Ocean atmosphere interactions of gases and particles

Liss, Peter S

Johnson, Martin T

2014

Springer, Berlin, Heidelberg

The oceans and atmosphere interact through various processes, including the transfer of momentum, heat, gases and particles. In this book leading international experts come together to provide a state-of-the-art account of these exchanges and their role in the Earth-system, with particular focus on gases and particles. Chapters in the book cover: i) the ocean-atmosphere exchange of short-lived trace gases; ii) mechanisms and models of interfacial exchange (including transfer velocity parameterisations); iii) ocean-atmosphere exchange of the greenhouse gases carbon dioxide, methane and nitrous oxide; iv) ocean atmosphere exchange of particles and v) current and future data collection and synthesis efforts. The scope of the book extends to the biogeochemical responses to emitted / deposited material and interactions and feedbacks in the wider Earth-system context.

This work constitutes a highly detailed synthesis and reference; of interest to higher-level university students (Masters, PhD) and researchers in ocean-atmosphere interactions and related fields (Earth-system science, marine / atmospheric biogeochemistry / climate). Production of this book was supported and funded by the EU COST Action 735 and coordinated by the International SOLAS (Surface Ocean- Lower Atmosphere Study) project office.

Business and Management

General and Civil Engineering

Materials

Geography

English

978-3-642-25642-4 (Print)

978-3-642-25643-1 (Online)

Springer Earth System Sciences

Peter S. Liss
Martin T. Johnson
Editors

Ocean-Atmosphere
Interactions of Gases
and Particles

Ocean-Atmosphere Interactions of Gases
and Particles

Springer Earth System Sciences

For further volumes:
http://www.springer.com/series/10178

Peter S. Liss • Martin T. Johnson
Editors

Ocean-Atmosphere
Interactions of Gases
and Particles

Editors
Peter S. Liss
Martin T. Johnson
Centre for Ocean and Atmospheric Sciences
School of Environmental Sciences
University of East Anglia
Norwich
United Kingdom

This publication is supported by COST.

ESF provides the COST Office through an EC contract

COST is supported by the EU RTD Framework programme

ISBN 978-3-642-25642-4
ISBN 978-3-642-25643-1 (eBook)
DOI 10.1007/978-3-642-25643-1
Springer Heidelberg New York Dordrecht London
Library of Congress Control Number: 2013955001
# The Editor(s) (if applicable) and the Author(s) 2014. The book is published with open access at
SpringerLink.com.

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Preface

This book is an outcome and an important part of the legacy of COST Action 735,
whose overall aim was to develop tools for estimating air-sea fluxes of compounds
important for climate, air quality and ocean productivity. The action was closely
allied with the SOLAS (Surface Ocean – Lower Atmosphere Study) project of the
International Geosphere-Biosphere Programme (IGBP), with both having very similar objectives. Because of this, they were mutually supportive in many ways.
The action ran for 5 years, starting in September 2006 and ending in September
2011. It involved more than 300 scientists mainly from Europe (77 % from the
following 18 countries: Belgium, Cyprus, Denmark, Finland, France, Germany,
Greece, Hungary, Ireland, Italy, Netherlands, Norway, Poland, Spain, Sweden,
Switzerland, Turkey and the United Kingdom) but with a significant number
from outside Europe, so that in total scientists from 30 countries participated.
One third of the participants were female. The action had a reciprocal agreement
with New Zealand. Additional information about the action can be found at
http://www.cost-735.org/.
The action operated through two types of activity – working group meetings
(21 being held in total) and short-term scientific missions, which allowed 19 younger
scientists to work in other laboratories in the action. The work of the action was
overseen and directed by a Management Committee that met eight times.
Many scientific publications have been produced from the work supported by the
action; it has also enabled many young scientists working for their Ph.Ds. to broaden
their vision and learn techniques not available in their home laboratories. In addition
and central to the aims of the action, several important databases have been assembled and made available through public data centres. This has in general not involved
making new measurements but the gathering together and collating in a coherent
format of existing data, much of which was not readily available previously. Notable
databases made with support from the action include IRONMAP (atmospheric
aerosol iron measurements); HalOcAt (ocean and atmospheric organo-halocarbon
data); MEMENTO (marine measurements of methane and nitrous oxide); SOCAT
(global surface ocean carbon dioxide database), a comprehensive dimethyl sulphide
database, which tripled the previous readily available number of surface ocean
measurements; and finally an ongoing effort to assemble measurements of aerosol
and rain composition (trace metals and nutrients) made from ships at sea. These
various datasets will be an important legacy of the action, and we expect them to
prove vital in the future development of the subject.
v

vi

Preface

Given the large amount of research supported by the action, it was decided to
produce this book to record the current state of knowledge in the area. Each of the
five chapters has several Lead Authors and a larger number of Contributing Authors,
using the IPCC authorship model. The Lead Authors together constituted the editorial board for the book. Lead authors are identified in the contacts list which follows
and by the inclusion of their email addresses at the beginning of each chapter. Each
chapter was reviewed by an external reviewer in addition to one member of the
editorial board not associated with the chapter. We have tried to have as much
consistency of nomenclature and units as possible but have not imposed this where
non-standard usage is well established and accepted. Although we have tried to keep
redundancy of material between chapters to a minimum, it has not been removed
entirely. We consider this is acceptable and even desirable since each chapter can be
downloaded independently and so needs to be complete within itself. Topics within
each chapter are dealt with in considerable detail and at a research level; so we expect
the book to be mainly of interest and constitute a fundamental text for research
workers, including graduate students.
Many people should be thanked and congratulated for the success of the action
and, consequently, for making this book possible. Firstly, thanks are due to the
participants in the scientific meetings and members of the Management Committee,
as well as the young researchers who took the opportunity to go on short-term
scientific missions. The SOLAS office at UEA carried much of the administrative
burden of the action, along with COST officers, rapporteurs and the administrative
staff.
We are grateful to the reviewers of the chapters who made many very useful and
perceptive suggestions. Rosie Cullington did a large amount of editorial work on the
reference lists for several of the chapters. Kath Mortimer put in a huge and sustained
effort at all stages of the production of the book; without her efforts it is unlikely that
the project would have been completed. Her thoroughness, excellent planning,
persuasiveness and stamina are truly remarkable. We are deeply indebted to all of
these people.
UEA, Norwich, UK
ICM-CSIC, Barcelona, Spain
UEA, Norwich, UK

Peter S. Liss
COST 735 Chair and Book Editor
Rafel Simó
COST 735 Vice-Chair
Martin T. Johnson
Book Editor

Acknowledgments

The authors would like to acknowledge the following organisations without whose
funding this publication and the meetings which supported this work would not have
been possible:
• COST Action ES0801: The ocean chemistry of bioactive trace elements and
paleoclimate proxies; www.costaction.earth.ox.ac.uk/
• European Cooperation in Science and Technology (COST); www.cost.eu
• European Space Agency (ESA); www.esa.int
• International Geosphere-Biosphere Programme (IGBP); www.igbp.net
• Land-Ocean Interactions in the Coastal Zone (LOICZ); www.loicz.org
• Natural Environment Research Council (NERC); www.nerc.ac.uk
• Surface Ocean-Lower Atmosphere Study (SOLAS); www.solas-int.org
• University of East Anglia (UEA); www.uea.ac.uk

vii

.

COST – European Cooperation in Science
and Technology

COST – European Cooperation in Science and Technology – is an intergovernmental
framework aimed at facilitating the collaboration and networking of scientists and
researchers at European level. It was established in 1971 by 19 member countries and
currently includes 35 member countries across Europe, and Israel as a cooperating state.
COST funds pan-European, bottom-up networks of scientists and researchers
across all science and technology fields. These networks, called ‘COST Actions’,
promote international coordination of nationally-funded research.
By fostering the networking of researchers at an international level, COST enables
break-through scientific developments leading to new concepts and products, thereby
contributing to strengthening Europe’s research and innovation capacities.
COST’s mission focuses in particular on:
• Building capacity by connecting high-quality scientific communities throughout
Europe and worldwide;
• Providing networking opportunities for early career investigators;
• Increasing the impact of research on policy makers, regulatory bodies and
national decision makers as well as the private sector.
Through its inclusiveness, COST supports the integration of research
communities, leverages national research investments and addresses issues of global
relevance.
Every year thousands of European scientists benefit from being involved in COST
Actions, allowing the pooling of national research funding to achieve common goals.
As a precursor of advanced multidisciplinary research, COST anticipates and
complements the activities of EU Framework Programmes, constituting a “bridge”
towards the scientific communities of emerging countries. In particular, COST
Actions are also open to participation by non-European scientists coming from
neighbour countries (for example Albania, Algeria, Armenia, Azerbaijan, Belarus,
Egypt, Georgia, Jordan, Lebanon, Libya, Moldova, Montenegro, Morocco, the
Palestinian Authority, Russia, Syria, Tunisia and Ukraine) and from a number of
international partner countries.
COST’s budget for networking activities has traditionally been provided by
successive EU RTD Framework Programmes. COST is currently executed by the
European Science Foundation (ESF) through the COST Office on a mandate by the
European Commission, and the framework is governed by a Committee of Senior
Officials (CSO) representing all its 35 member countries.
More information about COST is available at www.cost.eu.
ix

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Contents

1

Short-Lived Trace Gases in the Surface Ocean and the Atmosphere . . .
Peter S. Liss, Christa A. Marandino, Elizabeth E. Dahl, Detlev Helmig,
Eric J. Hintsa, Claire Hughes, Martin T. Johnson, Robert M. Moore,
John M.C. Plane, Birgit Quack, Hanwant B. Singh, Jacqueline Stefels,
Roland von Glasow, and Jonathan Williams
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Sulphur and Related Gases . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2.1 DMS(P) in the Surface Ocean . . . . . . . . . . . . . . . . . . . . . .
1.2.1.1 Ecosystem Dynamics . . . . . . . . . . . . . . . . . . . . .
1.2.1.2 DMS Yield . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2.1.3 Predicted Impact of Climate Change . . . . . . . . . .
1.2.2 Other Sulphur and Related Gases in the Surface Ocean . . .
1.2.2.1 Carbonyl Sulphide . . . . . . . . . . . . . . . . . . . . . . .
1.2.2.2 Carbon Disulphide . . . . . . . . . . . . . . . . . . . . . . .
1.2.2.3 Hydrogen Sulphide . . . . . . . . . . . . . . . . . . . . . . .
1.2.2.4 Methanethiol . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2.2.5 Dimethyl Selenide . . . . . . . . . . . . . . . . . . . . . . .
1.2.3 Atmospheric Sulphur and Related Gases . . . . . . . . . . . . . .
1.2.3.1 Chemistry of Sulphur in the Marine Boundary
Layer (MBL) . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2.3.2 CLAW Hypothesis . . . . . . . . . . . . . . . . . . . . . . .
1.3 Halocarbon Gases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.1 Chlorinated Compounds . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.1.2 Methyl Chloride . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.1.3 Dichloromethane . . . . . . . . . . . . . . . . . . . . . . . .
1.3.1.4 Tri- and Tetrachloroethylene . . . . . . . . . . . . . . . .
1.3.1.5 Chloroform . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.2 Brominated Compounds . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.2.1 Methyl Bromide . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.2.2 CHBr3, CH2Br2 and Other Polybrominated
Methanes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.3 Iodinated Compounds . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.3.1 Iodomethane . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.3.2 Other Mono-Iodinated Iodocarbons . . . . . . . . . . .
1.3.3.3 Di- and Tri-Halogenated Compounds . . . . . . . . .
1.3.4 Halogens in the Marine Atmospheric Boundary Layer . . . .

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1.4

2

Non-Methane Hydrocarbons (NMHCs) . . . . . . . . . . . . . . . . . . . .
1.4.1 Oxygenated Volatile Organic Compounds (OVOCs) . . . . .
1.4.1.1 Atmospheric Importance of OVOCs . . . . . . . . . .
1.4.1.2 Atmospheric Budget . . . . . . . . . . . . . . . . . . . . . .
1.4.1.3 Surface Ocean Processes . . . . . . . . . . . . . . . . . .
1.4.2 Alkanes and Alkenes . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.4.3 Alkyl Nitrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.4.4 Hydrogen Cyanide (HCN) and Methyl Cyanide (CH3CN) . . . .
1.5 Ozone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.6 Nitric Oxide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.7 Ammonia and Amines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.7.1 Ammonia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.7.2 Amines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.8 Hydrogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.9 Carbon Monoxide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.10 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Transfer Across the Air-Sea Interface . . . . . . . . . . . . . . . . . . . . . . . .
Christoph S. Garbe, Anna Rutgersson, Jacqueline Boutin, Gerrit de Leeuw,
Bruno Delille, Christopher W. Fairall, Nicolas Gruber, Jeffrey Hare,
David T. Ho, Martin T. Johnson, Philip D. Nightingale, Heidi Pettersson,
Jacek Piskozub, Erik Sahlée, Wu-ting Tsai, Brian Ward, David K. Woolf,
and Christopher J. Zappa
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1 Microscale Wave Breaking . . . . . . . . . . . . . . . . . . . . . . . .
2.2.2 Small Scale Turbulence . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.3 Bubbles, Sea Spray . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.4 Wind-Generated Waves . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.5 Large-Scale Turbulence . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.6 Rain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.7 Surface Films . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.8 Biological and Chemical Enhancement . . . . . . . . . . . . . . .
2.2.9 Atmospheric Processes . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3 Process Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.1 Interfacial Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.1.1 Thin (Stagnant) Film Model . . . . . . . . . . . . . . . .
2.3.1.2 Surface Renewal Model . . . . . . . . . . . . . . . . . . .
2.3.1.3 Eddy Renewal Model . . . . . . . . . . . . . . . . . . . . .
2.3.1.4 Surface Penetration . . . . . . . . . . . . . . . . . . . . . .
2.3.1.5 Air-Side Transfer . . . . . . . . . . . . . . . . . . . . . . . .
2.3.2 Direct Numerical Simulations (DNS) and Large
Eddy Simulations (LES) . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4 Exchanged Quantities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4.1 Physical Quantities . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4.2 Gases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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2.4.3

Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4.3.1 Dry Deposition . . . . . . . . . . . . . . . . . . . . . . . .
2.4.3.2 Wet Deposition . . . . . . . . . . . . . . . . . . . . . . . .
2.5 Measurement Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.5.1 Small-Scale Measurements Techniques . . . . . . . . . . . . . .
2.5.1.1 Particle-Based Techniques . . . . . . . . . . . . . . . .
2.5.1.2 Thermographic Techniques . . . . . . . . . . . . . . . .
2.5.2 Micrometeorological Techniques . . . . . . . . . . . . . . . . . .
2.5.3 Mass Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.5.3.1 Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.5.3.2 Scales (Spatial and Temporal) . . . . . . . . . . . . . .
2.5.3.3 Accuracy and Limitations . . . . . . . . . . . . . . . . .
2.5.3.4 Current and Recent Field Studies . . . . . . . . . . .
2.5.4 Profiles of pCO2 Near the Surface . . . . . . . . . . . . . . . . . .
2.5.5 Method Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.6 Parameterization of Gas Exchange . . . . . . . . . . . . . . . . . . . . . . .
2.6.1 Wind Speed Relationships . . . . . . . . . . . . . . . . . . . . . . .
2.6.2 Surface Roughness, Slope . . . . . . . . . . . . . . . . . . . . . . .
2.6.3 NOAA-COARE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.6.4 Energy Dissipation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.6.5 Evaluating and Selecting Transfer Velocity
Parameterisations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.7 Sea Ice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.8 Applications of Air-Sea Gas Transfer . . . . . . . . . . . . . . . . . . . . .
2.8.1 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.8.2 Remote Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.8.3 Inventories, Climatologies Using In Situ Data . . . . . . . . .
2.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3

Air-Sea Interactions of Natural Long-Lived Greenhouse
Gases (CO2, N2O, CH4) in a Changing Climate . . . . . . . . . . . . . . . .
Dorothee C.E. Bakker, Hermann W. Bange, Nicolas Gruber,
Truls Johannessen, Rob C. Upstill-Goddard, Alberto V. Borges,
Bruno Delille, Carolin R. Löscher, S. Wajih A. Naqvi,
Abdirahman M. Omar, and J. Magdalena Santana-Casiano
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1 Atmospheric Greenhouse Gases from Ice Cores . . . . . . . . .
3.2 Surface Ocean Distribution and Air-Sea Exchange of CO2 . . . . . .
3.2.1 Global Tropospheric CO2 Budget . . . . . . . . . . . . . . . . . . .
3.2.2 Processes Controlling CO2 Dynamics in the Upper
Water Column . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.3 Surface Ocean fCO2 and Air-Sea CO2 Fluxes in the
Open Ocean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.3.1 Surface Ocean fCO2 Distribution . . . . . . . . . . . .
3.2.3.2 Multi-Year Changes and Trends . . . . . . . . . . . . .
3.2.3.3 Comparison of Air-Sea CO2 Flux Estimates . . . .
3.2.3.4 Sea Ice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.3.5 Coastal to Open Ocean Carbon Exchanges . . . . .

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3.2.4

Air-Sea CO2 Fluxes in Coastal Areas . . . . . . . . . . . . . . . . .
3.2.4.1 Continental Shelves . . . . . . . . . . . . . . . . . . . . . . .
3.2.4.2 Near-Shore Systems . . . . . . . . . . . . . . . . . . . . . . .
3.2.4.3 Multi-Year Changes and Trends . . . . . . . . . . . . . .
3.3 Marine Distribution and Air-Sea Exchange of N2O . . . . . . . . . . . . .
3.3.1 Global Tropospheric N2O Budget . . . . . . . . . . . . . . . . . . . .
3.3.2 Nitrous Oxide Formation Processes . . . . . . . . . . . . . . . . . .
3.3.2.1 Denitrification . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.2.2 Nitrification . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.2.3 N2O Formation by Dissimilatory Nitrate
Reduction to Ammonium . . . . . . . . . . . . . . . . . . .
3.3.3 Global Oceanic Distribution of Nitrous Oxide . . . . . . . . . . .
3.3.4 Coastal Distribution of Nitrous Oxide . . . . . . . . . . . . . . . . .
3.3.5 Marine Emissions of Nitrous Oxide . . . . . . . . . . . . . . . . . .
3.4 Marine Distribution and Air-Sea Exchange of CH4 . . . . . . . . . . . . .
3.4.1 Global Tropospheric CH4 Budget . . . . . . . . . . . . . . . . . . . .
3.4.2 Formation and Removal Processes for Methane . . . . . . . . . .
3.4.3 Global Oceanic Distribution of Methane . . . . . . . . . . . . . . .
3.4.4 Coastal Distribution of Methane . . . . . . . . . . . . . . . . . . . . .
3.4.4.1 Coastal Sediments . . . . . . . . . . . . . . . . . . . . . . . .
3.4.4.2 Coastal Waters . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.4.3 Methane Hydrates . . . . . . . . . . . . . . . . . . . . . . . .
3.4.5 Marine Emissions of Methane . . . . . . . . . . . . . . . . . . . . . .
3.5 Impact of Global Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5.1 Future Changes in the Physics of the Oceanic Surface
Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5.1.1 Carbon Dioxide in the Open Ocean . . . . . . . . . . . .
3.5.1.2 Carbon Dioxide in Coastal Seas . . . . . . . . . . . . . .
3.5.1.3 Nitrous Oxide and Methane . . . . . . . . . . . . . . . . .
3.5.2 Ocean Acidification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5.2.1 Carbon Dioxide . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5.2.2 Nitrous Oxide and Methane . . . . . . . . . . . . . . . . .
3.5.3 Deoxygenation and Suboxia in the Open Ocean . . . . . . . . .
3.5.4 Coastal Euthrophication and Hypoxia . . . . . . . . . . . . . . . . .
3.5.5 Changes in Methane Hydrates . . . . . . . . . . . . . . . . . . . . . .
3.6 Key Uncertainties in the Air-Sea Transfer of CO2, N2O and CH4 . . .
3.6.1 Outgassing of Riverine Carbon Inputs . . . . . . . . . . . . . . . . .
3.6.2 Heterogeneity in Coastal Systems . . . . . . . . . . . . . . . . . . . .
3.6.3 Sea Ice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.6.4 Parameterising Air-Sea Gas Transfer . . . . . . . . . . . . . . . . .
3.6.5 Data Collection, Data Quality and Data Synthesis . . . . . . . .
3.7 Conclusions and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.7.1 Carbon Dioxide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.7.2 Nitrous Oxide and Methane . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

126
126
129
129
130
130
130
130
131
132
132
134
135
137
137
137
139
139
139
142
143
146
147
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147
149
150
150
150
152
152
153
153
154
154
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155
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156
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4

Ocean–Atmosphere Interactions of Particles . . . . . . . . . . . . . . . . . . .
Gerrit de Leeuw, Cécile Guieu, Almuth Arneth, Nicolas Bellouin,
Laurent Bopp, Philip W. Boyd, Hugo A.C. Denier van der Gon,
Karine V. Desboeufs, François Dulac, M. Cristina Facchini,
Brett Gantt, Baerbel Langmann, Natalie M. Mahowald,
Emilio Marañón, Colin O’Dowd, Nazli Olgun, Elvira Pulido-Villena,
Matteo Rinaldi, Euripides G. Stephanou, and Thibaut Wagener
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Aerosol Production and Transport in the Marine Atmosphere . . . . .
4.2.1 Sources of Aerosol in the Marine Atmosphere . . . . . . . . . . .
4.2.1.1 Sea Spray Aerosol Production . . . . . . . . . . . . . . . .
4.2.1.2 Organic Enrichment of Particulate Organic
Matter in Sea Spray Aerosol . . . . . . . . . . . . . . . . .
Laboratory Studies . . . . . . . . . . . . . . . . . . . . . . . .
Global Distribution of Organic Enrichment . . . . . .
4.2.1.3 Secondary Aerosol Formation in the Marine
Atmospheric Boundary Layer . . . . . . . . . . . . . . . .
Secondary Inorganic Aerosol Formation . . . . . . . .
Secondary Organic Marine Aerosol . . . . . . . . . . .
New Particle Formation in the Marine Boundary
Layer? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.2 Non-Marine Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.2.1 Desert Dust . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.2.2 Volcanic Gases, Aerosols and Ash . . . . . . . . . . . .
4.2.2.3 Global Emissions of Biogenic Volatile Organis
Compounds (BVOC’s) from Terrestrial
Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.2.4 Anthropogenic Emissions . . . . . . . . . . . . . . . . . . .
Anthropogenic Land-Based Emissions . . . . . . . . .
Uncertainty in Global Anthropogenic Emissions . . .
Global Biomass Burning Emissions . . . . . . . . . . .
International Shipping Emissions . . . . . . . . . . . . .
Comparison and Evaluation of Different Emission
Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.3 Ageing and Mixing of Aerosols During Transport . . . . . . . .
4.2.3.1 Chemical Ageing of Organic Aerosols . . . . . . . . .
4.2.3.2 Internal Mixing . . . . . . . . . . . . . . . . . . . . . . . . . .
Dust/Inorganic Species . . . . . . . . . . . . . . . . . . . . .
Dust/Organic Species . . . . . . . . . . . . . . . . . . . . . .
Sea Salt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Future Directions . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.4 Dust-Mediated Transport of Living Organisms and
Pollutants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Direct Radiative Effects (DRE) . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Effects on Cloud Formation and Indirect Radiative Effects . . . . . . .
4.5 Deposition of Aerosol Particles to the Ocean Surface
and Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

171

171
174
174
174
176
179
180
182
182
183
184
186
186
189

191
193
193
193
194
194
195
196
196
197
197
198
200
200
201
202
204
206

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Contents

4.5.1

Deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.1.1 Iron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.1.2 Phosphorus . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.1.3 Nitrogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.1.4 Deposition of Other Species . . . . . . . . . . . . . . . . .
4.5.2 Elements of Biogeochemical Interest and Their
Chemical Forms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.3 Dissolution- Scavenging Processes . . . . . . . . . . . . . . . . . . .
4.5.4 Atmospheric Impacts in HNLC and LNLC Areas . . . . . . . .
4.5.4.1 Experimental: Large Scale Fertilisation
Experiments (Fe, P) . . . . . . . . . . . . . . . . . . . . . . .
4.5.4.2 Experimental: Microcosms . . . . . . . . . . . . . . . . . .
Main Results Obtained from the Microcosm
Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.4.3 Experimental: In Situ Mesocosms . . . . . . . . . . . . .
4.5.4.4 Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.5 Particulate Matter and Carbon Export . . . . . . . . . . . . . . . . .
4.6 Summary and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5

206
206
207
209
209
209
211
213
213
215
215
218
221
222
224
227

Perspectives and Integration in SOLAS Science . . . . . . . . . . . . . . . . . 247
Véronique C. Garçon, Thomas G. Bell, Douglas Wallace, Steve R. Arnold,
Alex Baker, Dorothee C.E. Bakker, Hermann W. Bange, Nicholas R. Bates,
Laurent Bopp, Jacqueline Boutin, Philip W. Boyd, Astrid Bracher,
John P. Burrows, Lucy J. Carpenter, Gerrit de Leeuw, Katja Fennel,
Jordi Font, Tobias Friedrich, Christoph S. Garbe, Nicolas Gruber,
Lyatt Jaeglé, Arancha Lana, James D. Lee, Peter S. Liss, Lisa A. Miller,
Nazli Olgun, Are Olsen, Benjamin Pfeil, Birgit Quack, Katie A. Read,
Nicolas Reul, Christian Rödenbeck, Shital S. Rohekar, Alfonso Saiz-Lopez,
Eric S. Saltzman, Oliver Schneising, Ute Schuster, Roland Seferian,
Tobias Steinhoff, Pierre-Yves Le Traon, and Franziska Ziska
5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling
and Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248
5.1.1 In Situ Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248
5.1.1.1 ARGO (T, S, O2) . . . . . . . . . . . . . . . . . . . . . . . . . 248
5.1.1.2 Ocean Observatories . . . . . . . . . . . . . . . . . . . . . . 250
5.1.1.3 Atmospheric Observatories . . . . . . . . . . . . . . . . . . 250
5.1.1.4 Monitoring Reactive Trace Species in the
Marine Atmosphere: Highlights from the Cape
Verde Observatory . . . . . . . . . . . . . . . . . . . . . . . . 251
5.1.1.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
5.1.2 Earth Observation Products . . . . . . . . . . . . . . . . . . . . . . . . 255
5.1.2.1 Altimetry, SST, Winds, Sea State . . . . . . . . . . . . . 256
5.1.2.2 Sea Surface Salinity . . . . . . . . . . . . . . . . . . . . . . . 260
5.1.2.3 Marine Carbon Observations from Satellite Data:
Ocean Color/PIC/POC . . . . . . . . . . . . . . . . . . . . . 261
5.1.2.4 Sea Ice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264
5.1.2.5 Aerosols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266

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xvii

5.1.2.6

Satellite Measurements of Trace Gases Over
the Oceans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.2.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.3 Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.3.1 Global Perspective, Prognostic IPCC
and Hindcast . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.3.2 Regional Perspectives from High-Resolution
Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.3.3 Inverse Modelling . . . . . . . . . . . . . . . . . . . . . . . .
5.1.3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.4 SOLAS/COST Data Synthesis Efforts . . . . . . . . . . . . . . . . .
5.1.4.1 MEMENTO (MarinE MethanE and
NiTrous Oxide) Database . . . . . . . . . . . . . . . . . . .
5.1.4.2 HalOcAt (Halocarbons in the Ocean
and Atmosphere) . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.4.3 DMS-GO (DMS in the Global Ocean) . . . . . . . . . .
5.1.4.4 The Surface Ocean CO2 ATlas (SOCAT) . . . . . . .
5.1.4.5 Aerosol and Rainwater Chemistry Database . . . . .
5.1.4.6 A Data Compilation of Iron Addition
Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Examples of SOLAS Integrative Studies . . . . . . . . . . . . . . . . . . . .
5.2.1 DMS Ocean Climatology and DMS Marine Modelling . . . .
5.2.1.1 Global Climatologies Based on Observations . . . . .
5.2.1.2 Diagnostic Approaches: Based on Empirical
Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.1.3 Prognostic Modelling: From 1D to 3D . . . . . . . . .
5.2.1.4 Examples of Applications . . . . . . . . . . . . . . . . . . .
Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . .
Iron Fertilisation . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.2 North Pacific Volcanic Ash and Ecosystem Response . . . . .
5.2.3 CO2 in the North Atlantic . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.4 Global Distribution of Sea Salt Aerosols . . . . . . . . . . . . . . .
5.3 Perspectives for the Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

267
270
270
271
272
274
275
276
276
276
278
279
280
281
283
284
284
284
284
284
286
286
286
287
289
292
293
294

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307

.

Contributors

Almut Arneth is Professor and Head of Division of
Ecosystem-Atmosphere Interactions at the Karlsruhe Institute of Technology, Institute of Meteorology and Climate
Research/Atmospheric Environmental Research. Her
research interests are in the interactions of climate change,
land use change, vegetation dynamics and terrestrial biogeochemical cycles, and the feedbacks existing in that
system.

Steve Arnold is a Senior Lecturer in the School of Earth
and Environment at the University of Leeds. His research
interests are in the chemistry of the lower atmosphere
and interactions between air quality, climate and the
biosphere.

xix

xx

Alex Baker is a Reader in Marine and Atmospheric
Chemistry in the School of Environmental Sciences at
the University of East Anglia in Norwich, UK. He has
studied the marine and industrial chemistry of humic
material and the biogeochemistry of trace metals in seawater and, since 1997, has worked primarily on aerosol
biogeochemistry in the marine boundary layer.

Dorothee Bakker is a Research Officer in the School of
Environmental Sciences at the University of East Anglia
in Norwich, UK. Her research is on processes controlling
the carbon sink in shelf seas and the oceans, notably the
roles of marine biota, ocean circulation, iron supply, sea
ice and ocean acidification. Dorothee has a strong interest in creating better access to marine biogeochemical
data, e.g. via the Surface Ocean CO2 Atlas (SOCAT)
(www.socat.info).

Hermann Bange is a Professor of Marine Chemistry in
the Marine Biogeochemistry Research Division of
GEOMAR in Kiel, Germany. His research interests are
in the marine biogeochemistry of trace gases (N2O, CH4
and DMS) and the marine nitrogen cycle. He was
Co-chair of Working Group 3 of COST Action 735.
Currently, he is the Coordinator of the SOPRAN
(Surface Ocean Processes in the Anthropocene; www.
sopran.pangaea.de) project, which is a German contribution to SOLAS.

Contributors

Contributors

xxi

Nick Bates is Senior Scientist at the Bermuda Institute of
Ocean Sciences (BIOS), a US scientific research institution
based in Bermuda. He is a Chemical Oceanographer whose
research is focused on the ocean carbon cycle, marine
biogeochemistry and ocean acidification. At present, he is
Principal Investigator for two ocean time series near
Bermuda, namely the Bermuda Atlantic Time-series
Study (BATS) and Hydrostation S. Other projects include
the investigations into the impact of ocean acidification on coral reef ecosystems and
studies of the ocean carbon cycle in the North Atlantic Ocean, Arctic Ocean and
surrounding shelf seas, and the Southern Ocean.

Tom Bell is a Senior Scientist at the Plymouth Marine
Laboratory, Plymouth, UK. His research interests are
in ocean–atmosphere interactions and surface ocean
trace gas biogeochemistry. His recent work has been
to make direct measurements of DMS air-sea flux by
eddy covariance using Atmospheric Pressure-Chemical
Ionisation Mass Spectrometry (API-CIMS).

Nicolas Bellouin is a Senior Climate Research Scientist
at the Met Office Hadley Centre in Exeter, UK. He
investigates the role of atmospheric aerosols within the
Earth system by using large-scale numerical models and
satellite observations.

xxii

Laurent Bopp is a Senior Scientist in the modelling
department of the Laboratoire des Sciences du Climat et
de l’Environnement (LSCE) at l’Institut Pierre-Simon
Laplace (IPSL) in Paris, France. He graduated from
Pierre-et-Marie-Curie University and was a Visiting Scientist at both the Max Planck Institute in Jena, Germany,
and the University of East Anglia in Norwich, UK. Most
of his research focuses on marine biogeochemical cycles
and on how marine ecosystems and the ocean carbon cycle
respond to natural and anthropogenic climate change.

Alberto Borges is FRS-FNRS Research Associate at the
University of Liège where he heads the Chemical Oceanography Unit. His research interests are in carbon cycling
across aquatic systems including freshwater ecosystems
(lakes and rivers), coastal ecosystems (estuaries, seagrass
beds, mangroves and continental margins), and open
ocean.

Jacqueline Boutin is a Research Scientist at Laboratoire
d’Océanographie et du Climat-Expérimentation et
Approches Numériques in Paris, France. Her research
interests are in air–sea exchange particularly involving
upper ocean variability and carbon dioxide and freshwater fluxes.

Philip Boyd is a Professor of Ocean Biogeochemistry
based at the Institute for Marine and Antarctic Science,
University of Tasmania. His research interests include
trace metal biogeochemistry and exploring the links
between dust, iron supply and altered ocean carbon
biogeochemistry.

Contributors

Contributors

xxiii

Astrid Bracher is leading the Helmholtz University
Young Investigator’s Group Phytooptics http://www.awi.
de/en/go/phytooptics at the Climate Section of AWI http://
www.awi.de/en/ and at the IUP at the University of
Bremen http://www.iup.uni-bremen.de/eng/. She is an
adjunct professor at the Department of Physics at the
University of Bremen. Her studies are in the field of biooptical in situ sampling techniques and ocean colour
retrieval techniques. Her recent studies focus on retrieving
new bio-optical information (phytoplankton functional
types, vibrational Raman scattering, fluorescence) from
hyperspectral satellite data.
John Burrows is Professor of Atmospheric and Oceanic
Physics at the University of Bremen, Director of the
Institute of Environmental Physics at the University of
Bremen and a Fellow of the Natural Environmental
Research Council’s Centre for Ecology and Hydrology.
His research is focused on Earth system and observation
science, atmospheric physics and chemistry, kinetics and
spectroscopy. He has pioneered the development of the
remote sensing of tropospheric trace gases from space.
He is also President of the International Commission on
Atmospheric Chemistry and Global Pollution.
Lucy Carpenter received her BSc (hons) in Chemistry
from the University of Bristol and studied for a PhD
(awarded in 1996) in the subject of peroxy radicals in
the lower atmosphere at the University of East Anglia.
After postdoctoral research at the University of East
Anglia and the University of Leeds, she moved to the
Department of Chemistry, University of York, as a Lecturer
in 2000 and was awarded a personal chair in 2009. Her
research addresses the atmospheric impacts of marinederived trace gases, particularly organohalogens. In 2006,
she was awarded a Philip Leverhulme Prize in Earth, Ocean
and Atmospheric Sciences.
Elizabeth Dahl is an Associate Professor of Chemistry at
Loyola University, Maryland, USA. Her research focuses
on the sources of alkyl nitrates in natural waters and the
impact on tropospheric chemistry. She has a PhD in Earth
System Science from the University of California, Irvine,
and has mentored over a dozen undergraduate research
students during her time at Loyola. When she is not
working she enjoys spending time on kitchen chemistry
with her future scientists.

xxiv

Gerrit de Leeuw is a Research Professor at the Finnish
Meteorological Institute and in the Department of Physics at the University of Helsinki, both in Helsinki,
Finland, while being also affiliated to TNO, Utrecht,
the Netherlands. His primary research interests are satellite remote sensing of aerosols, the production of sea
spray aerosol and the application of satellite data to
climate change and air quality as well as to
ocean–atmosphere and land–atmosphere interactions.

Bruno Delille is FRS-FNRS Research Associate at the
Chemical Oceanography Unit of the University of Liège.
His research interests are in climate gas dynamics in
polar ocean with an emphasis on sea ice.

Hugo Denier van der Gon is a Senior Researcher in the
Department of Climate, Air and Sustainability of the
Netherlands Organisation for Applied Scientific
Research (TNO), Utrecht, the Netherlands. His current
research interests are (i) improving emission inventories
of non-CO2 greenhouse gases and air pollutants for use in
atmospheric chemistry and transport models, (ii) chemical speciation and source apportionment of particulate
matter emissions and (iii) the use of satellite data to
validate emission estimates.

Karine Desboeufs is Assistant Professor in the LISA
(Laboratoire
Interuniversitaire
des
Systèmes
Atmosphériques) at the University of Paris Diderot,
France. Her research activities include the study of dust
(and associated nutrients) cycling in the atmosphere and
its biogeochemical impact after deposition to the ocean.

Contributors

Contributors

xxv

François Dulac is a Senior Research Scientist appointed
by the French Commissariat à l’Energie Atomique et aux
Energies Alternatives (CEA) in the Laboratoire des
Sciences du Climat et de l’Environnement (LSCE) of
CEA, CNRS, and the University of Versailles StQuentin, at Gif-sur-Yvette, France. His research interests
are in tropospheric chemistry with a focus on aerosols
and their impacts. He is particularly experienced in desert dust and remote sensing and currently focuses on the
Mediterranean region (http://charmex.lsce.ipsl.fr).

Cristina Facchini is Research Director at the Institute of
Atmospheric Sciences and Climate (ISAC) of the
National Research Council (CNR) in Bologna, Italy.
Her research interest is in the effect of changes in atmospheric composition on global change and climate.
Cristina’s specific field of research in the last 15 years
has been the organic composition of aerosol particles,
their related physical properties and their interaction
with atmospheric water. She is presently Lead Author of Intergovernmental Panel
on Climate Change AR5-WG1.

Chris Fairall is a physicist at National Oceanic and
Atmospheric Administration (NOAA)’s Earth System
Research Laboratory in Boulder, Colorado, where he
heads the Weather and Climate Physics Branch. He has
spent decades developing/deploying air–sea interaction
observing systems for ships and aircraft. His work is
devoted to making direct measurements for verifying
and improving the representation of air–sea interaction
processes (surface evaporation, absorption of heat, generation of waves, uptake of carbon dioxide) in climate
models used for climate change projections.

Katja Fennel holds a Canada Research Chair in Marine
Prediction and is Associate Professor in the Department of
Oceanography at Dalhousie University in Halifax,
Canada. Her research interests are in biogeochemical
modelling and data assimilation. She is a collaborator in
major research projects, such as the Ocean Tracking Network and the U.S. Integrated Ocean Observing System
(US IOOS) Modeling Testbed, and is a member of the
IMBER/LOICZ Continental Margins Task Team and the
CLIVAR Working Group on Ocean Model Development.

xxvi

Jordi Font is a Research Professor in the Institute of
Marine Sciences, Spanish Research Council, in
Barcelona, Spain. He is a Physical Oceanographer now
involved in ocean remote sensing, with the main focus of
interest on the measurement of sea-surface salinity using
microwave interferometric radiometry. At present, he is
Co-lead Investigator for the Soil Moisture and Ocean
Salinity (SMOS) satellite mission of the European
Space Agency.

Tobias Friedrich is currently Postdoctoral Fellow at the
International Pacific Research Center at the University of
Hawaii. His research interests encompass natural and
anthropogenic climate and carbon cycle variability,
paleoceanography and Earth system modelling.

Brett Gantt is a Postdoctoral Researcher in the Department
of Marine, Earth, and Atmospheric Sciences at North
Carolina State University in Raleigh, North Carolina,
USA. His research interests are in ocean–atmosphere interaction particularly involving sea spray aerosol and biogenic
volatile organic compounds.

Contributors

Contributors

xxvii

Christoph Garbe is the Head of an independent research
group at the Interdisciplinary Center for Scientific Computing (IWR) at the University of Heidelberg. His
research interests are in environmental transport processes and in digital image processing. Applications
range from small-scale transport models at the air–water
interface to satellite remote sensing. Christoph Garbe is
member of the SOLAS scientific steering committee and
principal investigator at the Intel Visual Computing
Institute at Saarland University.

Véronique Garçon is a CNRS Senior Scientist at
Laboratoire d’Etudes en Géophysique et Océanographie
Spatiales (LEGOS) in Toulouse, France. Her research
interests include marine biogeochemistry and ecosystem
dynamics, large-scale circulation and tracers, global carbon and nitrogen cycles, physical–biological interactions,
eastern boundary upwelling and Oxygen Minimum Zone
(OMZ) systems, and biogeochemical climatic monitoring.
She joined Centre national de la recherche scientifique (CNRS) in 1985 at the Institute
of Physics of the Globe in Paris after a postdoctoral experience at MIT. She sits on
many national and international Scientific Steering Committees (i.e. IFREMER,
JGOFS, SOLAS, etc.)
Nicolas Gruber is Professor of Environmental Physics at
ETH Zürich, Zürich. His research interests include the
study of biogeochemical cycles on regional to global
scales and on timescales from months to millennia, with
a particular focus on the interaction of these cycles with
the Earth’s climate system. He has co-authored a textbook
with Jorge Sarmiento, Ocean Biogeochemical Dynamics,
and has received several awards, among which is the
Rosenstiel Award from the University of Miami.

Cécile Guieu is a Senior Scientist at Centre National de
la Recherche Scientifique and works at Laboratoire
d’Océanographie in Villefranche sur Mer (LOV),
France. She is a Marine Biogeochemist. Her research
interests concern atmospheric inputs of nutrients and
particles and how they impact marine biogeochemical
cycles and carbon export. She has been a member of the
SOLAS Scientific Steering Committee since 2009.

xxviii

Jeff Hare is the Deputy Director for PIFSC Projects at the
Joint Institute for Marine and Atmospheric Research
(JIMAR) at the University of Hawaii at Manoa. His
research interests are in the measurement and
characterisation of air–sea mass and heat fluxes and in the
development of transfer parameterisations. He previously
served as Executive Officer for the Surface Ocean – Lower
Atmosphere Study (SOLAS) International Project Office at
the University of East Anglia in the UK (2005–2008).

Detlev Helmig is a Research Professor at the Institute of
Arctic and Alpine Research at the University of
Colorado, Boulder. His research entails studies of
surface–atmosphere gas exchange and atmospheric
chemistry and transport, with an emphasis on the ocean
and cryosphere environment. He is Editor in Chief of the
Atmospheric Science Domain of the non-profit, open
access journal Elementa.

Eric Hintsa is a Research Scientist at the Cooperative
Institute for Research in Environmental Sciences
(CIRES) at the University of Colorado, Boulder, and
the National Oceanic and Atmospheric Administration’s
Global Monitoring Division. His research interests
include the sources, chemistry and transport of trace
gases in the troposphere and stratosphere.

David Ho is a Professor in the Department of Oceanography at the University of Hawaii in Honolulu, Hawaii,
USA. His research interests include air–sea gas
exchange, tracer oceanography and coastal carbon
cycling.

Contributors

Contributors

xxix

Claire Hughes is a Lecturer in Environmental Chemistry in the Environment Department at the University of
York, UK. Her research interest is in understanding the
mechanisms of biogenic trace gas production in the
marine environment.

Lyatt Jaeglé is a Professor of the Atmospheric Sciences
at the University of Washington. She is an atmospheric
chemistry modeller, and her research interests are in
long-range transport of pollution, biogeochemical
cycling of mercury and surface emissions of chemical
constituents.

Truls Johannessen is a Professor at the Geophysical
Institute and the Bjerknes Centre for Climate Research,
of Chemical Oceanography/Biogeochemistry/Earth
Sciences at the University of Bergen and Uni Research.
His special research interests are in marine climates,
geochemistry, oceanography and earth systems. He is
an author or co-author of about 70 scientific papers and
a Steering Committee member in a number of international programmes under the auspices of JGOFS, SCOR
and IOC. He was a member of the SOLAS Scientific
Steering Committee from 2000 to 2008.

xxx

Martin Johnson is a Lecturer in Marine Science at the
University of East Anglia in Norwich, UK. His main
research interests lie in two related areas: the biogeochemical cycling of nitrogen compounds, particularly
in aquatic environments, and their role in driving the
biological carbon pump; and the physics, chemistry and
biology of ocean–atmosphere trace gas exchange. He
uses direct measurements and incubation experiments
to quantify processes in the field and numerical and
statistical modelling to constrain estimates of biogeochemical fluxes.

Arancha Lana is a postdoctoral researcher at the Mediterranean Institute for Advanced Studies (IMEDEA) in
the Balearic Islands, Spain. She recently obtained her
PhD, focused in marine aerosols, their precursors and
their influence on clouds over the global ocean at
Institut de Ciències del Mar (ICM-CSIC), in Barcelona.
Her research interests are actually moving into Operational Oceanography, and the use of HF Radar to study
ocean motion and surface currents.
Baerbel Langmann is a Senior Scientist at the Institute
of Geophysics of the University of Hamburg, Germany.
Her highly interdisciplinary research contributes to
current topics in climate research. She investigates the
importance of anthropogenic versus natural trace species emissions, atmospheric photochemical composition, aerosols, clouds and precipitation by regionalscale numerical modelling.

James Lee is an National Centre for Atmospheric Science (NCAS) Research Scientist in the Department of
Chemistry, University of York. His research interest is in
ozone photochemistry in remote and polluted
environments, in particular the effect of long-term
changes in NOx levels.

Contributors

Contributors

xxxi

Pierre-Yves Le Traon is a Senior Oceanographer at
IFREMER. His research interests include satellite and in
situ observing systems, sea level and ocean circulation, and
operational oceanography. He is the coordinator of the
Euro-Argo Research Infrastructure and member of the
International Argo Steering Team. He was Vice Chair of
the Global Ocean Data Assimilation Experiment
(GODAE) from 2002 to 2008. He received the Fridtjof
Nansen Medal from the EGU in 2012.

Peter Liss is a Professorial Fellow in the School of
Environmental Sciences at the University of East Anglia
in Norwich, UK. His research interests are in
ocean–atmosphere interaction particularly involving
trace gases. He served as Chairman of the International
Geosphere-Biosphere Programme (IGBP) for 5 years
and was subsequently Chair of its Surface Ocean –
Lower Atmosphere Study (SOLAS). He is a Fellow of
the Royal Society.
Carolin Löscher works as a Postdoc in the Department
of Microbiology at Christian-Albrecht University, Kiel,
Germany. Her main research focus is set on the
biological formation of nitrous oxide by microorganisms
in tropical ocean areas. After studying biological oceanography at IFM-GEOMAR, Kiel, Germany, she worked
on the sensitivity of the oceanic biological nitrogen cycle
to changes in dissolved oxygen during her PhD from
2008 on and received her doctoral degree in December
2011.
Natalie Mahowald is an Associate Professor of Atmospheric Sciences in the Department of Earth and Atmospheric Sciences at Cornell University. Her research is
focused on understanding global- and regional-scale
atmospheric transport of biogeochemically important
species such as desert dust. She is interested in how
humans are perturbing the natural environment, especially through perturbations to aerosols. She uses a combination of three-dimensional global transport and
climate models and analysis of satellite and in situ data.

xxxii

Christa Marandino is a Junior Professor in the Chemical Oceanography Department at GEOMAR in Kiel,
Germany. Her research focuses on performing direct
open ocean flux measurements of dimethyl sulphide
(DMS) and oxygenated volatile organic compounds
(OVOCs, e.g. acetone and acetaldehyde) using the eddy
covariance technique. She is also interested in surface
ocean cycling of OVOCs.

Emilio Marañón is Associate Professor at the University of Vigo, Spain, since 1999. He studies the ecology
and biogeochemical role of marine phytoplankton, with
special interest in primary production, nitrogen fixation
and calcification.

Lisa Miller is a Research Scientist at the Centre for
Ocean Climate Chemistry at the Institute of Ocean
Sciences in British Columbia. She studies the marine
carbon cycle in relation to air–sea exchange, with particular focus on the roles of sea ice and the sea-surface
microlayer.

Robert Moore is a Professor in the Department of
Oceanography at Dalhousie University, Halifax, Canada.
His research interest is in chemical oceanography with a
focus on trace gases that are reactive in the atmosphere.
A current focus of his research is the marine biogeochemistry of molecular hydrogen and the relationship
between its oceanic distribution and nitrogen fixation.

Contributors

Contributors

xxxiii

Kath Mortimer is former Project Officer for the Surface
Ocean Lower Atmosphere Study (SOLAS) where she
focussed on projects such as the SOLAS Summer
School, Open Science Conference, website and
publications. As a part of this role she co-ordinated
the activities of COST Action 735 including the publication of this book, Ocean-Atmosphere Interactions of
Gases and Particles. She continues to work in research
project management for the University of East Anglia,
Norwich, UK.

Wajih Naqvi is the Director of the CSIR-National Institute of Oceanography, Dona Paula, Goa, India. His
research interests are in the biogeochemistry of oxygendeficient aquatic systems, especially transformations
involving climatically important gases. He has actively
participated in the planning and implementation of several IGBP projects (JGOFS, LOICZ and IMBER) at
national and international levels. He is currently the
Co-chair of Sustained Indian Ocean Biogeochemical
and Ecological Research (SIBER).
Phil Nightingale is a scientist at the Plymouth Marine
Laboratory (PML). His research interests include air-sea
gas exchange, the cycling of trace gases in seawater and
Lagrangian tracers. He obtained his BSc from the School
of Chemical Sciences and PhD from the School of Environmental Sciences, both at the University of East
Anglia. He has been working at PML since 1995.

Colin O’Dowd is Professor of Physics and Director of
the Centre for Climate and Air Pollution Studies at the
National University of Ireland, Galway, Ireland. His
research interests include aerosol formation, transformation and climate effects with particular focus on marine
aerosols. He has received the following honours:
Smoluchowski Award, Fellow of the Institute of Physics
and the Royal Meteorological Society, Doctorate of Science, Member of the Royal Irish Academy, and the
Institute of Physics Appleton Medal.

xxxiv

Nazli Olgun finalised her PhD studies at the Helmholtz
Centre for Ocean Research (GEOMAR) in Kiel,
Germany. Her research interest is in the biogeochemical cycling of nutrients in the oceans involving the
impacts of volcanic eruptions. She obtained her
bachelor’s degree in the Department of Geological
Engineering, Middle East Technical University,
Ankara, Turkey. She did her master’s study at the
Coastal Research Laboratory at the University of Kiel.

Are Olsen is Associate Professor at the Geophysical
Institute/Bjerknes Center for Climate Research, University of Bergen. He spends his time doing research on
marine biogeochemistry focusing in particular on
ocean uptake of fossil fuel CO2, air-sea CO2 exchange
and δ13C dynamics. He also spends time on promoting
ocean carbon data storage and access through SOCAT
and GLODAPv2.

Abdirahman Omar is a Research Scientist and leader
of the Biogeochemistry research group at Uni Climate,
Uni Research Ltd. His research interests include
the response of the oceanic carbon sink to increasing
atmospheric carbon dioxide (CO2) and the role of
sea ice and brine formation in the oceanic uptake of
atmospheric CO2.

Heidi Pettersson is the Head of the Wave and Sea Level Group in the Marine
Research Unit at the Finnish Meteorological Institute in Helsinki, Finland. Her
research interests are in wind-generated waves and their role in the air–sea
interactions, particularly in the exchange of carbon dioxide. She is an Adjunct
Professor of Geophysics/Physical Oceanography (University of Helsinki).

Contributors

Contributors

xxxv

Benjamin Pfeil is a Scientific Data Manager at the
University of Bergen/Bjerknes Centre for Climate
Research, Norway. His main interest is in data management in the field of chemical oceanography with an
emphasis on data management of surface carbon dioxide
measurements and producing data products with those
data (e.g. SOCAT).
Jacek Piskozub is the Head of Physical Oceanography
Department at the Institute of Oceanology, Polish Academy of Sciences, in Sopot, Poland. His research interests
include ocean optics, air–sea interaction, marine aerosols
as well as ocean-related processes influencing the
climate.

John Plane is Professor of Atmospheric Chemistry at the
University of Leeds, UK. His current research interests
include halogens in the marine troposphere and the
impact of cosmic dust on planetary atmospheres. He
was a Research Fellow at Cambridge University from
1982 to 1985 and then Associate Professor at the University of Miami. In 1991, he moved to the University of
East Anglia, becoming Professor of Environmental Science in 1999. He has been at Leeds since 2006.

Elvira Pulido-Villena is a Research Scientist at the
Mediterranean Institute of Oceanography in Marseilles,
France. Her research interests are in the effects of atmospheric deposition on bacterial activity and dissolved
organic matter cycling in the surface ocean.

xxxvi

Birgit Quack is a Senior Scientist in the Chemical
Oceanography Research Unit at the GEOMAR,
Helmholtz Centre for Ocean Research, in Kiel, Germany.
Her research interests are in the air–sea exchange of
marine-chlorinated, -brominated and -iodinated halocarbons and in the investigation of their sources and the
driving factors for their regional and temporal variable
oceanic emission strengths.

Katie Read is a Postdoctoral Researcher in the National
Centre of Atmospheric Science (NCAS) based at the
University of York, UK. Her research interest is in the
long-term monitoring of trace gases in the boundary
layer.

Nicolas Reul has been a Researcher in the Laboratory of
Oceanography from Space at IFREMER, France, since
2001. His research interests are in ocean–atmosphere
interactions and ocean remote sensing, particularly
involving sea-surface salinity remote estimation from
space.

Matteo Rinaldi is a Researcher at the Institute of Atmospheric Sciences and Climate (ISAC) of the Italian National
Research Council (CNR) in Bologna, Italy. His research
interests are in ocean–atmosphere interactions particularly
involving the formation and atmospheric evolution of
organic aerosols. He received his PhD in Environmental
Sciences from the University of Urbino, Italy, in 2009,
defending a thesis titled “Marine aerosol: distinguishing
properties of primary and secondary components.”

Contributors

Contributors

xxxvii

Christian Rödenbeck works as a Scientist at the Max
Planck Institute for Biogeochemistry in Jena. His research
interest is in the quantification of global biogeochemical
fluxes from atmospheric, oceanic and remotely sensed data.

Shital Rohekar works as a SOLAS Project Integrator in
the School of Environmental Sciences at the University of
East Anglia, Norwich, UK. As a Project Integrator, she has
contributed towards the development of an aerosol–rain
chemistry database and the MEMENTO database. She
plans to analyse the data products from these global
databases and estimate the air–sea fluxes for the global
oceans. Shital holds a PhD from the University of
Cambridge and a master’s degree from the University of
Pune, India, specialising in Atmospheric Sciences.

Anna Rutgersson is a Professor of Meteorology in the
Department of Earth Sciences at Uppsala University in
Sweden. Her research interest is in air–sea interaction
processes, particularly involving physical processes
controlling the efficiency of gas exchange. She also
works with the impact of moving surface waves on
air–sea exchange and on turbulence in the atmosphere.
Her research includes both measurements, mainly using
micrometeorological methods, and modelling.

Erik Sahlée is a Senior Lecturer in Meteorology at
Uppsala University. His research is related to boundary-layer processes at the air–water interface, including
exchange processes of energy and matter and waveatmosphere interactions.

xxxviii

Alfonso Saiz-Lopez received his PhD from the University of East Anglia in 2005. He was a Postdoctoral
Scholar at the Jet Propulsion Laboratory, NASA, and at
the Harvard-Smithsonian Center for Astrophysics. In
2009, he was appointed Senior Research Scientist and
currently heads the Laboratory for Atmospheric and Climate Science at the Spanish Research Council (CSIC).
Since 2009, he is Affiliate Scientist at NCAR. His
research group focuses on atmospheric halogens and
their impact on atmospheric chemistry and climate.

Eric Saltzman is Professor of Earth System Science at
the University of California, Irvine. His research
involves studying the ocean–atmosphere exchange and
atmospheric cycling of climate-active trace gases and
aerosols and reconstructing the history of the Earth’s
atmosphere from polar ice cores.

Magdalena Santana-Casiano is Professor of Chemical
Oceanography in the Faculty of Marine Science at the
Universidad de Las Palmas de Gran Canaria, Spain. Her
research interests are in the chemical behaviour of iron in
seawater and the changes in the carbon dioxide system
with special focus on marine acidification.

Oliver Schneising is a Research Scientist at the Institute
of Environmental Physics at the University of Bremen,
Germany. His research interest is in satellite remote
sensing particularly involving the atmospheric greenhouse gases carbon dioxide and methane.

Contributors

Contributors

xxxix

Ute Schuster is a Senior Research Fellow in the College
of Life and Environmental Sciences at the University of
Exeter, Exeter, UK, having moved there in July 2013
from the School of Environmental Sciences at the University of East Anglia, Norwich, UK. Her research
interests are in the marine carbon cycle, the biogeochemical drivers of its observed variability in both time and
space, and its interaction with climate change.

Roland Séférian is a Researcher/Engineer at the Centre
National de Recherche de Météo-France (CNRMGAME) in the Laboratoire des Sciences du Climat et
de l’Environnement (LSCE) in Gif-sur-Yvette, France.
His research mainly focuses on climate marine carbon
cycle feedbacks and contributions of the near-term or the
long-term variability.

Hanwant Singh is a Senior Scientist in the Earth Science
Division of NASA Ames Research Center. His primary
research goal is to better understand the impact of human
activities on the chemistry and climate of the Earth’s
atmosphere through direct observations and data analysis. He is also the Editor-in-Chief of the international
journal Atmospheric Environment.

Jacqueline Stefels is a Senior Scientist and Lecturer at
the University of Groningen, within the group Ecophysiology of Plants. She is a marine phytoplankton biologist.
Her research interests are in the biological and environmental chemistry of dimethyl sulphide and related
compounds in oceans and sea ice, the role of Scompounds in phytoplankton physiological adaptation
to environmental stress and climate modelling. She is member of the SOLAS
Scientific Steering Committee and Co-chair of SCOR-WG140 on Biogeochemical
Exchange Processes at the Sea-Ice Interfaces.

xl

Tobias Steinhoff is a Postdoc at the GEOMAR,
Helmholtz Centre for Ocean Research, in Kiel, Germany.
His research interests are in the ocean carbon and oxygen
cycles and especially the air–sea gas exchange of CO2.
Furthermore, he is involved in the development of new
observational techniques for the carbon and oxygen
cycles.

Euripides Stephanou is a Professor of Environmental
Chemistry at the University of Crete. His research
interests are in the biogeochemical cycles of organic
compounds and organic aerosol formation. He has been
Coordinator and Principal Investigator in numerous
European projects. He is a member of the ERC Panel
Earth System Science and Editor of Environmental Science and Pollution Research. He is now the Rector of the
University of Crete.

Wu-ting Tsai is a Professor in the Department of Engineering Science and Ocean Engineering at the National
Taiwan University in Taipei, Taiwan. His research
interests are in developing numerical models to simulate
flows in close proximity to the air–sea interface which
involve surface waves and turbulence and in applying
these models to study the microscale air–sea interaction
processes and their potential impact on a global scale.

Robert Upstill-Goddard is Professor of Marine Biogeochemistry in the School of Marine Science and Technology at Newcastle University. His research interests include
the sea-surface microlayer, geophysical controls of
air–sea gas exchange and marine emissions of methane
and nitrous oxide. He has served on national and international committees, contributed to the European Nitrogen
Assessment and the CARBOEUROPE report on GHG emissions from European
waters and co-edited proceedings of an international symposium on GHG in mangrove
coasts.

Contributors

Contributors

xli

Roland von Glasow is a Professor of Atmospheric
Sciences in the School of Environmental Sciences at
the University of East Anglia in Norwich, UK. The
work of his group focuses on tropospheric halogen chemistry (marine boundary layer, polar regions, volcanic
plumes, salt lakes, free troposphere), the background
chemistry of the marine boundary layer, and the microphysics of aerosols and clouds in the marine boundary
layer.

Thibaut Wagener is an Assistant Professor at the Mediterranean Institute of Oceanography of the University of
Aix-Marseille in Marseille, France. His research interest
is in the deposition of atmospheric particles in the ocean
with a special focus on trace metal fluxes.

Douglas Wallace is Canada Excellence Research Chair
(CERC) in Ocean Science and Technology at Dalhousie
University, Canada, and former Professor of Marine
Chemistry at the Leibniz Institute of Marine Sciences,
Germany. His research focuses on developing new
observation instruments that are more sensitive in
detecting biogeochemical oceanic change than current
methods.

Brian Ward is a Lecturer in the School of Physics at
National University of Ireland, Galway (NUIG) and is
head of the AirSea Laboratory. His research interests are
related to small-scale processes on both sides of the air–sea interface and their impact on ocean–atmosphere
exchange. He is a member of the SOLAS Scientific
Steering Committee and the WCRP Data Advisory
Council.

xlii

Jonathan Williams is an Atmospheric Chemist.
Presently, he is a research group leader at the Max Planck
Institute with a focus on volatile organic compounds
(VOC) from forests and oceans. He has participated in
many international field campaigns on aircraft, ships and
at ground stations.

David Woolf is a Reader in Marine Physics at HeriotWatt University, based in Orkney, Scotland. His research
interests include air–sea interaction, climate, Earth
observation and renewable energy. He has been engaged
in research on the influence of waves, wave breaking and
bubbles on the air–sea exchange of gases and particles
throughout his career. He is currently leading a European
Space Agency project (www.oceanflux-ghg.org/) aimed
at improving air–sea gas exchange climatologies.

Christopher Zappa is a Lamont Associate Research
Professor at the Lamont-Doherty Earth Observatory of
Columbia University in Palisades, NY, USA. He was an
ONR Young Investigator from 2004 to 2007. He is
dedicated to understanding the processes that affect
ocean–atmosphere interaction and their boundary layers.
His focus includes wave dynamics and wave breaking;
the effect of near-surface turbulence on heat, gas and
momentum transport; airborne infrared, multispectral
and polarimetric remote sensing; upper ocean processes
and polar ocean processes; and coastal and estuarine
dynamics.

Contributors

Contributors

xliii

Franziska Ziska is a PhD student at the University of
Kiel working at the GEOMAR, Helmholtz Centre for
Ocean Research, in Kiel, Germany. Her research interest
is in the global distribution of oceanic and atmospheric
halocarbon concentrations including bromoform,
dibromomethane and methyl iodide and their future
trends.

.

Lead Authors

Dorothee C.E. Bakker Centre for Ocean and Atmospheric Sciences, School
of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK,
d.bakker@uea.ac.uk
Hermann W. Bange GEOMAR Helmholtz Centre for Ocean Research Kiel,
Düsternbrooker Weg 20, 24105 Kiel, Germany, hbange@geomar.de
Thomas G. Bell Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth,
PL1 3DH, UK. Previously at: Department of Earth System Science, University of
California Irvine, CA 92697, USA; and Centre for Ocean and Atmospheric
Sciences, School of Environmental Sciences, University of East Anglia, Norwich,
NR4 7TJ, UK, tbe@pml.ac.uk
Gerrit de Leeuw Climate Change Unit, Finnish Meteorological Institute, Helsinki,
Finland; Department of Physics, University of Helsinki, Helsinki, Finland; TNO,
P.O. Box 800153508 TA Utrecht, The Netherlands, gerrit.leeuw@fmi.fi
Christoph S. Garbe IWR, University of Heidelberg, Speyerer Street 6, 69115
Heidelberg, Germany, christoph.garbe@iwr.uni-heidelberg.de
Véronique C. Garçon LEGOS, Centre National de la Recherche Scientifique,
31401 Toulouse, Cedex 9, France, veronique.garcon@legos.obs-mip.fr
Nicolas Gruber Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich,
Universitätsstrasse 16, 8092 Zürich, Switzerland, nicolas.gruber@env.ethz.ch
Cécile Guieu Laboratoire d’Océanographie de Villefranche, CNRS, University
Paris 6, Villefranche sur Mer, France, guieu@obs-vlfr.fr
Truls Johannessen Geophysical Institute & Bjerknes Centre for Climate Research,
University of Bergen, Allégaten 70, NO-5007 Bergen, Norway
truls.johannessen@gfi.uib.no
Martin T. Johnson Centre for Ocean and Atmospheric Sciences, School of
Environmental Sciences, University of East Anglia, Norwich, NR47TJ, UK;
and Centre for environment, fisheries and aquaculture science, Pakefeld Road,
Lowestoft, Suffolk, NR33 0HT, UK, martin.johnson@uea.ac.uk (Editorial board)

xlv

xlvi

Peter S. Liss Centre for Ocean and Atmospheric Sciences, School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK; and Department
of Oceanography, Texas A & M University, College Station, TX 77843, USA,
p.liss@uea.ac.uk; liss@geos.tamu.edu
Christa A. Marandino GEOMAR Helmholtz Centre for Ocean Research Kiel,
Düsternbrooker Weg 20, 24105 Kiel, Germany, cmarandino@geomar.de
Anna Rutgersson Department of Earth Sciences, Uppsala University, Villavägen
16, SE-752 36 Uppsala, Sweden, anna.rutgersson@met.uu.se
Rob C. Upstill-Goddard School of Marine Science and Technology, Newcastle
University, Newcastle upon Tyne, NE1 7RU, UK, rob.goddard@ncl.ac.uk
Douglas Wallace Department of Oceanography, Dalhousie University, 1355
Oxford Street, PO BOX 15000, B3H 4R2 Halifax, NS, Canada
douglas.wallace@dal.ca

Lead Authors

Contributing Authors

Almuth Arneth Division of Ecosystem-Atmosphere Interactions, Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research/Atmospheric
Environmental Research, Kreuzeckbahn Str. 19, 82467 Garmisch-Partenkirchen,
Germany, almut.arneth@kit.edu
Steve R. Arnold School of Earth & Environment, Institute for Climate and
Atmospheric Science, University of Leeds, Leeds, LS2 9JT, UK
s.arnold@leeds.ac.uk
Alex Baker
Centre for Ocean and Atmospheric Sciences, School
of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK,
alex.baker@uea.ac.uk
Dorothee C.E. Bakker Centre for Ocean and Atmospheric Sciences, School
of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK
d.bakker@uea.ac.uk
Hermann W. Bange GEOMAR Helmholtz Centre for Ocean Research Kiel,
Düsternbrooker Weg 20, 24105 Kiel, Germany, hbange@geomar.de
Nicholas R. Bates Bermuda Institute of Ocean Sciences, GE01 St George’s,
Bermuda, nick.bates@bios.edu
Nicolas Bellouin Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB,
United Kingdom, nicolas.bellouin@metoffice.gov.uk
Laurent Bopp Centre National de la Recherche Scientifique (CNRS), Laboratoire
des Sciences du Climat et l’Environnement (LSCE), Orme Des Merisiers, 91191
Gif sur Yvette, France, laurent.bopp@lsce.ipsl.fr
Alberto V. Borges Chemical Oceanography Unit, Institut de Physique (B5),
University of Liège, B-4000 Liège, Belgium, alberto.borges@ulg.ac.be
Jacqueline Boutin LOCEAN/CNRS/UPMC/IRD, Université Pierre et Marie Curie,
75252 Paris Cedex O5, France, jb@locean-ipsl.upmc.fr
Philip W. Boyd Institute for Marine and Antarctic Science, University of Tasmania,
7005 Hobart, TAS, Australia, philip.boyd@utas.edu.au
Astrid Bracher Alfred-Wegener-Institute for Polar & Marine Research,
Bremerhaven and Institute of Environmental Physics, University of Bremen,
Bremen, Germany, astrid.bracher@awi.de

xlvii

xlviii

Contributing Authors

John P. Burrows Institute of Environmental Physics and Remote Sensing, University of Bremen, Postfach 330440, 28334 Bremen, Germany
burrows@iup.physik.uni-bremen.de
Lucy J. Carpenter Department of Chemistry, University of York, York, YO10
5DD, UK, lucy.carpenter@york.ac.uk
Elizabeth E. Dahl
eedahl@loyola.edu

Loyola University Maryland, Baltimore, MD, USA,

Gerrit de Leeuw Climate Change Unit, Finnish Meteorological Institute, Helsinki,
Finland; Department of Physics, University of Helsinki, Helsinki, Finland; TNO,
P.O. Box 800153508 TA Utrecht, The Netherlands, gerrit.leeuw@fmi.fi
Bruno Delille Chemical Oceanography Unit, University of Liège, Allée du 6 Août,
4000 Liège, Belgium, bruno.delille@ulg.ac.be
Karine V. Desboeufs LISA/IPSL, CNRS, Universités Paris Est Créteil and Paris
Diderot, Créteil, France, desboeufs@lisa.u-pec.fr
François Dulac LSCE/IPSL, CNRS/CEA/UVSQ, Gif sur Yvette, France
francois.dulac@cea.fr
M. Cristina Facchini Institute of Atmospheric Sciences and Climate (ISAC),
National Research Council (CNR), Via Gobetti 101, 40129 Bologna, Italy
mc.facchini@isac.cnr.it
Christopher W. Fairall Physical Sciences Division, NOAA Earth System
Research Laboratory, Boulder, CO, USA, chris.fairall@noaa.gov
Katja Fennel Department of Oceanography, Dalhousie University, 1355 Oxford
Street, PO BOX 15000, B3H 4R2 Halifax, NS, Canada, katja.fennel@dal.ca
Jordi Font Department of Physical Oceanography, Institut de Ciènces del MarCSIC, 08003 Barcelona, Spain, jfont@icm.csic.es
Tobias Friedrich School of Ocean and Earth Science and Technology, University
of Hawai, 96822 Honolulu, HI, USA, tobiasf@hawaii.edu
Brett Gantt North Carolina State University, Raleigh, NC, USA
bdgantt@gmail.com
Christoph S. Garbe IWR, University of Heidelberg, Speyerer Street 6, 69115
Heidelberg, Germany, christoph.garbe@iwr.uni-heidelberg.de
Hugo A.C. Denier van der Gon TNO, P.O. Box 80015, 3508 TA Utrecht,
The Netherlands, hugo.deniervandergon@tno.nl
Nicolas Gruber Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich,
Universitätsstrasse 16, 8092 Zürich, Switzerland, nicolas.gruber@env.ethz.ch
Jeffrey Hare Joint Institute for Marine and Atmospheric Research (JIMAR),
University of Hawaii at Manoa, 96822 Honolulu, HI, USA, jeff.hare@noaa.gov
Detlev Helmig Institute of Arctic and Alpine Research, Boulder, CO, USA
detlev.helmig@colorado.edu

Contributing Authors

xlix

Eric J. Hintsa University of Colorado and NOAA Global Monitoring Division,
Boulder, USA, eric.j.hintsa@noaa.gov
David T. Ho Department of Oceanography, University of Hawaii, 1000 Pope Road,
96822 Honolulu, HI, USA, ho@hawaii.edu
Claire Hughes Environment Department, University of York, Heslington, York,
YO10 5DD, UK, c.hughes@york.ac.uk
Lyatt Jaeglé Department of Atmospheric Sciences, University of Washington,
BOX 351640, 98195 Seattle, WA, USA, jaegle@uw.edu
Martin T. Johnson Centre for Ocean and Atmospheric Sciences, School of
Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK; Centre
for environment, fisheries and aquaculture science, Pakefeld Road, Lowestoft, Suffolk, NR33 0HT, UK, martin.johnson@uea.ac.uk
Arancha Lana Department of Marine Technologies, Operational Oceanography
and Sustainability, IMEDEA-CSIC, 07190 Balearic Islands, Spain
alana@imedea.uib-csic.es
Baerbel Langmann
Institute of Geophysics, University
KlimaCampus, Hamburg, Germany, baerbel.langmann@zmaw.de

of

Hamburg,

James D. Lee Department of Chemistry, University of York, YO10 5DD York,
UK, james.lee@york.ac.uk
Peter S. Liss Centre for Ocean and Atmospheric Sciences, School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK; Department of
Oceanography, Texas A & M University, College Station, TX, 77843, USA
p.liss@uea.ac.uk, liss@geos.tamu.edu
Carolin R. Löscher Institute of Microbiology, Christian-Albrechts University
Kiel, Biologiezentrum, Am Botanischen Garten 1-9, D-24118 Kiel, Germany,
cloescher@ifam.uni-kiel.de
Natalie M. Mahowald Department of Earth and Atmospheric Sciences, Cornell
University, Snee 2140, 14853 Ithaca, NY, USA, mahowald@cornell.edu
Emilio Marañón Departamento de Ecologı́a y Biologı́a Animal, Facultad de
Ciencias del Mar, University of Vigo, Vigo, Spain, em@uvigo.es
Lisa A. Miller Institute of Ocean Sciences, Fisheries and Oceans Canada, Sidney,
BC V8L 4B2, Canada, lisa.miller@dfo-mpo.gc.ca
Robert M. Moore Department of Oceanography, Dalhousie University, 1355
Oxford Street, PO BOX 15000, B3H 4R2 Halifax, NS, Canada, robert.moore@dal.ca
S. Wajih A. Naqvi CSIR-National Institute of Oceanography, Dona Paula, Goa
403 004, India, naqvi@nio.org
Philip D. Nightingale Plymouth Marine Laboratory, Prospect Place, The Hoe,
PL1 3DH Plymouth, UK, pdn@pml.ac.uk
Colin O’Dowd School of Physics, National University of Ireland, Galway, Ireland,
colin.odowd@nuigalway.ie

l

Nazli Olgun GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker
Weg 20, 24105 Kiel, Germany, nolgun@ifm-geomar.de, nazliolgun.kiyak@itu.edu.tr
Are Olsen Geophysical Institute and Bjerknes Centre for Climate Research, University of Bergen, Allégaten 70, NO-5007 Bergen, Norway
are.olsen@gfi.uib.no
Abdirahman M. Omar Bjerknes Centre for Climate Research, Uni Research,
Allégaten 55, 5007 Bergen, Norway, abdir.omar@uni.no
Heidi Pettersson Marine Research Unit, Finnish Meteorological Institute, (Erik
Palménin aukio 1, 00560 Helsinki), P.O.Box 503, FI-00101 Helsinki, Finland, heidi.
pettersson@fmi.fi
Benjamin Pfeil University of Bergen/Bjerknes Centre for Climate Research,
Allégaten 55, 5007 Bergen, Norway, benjamin.pfeil@gfi.uib.no
Jacek Piskozub Institute of Oceanology, Polish Academy of Sciences,
Powstancow Warszawy 55, 81-712 Sopot, Poland, piskozub@iopan.gda.pl
John M.C. Plane University of Leeds, Leeds, LS2 9JT, UK
j.m.c.plane@leeds.ac.uk
Elvira Pulido-Villena Aix Marseille Université, CNRS/INSU, IRD, Mediterranean Institute of Oceanography (MIO), UM 110, 13288 Marseille (France)
elvira.pulido@univ-amu.fr
Birgit Quack GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker
Weg 20, 24105 Kiel, Germany, bquack@geomar.de
Katie A. Read National Centre of Atmospheric Science (NCAS), University of
York, York, YO10 5DD, UK, km519@york.ac.uk
Nicolas Reul IFREMER, Laboratoire d’Océanographie Spatiale, Centre Méditerranée,
Zone Portuaire de Brégaillon, CS20 330, 83507 La Seyne-sur-Mer Cedex, France,
nicolas.reul@ifremer.fr
Matteo Rinaldi Institute of Atmospheric Sciences and Climate (ISAC), National
Research Council (CNR), Via Gobetti 101, 40129 Bologna, Italy
m.rinaldi@isac.cnr.it
Christian Rödenbeck Max Planck Institute for Biogeochemistry, Jena, Germany,
christian.roedenbeck@bgc-jena.mpg.de
Shital S. Rohekar Centre for Ocean and Atmospheric Sciences, School
of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK,
s.rohekar@uea.ac.uk
Erik Sahlée Department of Earth Sciences, Uppsala University, Villavägen 16,
SE-752 36 Uppsala, Sweden, erik.sahlee@met.uu.se
Alfonso Saiz-Lopez Institute of Physical Chemistry Rocasolano - CSIC, 28006
Madrid, Spain, a.saiz@csic.es
Eric S. Saltzman Department of Earth System Science, University of California,
92697 Irvine, CA, USA, eric.saltzman@uci.edu

Contributing Authors

Contributing Authors

li

J. Magdalena Santana-Casiano Facultad de Ciencias del Mar, Universidad de
Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain,
jmsantana@dqui.ulpgc.es
Oliver Schneising Institute of Environmental Physics and Remote Sensing,
University of Bremen, Postfach 330440, 28334 Bremen, Germany
oliver.schneising@iup.physik.uni-bremen.de
Ute Schuster College of Life and Environmental Sciences, University of Exeter,
Hatherly Laboratories, Prince of Wales Road, Exeter, EX4 4PS, UK
u.schuster@exeter.ac.uk
Roland Séférian CNRM-GAME/GMGEC/ASTER & IPSL/LSCE, Toulouse,
France, roland.seferian@lsce.ipsl.fr, roland.seferian@meteo.fr
Hanwant B. Singh NASA Ames Research Centre, Mountain View, CA, USA,
hanwant.b.singh@nasa.gov
Jacqueline Stefels Centre for Life Sciences, Ecophysiology of Plants, University
of Groningen, Groningen, The Netherlands, j.stefels@rug.nl
Tobias Steinhoff GEOMAR Helmholtz Centre for Ocean Research Kiel,
Düsternbrooker Weg 20, 24105 Kiel, Germany, tsteinhoff@geomar.de
Euripides G. Stephanou Department of Chemistry-University of Crete, Environmental Chemical Processes Laboratory (ECPL), GR-71003 Voutes-Heraklion,
Greece, stephanou@chemistry.uoc.gr
Pierre-Yves Le Traon IFREMER and Mercator Ocean, Toulouse, France
pierre.yves.le.traon@ifremer.fr
Wu-ting Tsai Department of Engineering Science and Ocean Engineering,
National Taiwan University, 10617 Taipei, Taiwan, wttsai@ntu.edu.tw
Roland von Glasow Centre for Ocean and Atmospheric Sciences, School of
Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK,
r.von-glasow@uea.ac.uk
Thibaut Wagener Aix Marseille Université, CNRS/INSU, IRD, Mediterranean
Institute of Oceanography (MIO), UM 110, 13288 Marseille, France
thibaut.wagener@univ-amu.fr
Brian Ward School of Physics, National University of Ireland, Galway, Ireland,
bward@nuigalway.ie
Jonathan Williams Max Planck Institute for Chemistry, Department of Atmospheric
Chemistry, Hahn-Meitner Weg 1, D-55128 Mainz, Germany
jonathan.williams@mpic.de
David K. Woolf International Centre for Island Technology, Heriot-Watt University, The Old Academy, Back Road, Stromness, Orkney, Scotland KW16 3AW
d.k.woolf@hw.ac.uk
Christopher J. Zappa Lamont-Doherty Earth Observatory, Columbia University,
61 Route 9W, 10964 Palisades, NY, USA, zappa@ldeo.columbia.edu
Franziska Ziska GEOMAR Helmholtz Centre for Ocean Research Kiel,
Düsternbrooker Weg 20, 24105 Kiel, Germany, fziska@geomar.de

1

Short-Lived Trace Gases in the Surface Ocean
and the Atmosphere
Peter S. Liss, Christa A. Marandino, Elizabeth E. Dahl, Detlev Helmig,
Eric J. Hintsa, Claire Hughes, Martin T. Johnson, Robert M. Moore,
John M.C. Plane, Birgit Quack, Hanwant B. Singh, Jacqueline Stefels,
Roland von Glasow, and Jonathan Williams

Abstract

The two-way exchange of trace gases between the ocean and the atmosphere
is important for both the chemistry and physics of the atmosphere and the
biogeochemistry of the oceans, including the global cycling of elements. Here
we review these exchanges and their importance for a range of gases whose
lifetimes are generally short compared to the main greenhouse gases and which
are, in most cases, more reactive than them. Gases considered include sulphur
and related compounds, organohalogens, non-methane hydrocarbons, ozone,
ammonia and related compounds, hydrogen and carbon monoxide. Finally, we
stress the interactivity of the system, the importance of process understanding for
modeling, the need for more extensive field measurements and their better
seasonal coverage, the importance of inter-calibration exercises and finally the
need to show the importance of air-sea exchanges for global cycling and how
the field fits into the broader context of Earth System Science.

1.1

Introduction

Despite their seemingly low abundances, short-lived
trace gases in the atmosphere critically influence
global climate change, stratospheric ozone chemistry,
and the oxidative capacity of the atmosphere. Because
the ocean–atmosphere interface covers a large extent
of the Earth’s surface, the ocean is a major control on
the atmospheric budget of many trace gases. Further,
the chemical, biological, and physical processes that
occur around this interface have a large impact on

P.S. Liss (*)
e-mail: p.liss@uea.ac.uk
C.A. Marandino (*)
e-mail: cmarandino@geomar.de

trace gas cycling between the oceanic and atmospheric
reservoirs.
In this chapter we present current knowledge on
surface ocean cycling processes, atmospheric reactivity
and importance, and the influence of air-sea exchange
for a suite of trace gases which generally have shorter
atmospheric lifetimes than those discussed in Chap. 3
(i.e. CO2, N2O and CH4). We focus not only on research
from the past 10 years, but also on topics where
much uncertainty remains. Unfortunately, not all of
the important issues can be addressed here. Notably
missing is detailed information about trace gas cycling
in polar regions and particularly over-ice processes,
including the role of frost flowers.
The chemical species discussed in the chapter are
intimately related to the topics discussed in Chaps. 2, 4,
and 5 of this book. More accurate parameterisations
of gas exchange will allow for better calculations of

P.S. Liss and M.T. Johnson (eds.), Ocean-Atmosphere Interactions of Gases and Particles,
Springer Earth System Sciences, DOI 10.1007/978-3-642-25643-1_1, # The Author(s) 2014

1

2

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

oceanic emissions and uptake (Chap. 2). Many
of the gases described, such as sulphur gases and
non-methane hydrocarbons, are important for marine
boundary layer particle formation and cloud coverage
(Chap. 4). And, finally, predictive tools for the impacts
of future environmental variability on these gases are
required (Chap. 5).
In the chapter we have tried to rationalise the use
of units wherever possible. However, different units
are used by, for example, the atmospheric and ocean
communities, the former often using volume units
(e.g. ppt by volume), whereas the latter use mass or
molar units (both of which are found widely). So, to
avoid confusion, we have not tried to standardise on a
common unit for all measurements reported here but
have allowed the most common usage to remain. In the
case of mass units we have tried to use molar units as
much as possible but for instance have allowed mass
units for global fluxes, since this is what is routinely
done in that community.

1.2

Sulphur and Related Gases

Based largely on his own field measurements,
Lovelock et al. (1972) suggested that dimethylsulphide
(DMS) represents more than 50 % of the natural
sulphur emission to the atmosphere, so closing the
previous large gap in the global sulphur budget.
This discovery stimulated much research as well as
the so-called CLAW hypothesis (Charlson et al. 1987,
see Sect. 1.2.3.2), which suggested an important role
of DMS in climate regulation. Over the last decades,
the biological sources of oceanic DMS and its
precursor dimethylsulphoniopropionate (DMSP), the
ocean–atmosphere flux of DMS, its atmospheric
oxidation and climate relevance have been studied in
detail. This section reviews the relevant processes of
sulphur cycling, via DMS as well as other sulphur
compounds, between the ocean and atmosphere and
their potential climatic relevance.

1.2.1

DMS(P) in the Surface Ocean

1.2.1.1 Ecosystem Dynamics
DMS originates from dimethylsulphoniopropionate
(DMSP), a compound solely produced by phyto-

plankton. DMSP is thought to regulate osmotic pressure in the cells of some plankton species and
may also serve an anti-grazing function (Kirst et al.
1991). The conversion of DMSP to DMS is enzymatically mediated by algal or bacterial DMSP lyases.
With respect to regulation of DMSP biosynthesis, currently two hypotheses exist: the anti-oxidant hypothesis
(Sunda et al. 2002) and the overflow-metabolism hypothesis (Stefels 2000). Both hypotheses predict increased
DMSP production under high-light conditions, but the
difference is associated with UV-radiation (UVR),
which would result in increased DMSP production in
the view of the anti-oxidant hypothesis, but in a reduction according to the overflow hypothesis. The issue is
currently unresolved, since the number of studies that
differentiate between conditions with and without UVR
is very limited.
In marine ecosystems, DMSP, DMS and its oxidation product dimethylsulphoxide (DMSO) are dynamically linked through a myriad of interactions among
biological and chemical parameters (Stefels et al.
2007). As a result, algal biomass parameters often
show different temporal transitions than the sulphur
compounds. In fact, an overarching correlation was
found between DMS surface-water concentration
and the average radiation in the surface mixed layer,
or solar radiation dose (SRD), and not with phytoplankton biomass (Vallina and Simo 2007). This
mismatch between source and end product is called
the DMS-summer-paradox and has been observed
in large areas of the subtropical and temperate oceans
(Dacey et al. 1998; Simo and Pedros-Alio 1999).
The simplicity of the correlation between SRD
and DMS concentration makes it a useful parameter
in large-scale models, but it cannot be used as a
mechanistic explanation of underlying processes.
Moreover, in other parts of the North Atlantic Ocean,
no significant correlation was found between SRD and
seasonal variations in DMS (Belviso and Caniaux
2009; Derevianko et al. 2009). On a global scale, the
correlation only exists after removing the variance in
the global DMS database through averaging into a
single value per SRD range (Derevianko et al. 2009).
Other factors may therefore play more important
roles in determining the distribution of DMS, whereby
interactions with light can take place.
Effects of changes in light condition on ecosystem
processes are associated with shifts in species

1.2 Sulphur and Related Gases

composition. The current view is that bacteria are
more susceptible to high radiation than phytoplankton
species (Herndl et al. 1993; Simo and Pedros-Alio
1999), but shifts also occur within the phytoplankton
realm. The ability to produce DMSP may be of
competitive advantage for algae, not only because of
its potential protective physiological function under
high-light conditions, but also because of adverse
effects on grazers. Applying different scenarios of a
dynamic model to the Sargasso Sea indicated that
solar-radiation-induced DMS exudation by phytoplankton is the major contributor to the DMS summer
paradox (Vallina et al. 2008). In a review, with
the aim to assist sulphur modellers, Stefels et al.
(2007) recommend distinguishing six functional phytoplankton groups, based on their size, DMSP production
and conversion ability, and interactions with grazers.
Shifts in dominance of these groups in association
with shifts in bacterial community will have drastic
consequences for the turnover of organic carbon and
sulphur compounds.

1.2.1.2 DMS Yield
Given the many conversion pathways that originate
from DMSP, it is difficult to assess what fraction
of DMSP ultimately ends up as atmospheric DMS.
As a general rule, the fraction of DMSP produced
that is emitted to the atmosphere as DMS is thought
to be between 1 % and 10 % (Bates et al. 1994).
This far larger DMSP pool therefore leaves room
for a several-fold change in the DMS flux, which
could arise when shifts in any of the conversion
pathways occur. Unfortunately, there is no database
of DMSP concentrations comparable to the one for
DMS, which makes it difficult to verify underlying
ecosystem pathways (see Chap. 5 for discussion of
such databases).
The potential for shifts in the DMS yield from
DMSP can be illustrated with four scenarios (Fig. 1.1).
The terms “Low” and “High DMSP” refer to variation in the species composition of a system, where
one system is comprised of species that do not
produce large amounts of DMSP – such as diatoms,
prochlorophytes and cyanophytes – and the other of
species that do produce DMSP – such as haptophytes,
chrysophytes and dinoflagellates (Stefels et al. 2007).
The term “High stress” refers to conditions that adversely affect algal or bacterial physiology, such as
nutrient limitation and light inhibition.

3

Under low-stress conditions (Fig. 1.1a, b), DMS
produced directly by algae is usually found to be
low (compare exponentially growing algal cultures
that have little DMS), and most of the DMSP is
released from algal cells through grazing or viral
lysis. DMSP has been found to be a major source of
reduced sulphur for bacteria, but there is not always
a good match between production and consumption.
Although bacteria can also cleave DMSP into
DMS, the more preferred metabolic pathway is demethylation, which does not yield DMS. About onethird of surface-ocean bacteria are capable of
demethylating DMSP (Howard et al. 2006). It provides
more energetic benefits than cleavage and is a relatively
economic way to assimilate reduced sulphur. Kiene
and co-workers proposed a model in which bacterioplankton will prefer the demethylation over the cleavage pathway at low dissolved DMSP concentrations
(Kiene et al. 2000) (Fig. 1.1a). It is suggested that
the total sulphur demand of bacteria can be derived
in this way and, as such, this pathway can be directly
linked to bacterial production. At higher dissolved
DMSP concentrations (Fig. 1.1b), the DMSP fraction
that is not assimilated would then be available to the
cleavage pathway. In other words, the fraction that
is converted to DMS depends on the biomass and
growth of the bacterial community; if the bacterial
sulphur demand were relatively small, a larger portion
of DMSP would be converted to DMS, either by
bacterial or algal enzymes.
Under high-stress conditions (Fig. 1.1c,d), such
as nutrient limitation or light inhibition, the relative
amount of DMS produced by algae directly is often
higher. If DMS is released under conditions of high
light, UV radiation will photo oxidise an important
part of DMS into DMSO. High-stress conditions are
also often detrimental to bacteria (Herndl et al. 1993),
which would then result in a reduced S-demand
and hence an increase in the relative contribution of
the cleavage pathway that produces DMS. The net
result of the different scenarios with respect to
DMS emission is with our current knowledge difficult
to quantify, but may vary by over an order of
magnitude.

1.2.1.3 Predicted Impact of Climate Change
Accurate implementation of DMS cycling in global
climate models requires the validation of such models
with DMS field measurements. A first global DMS

4

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

Fig. 1.1 Schematic representation of the major pathways
within the marine sulphur cycle and the impact of four different
regimes on the relative contribution of each pathway and
ultimately on the fraction of DMSP that is emitted to the
atmosphere as DMS. “Low DMSP” and “High DMSP” refer to

variation in the species composition of a system under blooming
or non-blooming conditions. The stress term refers to conditions
that adversely affect algal or bacterial physiology, such as
nutrient limitation and light inhibition. Thickness of the arrows
represents an estimate of the magnitude of the process

climatology, derived exclusively from field data, was
published more than a decade ago (Kettle et al. 1999;
Kettle and Andreae 2000). The number of surface DMS
data available at that time was approximately 17,000.
They are deposited at the Global Surface Seawater
(GSS) DMS database maintained by NOAA/PMEL,
which is freely accessible at: http://saga.pmel.noaa.
gov/dms/. Since the first data collection by Kettle and
co-workers, the scientific community was encouraged
to upload new data, which increased the number of
DMS measurements threefold. Following this increase,
a joint initiative of the SOLAS Project Integration,

COST Action 735, and EUR-OCEANS was launched
to produce an updated DMS climatology (Lana et al.
2011), which is illustrated by the global monthly climatology shown in Fig. 1.2. The DMS climatology is now
available and posted for open access at the SOLAS
Project Integration website (www.bodc.ac.uk/solas_
integration/, see Chap. 5 for further detail).
The availability of a DMS climatology allowed
verification of model results and hence the inclusion
of DMS in global ocean models. A first evaluation of
nine models has been published: four global 3D models
and five local 1D models (Le Clainche et al. 2010).

1.2 Sulphur and Related Gases

5

Fig. 1.2 Monthly climatology of DMS concentrations
(nmol L
1). Note that the scale is capped at 15 nmol L
1 to
ensure readability of the plots, although only a few specific

regions exceed 15 nmol L
1 DMS concentration (Reproduced
from Lana et al. (2011) by permission of the American
Geophysical Union)

The (in)ability of the models to predict the summerDMS maximum that is often observed at low to mid
latitudes, appeared to significantly affect estimates
of global DMS emissions predicted by the models.
A major conclusion of this intercomparison was
that prognostic DMS models need to give more
weight to the direct impact of environmental forcing
(e.g. irradiance) on DMS dynamics to decouple them
from ecological processes. Potential controlling effects

of light and nutrients on DMSP biosynthesis by
algae, resulting in shifts in C:S ratios or species
composition, were also found to be key issues.
Global climate change is predicted to result in
shallowing of the mixed layer depth at some places
with increased storm events at others. This would
have drastic impacts on the surface-ocean light environment and nutrient inputs and consequently on
plankton development (Schmittner 2005). Predictions

6

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

of the effects on DMS flux have yielded very
different outcomes: Kloster and co-workers predict
a 10 % reduction in global DMS flux by the end
of the century (Kloster et al. 2007); Vallina et al.
(2007) found only a 1 % increase in net global DMS
concentrations upon a 50 % increase in atmospheric
CO2; Cameron-Smith and co-workers simulated a
50 % increase in DMS flux to the atmosphere over
large regions of the Southern Ocean when atmospheric
CO2 increases to 970 ppm (Cameron-Smith et al. 2011).
The latter result was related to concurrent sea-ice
changes and ocean ecosystem composition shifts. In
a comparison with previous model outcomes, the
authors concluded that increasing model complexity
appears to be associated with reduced DMS emission at
the equator and increased emissions at high latitudes.
The variation in model outcomes illustrates how
important it is to improve our understanding of the
underlying ecosystem processes.
The different scenarios presented in Fig. 1.1
show that under different environmental conditions
the DMS yield from DMSP can shift by possibly
an order of magnitude. This can potentially result
in changes in DMS emission of several hundreds of
percent, an extent that the recent publication from
Quinn and Bates (2011), which argues for the retirement of the CLAW hypothesis (see Sect. 1.2.3.2),
did not consider possible. Whether such shifts are
plausible at the global scale remains to be seen.
Since DMS in mechanistic models is dynamically
linked to the production of DMSP by phytoplankton,
validating this model parameter would help us
to understand the underlying controlling factors.
Building a DMSP database comparable to the current
DMS database would therefore be of great value.

1.2.2

Other Sulphur and Related Gases
in the Surface Ocean

1.2.2.1 Carbonyl Sulphide
Carbonyl sulphide (COS) is the most abundant sulphur
gas in the atmosphere which has led to the study of airsea exchange as a source. Measurements in surface
seawaters indicate a net flux from the ocean to the
atmosphere of approximately 0.1 Tg COS year
1 in
the open ocean and 0.2 Tg COS year
1 in the coastal
ocean (with considerable uncertainty, Watts 2000;
Kettle et al. 2002). COS is produced photochemically

in the surface ocean from the interaction of UV
light with coloured dissolved organic matter (CDOM)
(Uher and Andreae 1997) and its main loss mechanism
in the water column is hydrolysis (Andreae and Ferek
1992). Seawater concentrations can vary between 1 and
100 pM (~2 orders of magnitude less than DMS) and
typical atmospheric concentrations are about 500 ppt(v)
(Kettle et al. 2001). It has been suggested that in situ
degradation rates are high in the surface ocean. Diurnal
cycling of COS in surface waters has been reported,
during which the ocean acts as an atmospheric sink
late at night and early in the morning and as a source
for the remainder of the day (e.g. Kettle et al. 2001).
Approximately 40 % of the total atmospheric source
of COS can be accounted for by oceanic emissions.

1.2.2.2 Carbon Disulphide
There are limited published measurements of carbon
disulphide (CS2) in the surface ocean. CS2, like COS,
is known to have a photochemical source from CDOM
(Xie et al. 1998), as well as a biological source
(Xie and Moore 1999). There is no significant sink of
CS2 in the water column except air-sea exchange,
although a small diurnal signal has been observed
(Kettle et al. 2001). Surface seawater concentrations
between 5 and 150 pM have been reported (highest
values in upwelling areas, Kettle et al. 2001). The
estimated global oceanic flux to the atmosphere is
0.13–0.24 Tg CS2 year
1 (Xie and Moore 1999).
This constitutes between 20 % and 35 % of the total
atmospheric source of CS2 (Watts 2000).
1.2.2.3 Hydrogen Sulphide
Hydrogen sulphide (H2S), originally thought to be
the dominant volatile sulphur compound in the oceans,
makes only a minor contribution to the total marine
flux of sulphur to the atmosphere (Watts 2000) and so
to the global cycling of sulphur. The ocean source to
the atmosphere has been estimated as 1.8 Tg year
1,
which is about 25 % of the total atmospheric source
of H2S (Watts 2000). In the ocean water column,
H2S is produced mainly as part of the hydrolysis
of COS (Elliott 1989) and from degradation of
particulate organic material. In addition, there is some
evidence for a direct algal source (Andreae et al. 1991).
H2S is rapidly oxidised in surface waters (2–50 h),
with an expected predawn maximum in its diurnal
cycle (Andreae 1990). This fast oxidation is made
manifest in particular environments, such as upwelled

1.2 Sulphur and Related Gases

waters off Namibia, where microgranules of elemental sulphur arising from oxidation of sulphide
can be detected by remote sensing (Weeks et al.
2002; Brüchert et al. 2009).

1.2.2.4 Methanethiol
Methanethiol (CH3SH), also known as methylmercaptan, is produced from DMSP in a pathway that
competes with DMS production and may even be the
dominant product in some cases (Kiene et al. 2000).
However, CH3SH is thought to be rapidly removed
from the water column due to assimilation into
proteins by marine bacteria (Kiene 1996) and reaction
with dissolved organic matter (Kiene et al. 2000).
CH3SH may also have a small photochemical sink in
seawater (Flock and Andreae 1996). Seawater concentrations are assumed to be considerably lower than for
DMS, although few measurements have been made
(Kiene 1996). However, what data there are indicate
that levels of CH3SH in the Atlantic Ocean range
from 150 to 1,500 pM and are typically about 10 %
those of DMS (Kettle et al. 2001). Open ocean values
were close to 300 pM, but increased dramatically in
upwelling and coastal regions, such that the mean
concentration of CH3SH was as high as 20 % that of
DMS. In some areas the ratio of CH3SH to DMS
was unity. This would make CH3SH the second most
dominant volatile sulphur compound in seawater and
suggests that CH3SH needs to receive greater consideration in future studies of DMS production or
DMSP transformation and in estimates of the flux
of biogenic sulphur from the oceans.
1.2.2.5 Dimethyl Selenide
There are considerable similarities between the
biogeochemical cycles of the Group VIb elements
sulphur and selenium in the marine environment.
For example, both can be methylated by microorganisms to form volatile species which are required
to balance their geochemical budgets. However,
although DMS as the volatile S form has been extensively studied, there has been much less attention
paid to volatile Se. One of the few studies is that
of Amouroux et al. (2001), who measured volatile Se
in surface waters of the North Atlantic in summer.
They found that the dominant forms were DMSe
(the direct analogue of DMS) and the mixed S/Se
compound DMSeS, and calculate that emissions from
this part of the oceans at the period of measurement
(6.4 (range 1.4–17.9) nmol m
2 d
1) were of the right

7

order of magnitude to balance the Se budget, if they
applied globally. As shown in Fig. 1.3, the positive
relationships between seawater concentrations of DMSe
and DMS and DMSe and coccolithophore carbon
were interpreted to mean that, like DMS, DMSe is
an algal product.
On emission to the atmosphere, the volatile Se
compounds will be oxidised and incorporated into
particulate phases in pathways which are probably
analogous to those discussed in Sect. 1.2.3 for DMS,
although the four orders of magnitude smaller flux
of volatile Se compared to S means that it’s significance for atmospheric properties will be negligible.
However, some of the sea-to-air flux of Se will be
dry and wet deposited to the land where it could
have implications for human health (Rayman 2000).
The pathway of Se from the oceans to land via the
atmopshere is supported by measurements of the Se
content of mosses in Norway which show decreased
concentrations with distance from the sea, indicating
a marine source (E. Steinnes, 2003, personal
communication).

1.2.3

Atmospheric Sulphur and Related
Gases

The recent reviews by Faloona (2009) and Vogt
and Liss (2009) provide a detailed picture of the
literature regarding sulphur cycling in the marine
boundary layer. Here we focus on developments in
the last 5–10 years after a few introductory remarks.
Sulphur is present in the atmosphere in both gaseous
and particulate forms. It is emitted by natural processes
such as from volcanoes, the ocean, vegetation and
natural biomass burning as well as anthropogenic
processing, mainly combustion of sulphur-containing
fuels (oil, coal, anthropogenic biomass burning)
and some smeltering processes. Sulphur is emitted in
various oxidation states (e.g., -II: COS, H2S, CS2,
DMS; þIV: SO2; þVI: H2SO4) and is oxidised
in the atmosphere, eventually resulting in sulphate
(oxidation number þ VI) unless deposited prior to
complete oxidation.
The natural marine source is dominated by DMS.
In some ocean regions, especially in the northern
hemisphere, anthropogenic sulphur emissions from
shipping (as SO2 and particulate sulphate) are already
of similar magnitude to natural marine sulphur
emissions (Corbett et al. 1999) and shipping emissions

8

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

Fig. 1.3 Relationship
between DMSe and
coccolithophorid carbon
concentrations (a) and DMSe
and DMS (b) in surface waters
of the North Atlantic in June
1998 around 60  N, 20  W.
Error bars represent the
relative standard deviation
of the reproducibility of
the analytical methods
on duplicate samples
(Reproduced from Amouroux
et al. (2001) by permission
of Elsevier)

are expected to increase in the next decades
(e.g. Eyring et al. 2010). The estimation of source
strengths is not an easy task and comes with
large uncertainties. For an oceanic gas, such as DMS,
the “bottom-up” flux estimate requires reliable information about the oceanic concentrations (as indicated
above) as well as the sea-air flux which depends on
its temperature-dependent solubility, wind speed and
other poorly quantified factors such as surfactants
and sea state. The atmospheric concentration of
DMS (typically a few nmol m
3) is often ignored in
estimating sea to air fluxes of DMS, since relative to
seawater concentrations, it is very small and thereby

an error of only a few percent is made. The sea-to-air
flux of DMS and similar gases is a non-linear function
of wind speed, so it is important to include wind
speed variations and possibly even gusts in global
estimates of its flux. Kettle and Andreae (2000) showed
that depending on the choice of the sea-air-exchange
parameterisation, the global flux can vary between
15 and 33 Tg(S) year
1 using the same database for
oceanic DMS. Using an approximately threefold
larger data set, Lana et al. (2011) estimated the global
DMS flux to be 28.1 (17.6–34.4) Tg(S) year
1, which
is about 17 % higher than that calculated by Kettle
and Andreae (2000).

1.2 Sulphur and Related Gases

The “top-down” approach to estimating fluxes
involves the balancing of sources and sinks while
reproducing atmospheric concentrations. This is usually
done with global chemistry transport models. Faloona
(2009) summarises the resulting estimates from a series
of global models and highlights the uncertainties
in the calculation of dry and wet deposition, showing
that the largest individual source of variability in
the sulphur cycle in global models is the wet deposition of sulphate aerosol, followed by deposition and
heterogeneous oxidation of SO2. Nevertheless, the
models he cites have a range of DMS flux estimates,
from 10.7 to 27.9 Tg(S) year
1, which is roughly
within the range of the bottom-up estimates given
above. See also Chap. 5 for further information on
oceanic DMS climatology and models.

1.2.3.1 Chemistry of Sulphur in the Marine
Boundary Layer (MBL)
Once emitted, all reduced sulphur compounds are
oxidised in the atmosphere until they are either deposited or reach the highest possible oxidation state
for sulphur, þVI (H2SO4, sulphate). In the gas phase,
the most important oxidant is the OH radical,
which oxidises almost all sulphur compounds, albeit
with different rates (see e.g. Atkinson et al. 2007,
for a recent compilation of kinetic data). The oxidation
of DMS follows either the abstraction pathway
(OH at higher temperature, NO3, Cl) or the addition
pathway (OH at lower temperature, BrO). Only the
abstraction pathway leads to the formation of H2SO4,
which is the only sulphur compound with sufficiently
low vapour pressure to allow the formation of new
aerosol particles (e.g. Kreidenweis and Seinfeld 1988).
Many oxidised sulphur compounds (e.g., dimethylsuphoxide (DMSO), methanesulphonic acid (MSA),
sulphur dioxide (SO2)) are soluble and can be oxidised
further, and often more quickly, in aerosol particles
and cloud droplets. The main aqueous oxidants are
H2O2, O3, O2 (if metal catalysed) and hypohalous
acids (HOCl, HOBr).
The aqueous oxidation of S (IV) by O3 is very pH
dependent and only efficient in particles with pH > 6;
at high pH, it is orders of magnitude faster than
oxidation by H2O2 (see, for example, Fig. 5 in Faloona
2009). This is most relevant for fresh sea-salt particles
which initially have the pH of surface ocean water
(pH 8.1) or slightly higher due to concentration effects
immediately after emission. A particle with a pH

9

5.6 is in equilibrium with atmospheric CO2 and
strong acids, such as H2SO4 and HNO3, often reduce
the pH even further. Therefore, aqueous oxidation
of S (IV) by O3 is expected to be relevant only
during the initial acidification of sea salt particles
when the pH is still above the threshold for efficient
oxidation by ozone. Depending on the availability
of gas phase acidity, sea salt particles are usually
acidified within minutes to tens of minutes for submicron particles but for super-micron particles this
is extended to 1–3 h (von Glasow 2006) allowing
for very efficient heterogeneous oxidation of S(IV),
as earlier suggested by Sievering et al. (1999) and
Chameides and Stelson (1992). Based on laboratory
experiments, Laskin et al. (2003) suggested that the
surface reaction of OH on sea salt might produce
additional alkalinity in sea salt aerosol and hence
increase the relevance of the O3 oxidation pathway.
However, their extrapolation to ambient conditions
was criticised by Sander et al. (2004), Keene and
Pszenny (2004), and von Glasow (2006) who all
suggested that this pathway might only be minor
under atmospheric conditions. Sievering et al. (2004)
interpreted their measurements of calcium excesses in
coarse aerosol at Baring Head, New Zealand, as evidence for increased buffering of sea salt which would
strongly increase the relevance of the aqueous O3
oxidation pathway. A larger scale view of the role of
sea salt aerosol alkalinity was presented by Alexander
et al. (2005) who measured sulphate aerosol isotopic
composition from a cruise in the Indian Ocean that
suggest a large role of the ozone pathway for sulphate
formation. Using a global 3D model that includes the
titration of alkalinity in sea salt aerosol, they managed
to reproduce these data. They conclude that the formation of sulphate in sea salt aerosol is limited by the flux
of alkalinity and its acidification mainly by HNO3.
Alexander et al. (2012) combined oxygen isotope
measurements of non-sea salt sulphate from North
Atlantic samples with simulations from a global chemical transport model and found that in-cloud oxidation
of S (IV) by O3 represents over one-third (36–37 %)
of total in-cloud sulphate production on average.
They also considered and found the importance of
hypohalous acids to be negligible in winter conditions
but contributing about 20 % to in-cloud S (VI) formation in summer.
Often it is assumed that OH is the only relevant
oxidant for DMS, but this is not necessarily the

10

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

case. In winter, night-time oxidation by NO3 becomes
important (Koga and Tanaka 1993, 1996; von Glasow
and Crutzen 2004) and the confirmation of the presence of BrO in the MBL (e.g., Saiz-Lopez et al. 2007b;
Read et al. 2008b; Martin et al. 2009) highlights the
relevance of this oxidant (see below). The following
list is meant to give an idea of DMS lifetimes
(at T ¼ 280 K, kinetic data from Atkinson et al.
2007) to reaction with the main gas phase oxidants:
τ(OH ¼ 106 molecules cm
3) ¼ 29 h, τ(NO3 ¼ 5
pmol mol
1) ¼ 18 h, τ(BrO ¼ 1 pmol mol
1) ¼ 21 h,
τ(Cl ¼ 104 molecules cm
3) ¼ 84 h.
As the formation of SO2 from the intermediates
takes additional time, any local increases in DMS
concentrations will affect SO2 concentrations only
days downwind, which usually equates to hundreds to
thousands of kilometres in the MBL (e.g. Woodhouse
et al. 2010). It is noteworthy that the oxidation of DMS
by BrO is relevant on a global scale even when BrO is
present only in mixing ratios of tenths of a pmol mol
1
(e.g. von Glasow et al. 2004; Breider et al. 2010).
The conversion efficiency of DMS to SO2 is
important for estimating any climate feedbacks
(see Sect. 1.2.3.2). Reaction chamber experiments
show the net SO2 yields to be in the range of 0.7–0.8
at room temperature (Barnes et al. 2006 and references
therein). However, the measurements were often
performed at NOx levels that are much higher than
those typically found in the MBL. The laboratory
study of NOx oxidation by Patroescu et al. (1999)
indicated an increase in the SO2 yield at lower NOx
levels. Faloona (2009) compiled the conversion efficiency from many process and global models and
found an average conversion efficiency of 71 % and
a range of 31–98 %. Estimates of this conversion
efficiency in an individual process model cover an
even larger range (14–96 %, von Glasow and Crutzen
2004) due to remaining uncertainties in the kinetics,
but also due to differences in meteorological, as
well as chemical, conditions. The model study
by von Glasow and Crutzen (2004) suggests higher
conversion efficiencies for tropical regions and lower
efficiencies for colder (winter) and cloudy conditions.
The conversion efficiency of DMS to SO2 from field
studies is also large. Bandy et al. (1992) presented
measurements from the northeast Pacific showing a
low (but unquantified) DMS to SO2 conversion efficiency. They concluded that SO2 is not an important
intermediate in the oxidation of DMS. Measurements

on Christmas Island in July and August 1994 indicated
a DMS to SO2 conversion efficiency of about 62 %
(Bandy et al. 1996), whereas Gray et al. (2010) calculated a DMS to SO2 conversion efficiency of 73 %
for more recent measurements at Christmas Island
(see below).
The potential of the oxidation of DMS by BrO and
Cl has received a lot of attention, initially mainly
based on laboratory data and model calculations
(e.g., Barnes et al. 1991; Toumi 1994; Ingham et al.
1999; von Glasow et al. 2002) and more recently
increasingly based on field measurements. Saiz-Lopez
et al. (2004) measured 2–6 pmol mol
1 of BrO at Mace
Head, Ireland and stressed its importance for DMS
oxidation. Saiz-Lopez et al. (2007b) presented a
year-long time series of BrO at Halley, Antarctica
showing its presence during most of the year from
several pmol mol
1 up to 20 pmol mol
1. Read et al.
(2008b) calculated that these BrO levels have a
substantial influence on DMS cycling, increasing
the production of MSA ninefold. During 8 months
of measurements at Cape Verde, Read et al. (2008a)
detected on average 2.4 pmol mol
1 of BrO. Mahajan
et al. (2010a) showed by comparing model results
and observational data that under these conditions
BrO would contribute about 50 % to DMS oxidation.
Interestingly all published measurements of BrO in
the MBL are from the Atlantic, which indicates
the need for studies elsewhere. Lawler et al. (2009)
presented measurements of Cl2 and HOCl at Cape
Verde and their modelling suggested that under these
conditions BrO would contribute about 36 % and
Cl about 9 % of the DMS oxidation rate, the rest
being by OH. For the first time, Lawler et al. (2011)
measured HOCl in addition to Cl2 and their surprising
results lead to even higher estimates of Cl atom
concentrations. The global model study with a 3D
aerosol-chemistry model by Breider et al. (2010)
suggested a global contribution of BrO to DMS oxidation of about 16 %. Interestingly, they showed that
an increase in DMS flux increased the abundance
of BrO due to production of additional acidity and
resulting enhanced release of bromine from sea salt
aerosol. They suggest a possible feedback between
DMS, acidity, sea salt and bromine that would act
to reduce the sensitivity of the lifetime of DMS to
increases in DMS emissions.
Sciare et al. (2000a) concluded, based on observations during a cruise in the Atlantic (October –

1.2 Sulphur and Related Gases

November 1996), that oxidants other than OH
must have been involved in the oxidation of DMS.
In contrast, data from Amsterdam Island (Sciare et al.
1998, 2000b, 2001) suggest that additional oxidants
for DMS are not needed to close the budget for this
location in the southern Indian Ocean. Nowak et al.
(2001) discussed observations of DMS and DMSO
during the PEM-Tropics B campaign (tropical Pacific,
March-April 1999) and show that measured DMSO
concentrations are inconsistent with DMS oxidation
solely by OH. As the biggest mis-match occurs
at night, they discount BrO as a potential reason.
Wingenter et al. (2005) concluded, based on hydrocarbon ratios, that an additional oxidant must
have been present during their measurements in the
equatorial Pacific, equivalent to 6
8  104 atoms cm
3
of Cl or 1.3 pmol mol
1 of BrO. However, during the
Pacific Atmospheric Sulfur Experiment (PASE, based
on Christmas Island, August – September 2007),
Conley et al. (2009) concluded that the DMS budget
can be closed without invoking oxidants other
than OH. Using a 1D, model Gray et al. (2010) also
managed to close the DMS budget based on OH
oxidation only, but they found that BrO up to levels
of about 1 pmol mol
1 would still be consistent with
the data. They estimate the conversion efficiency of
DMS to SO2 to be 73 %. The presence of 1 pmol
mol
1 of BrO would require a 14 % higher ocean
flux of DMS, which would also reduce the DMS
to SO2 conversion efficiency to 60 %. They found
no evidence of relevant non-DMS sources of sulphur
to the MBL. Furthermore, they calculated a dry
deposition velocity of SO2 that was only 50 % of
that used in the global GEOS-Chem model hinting
at further unknowns in the global budget of sulphur.
Yang et al. (2011) investigated the sulphur budget
during the VOCALS campaign (October–November
2008) in the south-east Pacific (west of Chile). DMS
was the predominant source of sulphur to the MBL
outside the regions affected by continental outflow.
The most important loss for SO2 was in-cloud
oxidation. They also found a distinct diurnal cycle
in SO42
in the MBL with a rapid rise after sunset
and decay for the remainder of the day. The authors
suggested that meteorology (night time recoupling
of the MBL and precipitation scavenging) was the
driving force for this variability
In summary, there appear to be large regional
differences in oxidation pathways, which probably is
not surprising given the large range of environmental

11

conditions encountered in these different regional
studies. In order to reach an improved understanding
of the global relevance of the sulphur lifecycle
(emission, transformation, deposition), more detailed
studies in all seasons are required in the MBL of
different oceans.

1.2.3.2 CLAW Hypothesis
Charlson et al. (1987) proposed a potential link between
DMS emission from the oceans and production of new
aerosol particles (see chapter 4 for a discussion of
marine aerosols), resulting in changes in cloud albedo
which could influence the temperature in the surface
ocean and/or the amount of UV that reaches the ocean.
This could, in turn, influence the production of DMS and
hence its flux to the atmosphere. In their original paper,
they mentioned that the sign of the impact of changes in
cloud albedo was uncertain, but a decrease of oceanic
DMS production following an increase in cloud albedo
would constitute a negative feedback, i.e. a climate
stabilising mechanism. This idea is usually referred
to as the CLAW hypothesis (using the first letter of the
authors’ names, see Fig. 1.4) and has triggered much
research since it was first published a quarter of a century ago (Charlson et al. 1987), especially in the field of
ocean biogeochemistry and atmospheric particle formation. However, the fact that the hypothesis involves
biological, chemical and physical interactions in the
ocean and atmosphere over varying time and space
scales makes it arguably untestable as a whole. Socalled Earth System models that aim to include all
components of the earth system (ocean and atmospheric physics and chemistry as well as ocean biogeochemistry) might, in principle, be able to test the
CLAW hypothesis. However, many feedbacks are not
well understood and are probably non-linear, hence
their replication in a global model will be imperfect.
Earth System models have shown inconclusive results,
some predicting slight increases and others slight
decreases in DMS fluxes for future climate simulations
(e.g. Bopp et al. 2003; Gabric et al. 2001, 2004; Kloster
et al. 2007; Vallina et al. 2007; Cameron-Smith et al.
2011). All studies found large regional differences, so a
climate feedback – if present – may not be acting
globally. Our limited knowledge of the strong
interactions between physics, biogeochemistry and
DMS make quantitative modeling of the whole system
very challenging.
A crucial link for the CLAW hypothesis to act
as a climate stabilising feedback is that an increase

12

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

Fig. 1.4 Schematic of the CLAW hypothesis after Charlson
et al. (1987) with additions (in bold) based on von Glasow
and Crutzen (2004). Plus (minus) symbols denote positive
(negative) feedbacks. Solid arrows indicate ‘leads to’, dotted
arrows indicate fluxes (to/from the ocean), whereas dash-dotted
arrows indicate uptake to aerosol particles and cloud droplets.
Key additions to the CLAW hypothesis are the ‘short-cut’ in the

atmospheric sulphur cycle by uptake of DMS and its gas-phase
oxidation products on sea salt aerosol with a short lifetime and,
therefore, rapid deposition to the ocean; the negative feedback
of the growth of existing CCN on cloud albedo; and the
importance of sea salt as CCN, thereby possibly reducing the
relative role of sulphate aerosol as CCN (Reproduced with
permission from von Glasow (2007), copyright CSIRO Publishing)

of solar radiation at the ocean surface leads to
higher DMS concentrations in the ocean. As discussed
earlier in this chapter, Vallina and Simó (2007)
presented a study where they could in fact show
that the “solar radiation dose” (SRD, a measure of
the daily integral amount of UV radiation in the
surface ocean mixed layer) correlates very well with
surface ocean DMS concentrations all over the
globe, irrespective of temperature, latitude or plankton
biomass. Derevianko et al. (2009) re-examined this
relationship and found that only 14 % of the variation
in DMS could be explained by SRD, rather than 95 %
as found by Vallina and Simó (2007). Derevianko
et al. (2009) explain this striking difference by the
fact that Vallina and Simó (2007) binned their data
in latitude bands, whereas they used the individual
data points in their analysis. Additionally, Derevianko
et al. (2009) found that most of the correlation between
SRD and DMS resulted from locations where the
mixed layers deepens, resulting in a reduction of
both SRD and DMS, highlighting the importance of
ocean physics.

Many studies investigated potential links between
DMS, MSA, aerosol and cloud properties especially
in the mid and high southern latitudes, as this
region is least influenced by anthropogenic activity
(see references in Vogt and Liss 2009). These
parameters were often found to co-vary but a causal
link, as suggested by the CLAW hypothesis, could
not be established definitively. Complexities of atmospheric chemistry were pointed out by von Glasow
and Crutzen (2004) and von Glasow (2007), stressing
that most sulphate derived from atmospheric DMS
oxidation will likely be taken up on existing CCN
rather than form new ones, which could lead to
darker rather than brighter clouds. The role of sea
salt as CCN might have also been underestimated
in the past (e.g. Lewis and Schwartz 2004; Smith
2007) as well as that of primary organic marine aerosol (e.g. O’Dowd et al. 2004; Leck and Bigg 1999).
These effects make detection of any CLAW-related
feedbacks even more difficult.
Aerosol indirect effects on climate, by changing
cloud reflectivity or lifetime, are potentially very

1.3 Halocarbon Gases

important but are still associated with large
uncertainties (e.g. IPCC 2007). Therefore, much
research is aimed at elucidating such indirect aerosol
effects. These studies have shown an amazing range of
complex microphysical and dynamical feedbacks and
have also led to the suggestion that on a global scale
the aerosol–cloud–precipitation system is buffered
(Stevens and Feingold 2009) and might be less sensitive to aerosol perturbations than previously thought.
This further complicates the detection of any potential
CLAW-like feedback, as the original proposal about
a DMS–cloud link was rather simplistic in as
much that an increase in DMS would increase cloud
albedo. This is not always the case, as cloud dynamics
(including heating, mixing with outside air) as well
as aerosol size distribution and composition play
major roles in the lifecycle of clouds and hence
their climatic impacts (e.g. Stevens and Feingold
2009 and reference therein).
A recent study with a global aerosol microphysics
model confirmed the importance of DMS in maintaining the aerosol burden (Korhonen et al. 2008).
It also highlighted the non-linearity of the processes
involved and that the effect of various sulphur sources
on aerosol concentrations is not simply additive. They
found that DMS emissions increase CCN number
between November and April in the latitude band
30–40  S but at higher latitudes only between
December and February, where the effect was also
less pronounced.
Wingenter et al. (2007) suggested a CLAW-based
climate geo-engineering approach to cool climate by
fertilising the ocean and hence increase DMS
concentrations. Vogt et al. (2008) pointed out several
shortcomings in the argument and calculations and
questioned the effectivity of such an approach.
Woodhouse et al. (2008) showed, using a global
aerosol microphysics model, that an increase in
CCN number would be only about 1.4 % for such
a fertilised patch. Moreover, they showed how
non-regional the impact of this geo-engineering
approach would be on CCN number, which is easily
explained by the timescales of DMS oxidation and
CCN formation and growth, i.e. on the order of
days or hundreds to thousands of kilometres. The
same group (Woodhouse et al. 2010) used current
and future sea surface DMS climatologies to estimate
the potential effect of changes in DMS on CCN
number. They found a very low sensitivity of CCN

13

number to changes in DMS: a change of only
0.02 % in CCN number per 1 % change in ocean
DMS concentrations in the Northern hemisphere,
compared to 0.07 % for the Southern hemisphere.
Given that expected future changes in ocean DMS
concentrations are in the range of 1 %, their study
suggests that any CLAW feedback on climate would
be very small, similar to interannual variations. The
uncertainty of current and future DMS flux estimates
has to be noted (see earlier discussion in Sect. 1.2.1.3),
but given the very low sensitivity of CCN number
to the DMS flux the conclusion is unlikely to be
affected.
In a recent review, Quinn and Bates (2011)
questioned the impact of DMS on global climate.
They discuss all three steps of the feedback loop: 1.
DMS as a significant source of CCN to the marine
boundary layer; 2. The impact of DMS-derived CCN
on cloud albedo; and 3. The response of oceanic
DMS production to changes in surface temperature
and/or incident solar radiation. Quinn and Bates
(2011) conclude that the role of DMS in each step is
in fact very limited and that it is time to retire the
CLAW hypothesis. They identify significantly more
non-DMS sources of marine boundary layer CCN,
such as sea salt and organic material. They cite
studies indicating that there is little or no theoretical
or observational basis for boundary layer nucleation
due to DMS derived sulphur species. Finally, they
determine that the initial hypothesis was too simple
to capture the relationship between aerosols, clouds,
and Earth’s albedo. However, Quinn and Bates (2011)
do acknowledge that it is possible that a direct link
between biological production and climate effects
can exist, but that it is more complicated than
described in the CLAW hypothesis.

1.3

Halocarbon Gases

Halogenated hydrocarbons (halocarbons) affect the
‘oxidizing capacity’ of the atmosphere, primarily as
a result of their influence on ozone concentration
in both the troposphere and the stratosphere. Whereas
a number of man-made compounds contribute to
the chlorine supply, bromine and iodine are supplied
to the atmosphere mainly as short-lived natural
species. The natural sources exert a stronger influence
on ozone now than in the past, since synergetic

14

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

halogen cycles amplify ozone destruction when coupled with the increased levels of chlorine (Lary 1996;
von Glasow 2008).
The temporal and spatial distribution of the highly
variable oceanic emissions is uncertain and the
oceanic sources of the organic halogen compounds
are not well understood. Production and loss processes, both biological and chemical, will influence
the oceanic distribution of halogenated compounds
and are known to be closely coupled in the marine
environment. Other processes affecting halogen
distributions include physical transport within the
ocean, air–sea exchange, phytoplankton abundance
in the open ocean and macroalgae distributions in
coastal waters.
Phytoplankton and macroalgae have been identified
as sources for halocarbons in seawater. Sea surface
temperature, which determines the solubility of the
compounds, together with biological production and
radiation, is a key factor in controlling seasonal
and diurnal variability of concentrations in surface
seawater. Anthropogenic sources from the atmosphere
and water discharges also contribute to their environmental variability.
The supply of halocarbons to the atmosphere can be
altered by changes within the surface ocean associated
with climate change, and changes in surface ocean
physics, chemistry and biology. Our ability to predict
such effects is limited, in part because surface seawater sources and sinks are poorly known and quantified,
and the relative importance of production and degradation processes, revealed in laboratory studies, are
obscured in the natural environment. It is necessary to
investigate the kinetics of photochemical, chemical
and biological processes in the water, in organisms,
on particles, and in the sea surface microlayer. While
in this section we address mainly the oceanic emission
of the compounds, it is the case that the background
concentrations of many trace gases in ocean surface
waters are dictated by the overlying atmospheric
mixing ratios. The high atmospheric background
concentrations of several gases lead to high surface
ocean concentrations, which affects our ability to detect
and quantify natural marine sources, particularly for
some chlorinated compounds. To improve our understanding of future changes in oceanic emissions and
thus their contribution to tropospheric and stratospheric

chemistry we need: more measurements in oceanic hotspot regions; introduction of direct flux measurement
techniques; the combining of existing data and intercalibrations within the global database (see Chap. 5 for
information on the halocarbon database); and reliable
predictive parameterisations. In this section, we outline
some of the biogeochemical cycles of halocarbons in
surface waters as a prerequisite for understanding future
concentration and emission developments.

1.3.1

Chlorinated Compounds

1.3.1.1 Introduction
With the possible exception of methyl chloride
(CH3Cl), most of the compounds that contain Cl as
the sole halogen do not have a primary ocean source.
While CH3Cl is mainly naturally occurring, trichloroethylene (TCE) and perchloroethylene (PCE) are
predominantly anthropogenic, and dichloromethane
(CH2Cl2) is about 70 % anthropogenic (Cox et al.
2003). The anthropogenic compounds have time
varying concentrations in the atmosphere, typically
increasing in the mid-twentieth century, and in some
cases declining more recently, e.g. CH2Cl2 (at Mace
Head, Simmonds et al. 2006), and chloroform CHCl3
(from firn data, Trudinger et al. 2004). The predominance of non-marine sources in the global budget for
these gases means that surface water concentrations
will be largely controlled by concentrations in the
atmosphere. A consequence is that detecting and
quantifying natural marine sources of these chlorinated
compounds is more challenging than for a gas such as
CH3I, which has a very low atmospheric background
concentration. Chlorinated gases have widely differing atmospheric lifetimes, ranging from around 7 days
for TCE (Simmonds et al. 2006), through 3–4 months
for tetrachloroethylene (Olaguer 2002), ca. 5 months
for CH2Cl2 (McCulloch and Midgley 1996), ca. 6
months for CHCl3 (O’Doherty et al. 2001), 1–1.5
years for CH3Cl, (Xiao et al. 2010) to ca. 35 years
for carbon tetrachloride (CCl4) (Prinn et al. 1999). As
the lifetimes are dependent on atmospheric OH,
concentrations vary with both season and latitude.
Further influences on the latitudinal variation in concentration are the degree to which industrial emissions
contribute to the global budget, and the importance of

1.3 Halocarbon Gases

natural terrestrial sources and sinks. Lifetimes of the
gases in ocean waters play an additional role in the
distributions. Methyl chloride is relatively short-lived
on account of microbial consumption, while PCE and
TCE are apparently much longer lived in the ocean
than in the atmosphere leading to concentrations that
increase with depth.

1.3.1.2 Methyl Chloride
Our improving knowledge of sources and sinks of
methyl chloride (CH3Cl) has revealed a lesser role
for ocean sources than previously thought. Chemical
loss in the atmosphere of about 7  1010 mol year
1 is
matched by a net ocean supply of about 5 % of that
amount. It has been found that warm waters tend to be
supersaturated with increasing temperature, while cool
waters (below about 12  C) are undersaturated (Moore
et al. 1996a). It is clear from decreasing concentrations
of CH3Cl with depth in the ocean that CH3Cl
sinks exist within the water column. Measurements
of isotopically labelled CH3Cl have revealed biological uptake rate constants in the range 0–0.22 d
1
(mean 0.07 d
1) in Southern Ocean surface waters
(Tokarczyk et al. 2003a) and up to 0.3 d
1 in coastal
waters (annual mean 0.07 d
1, Tokarczyk et al.
2003b). Among the most firmly established marine
sources of CH3Cl are those from reactions of CH3I
and CH3Br with Cl
in seawater, the rates of which are
known to be strongly temperature dependent. There is
evidence for more direct biological production of
CH3Cl from laboratory studies of algal cultures (e.g.
Scarratt and Moore 1996, 1998), though these normally do not uniquely identify the source as the
cultured alga rather than associated bacteria. Furthermore, the measured rates are very modest in comparison with estimates of CH3Cl production in ocean
waters. For example, taking the most prolific phytoplankton producer of CH3Cl amongst those studied,
Phaeocystis, was estimated to be able to account for
only 0.5 % of oceanic production (Scarratt and Moore
1998) based on estimates of the abundance of the
organism within oceanic blooms and their areal extent.
Other limitations of these studies include the major
differences that exist between culture conditions and
the ocean. Even when production has been
demonstrated, it is important to be able to relate it to
the growth phase of the organism. Thus, while Ooki
et al. (2010) report enhanced concentrations of CH3Cl
associated with elevated chlorophyll-a in the NW

15

Pacific (SST 18  C), the interpretation cannot be
more specific than an indication of a biological source,
with a wide range of algae, bacteria or other organisms
potentially being responsible.
It has more recently been shown that photochemical breakdown of coloured dissolved organic
matter in seawater is a source of CH3Cl (Moore
2008). This source is likely to be more significant
on a unit area basis in estuarine and coastal waters
than in the open ocean, though Ooki et al. (2010)
speculate that it could account for highly elevated
concentrations in warm, (29  C) low chlorophyll
waters of the subtropical NW Pacific.

1.3.1.3 Dichloromethane
The oceanic distribution of dichloromethane (CH2Cl2)
is remarkably different from that of methyl chloride.
The short atmospheric lifetime (ca. 5 months) and
anthropogenic sources together cause a large asymmetry in atmospheric concentrations between the two
hemispheres and, in turn, different atmosphericallysupported concentrations in the respective surface
waters. Vertical profiles of CH2Cl2 can show increases
in concentration with depth, as do the long-lived,
anthropogenic Freons (Moore 2004). It appears that
CH2Cl2, like Freons, enters the deep ocean at high
latitude sites of vertical convection. The lifetime in
seawater is apparently long enough to maintain a
clear similarity in profile with the very long-lived
Freon tracers. Seasonal variations of CH2Cl2 concentration in the atmosphere will tend to supply the
compound to the ocean in winter and yield summertime supersaturations in surface waters that do not
reflect in situ marine production but rather a
recycling of the gas to the atmosphere. While the
major atmospheric influences on CH2Cl2 concentration will make smaller natural sources difficult to
discern and quantify, it may be predicted that some
production occurs in seawater by reactions of chloride
with precursors such as chloroiodomethane (CH2ClI),
which is both biogenic and formed by light catalysed
reaction between di-iodomethane and chloride (Jones
and Carpenter 2005). Additionally, the compound may
be produced biologically by processes analogous to
those involving haloperoxidases that yield dibromomethane. Ooki and Yokouchi (2011) provide
evidence for in situ production by quantifying excess
saturation anomalies which have had the influence
of sea surface temperature rise on CH2Cl2

16

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

supersaturation removed, as well as the influence of
seasonal change of CH2Cl2 in the atmosphere. Their
results provide evidence for efflux to the atmosphere
between 10  S and 40  S and also for the source
being derived from the precursors CH2ClI and CH2Cl2,
but their claim for direct biogenic production is less
well founded.

1.3.1.4 Tri- and Tetrachloroethylene
There are few published measurements of tri- and
tetrachloroethylene (C2HCl3-TCE, C2Cl4-PCE, respectively) in seawater. These two compounds have
primarily industrial sources, with production and
release to the atmosphere displaying a declining trend
in the case of PCE based on measurements at Mace
Head (Simmonds et al. 2006). TCE was reported to
have relatively constant emissions between 1988 and
1996, as estimated from production data. Surface
waters will be influenced by the latitudinal and seasonal
variations in atmospheric mixing ratio, tending towards
higher and lower concentrations in winter and summer,
respectively. Supersaturation has been reported for both
compounds in the N. Atlantic during summer (Moore
2001) and it was argued that this is likely to reflect
release of gases taken up during winter months. Deep,
cooler waters had higher concentrations, increasing
with depth, that are likely to result from high latitude
ventilation and relative stability of both compounds in
seawater. Moore (2001) reported concentration maxima
of TCE at a depth of 50 m in the western Atlantic
(ca. 21  N) similar to those frequently seen for biogenic
compounds such as isoprene, but physical processes
(advection) could provide an alternative explanation.
A report of TCE and PCE production by red algae
(Abrahamsson et al. 1995) proved controversial with
two later studies of the same algae showing no evidence
for production (Marshall et al. 2000; Scarratt and
Moore 1999). Marshall et al. (2000) suggested that
TCE and PCE could potentially be produced in those
experiments by dehydrohalogenation of 1,1,2,2tetrachloroethylene and pentachloroethane, respectively; the significance of any such production is questionable in the absence of data on the precursors in
ocean waters. Studies of these compounds in waters of
the southern hemisphere, where the atmosphericallysupported background concentration is lower, would
be especially interesting and useful in identifying and
quantifying any marine production.

1.3.1.5 Chloroform
The major sources of chloroform to the atmosphere are
soils and anthropogenic emissions, which Trudinger
et al. (2004) estimate from firn measurements account
for more than half of current emissions. Nightingale
et al. (1995) reported that early estimates of the flux
of chloroform (CHCl3) from the ocean, based on
few measurements and with an assumed high transfer
velocity (Khalil et al. 1983), are likely to be too
high by about a factor of four. Little has been
published on CHCl3 distributions in the ocean, but
there is evidence for higher concentrations in both
intermediate and deep waters of the Atlantic than
near the surface. In the case of TCE, PCE, and
CH2Cl2, this has been attributed to influx of the gases
in regions of deep convection, coupled with relatively
long lifetimes in seawater compared with the atmosphere (Moore 2001, 2004); it is reasonable to interpret the CHCl3 distribution in the same way. Seasonal
and spatial fluctuations in both atmospheric concentrations and sea surface temperatures will lead to
variations in magnitude, and perhaps direction, of
CHCl3 fluxes across the ocean surface.
Nightingale et al. (1995) reported the first direct
evidence that some species of macrophytes release
CHCl3. It is possible that a chloroperoxidase is
responsible, but if the mechanism involves HOCl
release, the nature and concentration of halogenated
products is evidence that the CHCl3 must be have
been produced intracellularly. A macrophyte source
would tend to elevate concentrations in coastal
waters. Two laboratory studies showed that the
benthic microalga, Porphyridium purpureum, produced
CHCl3 (Scarratt and Moore 1999; Murphy et al. 2000)
by an unidentified mechanism; these findings tell us
little directly about producers in ocean waters since
the organism studied occurs only in shallow waters.

1.3.2

Brominated Compounds

1.3.2.1 Methyl Bromide
The oceans play an important role in the geochemical
cycle of methyl bromide (CH3Br), contributing
approximately half of the stratospheric bromine burden.
Oceanic emissions contribute roughly 35 % of all
known natural and anthropogenic sources to the
atmosphere, while industrial production is decreasing
due to the Montreal protocol (WMO 2011). The

1.3 Halocarbon Gases

ocean is both a source and a sink of bromine for the
atmosphere (0.5 and 0.6 Gmol Br year
1, respectively)
in the form of CH3Br (King et al. 2002). Due to
declining atmospheric concentrations (Yvon-Lewis
et al. 2009), the ocean is now less undersaturated
(i.e. closer to equilibrium between the ocean and the
atmosphere) with CH3Br than in the period 1996–1998,
when atmospheric mixing ratios rose above 10 ppt.
Preindustrial mixing ratios of CHBr3 range from 5 to
5.5 ppt, (Saltzman et al. 2008), indicating substantial
natural sources. Sea surface temperature (SST) can be
used as proxy for the oceanic saturation state of CH3Br
(Groszko and Moore 1998; King et al. 2002). While
polar and tropical regions are undersaturated the entire
year, temperate waters show strong seasonal cycles
(Baker et al. 1999), with supersaturations in summer
months. Oceanic surface waters with temperatures
between 12  C and 20  C are, in general, supersaturated. However, Lobert et al. (1995) concluded that
coastal and upwelling regions are sources of methyl
bromide (CH3Br) to the atmosphere independent of
SST, which can possibly be resolved if marine
primary productivity is included in the modeling
(Anbar et al. 1996). Hydrolysis and reaction with
chloride, as well as biological degradation, are the
major loss processes from the ocean, and the degradation rates (chemical: 0.04 d
1, biological: 0–0.26 d
1)
can match the sea to air exchange rates (Zafiriou
1975; Elliott and Rowland 1995; Yvon-Lewis et al.
2002). Since the consumption reactions are extremely
temperature-sensitive, all oceanic temperature variations have large effects on the concentration of CH3Br
in seawater and therefore the exchange between the
atmosphere and the ocean.
The net flux of CH3Br is also sensitive to variations in the rate of CH3Br production. The natural
oceanic production mechanism is unknown but likely
phytoplankton in the surface layer are involved.
Supersaturations of CH3Br have been observed in
coastal waters off Tasmania, especially in the presence
of Phaeocystis (Sturrock et al. 2003), possibly due
to nutrient limiting conditions. Laboratory studies
have revealed CH3Br production from a variety of
phytoplankton species (Saemundsdottir and Matrai
1998; Scarratt and Moore 1998) and macroalgae
(Gschwend et al. 1985; Manley and Dastoor 1987).
However, all observed rates and inferred global
estimates are insufficient to support the observed
seawater concentrations and global fluxes. The coastal
ocean can be a highly productive region for CH3Br,

17

due to enhanced biological processes, and including
its emissions may increase the estimates of global
oceanic emissions by 1–9 % (Hu et al. 2010). While
abiotic production of CH3Br can occur from the
degradation of organic matter (Keppler et al. 2000),
the relative importance of this process compared to
biological generation, likely involving methyl transferases (Wuosma and Hager 1990), is not known.
Future work should include increasing the amount
of data representing global coastal regimes and information on seasonal variations. Since the direction
and magnitude of CH3Br exchange between the
atmosphere and ocean is very sensitive to temperature
and marine productivity, future measurements of
marine CH3Br, temperature and primary production
should be combined with models to determine the
relationship between marine biological activity and
CH3Br production.

1.3.2.2 CHBr3, CH2Br2 and Other
Polybrominated Methanes
The oceans are also a source of reactive bromine
to the atmosphere in the form of short-lived
brominated methanes, including bromoform (CHBr3)
and dibromomethane (CH2Br2). These compounds
represent the largest known natural contribution
to atmospheric organic bromine and are recognised
as an important source of reactive bromine to the
troposphere and lower stratosphere, where they may
contribute up to 40 % of O3 depletion in mid latitudes
(Salawitch et al. 2005; Salawitch 2006; Yang et al.
2005; WMO 2011).
The magnitude of the oceanic emissions is
uncertain and ranges from 3 to 22 Gmol Br
year
1, with a recurring mean of around 10 Gmol Br
year
1 for CHBr3, and from 0.5 to 3.5 Gmol Br year
1
for CH2Br2 (Quack and Wallace 2003; WMO 2011).
Elevated atmospheric concentrations have been observed
over the tropical oceans (Class and Ballschmiter
1988a; Atlas et al. 1993; Schauffler et al. 1998)
and were linked to oceanic supersaturations of the
compounds and especially to productive upwelling
areas. Equatorial surface waters of the tropical Atlantic
were indeed identified as a significant CHBr3 source to
the atmosphere, with CHBr3 production occurring in
the deep chlorophyll maximum (Quack et al. 2004).
Generally the oceanic distributions of the brominated
compounds, bromoform, dibromomethane (CH2Br2),
dibromochloromethane (CHBr2Cl) and bromodichloromethane (CHBrCl2), are associated with the

18

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

abundance of phytoplankton in the open ocean and
macroalgae in the coastal waters (Baker et al. 1999;
Carpenter and Liss 2000; Nightingale et al. 1995;
Arnold et al. 2010), with CHBr3 showing an order
of magnitude higher concentration compared with
the other compounds in source regions. Pronounced
seasonality of the brominated compounds has been
observed in coastal regions, with elevated concentrations during summer months (Klick 1992; Hughes
et al. 2009; Orlikowska and Schulz-Bull 2009).
Diurnal variations are also observed with elevated
concentrations around midday (Ekdahl et al. 1998;
Karlsson et al. 2008).
Laboratory studies have shown CHBr3 production
from arctic diatom cultures (Moore et al. 1996b). Since
then, high field concentrations of brominated trace
gases have generally been found to be associated with
diatom-rich open waters, especially upwelling systems
(e.g. Baker et al. 2000). Indeed, significant correlations
with CHBr3 were found for low concentrations of
diatom marker pigments in the Mauritanian upwelling.
However, CHBr3 concentrations do not continue to
increase with high diatom abundances and chlorophyll
a values (Quack et al. 2007). Biological sources of
CHBr3 also correlated with degradation pigments
and the decay of organic matter. Cyanobacteria
have been shown to be a bromocarbon source in the
Baltic Sea (Karlsson et al. 2008). The parameterisation
of CHBr3 concentration with chlorophyll a and
mixed layer depth has been tested (Palmer and
Reason 2009) and found in some tropical areas
to satisfactorily reproduce observed concentrations.
In coastal regions macroalgae are thought to be
the major marine sources (Manley et al. 1992;
Nightingale et al. 1995; Carpenter and Liss 2000)
of bromocarbons, while anthropogenic contamination
by industrial or municipal effluents may overwhelm
the natural sources in some areas (Quack and Wallace
2003). While advection of different water masses
generally influences the oceanic distribution, oceanic
supersaturations of the compounds can also be caused
by the advection of coastal waters enriched in
macroalgal releases (Carpenter and Liss 2000; Raimund
et al. 2011) and emissions from floating macroalgae
(Moore and Tokarczyk 1993).
The biological production of polybrominated
compounds occurs during the enzymatic oxidation of
bromine by bromoperoxidases and chloroperoxidases
in the presence of hydrogen peroxide, resulting in

the halogenation of organic compounds with activated
terminal methyl groups (Theiler et al. 1978; Neidleman
and Geigert 1986). The formation of CHBr3 during
the oxidation of organic matter is chemically favoured
over CH2Br2, since intermediate products are stabilised by additional halogen atoms, and thus CHBr3
is the major product in oceanic environments (Wade
1999). The other bromocarbons have been identified
as minor products from phytoplankton and macro
algae cultures (Theiler et al. 1978; Gschwend et al.
1985; Manley et al. 1992; Tokarczyk and Moore 1994;
Nightingale et al. 1995). CHBr3 has generally longer
lifetimes compared with CH2Br2 in the oceanic environment under aerobic and anaerobic conditions,
though in the case of the reductive hydrogenolysis,
CHBr3 reacts faster than CH2Br2 (Bartnicki and Castro
1994; Goodwin et al. 1997). Thus reductive hydrogenolysis of CHBr3 could be a process that generates
CH2Br2 in anoxic environments (Vogel et al. 1987;
Tanhua et al. 1996). CH2Br2 concentrations have
indeed been found to anticorrelate with CHBr3 and
increase in deeper waters, suggesting that this process
may be possible (Quack et al. 2007). Alternatively,
advection of subducted open ocean waters could
possibly explain this pattern. Chemical conversion
losses from hydrolysis, with half-lives of 183 years
for CH2Br2 and 686 years for CHBr3 (Mabey and
Mill 1978), and halogen exchange, with a half-live
of 5–74 years for CHBr3 (Geen 1992), also serve
as sources for other brominated compounds, especially
for the mixed bromochloromethanes. The progressive
abiotic substitution of bromine in CHBr3 with chloride
is suggested as the main oceanic source for dibromochloromethane (CHBr2Cl) and bromodichloromethane
(CHBrCl2) in the ocean, and an in situ contributor
of chloroform (CHCl3) (Class and Ballschmiter 1988b).
The background concentration ratio of trace gases
in the surface ocean is strongly influenced by air-sea
exchange with the overlying atmosphere. Since CH2Br2
has a longer lifetime in the atmosphere (WMO 2011)
than CHBr3, the concentration ratio between CH2Br2
and CHBr3 in both atmosphere and ocean increases
away from coastal source regions towards the open
ocean. Thus, remote open ocean surface waters contain
relatively more CH2Br2 than CHBr3, giving ratios
up to >1, while ratios in source regions are around
0.1. Transport of halocarbons may occur through
the atmosphere from coastal source regions to ocean
surface waters (Carpenter and Liss 2000).

1.3 Halocarbon Gases

Future work in this area should include halocarbon
measurements in water and air in concert with phytoplankton biomass and productivity, chemical and
physical parameters. In this way their distributions
and correlations can be evaluated and coupled with
numerical modeling of source and sink processes
(Hense and Quack 2009).

1.3.3

Iodinated Compounds

Transfer from the ocean reservoir to the atmosphere and
then to land is an important pathway in the biogeochemical cycle of iodine, with important implications
for human health. The iodocarbons, including volatile
mono-, di-, and tri-halogenated organic compounds,
are produced in seawater and are believed to play an
important role in mediating this transfer.

1.3.3.1 Iodomethane
One volatile organic iodine compound that has
generated much interest is iodomethane (CH3I) with
estimates of the sea-to-air transfer rate ranging
from 0.9 to 9.2 Gmol year
1 (reviewed by Bell et al.
2002). These fluxes are far in excess of those reported
for other sources of CH3I including biomass burning
(<0.1 Gmol year
1, Andreae et al. 1996), rice paddies
(0.1–0.5 Gmol year
1, Muramatsu and Yoshida 1995),
peatlands (< 0.1 Gmol year
1, Dimmer et al. 2001)
and wetlands (< 0.1 Gmol year
1, Dimmer et al.
2001), making the oceans the dominant source of
CH3I to the atmosphere.
CH3I production has been observed in experimental incubations of marine seaweeds (Nightingale
et al. 1995; Carpenter et al. 2000), phytoplankton
(Moore et al. 1996b; Manley and de la Cuesta 1997),
cyanobacteria (Smythe-Wright et al. 2006; Brownell
et al. 2010; Hughes et al. 2011), bacteria (Amachi
et al. 2001; Fuse et al. 2003), detrital aggregates
(Hughes et al. 2008b) and in irradiated sterile
seawater, suggesting photochemical formation (Moore
and Zafiriou 1994; Richter and Wallace 2004). The
relative importance of each of these sources to the
oceanic inventory of CH3I is currently unknown.
Estimates of global CH3I production by seaweeds
(0.4  10
2 Gmol year
1, Manley and Dastoor 1988)
and phytoplankton (0.8  10
2 Gmol year
1, Manley
and de la Cuesta 1997) suggest that these organisms do
not contribute significantly. However, the calculations
on which these estimates are based rely on the

19

extrapolation of results obtained from surveys of a
limited number of organisms under laboratory
conditions which may not be a true reflection of the
natural situation. This is an important consideration as
algal CH3I production has been found to vary
depending on the species and environmental
conditions, such as nutrient-limitation (SmytheWright et al. 2010), grazing (Smythe-Wright et al.
2010), light-levels (Moore et al. 1996b; Laturnus
et al. 1998), mechanical damage (Nightingale et al.
1995) and desiccation (Nightingale et al. 1995). The
only sources which some studies have suggested could
contribute significantly to global or regional CH3I
production in the marine environment are photochemistry and cyanobacteria. Richter and Wallace (2004)
suggest that 50 % of the average daily flux of CH3I
from the tropical Atlantic surface layer could be due to
photochemistry.
The extrapolation of laboratory production rates
suggests that the marine cyanobacterium Prochlorococcus
could contribute significantly to CH3I production in
the eastern tropical Atlantic (Hughes et al. 2011) and
calculations presented in Smythe-Wright et al. (2006)
suggest that this organism is also an important global
source producing 4.3 Gmol year1. However, orders
of magnitude variability in the CH3I production
rate have been observed in laboratory cultures of
Prochlorococcus between the three studies on this
topic published to date (Smythe-Wright et al. 2006;
Brownell et al. 2010; Hughes et al. 2011). Hughes
et al. (2011) suggest that CH3I production by
Prochlorococcus is strongly dependent on cell physiological state. Therefore, the different incubation
conditions used in these three studies could explain
this variability and should be considered when
extrapolating laboratory production rates to the natural
environment. Other sources such as bacteria (Amachi
et al. 2001; Fuse et al. 2003) and detrital aggregates
(Hughes et al. 2008b) are not understood sufficiently
to allow accurate global flux estimates to be made.
Several mechanisms for CH3I production in seawater have been proposed:
1. A photochemical CH3I source has been suggested
to involve a radical recombination pathway
(Eq. 1.1, Moore and Zafiriou 1994) but more information on the mechanism is needed.
CH3  þ I

▸ CH3 I

(1.1)

20

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

2. Proposed pathways by which biological activity
produces CH3I include methyl group transfer to
iodide via S-adenosyl-L-methionine (SAM)dependent methyltransferases (Eq. 1.2, Amachi
et al. 2001; Ohsawa et al. 2001), the production of
methylating agents such as methylcobalamin or
methyl vitamin B12 (Eq. 1.3, Manley 1994) and
the breakdown of higher molecular weight organic
compounds (Fenical 1982).

I
þ SAM
▸CH3 I
þ s-adenosyl-L-homocysteine ðSAHÞ
CH3 -B12 ðCo3þ Þ þ I

þ B12s ðCo1þ Þ

(1.2)

▸ CH3 I
(1.3)

Loss processes for CH3I in seawater include gas
exchange in surface waters (Liss and Slater 1974), nucleophilic (SN2) substitution with Cl
(Elliott and Rowland
1993), and possibly bacterial breakdown (Bell et al.
2002). Hydrolysis is generally an order of magnitude
lower than nucleophilic substitution (Moelwyn-Hughes
1938) and the nature of the absorbance cross-section of
CH3I means that photolysis is not significant (Zika et al.
1984). The relative importance of gas exchange and the
reaction with Cl
varies depending on windspeed and
seawater temperature, but under certain conditions loss
rates due to these processes can be comparable (Bell
et al. 2002). A wide range of methanotrophic and nitrifying bacteria have been found to be capable of breaking
down CH3I (McDonald et al. 2002). Methyl halides are
molecular analogues of methane (CH4) and ammonium
(NH4+) and therefore act as competitive inhibitors of
methane monooxygenase (MMO) and ammonium
monooxygenase (AMO). To account for discrepancies
between modelled and measured CH3I concentrations in
mid to high latitude ocean waters, Bell et al. (2002)
propose that the bacterial breakdown rate is 0.24 d
1.
However, the mean non-chemical loss rate observed in
incubations of North Atlantic seawater by Moore (2006)
using 13CH3I as a tracer was 7 % d
1, suggesting that
this process is less significant.

1.3.3.2 Other Mono-Iodinated Iodocarbons
Other volatile mono-halogenated iodocarbons including iodoethane (C2H5I), 1- and 2-iodopropane (C3H7I)
and the iodobutanes (C4H9I) have been detected in

seawater (Klick and Abrahamsson 1992) but their
generally low concentrations and their relatively
higher solubility means that they make a much smaller
contribution to sea-to-air iodine flux than CH3I. Their
production has been observed alongside CH3I in
incubations of marine seaweeds (Carpenter et al.
2000), phytoplankton (Moore et al. 1996b) and detrital
aggregates (Hughes et al. 2008b).

1.3.3.3 Di- and Tri-Halogenated Compounds
The marine environment is also known to be a source
of reactive di- and tri-halogenated iodocarbons including chloroiodomethane (CH2ClI), di-iodomethane
(CH2I2), and bromoiodomethane (CH2BrI), which,
due to their relatively short atmospheric lifetimes,
play a more important role in the chemistry of
the marine boundary layer (MBL) than the monoiodinated organics. Martino et al. (2009) have
measured iodoform in a laboratory study but, as far
as we are aware, there are no published measurements
in marine waters. Recent work has also examined the
relative importance of organic versus inorganic iodine
(i.e. I2) flux to the marine boundary layer with varying
results (e.g. Jones et al. 2010; Mahajan et al.
2010b). The production of the di- and tri-halogenated
iodocarbons has been seen in laboratory experiments
involving marine seaweeds (Carpenter et al. 2000),
phytoplankton (Moore et al. 1996b), bacteria (Fuse
et al. 2003) and ozone (O3) deposition (Martino et al.
2009). Although they share common sources, the
mechanisms of formation for the mono-, di- and trihalogenated iodocarbons are different. The biological
formation of reactive iodocarbons is known to be
catalysed by haloperoxidase enzymes which have
been detected in a wide range of marine organisms
and catalyse the oxidation of halide ions by hydrogen
peroxide (H2O2) and formation of HOI/I2 (Butler and
Walker 1993). The O3 deposition pathway identified
by Martino et al. (2009) involves the reaction between
O3 and I
at the sea surface to form I2/HOI (Eq. 1.4a, b).
Another mechanism by which the reactive iodine
precursor of di- and tri-halogenated iodocarbons could
be formed in the marine environment is the reaction
between reactive oxygen species (e.g. H2O2) and I

released during oxidative stress, as was observed for
the kelp Laminaria by Kupper et al. (2008). In all
cases the I2/HOI formed reacts with dissolved organic
substrates (Truesdale and Luther 1995) to yield the
di-/tri-halogenated organics.

1.3 Halocarbon Gases

O 3 þ I
þ H 2 O
HOI þ I
◂

21

▸ HOI þ O2 þ OH

▸ I2 þ OH




(1.4a)
(1.4b)

Losses of the more reactive iodocarbons in seawater are dominated by gas exchange and photolysis.
Photolysis is an important breakdown pathway in surface waters for compounds such as CH2I2 (Jones and
Carpenter 2005; Martino et al. 2005). For example,
Martino et al. (2005) report photolytic lifetimes of
CH2I2 and CH2ClI in surface waters (at 15  C and a
solar zenith angle of 0  ) are on the orders of minutes
and hours, respectively. The photolytic breakdown of
CH2I2 is also an iodocarbon source as this generates
CH2ClI with a yield of up to 30 % (Martino et al.
2005). As with all trace gases, the rate of exchange
with the atmosphere can be quantified using known
formulations (Liss and Slater 1974) and parameterisations (e.g. Nightingale et al. 2000). In general,
published rates of sea-air transfer consider only the
water-phase resistance to transfer (kw). However,
recent work (Archer et al. 2007; Johnson 2010)
suggests that the air-phase resistance to transfer (ka)
is also an important control on the sea-to-air flux of the
more soluble reactive iodocarbons, and that the total
resistance to transfer (Kw) should be used in the calculation of emission rates. For example, the flux rate of
CH2I2 is reduced by 40 % at 5  C, and 20 % at 30  C
when Kw is used to calculate the flux instead of kw
(Johnson 2010). Further information on air-sea gas
transfer rates can be found in Chap. 2.

1.3.4

Halogens in the Marine
Atmospheric Boundary Layer

This section describes recent advances in understanding the impacts of halogen chemistry in the marine
boundary layer. During the past decade there has been
an explosion of interest in iodine chemistry, which is
described here in some detail. For the chemistry of
bromine and chlorine, on which there has been rather
less work in the past 5 years and which is discussed in
less detail here, the interested reader is referred to the
review by von Glasow and Crutzen (2007). One point
of note in chlorine chemistry is that the discovery
during the last 3 years of the widespread abundance
of nitryl chloride (ClNO2) is rekindling interest in this
halogen (Thornton et al. 2010).

The potential importance of iodine to the gas-phase
chemistry of the troposphere was noted over 30 years
ago (Chameides and Davis 1980), starting with its
potential for changing the atmospheric oxidising
capacity, both via ozone (see Sect. 1.5) destruction
and changes to the chemistry of hydrogen oxide (OH
and HO2 ¼ HOx) and nitrogen oxide (NO and NO2 ¼
NOx) radicals. However, one common conclusion of
the early modeling studies on iodine chemistry in the
atmosphere was an absence of kinetic data and
sparseness of information concerning oceanic iodine
sources, which made reliable assessments of the
importance of iodine chemistry difficult. Furthermore,
apart from measurements of iodine enrichment in
sea-salt aerosol (Moyers and Duce 1972), no iodine
species had actually been observed in the atmosphere.
During the past decade, the gas-phase iodine species atomic iodine (I), molecular iodine (I2), iodine
monoxide (IO) and iodine dioxide (OIO) have all
been detected in the atmosphere for the first time, in
locations ranging from Antarctica to the equatorial
marine boundary layer. These observations have
been made using the techniques of differential optical
absorption spectroscopy (DOAS) (Alicke et al. 1999;
Allan et al. 2000; Saiz-Lopez and Plane 2004), cavity
ring-down spectroscopy (Wada et al. 2007), laser
induced fluorescence (Whalley et al. 2007), resonance
fluorescence (Bale et al. 2008; Gómez Martı́n et al.
2011), inductively coupled plasma mass spectrometry
(Saiz-Lopez et al. 2006a), and atmospheric pressure
chemical ionisation with tandem mass spectrometry
(Finley and Saltzman 2008). Measurements of IO
have very recently been extended to satellite-based
DOAS (Saiz-Lopez et al. 2007a; Schönhardt et al.
2008). The resulting confirmation of active and widespread iodine chemistry has produced a huge increase
of interest in this halogen.
These discoveries have been accompanied by
improved knowledge regarding the fluxes and atmospheric concentrations of short-lived organic and
inorganic iodine source compounds, as well as significant developments in laboratory and quantum theory
studies of the reaction kinetics, photochemistry and
heterogeneous chemistry of iodine species (SaizLopez et al. 2012). In the past 5 years there has been
active laboratory research on IO, OIO, IONO2 and the
higher iodine oxides (I2Ox, x ¼ 2–5) (Dillon et al.
2008; Gómez Martı́n et al. 2009; Gravestock et al.
2005; Joseph et al. 2007; Kaltsoyannis and Plane
2008; Plane et al. 2006).

22

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

Fig. 1.5 Schematic diagram
of iodine chemistry in the
marine boundary layer. For
the gas phase reactions shown
rate coefficients and
photolysis cross-sections have
been measured in the
laboratory. Reactions that are
thermodynamically possible
but for which rates and crosssections have not been
measured are indicated with a
question mark. The night-time
reaction between I2 and NO3
has been omitted for clarity
(Figure adapted from SaizLopez et al. (2012))

Figure 1.5 is a schematic diagram of our current
understanding of tropospheric iodine chemistry. The
elucidation of important reaction pathways has
emerged from atmospheric models (containing the
new laboratory reaction rate coefficients) that have
been used to reproduce measured concentrations of
I, IO, OIO, I2 and observed new particle formation,
as well as shifts in the observed ratios of OH/HO2 and
NO/NO2. These modelling studies have been reviewed
in detail recently (Saiz-Lopez et al. 2012). The major
source of atmospheric iodine is through the evasion
from the ocean of a range of compounds (see
Sect. 1.3.3): monohalogenated organic compounds
such as methyl iodide (CH3I), ethyl iodide (C2H5I)
and propyl iodide (1- and 2-C3H7I); reactive
polyhalogenated compounds such as chloroiodomethane (CH2ICl), bromoiodomethane (CH2IBr)
and diiodomethane (CH2I2); and I2 (Saiz-Lopez et al.
2012). These compounds photodissociate in the atmosphere to generate iodine atoms. In the case of I2,
which photolyses most rapidly (< 10 s), I atoms are
produced just above the ocean surface. The longerlived compounds, such as CH3I which has a lifetime of
about 2 days, provide a source of iodine throughout the
troposphere. It should be noted that, in contrast to
iodine, the major source of bromine and chlorine in
the marine boundary layer is sea-salt aerosol, from

which the Br
and Cl
ions are removed to the gas
phase by the uptake and subsequent reaction of species
such as N2O5, HOI, HOBr and IONO2 (von Glasow
and Crutzen 2007).
As shown in Figure 1.5, I atoms produced by photolysis of these precursors mostly react with ozone to
form the IO radical. A rich inorganic gas-phase chemistry then ensues, driven by rapid photochemistry and
reactions with HOx and NOx species. The iodinecatalysed depletion of O3 occurs via three catalytic
cycles (identified so far). First, IO can deplete O3
through its self reaction to form the OIO radical:
Cycle 1.

IO þ IO
OIO þ hν
ðI þ O3

▸ I þ OIO

(1.5)

▸ I þ O2

(1.6)

▸ IO þ O2 Þ  2
net : 2O3

▸ 3O2

(1.7)

OIO has strong absorption bands in the visible from
about 580 to 630 nm. Although the photodissociation
of OIO has been a matter of recent controversy, it is
now clear that absorption in these bands leads to
photolysis to I + O2 with a quantum yield close to 1
(Gómez Martı́n et al. 2009). It should be noted that

1.3 Halocarbon Gases

23

OIO also forms with a branching ratio of about 80 %
from the reaction of IO and BrO. This cross-reaction
therefore links bromine and iodine chemistry, and
can significantly increase Br-catalysed ozone loss
(Mahajan et al. 2010b; Read et al. 2008a). Cycle 1 is
potentially very O3-depleting, but its efficiency is
second-order in IO (Eq. 1.5), so that it becomes most
important when the IO (or BrO) mixing ratio is more
than several parts per trillion (ppt). At lower halogen
oxide concentrations, reaction with HO2 radicals
becomes important:

IONO2 enhances the release of chlorine and bromine
from sea-salt particles into the gas phase, which can
then cause further O3 depletion (McFiggans et al.
2002). Two recent studies have revealed high levels
of IO and OIO in polluted environments (Mahajan
et al. 2009a; Stutz et al. 2007). Subsequent work
using quantum chemistry calculations has shown that
IONO2 should rapidly recycle back to I2 by reaction
with I (Kaltsoyannis and Plane 2008):

Cycle 2.

The resulting I atoms, from I2 photolysis, will react
with IONO2, rather than O3, if the ratio [IONO2]/[O3] is
greater than about 0.01 (Kaltsoyannis and Plane 2008).
This sequence represents an autocatalytic cycle that
will limit the buildup of IONO2, and explains why
iodine chemistry is active even in a relatively high
NOx environment (Mahajan et al. 2009b).
The ratio of NO2 to NO is controlled principally by
the reactions:

IO þ HO2
HOI þ hν
I þ O3

net : HO2 þ O3

▸ HOI þ O2

(1.8)

▸ I þ OH

(1.9)

▸ IO þ O2
▸ OH þ 2O2

(1.10)

In semi-polluted environments, IONO2 formation
can also lead to O3 depletion:

IONO2 þ I

NO2 þ hν
Cycle 3.

IO þ NO2
IONO2 þ hν
I þ O3

▸ IONO2

(1.11)

▸ I þ NO3

(1.12)

▸ IO þ O2

NO3 þ hν

▸ NO þ O2

NO þ O3

▸ NO2 þ O2

net : 2O3

▸ 3O2

(1.13)
(1.14)

NO þ O3

However, although the quantum yield for photolysis of IONO2 in the near-UV (Eq. 1.11) is close to 1
(Joseph et al. 2007), the major photolysis pathway
(~80 %) of NO3 produces NO2 + O (rather than the
minor pathway, Eq. 1.12), leading to no overall O3
depletion. Thus, this cycle can only operate at ~20 %
efficiency.
The results in Figure 1.5 show that the HOI and
IONO2 formed in Eqs. 1.8 and 1.10, respectively, can
also be removed from the gas phase by recycling
through sea-salt aerosol. In the case of IONO2 this
provides an efficient route for converting NOx to
NO3
ions in the aerosol (Mahajan et al. 2009a;
Stutz et al. 2007). Moreover, the uptake of HOI and

(1.16)

▸ NO þ O

(1.17)

▸ NO2 þ O2

(1.18)

In the presence of significant IO (or BrO)
concentrations, the balance will be shifted towards
NO2 (McFiggans et al. 2000, 2010; Saiz-Lopez et al.
2008):
IO þ NO

(1.15)

▸ I2 þ NO3

▸ I þ NO2

(1.19)

where the halogen oxide plays a similar role to peroxy
radicals such as HO2 and CH3O2. The HO2/OH ratio in
the presence of IO will be reduced by the combination
of Eqs.1.8 and 1.9 (Bloss et al. 2005; McFiggans et al.
2000; Saiz-Lopez et al. 2008). Compared to the analogous bromine chemistry, this cycle is particularly
important because the IO + HO2 reaction is fast and
HOI photolyses more readily than BrO (Atkinson et al.
2007).
The source of I atoms shown in Fig. 1.5 is the
photolysis of oceanic precursors (see Sect. 1.3.3),
which is therefore restricted to daytime. However,
nocturnal IO has also been observed (Mahajan et al.
2009b; Saiz-Lopez and Plane 2004). It has been proposed that the source of IO is the reaction between the
NO3 radical and I2, to give I atoms:

24

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

I2 þ NO3

▸ I þ IONO2

(1.20)

followed by reaction with O3 (Eq. 1.7). The reaction
between IO and NO3 then produces OIO, which has
also been observed at night (Mahajan et al. 2009b;
Saiz-Lopez and Plane 2004; Saiz-Lopez et al. 2006b):
IO þ NO3

▸ OIO þ NO2

(1.21)

Equation 1.21 has recently been studied in the
laboratory (Dillon et al. 2008); the measured rate
constant is close to the estimated value that is required
to model the nocturnal concentration of OIO (SaizLopez et al. 2006b).
The presence of fine particles (diameter < 100 nm)
in the coastal atmosphere has been long established
(Aitken 1895). Initially, it was postulated that the
secondary production of these particles (i.e., growth
through condensation of vapour, rather than primary
emission into the atmosphere) involved sulphuric acid,
likely derived from the oxidation of DMS (see
Sect. 1.2.3.1). However, strong evidence for the
important role of iodine compounds was provided by
the correlation of daytime tidal cycles in iodocarbons
(Carpenter et al. 1999), IO (Alicke et al. 1999; Allan
et al. 2000), and ultrafine particle bursts (O’Dowd
et al. 1999; O’Dowd et al. 1998). Probably the most
comprehensive characterisation of coastal ultrafine
particle properties was carried out during the Particle
Formation in the Coastal Environment (PARFORCE)
project at Mace Head, Ireland (O’Dowd et al. 2002a).
The explosive appearance of particles between 3 and
10 nm diameter (referred to as particle bursts) was
commonly observed during daytime low tide
(Flanagan et al. 2005). The simultaneous reduction in
measured gaseous H2SO4 concentration, and increase
in particle surface area and hygroscopicity, indicated
that the condensation of H2SO4 plays a role in the
subsequent growth of the particles. This has since
been confirmed in laboratory experiments (Saunders
et al. 2010). Furthermore, particle analysis identified
the presence of both iodine and sulphur in the majority
of particles (Mäkelä et al. 2002). In the more recent
North Atlantic Marine Boundary Layer Experiment
(NAMBLEX), observations show that the dominant
iodine precursor at Mace Head is actually I2 (SaizLopez and Plane 2004), released from exposed

macroalgae (especially Laminaria) at low tide
(McFiggans et al. 2004).
Since many coastal regions are semi-polluted, it has
been important to establish whether iodine-mediated
particles are formed at sites where NOx is above
1 ppb. The Reactive Halogens in the Marine Boundary
Layer (RHaMBLe) coastal experiment at Roscoff
(France) showed that this was indeed the case:
substantial particle bursts with concentrations up to
3  105 cm
3 were observed (McFiggans et al. 2010,
Fig. 1.6). Importantly, the particles were then seen to
grow to sizes at which they were active as cloud condensation nuclei (CCN). This experiment therefore
demonstrated that the iodine chemistry involved in
particle formation was not shut down in semi-polluted
air by the NOx reactions shown in Fig. 1.5 (Mahajan
et al. 2009b).
The observation that iodine-mediated particles can
grow to CCN size lends support to an earlier postulate
that the iodine-enhanced marine particle number
might be sufficient to affect global radiative forcing
(O’Dowd et al. 2002b). To test this requires an
understanding of the precise role that iodine plays
in ultrafine particle formation. Almost uniquely in
the atmosphere, iodine oxide vapours are able to
form iodine oxide particles (IOPs) spontaneously
(i.e. there is no free energy barrier). As shown in
Fig. 1.5, laboratory studies have established that
the higher iodine oxides I2O3 and I2O4 form from IO
and OIO recombination (Saiz-Lopez et al. 2012).
Although one laboratory study of IOP formation
from the photolysis of CH2I2 in O3 has indirectly
inferred the tetra-oxide form (Jimenez et al. 2003),
transmission electron microscope analysis of particles
generated photochemically from molecular I2 and O3
in dry conditions showed that the IOPs were essentially I2O5 (Saunders and Plane 2005). Saunders and
Plane speculated that I2O5 might form in the gas phase
through the oxidation of I2O4 by O3, and that this very
stable iodine oxide then polymerised to produce IOPs.
However, IOPs can also form without O3 being present, almost certainly initiated by the spontaneous
polymerisation of I2O3 and I2O4 (Saunders et al.
2010). These particles must then restructure in the
solid phase to I2O5 and I2. Under dry conditions, a
kinetic study of IOP formation showed that the
particles form fractal-like, aggregate structures

1.3 Halocarbon Gases

25

Fig. 1.6 Particle number
concentrations and size
distribution evolution during
the RHaMBLe Roscoff field
project (Reprinted with
permission from Saiz-Lopez A,
Plane JMC, Baker AR,
Carpenter LJ, Glasow Rv,
Martı́n JCG, McFiggans G,
Saunders RW (2012)
Atmospheric chemistry of
iodine. Chem Rev
112:1773–1804. Copyright
2012 American Chemical
Society)

(Saunders and Plane 2006), which collapse when
humidified (Saunders et al. 2010).
One important question concerns the growth of
IOPs in the presence of condensable vapours (water,
ammonia and both mineral and organic acids) in the
marine atmosphere. This is because the supply of
iodine oxides is limited, so that once IOPs form
they will only grow by condensation of these other
vapour species to sizes (> 50 nm diameter) where the
particles can have a significant impact on climate
either directly (scattering and absorption of solar
radiation) or indirectly (enhancement of CCN)
(McFiggans et al. 2006). A recent laboratory study
has shown that the accommodation of H2SO4 vapour
on IOPs is very efficient, particularly at high relative
humidities (Saunders et al. 2010). The growth of IOPs
in the remote MBL to sizes at which their role as CCN
may become important is therefore likely to be
governed by the uptake of H2SO4, accompanied by
H2O and NH3 to maintain the pH close to neutral
(Kulmala and Kerminen 2008). A recent modelling
study (Mahajan et al. 2010a) examined the rate of
production of iodine-mediated CCN in the MBL, as a
function of the mean daytime IO concentration. There
is a highly non-linear relationship, as expected from
the chemistry of IOP formation (Fig. 1.5). Another
critical factor is the background aerosol surface area,
because the loss to background aerosols through
uptake is usually faster than growth by coagulation
and condensation (except at high IO concentrations).
The result is that there is an extremely small

probability of forming new potential CCN particles
when [IO] < 5 ppt, which seems to be the case in
the remote open-ocean MBL (Allan et al. 2000;
Mahajan et al. 2010a; Read et al. 2008a). However,
IOPs may well produce CCN in environments with
higher IO concentrations, such as mid-latitude coastal
areas (Mahajan et al. 2009b; Saiz-Lopez and Plane
2004; Whalley et al. 2007) and Antarctica (SaizLopez et al. 2007b).
Iodine occurs at the low ng m
3 level in atmospheric aerosols from a wide variety of environments;
these concentrations are considerably enriched over
the seawater composition, as indicated by I/Cl and
I/Na ratios (Baker et al. 2000; Duce et al. 1967). This
enrichment most likely arises from the uptake of
gas-phase inorganic iodine species and small IOPs
(Fig. 1.5). A range of inorganic iodine species are
potentially present in the aerosol. These include I
,
HOI, I2, ICl, IBr and IO3
. However, because HOI
reacts rapidly with halide ions and the resulting
IX (X ¼ Cl, Br, I) species are insoluble (and
photochemically active), only the ionic species I

and IO3
are expected to accumulate appreciably. Of
these, I
participates in halogen activation reactions to
yield I2, and so model studies concluded that IO3

should be the only stable iodine species in aerosols
(McFiggans et al. 2000; Vogt et al. 1999). However,
measurements of I
/IO3
speciation in marine aerosol
have shown that the ratio between these two species is
highly variable, for reasons that are poorly understood
(Saiz-Lopez et al. 2012).

26

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

Soluble organic iodine (SOI) in marine aerosol is
ubiquitous (Baker 2005). The proportion of SOI
relative to IO3
is quite variable, although SOI is
frequently found to constitute the major fraction
(Gilfedder et al. 2008). There is also an appreciable
insoluble fraction of aerosol iodine, which is likely to
be either organic or iodine adsorbed to mineral or
black carbon surfaces (Baker et al. 2000; Gilfedder
et al. 2010; Tsukada et al. 1987; Xu et al. 2010).
Hydration of the higher iodine oxides (I2O4, I2O5)
associated with the nucleation of IOPs can account
for the presence of aerosol iodate. Aerosol SOI may
be introduced by primary emissions of iodinated
organic matter from the sea surface during bubble
bursting (Seto and Duce 1972). The sea surface
microlayer is enriched in organic matter and reactions
of O3 and I
at the sea surface are known to produce
iodinated organic matter (Martino et al. 2009). SOI
could also form via the reaction of aerosol organic
matter with HOI (Baker 2005). The interactions of
iodine and organic matter, and the degree to which
aerosol iodine can be recycled to the gas phase, are the
most significant unknowns in aerosol iodine chemistry
at present.

1.4

Non-Methane Hydrocarbons
(NMHCs)

1.4.1

Oxygenated Volatile Organic
Compounds (OVOCs)

Oxygenated volatile organic compounds (OVOCs) are
a subgroup of non-methane hydrocarbons consisting
of alcohols, aldehydes, ketones, and carboxylic acids
(e.g. methanol, acetaldehyde, acetone, and formic
acid). In this section only short chain OVOCs (<5 C
atoms) will be considered. Many of these gases are
ubiquitous in the atmosphere (Singh et al. 1995) and
can also play a significant role in atmospheric trace gas
chemistry. For example, Lewis et al. (2005) found that
acetone, acetaldehyde and methanol constituted up to
85 % of total NMHCs and 80 % of the OH radical sink
at Mace Head Ireland under maritime conditions. In
the following section, we discuss the atmospheric
importance and budgets of OVOCs, as well as the
role of the ocean as a control on their atmospheric
concentrations.

1.4.1.1 Atmospheric Importance of OVOCs
The build-up of biogenic and anthropogenic chemical
compounds in the atmosphere is in many cases largely
controlled by their reaction with the hydroxyl radical,
OH. The abundance of OH depends on a variety of
factors, such as light levels, temperature, and the presence of chemical precursors (Spivakovsky et al. 1990),
most importantly water vapour (H2O) and ozone (O3).
In certain regions of the atmosphere, such as the upper
troposphere, water vapour is not present at high
enough concentrations to account for the amount of
hydroxyl radical observed there. In these regions it has
been suggested that OVOCs are important chemical
precursors of OH (Singh et al. 1995; Wennberg et al.
1998; Lary and Shallcross 2000, Fig. 1.7). In the case
of acetone, Elias et al. (2011) show that there is
enhancement of this compound, although variable, in
the upper troposphere in convective regions during
summer, indicating transport of acetone from surface
sources. However, the acetone source of upper tropospheric OH is still poorly quantified (Sprung and Zahn
2010).
OVOCs can also be significant sources of RO2
(Eq. 1.23, Monks 2005; Mueller and Brasseur 1999;
Singh et al. 1994) and are therefore an important
control on tropospheric ozone (See Sect. 1.5), a harmful pollutant when present in large quantities (Prather
et al. 2003 and references therein, see Sect. 1.5). The
principal reactions involved in stratospheric ozone
formation are the Chapman reactions (Lelieveld and
Dentener 2000). However, these reactions cannot
account for the high concentrations of ozone in some
regions of the troposphere, such as urban areas.
Initially, the source of tropospheric ozone was
believed to be stratospheric-tropospheric exchange.
Later it was found that in highly polluted regions
ozone is principally formed by chemical reactions
involving hydrocarbons and nitrogen oxide chemical
species (NOx ¼ NO þ NO2 ),
RH þ OH
RO2 þ NO

O2

O2

▸RO2 þ H2 O

(1.22)

▸ NO2 þ HO2 þ RðC ¼ OÞ
(1.23)

HO2 þ NO
2NO2

hv; O2

▸ NO2 þ OH

(1.24)

▸ 2NO þ 2O3

(1.25)

1.4 Non-Methane Hydrocarbons (NMHCs)

27

Fig. 1.7 (a) Comparison of
HOx (OH + HO2) production
rates (dashed lines) with
model outputs (solid lines).
(b) Measured OH
concentrations (open symbols)
in a vertical atmospheric
profile over the Pacific Ocean.
Solid lines are model output.
In both panels the red line
includes acetone photolysis
and the blue does not (From
Wennberg PO, et al. (1998)
Hydrogen radicals, nitrogen
radicals, and the production of
O3 in the upper troposphere.
Science 279:49–53. Reprinted
with permission from AAAS)

Additionally, reactions of OVOCs have been
shown to form peroxyaceylnitrate (PAN), a reservoir
for reactive nitrogen compounds, and to form secondary organic aerosols and cloud condensation nuclei
(see Chap. 4), which influence the radiative budget
of the atmosphere (Singh et al. 1995; Blando and
Turpin 2000).

1.4.1.2 Atmospheric Budget
The atmospheric sources and sinks of OVOCs have
only recently begun to be investigated. There are more
detailed budgets for certain compounds, such as acetone and methanol (Heikes et al. 2002; Jacob et al.
2002, 2005; Millet et al. 2008, Schade and Goldstein
2006), but not for others, such as C2–C5 aldehydes
and carboxylic acids, although a few exist (Razavi
et al. 2011-formic acid, Millet et al. 2010 – acetaldehyde, and Naik et al. 2010 – ethanol). Several studies
use models and atmospheric measurements from aircraft to determine budgets (e.g. Jacob et al. 2002),
while more recently measurements from satellites
have become available (e.g. Elias et al. 2011 – methanol, Dufour et al. 2007 – methanol, Razavi et al. 2011
– methanol and formic acid, Rinsland et al. 2006, 2007
– methanol and formic acid; Harrison et al. 2011a,
b – acetone). Tables 1.1 and 1.2 present current estimates
of acetone and methanol atmospheric budgets,

respectively, and it is evident that there is considerable
discrepancy in the literature regarding these
compounds. In general, the main sources of OVOCs
are primary anthropogenic and biogenic emissions,
biomass burning, atmospheric oxidation of precursors
(such as methane, isoalkanes, monoterpenes and
methylbutanol), and plant decay. The main sinks are
wet and dry deposition, reaction with OH, and photochemical oxidation. It is possible to produce OVOCs
from atmospheric transformations of other OVOCS.
For example, acetaldehyde can be produced from
methyl ethyl ketone (Nadasdi et al. 2010; Lewis
et al. 2005) and ethanol (Millet et al. 2010; Lewis
et al. 2005), as well as formaldehyde from methanol
(Heikes et al. 2002), and ethanol from propanal (Naik
et al. 2010).
The large extent and productivity of the global
oceans dictate their potential to be a significant source
or sink for these compounds. Remote marine areas
are also of interest since they are the main convective
regions that cycle chemical species between the
upper and lower troposphere. In these regions oceanic
sources of OVOCs can be especially important for
upper tropospheric O3, OH, and NOx chemistry. Nonetheless, the role of the oceans in the atmospheric
budgets of OVOCs is largely unknown and intensely
debated.

28

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

Table 1.1 Acetone atmospheric source and sink estimates from several publications (as % of total)
Jacob et al. (2002)
Sources
Atmospheric oxidation
Biogenic
Biomass burning
Industry/Urban
Ocean
Sinks
Photolysis
OH oxidation
Dry deposition to land
Ocean

Marandino et al. (2005) –Sinks only

30
35
5
–
29
48
28
24

Shim et al. (2007) – sources only
–
20–40
19–55
8–30
2–4

20
16
8
62

Table 1.2 Methanol atmospheric source and sink estimates from several publications (in Tg year
1)
Sources
Ocean
Terrestrial plant growth
Atmospheric oxidation
Plant decay
Biomass/biofuel buring
Urban emissions
Sinks
Ocean
OH oxidation (gas phase)
Dry deposition to land
Wet deposition
In cloud OH oxidation (aqueous phase)
Inventory (Tg)
Lifetime (days)

Jacob et al. (2005)
170–330
–
100–160
50–100
5–40
10–20
1–10
206
10
129
55
12
<1
4
7

In the case of acetone, Singh et al. (2001, 2003,
2004) infer that the ocean is both a source and a sink
from tropospheric gradient measurements. Sinha et al.
(2007) show that the ocean can be both a source and a
sink depending on regional biological productivity and
light levels. Marandino et al. (2005) directly measured
the flux of acetone using the eddy correlation technique (see Chap. 2) over the north and equatorial
Pacific Ocean. They found that the flux (F, Fig. 1.8)
was always into the ocean (negative) and, when
normalised to wind speed (U), is directly proportional
to the atmospheric concentration of acetone (Ca). The
results of Mao et al. (2006) are in agreement with the
findings of Marandino et al. (2005).
For methanol, the ocean appears to be both a
large source and a large sink. Carpenter et al. (2004)
use DMS (solely oceanic in origin because it is

Heikes et al. (2002)
90–490
0–80
50–280
18–30
10–40
2–32
5–11
160–570
60–150
25–150
35–210
4–36
5–20
3.5–6.9
9

Millet et al. (2008)
242
85
80
37
23
12
5
242
101
88
40
13
<1
3.1
4.7

biogenically produced in the surface ocean, see
Sect. 1.2) and wind speed to calculate a net ocean
sink. Williams et al. (2004) also calculate an ocean
sink for methanol of 66  266 μmol m
2 d
1 in the N.
Atlantic, while Millet et al. (2008) agree that the ocean
is a net sink for methanol but can be a weak source
when background concentrations are low and the sea
surface temperature is warm.
For acetaldehyde, Lewis et al. (2005) state that
there is no statistically significant relationship between
DMS and acetaldehyde, indicating the ocean is probably not a source for acetaldehyde. Millet et al. (2010)
find large regional ocean sources for acetaldehyde.
More studies, including direct flux measurements,
are needed to constrain the role of the ocean in the
atmospheric budgets of a suite of OVOCs. Because
flux is directly proportional to the concentration

1.4 Non-Methane Hydrocarbons (NMHCs)

Fig. 1.8 Empirical relationship between the acetone air/sea
flux (F), horizontal wind speed (U), and the atmospheric concentration of acetone (Ca). Measurements were made over the
northern and equatorial Pacific Ocean during the summer season
(Reproduced from Marandino et al. (2005) by permission of the
American Geophysical Union)

gradient, more information is needed regarding surface ocean processes and OVOC cycling.

1.4.1.3 Surface Ocean Processes
There have been limited investigations of the surface
ocean concentrations, sources, and sinks of OVOCs.
This is mainly due to the fact that it is difficult to
reliably measure these compounds in seawater.
Measurement problems include loss from seawater
sample to the headspace during extraction, contamination of samples from surrounding air (especially in
laboratories that extensively use these compounds as
solvents), large Milli-Q blanks from UV photolysis,
and high solubility of these compounds in humid sample lines (Hudson et al. 2011).
Low molecular weight carbonyl compounds have
been detected in seawater as early as 1955 (Mopper
and Stahovec 1986 and references therein). Since then
the concentration of a variety of <5 C OVOCs have
been measured in the Delaware and Biscayne Bays,
the Caribbean, Sargasso, and Aegean Seas, and in the
Atlantic and Pacific Oceans. These compounds
include methanol, ethanol, 1- and 2-propanol, tertbutyl alcohol, formaldehyde, acetaldehyde, propional,
butyraldehyde, acetone, and pyruvate (Beale et al.

29

2010; de Bruyn et al. 2011; Dixon et al. 2011a, b;
Kieber and Mopper 1990; Marandino et al. 2005;
Mezcua et al. 2003; Mopper and Stahovec 1986;
Obernosterer et al. 1999; Williams et al. 2004; Zhou
and Mopper 1997). An example of measured acetone
atmospheric mixing ratios and surface ocean
concentrations from the Pacific Ocean (Marandino
et al. 2005) is shown in Fig. 1.9. These findings illustrate that the atmospheric acetone levels increase with
latitude (going north) but that the seawater levels
remain nearly constant. The atmospheric sources and
sinks of acetone are relatively well understood and the
measured change in atmospheric levels is probably
due to anthropogenic emissions. However, the explanation for the lack of trend in ocean concentrations is
unknown and points to the lack of understanding
of surface ocean processes that influence acetone
concentrations. In the case of methanol, Carpenter
et al. (2004) reports that the biggest uncertainty in
the methanol atmospheric budget is related to its concentration in seawater, which cannot be accounted for
by their calculated air-to-sea flux.
Most oceanic source process studies have focused
on the production of OVOCs from the photochemical/
photosensitized oxidation of dissolved organic matter
(DOM) (de Bruyn et al. 2011; Ehrhardt and Weber
1991; Kieber et al. 1990; Mopper and Stahovec 1986;
Mopper et al. 1991; Moran and Zepp 1997;
Obernosterer et al. 1999; Sinha et al. 2007; Zhou and
Mopper 1997). This process has been highlighted
as the most likely source of OVOCs in the surface
ocean (see also Sect. 1.5). Other measured sources of
OVOCs include biological production, air/sea
exchange, and oxidation of DOM via radical chemistry (Dixon et al. 2011a, b; Singh et al. 2003b, 2004;
Nemecek-Marshall et al. 1995; Mopper and Stahovec
1986). It is most likely that the production of OVOCs
in the surface ocean is from a combination of the
above processes. de Bruyn et al. (2011) found a combined effect between the photoproduction of acetaldehyde and acetone and the addition of NO3 radicals to
seawater and hypothesised that radical production
enhances OVOCs production. When they increased
oxygen levels in the seawater samples they measured
increased apparent quantum yields (AQY) for formaldehyde and acetone. In addition, while de Bruyn et al.
(2011) saw an increase in photoproduction of formaldehyde, acetone, and acetaldehyde with increasing
coloured DOM (CDOM), they found a non-linear

30

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

Fig. 1.9 Shipboard
measurements of acetone over
the equatorial and northern
Pacific Ocean during summer,
2004, top) Atmospheric
mixing ratios (Ca) and surface
ocean (Cw) acetone
concentrations, bottom)
Computed saturation state
from measured acetone values
(Figure adapted from
Marandino et al. (2005))

relationship at low CDOM levels (i.e. those more representative of the open ocean). The AQY increased
with decreasing CDOM levels indicating that there
is enhanced production efficiency with CDOM
photobleaching/humic substance breakdown. This is
in contrast to Kieber et al. (1990) who report a linear
relationship. The Kieber et al. (1990) study included
UV radiation while de Bruyn et al. (2011) did not.
Beale et al. (2010), although they did not perform direct
process studies, found that the ocean may
be an important source of ethanol (concentration
2–33 nmol L
1), and 1- and 2-propanol (concentrations
2–22 and 1–19 nmol L
1, respectively) to the atmosphere. They found enhanced concentrations of the
propanols in more biologically productive regions
(e.g. upwelling areas). Ethanol concentrations followed
different trends in different regions. In one area they
saw pronounced diurnal cycles with peaks for ethanol
at predawn, possibly indicating bacterial production.
They propose that when biological activity is low
there is a productivity switch to more photochemical
control on ethanol production. In the case of methanol
(discussed in more detail below), Dixon et al. (2011b)
measured concentrations of 151–296 nmol L
1. This is
the highest ever reported value for methanol in oligotrophic waters, with the peak in concentration at predawn. They calculated the production rate of methanol

needed to sustain these levels and the uptake rates, as
discussed below, and concluded that there must be a
large in situ source, unrelated to air-sea exchange.
Surface ocean OVOCs sink processes include
biological uptake, air/sea exchange, and oxidation
(both radical and photochemical) (Obernosterer et al.
1999; Singh et al. 2003b, 2004; Sinha et al. 2007; Sluis
and Ensign 1997). Methanol uptake by bacteria has
recently been reported by Dixon et al. (2011a, b). They
have found that methanol can be used as both an
energy and carbon source. Reported uptake rates are
2–146 nmol L
1 d
1 in surface waters, with enhanced
uptake in more biologically productive waters. They
hypothesise that methanol is an important carbon
source for bacteria in oligotrophic waters (up to
54 % and concentrations are a power function of
chlorophyll a concentrations when they are lower
than 0.2 μg L
1), where there is not enough DOM
produced by phytoplanktion to sustain bacterial
populations. Beale et al. (2010) also found evidence
of removal of alcohol by marine organisms, this
time ethanol. They found an anti-correlation between
biologically productive regions and ethanol
concentrations in the Atlantic Ocean.
Further process studies looking into the role of
biology, photochemistry, and air-sea exchange simultaneously are needed for a range of OVOCs to better

1.4 Non-Methane Hydrocarbons (NMHCs)

constrain how the ocean directly impacts the atmospheric concentrations of these trace gases.

1.4.2

Alkanes and Alkenes

Of relevance to the air-sea interface are the small,
more volatile, gas phase alkanes (e.g. methane
(discussed in Chap. 3), ethane, propane, butane) and
alkenes (e.g. ethene, propene, isoprene), (Donahue and
Prinn 1993). Global oceanic emissions of light alkanes
and alkenes have been estimated to be between 2.5 and
6 TgC year
1 (Plass-Dülmer et al. 1995; Warneck and
Williams 2011). This is small in comparison to terrestrial emissions (ca. 1,141 TgC year
1, Guenther et al.
1995), which are dominated by isoprene from tropical
plants (ca. 500 TgC year
1). As a comparison, marine
isoprene emissions, which were first measured by
Bonsang et al. (1992), have been recently estimated
to be between 0.3 and 1.9 Tg isoprene year
1 (Arnold
et al. 2009). Nonetheless, oceanic inputs of alkanes
and alkenes can have a potentially important role in
the remote marine boundary layer. In the atmosphere,
the alkenes, by virtue of their double bond, generally
react faster than alkanes with ambient oxidants (OH,
O3, NO3). When marine alkenes such as isoprene are
emitted in the vicinity of ship traffic emitting NOx,
rapid ozone production can result (Williams et al.
2010, see Sect. 1.5). Surface water concentrations of
alkanes and alkenes result from direct biological production (from seaweed or phytoplankton) and from
photochemical degradation of dissolved organic matter (DOM) by UV light (Bonsang et al. 1988; Ratte
et al. 1993, 1995, 1998; Riemer 2000). Seawater
concentrations of isoprene have been shown to vary
from 0 to 55 pmol L
1 in the North Sea, peaking in
the summer months when light and phytoplankton
populations are highest (Broadgate et al. 1997). Diurnal variations in emissions related to irradiance have
been observed under clean Southern Ocean conditions
(Bonsang et al. 2008). Laboratory based studies of the
isoprene emission rates from various phytoplankton
monocultures have shown strong emissions from
cyanobacteria and weak emissions from diatoms
(Shaw et al. 2003; Bonsang et al. 2010). In the Mediterranean, ethene concentrations in excess of 200 pmol
L
1 have been reported (Ratte et al. 1995), while in
estuarine waters in Florida ethene reached circa 500
pmol L
1 with carbon equivalent alkanes at several

31

times lower concentrations. Alkenes, and to a lesser
extent alkanes, in seawater are generally found to
be supersaturated with respect to the atmosphere.
Production rates of ethene have been shown to vary
by more than an order of magnitude, between 0.22
pmol L
1 min
1 in transparent oligotrophic waters
and 6.11 pmol L
1 min
1 in estuarine waters (Riemer
et al. 2000). Concentrations of both alkanes and
alkenes have been observed to decrease with increasing carbon number in surface waters (Plass-Dülmer
et al. 1995). Water column measurements by Ratte
et al. (1995) in the Mediterranean, North Atlantic
and North Sea showed alkanes have pronounced maxima at the bottom of the euphotic zone, whereas alkene
concentrations were maximum near the surface
Marine emissions of isoprene have been a focus of
renewed interest in recent years following the suggestion that it was the link between observed correlations
between phytoplankton and cloudiness in the Southern
Ocean (Meskhidze and Nenes 2006). Modelling studies based on in situ measurements from the same
region concluded that isoprene plays an insignificant
role in modulating marine aerosol (Arnold et al. 2009,
see Sect. 1.5). This was based on its modest emission
source (0.31–1.9 Tg year
1) and low aerosol production efficiency (ca. 2 %). In contrast, Gantt et al.
(2009) found that isoprene significantly impacts submicron marine organic carbon and concluded that it
therefore could affect shallow marine clouds and
thereby influence climate. In 2008, the first marine
emissions of monoterpenes were reported (Yassaa
et al. 2008). Exploratory global model studies based
on this single dataset suggest that monoterpenes may
also impact organic aerosol formation (Luo and Yu
2010). A review of marine isoprene and monoterpenes
has been provided by Shaw et al. (2010). From this it
has been suggested that marine organic emissions
should be introduced into regional air quality models
for the western United States (Gantt et al. 2010). All of
the recent modelling assessments suffer from the current paucity of open ocean isoprene and monoterpene
measurements.

1.4.3

Alkyl Nitrates

The oceans are a source of low-molecular weight alkyl
nitrates to the troposphere, particularly methyl
(MeONO2) and ethyl nitrates (EtONO2) (Atlas et al.

32

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

1992, 1993; Blake et al. 1999, 2003; Fischer et al.
2002; Jones et al. 1999; Swanson et al. 2004). Tropospheric measurements indicate that, of the alkyl
nitrates in marine air masses, methyl nitrate is
dominant and that they exhibit positive correlations
between methyl nitrate and ethyl, isopropyl and
n-propyl nitrate (Beyersdorf et al. 2010). Both atmospheric and oceanic measurements indicate that the
source of tropospheric alkyl nitrates varies with location. The equatorial oceans appear to be a net source
region with saturation anomalies as high as 1,500 %
(Blake et al. 2003; Chuck et al. 2002; Dahl et al. 2005).
Higher latitude areas of the oceans can be a source or a
sink; however, saturation anomaly data is sporadic and
does not account for seasonality (Chuck et al. 2002;
Hughes et al. 2008a). Few shipboard studies of alkyl
nitrate concentrations have been carried out. Surface
ocean measurements have been made off the eastern
U.S. coast (Russo et al. 2010), in the Atlantic Ocean
(Chuck et al. 2002; Dahl 2005), in the Pacific Ocean
(Dahl et al. 2005) and in the Southern Ocean (Hughes
et al. 2008a). Shipboard measurements of alkyl
nitrates have indicated that ocean mixed layer alkyl
nitrate concentrations can be up to 100 pmol L
1 in
equatorial regions (Chuck et al. 2002; Dahl et al.
2007). This represents considerable water supersaturation relative to atmospheric concentrations that were
up to 40 ppt during the PEM-Tropics A experiment
(Blake et al. 2003).
Using observations from surface ocean measurements
in the equatorial Pacific, Neu et al. (2008) estimated an
average flux of 26.7 mg MeONO2 m
2 year
1 and
7.18 mg EtONO2 m
2 year
1 for the region. Their
model calculations indicated that the oceanic source of
alkyl nitrates is important to the global ozone cycle (see
Sect. 1.5) and leads to the formation of between 15 %
and 20 % of tropospheric ozone in the western Pacific
(Neu et al. 2008). This is the first time that oceanic
measurements of alkyl nitrates were linked to an atmospheric chemistry model. Given the findings from the
model and the lack of oceanic measurements, it is clear
that more work is needed to quantify the levels of alkyl
nitrate in the oceans as well as how they vary seasonally.
Alkyl nitrates are also present at depth. Depth
profiles in the equatorial Pacific show high
concentrations of alkyl nitrates in the mixed layer
compared to areas outside the equatorial region
(Dahl et al. 2007). Depth profiles of alkyl nitrates in
an upwelling region of the North Atlantic correlated

with chlorophyll, suggesting a biological source
(Chuck et al. 2002). Chuck et al. (2002) conducted
depth profile measurements of methyl and ethyl nitrate
in the Atlantic Ocean and suggested that alkyl nitrate
production: (1) may be associated with algal production in some areas; (2) may be linked to nitrate ions;
and (3) may be photochemically produced in areas
affected by continental run-off (i.e. high nutrient,
high DOM). The loss rates of alkyl nitrates in seawater
have not been measured, but lifetimes have been
estimated to be over a year at 25  C and to increase
with decreasing temperature (Dahl et al. 2007).
Several possible mechanisms for oceanic alkyl
nitrate production have been hypothesised
(Ballschmiter 2002; Chuck et al. 2002; Dahl 2005;
Dahl et al. 2003; Moore and Blough 2002). To date
only two mechanisms have been studied: (1) the
photochemically initiated reaction of alkyl peroxy
radicals with nitric oxide (Dahl 2005; Dahl et al.
2003; Moore and Blough 2002) and (2) biological
production by phytoplankton (Dahl et al. 2012b).
The reaction of alkyl peroxy radicals with nitric
oxide in the aqueous phase is the same mechanism of
alkyl nitrate formation in the gas phase.
ROO þ NO

▸ ROONO

▸ RONO2 (1.26a)

▸ RO þ NO2

(1.26b)

The yield of this reaction is 25–75 % in the aqueous
phase for C1–C3 alkyl nitrates (Dahl et al. 2003)
which is much higher than the gas phase yields
(< 5 %) (Atkinson et al. 1982). The rate of reaction
of organic peroxy radicals with nitric oxide is of
the order of 109 L mol
1 s
1 in the aqueous phase
(Padmaja and Huie 1993). Studies of this reaction
pathway in seawater indicate that it can be initiated
by photochemistry (Dahl et al. 2003; Moore and
Blough 2002). Alkyl peroxy radicals can be formed
in seawater from photolysis of dissolved organic matter (DOM) and NO can be generated in seawater by the
photolysis of nitrite (Blough 1997; Zafiriou and
MacFarland 1981). The photochemical production of
alkyl nitrates in seawater appears to be highly dependent upon the availability of NO (Dahl and Saltzman
2008; Dahl et al. 2003; Moore and Blough 2002).
Recent studies have also indicated that alkyl nitrate
speciation depends on location and, potentially,
sources of DOM (Dahl et al. 2012a).

1.5 Ozone

Biological production of alkyl nitrates is
hypothesised to occur as a result of the formation of
nitric oxide during normal cell function. It has been
suggested that phytoplankton produce nitric oxide
under normal growth conditions and as a response to
infochemicals from wounded or dying cells (Kim et al.
2006; Vardi et al. 2006; Zhang et al. 2006). Previous
studies of the aqueous phase reaction of alkyl peroxy
radicals with NO to form alkyl nitrates indicate that
they will form readily whenever the precursors occur
in solution. Current work indicates that alkyl nitrate
concentrations increase when cells are exposed to
chemicals that stimulate production of nitric oxide
and that cultures of marine phytoplankton are significantly higher (nmol L
1) in alkyl nitrate concentration
than in control solutions (pmol L
1 or lower) (Dahl
et al. 2012b). In addition, there is likely to be a bacterial source of alkyl nitrates from nitrification/denitrification processes (Hughes et al. 2010).

33

(Bange and Williams 2000). Over the marine boundary layer concentrations decline significantly as
expected from an oceanic sink (de Gouw et al. 2003;
Singh et al. 2003a). With the exception of Williams
et al. (2004) for acetonitrile, there are few direct
measurements in ocean waters, although calculations
suggest that the surface oceans are likely to be substantially under-saturated (Table 1.3). The exact
mechanisms by which HCN and CH3CN are broken
down in the oceans are not known but a variety of
bacteria can degrade these species and biological processes are expected to be important. Both HCN and
CH3CN are a small source of nitrogen to the oceans.
As shown in Table 1.3, the global budget of HCN and
CH3CN can be roughly balanced but many of the
key parameters remain poorly quantified. Seawater
measurements are critically needed in order to quantify the ocean sink of these compounds.

1.5
1.4.4

Hydrogen Cyanide (HCN)
and Methyl Cyanide (CH3CN)

HCN and CH3CN (also called acetonitrile) are abundant components of the earth’s atmosphere and have
been measured with a variety of in situ methods and
observed remotely from satellites (de Gouw et al.
2003; Singh et al. 2003a; Pumphrey et al. 2011).
They have been used extensively as potentially specific tracers of terrestrial biomass combustion (Singh
et al. 2010). The current consensus is that biomass
combustion is the main source of both HCN and
CH3CN and the oceans are the primary sink. There
are strong seasonal cycles in the abundances of atmospheric CH3CN and HCN that are linked to seasonal
cycles of forest fires and other biomass combustion
sources (Li et al. 2000). The industrial emissions of
both CH3CN and HCN are negligible even though
substantial quantities of HCN ( 0.40 Tg(N) year
1)
are manufactured as a fumigant. There are indications
that under certain conditions automobile exhaust is a
small source of HCN.
From measured enhancement ratios with respect
to CO in biomass burning plumes, global sources of
1 Tg (N) year
1 for HCN and 0.5 Tg (N) year
1 for
CH3CN are estimated. Chemical removal processes
(such as hydrolysis) have been determined to be
quite slow both in the atmosphere and in the oceans

Ozone

Ozone in the stratosphere, at parts per million levels,
filters out harmful UV radiation, protecting life on
earth. In the troposphere, albeit at three orders of
magnitude lower concentration, ozone exhibits mostly
harmful properties. Here, ozone acts as a potent greenhouse gas. The warming effect of ‘anthropogenic’, i.e.
man-caused, ozone is estimated at 0.35 W m
2 (IPCC
2007), which ranks ozone third in the list of gases
contributing to anthropogenic greenhouse warming.
Ozone is also well known for its oxidising effects,
damaging both plant and animal life in terrestrial
environments. These detrimental effects, in contrast
to most other atmospheric species, are already noticeable at levels 2–3 times the ozone tropospheric background concentration.
Ozone in the lower atmosphere arises from two
sources. Transport from the stratosphere provides a
10–15 ppb background mole fraction. Ozone is also
formed and destroyed by photochemical reactions in
the troposphere. Anthropogenic emissions of nitrogen
oxides and volatile organic compounds have altered
the ozone natural cycle, and resulted in a 2–3 times
increase in lower tropospheric ozone background
concentrations (Lamarque et al. 2005). Close to precursor emission sources, ozone can be further
enhanced. Tropospheric ozone levels are typically
higher over land than over the ocean. However,

34

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

Table 1.3 An estimate of the global budget of HCN and CH3CN (Adapted from Singh et al. 2003a and references therein, and Lupu
et al. 2009)
Parameters
Global atmospheric burden (TgN)
Global Source (>95 % BB) (Tg (N) year
1)
Residence time due to oceanic sink (months)
Residence time due to OH reaction (months)
Global mean residence time (months)
Calculated ocean surface under-saturation

elevated ozone can be observed for days within the
marine boundary layer (MBL) in continental outflow
(Li et al. 2002). In the remote MBL ozone falls to
between 15 and 35 ppb, significantly lower than in the
marine lower free troposphere (Allen et al. 2011),
indicating that the MBL is a net ozone sink. Longterm monitoring and comparison of historical data sets
point towards an increase of MBL ozone, albeit at a
slower rate than observed over land (Lelieveld et al.
2004; Oltmans et al. 2006).
The second most important sink of ozone, after
photochemical destruction, is surface uptake. The
uptake rate, commonly expressed as a deposition
velocity, varies greatly over the Earth’s surfaces.
Ozone deposition rates are highest over vegetated
land, reaching maxima of 2 m s
1 (Wesely and
Hicks 2000). Although ozone is soluble in water, it is
unreactive with it alone but can react with certain
solutes (see below). Overall, oceanic ozone uptake is
significantly slower than over land. Nonetheless, due
to the large ocean surface area, the world’s oceans are
estimated to account for ~ 1/3 of the global ozone
deposition sink (Ganzeveld et al. 2009).
Ozone deposition to the ocean surface and ozoneseawater chemical interactions have recently received
renewed attention. Earlier research was hampered
by limitations on approaches for directly measuring
the ozone flux to the ocean surface. Because of
this, controlling parameters were primarily studied
in laboratory experiments. Ambient scale flux
measurements have relied on flux gradient measurements from lighthouses (Tiefenau 1973), turbulent flux
measurements by eddy covariance (EC) using airplanes
(Lenschow et al. 1982), and most recently, EC ozone
flux measurements from ocean research vessels
(Bariteau et al. 2010). Ozone concentrations and fluxes
over Arctic sea ice have recently been measured by

HCN
0.5
1.1–1.3
5.3
63
5.0
20–30 %

CH3CN
0.3
0.5
9.2
23
6.6
5–10 %

means of automated, self-powered instrument packages
(Knepp et al. 2010; Bottenheim et al. 2009).
Ozone-water deposition rates reported in the literature range from 0.01 to 0.15 m s
1, albeit most results
fall between 0.01 and 0.05 cm s
1. This uptake is ~ 40
times faster than expected from ozone dissolution in
seawater alone. A chemical enhancement mechanism
(explained in Chap. 2) has been postulated (Chang
et al. 2004). Prime candidates for the oceanic reactivity are the iodide ion (I
) as well as organic
constituents (Oh et al. 2008). The results of a recent
laboratory study indicate that these two reactants are
about equally important in determining the deposition
velocity of ozone (Martino et al. 2012). Most likely,
these reactions occur in the ocean surface microlayer
or the immediately underlying seawater. Ozone flux
data obtained at sea point to greater ozone deposition
in coastal waters, likely due to the enhanced ocean
surfactants in those regions (Helmig et al. 2012). This
gradient in marine ozone uptake was also noted in the
review of the oceanic ozone flux literature and
modeling work (Ganzeveld et al. 2009). Ozone uptake
appears to increase with wind speed and sea surface
temperature (Chang et al. 2004; Helmig et al. 2012).
However, these do not seem to be simple
dependencies, but are probably also determined by
concentrations and reactivity of the waterside
reactants.
The parameterisation developed by Fairall et al.
(2007) considers the reaction of ozone with watersoluble reactants, turbulent-molecular transport, a
thin penetration depth of ozone and with reactivity
being primarily in the surface microlayer. Calculations
from this model indicate an enhancement of up to a
factor of three in the oceanic ozone uptake rate during
high winds. Integration of this parameterisation into a
global atmospheric chemistry model showed a

1.7 Ammonia and Amines

surprisingly low spatial and seasonal variability of
ozone fluxes to the oceans, due to compensating
effects in the main drivers, i.e. atmospheric turbulence, and oceanic chemical and physical properties
(Ganzeveld et al. 2009). The most fundamental finding
of this research is that chemical reactants in the surface
ocean are the primary drivers of ozone uptake. This is
a well-known mechanism, as ozone is often used as an
oxidising agent in domestic, municipal and industrial
water disinfection. Those applications have shown
that the ozone reaction with organic constituents
results in production of soluble oxygenated organics,
such as aldehydes, ketones, and acids (von Gunten
2003). Similar processes are expected to arise from
ozone deposition to the ocean, suggesting that ozone
reactions at or near the ocean surface oxidise organic
material and constitute a source of oxygenated organic
compounds (see Sect. 1.4.1). At present it is uncertain
if and how reactions with ozone affect the oceanic
microlayer, dissolved organic material, and ocean biogeochemistry. The increase in the ozone burden seen
in the MBL implies that there has been an increase in
the ozone ocean uptake rate since pre-industrial times.
Following the arguments presented above nurtures
speculation that this may have also driven changes in
ocean surface chemical processing and possibly
aquatic life, though experimental evidence for this
effect is lacking thus far.

35

of NO to the atmosphere, although as far as we are
aware the size of the flux has not been estimated.

1.7

Ammonia (NH3) is a polar, strongly soluble compound
which is ubiquitous in the environment and is important in the atmosphere as the dominant gas phase base
(e.g. Quinn et al. 1992). It has been shown to neutralise
between 50 % and 100 % of acidity in aerosol in
marine air (e.g. Savoie et al. 1993, Johnson and Bell
2008). The amines, derivatives of ammonia (mostly
organic, general formula RxNHy) are also basic, soluble and contribute to neutralisation of atmospheric
acidity. In the aqueous phase, ammonia and the amines
(NHx) are partitioned between their (basic) molecular
forms and their protonated (weakly acidic) ionic forms,
e.g. ammonium, NH4+ and dimethylammonium,
(CH2)2NH2+. The base dissociation constants (pKb)
for ammonia and the aliphatic amines tends to be
similar and of the order of 5 (with the amines generally
somewhat more basic than ammonia); pKb values for
aromatic amines can vary substantially, but they tend
to be weaker bases than ammonia.

1.7.1

1.6

Nitric Oxide

Nitric oxide (NO) is important in the atmosphere due
to its role in the cycling of ozone (see Sect. 1.5).
However, little is known about its transfer across
the air-sea interface. There is evidence from
measurements in the equatorial Pacific that NO can
be formed photochemically in the surface oceans
(Zafiriou et al. 1980). The formation of NO is by
photolysis of nitrite ions, with the major destruction
pathway being reaction with O2
, which can also mop
up any NO from the atmosphere entering the surface
microlayer (Blough and Zafiriou 1985). Extrapolation
of the limited field data to wider areas is problematic
since both light of the correct wavelength
(295–410 nm) and the presence of nitrite, whose concentration is very variable, are required. The tentative
conclusion is that the oceans are likely to be a source

Ammonia and Amines

Ammonia

Ammonia is produced as a byproduct of biological
activity, e.g. from the breakdown of nitrogen-containing
organic compounds. There are natural terrestrial and
marine sources of ammonia to the atmosphere
(Bouwman et al. 1997; Dentener and Crutzen 1994)
and strong anthropogenic sources, including agricultural, domestic and industrial wastes (e.g. Sutton et al.
2008; Dentener et al. 2006). In the atmosphere it readily
reacts with acidic gases and particles to enter the particulate phase as NH4+ (either in aqueous or dry particles,
see chap. 4 for a discussion of particles), from which it
is deposited to land or water surfaces. This is the major
sink for NH3, while oxidation by OH radical is relatively slow (Dentener and Crutzen 1994). The half-life
of NH3 in the troposphere is dependent on the net
acidity of the atmosphere but is thought to be of the
order of 1–2 days, whereas the lifetime of NH4+ in
aerosol particles and cloud water is of the order
of 5 days or more (Dentener and Crutzen 1994).

36

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

It is thought that in the past the ocean would have
been a net source of NHx (NH3 + NH4+) to the terrestrial environment, but since the industrial revolution
this net flux has reversed (Holland et al. 1999;
Galloway 2004). Thus, NHx is transported from the
terrestrial to the marine atmosphere and deposited on
the ocean surface. However, given the relatively short
lifetimes, concentrations in the atmosphere decrease
rapidly away from terrestrial source regions, so the
large anthropogenic flux of NHx probably affects
only the coastal and shelf seas significantly. Typically,
NH3 concentrations in the marine atmosphere are
of the order of 0.1–10 nmol m
3 (mid-to-high ppt
concentrations), but they can be orders of magnitude
higher in polluted terrestrial environments (Johnson
et al. 2008; Dentener et al. 2006).
In open ocean seawater, NHx exists predominantly
as ammonium with concentrations in the nmol L
1
range; concentrations of 1 μmol L
1 or higher may
occur frequently in some coastal and shelf seas but are
uncommon in the open ocean (Johnson et al. 2008).
Ammonium is both a nutrient and a waste product. As
a nutrient its uptake by both phytoplankton and bacteria
is energetically favourable relative to nitrate and it can
be released or produced at various stages in the marine
microbial nitrogen cycle, e.g. from ‘leaky’ algal cells
to heterotrophic breakdown of nitrogen-containing
dissolved organic matter (e.g. Capone 2000). As such,
during productive periods, it tends to be cycled
extremely rapidly (turnover of the entire standing
stock on the order of hours to days) and temporary
decoupling of uptake from remineralisation can lead to
substantial transient increases in NH4+ concentration
over wide areas (Johnson et al. 2007). There is some
evidence of photochemical production of ammonium
from dissolved organics, which could also contribute
to the turnover rate (Kitidis et al. 2006, but see also
Grzybowski 2003). The amount of NH3 available to
transfer across the air-sea interface depends strongly
on pH and temperature (Bell et al. 2007), as does the
Henry’s law solubility of ammonia in water (Johnson
et al. 2008). The partial pressure of NH3 over the open
ocean calculated from typical concentrations, pH and
temperatures is in the range 0.1–5 nmol m
3, which is
of the same order as atmospheric concentrations,
demonstrating that ammonia tends to be close to equilibrium and that the magnitude and direction of the flux
can be very sensitive to factors controlling the acid

dissociation and Henry’s law equilibrium positions.
Johnson et al. (2008) find that the major factor
controlling the flux of NH3 at a global scale is
temperature, with higher seawater concentrations but
downward fluxes at high latitudes, and lower seawater
concentrations but ocean emissions at low latitudes.
However, transient biologically-driven peaks in NH3
concentration in the surface ocean can drive emission
events in regions where the flux might typically be into
the ocean (Johnson et al. 2007). The finding that the
high latitudes appear to be predominantly a sink for
ammonia is somewhat at odds with strong circumstantial evidence for a North Atlantic marine ammonia
source presented by Jickells et al. (2003). This may be
due to advection from lower latitudes or sampling during transient emission periods. However, further simultaneous measurements of concentrations in the ocean
and atmosphere are required to resolve this apparent
discrepancy, and to improve estimates of the net global
emission of ammonia from the ocean, previous
estimates of which lie between 5 (Galloway 2004) and
13 (Schlesinger and Hartley 1992) Tg NH3-N year
1. In
the light of evidence of strong downward fluxes at high
latitudes (Johnson et al. 2008), future estimates are
likely to be lower as low latitude emissions will to
some extent be balanced by high latitude uptake.
An alternative mechanism controlling NH3 flux has
been proposed by Johnson and Bell (2008) to explain
the near-constant ratio of ammonium to non-sea-salt
sulphate in aerosol particles in the remote marine
environment (see Sect. 1.2.1). They invoke titration
of acidic DMS oxidation products with gas phase
ammonia, the rate of which in inversely proportional
to the degree of neutralisation in the particle phase (i.e.
to the NH4+: nssSO42
ratio). Thus, the DMS emission
(and subsequent oxidation) can control the gas phase
ammonia concentration and thus the oceanic emission,
given that the atmosphere and surface ocean are generally close to equilibrium with respect to ammonia.
It is important to note that, as a soluble gas, the airsea exchange of ammonia is controlled by transfer on
the air-side of the interface, particularly as its water
phase transport is likely to be significantly chemically
enhanced by protonation (Johnson et al. 2011) (as
further discussed in Chap. 2). The same applies for
the majority of the amines, discussed below. This
means that calculating the rate of transfer requires
application of a ka (air-side) transfer velocity term

1.8 Hydrogen

rather than the typically applied kw (water side) term
used for less soluble gases such as CO2, N2O, DMS
etc. (e.g. Liss and Slater 1974; Johnson 2010).

1.7.2

Amines

A recent review has identified >150 amine species
which have been measured in the atmosphere or are
known to be produced (Ge et al. 2011). Many of these
have only industrial sources, but a large number are
also known to be biogenically produced and are therefore likely to be formed by biological activity in the
surface ocean in a similar manner to ammonium.
Others have specific natural production pathways,
such as hydrazine (N2H4) which is an intermediary
species in the anaerobic bacterial oxidation of ammonia with nitrite (Anammox, e.g. Ward 2003).
In both ocean and atmosphere the most abundant
volatile amines are the low molecular weight aliphatic
amines of carbon number <6 (Ge et al. 2011), typically occurring at between 1 and 3 orders of magnitude
lower concentration than ammonia (Gibb et al. 1999)
in the ocean and between 2 and 5 orders of magnitude
lower in the atmosphere. There is an increasing body
of atmospheric concentration data for the low molecular weight alkyl amines (e.g. Müller et al. 2009).
The significance of both ammonia and the amines
in particle formation is currently poorly understood
(see Chap. 4). For ammonia the literature is contradictory, with some experimental and modelling studies
suggesting that ammonia significantly enhances ternary new particle formation from water and sulphuric
acid (Coffman and Hegg 1995; Korhonen et al. 1999);
and other evidence suggesting little effect (e.g. Yu
2006). However, recent works suggest that ammonia
can play an important role in particle formation and
growth (Kirkby et al. 2011; Benson et al. 2011). The
amines have been postulated as even stronger agents
than ammonia in ternary nucleation (Kurten et al.
2008), due to their higher basicity and higher molecular weight; as well as being significant components of
the secondary organic aerosol fraction (e.g. Facchini
2008; Wang et al. 2010). In order to better understand
the role of the oceans as sources/sinks of ammonia and
the amines and their importance in atmospheric chemistry, substantial further observational data is required.

37

1.8

Hydrogen

There is growing interest in hydrogen (H2) in view of
its role in affecting the oxidising capacity of the atmosphere and also because of the potential for increased
emissions in a future ‘hydrogen economy’. The production and flux to the atmosphere of H2 formed in the
oceans constitutes about 5 % of the total current global
production of the gas (IPCC 2007). The majority of the
production is from the photochemical oxidation of
methane in the atmosphere (see Chap. 3: Sect. 3.4),
with deposition to soils being by far the major sink
(Rhee et al. 2005).
The near surface waters of the ocean are frequently
at, or well above, saturation with the atmosphere,
indicating net production. Below the upper tens of
meters of the water column, concentrations decline
and, in the case of 8 profiles to 1,000 m in the central
equatorial Pacific, become strongly undersaturated,
indicating net consumption, probably bacterial
(Moore et al. 2009). Processes having the potential to
account for supersaturations have long been
recognised as anaerobic bacterial activity in detrital
particles, nitrogen fixation by cyanobacteria, and
photo-oxidation of dissolved organic material (Conrad
and Seiler 1986). However, there is no definitive
answer as to the relative importance of these three
processes (or others) for the formation of H2 in surface
seawater. In the study by Moore et al. (2009) it was
apparent that the highest concentrations observed in
the water column coincided with high levels of nitrogen fixation. A more recent study (Moore, 2012, personal communication) including about 3,000 surface
measurements along a meridional transect in the
Atlantic (cruise AMT 20) showed marked elevations
in hydrogen concentrations in surface waters between
about 30  N and 30  S. A laboratory study by Wilson
et al. (2010) showed that diazotrophic bacteria varied
greatly in their net release of H2 during nitrogen fixation, with Trichodesmium erythraeum yielding an
order of magnitude more H2 than the unicellular cyanobacterium Cyanothece, and about two orders more
than Crocosphaera watsonii. With regard to
photoproduction in ocean surface waters, Punshon
and Moore (2008) showed from a study of lake and
coastal waters that hydrogen is produced during irradiation with sunlight, with the rate being dependent on

38

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

the concentration of coloured dissolved organic matter
(CDOM). They concluded that while it is unlikely that
photochemistry can account fully for the widespread
supersaturation of H2 in low latitude oligotrophic
ocean waters, it may be a contributor.
Whatever the operative production processes, the
measured supersaturations lead to a net flux of H2 from
the oceans to the atmosphere, estimates of which range
from 3 to 6 Tg year
1, with an uncertainty on each
estimate of 50 % or more (Schmidt 1974; Conrad and
Seiler 1986; Novelli et al. 1999; Rhee et al. 2005).

1.9

Carbon Monoxide

Since the pioneering work of Swinnerton et al. (1970),
the ocean is known to be a source of carbon monoxide
(CO) to the atmosphere. This compound is important
for atmospheric chemistry as its oxidation by the
hydroxyl radical, OH (its main sink) leads, in presence
of nitrogen oxide, to the formation of ozone (see Sect.
1.5), a pollutant and greenhouse gas. Although the
evaluation of its global oceanic source led to estimates
ranging over two orders of magnitude (from 4 to 600
Tg CO year
1), the more recent estimates lie in the
lower part of this range (Bates et al. 1995; Stubbins
et al. 2006b). Therefore, the oceanic source of CO is
likely to play a minor role in its global budget (surface
emissions > 1,000 Tg CO year
1, Duncan et al. (2007)),
which is dominated by fossil fuel, biomass burning and
secondary emissions. Nevertheless, in remote regions,
oceanic emissions from CO may affect the regional
oxidising capacity of the troposphere and therefore
deserve to be better characterised and quantified.
More than three decades ago, the main source and
sink of oceanic CO were established as photooxidation of dissolved organic matter under UV light
and microbial consumption, respectively (Conrad and
Seiler 1980; Wilson et al. 1970). Further work on the
main CO source and sink in the ocean has since been
performed, allowing a better understanding and quantification of these processes, which are dependent on
several biogeochemical or physical parameters, such
as UV irradiance at the seawater surface, quantum
efficiency of the conversion of DOC to CO, the attenuation coefficient of UV radiation in the water layers
in relation to their DOC content, and microbial consumption (Day and Faloona 2009; Johnson and Bates

1996; Stubbins et al. 2006a, 2008; Valentine and Zepp
1993; Xie et al. 2005; Zafiriou et al. 2003; Zhang et al.
2006; Ziolkowski and Miller 2007; Zuo and Jones
1995). A biotic source of CO originating from
macro-algae was also investigated by different groups
(Loewus and Delwiche 1963; Troxler and Dokos
1973), but King (2001) concluded that this source is
not a significant fraction of the global oceanic source.
Recently, laboratory studies of trace gas emissions by
various phytoplankton species have shown that CO is
also emitted directly by phytoplankton under photosynthetically available radiation (Gros et al. 2009).
Cyanobacteria and diatoms were the largest CO
emitters and this source could account for up to 20 %
of the CO production in seawater. Finally, another
source of oceanic CO is the so-called “dark production” which has been reported by several groups (Day
and Faloona 2009 and references therein) and which is
needed to explain non-zero CO values below the
euphotic zone (Kettle 2005).
The first detailed measurements of CO in the mixed
layer of the oceanic water column in the Pacific Ocean
showed that it varies seasonally and regionally with
daily means between 0.1 and 4.7 nmol L
1 (Bates
et al. 1995). CO showed pronounced diurnal cycles
with maxima in the afternoon, in agreement with the
photochemical nature of the main source in water.
Since then, measurements of CO have been performed
only over a few additional areas in the open ocean.
Stubbins et al. (2006b) determined values between
0.2 and 2.6 nmol L
1 during an Atlantic Meridional
cruise (from 35  S to 54  N). Gros et al. (unpublished
data) measured values around 1 nmol L
1 in the
Southern Atlantic Ocean, with levels rising to
20 nmol L
1 in a phytoplankton bloom near the
Argentinian coast. Xie et al. (2009) conducted
measurements in the open waters of the South Eastern
Beaufort Sea at two different seasons with mean
concentrations of 0.45 and 4.7 nmol L
1 in spring
and autumn, respectively.
In shelf waters, CO concentrations were also found to
be variable but with values in the upper range of those
measured in the open ocean. Day and Faloona (2009)
found values between 2.7 and 17 nmol L
1 for a
Californian upwelling system; Xie et al. (2005)
measured values around 1 nmol L
1 in the Beaufort
Sea and Gulf Stream and 2–18 nmol L
1 in the coastal
NW Atlantic. Yang et al. (2010, 2011) have recently

1.10 Concluding Remarks

investigated the East China Sea and the Yellow Sea and
observed values between 0.13 and 2.3 nmol L
1 (mean
of 0.68 nmol L
1) in November and values between 0.12
and 6.99 nmol L
1 (mean of 2.24 nmol L
1) in April.
Sea-to-air CO fluxes have been shown to vary over
two orders of magnitude, depending on the source
(which is variable geographically and seasonally)
and sink strengths, but also strongly dependent on
wind speed. Based on concomitant measurements of
CO in seawater and the atmosphere over the Pacific
Ocean, Bates et al. (1995) calculated zonal average
fluxes ranging from 0.25 to 13 μmol CO m
2
day
1and Stubbins et al. (2006b) have estimated
fluxes from 0.4 to 12.1 μmol CO m
2 day
1.
New approaches are becoming available to estimate photoproduction of CO in water on a global
scale, as shown recently by the study of Fichot and
Miller (2011) who derived CO production fluxes from
remotely sensed ocean colour and modelled solar
irradiance.
Despite the large temporal and spatial variability of
the oceanic source of CO, it is usually considered a
constant yearly mean in global model studies. Further
work combining field measurements (in less
documented areas) and new approaches to derive
global fluxes will help to refine estimates of the marine
CO source.

1.10

Concluding Remarks

In this chapter we have attempted to summarise in
some detail present knowledge of the main trace
gases whose transfer across the air-sea interface is
environmentally important from local to global scales.
This has been done with sections for each gas or group
of gases. But we are aware that the real system does
not follow such arbitrary chemical sectioning. For
example, it is clear that in the atmosphere there is
much interaction between various gases and also
between the gas and particulate phases (both secondary particle formation and reactions on existing
particles). We have tried to deal with this by crossreferencing to relevant sections both within the chapter and across to other chapters, but a more integrated
approach would have been desirable.
An example of how focusing on just one gas can
lead to misunderstanding is DMS and the CLAW
hypothesis. Although the idea that evasion of a

39

biologically-derived gas, DMS, might affect the formation of CCN was on its promulgation a quarter of a
century ago a hugely powerful one, at that time the
complexity of particle formation in the atmosphere
was not well appreciated. Now we are aware that
several gases and possibly pre-existing particles are
likely to be involved in CCN production and so
the original concept needs reframing in the light of
the complexity of our present understanding (see, for
example, Sect. 1.3.4). This is still a very active
research area, as is clear from several earlier sections,
and we have consciously not tried to come to an
overall conclusion to the ongoing discussion.
We have tried to show how air-sea gas fluxes are
driven by processes in both the atmosphere and oceans
and thereby affect and are affected by the properties of
both. In the future, research effort will need to be put
into understanding processes at work in both the surface
oceans and marine troposphere because the sophisticated modelling discussed in almost every part of the
chapter is highly dependent on such understanding.
Another thread that comes out very strongly from
our synthesis is the importance of field measurements.
The chemical nature and low concentrations of many
of the trace gases makes them difficult to measure
accurately. Further, in terms of remoteness and difficulty of sampling, the sea surface and overlying atmosphere are some of the most difficult places from which
to obtain reliable measurements. This is particularly
acute in terms of remote locations, such as the Arctic
and Southern Oceans, and the lack of data covering all
seasons is often due to the difficulty of fieldwork in
winter and under other adverse conditions. Maybe in
the future, satellite and other autonomous measurement platforms will lessen the dependence on ships
and aircraft and so, increase both the temporal and
spatial coverage of our measurements. This requires
investment in miniaturising, automating, and development of new sensitive and stable sensors that can be
deployed unattended. If for no other reason, environmental measurements are vital for calibrating and
validating modelling studies. Other aspects that need
attention are the need for international inter-calibration
of measurement techniques and the importance of
developing the reliability of techniques for direct flux
measurements and their application to a greater range
of gases than is possible at present.
In the chapter authors have, where possible,
indicated how air-sea trace gas fluxes may change

40

1 Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

under expected future global change, although this is
necessarily quite speculative. One area that has come
into prominence recently is ocean acidification and
there has been some study of how lowered future
ocean pH may alter trace gas concentrations in seawater. For example, mesocosm experiments have shown
significant downward changes in DMS and DMSP
concentrations under doubled CO2 (Avgoustidi et al.
2012; Hopkins et al. 2010), and for DMS only (Archer
et al. 2012). However, this is not always the case and
increases in concentrations with lowered pH have been
reported by Kim et al. (2010) for both DMS and DMSP
and by Archer et al. (2012) for DMSP only. Hopkins
et al. 2010 found substantial downward changes in
concentrations of organo-halogen gases in a mesocosm
experiment with seawater under elevated CO2.
Another aspect of global change that may affect
air-sea trace gas fluxes is the role of iron and other
nutrients depositing from the atmosphere into the
ocean (discussed in detail in Chap. 4) or coming into
surface waters from below. The 13 in situ iron
fertilisation experiments conducted over the last 20
years give some indications of how changing iron
amounts from dust or other sources might affect
ocean biogeochemistry and so lead to changed
air-sea fluxes of trace gases (see Williamson et al.
2012 for a general overview of these experiments
and Law 2008 for a review of the effect on trace
gases). The best studied gas in this context is DMS
where in many of the experiments concentrations of
the gas (and its precursor DMSP) have been found to
increase substantially (Turner et al. 1996, 2004). But
this is not always the case since in the sub-Arctic
Pacific fertilisation with iron was found to decrease
or show no change in DMS (Levasseur et al. 2006;
Nagao et al. 2009). For a variety of other trace gases
including organo-halogens, isoprene and NMHCs the
situation is similarly mixed (Wingenter et al. 2004;
Liss et al. 2005; Hashimoto et al. 2009; Kato et al.
2009; Nagao et al. 2009). This means that predicting
the impact of changes in dust inputs on production of
these other gases and hence on, for example, atmospheric oxidising capacity and climate is extremely
difficult to predict given present understanding of the
complex interactions involved. As noted in Liss et al.
2005, this complexity would need to be taken into
account in any proposal to ameliorate global climate
by fertilising the oceans with iron.

Throughout the chapter we have endeavoured to
show how gas transfer between ocean and atmosphere
fits both conceptually and quantitatively into the
global cycling of elements. In the future, further attention will need to be paid to how air-sea exchange
contributes to the concept of Earth System Science,
particularly for forecasting how environmental
variables (e.g. wind, pH, dust deposition) are likely
to alter in the future under the substantial global
changes predicted for this century.

Open Access This chapter is distributed under the
terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any
medium, provided the original author(s) and source
are credited.

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2

Transfer Across the Air-Sea Interface
Christoph S. Garbe, Anna Rutgersson, Jacqueline Boutin,
Gerrit de Leeuw, Bruno Delille, Christopher W. Fairall,
Nicolas Gruber, Jeffrey Hare, David T. Ho, Martin T. Johnson,
Philip D. Nightingale, Heidi Pettersson, Jacek Piskozub, Erik Sahlée,
Wu-ting Tsai, Brian Ward, David K. Woolf, and Christopher J. Zappa

Abstract

The efficiency of transfer of gases and particles across the air-sea interface
is controlled by several physical, biological and chemical processes in the
atmosphere and water which are described here (including waves, large- and
small-scale turbulence, bubbles, sea spray, rain and surface films). For a deeper
understanding of relevant transport mechanisms, several models have been developed, ranging from conceptual models to numerical models. Most frequently the
transfer is described by various functional dependencies of the wind speed, but
more detailed descriptions need additional information. The study of gas transfer
mechanisms uses a variety of experimental methods ranging from laboratory
studies to carbon budgets, mass balance methods, micrometeorological
techniques and thermographic techniques. Different methods resolve the transfer
at different scales of time and space; this is important to take into account when
comparing different results. Air-sea transfer is relevant in a wide range of
applications, for example, local and regional fluxes, global models, remote sensing and computations of global inventories. The sensitivity of global models to the
description of transfer velocity is limited; it is however likely that the
formulations are more important when the resolution increases and other processes in models are improved. For global flux estimates using inventories or
remote sensing products the accuracy of the transfer formulation as well as the
accuracy of the wind field is crucial.

2.1

Introduction

The transfer of gases and particles across the air-sea
interface depends not only on the concentration difference between the water and the air, but also on the

C.S. Garbe (*)
e-mail: christoph.garbe@iwr.uni-heidelberg.de
A. Rutgersson (*)
e-mail: anna.rutgersson@met.uu.se

efficiency of the transfer process. The efficiency of the
transfer is controlled by complex interaction of a variety
of processes in the air and in the water near the interface.
Here we treat both gases and particles since the transfer,
to some extent, is governed by similar mechanisms.
Studies of transfer across the air-sea interface
include a variety of methods and techniques ranging
from laboratory studies, modeling and large-scale
field studies. Various methods reach somewhat different conclusions, due to representation of different

P.S. Liss and M.T. Johnson (eds.), Ocean-Atmosphere Interactions of Gases and Particles,
Springer Earth System Sciences, DOI 10.1007/978-3-642-25643-1_2, # The Author(s) 2014

55

56

2

mechanisms, but also because of uncertainties in the
different methodologies.
Much of the interest in air-sea gas and particle
transfer relates to global or regional phenomena, by
using models or remote sensing products to determine
oceanic sinks and sources of gases and particles. Upscaling of transfer involves relating the transfer to the
environmental factors that influence the exchange.
Figure 2.1 summarises factors most likely to be of
great importance. Most commonly the transfer is
described by a wind speed dependent function. There
is, however, increasing understanding that a variety of
processes influence gases of varying properties (like
solubility) and particles differently.
The transport of a quantity is characterised by the
transfer velocity, k. The transfer velocity is given by
the flux F of the transported quantity divided by the
“concentration difference” of the quantity between
surface (C0) and the bulk (Cbulk):
k¼

F
:
C0  Cbulk

(2.1)

For the transport of CO2, the water-sided transfer
velocity kw is often related to the partial pressure
difference ΔpCO2 between air and water by kw ¼
F/(αΔpCO2). The transfer velocity k will then depend
both on the Schmidt number (Sc) and the dimensionless solubility α. It is related to the friction velocity
u* and the Schmidt number Sc ¼ ν/D given by
k ¼ u βðsÞScnðsÞ ;

(2.2)

where both β (s) and n(s) depend on parameters
describing the surface conditions and the related transport processes. If heat is the transported quantity of
interest, then Sc is substituted by the Prandtl number
Pr ¼ ν/ϰ in this equation. Here ν is the kinematic
viscosity, D is the molecular mass diffusivity and ϰ
is the thermal diffusivity.
There exist a number of previous books and
reviews concerning air-sea gas and particle transfer
(e.g. Fairall et al. 2000; Wanninkhof et al. 2009; Le
Quéré and Saltzman 2009; de Leeuw et al. 2011). Here
we summarise mechanisms of importance, measurement techniques frequently used to investigate the
transfer, present state-of-the-art parameterisations

Transfer Across the Air-Sea Interface

and applications of the air-sea transfer description.
The accuracy of the various methods and the importance of measurement uncertainty for global flux
estimates are further discussed.

2.2

Processes

The major processes influencing the air-sea transfer
velocity are described here. This includes processes
important for a relatively insoluble gas like CO2:
micro scale wave breaking, small and large scale turbulence in the water, waves, bubbles, sea spray, rain
and surface films (see Fig. 2.1). In addition biological
and chemical enhancement is described having been
recognised as important for a small number of gases
(including O3 and SO2). Atmospheric processes are
also briefly described due to the importance for turbulence generation at the surface as well as their direct
importance for the transfer velocity of air-side controlled (soluble) gases.

2.2.1

Microscale Wave Breaking

Microscale wave breaking, or microbreaking (Banner
and Phillips 1974), is the breaking of very short wind
waves without air entrainment and begins at wind
speeds well below the level at which whitecaps appear
(Melville 1996). Laboratory and field observations
show microbreaking waves are ~ 0.1–1 m in length
and ~ 0.01–0.1 m in amplitude and have a bore-like
crest with parasitic capillary waves riding on the forward face (Zappa et al. 2001).
Other defining characteristics exist between
microbreaking and whitecapping. Oh et al. (2008)
have shown that a single strong coherent structure
develops beneath a large-scale whitecapping breaking
crest. The coherent structure rotates in the same sense
as the wave orbital motion. In contrast, a series of
coherent structures whose rotation sense is not fixed
are generated beneath microscale breakers. However,
the overall characteristics of the spatio-temporal evolution of the coherent vortical structures are qualitatively the same between the two types of wave
breaking. It is also significant that unlike energetic
whitecapping breakers, no jet is formed at the crest
of a microscale breaking wave, suggesting that surface

2.2

Processes

Parameters

57

Processes

Kinematic
Forcing

Quantities
(For Example CO2 )

Thermodynamic
Forcing

Micro Breaking
Wind
Small Scale
Turbulence
Heat Flux

Fetch

Turbulence
in the Air

Large Scale
Turbulence
Waves

Mixing Depths

Bubbles, Spray

Chemi. / Biol.
Properties

Rain

Transfer
Velocity

k

Partial
Pressure
Difference
Δ pCO2

Turbulence
in the Water

Dynamics at
the Interface

Sea Surface
Temperature

Transport

Air-Sea CO2 Flux

Biology

Surface Films

Fig. 2.1 Simplified schematic of factors influencing air-sea
CO2 fluxes. On the right are factors that affect the air-sea
pCO2 difference (thermodynamic forcing). On the left are

environmental forcing factors that control the efficiency of the
transfer (kinematic forcing)

tension has a significant impact on the breaking of
waves shorter than 1 m (Tulin and Landrini 2001).
Instead, the bore-like crest of the microbreaker can
propagate for a considerable distance without significant change of shape. Microbreakers inherently lose a
significant portion of their height and dissipate their
energy during the breaking.
Laboratory experiments in wind-wave tunnels provide a growing body of evidence that turbulence
generated by microscale wave breaking is the dominant mechanism for air-water gas transfer at low to
moderate wind speeds. Laboratory measurements
indicate that a wave-related mechanism regulates gas
transfer because the transfer velocity correlates with


the total mean-square wave slope, S2 (Jähne et al.
1987). Wave slope characterises the stability of water
waves (e.g. Longuet-Higgins et al. 1994) and certain
limiting values of slope are typically used to detect and
define wave breaking (e.g. Banner 1990). Therefore,
Jähne et al. (1987) have argued that wave slope is
representative of the near-surface turbulence produced
by microscale wave breaking.
Microbreaking is widespread over the oceans, and
Csanady (1990) has proposed that the specific manner
by which it affects kw is the thinning of the aqueous
mass boundary layer (MBL) by the intense surface
divergence generated during the breaking process.
Infrared (IR) techniques have been successfully
implemented in the laboratory to detect this visually

ambiguous process and have been used to quantify
regions of surface renewal of the MBL caused by
microbreakers (Jessup et al. 1997a). Analogous to the
MBL, Wells et al. (2009) suggest that an appreciable
change in skin temperature of the thermal boundary
layer requires a large strain rate, such as might be
generated by high vorticity wavelets (Okuda 1982) or
microbreaking waves. Skin layer straining may be
important for large Péclet (Pe) number flows. The
Péclet number is defined to be the ratio of the rate of
advection to the rate of diffusion of a physical quantity.
In the context of heat transport, the Péclet number is
given by the product of the Reynolds number and the
Prandtl number. In the context of mass diffusion, the
Péclet number is the product of the Reynolds number
and the Schmidt number. For example, Peirson and
Banner (2003) and Turney et al. (2005) measured
surface divergence of approximately 1–10 s1 for
microbreaking surface waves, yielding Pe ~ 10–100
for a 0.1 cm skin layer. In addition, flow separation
can produce high vorticity wavelets with Pe ~ 200
(Csanady 1990). On the basis of the experimental
data, the variation of the skin temperature for steady
large Péclet number flows is reasonably well described
ffi
Q pffiffiffiffiffi
π
by ρC
2κE, where Q is the net heat flux, ρ is the
p
density, Cp is the specific heat capacity at constant
pressure, κ is the thermal diffusivity and E is the strain
rate (Wells et al. 2009). The IR signature provides
qualitative evidence of the turbulent wakes of

58

microbreakers that is consistent with Csanady (1990)
idea for the effect of microbreaking on the MBL.
In the infrared imagery, the microbreaking process
is observed to disrupt the aqueous thermal boundary
layer, producing fine-scale surface thermal structures
within the bore-like crest of the microbreaking wave
and in its wake. In the slope imagery, microbreaking
generates three-dimensional dimpled roughness
features of the bore-like crest and in its wake. The
spatial scales of the fine-scale thermal structures and
the three-dimensional roughness features within the
bore-like crests of microbreakers were observed to
be ~ 102 m, the same as the length scale for the
vortical eddy structures measured beneath windforced waves by Siddiqui et al. (2004). Furthermore,
the dimpled roughness features of the bore-like crest
correspond directly to the warmest fine-scale features
of the skin-layer disrupted by microbreaking. The
implication is that the fine-scale thermal structures
and the dimpled roughness features are directly related
to the near-surface turbulence generated at the borelike crests. Because near-surface turbulence increases
kw, microbreakers are likely to be the mechanism that
enhances both heat and mass transfer.
Understanding the nature and key features of the
interfacial flows associated with small-scale waves is
fundamental to explaining the prominent enhancement
of air-water exchange that occurs in their presence.
Within a few hundred microns of the surface of laboratory microscale breaking wind waves, the mean surface drift directly induced by the wind on the upwind
faces and crests is (0.23  0.02)u*a in the trough
increasing to (0.33  0.07)u*a at the crest, where u*a
is the wind friction velocity (Peirson and Banner
2003). Substantial variability in the instantaneous surface velocity exists up to approximately  0.17u*a in
the trough and 0.37u*a at the crest. Peirson and
Banner (2003) attribute this variability primarily to
the modulation of the wave field, although additional
contributions arise due to the influence of transient
microscale breaking or from near-surface turbulence
generated by shear in the drift layer or from
fluctuations in wind forcing.
Microscale wind-wave breaking plays an important
role in the direct transport of fluid from the surface to
the turbulent domain below. The toe of a microscale
breaker spilling region exhibits an intense and highly
localized convergence of surface fluid, with convergence rates generally exceeding 100 s1 (Peirson and

2

Transfer Across the Air-Sea Interface

Banner 2003). However, the variability in mean surface drift along the upwind (windward) faces of the
waves produces mean surface divergences of between
0.2 and 1.3 s1 with the occurrence of locally intense
flow divergence observed to be only of order 10 s1.
Therefore, the convergence zones at the toes of spilling regions are significantly more energetic and efficient than these windward divergence zones.
The phase-averaged characteristics of the turbulent
velocity fields beneath steep short wind waves suggest,
that under conditions of short fetches and moderate
wind speeds, a wind-driven water surface can be
divided into three regions based on the intensity of
the turbulence (Siddiqui and Loewen 2010). The
regions are the crests of microbreaking waves, the
crests of non-breaking waves and the troughs of all
waves. The turbulence is most intense beneath the
crests of microbreaking waves. In the crest region of
microbreaking waves, coherent structures are observed
that are stronger and occur more frequently than
beneath the crests of non-breaking waves. Beneath
the crests of non-breaking waves the turbulence is a
factor of 2–3 times weaker and beneath the wave
troughs it is a factor of 6 weaker.
The mean velocity profiles in the wind drift layer
formed beneath short wind waves have been shown to
be logarithmic and the flow is hydrodynamically
smooth at short fetch up to moderate wind speeds
(Siddiqui and Loewen 2007). The turbulent kinetic
energy dissipation rate for these conditions is significantly greater in magnitude than would occur in a
comparable wall-layer. At a depth of 1 mm, the dissipation rate is 1.7–3.2 times greater beneath
microbreaking waves compared to non-breaking
waves. In the cresttrough region, 40–50 % of the
dissipation is associated with microbreakers. These
results demonstrate that the enhanced near-surface
turbulence in the wind drift layer is the result of
microscale wave breaking.
Terray et al. (1996) proposed that the turbulent
kinetic energy dissipation rate due to wave breaking
is a function of depth, friction velocity, wave height
and phase speed. Vertical profiles of the rate of dissipation showed that beneath microscale breaking
waves there were two distinct layers (Siddiqui and
Loewen 2007). Immediately beneath the surface, the
dissipation decayed as ζ 0.7 and below this in the
second layer it decayed as ζ 2 where ζ is the vertical
coordinate in the wave-following Eulerian system.

2.2

Processes

The enhanced turbulence associated with microscale
wave breaking was found to extend to a depth of
approximately one significant wave height. The only
similarity between the flows in these wind drift layers
and wall-layers is that in both cases the mean velocity
profiles are logarithmic.
A comprehensive study was performed to show that
microbreaking is the dominant mechanism governing
kw at low to moderate wind speeds (i.e. nominally up
to 10 ms1) (Zappa et al. 2001, 2004). Simultaneous
and collocated IR and wave slope imagery
demonstrated that the IR signature of the disruption
of the aqueous thermal boundary layer corresponds
directly to the wakes of microbreakers (Zappa et al.
2001). Concurrently, simultaneous particle image
velocimetry and IR imagery showed that vortices are
generated behind the leading edge of microbreakers
and that the vorticity correlated with kw (Siddiqui et al.
2001). Furthermore, kw was shown to be linearly
correlated with the fraction of water surface covered
by the wakes of microbreaking (AB) and this correlation was invariant with the presence of surfactants
(Zappa et al. 2001). This correlation is evidence for a
causal link between microbreaking and gas transfer.
Zappa et al. (2004) showed that the surface renewal in
the wakes of microbreakers enhanced transfer by a
factor of 3.4 over that in the background. Furthermore,
microbreaking directly contributed up to 75 % of the
transfer across the air-water interface under moderate
wind speed conditions. Zappa et al. (2004) argued that
these results show conclusively that microbreaking is
an underlying mechanism that explains the observation of enhanced gas transfer in the presence of waves
and may govern air-sea gas transfer at low to moderate
wind speeds.
Microbreaking events directly enhance heat and gas
transfer and produce surface roughness elements that


contribute directly to S2 . Also, AB was correlated


with S2 . Since many capillary and non-breaking


gravity waves may dominate S2 , these results suggest there is an indirect link between microbreaking


and the correlation of transfer velocity with S2 .
Furthermore, the modulation of capillary waves as
microscale breaking waves evolve and the transient
roughness features associated with microbreaking
have shown promise in identifying individual
microbreaking events. A comprehensive examination

59

of the IR and wave slope imagery data should clarify


the relationship between microbreaking and S2 .
The correlation of kw with wave slope has shown
results similar to the correlation of kw with AB. Jähne


et al. (1987) observed a correlation of kw with S2 for
both fetch-limited and unlimited fetch cases, and Frew
(1997) and Bock et al. (1999) showed the correlation
was independent of surfactant for the unlimited fetch
case. Zappa et al. (2004) suggest that the reason kw
correlates with wave slope is that microbreaking is the
wave-related mechanism controlling gas transfer and


contributes to S2 .


The greatest contribution of microbreaking to S2
occurs in areas not directly affected by active
microbreaking. The density of capillary waves present
during the microscale wave breaking process is less than
for non-breaking waves. Specifically, the capillary
waves on the forward face of the bore-like crest become
extremely short during the most intense initiation of
microscale breaking when steep slopes occupy the dimpled bore-like crest. The wave field slope characteristics
are constantly evolving throughout the growth process
of the wave packet from the moment capillary waves are
formed to the moment that microscale wave breaking
occurs. The wave evolution up to microbreaking
incorporates contributions of slopes from waves of all
scales. Microscale wave breaking is of short duration
and merely one component of this wind-wave cycle.
Dense packets of capillary-gravity waves ubiquitous in
regions not affected by microbreaking dominate the
contribution to the total mean-square wave slope.
Since the bore-like crest produces the signature of
breaking detected as AB in the infrared imagery, only the
steep slopes associated with the front of the actively
breaking crest and the wake of a microscale breaker


influence S2 . Capillary waves have been shown to


contribute significantly to S2 (Bock et al. 1999) and
are observed to be transient during the microscale breaking process. The fact that capillary waves are damped by
surfactants, coupled with the fact that the smallest scale
waves have been shown to correlate with the gas transfer
velocity, suggests that capillary waves characterise
the importance of the wave field to gas transfer. The
potential for capillary waves as a direct mechanism
for gas transfer has been demonstrated both experimentally (Saylor and Handler 1997) and theoretically
(Coantic 1986; Szeri 1997; Witting 1971). However,

60

2

microbreaking clearly dominates over these capillary
wave processes. The contribution of capillary waves to

2
S is symptomatic of a simultaneous increase in
overall microbreaking and the densely structured
capillary-gravity wave system that increases with wind


forcing. The link between kw and S2 is more complicated than simply stating that microbreaking is the direct
link. It is more likely that microbreaking controls kw,
the capillary-gravity wave system facilitates
microbreaking, and the non-breaking capillary and


gravity waves contribute significantly to S2 .
Breaking of small-scale waves can be affected by
longer waves in a number of ways that provide insights
into the hydrodynamics of formation, propagation and
evolution of microbreaking waves. One of them is
modulation of the train of the short waves riding the
underlying large-scale waves (Babanin et al. 2009).
The large-scale waves compress the short-wave
lengths at their front face and extend those at the rear
face. As a result, the front-face small waves become
steeper and frequently break (Donelan 2001). Smallscale breaking is also affected by the breaking of large
waves (Banner et al. 1989; Manasseh et al. 2006;
Young and Babanin 2006). The growth of short
waves is affected by the existence of energetic
breaking waves, which not only modulate the
microbreakers, but virtually eliminate them as the
microbreakers are overcome by the energetic breakers.
In principle, short waves can be dissipated without
breaking, because of interaction with the turbulent
wake of the large breaker (Banner et al. 1989). Regardless, microbreakers begin growing again from very
short waves and their eventual length is determined
by the effective fetch between energetic breakers.

2.2.2

Small Scale Turbulence

The kinematic viscosity and thermal diffusivity of
water differ by an order of magnitude. Hence, the
thermal sublayer is embedded within the viscous
sublayer, its momentum analogue.
This points to the significance of small-scale processes, such as near-surface turbulence, for the transport of heat and gas though the interface (McKenna
and McGillis 2004). Eddies close to the surface bring
“fresh” bulk water to the surface from which gases can

Transfer Across the Air-Sea Interface

diffuse in or out of the water. Overall this leads to
enhanced gas exchange.
Wind generates turbulence at the air-water interface directly through shear or indirectly through the
wave field. Turbulence generated by wind-induced
waves will be covered in Sect. 2.2.4. In this section,
we are focusing on shear induced turbulence.
The turbulence generated at the air-water interface
can be isotropic or, more importantly, coherent. Numerical simulations performed by Tsai et al. (2005) and
experiments conducted by Rashidi and Banerjee
(1990) have shown that shear stress alone without
waves is sufficient for coherent turbulent structures to
evolve. The structure of coherent shear induced turbulence has implications for turbulent kinetic energy dissipation (c.f. Sect. 2.6.4). The shear induced turbulence
is of a similar scale to the shear layer thickness (Hunt
et al. 2011). Infrared thermography will be introduced in
Sect. 2.5.1.2. In infrared images of the water surface, the
near surface turbulence becomes visible because areas
of surface divergence consist of water parcels that have
been in contact with the water surface for a longer time.
In the presence of a net heat flux away from the interface, such water parcels cool down and evolve into cold
streaks. The opposite holds true for upwelling regions.
The temperature distribution of such coherent structures
visualized with thermography can be seen in Fig. 2.2.
Such patterns were termed fish-scales by Tsai et al.
(2005) and Handler et al. (2001) and consist of narrow
cold bands of high speed fluid.
The narrow dark streaks form the characteristic
intersections when they encounter slow warm plumes
that “burst” into the surface. Schnieders et al. (2013)
performed a statistical analysis of the streak spacing.
These spacings were extracted accurately using an
image processing approach based on supervised learning.
The streaky pattern observed at the water surface is
very similar to the pattern of low speed streaks near
no-slip walls, as described by Nakagawa and Nezu
(1981) and Smith and Paxson (1983). They found the
spacing between these streaks to be lognormally
distributed with a mean dimensionless streak spacing
of l+ ¼ lu*w/ν ¼ 100, where the mean streak spacing
is given by l, the friction velocity by u*w and the
kinematic viscosity by ν. The factor u*w/ν which
when divided by l is dimensionless is estimated to be
the inverse of the thickness of the thermal boundary
layer (Grassl 1976).

2.2

Processes

a

1 cm

61

29,1cm

b
20.6

21.05

21
20.55
20.95

20.9
20.5

l theo
lexp
Fig. 2.2
different
expected
the case

20.85

20.8

ltheo
lexp

20.45

Thermographic images of the water surface at two
wind conditions. λtheo ¼ 100uw =ν describes the
value of the mean dimensionless streak spacing in
of low speed streaks near a no-slip wall, with the

friction velocity given by u*w, and the kinematic viscosity by
ν. λexp ¼ lexp uw =ν describes the mean dimensionless streak
spacing, where l is the determined mean streak spacing for
each case (Images reproduced from Schnieders et al. (2013))

The generally agreed scaling of l+ ¼ lu*w/ν ¼ 100
leads to the scaling of the streak spacing with the
friction velocity in water u*w. With increasing u*w
the spacing of the streaks is therefore supposed to
decrease linearly in the regime of low wind speeds
(Scott et al. 2008). Schnieders et al. (2013) performed
a statistical analysis of the streak spacing in different
laboratory facilites in different conditions. They found
the spacing to plateau for higher wind speeds.
Although the fish-scale pattern seems to be universal and has been described by several authors, the
process that causes these streaks has not been conclusively identified. It has been suggested (Tsai et al.
2005) that low speed streaks near no-slip walls and
high speed streak near the water surface are caused by
a similar process. Tsai et al. (2005) point out that one
possible mechanism causing these streaks is that of
horseshoe vortices that form in the turbulent shear
layer by turning and stretching the spanwise vortices
(see Kline et al. (1967)). These coherent turbulent
structures move upward until their upper ends burst
into the surface and subsequently lead to upwelling of
warmer and slower water at the surface.
Chernyshenko and Baig (2005) suggest another
mechanism for the formation of streaks. In their
scheme, the lift-up of the mean profile in combination
with shear stress and viscous diffusion lead to the
formation of the streaky pattern. Currently, no

conclusive measurements of these processes have
been conducted.

2.2.3

Bubbles, Sea Spray

Transfer of matter across the air-sea interface may
occur not only by molecular diffusion of volatiles
across the sea surface but also through the mediation
of bubbles and sea spray. Non-volatile material can
only be ejected from the sea into the lower atmosphere
within sea spray. Sea spray can be produced by “tearing” of the sea surface by the action of the wind
(“spume drops”); which is most efficient near the
crests of breaking waves and sharply crested waves.
This tearing process is mostly effective at wind speeds
greater than 10 ms1 (Monahan et al. 1983), though it
can be observed at the crest of breaking waves at
moderate wind speeds. The spume droplets are generally large, ranging from tens of micrometres to a few
millimetres. These large droplets will generally only
be airborne for a short time (seconds to minutes) but
are significant for their role in the transfer of moisture
and latent heat (Andreas 1992). Smaller droplets that
are more likely to evolve over hours or days in the
marine atmosphere are almost entirely associated with
the bursting of bubbles at the sea surface. A comprehensive review of sea salt aerosol production was

62

presented by Lewis and Schwartz (2004), the production of sub-micron sea spray aerosol particles was
recently reviewed by de Leeuw et al. (2011). An
overview of both production of sea spray aerosol and
effects of aerosols produced over land on biogeochemical processes in the ocean is presented in Chap. 4 of
this book (de Leeuw et al. 2013).
Bubble generation at the sea surface is associated
with all types of precipitation falling on the sea surface
and can result from supersaturation of air in the upper
ocean, but is primarily associated with air entrainment
within breaking waves. When these bubbles burst at
the sea surface they produce two types of drops, jet
drops and film drops (Blanchard 1963; Woolf et al.
1987). Jet drops are pinched off from the
“Worthington jet” that projects from the open cavity
of a bursting bubble and their initial radii are typically
a tenth of the radius of the parent bubble. Jet drops are
inferred to be the main source of the important subset
of marine aerosol particles with radii between 1 and
25 μm radius (de Leeuw et al. 2011). Film drops were
originally assumed to be formed from the shattering of
the film cap of large bubbles, but a more complicated
picture emerged from later investigations (Spiel
1998). Nevertheless the numerous small sea spray
particles in the lower atmosphere (approximately
10 nm–1 μm) are associated with film drop production.
The study of small sea spray particles underpins our
understanding of direct and indirect radiative effects of
this aerosol (the latter through their role in cloud microphysics) and chemical reactions and pathways in the
lower atmosphere involving both the sea spray
particles and natural and anthropogenic volatiles.
Models and quantitative estimates of these processes
requires a “sea spray source function” (SSSF) and
estimates of this function have recently been reviewed
(de Leeuw et al. 2011). Several methods have been
proposed for estimating SSSF including those based
on a balance between production and deposition (wet
or dry), micrometeorological methods and the whitecap method. Micrometeorological methods are
showing considerable promise, but most estimates are
based on the whitecap method. The whitecap method
requires an assessment of aerosol production by a
“standard whitecap”, usually by measuring the aerosol
production by a simulated whitecap in the laboratory,
which is then scaled to the ocean using predictions of
whitecapping. A serious problem remains since “orderof-magnitude variation remains in estimates of the

2

Transfer Across the Air-Sea Interface

size-dependent production flux per white area” (de
Leeuw et al. 2011). An additional uncertainty arises
from the challenge of parameterising whitecap coverage W (see below).
When bubbles are formed they will accumulate
material on their surface that was previously on the sea
surface. As they are mixed through the upper ocean, the
bubbles may scavenge further material (primarily
surface-active) from the water column. Some bubbles
will dissolve leaving fragments (a “microbubble”
enclosed in a shell of organics, or particles), but most
surface and burst. Material carried to the surface on the
bubble, or skimmed from the sea surface in the bursting
process may be ejected on the sea spray. The cycling of
organic material described above represents another role
of bubbles in geochemical transport and air-sea transfer.
It is recognised that the sea surface microlayer is highly
dynamic when waves are breaking and constantly
renewed by the action of bubbles (Liss et al. 1997). It
follows that bubbles and spray have an indirect effect on
interfacial transfer through their effect on the composition of the sea surface (see Sect. 2.2.7). The sea spray
aerosol will be enriched in organic material and much of
the current interest and progress in studying that aerosol
focuses on the organic content and its effects (de Leeuw
et al. 2011).
Air-sea transfer of volatiles is also dependent on
bubbles and spray. In principle, sea spray should have
some effect on the deposition or exchange of soluble
gases across the atmospheric surface layer, but this
pathway is usually not explicitly identified in
parameterisations of air-side transfer velocity (Johnson
2010). Far more attention has been given to the role of
bubbles in the air-sea exchange of poorly soluble gases.
As already mentioned, bubbles may influence air-sea
gas transfer through their influence on the composition
of the sea surface. Both breaking waves and surfacing
bubbles generate turbulence and this may enhance
transfer at the sea surface. The greatest influence on
poorly soluble gases appears to be through “bubblemediated transfer”, defined as the net transfer of gas
across the surface of bubbles while they are submerged
(Woolf and Thorpe 1991). Both laboratory experiments
and numerical calculations suggest that bubblemediated transfer is a highly effective mechanism for
poorly soluble gases. The effect of bubbles on gas
transfer can be seen in Fig. 2.3 where the transfer
velocity kw is shown for both CO2 and dimethylsulfide
(DMS). DMS has a much higher solubility coefficient

2.2

Processes

63

160
l

EC CO2 data
EC DMS data
bubble k(CO2_660), Woolf 1997
bubble k(DMS_660), Woolf 1997
Kw_DMS, Woolf 1997, Johnson 2010

kw_660 / cm.hr−1

120

80

40
l

l
l
l
l l

0

l

0

l
l l

l

l l

l

l

l l

l l l l

5

10

15

20

−1

wind speed / m.s
Fig. 2.3 The magnitude of the bubble effect on transfer velocity for the relatively soluble gas dimethylsulpide (DMS) compared to less soluble CO2. Pink dots summarise eddy covariance
estimates of DMS transfer velocity to date and grey squares
those for CO2 (detailed in Fig. 2.10). These are plotted over
predicted k660-normalized transfer velocities from the bubble
model of Woolf (1997) for DMS (dark pink solid line) and CO2
(solid black line), using solubility at 15  C from Johnson (2010).
For DMS, the bubble-mediated transfer is very small, so the pink

shaded area effectively represents the diffusive transfer, which
is approximately equal for both CO2 and DMS (at the same
Schmidt number), with the grey shaded area showing the additional bubble-mediated transfer for CO2. Total transfer velocity,
KW, for DMS is also calculated using the scheme of Johnson
(2010), applying the Woolf (1997) bubble parameterization for
kw and the Johnson (2010) ka term (Figure by M.T. Johnson,
shared under creative commons license at http://dx.doi.org/
10.6084/m9.figshare.92419)

than CO2. When the effect is scaled to the ocean by
variants of the whitecap method, bubble-mediated gas
transfer is calculated to contribute substantially to the
transfer of poorly soluble gases (Keeling 1993; Woolf
1993, 1997; Asher et al. 1996). The inclusion of
bubble-mediated transfer is crucial to the successful
application of physically-based models such as
NOAA-COARE (see Sect. 2.6.3) to the air-sea transfer
of carbon dioxide and other poorly soluble gases
(Fairall et al. 2011).
Bubble-mediated gas transfer is distinct in several
respects from direct transfer of gas across the sea
surface. Firstly, since bubbles may dissolve and are
always subject to additional pressure, bubblemediated transfer is asymmetric with a bias to invasion
of gas (Woolf and Thorpe 1991). Secondly, through

the change in composition of a bubble while it is
submerged, the contribution of bubble-mediated transfer to the water-side transfer velocity of a gas is
dependent on solubility and has a complicated dependence on Schmidt number. This feature complicates
the interpretation of dual tracer experiments
(Sect. 2.5.3; Asher and Wanninkhof 1998) and affects
the applicability of gas transfer parameterisations
across gases and the range of water temperature (see
Sect. 2.6.5). Some estimates of the sensitivity of transfer velocity to whitecap coverage are depicted in
Fig. 2.4, emphasising particularly the theoretical sensitivity to solubility and structural uncertainty relating
to the specific model.
The precise dependences of bubble-mediated transfer on the environment and the molecular properties of

64

2

Transfer Across the Air-Sea Interface

Fig. 2.4 Sensitivity of gas transfer velocity to whitecap coverage (kw/W in cm(h %)1) plotted against logarithm of Ostwald
solubility (log10 α). Based on the original “independent bubble
model” (filled diamonds) and “dense plume model” (open
squares) of Woolf et al. (2007). Dense plume model calculated

for a plume void fraction of 20 %. All transfer velocities calculated for seawater at 20  C using gas constants suggested by
Wanninkhof et al. (2009) and normalised to a Schmidt number
of 660

the dissolved gas require further investigation (Woolf
et al. 2007). A third important feature of bubblemediated gas transfer is that since it is dependent on
wave breaking, whitecapping and the dispersion by
mixing processes in the upper ocean (see
Sect. 2.2.5), its environmental dependence (on wind
speed, sea state, water temperature . . .) is a function of
the environmental dependence of these processes. In
particular, in common with the sea spray source function, the environmental dependence of whitecap coverage is critical for quantification of bubble-mediated
gas transfer.
Whitecap coverage W is a practical but enigmatic
measure of the whitening of the sea surface associated
with wave breaking, air entrainment, bubble plumes
and surface foam. Its indefinite nature and practical
obstacles to its systematic measurement make applying field measurements and parameterisations of
whitecapping within the whitecap method difficult
(de Leeuw et al. 2011). Nevertheless, studies of historical data sets (Bortkovskii and Novak 1993; Zhao

and Toba 2001) have elucidated the environmental
dependence of whitecapping. There have also been
significant advances in its systematic measurement
(Callaghan and White 2009) that enable more detailed
analysis of environmental dependence (GoddijnMurphy et al. 2011). Wind speed is the main driver
of whitecapping and wind-speed-only parameterisations are useful (Monahan and O’Muircheartaigh
1980; Goddijn-Murphy et al. 2011), but their accuracy
for instantaneous whitecap coverage is poor. Recent
measurements all indicate that the whitecap fraction is
lower than that predicted by Monahan and
O’Muircheartaigh (1980) which is most frequently
used in models (de Leeuw et al. 2011). It may be
inferred that the sea spray source function and gas
transfer velocities are also likely to vary greatly at a
given wind speed. Water temperature and sea state
appear to be major additional factors in determining
whitecap coverage, and by implication aerosol production and gas transfer. Long et al. (2011) suggests
scaling particle production by wave-breaking energy.

2.2

Processes

2.2.4

Wind-Generated Waves

Wind-generated waves affect processes at the air-sea
interface in several ways. In the form of variable
roughness elements and group speeds, they interact
with and modify the wind field above the sea surface
(c.f. Sects. 2.2.9 and 2.4.1). The Stoke’s drift caused
by the waves is involved in the generation of the
Langmuir circulation which, in turn, is one of the
mixing processes of the surface layer of the ocean
(Sect. 2.2.5) and affects the distribution of surfactants
(Sect. 2.2.7). Breaking waves inject bubbles
(Sect. 2.2.3) and cause turbulence in the surface waters
contributing to the surface processes and to the mixing
of the surface layer. Another source of turbulence is
the microscale breaking of short waves (Sect. 2.2.1).
The properties of the irregular wave field cannot be
parameterised by wind speed alone. For example, wave
breaking depends on the wave steepness and not on the
wind speed. In addition to the wind speed, the development of waves depends also on wind duration, fetch,
the shape of the basin, water depth and atmospheric
stratification. Typically, the wave field consists of one
or several wave systems, e.g. locally generated waves
and a swell originating from a distant storm. Their
influence on the interaction with the atmosphere varies
with state of development, the relative dominance of
the wave systems and the difference of their directions
(e.g. Donelan et al. 1997; Drennan et al. 1999; Veron
et al. 2008). The properties of the wave field can be
quite different in the oceans to that in marginal seas.
While in the oceans the waves more often reach full
development, in areas closer to the shore waves are
typically strongly forced due to the limited fetch and
are thus steeper. Depending on the fetch geometry, the
direction of the waves can differ from the wind direction (Pettersson et al. 2010). If the sea is shallow
enough for the waves to ‘feel’ the bottom, there will
be further changes in the steepness of the waves and
changes in wave directions leading to areas of wave
energy convergence and divergence. In high latitudes
the seasonal ice cover forms a changing fetch and fetch
geometry affecting the properties of the wave field
adjacent to the ice edge.
For transfer across the air-sea interface the turbulence in the subsurface layer is one of the key factors.
When present, the breaking waves contribute strongly
to the subsurface turbulence. Babanin (2006) has

65

suggested that the orbital motion of the waves causes
turbulence also in the absence of the breaking waves.
In view of the importance of the water-side turbulence for the processes at the interface and the surface
layer of the ocean, studies have been undertaken on the
dissipation of the turbulent kinetic energy in the upper
layer. The experimental data needed in these studies
are not easy to obtain (see Terray et al. (1996) and
Soloviev et al. (2007) for a review), but the limited
data shows that in the presence of breaking waves the
dissipation values are higher than given by the sheardriven wall layer approach (e.g. Agrawal et al. 1992;
Osborne et al. 1992; Anis and Moum 1995; Drennan
et al. 1996; Terray et al. 1996; Phillips et al. 2001;
Zappa et al. 2007). Anis and Moum (1995) measured
high dissipation rates at depths greater than the height
of the breaking waves and suggested that the orbital
motions of swell could be one possible mechanism
that transports the wave induced turbulence to deeper
depths. On the other hand, Terray et al. (1996) found
that the dissipation has a constant value to a certain
depth below which the wall-layer behaviour is found.
Their premise was that the dissipation is balanced with
the wind input and the depth of the constant dissipation layer is of the order of the significant wave height.
Based on his similarity analysis, Kitaigorodskii (1984,
2011) proposed the existence of a constant dissipation
rate under breaking waves. He concluded that the
dissipation is dependent on the water-side friction
velocity and the turbulent viscosity constant in the
layer of constant dissipation. In both approaches
(Terray et al. 1996; Kitaigorodskii 1984, 2011), the
dissipation is dependent on the state of development of
the waves. Sutherland et al. (2013) compared
measured dissipation rates in the surface ocean boundary layer to various scalings (e.g. Terray et al. (1996)
and Huang and Qiao (2010)). They found that the
depth dependence was consistent with that expected
for a purely shear-driven wall layer. Many dissipation
profiles scaled with a Stokes drift-generated shear,
suggesting there may be occasions where the shear in
the mixed layer are dominated by wave-induced
currents.
So far there have been only a few experimental
studies on the dependence of gas exchange on the
dissipation caused by the breaking waves. Zappa
et al. (2007) demonstrated the dependence of gas transfer velocity on the subsurface dissipation rate.

66

2

Presently there are on-going field campaigns aiming to
study this question (e.g. Pettersson et al. (2011) and
using the ASIP profiler see Sect. 2.6.4). Kitaigorodskii
(2011) proposed a wave-age-dependent transfer velocity based on his considerations of the dissipation rate
and previously published data. Soloviev et al. (2007)
have proposed a gas exchange parameterisation based
on present knowledge of the different processes in the
exchange. Their model includes three sources of turbulence, the convective, shear and wave-induced turbulence. The model predicted that at wind speeds of up to
10 ms1 the exchange is not dependent on the wave age
due to factors that cancel each other. At higher wind
speeds, the bubble-mediated transfer is dominating.
Due to the lack of coincident observations, this
behaviour could not be confirmed and also hindered a
detailed analysis of the relative importance of different
sources of the turbulence.

2.2.5

Large-Scale Turbulence

Upper ocean dynamics is dominated by sheargenerated eddies (small-scale turbulence) that coexist
with buoyant plumes and wave-generated coherent
structures (large-scale turbulence). The buoyant large
eddies are generated by cooling at the surface resulting
in convection extending to the bottom of the mixed
layer, at which depth stability suppresses turbulence
(Csanady 1997).
The surface cooling (net heat flux and evaporation)
increases density of the surface water, thereby enhancing buoyancy. The buoyancy flux is defined according
to e.g. Jeffery et al. (2007) as:
B¼

gaQnet gβsal Qlat
þ
cpw ρw
λρw

(2.3)

where a is the thermal expansion coefficient, g acceleration due to gravity, Qnet is the net surface heat flux
(i.e. sensible and latent heat flux plus net long-wave
radiation), cpw is the specific heat of water, ρw is the
density of water, βsal is the saline expansion coefficient, Qlat is the latent heat flux, and λ is the latent heat
of vaporisation. Following convective scaling in the
atmosphere (see Eq. 2.11), the characteristic velocity
scale of the turbulence generated by convection (the
water-side convective velocity scale) is defined as
follows (MacIntyre et al. 2002):

Transfer Across the Air-Sea Interface

w ¼ ðBzml Þ1=3

(2.4)

where zml is the depth of the mixed layer. According to
Eq. 2.4, a stronger buoyancy flux (a larger value of B)
and a deeper mixed layer (a larger value of zml) produce
enhanced convective mixing in the water. The convective velocity scale exhibits diurnal as well as seasonal
cycles depending on variations in surface heating and
variations of mixed layer depth. Rutgersson et al. (2011)
suggested relating the convective velocity scale to the
friction velocity of the water (u*w) as u*w/w*. This
parallels the description of atmospheric flow by
combined convective and shear-generated turbulence,
and characterises the comparative energetic roles of
surface shear and buoyancy forces (Zilitinkevich
1994). This scaling can be used to express the additional
parallel resistance to transfer initiated by the water-side
convection. When wind is in the low to intermediate
speed regime, convection is important for mixing, and it
has in several studies been shown to enhance surface gas
transfer (MacIntyre et al. 2002; Eugster et al. 2003;
McGillis et al. 2004b; Rutgersson and Smedman 2010;
Rutgersson et al. 2011). Water-side convection is
expected to dominate in situations with cooling at the
water surface (during the night, or during advection of
cold air masses) and deepening of the mixed layer.
The interaction between the mean particle drift of
surface waves (Stokes drift) and wind-driven surface
shear current generates Langmuir circulation consisting
of counter-rotating vortices roughly parallel to the wind
direction (Langmuir 1938). The Langmuir circulation
can be seen at the surface by the collection of surface
foam in meandering lines in the along-wind direction or
by sub-surface observations, following bubbles trapped
in downwelling regions between vortices and current
profiles (Smith 1998; Thorpe 2004; Gargett and Wells
2007). McWilliams et al. (1997) defined a turbulent
Langmuir number describing the relative influence of
directly wind-driven shear and Stokes drift:
 1=2
ua
La ¼
uS

(2.5)

where u*a is the atmospheric friction velocity and us is
the surface Stokes velocity. Sullivan and McWilliams
(2010) summarise several studies focusing on the
Langmuir mechanism and it is clear that Langmuir
circulation (or Langmuir turbulence) greatly enhances

2.2

Processes

turbulent vertical fluxes of momentum and heat at the
surface.
A regime diagram for classifying turbulent large
eddies in the upper ocean was suggested by Li et al.
(2005) using the Hoennecker number (Li and Garrett
1995) as a dimensionless number comparing the unstable buoyancy force driving thermal convection with
the wave forcing driving the Langmuir circulation.
According to Li et al. (2005), the wind driven upper
ocean is dominated by Langmuir turbulence under
typical sea state conditions. Transition from Langmuir
to convective turbulence occurs with strong thermal
convection, relatively low winds and small surface
velocity. An alternative descrition of the relative role
of Langmuir and convectively generated turbulence is
given by Belcher et al. (2012) as zml/LL, where LL is a
convective-Langmuir number stability length as an
analogue to Obukhov length for convective-shear
turbulence.
Large scale turbulence disrupts the molecular
sublayer and initiates a more efficient gas transfer at
the surface. The large scale turbulence is dominated by
Langmuir circulation or buoyancy depending on environmental conditions.

2.2.6

Rain

Because rain events in nature are episodic and rain
rates are variable, only a few field studies have been
conducted. These experiments, using natural rain and
geochemical mass balances of O2 invasion and SF6
evasion in small plastic pools (Belanger and Korzun
1990, 1991; Ho et al. 1997), demonstrated that rain
could significantly enhance kw(660), although the
exact relationship between rain and kw(660) was not
established in these studies.
Most systematic studies on the effect of rain on airwater gas exchange have been conducted in the laboratory using a rain simulator and a receiving tank.
These studies began in the 1960s, where initial laboratory studies using O2 invasion employed a rain
simulators that had only 8–12 nozzles so only produced a few raindrops at a time, and only investigated
a range of rain rates up to 17 mm h1 (Department of
Scientific and Industrial Research 1964; Banks and
Herrera 1977; Banks et al. 1984). In the last two
decades, systematic laboratory studies examining the
full range of rain rates encountered in nature have

67

quantified the effect of rain on gas exchange (Ho
et al. 1997; Takagaki and Komori 2007), the interaction between rain and wind (Ho et al. 2007), the
mechanism behind the enhancement (Ho et al. 2000),
the effect in saltwater Ho et al. (2004); Zappa et al.
(2009). Furthermore, some modeling studies have
been conducted to examine the potential effect of
rain on air-sea CO2 exchange (Komori et al. 2007;
Turk et al. 2010).
In laboratory experiments using evasion of He,
N2O, and SF6 or invasion of CO2, rain has been
shown to enhance the rate of gas exchange significantly, and the relationship between kw(660) and rain
can easily be related to either the kinetic energy flux or
momentum flux of rain, both of which encompass
variability in rain rate and drop size (Ho et al. 1997;
Takagaki and Komori 2007). Enhancement in kw(660)
is due mostly to increased near surface turbulence,
whereas bubbles play a minor role (Ho et al. 2000).
Experiments in saltwater demonstrate that the relationship between rain and kw(660) is the same as in
freshwater, but density stratification could inhibit vertical mixing and decrease the overall gas flux (Ho et al.
2004; Zappa et al. 2009).
Some SF6 evasion experiments have been
conducted to examine the combined effects of rain
and wind on gas exchange. Initial results indicated
that the effect of rain and wind might be linearly
additive (Ho et al. 2007), but further experiments
have shown that the enhancement effects of rain
fades with increasing wind speeds (Harrison et al.
2012), and wind speeds in the initial experiments
were not high enough to exhibit that effect. While
the exact mechanistic interaction between wind and
rain has not been determined, it has been shown that
when Kinetic Energy Flux (KEF) from wind (calculated from u*) is greater than KEF from rain, the
enhancement effects of rain is diminished (Harrison
et al. 2012).
Modeling studies using relationships derived in the
laboratory shows that the effect of rain on air-sea CO2
exchange is insignificant on a global scale, but could
be important on regional scales (Komori et al. 2007;
Turk et al. 2010). However, those studies made simple
assumptions that remain to be tested, including those
about the dynamics of rain falling on the ocean, and
about how rain and wind interact.
Future experiments should examine the effect of
temperature change caused by rainfall on gas

68

2

exchange, the interaction of rain with surfactants, and
detail the mechanism behind the interaction of rain
and wind and how they affect gas exchange. Field
experiments should also be conducted in areas likely
to be impacted by rain.

2.2.7

Surface Films

Surfactants can influence air-sea gas transfer rates via
several mechanisms. Firstly, the presence of
concentrated insoluble surfactant films (slicks) can
act as a barrier to gas exchange, either by forming a
condensed monolayer on the sea surface (Springer and
Pigford 1970) or by providing an additional liquid
phase that provides a resistance to mass transfer
(Liss and Martinelli 1978). However, in the field, this
effect is believed to be important only at low wind
speeds, as slicks are easily dispersed by wind and
waves (Liss 1983). The main effect of surface-active
material is believed to be due to the occurrence of
soluble surfactants that alter the hydrodynamic
properties of the sea surface and hence turbulent
energy transfer. Their presence reduces the roughness
of the sea surface and hence the rate of micro-scale
wave breaking (see Sect. 2.2.1), lowers sub-surface
turbulence (see Sect. 2.2.2) and impacts on rates of
surface renewal (see Sect. 2.3.1).
Early experiments in wind/wave tanks showed that
artificial surfactants could cause reductions in kw of up
to 60 % for a given wind speed (Broecker et al. 1978).
Indeed, contamination of the air-water interface by
surface films was a common problem, particularly in
circular tanks where there was no “beach” at the end of
the tunnel for the film to collect (Jähne et al. 1987).
Later experiments found that the presence of
surfactants influenced the point at which small scale
waves were observed at the water surface, co-incident
with a rapid increase in kw (Frew 1997). Even more
interesting were observations that kw could vary with
biological activity (Goldman et al. 1988) due to the
exudation of soluble surface-active material (including carbohydrates, lipids and proteins) by phytoplankton (Frew et al. 1990).
Supporting evidence came from laboratory
experiments using seawater collected on a transect
from the United States of America to Bermuda (Frew
1997) indicating that the decrease in kw correlated

Transfer Across the Air-Sea Interface

inversely with bulk-water chlorophyll, dissolved
organic carbon (DOC) and coloured dissolved organic
matter (CDOM), see Fig. 2.5. The relationship of kw
with CDOM is important because this parameter can
be determined remotely by satellite.
Although the importance of surfactants in determining air-sea gas transfer rates has long been
recognised, it has proven exceedingly difficult to
demonstrate that they exert a measurable effect in
the field. Perhaps the first direct evidence came from
a study of air-sea gas transfer rates using the heat flux
technique (see Sect. 2.5.1) off the coast of New
England in fairly light winds (Frew et al. 2004). The
transfer of heat was measured inside and outside of a
naturally occurring CDOM-rich slick and was found
to be substantially inhibited by the presence of the
slick (from 4.1 to 1.3 cm hr1). Due to the patchy
nature of the slick and changeable wind speed the
exact magnitude of the inhibition due to the slick is
unclear.
It is commonly thought that surfactants may only
be important in retarding gas transfer at very low
winds. However, a big unknown is whether exchange
rates might be reduced at higher winds when, despite
wave breaking, surfactants are predicted to be brought
back to the sea surface via bubble scavenging (Liss
1975). Support for this hypothesis came from Asher
et al. (1996) who demonstrated, in a series of laboratory experiments, that soluble surfactants inhibited gas
transfer even under wave-breaking conditions. Very
recently, Salter et al. (2011) reported on a deliberate
large scale release of an artificial surfactant (oleyl
alcohol), in the north-east Atlantic Ocean. Gas transfer
rates were measured by both the dual tracer technique
(see Sect. 2.5.3) and direct covariance measurements
of the DMS flux (see Sect. 2.5.2). Air-sea gas transfer
rates were reduced by about 50 % at 7 ms1 and were
still impacted by the presence of the surfactant at high
wind speeds; kw was lowered by 25 % at 11 ms1.
Whilst the Salter et al. (2011) study has proven
that surfactants can significantly impinge on gas
transfer in the world’s oceans, it is not yet clear that
they do so at ambient levels. A recent study (Wurl
et al. 2011) found many of the world’s oceans (subtropical, temperate and polar) are covered to a significant extent by surfactants. Enrichments in the sea
surface microlayer compared to bulk seawater were
observed to persist at wind speeds of up to 10 ms1

2.2

Processes

69

Fig. 2.5 Correlations of kw with (a) surfactant concentration, (b) in situ fluorescence, (c) dissolved organic carbon (DOC) and
(d) coloured dissolved organic matter (CDOM) fluorescence at 450 nm (Figure reproduced from Frew (1997))

consistent with the observations of the Salter et al.
(2011) study. Clearly, this is an area worthy of further
effort.
How important might surfactants be on a global
basis? Asher (1997) predicted a global decrease of
20 % in the net sea to air flux of carbon dioxide based
on the reasonable assumption that surfactant
concentrations would scale with primary productivity.
Tsai and Liu (2003) estimated that the net uptake of
CO2 could be reduced by between 20 % and 50 %; the
range reflecting the uncertainty in measurements of
surfactants and their impact on gas transfer. However,
a simple relationship between chlorophyll and a
reduction in kw may be unrealistic as sea surface
surfactant enrichments have been found to be greatest
in oligotrophic (i.e. low productivity) waters rather
than, as might have been expected, in highly productivity waters (Wurl et al. 2011); presumably due to
the greater occurrence of bacterial degradation in the
latter waters. Nightingale et al. (2000a) found no
decline of gas transfer rates during the development
of a large algal bloom in the equatorial Pacific. The
implication of these studies of surfactants and air-sea
gas transfer is that wind speed may not be the best
parameter with which to parameterise kw in the
oceans, particularly in biologically productive
regions.

A reasonable correlation with the total mean square
wave slope for both filmed and film-free surfaces,
particularly of shorter wind waves, suggested that
this parameter, although difficult to measure at sea,
might be a more useful predictor of kw (Jähne et al.
(1987) – see Sect. 2.6.2). This has been shown experimentally in the field by Frew et al. (2004) who found
that kw was better correlated to the mean square wave
slope than to wind speed, specifically when winds
were below 6 ms1 and when CDOM levels were
enhanced in the microlayer.

2.2.8

Biological and Chemical
Enhancement

The enhancement of gas transfer by chemical reaction
within the mass boundary layer(s) has been recognised
as important for a small number of gases including O3
(Fairall et al. 2007) and SO2 (Liss 1971), and potentially important for CO2 (Hoover and Berkshire 1969;
Wanninkhof and Knox 1996; Boutin and Etcheto
1995). Suitably rapid reactions serve to ‘steepen’ the
concentration gradient of the gas and thus reduce the
effective depth of the mass boundary layer, thus leading to an enhancement in the transfer velocity (Johnson et al. 2011).

70

Note that whilst reversible reactions such as the
hydration of CO2 will act to buffer a reaction in either
direction and thus lead to enhancement of fluxes into
and out of the ocean (albeit assymetrically where forward and reverse reactions occur at different rates),
irreversible reactions have the capacity to inhibit fluxes
by a similar mechanism (i.e. to decrease the steepness
of the concentration gradient where either a production
reaction acts against a flux into the ocean or a breakdown reaction acts against a flux out) (Johnson et al.
2011). Here we will refer only to enhancement, but the
reader should be aware that enhancement factors can
be negative in some situations.
The enhancement of CO2 exchange by hydration to
carbonic acid and subsequent acid dissociation has
been estimated to account for between 0 % and 20 %
of the global CO2 flux estimated from the 14C technique (Keller 1994). Unlike the near-instantaneous
hydration of SO2 (Liss 1971), the hydration of CO2
is relatively slow and its enhancement is thought to be
important only when turbulent forcing is weak (and
thus the timescale of transport across the mass boundary layer is relatively large). The effect on global CO2
fluxes is rather complex. Boutin and Etcheto (1995)
estimate that the net global atmosphere-to-ocean CO2
flux is reduced by approximately 5 % due to the bias
introduced by outgassing areas being generally
associated with low average winds. On a local scale,
CO2 hydration must enhance the flux at low or zero
wind speeds. The magnitude of this effect is
represented in the hybrid parameterisation of
Wanninkhof et al. (2009) as a constant component of
kw of 2.3 cm h1. Interactions between different
compounds and reactions might lead to more complex
behaviour than simple first-order enhancement. For
instance, air-water mass transfer of CO2 can be
inhibited or enhanced by the mass transfer of NH3
due to the reversible and pH dependent formation of
ammonium carbamate (Budzianowski and Koziol
2005), although this phenomena has not been studied
in the natural environment.
Just as physico-chemical processes may enhance
mass transfer by modifying the concentration gradient
in the mass boundary layers, biological activity might
achieve the same, at least on the water side of the
interface. Microbial communities at the ocean surface
tend to be different from bulk water communities,
often with considerably enhanced population densities
(Cunliffe et al. 2009), which would lead to potentially

2

Transfer Across the Air-Sea Interface

rapid processing of bio-active compounds. There is
some circumstantial evidence for the asymmetrical
biologically-mediated transfer of methane (UpstillGoddard et al. 2003), and O2/CO2 (Garabetian 1991;
Matthews 1999), but these processes are not well
studied.

2.2.9

Atmospheric Processes

Within a few millimetres of the water surface there is a
thin sublayer dominated by molecular diffusion. Above
the molecular diffusion layer is the atmospheric surface
layer where the vertical transport is dominated by turbulent eddies; it extends upwards from the molecular layer
to a rather poorly defined distance (ranging from
approximately 10–100 m). Further away from the surface (in the Ekman layer) the Coriolis effect gradually
changes the flow. These layers make up the Atmospheric Boundary Layer (ABL), in which the presence
of the surface has a profound effect on the flow. Traditionally the atmospheric surface layer is described by the
Monin-Obukhov Similarity Theory (MOST), it assumes
stationary and homogeneous conditions and a solid surface (Panofsky and Dutton 1984). The fluxes are
approximated to be constant with height (within 10 %)
and it is thus enough to describe the flux at just one
height. Using MOST the turbulent surface fluxes are
often expressed using the bulk aerodynamic formula.
Stress (τ), heat and scalar fluxes can be written as:
τ ¼ ρa u2a ¼ ρa CD ðUz  U0 Þ2

(2.6)

H ¼ ρa cp w0 θ0 ¼ ρa cp CH Uz ðTz  T0 Þ

(2.7)

C ¼ ρa cp w0 c0 ¼ DC Uz ðCz  C0 Þ

(2.8)

where ρa is the air density, u*a the friction velocity on
the air-side, CD, CH and DC are the transfer
coefficients for momentum heat and scalars at the
specific height z. Transfer coefficients for scalars (Dalton number) can be related to transfer velocity by
DC ¼ k/Uz. Wind speed, temperature and scalar
values at height z are Uz, Tz and Cz, corresponding
parameters at the surface are U0, T0 and C0.
In the atmosphere, gradients of wind, temperature,
and scalars are dependent on the atmospheric stratification, which also influences the transfer coefficients.
Neutral stratification (giving logarithmic profiles,
assuming MOST) is used as the reference state and

2.2

Processes

71

flux coefficients are then normalised using the actual
stratification. Stratification can be expressed in terms
of the Monin-Obukhov length (L ¼ 

u3a T

κgw0 θ0 v

, where

w0θ0 v is the surface virtual potential temperature
flux). For unstable atmospheric stratification L < 0
and for stable atmspheric stratification L > 0. Turbulence in the atmosphere (and thus the vertical
gradients) is stability dependent and the nondimensional gradients of wind, temperature and
scalars are expressed as:
ϕα ¼

dχ κz
dz χ 

(2.9)

where α ¼ m, h, c and χ ¼ U, θ and C. The ϕ functions are expressed by empirical expressions for
wind (ϕm) and temperature (ϕh); see Högström (1996)
for a review of expressions for momentum and heat,
and in Edson et al. (2004) functions for humidity are
given. McGillis et al. (2004a) suggest the same
expressions for CO2 as for humidity. For stable stratification, expressions from Holtslag and De Bruin
(1988) are frequently used.
Over the sea in the presence of surface gravity
waves the wave boundary layer (WBL) is the atmospheric layer that is directly influenced by surface
waves. For a growing sea the WBL is of the order of
1 m (Janssen 2004) and for swell waves it is significantly larger, it can even extend throughout the ABL
(Smedman et al. 1994).
The neutral transfer coefficients are related to the
roughness length defined as the intersect of the logarithmic profiles with the surface value. Roughness
length for momentum (z0) is crudely related to the
roughness of the surface. Charnock (1955) expressed
z0 over the ocean as:
z0 ¼ α

u2a
g

(2.10)

where the Charnock coefficient, α, is a constant or
described as a function of the state of the waves,
where younger waves are expected to give a rougher
surface (Fairall et al. 2003; Drennan et al. 2003;
Carlsson et al. 2009). For temperature and scalars it is
more complicated (Garratt 1992). The scalar roughness
lengths can basically be expressed by the velocity
roughness length, friction velocity and Schmidt number

(see Fairall et al. (2000) for a discussion of different
approaches). When the flow is aerodynamically smooth,
a thin viscous sublayer exists adjacent to the surface.
Stability is a dominating parameter in the atmosphere since it determines the scale of the turbulence
and thus the efficiency of eddy transport. For stable
stratification, turbulence is suppressed, being
dominated by intermittent turbulent events and atmospheric gravity waves and with a low boundary layer
height. For unstable stratification, the convection at
the surface enhances turbulence initiating convective
eddies. The mean wind can be close to zero, but there
is a non-zero wind component due to the gustiness.
Godfrey and Beljaars (1991) suggested adding a gustiness wind component proportional to the convective
scaling velocity (Deardorff 1970):

wa ¼

g
T

zi w0 θ0 v

1=3
(2.11)

where zi is the height of the ABL. For specific
conditions (during free convective conditions, swell
or low boundary layer height) the wind gradients are
altered (Beljaars 1995; Fairall et al. 2003; Guo et al.
2004; Högström et al. 2008). For gradients of temperature and scalars, free convection is important, but less
is known about swell and boundary layer height.
Over land, stratification is mainly determined by
the diurnal cycle due to effective radiative heating and
cooling of land surfaces. Over sea, the diurnal cycle is
not as dominating due to the larger heat capacity of
water. Then stronger atmospheric stratification occurs
during advection of air masses with a different temperature to the water surface. Strongly stratified
conditions thus occur in areas close to coasts or with
great horizontal temperature gradients.
For momentum, heat, and gases with high solubility
(like water vapour) the atmosphere induces the major
resistance to transfer. For gases with low solubility,
processes in the water contribute the main resistance to
transfer. Taking atmospheric stability into account
when determining transfer coefficients makes a significant difference when calculating fluxes of momentum, heat and humidity. For CO2 the effect is
relatively minor, up to about 20 % for low wind speeds
(Rutgersson and Smedman 2010).

72

2.3

2

Process Models

To gain a deeper understanding of relevant transport
mechanisms, several models have been developed.
These range from conceptual models to numerical
models based on first principles. Conceptual models
are major simplifications of the actual processes and
frequently address one dominant process taking place.
Nevertheless, they are appealing as they can be used for
deriving certain properties of the transport, such as
gradients, fluxes or Schmidt number exponents. Such
models will be discussed in Sect. 2.3.1. These simplistic
models cannot describe interfacial properties, such as
temperature distributions or the wave field. Numerical
simulations based on first principles may be used for
addressing such problems. The current state-of-the-art
of such models is presented in Sect. 2.3.2.

Transfer Across the Air-Sea Interface

processes. The diffusive flux through the water side
stagnant film can be written as
F ¼ D

Interfacial Models

The ocean–atmosphere exchange of insoluble gases
and sparingly soluble chemical species such as CO2,
as well as other properties such as heat and momentum, is controlled by the mass boundary layer
occupying the upper 10–100 μm of the ocean surface.
Within this boundary layer, molecular diffusive transport tends to dominate over turbulent transport, with
increased turbulent forcing leading to an increase in
the rate of exchange by a reduction of the thickness of
the diffusion-dominated domain. Various models have
been proposed to represent such diffusion-mediated
transport across the air-sea interface, and these lead
to different dependency of exchange kinetics (i.e. the
transfer velocity) on the diffusivity of the tracer (due
to differences in the balance between the diffusive and
turbulent processes controlling exchange). These
models are described below, along with brief consideration of analogous models for the transport on the
air-side of the interface for soluble gases.

2.3.1.1 Thin (Stagnant) Film Model
The simple thin film model (Whitman 1923) applied to
the air-sea interface by Liss and Slater (1974),
represents the sea-surface as a flat, solid boundary,
with stagnant mass boundary layers on either side,
through which diffusion is the sole transport

(2.12)

where D is the diffusivity of the gas in the medium, C
is concentration of the gas of interest and z is the
vertical depth of the mass boundary layer. The thickness of the stagnant film must also be a function of the
properties of the medium, notably the viscosity. We
can rewrite the equation in terms of a transfer velocity
of the water side, kw as follows:
F ¼ kw ΔC

(2.13)

where
kw ¼

2.3.1

dC
dz

D
δc

(2.14)

It can be seen from this approach that the transfer
velocity is a function of both the gas properties (D) and
the thickness of the thin film (δc), and has units of
velocity (m2s1 m1 ¼ ms1). This transfer velocity
(also known as piston velocity) represents the rate of
vertical equilibration between water column and
atmosphere. Equation 2.14 is directly proportional to
the diffusivity of the gas. The effect of (e.g. winddriven) turbulence is to reduce the effective depth of
the mass boundary layer and thus reduce the resistance
to transfer.
Where the stagnant film models describe discrete
layers where turbulent and diffusive mixing dominate
transfer, the rigid boundary or solid wall models (e.g.
Deacon 1977; Hasse and Liss 1980) apply a velocity
profile at either side of the interface to describe a
smooth transition between molecular and turbulent
transport regimes. Such models predict that the transfer velocity is proportional to D2/3, demonstrating that
as turbulence plays a (modest) role in determining the
rate of transfer in such a model, the diffusivity of the
tracer becomes somewhat less important in determining the rate of exchange than for the stagnant film
model. This diffusivity dependency has been
demonstrated rather conclusively for a smooth (laminar flow) water surface by tank and wind tunnel heat
and gas exchange experiments (see e.g. Liss and
Merlivat 1986, for a summary).

2.3

Process Models

73

2.3.1.2 Surface Renewal Model
A second class of models has historically been applied
to interfacial exchange problems, where the diffusionlimited surface water layer is episodically and instantaneously replaced by bulk water from below
(e.g. Higbie 1935; Danckwerts 1951). This episodic
replacement leads to an increased role for turbulence
in bringing water to the surface to exchange with the
atmosphere. The details of the various surface renewal
models differ; for example Higbie (1935) assumes a
single turbulence-dependent renewal rate, whereas
Dankwerts describes a statistical distribution of
possible renewal timescales which is modulated by
turbulent forcing. However, they all demonstrate the
same dependence of the transfer velocity on D1/2
which results from the description of an average flux
resulting from episodic replacement of the surface
with water of maximum disequilibrium and
subsequent reduction in flux as this disequilibrium is
reduced prior to the next renewal event. The general
form of the surface renewal model transfer velocity
term is
kw /

 12
D
τ

rate, (along with molecular diffusivity) and can be
generalised as
kw ¼ D=δc

with
1=4

δc ¼ ðD=νÞ1=2 δν ¼ ðD=νÞ1=2 ðν3 =εÞ
) kw ¼ bS1=2
ðενÞ1=4
c

(2.16)

(2.17)

where f (εν) is some function of ε and the kinematic
viscosity of water, ν. As with instantaneous surface
renewal models, the predicted transfer velocity is
1
found to vary with D2 . Recently, eddy renewal models
have been used with considerable success in predicting
not only observed heat fluxes from environmental and
laboratory water surfaces but also patterns of heat distribution – parallel streaks of warmer and colder water on
the surface (e.g. Hara et al. 2007; Veron et al. 2011). The
parallel development of eddy renewal models and surface infrared imaging has the potential to directly relate
surface turbulence measurements, mean surface
renewal timescales and transfer velocities leading to
alternative parameterisations of transfer velocities.

(2.15)

where τ is the renewal timescale. Surface renewal
models have been widely used in studies of heat fluxes
from water surfaces, including in the ocean, with generally good agreement at moderate to high winds (e.g.
Garbe et al. 2004). This is strong validation of the
dependency of the transfer velocity on the square root
of the diffusivity for non-smooth water surfaces.

2.3.1.3 Eddy Renewal Model
The surface renewal model describes periodic
‘disturbances’ of the surface by discrete events,
which are not physically described. Fortescue and
Pearson (1967) and Lamont and Scott (1970) developed the surface renewal approach by explicitly
modelling the physical processes at the interface
assuming eddy turbulence to be the dominating process
in transporting bulk water to the interface. This treatment models the eddy turbulence as a series of
stationary cells of rotating fluid in long ‘rollers’ in the
along-wind direction, alternatively converging and
diverging, leading to regions of upwelling and downwelling to and from the surface. The transfer velocity
in this model is related to ε, the energy dissipation

2.3.1.4 Surface Penetration
Whilst good agreement has been found between surface and eddy renewal models and heat fluxes and
distributions at water surfaces, it has been found that
these do not scale well to gas transfer velocities via the
diffusivity dependency (e.g. Atmane et al. 2004). This
led to the application of the surface penetration model
of Harriott (1962) to detailed heat and mass flux data
by Asher et al. (2004). The surface penetration model
differs from renewal models as it considers incomplete
replacement of the surface by eddy transport from the
bulk. This means that as well as eddy lifetime, the
transfer velocity is a function of the ‘approach distance’. The implication of this is that the diffusivity
dependence of transfer is not constant with turbulent
forcing. This is demonstrated by Asher et al. (2004) by
using a special case of the model of Harriott (1962) for
constant eddy approach distance and lifetime:
1
1
h
1  t 2
¼ þ
kw D 1:13 D

(2.18)

where h is the eddy approach distance and t is the
renewal timescale. The application of the surface penetration model is found by Asher et al. (2004) to

74

2

explain the apparent discrepancy between transfer
velocities of heat and mass through the surface water
layer from surface/eddy renewal models.
The diffusivity dependence of the transfer velocity
as predicted by surface penetration theory is not constant with turbulent forcing. This implies that as well
as step changes in the diffusivity transfer velocity
relationship (from kw / D2=3 to kw / D1=2 at the transition between smooth and rough surface regimes)
there is a continuous change in the exponent with
changing turbulent forcing.

2.3.1.5 Air-Side Transfer
A similar array of physical models of the transfer
velocity on the air-side of the interface exist (e.g.
Fairall et al. 2003; Jeffery et al. 2010), which are
principally concerned with the flux of water vapour
from water surfaces. Such models are also applicable
to other soluble gases or those whose transfer is significantly chemically enhanced in the water phase (and
thus under gas-phase control) and also to the dry deposition of particles to the water surface. Generally these
models show that, as in the water phase, the diffusivity
dependence on transfer is between D0.5 and D0.7 (e.g.
Fairall et al. 2003; Johnson 2010).

2.3.2

Direct Numerical Simulations (DNS)
and Large Eddy Simulations (LES)

High-resolution numerical simulation of the turbulent flow and heat/gas transport in the vicinity of
an air-water interface is an alternative approach
for studying interfacial transfer processes. The
computational approach, which is also called direct
numerical simulation (DNS), solves the posed initialboundary-value problem and resolves the flow fields
down to that of dissipation and diffusion scales, and
examines the detailed dynamics and transfer processes. The boundary-value problem consists of conservation equations of mass and momentum, and
advection–diffusion equations of temperature and
gas in the bulk fluids, subject to stress/flux balance
and kinematic constraint at the air-water interface.
The interfacial boundary conditions also govern the
dynamics of surface waves ranging from capillary
wavelets to gravity waves. Imposing the nonlinear
conditions accurately on the interface, which is also
an unknown to be solved, is the most challenging part

Transfer Across the Air-Sea Interface

in solving such a “fully-nonlinear” free-surface problem. Owing to the limit in previous computing capacity, DNS of three-dimensional turbulent flow
underneath a free-moving water surface was not feasible until recently. The numerical model of Fulgosi
et al. (2003) (also Lakehal et al. 2003) is among the
first attempts to consider the dynamical effect of a
deformable interface in numerical simulation of
interfacial turbulence and transfer processes. The
simulation, however, is confined to very small surface deformation and the maximum wave steepness
akwave (a is the amplitude and kwave the wavenumber)
never exceeds 0.01.
A more recent development by Tsai and Hung
(2007) successfully simulates the turbulent boundary
layer bounded by wind-generated surface waves and
the accompanying transfer processes up to immediate
surface steepness (maximum akwave  0.25). The
simulation resolves all modes of the turbulent flow,
including the coherent vortical structures (the renewal
eddies), the laminar sublayer immediately next to the
interface, and the Kolmogoroff scale turbulence, as
well as the interfacial motions, including the gravitydominant waves and the parasitic capillary ripples.
A representative three-dimensional surface profile
from the simulation is shown in Fig. 2.6 with the
corresponding distributions of temperature and surface
gas-flux density, streamwise vorticity is shown on a
vertical plane. The result clearly reveals high correlation between the surface temperature and the gas-flux
density. Two distinct signatures appear in these
distributions: elongated streaks and random localised
spots, indicative of different “surface renewal” processes in the underlying aqueous flow. The computed
distribution of surface temperature resembles the
measured thermal imagery of a wind-driven water
surface (e.g. Garbe et al. 2004; Smith et al. 2007).
Parasitic capillary wavelets form on the forward face
of the carrier gravity wave, as commonly observed in
wind-generated waves (e.g. Jähne and Riemer 1990).
The impact of these capillary wavelets on crossinterface transfer, however, is not as significant as
expected previously (e.g. Jähne et al. 1979). Note
that the initial condition in posing the numerical simulation consists of a pure gravity wave and random
velocity fluctuations without any prescribed vortical
structures and surface capillaries. The appearance of
these coherent eddies and parasitic ripples all arise
from the nonlinear dynamics of the surface waves
and the underlying turbulent boundary layer.

2.3

Process Models

75

Fig. 2.6 Representative surface profile of a simulated winddriven surface flow at the time instance of ten linear wave
periods from the start of the computation. The wave propagates
in the x direction. The wavelength of the carrier gravity wave is
7.5 cm. The contours superimposed on the surface, the front and

side planes depict the temperature distributions. The
corresponding distribution of gas-flux density is shown on an
additional surface above the air-water interface. The contours of
streamwise vorticity in the flow is also depicted on a crossstream vertical plane

Despite the success of the high-resolution numerical model in simulating realistic transfer processes of
wind-generated waves, DNS of interfacial turbulent
boundary-layer flow is still limited to low Reynolds
number (Re of the order of 103). The wavelength of
the simulated gravity wave is also restricted to
centimetres scale, and the wind speeds are limited to
low to immediate regime. This is due to the fact that
in DNS of the turbulent boundary layer the computation grid must be fine enough to resolve all spatial
scales of the velocity, temperature and dissolved gas
fields. For isotropic turbulence, the number of spatial
modes increases as Reynolds number to the power 3/4,
meaning the number of computation grids increases as
Re9/4. The minimum spatial scale of dissolved gas
field is even smaller than that of velocity fluctuation
(e.g., the diffusivities of dissolved CO2 and O2 are

~ 103 of the viscosity of water). If all of these modes
must be computed accurately, the required computer
capacity becomes overwhelming. As such, the current
DNS model is mainly applied to qualitative study of
transfer process (e.g. Tsai et al. 2013) or testing
of hypotheses in parameterization development
(e.g. Hung et al. 2011).
The new advances in computer hardware and the
use of parallel computing can certainly increase the
size of the physical domain and speed up the computation. An alternative approach will be to adopt the
method of large-eddy simulation (LES) in combination with new large-wave simulation (LWS). The
methodology of LES, which was first introduced in
atmospheric boundary layer research in the 1970s, is
increasingly used in process studies of the ocean mixed
later. However, unlike the LES model for ocean mixed

76

2

layer, in which both the subgrid turbulence and the
surface wave dynamics are parameterized, the proposed LES-LWS model for simulating air-water interfacial turbulent flow resolves not only the energetic
turbulent eddies but also the gravity-dominant surface
waves. In contrast to the well developed LES for
“wall” turbulent boundary layer, subgrid-scale (SGS)
parameterization for turbulent flow underneath a
deformable wavy surface is yet to be developed. In
addition, parameterization models of wave dynamics
of sub-surface-grid scales, including the interactions
among the SGS wavelets, the interactions between the
SGS wavelets and the resolved “large waves”, and the
interaction between the SGS wavelets and the turbulence underneath (e.g. Tsai and Hung 2010), also
require further development.

2.4

Exchanged Quantities

2.4.1

Physical Quantities

Transfer across the air-sea interface involves of a number of properties, the most prominent is the exchange of
momentum. Basically the exchange of momentum
originates from friction at the surface extracting energy
from the mean atmospheric flow. This is the major
source of energy for turbulence in the atmosphere and
the ocean, as well as for surface gravity waves. The
transfer of momentum over the sea is different to that
over land since the surface changes form as a response
to the atmospheric forcing. The transfer of momentum
can be expressed in terms of kinematic stress (τ) and
can be partitioned into turbulent, wave induced and
viscous components (Phillips 1977):
τ ¼ τt þ τw þ τν

(2.19)

the turbulent part (τt) depends on the wind shear, wave
induced part (τw) generated by the waves; the viscous
part (τν) is neglected from some millimetres above the
surface. Since the contribution from the different
components varies with height, it is possible that the
vertical flux is not constant with height within the
wave boundary layer (the layer directly influenced by
the waves). This also means that the surface stress is
highly wave dependent.
The kinematic stress τ is directly related to the
friction velocity u* by

Transfer Across the Air-Sea Interface

u ¼

rffiffiffi
τ
;
ρ

(2.20)

where ρ is the density of the fluid. The friction velocity
can be seen from the air-side (u*a) or from the waterside (u*w) and are related according to
rffiffiffiffiffi
ρw
(2.21)
ua ¼
uw :
ρa
For near neutral conditions one can consider heat to
act as a passive scalar, however, when the heat flux is
larger it is dynamically active and influences the turbulence in the atmosphere and the ocean. An upward
buoyancy flux influences the density of the lower atmosphere and acts to make the atmospheric stratification
unstable, in contrast a buoyancy flux directed to the
surface makes the atmospheric stratification stable.
The turbulent heat flux has a sensible component (direct)
and a latent component (indirect, linked to the energy
being used/released during evaporation/condensation).
For the water surface the buoyancy flux consists of
turbulent heat fluxes (sensible, latent heat) as well as
long wave radiation lowering the temperature of the
surface water. Together with evaporation acting to
make the surface more saline, this can act to make the
water surface buoyantly stable or unstable.
The transfer of momentum close to the surface is
caused by viscosity and pressure perturbations, but the
transfer of heat is ultimately caused by diffusivity. Thus
the transfer of momentum is more efficient and exhibits
a stronger wind speed dependence than the transfer of
heat. The latent heat flux is generally greater during
warmer conditions (in the tropics) as the specific humidity at saturation (controlling the surface value) is
strongly temperature dependent. Skin-temperature as
well as sea spray have been shown to influence the
sensible and latent heat (e.g. Fairall et al. 1996, 2003).

2.4.2

Gases

Similarly to heat, mass is a scalar quantity. However,
different gases will be transported at significantly different rates. Review articles exists on the issue (Jähne
and Haußecker 1998; Wanninkhof et al. 2009), hence
only a brief overview is given here.
The transfer velocity kw (defined in Sect. 2.1) will
depend both on the Schmidt number (Sc) and the
dimensionless solubility α. Both these quantities are

2.4

Exchanged Quantities

77

a

1

b

102

10

103

104

0

105

Air phase

z

Ca,0

Aqueous mass
boundary layer
(0.02–0.2 mm)

Kc < υ

C

Water
surface

Pentachlorophenol
Momentum

DDT

103

3)

Transition
zone

10

c

urfa

hs

oot

Sm

= 2/
e (n

ce (n
urfa

SO2 (pH<3)

)
= 1/2

Ethylacetate
0

102

Naphthalene

40

gh s

Rou

Benzene
H2S

Viscous boundary layer
(0.6–2 mm)

104

0

102

Solubility

Kc < D

Atrazine
Aniline

Heat

103

105

SO2 (pH=6)
Phenol DEHP

40
40
Chloroaniline
30

Cw,0 =

∼

H2 O

104

Air-side mass
boundary layer
(0.1–1 mm)

Ca,0

0

Air-side
control

Trichlorobenzene
Hexachlorobenzene
Trichloroethylene
0

1

1

CO2

40

10

0
Rn

40

Water-side
control

Water phase
zr

Reference level

O2
40

35
40

He

1

10

5
0

CO
0

Cw, bulk

z

CH4

102

25

SF6

103

5

104

Schmidt number Sc = /D

Fig. 2.7 (a) Schematic graph of the mass boundary layers at a
gas-liquid interface for a tracer with a solubility α ¼ 3. (b)
Schmidt number/solubility diagram including various volatile
tracers, momentum, and heat for a temperature range (1  C) as
indicated. Filled circles refer to a temperature of 20  C. The

material properties and depend on temperature and
salinity (Johnson 2010). A Schmidt number/solubility
diagram can be seen in Fig. 2.7. The solubility α is also
termed a partition coefficient. It generally (i.e not
when α ¼ 1) causes a concentration jump at the surface (c.f. Fig. 2.7). This is due to the thermodynamic
solubility equilibrium that is established between the
tracer concentration Ca in the gas phase and Cw in the
liquid phase. Directly at the interface, this results in
Cw;0 ¼ α  Ca;0 :

(2.22)

Cx,0 indicates the concentration in the phase x
directly at the surface.
A mass boundary layer exists on both sides of the
interface. This leads to a transfer resistance R, the
inverse of the transfer velocity k. The total transfer
resistance Rt is the sum of the transfer resistance Rw in
the water and Ra in the air-phase. Due to the solubility,
this transfer resistance will be different when viewed
from the air-side or the water-side of the interface:

regions for air-side, mixed, and waterside control to the transfer
process between gas and liquid phase are marked. At the solid
lines, the transfer resistance is equal in both phases (Adapted
from Jähne and Haußecker (1998))

Rta ¼ Ra þ Rw =α;

air-side :

1
1
1
¼ þ
kta ka α  kw
water-side :

(2.23)

Rtw ¼ α  Ra þ Rw ;

1
α
1
¼ þ
ktw ka kw

(2.24)

The ratio αkw/ka indicates which boundary layer
controls the transfer process, i.e. on which side of the
interface is the dominant transfer resistance. Figure 2.7
indicates which side of the interface dominates the
transfer resistance for a range of relevant gases.
The transfer velocity is related to the friction velocity
u*w and the Schmidt number Sc by
kw ¼ uw βðsÞScnðsÞ

(2.25)

where both β (s) and n(s) depend on parameters
describing the surface conditions. The Schmidt

78

2

number exponent n(s) transitions from n(s) ¼ 2/3
for a smooth interface to n(s) ¼ 1/2 for wavy
conditions. The exact functional form and details of
the transition are still subject to research. Equation 2.25 allows the scaling between different gas
species. Since the surface conditions will be the same
for the two species, the quotient between the transfer
velocities k1 and k2 of the species results in
k1
¼
k2

 nðsÞ
Sc1
Sc2

(2.26)

From the knowledge of the Schmidt numbers of two
species Sc1 and Sc2 and the transfer velocity of one (k1),
the transfer velocity of the other (k2) can easily be
calculated. Generally, the transfer velocities are scaled
to that of CO2 at 20  C in seawater with a Schmidt
number of Sc ¼ 660, while Sc ¼ 600 for CO2 in freshwater at 20  C. In the remainder of this chapter, we will
refer to Sc ¼ 660. In most studies, the Schmidt number
exponent is assumed to be n(s) ¼ 1/2.

2.4.3

Particles

Particle fluxes are governed by a set of processes of
different temporal and spatial scales. They are produced
at the sea surface (primary aerosol), as condensates from
gas molecules in the atmosphere (secondary aerosol), are
advected from continents (c.f. de Leeuw et al. 2013), are
removed by direct deposition at the sea surface as
explained below and wet depostion when they are
entrained in precipitation rain droplets. The particles
are also changed due to cloud processing. Here we follow the historical approach dividing the intertwined processes into sources and sinks. The sources are covered in
Chap. 4 while the sinks are the focus of this section.
The process of particle removal from the atmosphere by their collection on a solid or fluid surface
is called deposition. The particles can be scavenged
by precipitation (wet depostion) or be deposited
directly on a surface (dry deposition). A measure of
the efficiency of the deposition process is the deposition velocity vd, defined as the ratio of particle flux
F to the particle concentration. It has be noted that
although deposition velocity has the unit of ms1, in
general case it is not the velocity of any physical
object, not even necessarily the average vertical
speed of the particles. Especially, it should not be

Transfer Across the Air-Sea Interface

confused with the gravitational settling velocity vg.
The two parameters would have identical values
only if gravitational settling was the only process
leading to particle deposition. Deposition velocity
is also influenced by air turbulence, Brownian
motion of the particles and in the case of deposition
on the sea surface by the movement of the surface
itself (“water slip”).

2.4.3.1 Dry Deposition
In the case of dry deposition of particles to the sea
surface, the standard approach is the one proposed by
Slinn and Slinn (1980). In this the atmosphere is
divided into a constant flux layer dominated by turbulence and a thin “deposition layer” just above the
water interface where wind speed is close to zero and
air movement is dominated by diffusion and microphysical processes such as diffusiophoresis. The
particles in the deposition layer are assumed to have
their radius increased (aw) due to high humidity, compared to dry particle radius ad. The particle concentration at the sea surface is assumed to be zero (no
resuspension). In such a case, assuming that both the
turbulent and deposition layers have their respective
transfer velocities (analogous to gas transfer), k0 C and
k0 D which can be calculated separately, the deposition
velocity vd may be derived from
1
1
1 v g ð ad Þ
¼ þ 
vd kC kD
kC kD

(2.27)

where kC ¼ k0 C + vg(ad) and kD ¼ k0 D + vg(aw).
The formula is analogous to the one for parallel
resistance of electric current where the “overall transfer resistance”, v1
d , is the sum of the transfer
resistances for both layers reduced by gravitational
settling. This approach, similar to one used for gas
transfer velocity, allows for adding additional transfer
resistances for other processes, as well as to adjust the
two transfer velocity parameters. This explains its
popularity; it has been cited almost 300 times
according to the ISI Web of Science database.
However in the 32 years since Eq. 2.27 was created
it has also been the target of criticism from the theoretical point of view. The electrical analogy was
shown by Venkatram and Pleim (1999) to be incorrect
if transport cannot be represented in terms of a concentration gradient, as in the case of particle settling.
Equation 2.27 is not consistent with mass

2.4

Exchanged Quantities

79

conservation. A rigorous derivation based on mass
conservation (which implies fluxes being constant
with altitude) gives the following formula
vd ¼

vg



1  exp r vg

(2.28)

where r is total resistance to transport.
A special case of the general Eq. 2.28 occurs
assuming the viscous deposition layer not being
rate-determining has been independently derived by
Carruthers and Choularton (1986) for conditions of
neutral stability. It has been introduced to the marine
aerosol research field by Smith et al. (1991). Its use in
the gradient method of flux measurements by Petelski
and Piskozub (2006) has been recently criticised by
de Leeuw et al. (2011) on the ground that it does not
include processes such as impaction, molecular diffusion, or growth of particles due to increased relative
humidity (RH) near the sea surface, yielding dry
deposition velocities that are considerably greater
than those from most other formulations for particles
with radii smaller than several micrometers.
Another point where the Slinn and Slinn (1980)
description seems an oversimplification for most
marine conditions is the assumption of zero particle
concentration at the sea surface. Because the surface is
not only a sink but also source of particles (see Chap. 4
and de de Leeuw et al. (2011) for a review), this
condition is rarely true and particle fluxes are the net
result of two processes: aerosol production at the sea
surface and particle deposition (Nilsson et al. 2001).
Hoppel et al. (2002) suggested that when the two
processes are in equilibrium, deposition velocity at
any fixed height equals the gravitational settling velocity, allowing turbulent mixing processes to be
neglected. However, the time needed to achieve equilibrium for particles with radii smaller than about
5–10 μm is so long that other processes (such as wet
deposition) will remove them before the two fluxes are
in equilibrium. In a follow-up study, Hoppel et al.
(2005) proposed that in the case of a surface source,
both the source function and deposition velocity
should be considered above the interfacial (deposition)
layer. The logic behind this proposition is that
measurements of the surface source function are
made above the interface layer while the microscopic
processes occurring in the interface layer are poorly
characterised.

In situ direct measurements of deposition velocity
from ships and other platforms are inherently difficult.
Any such study would need to measure both particle
concentrations and deposition fluxes. Direct
measurements of fluxes (eddy correlation and similar
techniques) measure net fluxes that are the difference
between source and deposition fluxes. This method
also needs correction for platform motion, a nontrivial problem on research ships, but which could be
avoided by using spectral techniques (Sorensen and
Larsen 2010). This lack of experimental verification of
deposition velocity parameterisation (Andreas et al.
2010) is worrying, particularly considering that its
theoretical values are used to estimate source fluxes
using equilibrium methods (de Leeuw et al. 2011).
These assume that the source and sink terms are in
balance (an assumption unreliable for particles with
radii smaller than 10 μm (Hoppel et al. 2002)).

2.4.3.2 Wet Deposition
A separate process of aerosol particle removal from
the atmosphere is wet deposition, scavenging by
water droplets both within clouds and by precipitation below them (see Zhang and Vet (2006) for a
review). In-cloud scavenging consists of nucleation
scavenging (where particles become condensation
nuclei for cloud droplets) and impaction scavenging
(particles are collected by cloud droplets, raindrops
or snow crystals). The form of mathematical
description of wet deposition is usually based on
the concept of a scavenging ratio formulation
(Iversen 1989), where the dimensionless parameter
is the ratio of solution increase of the particles in the
precipitation element over the effective scavenging
height to its air concentration. This parameter is
expected to have higher values inside the cloud
than below it.
The details of the scavenging processes are still
poorly constrained due to scarcity of in situ
measurements from aircraft platforms. A climatology
of wet deposition scavenging ratios for the continental USA (Hicks 2005) showed that the ratios for
individual rain events are distributed over two orders
of magnitude, with standard deviations for weekly
averages being twice the mean value (for daily
predictions, the factor increases to about five).
Similarly, comparison of aerosol concentrations
measured over the oceans with the results of aerosol
transport models (Witek et al. 2007) shows that

80

2

model-measurements discrepancies increase with the
amount of precipitation, showing that wet deposition
is still the weakest link in aerosol transport models.
However, at the same time this result suggests that
presently used parametrisations of the aerosol
production function and deposition velocity, with all
the still existing uncertainties, are able to provide
reasonably good agreement with shipboard
measurements.
It has been recently proposed (Piskozub and
Petelski 2009) that marine aerosol composed of
water droplets is able to scavenge small particles
in a similar manner to precipitation droplets. Because
this (still unconstrained) process would be dependent
on the surface (aerosol production function rather
than precipitation) it should be included in
deposition velocity formulations as it is expected to
affect the flux by changing vertical concentration
gradients.

2.5

Measurement Techniques

There exists a variety of measurement techniques to
estimate the sea spray aerosol production (de Leeuw
et al. 2011) and the gas transfer velocity. Here we
mainly focus on the three most frequently used types
of methods for gas transfer velocity, small-scale
techniques, micrometeorological methods and massbalance techniques. The focus is on the methodology,
accuracy, advantages and disadvantages. In addition a
section on pCO2 near-surface profiles is included due
to the importance when estimating the transfer velocity or calculating air-sea CO2 fluxes. In the case of
large near-surface gradients, measurement depth is an
issue.

2.5.1

Small-Scale Measurements
Techniques

2.5.1.1 Particle-Based Techniques
In particle-based flow-measurement techniques, the
fluid is seeded with small (μm) particles for
visualizing the flow. In standard Particle Imaging
Velocimetry (PIV) techniques, the particles are
illuminated with a 2D laser light sheet (Raffel et al.
2007). Existing particle-based flow-measurement

Transfer Across the Air-Sea Interface

techniques can be grouped as proposed by Adrian
(1991) in three main categories:
Laser Speckle Velocimetry (LSV) which assesses
flow-information by analysing random interference
patterns caused by a high seeding density of tracer
particles,
Particle Tracking Velocimetry (PTV) which uses
lower seeding densities to enable tracking of single
particles through a long exposure time or multiple
images in a sequence, and
Particle Image Velocimetry (PIV) that extracts the
flow information from a fluid with a medium
seeding density where the displacement of small
groups of particles within an interrogation window
are analysed statistically.
Most proposed approaches in the third category use
correlation based methods to find similar particle
constellations within an interrogation window in consecutive image pairs. This mode is often referred to as
‘standard PIV’. These PIV techniques have been
widely used in laboratory measurements at the airwater interface, both on the water-side (Siddiqui and
Loewen 2010; Siddiqui et al. 2004; Turney et al. 2005;
Banner and Peirson 1998) or, more challengingly, on
the air-side (Reul et al. 2008; Shaikh and Siddiqui
2010; Troitskaya et al. 2011). Particularly for anisotropic turbulent flow, 2D measurement of velocities is
problematic as out of plane velocities are not captured
(Adrian 2005). Therefore, a strong effort has been put
into extending the measurement techniques to volumetric three-dimensional three-component (v3D3C).
These approaches can also be distinguished according
to the technique used to obtain 3D-velocity information and the methods used to measure volumetric
datasets. The most popular method to access threecomponents (3C) of the velocity field is to measure
the out-of-plane velocity using Stereoscopic methods
(Prasad 2000), extended by a technique called multiplane PIV by using multiple laser-sheets (Kähler and
Kompenhans 2000; Müller et al. 2001; Liberzon et al.
2004; Cenedese and Paglialunga 1989; Brücker 1996)
or intensity graded light sheets (Dinkelacker et al.
1992) for the reconstruction of the third velocity component. In the recent past many methods were
proposed to access volumetric information from 3D
flow fields. This was achieved using holographic
measurements (Hinsch 2002; Sheng et al. 2008), by
combining PIV with Doppler global velocimetry
(PIV/DGV) (Wernet 2004), using tomography

2.5

Measurement Techniques

(Elsinga et al. 2006; Schröder et al. 2008),
defocussing-based approaches (Pereira et al. 2000,
2007; Willert and Gharib 1992), scanning-light-sheet
methods (Burgmann et al. 2008; Hoyer et al. 2005;
Brücker 1995) and absorption based methods (Jehle
and Jähne 2008; Berthe et al. 2010; Voss et al. 2012).
For gaining a better understanding of transport processes at the air-water interface, flow structures relative to the location of the interface are of interest. Most
techniques measure in a Eulerian reference frame.
This makes an accurate extraction of the interface
location necessary. Absorption based methods do not
need this additional step, which may introduce further
sources of errors. They encode the depth below the
interface by light absorption in the dyed water body
(Jehle and Jähne 2008; Berthe et al. 2010; Voss et al.
2012). Recently, an approach was presented for using
one single plenoptic camera for assessing v3D3C
flows (Garbe et al. 2012). A micro–lens array in front
of the camera’s sensor allows the extraction of depth
and all-in-focus from computational photography.
While Steinbuck et al. (2010) have employed PIV
techniques in an autonomous profiler at depth 10–60 m
in the ocean, such measurements directly at the airwater interface are not practical.

2.5.1.2 Thermographic Techniques
Infrared measurements of the sea surface have been
used to detect breaking waves (Jessup et al. 1997a),
microscale breaking waves (Jessup et al. 1997b; Zappa
et al. 2001), internal wave structures (Zappa and
Jessup 2005), interfacial turbulence (Smith et al.
2007; Scott et al. 2008; Handler and Smith 2011;
Schnieders et al. 2013), the momentum flux (Garbe
et al. 2007; Garbe and Heinlein 2011) and also used to
infer gas flux (Jähne and Haußecker 1998; Garbe et al.
2003, 2004; Schimpf et al. 2004; Asher et al. 2004).
In thermographic technique, an infrared camera is
used for visualizing thermal patterns directly at the
interface. Generally, midrange (sensitive spectral
range: λ ¼ 3–5 μm) or longrange (sensitive spectral
range: λ ¼ 8–10 μm) thermal imagers are used. In
these spectral ranges, the penetration depth of the
radiation is  20 μm. Thermographic techniques are
distinguished as active or passive techniques. In passive techniques, visualized temperature fluctuations
occur from a natural net heat flux at the interface.
For active techniques, a heat source such as a laser is
used for imposing an external heat flux.

81

One such active thermographic technique is the
active controlled heat flux method proposed by Jähne
(1989). Haußecker and Jähne (1995) used an IRcamera to track a small patch at the water surface
heated up by a short pulse from a CO2 laser. The
temporal temperature decay of the patch was fitted
based on solving the diffusive transport equation and
utilising the surface renewal model (Higbie 1935;
Danckwerts 1951) as a first-order process. The time
constant of the decay was identified with the surface
renewal time scale and the heat transfer rate calculated. A further method for the analysis of the decay
curves was proposed by Atmane et al. (2004), in
which the diffusive transport is combined with a
Monte Carlo simulation of the renewal process based
on the surface penetration model (Harriott 1962).
Zappa et al. (2004) and Asher et al. (2004) measured
a scaling factor of roughly 2.5 between the gas and
heat transfer velocity when they applied the active
controlled flux technique. Following Asher et al.
(2004), the surface penetration theory provides a
more accurate conceptual model for air-sea gas
exchange. This is supported by the work of Jessup
et al. (2009) who found evidence for complete and
partial surface renewal at an air-water interface.
Passive techniques were used successfully to estimate the net heat flux, the temperature difference across
the thermal skin layer and parameters of the surface
renewal model by Garbe et al. (2003, 2004). Recently,
Schnieders et al. (2013) extracted characteristics of
interfacial turbulence from passive thermography.

2.5.2

Micrometeorological Techniques

Turbulence is an efficient transport mechanism for
both physical quantities such as heat and momentum
as well as mass transport i.e. gases and particles. If a
gradient of some air constituent exists and a turbulent
element, an eddy, moves in this gradient, the eddy
mixing of the air will result in transport, a flux, of
this air constituent from high concentration to low.
Micrometeorological techniques are commonly used
to measure the turbulent flux. In this section we will
briefly present two of these techniques. The most
direct technique is called the eddy-covariance (EC)
method (also called eddy-correlation method). The
turbulent flux can be directly obtained from the turbulent fluctuations of the wind speed components and the

82

2

air constituent in question. The turbulent fluctuations
are extracted from the measurement signal by the use
of Reynolds decomposition:
x ¼ x þ x0

(2.29)

where x is a measurement signal, overbar represents a
mean value and the prime deviation from the mean, a
perturbation. The mean value represents the mean flow
and the perturbation the turbulent fluctuations. Applying Reynolds averaging (see e.g. Stull 1988) to the
product between the vertical wind, w, and the air
constituent, c, yields the covariance term w0 c0 which
represents the vertical turbulent flux.
In order to capture all relevant fluctuations
contributing to the flux, the sampling needs to be
fast, usually > 10 Hz is required. In addition the measurement period needs to be long enough to include all
relevant scales but not too long since this risks including slower variations not related to the local turbulence, e.g. diurnal cycles and mesoscale variations.
Typically periods between 10 and 60 min are used.
Alternatively, if a fast response analyzer is unavailable the EC method can be modified by using disjunct
eddy sampling, originally presented by Haugen
(1978). The air constituent is sampled discontinuously, e.g. every 10 s, allowing the instrumentation
a longer time to analyse the sample. As long as the
sampling period is sufficiently short, the covariance
between the vertical wind and the air constituent will
still represent the flux. This version of the EC
method is called Disjunct Eddy Covariance (DEC)
and has been verified experimentally with the EC
method (e.g. Rinne et al. 2008).
The flux measured using eddy-covariance
techniques is representative for some surface area
upstream of the sensor, called the footprint. The spatial
and intensity distribution of the footprint is described
by the footprint function and is dependent on several
variables such as sensor height, atmospheric stability,
surface roughness length, wind speed and wind direction. Different approaches have been used to calculate
the footprint: analytical, Lagrangian and backward
trajectory models, see Vesala et al. (2008) for an
overview. The footprint is important in regions
where one would expect systematic variations of the
signal source at the surface. Typically most important
for coastal sites but also in experiments investigating

Transfer Across the Air-Sea Interface

effects of e.g. surfactants on fluxes, researchers must
be aware of where the flux footprint is positioned.
Although the EC-method is straightforward in principle, some issues need to be dealt with in the
processing of the signals such as: motion correction
(if measuring on a moving platform), dilution effects
(the so called Webb or WPL-correction (Webb et al.
1980)), salt contamination, correction for sensor separation, corrections for flow distortion (if measurements
are made from a bulky platform) and possibly
corrections related to flux losses in closed path
systems (see below). Some of these issues will be
presented here. The motion correction is needed to
remove effect of platform motions from the measured
wind speed. This can be done by measuring the platform motion, see e.g. Edson et al. (1998) or Anctil
et al. (1994) and is a necessary requirement when
using the EC-method on ships and buoys.
The dilution correction is applied when calculating
fluxes from density measurements of some air constituent like CO2. Density fluctuations may arise not only
by concentration variation of the air constituent but
also through variations in water vapour, temperature
and pressure (Webb et al. 1980; Fairall et al. 2000).
This correction can be applied in the post processing
or directly on the measured time series (Sahlée et al.
2008).
Measuring over the open ocean will cause salt
contamination on the sensors, and if not dealt with
properly will cause poor quality measurements. The
instrumentation must be washed regularly with fresh
water to remove salt contamination. Salt build up on
open path gas analysers may cause cross-talk between
the water vapour measurements and the CO2
measurements. A method to correct for this was
suggested by Peter K. Taylor and is presented in
Prytherch et al. (2010).
When two sensors are used to measure fluxes,
problems arise since the instruments are separated by
some distance and thus don’t measure in the same air
volume. This will cause an underestimation of the flux
since decorrelation arises between the measured
velocity and the measured scalar. This flux attenuation
is a function of separation distance, atmospheric stability and measurement height (determining the scale
of the turbulent eddies), see e.g. Nilsson et al. (2010)
or Horst and Lenschow (2009) for more details on
this issue.

2.5

Measurement Techniques

83

If measurements are planned over areas where
small fluxes are expected, i.e. in areas with a relatively
small air-sea gradient, a gas analyser should be chosen
accordingly. The resolution should be sufficient to
resolve the expected fluctuations. Otherwise the
measurements will be dominated by instrumental
noise (Rowe et al. 2011). Data with a low signal-tonoise ratio should be excluded from the analysis.
For measurements of gas fluxes, two types of gas
analysers are available, closed or open path sensors.
Open path sensor measure directly in the ambient air,
whereas closed path sensors measure in air that has
been drawn through some distance of tubing before
reaching the analyser. Both types have their merits
and complications. The open path is a more direct
measurement, but the sensor is exposed and thus has
problems with salt contamination and may be unable to
function during precipitation. The closed path sensor is
protected and may function also during precipitation
events. Also, if the tube is long enough heat loss in the
tubing makes the WPL-correction independent of temperature (Rannik et al. 1997; Sahlée and Drennan
2009). However, flux loss related to signal damping in
the tube might be significant and need to be corrected.
The closed path system requires pumps which are
energy consuming and thus these type of instrument
might be unsuitable on buoys where power is limited.
When measuring gas fluxes for which no fast
response sensor exists one can utilise the eddy accumulation method (or the DEC method mentioned
above). The eddy accumulation method is based on
conditional sampling where the upward and downward moving gases are collected in two separate
containers and analysed later. The total flux is then
the sum of the two covariances calculated separately
for the two containers (Desjardins 1977). Difficulties
arise since the two separate terms in the total covariance may be much larger than the total term i.e. they
need to be measured with great accuracy. Also, the air
flow to the containers needs to be proportional to the
magnitude of the vertical wind, which makes this a
difficult method to implement in practice. The relaxed
eddy accumulation method, REA, (Businger and
Oncley 1990) is based on the same principle but
combines it with the flux-variance similarity, thus the
flux can be written as:
w0 c0 ¼ bσ w ðcþ  c Þ

(2.30)

where σ w is the standard deviation of the vertical
velocity, c is the mean concentration of the sampled
gas and the sign denotes the direction of the vertical
wind, b is a coefficient  0.6, with little stability
dependence. Using Eq. 2.30 the air flow to the two
containers can be set at a constant rate making the
sampling easier and only dependent on the actual
sign of the vertical wind.
Commercially-available gas analysers used with
EC systems have typically been developed for use on
fixed platforms on land. Some instruments have been
found to introduce artificial signals when mounted on
moving platforms. Such inaccuracy can be dealt with
methodologically (e.g. Yelland et al. 2009) but there is
also room for improvement by creating sturdier
sensors. Recent development in methodology also
allows for correcting water-vapour cross-talk on
open-path sensors due to salt contamination Prytherch
et al. (2010). It is very likely that this methodology
will continue to develop. Future versions of current
sensors will likely be more sensitive allowing the
instrumentation to resolve smaller variations. This
will reduce the flux uncertainty (particular for the
smallest fluxes). Current efforts are ongoing to
develop new gas analysers. For example, one such
approach is based on photoacoustic spectroscopy
with a target sensitivity of 20 ppb for CO2.

2.5.3

Mass Balance

2.5.3.1 Techniques
On a global scale, inventories of radiocarbon (14C) in
the ocean (Broecker et al. 1985; Naegler et al. 2006;
Peacock 2004) produced by spallation in the atmosphere due to cosmic rays (natural) or aboveground
thermonuclear (bomb) testing have been used to determine the global mean gas transfer velocity for CO2.
Inventories are combined with wind data to predict
how this transfer might vary with wind speed
(Wanninkhof 1992). There has been some debate
about the accuracy of the 14C inventories and the
relationships of CO2 transfer with wind speed have
since been re-evaluated (Sweeney et al. 2007).
On timescales of process studies, two main geochemical mass balance technique utilising natural and
deliberately-injected tracers have been used in the
ocean to determine gas transfer velocities.

84

The first technique, called the radon deficit method,
assumes that 222Rn and 226Ra are in secular equilibrium in the mixed layer and below, and the observed
deficit of radon in the surface ocean is due to evasion
of 222Rn out of the ocean (e.g. Bender et al. 2011; Peng
et al. 1979; Roether and Kromer 1984).
The second technique involves the deliberate simultaneous injection of two volatile tracers with very
different diffusion coefficients (3He and SF6) in the
mixed layer of the ocean or into coastal seas, and the
subsequent monitoring of their concentrations with
time (e.g. Wanninkhof et al. 1993; Watson et al.
1991). Dilution will not alter the 3He/SF6 ratio,
whereas the gas exchange of 3He is faster than SF6,
and so the 3He/SF6 ratio will decrease with time, and
allow the gas transfer velocity to be determined.

2.5.3.2 Scales (Spatial and Temporal)
The 222Rn-deficit method has a time scale of about a
week, as this is dictated by the 222Rn half-life of 3.8
days, yielding a mean life of ca. 5.5 days, and has a
spatial resolution 100s of km. The 3He/SF6 technique
has a temporal resolution of at best 12–24 h, since there
needs to be a detectable change in the 3He/SF6 ratio. The
method typically has a spatial resolution of 10s of km.
2.5.3.3 Accuracy and Limitations
Besides the limitations imposed by the sampling
methods and ability of analytical instruments to measure 222Rn, 226Ra, 3He, and SF6 accurately and precisely, both methods have other complications.
The dual tracer technique was originally developed
for use in non-stratified coastal seas where the tracers
would be well mixed to the seafloor (Watson et al.
1991). However, when used in the open ocean, both
mass balance techniques depend on knowledge of the
mixed layer depths, see Ho et al. (2011a) for a detailed
discussion of mixed layer depth estimates derived
using different methods. Because a profile of 222Rn
reflects processes taking place over days to weeks, a
one-time estimate of the mixed layer depth may not
reflect its variability over time. This has been overcome by repeated sampling over a period of time
(Emerson et al. 1991). Also, with both methods,
there is the possibility of entrainment of water from
the pycnocline underlying the mixed layer either
through convection or internal waves, which could
alter the 222Rn deficit or 3He/SF6 ratios.

2

Transfer Across the Air-Sea Interface

In addition, the radon deficit method is confounded
by many complications, as documented by Roether
and Kromer (1984) and others. Among them:
1. The method relies on the assumption that 226Ra
concentrations throughout and below the mixed
layer is constant, which is not always the case;
2. The relatively long half life of 222Rn (3.8 days)
compared to gas exchange means that both the
wind history and the 222Rn deficit history have to
be known;
3. Because the 222Rn deficit is not horizontally homogeneous, advection can perturb the “steady state”
assumption.
A complication of the dual tracer technique is that it
actually determines the difference in the transfer
velocities of 3He and SF6. A knowledge of the Schmidt
number dependence is required to obtain an estimate
of the transfer velocity of either gas. Although lake
experiments (Watson et al. 1991) and multiple tracer
experiments in the North Sea. (Nightingale et al.
2000b) have shown that the value for n is close to
0.5 for the 3He/SF6 pair, this may not hold true under
high wind conditions when significant populations of
bubbles are expected to enhance air-sea gas transfer
and might cause a deviation from n ¼ 0.5 for 3He/SF6
(see Sect. 2.2.3 and Asher (2009)).

2.5.3.4 Current and Recent Field Studies
Most of the field studies with 222Rn were conducted in
the 1970s and 1980s (e.g. Kromer and Roether 1983;
Peng et al. 1979; Smethie et al. 1985). The method has
fallen into disuse because of the complications
confounding its use as described above, and the difficulty in determining a relationship between wind
speed and gas exchange. This is primarily due to the
multi-day half life of 222Rn, averaging significantly
different sea-surface and wind conditions. This
makes establishing a relationship with wind speed
particularly difficult.
The 3He/SF6 dual tracer technique has emerged as
the mass balance technique of choice in the ocean and
in coastal seas. Experiments have been conducted in a
variety of locations, the North Sea (Watson et al. 1991;
Nightingale et al. 2000b), the Georges Bank (Asher
and Wanninkhof 1998), the Florida Shelf (Wanninkhof
et al. 1997), the Atlantic Ocean (McGillis et al. 2001;
Salter et al. 2011), the equatorial Pacific (Nightingale
et al. 2000a) and the Southern Ocean (Wanninkhof
et al. 2004; Ho et al. 2006, 2011b).

2.5

Measurement Techniques

2.5.4

85

Profiles of pCO2 Near the Surface

To quantify the global exchange of CO2 between the
atmosphere and ocean with great accuracy requires the
inclusion of factors which influence the air–sea flux,
such as diurnal warming. This occurs at the sea surface
when incoming shortwave radiation can cause stratification in the absence of wind-induced mixing. Temperature differences of up to 3 K can occur across this
warm layer, which is ~ 10 m in depth (Ward 2006).
This warm layer influences the air-sea flux of CO2
because the partial pressure of CO2 in seawater has
an experimentally determined temperature dependence
under isochemical conditions (Takahashi et al. 1993):
h
i
0:0423ΔT WL
ΔpCOWL
2 ¼ pCO2 1 þ e

(2.31)

The diurnal warm layer enhances the flux of CO2 in
regions where the ocean acts as a CO2 source and
therefore reduces the oceanic carbon uptake (Olsen
et al. 2004).
An experiment was conducted off Martha’s Vineyard in 2002 (McNeil et al. 2006) in which in situ
profiles of temperature/salinity were acquired with a
seabird microcat, and pCO2 with water pumped from
depth to an equilibrator, where a sample of the headspace was analysed with a LI-COR LI-7000 IRGA.
The profile data and subsequent pCO2/temperature
relationship is shown in Fig. 2.8. There is a relatively
strong diurnal warm layer, with a ΔT of about 0.6  C
over the upper 4.5 m. There is a corresponding pCO2
gradient of about 12 μatm and the best fit provides
(∂pCO2/∂T)/pCO2 ¼ 0.062  C1, compared to the
value from Takahashi et al. (1993) of 0.0423  C1.
For a global net flux of CO2 from the atmosphere to
ocean, atmospheric pCO2 levels are about 2 %
(approximately 6 μatm) greater than the oceanic partial pressure for CO2, making measurements susceptible to small biases in the determination of the correct
partial pressure difference (McGillis and Wanninkhof
2006). Therefore the diurnal warm layer can mask the
true air-sea fluxes when calculated with measurements
of ΔpCO2 when the water concentration is measured
several meters below the sea surface.
Further work is required to understand the dependency of the ocean carbon system on temperature so
that the most appropriate value for the change in

seawater pCO2 with temperature can be determined
(McGillis and Wanninkhof 2006).

2.5.5

Method Evaluation

There are a large number of techniques to estimate gas
transfer velocities from measurements, here we
mainly discuss three different types, small-scale technique, micrometeorological technique (mainly EddyCovariance) and mass-balance technique (mainly
Dual Tracer). The spatial and temporal scales resolved
by each method are shown in Fig. 2.9.
Small scale techniques as outlined in Sect. 2.5.1
have significant advantages in laboratory conditions,
making spatio-temporally resolved water-side concentration measurements at the frame rate of the camera
used in the visualisation system feasible (up to 2,000
frames per second). Concentration gradients, flow
dynamics and turbulence in the air and in the water
can be resolved. This leads to a better understanding
of processes, their relevance to the transport and
their variability. To extract parameters from
these techniques, inverse problems have to be
solved, which introduces uncertainties. For field
measurements, most small-scale techniques are not
applicable, either because they are too delicate for
the field, because the set up and the lighting conditions
cannot be controlled, or because the water body cannot
be seeded with the tracer. Thermographic techniques
have been applied successfully in field campaigns.
However, besides having to deal with artefacts in
challenging conditions, they mainly measure the transport of heat with the transfer of gases being determined indirectly. Not all transfer mechanisms can be
captured from thermography. The most dominant of
these is bubble-mediated gas transfer, making thermographic techniques mainly applicable in low to
medium wind speed ranges. However, when thermographic measurement techniques are applicable in the
field, they make measuring natural variability or spatial homogeneity of fluxes possible. Not capturing
bubble-mediated transfer can also be an advantage
since its contribution to gas exchange can be assessed
through comparison with complementary measurement techniques. Small-scale measurement techniques
may therefore be very beneficial in combination with
other techniques such as eddy-correlation, as the

86

2

a

b

0.5

1

Transfer Across the Air-Sea Interface

436

6.09

6.085
434

1.5

6.08

2

6.075

2.5

3

3.5

430

6.07

6.065

428

pCO2 (μatm)

ln(pCO2) (μatm)

Depth (m)

432

6.06
426

4

6.055

424

4.5

422

424

426

432

430

428

434

436

438

6.05

pCO (μatm)
2

15.5

15.6

15.7

15.8

15.9

16

16.1

16.2

16.3

Temperature ( ° C)

16.4

6.045
15.4

15.6

15.8

16

16.2

422
16.4

Temperature (°C)

Fig. 2.8 Left: In situ profiles of temperature and pCO2. Right: Relationship between temperature and pCO2 for this profile.
Although this relationship is non–linear, the best fit provides (∂pCO2/∂T)/pCO2 ¼ 0.062  C1

assumptions underlying these techniques may be
verified under the measurement conditions.
For dual tracer techniques, 2–3 stations are typically
used and gas exchange on a time scale of 12–24 h can
be resolved, in addition to integrated measurements
over days to weeks. Eddy-Covariance measurements
resolve fluxes on a 30 min–1 h time-scale. However,
even during perfect conditions and no instrumental
problems, a single flux estimate for a 30-min period
using the covariance method would still be uncertain.
This is due to the fact that turbulence is not sampled
properly i.e. we cannot measure the ensemble mean,
instead we are limited to time averages at a fixed point
(i.e. tower and ship measurements) or spatial averages
(aircraft measurements) which are bound to be limited.
This means that we do not sample all possible
realisations that a turbulent field can attain given the
ambient atmospheric conditions. During conditions
with large variability of surface conditions within the
flux foot-print the variability of EC data is significantly
larger.

All methods have methodological problems as well
as instrumental uncertainties. For the dual tracer
methods relevant instrumental accuracy is 0.2–0.5 %
for 3He and 2 % for SF6, methodological difficulties
relate to determination of mixed layer depth which
typically adds 10 % uncertainty to the calculation of kw.
Discussions of uncertainty in eddy-covariance flux
measurements can be found in the literature, see e.g.
Fairall et al. (2000) and Vickers et al. (2010) and
references therein. Uncertainties arise from both
instrumental problems and methodological issues.
For CO2 the relative instrumental uncertainty is
estimated to be 17–20 % for tower-based fluxes
(Rutgersson et al. 2008; Vickers et al. 2010). The
uncertainty is higher for moving platforms (shipbased) in high-salinity environments. Better accuracy
is, however, acquired with gases that can be measured
with a larger signal-to-noise-ratio over sea, like DMS
(Huebert et al. 2004).
Eddy-Covariance is the most direct method since
the flux of interest is directly measured, it does not rely

2.6

Parameterization of Gas Exchange

10

Time Scale (h)

10

87

7

Natural
Bomb

5

1000

222
3

10

C

14

C

Rn

He/SF

6

Eddy
Covariance

0.1
Small Scale
Techniques

0.001
10

14

-5

10

-7

10

-5

0.001

0.1

10

1000

10

5

10

7

10

9

2

Spatial Scale (km )
Fig. 2.9 Spatial and temporal scales of different measurement techniques frequently applied to gas exchange at the air-water
interface

on assumptions about gas properties or approximations
concerning the turbulent structure of the atmospheric
boundary layer (Wanninkhof et al. 2009); the problem
is presently the relatively high instrumental uncertainty
in marine conditions, in particular for CO2. Dual
Tracer is an indirect method with the advantage of an
integrated measurement of the gas transfer velocity
with relatively high accuracy.
Wind stress at the ocean surface is responsible for
kinetic energy input into the water, which generates
turbulence. Several studies have shown a relationship
with the transfer velocity and the dissipation of turbulence kinetic energy ε (Asher and Pankow 1986;
Zappa et al. 2007). One can thus infer kw by measuring
dissipation in the water (see Sect. 2.6.4), although
operational measurements of ε (e.g. remote sensing
for assessing inventories), will be challenging.

2.6

Parameterization of Gas Exchange

Measuring transfer rates or transfer velocities of gas
exchange directly is challenging. Therefore it is appealing to relate them to readily accessible parameters that
dominate the transfer. These parameters might be the
wind speed at a certain reference height or surface
roughness. Such parameters can also be assessed globally from satellite remote sensing or are available in

simulations. In this section, different parameterisations
are examined. The most obvious ones are wind speed
dependent kw  U relationships or sea state dependent
ones through the surface roughness. The NOAACOARE algorithm is a mixture of basic interfacial
physics along with empirical additions such as bubbles
and tuneable parameters. It requires more parameters
than simple kw  U relationships but also probably
results in more accurate estimates. Parameterising gas
transfer with energy dissipation is appealing as it
relates the transport directly to turbulence but this
parameter is challenging to measure in itself.

2.6.1

Wind Speed Relationships

The processes mediating diffusive gas exchange
across the air-sea interface are complex, with multiple
physical controls (e.g. capillary waves, bubbles, small
scale eddy turbulence etc., cf Sect. 2.2). In the ocean
environment these processes are directly or indirectly
related to wind via turbulent forcing and wave breaking and as such wind speed is found to correlate well
with transfer velocity in observational data (Ho et al.
2011b). Wind is routinely measured and can be
derived from remotely sensed data and ground-truthed
by high precision instruments on ships (Wanninkhof
et al. 2009; Bender et al. 2011; Ho et al. 2011b); hence

88

it is likely to remain the key variable used in empirical
parameterisations of transfer velocities for the foreseeable future.
The first parameterisation of environmental airwater transfer velocity from wind speed (U) were
theoretical or based on simple laboratory experiments
(e.g. Liss and Slater (1974) and references therein,
Deacon (1977)). For the water-side transfer velocity
(kw), these have since been validated to first order by
wind tunnel experiments (e.g. Liss and Merlivat 1986)
and field studies as outlined previously in this chapter.
All of these methods yield a net flux over some
scale of area and time and thus enable calculation of
transfer velocity from flux (F) and the magnitude of
the concentration gradient between air and sea (ΔC),
following Eqs. 2.12 and 2.13. There is a considerable
range in the estimates of the kw  U relationship from
these methods (c.f. Fig. 2.10) – approximately a factor
of 2 at a typical average wind speed of 7 ms1
(e.g. Yang et al. 2011; Ho et al. 2011b; Johnson et al.
2011). However, this might be considered rather good
agreement considering the differing scales of space
and time over which the various methods operate e.g.
eddy covariance (102–103 m2 and minutes) and global
14
C estimates (1015 m2 and decades).
Generally, at least for mass balance techniques, the
field is tending to converge around a smaller range of
uncertainty in the kw  U relationship (for CO2),
bringing estimates from 14C inventory (e.g. Sweeney
et al. 2007), dual/multiple tracer experiments (e.g. Ho
et al. 2011b) and 222Rn distributions (e.g. Bender et al.
2011) into reasonable agreement, all broadly in line
with the commonly used parametrisation of Nightingale et al. (2000b), at least to within the experimental
uncertainty in mass balance techniques (Asher 2009;
Johnson et al. 2011). The Nightingale et al. (2000b)
parametrisation is largely based on the results of a
multiple tracer study conducted in the coastal North
Sea and as such is potentially subject to surfactant and
limited fetch effects which might inhibit kw. However,
their parametrisation compares well with that
presented for the equatorial Pacific (Nightingale
et al. 2000a) and more recent measurements in the
Southern Ocean (Ho et al. 2006, 2011b); suggesting
that the kw  U relationship as quantified by mass
balance techniques is remarkably robust to local and
regional conditions.
One key uncertainty in the parameterisation of kw
with U is the functional form of the relationship i.e.

2

Transfer Across the Air-Sea Interface

linear, quadratic, cubic, polynomial. A simple quadratic function (kw  U2) has been observed in windwave tanks (e.g. Wanninkhof and Bliven 1991). Such
relationships have been assumed in scaling global 14C
inventories (and inferred fluxes) to global average
wind speed (Wanninkhof 1992; Sweeney et al. 2007).
Simple and polynomial quadratic functions have been
found to fit dual tracer and 222Rn deficiency data well
(e.g. Wanninkhof 1992; Nightingale et al. 2000b; Ho
et al. 2011b).
The considerably greater transfer velocities
observed at high wind speeds in direct flux
measurements of CO2 via the eddy covariance technique (see Sect. 2.5.2) have led to suggestions of a
cubic relationship to wind speed as a best fit to the data
(e.g. McGillis et al. 2001; Prytherch et al. 2010),
although given the large uncertainties in the
measurements, a quadratic could also be fitted in
most cases. This has been explained by the proposed
cubic relationship between windspeed and
whitecapping (e.g. Monahan and Spillane 1984) and
the importance of whitecaps on bubble-mediated gas
exchange (e.g. Woolf 1997) at high winds plus a
reduction in gas transfer at low winds due to the
supposed presence of surfactants (see Sect. 2.2.7).
The cubic relationship is not supported by water-side
eddy covariance measurements of O2 and N2 exchange
by D’Asaro and McNeil (2007) using autonomous
floats deployed in hurricane-force winds. They show
a relationship that demonstrates much lower kw at
hurricane-force winds than predicted by the cubic
parameterisations of Wanninkhof and McGillis
(1999) and McGillis et al. (2001), although with kw
scaling to approximately the 6th power of windspeed
(McNeil and D’Asaro 2007). McNeil and D’Asaro
(2007) suggest that this is due to the much greater
bubble-mediated transfer for O2 and N2, which are
considerably more insoluble than CO2. Other studies
of oxygen (Kihm and Körtzinger 2010) and oxygen
and nitrogen (Vagle, McNeil and Steiner, 2010)
suggest considerably stronger bubble effects.
Evidence that CO2 exchange may be considerably
enhanced by bubbles comes from comparison with the
eddy covariance measurements of DMS fluxes at
high winds (see Fig. 2.3). This is summarised in
Yang et al. (2011), where a near-linear relationship
with wind speed is seen, and is quantitatively close to
the linear relationship predicted for the non-bubble
mediated component of the transfer velocity in the

2.6

Parameterization of Gas Exchange

89

a
250

Liss and Merlivat (1986)
McGillis et al 2001
Prytherch et al 2010a
Edson et al 2011
Best fit to all dual tracer data, Ho et al (2011b)
All dual tracer data
EC CO2, GasEx98, Edson et al (2011)
EC CO2, SO GasEx, Edson et al 2011
EC CO2, Knorr07, Miller et al (2009)
EC CO2, HIWASE, Prytherch et al (2010a)
EC DMS, multiple campaigns, Yang et al (2011)
EC DMS, Knorr07, Miller et al (2009)
EC DMS, Knorr06, Marandino et al (2009)

kw_660 / cm.hr−1

200

l
l

150

l

100

50

l
l

0

l ll
l
l
l l l l l
l
ll l
l

0

l

l
l l
l
l

5

l

l
l
l
l

l

l

l

l
l

l

10

wind speed / m.s−1

l

15

20

b
100

80

kw_600 / cm.hr−1

l

60
l

Liss and Merlivat (1986)
Nightingale et al (2000a)
Sweeney et al (2007)
Wanninkhof and McGillis (1999)
Wanninkhof (1992)
Global 14C constraint, Naegler(2009)
Best fit to all dual tracer data, Ho et al (2011b)
DT, Southern Ocean, Ho et al (2011b)
DT, Southern Ocean, Ho et al (2006)
DT, Southern Ocean, Wanninkhof et al (2004)
DT, equatorial Pacific, Nightingale et al (2000b)
DT, North Atlantic, McGillis et al (2001b)
DT, North Sea, Nightingale et al (2000a)
DT, Florida Shelf, Wanninkhof et al (1997)
DT, Georges Bank, Asher and Wanninkhof (1998)
Radon deficiency data, Bender et al (2011)

l

l

40

l
l

l

l

l

l
l

20
l

l

l

0
0

5

10

wind speed / m.s−1

Fig. 2.10 A comparison of different windspeed relationships
of the water side transfer velocity, kw. Measurements from eddy
covariance techniques are presented in (a), those from mass

15

20

balance techniques are shown in (b) (Figures by M.T. Johnson,
shared under creative commons license at http://dx.doi.org/
10.6084/m9.figshare.92419)

90

2

physically-based bubble model of Woolf (1997),
suggesting that DMS is sufficiently soluble to be unaffected by the bubble effect (Sect. 2.2.3), whilst CO2 is
not. This also suggests that, for gases of intermediate
solubility such as DMS, non-linear parameterisations
such as Nightingale et al. (2000b) may lead to substantial overestimates of fluxes at higher wind speeds.
For gases more soluble than DMS, where the air-side
transfer velocity becomes limiting, the relationship of
interest is that between wind speed and the air-side
transfer velocity (ka). This is less well constrained than
kw, with very little field validation of the empirical
models, particularly for gases other than water vapour
(e.g. Johnson 2010). Typically, however, ka  U
parameterisations are close to linear (e.g. Johnson 2010;
Sect. 2.6.3) and physically-based model predictions (e.g.
Duce and Tindale 1991; Jeffery et al. 2010) tend to agree
reasonably with wind-tunnel observations for trace gases
by Mackay and Yeun (1983).

2.6.2

Surface Roughness, Slope

Frew et al. (2004) have shown from field
measurements in coastal and offshore waters that the
gas transfer velocity better correlates with mean
square slope computed for the wave number range of
40–800 rad m1 than with the classically used wind
speed. This is consistent with laboratory observations
and the use of mean square slope (mss) was shown to
be of particular interest in case of surface films. This
study was performed from low to moderate wind
speed and the observed relationship between gas transfer velocity and mean square slope was linear.
Dual frequency measurements of altimeter like
TOPEX and JASON in Ku-band and C-band, provide
a mean of retrieving the mss contribution of small
scale waves between 40 and 100 rad m1, as derived
by a geometric optics model. Frew et al. (2007), after
calibrating satellite mss with field mss, derived k-mss
relationships and global kw maps. The main difficulty
is to calibrate satellite mss relationships and k-mss
relationships, due to few existing field measurements
of kw and mss and to even fewer collocations between
altimeter mss with these field measurements. Other
difficulties arise from the fact that at high wind speed
the effect of bubbles is not implicitly included in such
algorithms so that these algorithms underestimate kw
at high wind speed (Wanninkhof et al. 2009). kw fields
derived from altimeters are very undersampled

Transfer Across the Air-Sea Interface

compared to kw-fields deduced from satellite wind
speeds due to the poor spatial coverage of altimeter
measurements compared to scatterometer and microwave radiometer wind speeds.

2.6.3

NOAA-COARE

Details of the original development of the COARE
algorithm for meteorological fluxes (sensible heat,
latent heat, momentum) can be found in Fairall et al.
(1996, 2000), and the subsequent development of the
algorithm to include gas transfer is shown in Hare
et al. (2004) and Fairall et al. (2011).
Advancements in sensor technologies led to the
application of the micrometeorological direct covariance method to estimate air-sea fluxes at hourly time
scales on the atmospheric side of the interface. This
method was first successfully applied in the GasEx
field programmes beginning in 1998 (McGillis et al.
2001; Fairall et al. 2000).
The short time scale of the covariance estimates
enables observational investigations of the relationship of k to physical/chemical forcing beyond wind
speed – examples include wind stress, buoyancy flux,
surfactants, or surface gravity wave properties.
Physically-based parameterisations (Hare et al. 2004;
Soloviev 2007; Vlahos and Monahan 2009) are now
available that incorporate these additional forcing
factors and may lead to quite different transfer
properties for different gases. For example, the dependence of bubble-mediated exchange on gas solubility
implies the Wanninkhof (1992) formula may not be
appropriate for the fairly soluble biogenic gas
dimethylsulfide (DMS) (Woolf 1993; Blomquist
et al. 2006). Thus, application of a single wind-speed
formula for all gases is inconsistent with current
understanding of gas transfer physics (Fig. 2.11).
In the COARE gas transfer algorithm (COAREG),
the flux of a trace gas on the atmospheric side of the
interface is estimated as
Fc ¼ αka ðCw =α  Ca Þ ¼ αka ΔC
¼

αka
ua ΔC
ua

(2.32)

where ka is the transfer velocity on the air-side, α is
dimensionless solubility, Cw and Ca the mean concentration of the gas in the water and air at reference depth

2.6

a

Parameterization of Gas Exchange

91

b

250

250
TAO4
Sargasso05
DOGEE07
GX08
Wecoma04
Knorr06
Knorr07
MEHF01
COAREG3.0 A=1.3 B=1.0
COAREG3.1 A=1.6 B=1.8
W92
Average

GX98
GX01
GX08

200

200

GXMean
COAREG3.0 A=1.3 B=1.5

−1

150

k660 (cm hr )

−1

k660(cm hr )

MEHF01
COAREG3.1 A=1.6 B=1.8
W92

100

50

150

100

50

0
0

5

10

15

20

0

0

5

10

15

20

−1

−1

U10n (ms )

U10n (ms )

Fig. 2.11 (a) Gas transfer velocity for CO2 as a function of 10m neutral wind speed U10n from direct surface-based
observations. The black line is the mean of the data sets; the
error bars are statistical estimates of the uncertainty in the mean
computed. Symbols are: ◯ – GASEX98, * – GASEX01, ◊ –
SOGASEX08. The parameterisations shown are: blue solid line
– McGillis et al. (2001); black dotted line – COAREG3.0 CO2;
cyan dotted line – COAREG3.1 CO2 using tangential u*; red
dashed line – W92 (Wanninkhof 1992). (b) DMS gas transfer

coefficient as a function of 10-m neutral wind speed U10n from
direct surface-based observations. The black line is the mean of
the data sets; the error bars are statistical estimates of the
uncertainty in the mean computed. Symbols are: ◯ – TAO04
(Equatorial Pacific); □ – Sargasso Sea 05, ⊲ – DOGEE07, ◊ –
SOGASEX08, ◁ – Wecoma04, ▽ – Knorr06, * – Knorr07. The
parameterisations shown are: blue solid line – McGillis et al.
(2001), black dotted line – COAREG3.0 DMS; cyan dotted line
– COAREG3.1 DMS using tangential u*; red dashed line – W92

and height. The COAREG algorithm (current public
version is COAREG3.0) gives a simple form for the
transfer velocity (see Hare et al. 2004),

Scw the Schmidt number of water, ρw the density of
water, and hw ¼ 13.3/(Aφ).
In this expression, A is an empirical constant and φ
accounts for surface buoyancy flux enhancement of
the transfer. A similar expression is used for rat, but
the molecular sublayer thickness is explicitly
approximated by incorporating the velocity drag coefficient, Cd, which is a function of the atmospheric
measurement height, za,

ua
where
rw þ αra
1

1
rw ¼ rwt
þ kb =ua :

ka ¼

(2.33)

Here u*a/rwt represents molecular-turbulent transfer
which has a basis in air-sea physics and kb represents
bubble transfer based on empirical formulation. On the
atmospheric side we neglect spray-mediated gas transfer, so there is only molecular-turbulent transfer
ra ¼ rat.
In the COAREG model the individual terms are
represented as
 
rffiffiffiffiffi
ρw
zw
1
rwt ¼
þ
κ
ln
;
(2.34)
hw Sc1=2
w
ρa
δw
where zw is the water depth of the reference
measurements, δw the molecular sublayer thickness,



1=2
rat ¼ ha Sc1=2
ðza Þ  5 þ lnðSca Þ=ð2κÞ
a þ Cd
(2.35)
where ha ¼ 13.3. The bubble-driven part of the transfer is taken from Woolf (1997) as


1=n n
kb ¼ BV0 fwh α1 1 þ eαSc1=2
w

(2.36)

where B is a second adjustable constant, Vo ¼ 2,450
cm h1, fwh is the whitecap fraction, e ¼ 14, and

92

2

n ¼ 1.2 for CO2. The parameters A and B have been
adjusted to fit observations with values between 1 and
2. Note there is no capability at present to describe the
presence of surface films.
Recently COAREG was extended to include the
case of an atmospheric gas (such as ozone) that reacts
strongly in the ocean (Fairall et al. 2007). Using the
notation from Fairall et al. (2000), the budget equation
for the oceanic concentration of a chemical, Cw, is
@Cw
@½ðDc þ KÞ@Cw =@z
 aCw
þ UrCw ¼
@z
@t
(2.37)
where z is the vertical coordinate (depth for the ocean),
Dc is the molecular diffusivity of Cw in water, K the
turbulent eddy diffusivity, and the last term is the loss
rate of Cw due to reactions with chemical Y. Thus,
a ¼ RcyY, where Y is the concentration of the reacting
chemical, and Rcy the reaction rate constant. Assuming
that the concentration of Y is much larger than Cw so
that is remains effectively constant and that a is sufficiently large that Cw is completely removed within the
molecular sublayer, Garland et al. (1980) showed that
the water-side resistance is
ua pffiffiffiffiffiffiffiffi
¼ aDc
rw

(2.38)

Fairall et al. (2007) relaxed the requirement that the
reaction was confined to the molecular sublayer and
obtained a solution that allowed the ozone deposition
velocity to depend on the oceanic turbulence
uw pffiffiffiffiffiffiffiffi K1 ðξ0 Þ
¼ aDc
K0 ðξ0 Þ
rw

2 pffiffiffiffiffiffiffiffi
aDc :
kw uw

It is also clear that over the open ocean there is, on
average, a systematic change in the influence of waves
on air-sea turbulent exchange with increasing wind
speed. Attempts to find simple scaling expressions
that explain variations in surface roughness length
(or drag coefficient) due to variations in surface
wave properties have been of limited success
(Drennan et al. 2005).
The most recent results from application of the
algorithm to CO2 and DMS transfer for SO GasEx
can be found in Edson et al. (2011), while an analysis
of the implementation of COAREG for ozone is
outlined in Bariteau et al. (2010) and Helmig et al.
(2011). Current application of COAREG includes
input of satellite fields of sea surface temperature,
near surface humidity and temperature, and wind
speed to obtain global estimates of air-sea gas transfer
(Jackson et al. 2011).
One major technical problem is the poor performance of fast CO2 sensors at sea, which limit field
work to regions with very large air-sea differences in
CO2 concentration. Some clarity in scientific issues
could also come from high-quality eddy covariance
measurements of gases more soluble than DMS and
less soluble than CO2. There is considerable room for
progress on bubble-mediated transfer through field,
laboratory, and numerical modeling studies.
COAREG is publicly available from the authors of
(Fairall et al. 2011). The latest version of the code
(COAREG3.1) includes a scheme to partition the
wind stress into tangential and wave-induced
components (see Fairall et al. (2011) for more details).

(2.39)

2.6.4

Here K0 and K1 are modified Bessel functions of
order 0 and 1 and
ξ0 ¼

Transfer Across the Air-Sea Interface

(2.40)

COARE3.0 is a good fit to mean observed momentum and heat transfer coefficients over a considerable
range of wind speeds (more observations are above
18 ms1). However, at any specific wind speed bin
there is considerable scatter – more than can be
explained by atmospheric sampling variability alone.

Energy Dissipation

The flux of slightly soluble, non-reactive gases (i.e.
water side-controlled) between the ocean and atmosphere is determined from knowledge of the concentration difference across the diffusive layer as well as
the gas transfer velocity kw (see Eq. 2.14).
The ease of availability of wind speed U (m s1)
measurements has resulted in many attempts to develop
parameterisations between kw and U (see Sect. 2.6.1).
However, the primary mechanism for controlling kw is
shear-produced turbulence at the air-sea interface
(Salter et al. 2011; Asher 1997; Wanninkhof et al.
2009), whose generation mechanism is wind stress

2.6

Parameterization of Gas Exchange

93

acting on the ocean surface (Asher 1997; Kitaigorodskii
and Donelan 1984).
There has been much effort to describe kw in terms
of molecular diffusivity D (m2 s1), kinematic viscosity ν (m2 s1), and a function based on a velocity Q
(m s1) and length L (m) (Danckwerts 1951; Fortescue
and Pearson 1967; Lamont and Scott 1970; Deacon
1977; Ledwell 1984; Jähne et al. 1987; Brumley and
Jirka 1988; Soloviev and Schlüssel 1994; Fairall et al.
2000):

onset of the rain event before stratification plays a
dominant role. This result is important for predictive
modeling of kw as it allows inferring the surface value
of ε fundamental to gas transfer.
Lorke and Peeters (2006) took the approach of
using law of the wall scaling to describe ε (z) as a
function of depth z (m):

kw ¼ βScn f ðQ; LÞ

where κ ~ 0.41 is the von Kármán constant and uw ¼
pffiffiffiffiffiffiffiffiffiffi
τ=ρw is the friction velocity, τ is the wind stress
acting on the ocean surface which is parameterised
with knowledge of the drag coefficient CD and ρw is
the water density. Although this provides a mechanism
to derive the parameters to parameterise kw according
to Eq. 2.42, it is unlikely that law of the wall scaling
will adequately describe ε at the ocean surface (e.g.
Agrawal et al. 1992; Terray et al. 1996; Babanin and
Haus 2009).
Compared to wind speed, dissipation in the ocean is
much more challenging to measure as it requires
sophisticated instumentation and an ability to provide
data close to the air-sea interface for the application of
air-sea transfer parameterisation. Future progress
requires a new generation of experiments which combine turbulence data with estimates of gas transfer
velocities (Lorke and Peeters 2006).

(2.41)

where β is an experimentally determined constant and
Sc ¼ ν/D is the Schmidt number. The Schmidt number
exponent has been shown to vary between n ¼ 2/3
for conditions of low winds (e.g. Deacon 1977; Davies
1972) or where surfactants are present at the water
surface (Asher and Pankow 1986), to n ¼ 1/2 at
winds greater than 5 ms1 (Jähne et al. 1987). Lamont
and Scott (1970) used an eddy cell model to describe
f(Q, L) which resulted in a scaling relation:
kw ¼ 0:4Sc1=2 ðενÞ1=4

(2.42)

where ε (m3 s2) is the dissipation rate of kinetic
energy, a parameter that can be measured in the field.
Zappa et al. (2007) provided the first field
investigations that supported the mechanistic model
in Eq. 2.42 based on surface water turbulence that
predicts gas exchange for a range of aquatic and
marine processes. Their findings indicated that the
gas transfer rate varies linearly with the turbulent
dissipation rate to the ¼ power in a range of systems
with different types of forcing – in the coastal ocean,
in a macro-tidal river estuary, in a large tidal freshwater river, and in a model ocean.
An underlying assumption in Eq. 2.42 is that ε is
measured directly at the water surface. Since the profile of turbulence near the air-water interface may be
complicated by the interplay between wind, waves,
current shear and other processes, measurements at
depth will not be representative of ε at the surface
because the profile changes nonlinearly with environmental forcing. Zappa et al. (2009) implement the
Craig-Banner turbulence model, modified for rain
instead of breaking-wave turbulence, to successfully
predict the near-surface dissipation profile that varies
by two orders of magnitude over the top 50 cm at the

ε¼

2.6.5

u3w
κz

(2.43)

Evaluating and Selecting Transfer
Velocity Parameterisations

The above sections demonstrate that various different
turbulent forcing terms can be used to parameterise kw.
Wind speed, which is the most commonly used predictor for kw is only indirectly related to the key
forcing terms and hence a great deal of variability is
expected (and observed) in kw  U relationships. The
choice of most appropriate parameterisation depends
on both the forcing data available and the application
to which kw is to be used (e.g. spatial and temporal
scale, gas of interest).
For example, when considering fluxes of CO2 at the
global scale there is a constraint on the transfer velocity given the bomb 14C inventory of the ocean (e.g.
Sweeney et al. 2007). Thus, notwithstanding

94

uncertainties in both the inventory (Wanninkhof 1992;
Naegler et al. 2006) and the global average wind
speed, which may be as much as 1.3 m s–1 between
different datasets (Naegler et al. 2006), there is a
‘known point’ on the kw  U curve for global CO2
fluxes. Note that whilst parameterisations derived
from this value have tended to apply a quadratic relationship, as described in Sect. 2.6.1, there is no evidence from this global approach to support any
particular form for the kw  U relationship. Furthermore, whilst this is a valuable constraint on long
timescale global fluxes, it is not necessarily a constraint for regional or local fluxes or over shorter
timescales, due to variable and non-wind speed dependent forcings (e.g. fetch/wave slope, thermal stability,
etc.) or the effects of other processes such as chemical
or biological enhancement, rainfall etc.
Where wind speed is the only available forcing term
for a particular study on smaller-than-global scale (as
is commonly the case), an empirical kw  U
parameterisation must be employed to quantify the
flux of a gas given wind speeds averaged over an
appropriate timescale. Data on kw over a range of
wind speeds in the natural environment come primarily
from the dual tracer and eddy covariance approaches.
As noted above, significant variability due to processes
not directly related to wind speed is to be expected in
observations of such relationships, but even accounting for this there are apparent inconsistencies which
must be addressed. First and foremost is the apparently
greater transfer velocities observed by CO2 eddy
covariance measurements than by the dual tracer technique, particularly at higher wind speeds (Sect. 2.6.1).
It is possible that these two methods are measuring
empirically different properties – it has been suggested
by Ho et al. (2011b) that, whilst on the timescale of
eddy covariance measurements (minutes-hours) the
apparent transfer velocity varies with the cube of the
wind speed, over the timescale of a tracer study (days)
the vertical mixing of the bulk water becomes limiting
to the total tracer mass balance and thus the higherorder dependence is not apparent. Alternatively, Asher
(2009) suggests that the increasing importance of
bubbles and thus increasingly turbulence dominated
transfer at higher winds means that the scaling of kw
to Sc0.5 might break down, which would lead to errors
in the relationship calculated by the dual tracer
method.

2

Transfer Across the Air-Sea Interface

The dual tracer method also fails to account for the
effect of the solubility dependence of bubble-mediated
transfer. Bubble fluxes are predicted to be lower for
more soluble gases, and this is supported by laboratory
experiment (e.g. Rhee et al. 2007; Woolf et al. 2007)
and circumstantially by the lower and more linear
transfer velocities of DMS (compared to CO2)
observed in the field by various studies (Sects. 2.6.1
and 2.6.3, Figs. 2.3 and 2.10). Such effects, however,
should mean that the dual tracer method (based on the
exchange of 3He and SF6, which are considerably less
soluble than CO2) should predict greater fluxes than
those directly observed for CO2, which is the opposite
of what is observed. It has been suggested that such
inconsistencies may be reconciled by the application
of an improved representation of the bubble flux such
as that outlined by Woolf et al. (2007), which accounts
for the high void fraction in dense bubble plumes. This
may lead to ‘suffocation’ of the bubble flux (i.e. the
flux of gas into the bubbles starts to be limited by
the decreasing concentration in the water around the
bubble plumes). This effect will be largest for the most
diffusive gases and as such there is likely to be a
differential in the effect between the highly diffusive
3
He and rather less diffusive SF6. The result of this
effect when calculating kw from dual tracer data would
be an underestimation of kw (Woolf et al. 2007).
Such explanations may be able to reconcile all the
available data and may also satisfy the 14C and 222Rn
derived values for kw where appropriate, given that
they are subject to significant uncertainty themselves.
Given the current uncertainties associated with eddy
covariance and dual tracer methods and the magnitude
of the effect of bubbles on CO2 fluxes in the ocean
environment, it is impossible to determine which of
the above explanations for observed discrepancies
between methods is correct. It is possible that under
different regimes of thermal stability, wave field, fetch
and other forcings, the observed range of kw  U
relationships may all be valid in particular situations.
With forthcoming advances in CO2 sensors for eddy
covariance measurements, progress may be made
towards better understanding and resolution of the
above discrepancies.
With sufficient environmental forcing data, probably
the best estimates of transfer velocity can be achieved
by using physically-based models of boundary layer
interactions such as NOAA-COARE. The latest

2.7

Sea Ice

incarnation, COARE G 3.0 (Fairall et al. (2011), including a generalised scheme to enable application to any
gas following Johnson (2010)) might be considered the
‘state of the art’ in quantifying kw. Nonetheless it still
contains some physical parameterisations which are not
state of the art. For instance it applies the ‘classic’
bubble model of Woolf (1997) rather than the dense
bubble plume model mentioned above, and relies on
rather uncertain empirical parameterisations such as the
empirical windspeed-whitecapping relationship of
Monahan and O’Muircheartaigh (1980) which is subject to significant uncertainty (Johnson et al. 2011)
although recent developments are leading to considerable constraint of said uncertainty (Goddijn-Murphy
et al. 2011). Recent measurements of whitecap fraction
were discussed in de Leeuw et al. (2011) as outlined in
Sect. 2.2.3.
Selection of an appropriate transfer velocity must
be based on the requirements of the particular study. It
is important to recognise that parameterisations are not
necessarily universally applicable to gases of differing
solubilites, or to differing environmental situations.
However, it must also be acknowledged that in the
context of the wide range of other uncertainties
associated with quantifying air-sea gas fluxes
(summarised by Johnson et al. 2011), not least the
uncertainty in concentrations and selection of appropriate wind speed averaging, the uncertainty in kw
parameterisations is often relatively small.

2.7

Sea Ice

Sea ice affects air-sea exchanges of greenhouse gases
in ice covered waters in several ways. The first effect
is that sea ice prevents air-sea exchange of gases. First
studies of the vertical distribution of anthropogenic
CO2 in the Weddell Sea showed limited air-sea
exchange of CO2 in the winter surface water when it
is subducted and mixed with other water masses to
form Weddell Bottom Water (Weiss 1987; Poisson
and Chen 1987). This suggested that sea ice was acting
as an inert and impermeable barrier for gas exchange
so that most carbon cycle research has not considered
the possibility of either direct air-sea CO2 exchange in
the presence of sea ice or indirect air-ice-sea CO2
exchange related to sea ice melting (Bates and Mathis
2009; Tison et al. 2002). However, it is well
established that sea ice is a permeable medium,

95

depending of its brine volume fraction (Golden 2003;
Golden et al. 1998). Golden et al. (1998) showed that
sea ice is permeable to brine transport when brine
volume fraction is above 5 %, corresponding to a
temperature of 5  C and sea ice salinity of 5 – the
so-called “law of fives”-. Gas transport within sea ice
is therefore possible as observed by (Gosink et al.
1976) who reported CO2 and SF6 diffusion in sea ice
at 7  C. More recently, Loose et al. (2011b)
measured accurately diffusion of SF6 and O2 through
columnar sea-ice and reported diffusion coefficient
values within permeable artificial columnar ice of
1.3  104 cm2 s1 and 3.9  105 cm2 s1 for SF6
and O2, respectively. These values are rather low
compared to air-sea transfers in open water. However,
it must be borne in mind that sea ice covers about 7 %
of the Earth surface at its maximal seasonal extent and
represents one of the largest biomes on Earth. Even
though fluxes through sea ice are modest, integrated
over the global sea ice cover can potentially be significant (Delille et al. 2006).
Several studies reported measurements of direct
air-ice CO2 exchanges. Most studies reported air-ice
CO2 fluxes in spring and summer (Delille et al. 2006;
Semiletov et al. 2004; Zemmelink et al. 2006; Nomura
et al. 2010a, b; Papakyriakou and Miller 2011). Strikingly, some studies reported air-ice CO2 exchange
during the winter season when the air-ice interface is
supposed to be impermeable to gas exchanges
(Heinesch et al. 2009; Miller et al. 2011). Indeed,
despite the fact that sea ice can be seen as an ideal
environment for micro-meteorological measurement
of CO2 fluxes (Loose et al. 2011a) since the surface
is relatively smooth and levelled, air-ice CO2 fluxes
measurements are still in their infancy. Different
methods are used, without inter-comparison, and temporal and spatial coverage is scarce. In addition, the
early studies of Semiletov et al. (2004) and
Zemmelink et al. (2006) should be considered cautiously as they do not take into account bias of open
path sensor CO2 analysers in cold environments, as
pointed out by Burba et al. (2008). For the time being,
sea ice heterogeneity, gaps in understanding of sea ice
biogeochemistry, poor understanding of the role of
snow in controlling air-ice gas fluxes and poor temporal and spatial coverage prevent robust integration of
air-ice gas fluxes at large scale. Further studies are
required to provide such assessments.

96

Whatever the magnitude of air-ice gases fluxes, the
role of sea ice in gas exchange is not limited to the
ability to transfer gases from the ocean to the atmosphere. O2, CO2 and DMS observations within sea ice
show clearly that gas dynamics within sea ice are
different to that in the underlying layer. Several peculiar processes e.g. temperature change, brine concentration/dilution, brine transport, primary production
and respiration by sympagic1 microbial communities
and others biogeochemical processes affect gases
dynamics within sea ice (Delille et al. 2006, 2007;
Tison et al. 2010; Trevena and Jones 2006; Trevena
et al. 2000, 2003; Glud et al. 2002; Rysgaard and Glud
2004; Rysgaard et al. 2008), so that sea ice cannot be
seen as a simple open/closed pathway between ocean
and the atmosphere. Peculiar gas dynamics within sea
ice determine gas contents of sea ice and subsequently
air-ice partial pressure gradients of each gas. Therefore, the way and potential magnitude of air-ice gas
fluxes are controlled by gas dynamics within sea ice
rather than the gas content of the underlying water.
Rysgaard et al. (2011) recently reviewed the significance of sea ice related processes in terms of air-sea
CO2 exchange and pointed out the role of rejection of
CO2-rich brines in the underlying layer during sea ice
growth. Sea ice expels about 80 % of solutes, including gases, out of the ice matrix, mainly into the underlying water. In the Southern Ocean, this brine rejection
is one of the main drivers of Antarctic Deep Water
formation. Gases transported together with brines can
therefor potentially sink towards deep layers,
providing an efficient path-way for CO2 sequestration
(Rysgaard et al. 2011). In addition, Loose et al. (2009)
observed in artificial ice experiments that rejection of
gases (SF6 and O2) is enhanced compared to salts.
CaCO3 precipitation within sea ice as ikaite
(Papadimitriou et al. 2004; Dieckmann et al. 2008,
2010) has also been suggested as an efficient pathway
for atmospheric CO2 sequestration (Rysgaard et al.
2007) depending on the conditions where precipitation
takes place (Delille et al. 2006). In certain sea ice
permeability conditions, assuming that CaCO3
precipitates in the early sea ice growth phase (Assur
1958; Marion et al. 2009), CO2 produced by CaCO3
precipitation is rejected with brine to the underlying

1
A sympagic environment is one where water exists mostly as
solid ice.

2

Transfer Across the Air-Sea Interface

layer, while CaCO3 crystals remain trapped within the
ice matrix. In summer, CaCO3 dissolution and related
CO2 consumption within the ice or release of alkalinity depleted meltwater into the underlying water promote atmospheric CO2 uptake by the ice and
underlying water, respectively (Delille et al. 2006;
Rysgaard et al. 2007). In parallel to CO2-rich brine
rejection, this precipitation acts as an effective pathway for atmospheric CO2 sequestration (Rysgaard
et al. 2007, 2011; Delille et al. 2006).
The impact of sea ice transfer estimates can be very
important regionally. This is less the case at the global
scale, assuming impermeability of ice to be appropriate for many applications. Rysgaard et al. (2011)
recently provided a first tentative budget of the global
significance of sea ice related processes for atmospheric CO2 uptake. They assessed that sea-ice related
atmospheric CO2 uptake is about 45 TgC y1 and
71 TgC y1 in the Arctic and Southern Ocean, respectively. Oceanic CO2 up-take during the seasonal cycle
of sea ice growth and decay in ice-covered oceanic
regions therefore equals almost half of the net atmospheric CO2 uptake in ice-free polar seas. Finally,
remobilisation of CH4 held in East Siberian Arctic
Shelf sediments has been assessed to be a major contributor to the oceanic CH4 flux to the atmosphere
(Shakhova et al. 2010a, b). Shakhova et al. (2010b)
reported large bubbles of methane entrapped in fast
sea ice probably originating from ebullition from
sediments. Either the ice acts as a simple inert transient buffer for the ebullition CH4 flux or as more
complex pathway has still to be determined.

2.8

Applications of Air-Sea Gas
Transfer

There exist a wide range of applications in which
air-sea transport are important. In this section we
focus on applications in global models, large-scale
measurements from satellite remote sensing and
computations of global inventories. These applications are not exclusive but are the dominant topics
from air-sea gas exchange studies to global environmental research.

2.8

Applications of Air-Sea Gas Transfer

2.8.1

97

Models

Nearly all numerical models employed to date to simulate the cycling of carbon in the ocean are using the
parameterisation of Wanninkhof (1992) to estimate
the gas-transfer velocity, kw, i.e. a parameterisation
that suggests a quadratic dependence of kw on the
windspeed (see Sect. 2.6.1). Different coefficients
have been used with the quadratic dependence, but
we are not aware of any ocean model that uses a linear
(Krakauer et al. 2006), a cubic (Wanninkhof and
McGillis 1999), or piecewise linear function (Liss
and Merlivat 1986) for kw. The very small diversity
of parameterisations used today is a legacy of the
Ocean Carbon-cycle Model Intercomparison Project
(OCMIP-2), where it was decided to use the same
parameterisations for all models (Najjar and Orr
1998). In this particular study, kw was proposed to be
parameterised as follows:
kw ¼ ð1  fice Þ½a  ðU2 þ vÞðSc=660Þ1=2

(2.44)

Where fice is the sea-ice fraction, U2 the square of
the windspeed at 10 m, v is the variance of the
windspeed, added to reflect the non-linear nature of
the relationship, and Sc the Schmidt number. For
OCMIP, the instantaneous windspeed was first averaged to form a monthly climatology of U, and then
squared. v was similarly computed from the instantaneous windspeed. The coefficient a was then chosen
such that the global mean kw matched the global bomb
radiocarbon-derived estimate of Broecker et al.
(1986). This gave a coefficient a of 0.336 cm h1 s2
m2, which is slightly larger than the value of 0.31 cm
h1 s2 m2 proposed by Wanninkhof (1992) due to the
use of different winds.
This parameterisation has been carried forward
into nearly all ocean carbon cycle models since
then, with the only major development being the
suggestion that the coefficient a needs to be downscaled substantially to about 0.24 cm h1 s2 m2 in
order to take into consideration that Broecker et al.
(1986) bomb radiocarbon inventory estimate was too
large (Peacock 2004; Naegler et al. 2006; Sweeney
et al. 2007; Müller et al. 2008; Nägler 2009). This
gives a global mean transfer velocity kw for CO2 of
about 15 cm h1 instead of Broecker’s mean value of
19 cm h1.

Very few sensitivity studies have been performed to
investigate the influence of different gas exchange
parameterisations on the exchange of trace gases.
This is likely a consequence of the influential paper
by Sarmiento et al. (1992) where the authors
demonstrated little sensitivity of the uptake of anthropogenic CO2 with respect to different formulations of
the gas transfer velocity. This is because the rate limiting step for the uptake of this transient tracer is its
transport from the near surface ocean into the ocean’s
interior. But this lack of sensitivity does not apply to
the exchange of natural CO2, where a higher transfer
velocity generally leads to stronger fluxes (Fig. 2.12).
However, the modelled flux changes are less than proportional to the change in the gas transfer velocity,
since the surface ocean concentration of CO2 adjusts
to the change in the “resistance” across the air-sea
interface by generally decreasing the air-sea gradient
when kw is increased (Fig. 2.12c). Due to lateral transport and mixing, the altered CO2 concentration also
affects regions away from regions of strong air-sea
gradients, even leading to a change in the sign of the
gradient.
An even larger sensitivity to the choice of kw exists
for the air-sea transfer of the isotopes of CO2, i.e.,
14
CO2 and 13CO2, since the characteristic exchange
time-scale for these isotopes is about ten times longer
than that for CO2 itself (Broecker and Peng 1974).
This is a consequence of the fact that isotopic equilibrium across the air-sea interface needs to be
established by equilibrating all carbonate species in
the surface ocean, while for CO2, the shortcut reaction
2
involving HCO
3 and CO3 establish a faster equilibrium. Few numerical simulations have been performed
to investigate this sensitivity explicitly, but Broecker
and Maier-Reimer (1992) demonstrated that a doubling of the gas transfer velocity resulted in a substantial change in the surface ocean distribution of the
13 12
C/ C ratio with a near doubling of the range.
For DMS and N2O, virtually all models have used
the gas transfer formulation of Wanninkhof (1992) to
represent the air-sea flux of these gases (Le Clainche
et al. 2010; Jin and Gruber 2003; Suntharalingam et al.
2012). In contrast, for less soluble gases, such as argon
and neon, a broader set of parameterisations have been
employed, particularly to reflect the importance of
bubble mediated transfer. For the latter, most
modellers follow the suggestion of Asher and

98

2

Transfer Across the Air-Sea Interface

a

b

c

Sea-air flux (mol m-2 yr-1)

Difference in sea-air flux (k19-k15) (mol m-2 yr-1)

Difference in sea -air pCO2 (k19-k15) (µatm)

Fig. 2.12 Impact of the gas transfer velocity parameterisation on
the model simulated air-sea flux of CO2. (a) Annual mean sea-to-air
flux of CO2 in pre-industrial times simulated using a global mean
gas transfer velocity of 15 cm h1. (b) Difference in the annual mean
sea-to-air flux of CO2 in pre-industrial times between a simulation

using a global mean gas transfer velocity of 19 cm h1 and the
standard one with 15 cm h1. (c) Difference in the annual mean seaair partial pressure difference in preindustrial times between the
19 cm h1 and 15 cm h1 cases. Based on model simulations
executed with the global NCAR CCSM model (Graven et al. 2012)

2.8

Applications of Air-Sea Gas Transfer

Wanninkhof (1998) to scale the bubble-mediated flux
with the cubic power of the wind speed (Hamme and
Severinghaus 2007; Ito et al. 2011), but also other
powers have been used (Spitzer and Jenkins 1989).
In the last decade, global and regional modellers
have paid relatively little attention to the parameterisations used for the modelling the transfer of gases
across the air-sea interface. This is likely to change
in the coming years. On the one hand, the increasing
computational power permits modellers to increase the
spatial and temporal resolutions of their models, pushing them to the level where some of the assumptions
underlying the classical parameterisations may no longer be fulfilled. For example, when employing the
Wanninkhof (1992) parameterisation, most (global)
models use the parameters for long-term average
winds, while models are now being run with winds
that change every 6 h, requiring the parameters for
short-term winds. This change has not been
undertaken in most models, yet. But the need for a
reconsideration of the appropriateness of the chosen
parameterisations needs to go further. Processes such
as diurnal heating and cooling, eddies and fronts, and
organic slicks that were considered of secondary
importance at basin to global scales, may be important
at local to regional scales, thus requiring renewed
attention.
Furthermore, models are now increasingly being
used to assimilate ocean observations, including
pCO2. Given the substantial sensitivity of surface
ocean pCO2 to the value of kw (Fig. 2.12), it will be
necessary to better handle gas transfer in order to
exploit also the usefulness of pCO2 to determine the
rate of net community production, for example. With
the possible advent of large-scale observations of
dissolved oxygen by floats (Johnson et al. 2009), this
need will increase even further, as the interpretation of
the biologically generated O2 signal requires accurate
estimates of the amount of O2 that is exchanged with
the atmosphere.

2.8.2

Remote Sensing

Gas transfer velocity has been retrieved at global scale
from satellite measurements using mainly two
approaches:

99

1. The use of satellite wind speed and kw  U
relationships (see Boutin et al. (2009) for a review).
2. Or using mean square slope (mss) derived from
dual frequency altimeters (Glover et al. 2007).
Whatever the approach used, the big advantage of
the satellite measurements is that they provide a better
description of the spatio-temporal variability than
meteorological models or at-sea measurements. In
particular, a correct determination of kw following
point (1) requires a good knowledge of the statistical
moments of the wind speed (at least its average and
variance (see Wanninkhof et al. (2009))).
The main advantages of point (1) are related to the
excellent spatial and temporal coverage of the wind
speed owing to the large swaths of scatterometers and
microwave radiometers, and to the mature algorithms
for retrieving wind speed from satellite measurements
(the rms difference of satellite wind speed with
respect to buoy neutral wind speeds being on the
order of 1 m s–1 (Bourassa et al. 2010)), while field
measurements almost always provide kw and U simultaneously, but not always surface stress. Although
satellite wind speeds are expressed as neutral wind
speeds to facilitate the comparison with in situ
measurements, a scatterometer measures a backscatter
coefficient that is related to resonant Bragg scattering
by centimetric waves. Hence it is expected that
scatterometer measurements would better correlate
with kw than wind speed at 10 m height. But this is
almost impossible to demonstrate with field
measurements due to the spatio-temporal variability
of U (scatterometer measurements are integrated over
typically 25 km while kw is obtained from point
measurements) and temporal undersampling of satellite measurements (at best two measurements per day
while measurements of kw by dual tracer techniques
are integrated over several days). It is remarkable that
the use of kw  U relationship derived from dual tracer
experiments (Ho et al. 2006; Nightingale et al. 2000b)
and scatterometer wind speeds give global kw average
within the new 14C constraints without a-posteriori
adjustments (Boutin et al. 2009) as shown in
Fig. 2.13. Nevertheless, given the 30 % uncertainty
in the global kw average deduced from the 14C inventory (Sweeney et al. 2007), the remaining uncertainty
on the functional form of the kw  U relationship
(quadratic, cubic, or hybrid, see Wanninkhof et al.
(2009) for a review) and the remaining uncertainty

100

2

Transfer Across the Air-Sea Interface

25,0

k660 (cm/hr)

20,0

15,0

10,0

<kLM>
<kW>
<kN>
<kH>
k14C

5,0

0,0
Feb 1994- Jan 2001
(ERS1 & ERS2)

Aug 1999-July 2009
(QuikSCAT)

GEOSECS
inventories by
[Broecker et al., 1985]

New 14C constraint
[Naegler and Levin,
2006]

New 14C constraint
[Sweeney et al.,
2007]

New 14C and 13C
constraints [Krakauer
et al., 2006]

Fig. 2.13 Global averages of kw (in cm h1) deduced from long
time series of satellite wind speeds and kw  U relationships
(bar charts) (maroon bars indicate kLM (Liss and Merlivat
1986), yellow bars indicate kW (Wanninkhof 1992), green bars
indicate kN (Nightingale et al. 2000a), and blue bars indicate kH

(Ho et al. 2006)) and deduced from 14C global inventories (black
squares). Methodology to derive kw from satellite wind speed is
described in Boutin et al. (2009); KW fields are available at
http://cersat.ifremer.fr/Data/Discovery/By-product-type/
Gridded-products/

on the wind speed, the new 14C estimates are not
sufficient to fully constrain a kw  U relationship.
Method (2) has been developed because of experimental evidences that kw is better correlated to small
scale mss than to wind speed or wind stress (as
reviewed in Frew et al. (2007)). Nevertheless, it has
some limitations described in Sect. 2.6.2. Future large
swath altimeters will improve the spatial coverage but
in situ measurements of both mss and kw in various
regions of the globe are critical to definitely assess the
utility of this method with respect to the kw  U
method.

maps as it minimises the temperature dependence due
to the CO2 Schmidt number and to the CO2 solubility in
the air-sea CO2 flux computation) have mostly been
built from a single satellite instrument. KW derived
from ERS1, ERS2 and QuickSCAT wind speeds
between 1994 and 2009 are available at http://cersat.
ifremer.fr/Data/Discovery/By-product-type/Griddedproducts/K-CO2-QuikSCAT. Global averages of kw
derived from these scatterometer wind speeds are
presented on Fig. 2.13.
Using kw-mss relationships and after a thorough
cross-validation of Jason-1 and TOPEX, Glover et al.
(2007) have produced global kw fields from 1993 to
2005. Because of the quadratic or cubic dependency of
kw with U, the accuracy of the wind speed fields used
to build kw fields is a big issue and the merging of
several satellite wind speeds requires a thorough crossvalidation (e.g. Fangohr et al. 2008). Not only the
average wind speed must be well known but also at

2.8.3

Inventories, Climatologies Using
In Situ Data

Until now, long time series of kw or of CO2 exchange
coefficients, KW, (KW is usually preferred when building

2.9

Summary

least the second moment of the wind speed. On the
other hand, biases of a few tenths of meter per second
remain between the various types of satellite wind
speeds because of the difficulty of validating satellite
wind speeds integrated over typically 25 km with point
measurements of wind speeds in the field and because
the various satellite instruments are not sensitive to the
same surface state. For instance, a microwave radiometer or an altimeter will better sense high wind speeds
than a scatterometer; a Ku-band scatterometer will be
closer to friction velocity than a C-band scatterometer
but will be more affected by rain.
Recently, the numerous satellite wind speeds available since 1987, and the existence of biases between
various satellite wind products have motivated the
development of cross-calibrated multiplatform surface
wind products (CCMP) (e.g. Atlas et al. 2011). Such
merged products are necessary to ensure long term
continuity of wind speed records (in particular
scatterometer measurements are not continuous), and
are very useful for monitoring KW using existing kw 
U relationships and for computing the air-sea CO2 flux
from CO2 partial pressure. Mean kw deduced with a
quadratic kw  U relationship and CCMP wind speeds
between 2000 and 2009 and between 50  S and 50  N
agrees to within 2.5 % with mean kw deduced from
QuickSCAT products available at Ifremer.2

2.9

Summary

Transfer across the air-sea interface is traditionally
described by a wind-speed dependent function,
where different studies indicate different functional
forms and scaling dependencies. Measurements from
mass-balance techniques seem to converge at a quadratic wind speed dependence (e.g. Nightingale et al.
2000b), agreeing with estimates from 14C inventories.
Eddy covariance techniques tend to give higher
values, in particularly at higher winds, partly
explained by the enhancement due to bubble mediated
transfer (not fully captured by mass-balance
techniques). It is generally agreed that wind is not
the only forcing factor of importance. For better
understanding additional processes need to be taken

2
http://cersat.ifremer.fr/data/discovery/by_product_type/
gridded_products/k_co2_quikscat

101

into account (micro breaking waves, small- and large
scale turbulence, waves, bubbles and sea spray, surface films, chemical and biological enhancement, rain
and atmospheric processes etc.). This is particularly
important when more detailed information is required
in time and space. Further understanding of the transfer can be obtained by measurements, where it is
necessary to consider the strengths/weaknesses of the
various methods and the scale being resolved by each
method. High-resolution numerical simulation is an
alternative approach for studying transfer mechanisms
with the potential of giving detailed information on the
action/interaction of various mechanisms.
Global transfer velocities are often derived from
remote sensing or various inventories and use wind
speed estimates from satellites or surface wind speed
products and kw  U based relationships, although
mean squares slopes from altimeter data are sometimes used. Here the accuracy of the satellite products
and resolution in time and space are important factors
to consider when used for flux calculations. Air-sea
transfer velocities are frequently used in models.
Fluxes derived from models or modelled marine biogeochemical processes have shown relatively small
sensitivity to the formulation of transfer velocity,
partly due to feed-back mechanisms in the models
(the air-sea gradient is slightly adjusted) but the
modelling community have not focused much on the
issue. Global gas flux estimates calculated using airsea gradients estimated from global inventories (from
in situ data or remote sensing) are more sensitive to the
formulation of transfer velocity than modelled
estimates.
To be able to derive more accurate regional and
global methods of determining the transfer velocity it
is necessary to increase our understanding of the
dependence of various processes and their relative
importance. The variation in time and space can then
be better explained and described. To achieve this, it is
suggested to combine different measurement
techniques and use gases of different solubility.

Open Access This chapter is distributed under the
terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any
medium, provided the original author(s) and source
are credited.

102

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3

Air-Sea Interactions of Natural Long-Lived
Greenhouse Gases (CO2, N2O, CH4)
in a Changing Climate
Dorothee C.E. Bakker, Hermann W. Bange, Nicolas Gruber,
Truls Johannessen, Rob C. Upstill-Goddard, Alberto V. Borges,
Bruno Delille, Carolin R. Löscher, S. Wajih A. Naqvi,
Abdirahman M. Omar, and J. Magdalena Santana-Casiano

Abstract

Understanding and quantifying ocean–atmosphere exchanges of the long-lived
greenhouse gases carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4)
are important for understanding the global biogeochemical cycles of carbon and
nitrogen in the context of ongoing global climate change. In this chapter we
summarise our current state of knowledge regarding the oceanic distributions,
formation and consumption pathways, and oceanic uptake and emissions of CO2,
N2O and CH4, with a particular emphasis on the upper ocean. We specifically
consider the role of the ocean in regulating the tropospheric content of these
important radiative gases in a world in which their tropospheric content is rapidly
increasing and estimate the impact of global change on their present and future
oceanic uptake and/or emission. Finally, we evaluate the various uncertainties
associated with the most commonly used methods for estimating uptake and
emission and identify future research needs.

3.1

Introduction

Carbon dioxide (CO2), nitrous oxide (N2O) and methane
(CH4) are long-lived atmospheric greenhouse gases,
whose global budgets are substantially determined by

D.C.E. Bakker (*)
e-mail: d.bakker@uea.ac.uk
H.W. Bange (*)
e-mail: hbange@geomar.de
N. Gruber (*)
e-mail: nicolas.gruber@env.ethz.ch
T. Johannessen (*)
e-mail: truls.johannessen@gfi.uib.no
R.C. Upstill-Goddard (*)
e-mail: rob.goddard@ncl.ac.uk

the marine system. Understanding and accurately
predicting the evolution of the marine CO2 sink and
the marine emissions of N2O and CH4 is of great importance for future climate change scenarios as used in
studies for the Intergovernmental Panel on Climate
Change (Denman et al. 2007).
The tropospheric dry mole fractions of these three
greenhouse gases (Box 3.1) have been increasing
since the industrial revolution, principally reflecting
anthropogenic inputs, but also comparatively small
fluctuations in the balance of natural sources and sinks.
The tropospheric abundance of CO2 has been regularly
monitored since the late 1950s, and those of N2O and
CH4 since the 1970s (http://agage.eas.gatech.edu/index.
htm; http://www.esrl.noaa.gov/gmd) (Fig. 3.1). Table 3.1
summarises the tropospheric abundances, lifetimes, and
radiative forcings of CO2, N2O and CH4 for 2005

P.S. Liss and M.T. Johnson (eds.), Ocean-Atmosphere Interactions of Gases and Particles,
Springer Earth System Sciences, DOI 10.1007/978-3-642-25643-1_3, # The Author(s) 2014

113

114

3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

Box 3.1

Atmospheric gases are quantified by their dry mole fraction, given in units of ppm or μmol mol
1 for CO2
and in units of ppb or nmol mol
1 for N2O and CH4. In this chapter we report annual fluxes in Pg C year
1
for CO2, in Tg C year
1 for CH4 and in Tg N year
1 for N2O. One Pg (Petagram) is equivalent to 1015 g
and one Tg (Teragram) is equivalent to 1012 g. The troposphere is the lower part of the atmosphere and
extends from the Earth’s surface to the tropopause at 10–15 km height. In this chapter we are mainly
concerned with the troposphere, unless specified otherwise.

Fig. 3.1 Atmospheric concentrations of carbon dioxide, methane and nitrous oxide over the last 2,000 years (Reproduced from
Forster et al. (2007) by permission of the IPCC)
Table 3.1 The tropospheric dry mole fractions, radiative forcings (RF) and lifetimes (adjustment time) of CO2, N2O and CH4
(After Forster et al. 2007; WDCGG 2012)
Gas
CO2
N2O
CH4

Abundance in 2010
389.0 ppm
323.2 ppb
1,808 ppb

Abundance in 2005
379  0.65 ppm
319  0.12 ppb
1,774  1.8 ppb

RF in 2005 (W m
2)
1.66
0.16
0.48

Lifetime (years)
See belowa
114
12

a

No single adjustment time exists for CO2 (Joos et al. 2001), as the rate of removal of CO2 from the troposphere is determined by the
removal rate of carbon from the surface ocean (Annexe I in IPCC 2007). An approximate value of 100 years may be given, while
decay constants of 172.9 to 18.51 and 1.186 years have been used in models (Forster et al. 2007).

(Forster et al. 2007). An increase in the dry mole
fractions (Box 3.1) of these long-lived greenhouse
gases leads to tropospheric warming and stratospheric
cooling, which may impact on chemical reaction rates
and atmospheric dynamics (Wayne 2000). Other effects
of changes in the dry mole fractions of these gases are
listed in Table 3.2.
Global CO2 emissions are currently increasing
exponentially, primarily reflecting the accelerating
development of large emerging economies such as

China and India (Friedlingstein et al. 2010). If
sustained, this recently rapid growth in tropospheric
CO2 may precipitate critical climate and other global
environmental changes, possibly faster than previously identified (e.g. IPCC 2007).
Given its current tropospheric growth and the ongoing decline in chlorofluorocarbon (CFC) emissions,
N2O may soon replace CFCs as the fourth most important greenhouse gas after water vapour (H2O), CO2 and
CH4 (Forster et al. 2007). The major sink of N2O is

Oceanic contribution to
contemporary atmospheric budget

Impact of environmental change on air-sea gas exchange in the twenty-first century
Coastal
Ocean
Open ocean
eutrophication and
Global warming
acidification deoxygenation
hypoxia
CO2 Inert
Coastal CO2 sink
Net ocean sink for about 30 % of CO2 The net ocean sink likely to decrease Small effect Small effect
emissions from human activity partly by 2100
likely to increase
on ocean
mitigates climate change; the ocean
CO2 sink
ultimately controls atmospheric CO2
content
Large increase in
N2O Largely inert in the troposphere; Open ocean natural N2O source,
Increase of subsurface production
Unknown
Negligible or small
the coastal N2O
depletion of stratospheric O3
coastal regions (incl. rivers)
increase in the open
source
anthropogenic N2O source,
ocean N2O source
equivalent to 20 % and 10 % of the
global N2O emissions
Negligible or small
Unknown
Negligible or small
CH4 Regulates tropospheric O3 and OH Natural open ocean and coastal CH4 Increased CH4 release from CH4
increase in coastal
sources equivalent to 10 % of the
hydrates and increased CO2 release
radical; affects stratospheric O3
increase in the open
chemistry; CO2 source
CH4 source
global, natural CH4 sources;
upon oxidation of hydrate-derived
ocean CH4 source
continental CH4 seeps poorly known CH4

Gas Role in atmospheric chemistry

Table 3.2 Air-sea exchange and impact of the long-lived greenhouse gases CO2, N2O and CH4 in a changing climate, as discussed in this chapter. See Table 3.1 for the radiative
forcings and atmospheric lifetimes

3.1
Introduction
115

116

3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

Table 3.3 Sources and sinks of tropospheric CH4. Ranges are
derived from estimates for the period 1983–2004, as compiled
by Denman et al. (2007)
CH4 sources and sinks
(Tg C year
1)
Natural sources:
Wetlands
Termites
Oceans, incl coastal regions
Hydrates
Geological sources
Wild animals
Wildfires
Total natural sources
Anthropogenic sources:
Energy
Coal mining
Gas, oil, industry
Landfills and waste
Ruminants
Rice agriculture
Biomass burning
Vegetation
Total anthropogenic sources
Total sources:
Sinks:
Tropospheric OH radical
Soils
Stratospheric loss
Total sinks

75–173
15–22
3–11
3–4
3–11
11
2–4
109–195
56–58
23–36
36–68
26–52
57–142
23–84
11–66
27
198–321
377–458
321–383
20–26
22–34
386–436

the stratosphere where it photochemically decomposes
via reaction with O(1D) (oxygen singlet D) to form
nitric oxide (NO) radicals. The latter represent a
major removal pathway for stratospheric ozone (O3)
(Crutzen 1970; Ravishankara et al. 2009). Indeed,
N2O is expected to become the dominant O3 depleting
compound during the twenty-first century (Ravishankara
et al. 2009).
CH4 is the most abundant organic species in the
troposphere, where it influences oxidising capacity and
regulates levels of O3 and OH (hydroxyl free radical).
Oxidation of CH4 by OH to CO2 and CO (carbon
monoxide) is its major tropospheric sink (Table 3.3).
In the stratosphere photo-oxidation of CH4 is a major
source of stratospheric H2O, which influences both
tropospheric warming and stratospheric cooling
(Michelsen et al. 2000), and a small source of stratospheric CO2. CH4 plays a complex role in stratospheric
O3 chemistry (Wayne 2000). Additional stratospheric

CO2 arises from rapid CO oxidation. However, these
two CO2 sources are minor and as there are no
recognised stratospheric sinks for CO2 (Hall and Prather
1993); any variation in stratospheric CO2 principally
reflects the inflow of tropospheric air masses.

3.1.1

Atmospheric Greenhouse Gases
from Ice Cores

Analysis of the composition of fossil air trapped in ice
cores has extended the tropospheric histories of all three
gases, to ~800,000 years before present (YBP) for CO2
(Petit et al. 1999; EPICA community members 2004;
Lüthi et al. 2008) and to ~650,000 YBP for N2O and
CH4 (Spahni et al. 2005). These data show that during
the last 650,000 years CO2 has varied from ~170 ppm
during glacials to ~280 ppm during interglacials, while
during the preceeding 100,000 years the range was
somewhat smaller. For comparison, tropospheric CO2
increased from 280 ppm pre-industrially to 389 ppm in
2010 (Forster et al. 2007; WDCGG 2012). Changes in
ocean circulation and biology and the feedbacks
between them have been invoked to explain the glacial/
interglacial fluctuations of tropospheric CO2 but understanding the precise mechanistic details remains a
substantial challenge (Jansen et al. 2007). Over the
last 420,000 years, tropospheric CO2 has tracked
reconstructed changes in Antarctic temperature with a
time lag of several hundred to a thousand years
(Mudelsee 2001), implying that changes in the physical
climate system such as temperature and the extent of
glaciers have initiated changes in the global carbon cycle
and tropospheric CO2. The carbon cycle then has
responded by amplifying these initial perturbations
through positive carbon-climate feedbacks. Today the
situation is fundamentally different in that the increasing
greenhouse gas content drives changes in climate and
environment.
Variation in stratospheric N2O between 200 and
280 ppb during the past 650,000 years (Spahni et al. 2005)
can be attributed to concurrent natural changes in both the
terrestrial and the oceanic sources (Sowers et al. 2003;
Flückiger et al. 2004). Since the pre-industrial era the
mean tropospheric N2O dry mole fraction has increased
from 270  7 to ~323 ppb. The current tropospheric N2O
growth rate of about 0.7 ppb year–1 can primarily be
attributed to the continued increased use of nitrogen
fertilisers (Forster et al. 2007; Montzka et al. 2011).

3.2

Surface Ocean Distribution and Air-Sea Exchange of CO2

The tropospheric dry mole fraction of CH4 has
varied from ~400 ppb during glacials to ~700 ppb during interglacials. The current average tropospheric CH4
dry mole fraction is ~1,808 ppb, reflecting large and
growing anthropogenic CH4 fluxes since the preindustrial era (Table 3.3). Even so, tropospheric CH4
growth is temporally quite variable. High annual
growth rates of ~20 ppb year
1 during the 1970s were
followed by growth rates of ~9–13 ppb year
1 through
the 1980s, 0–13 ppb year
1 through most of the 1990s,
almost zero growth during the late 1990s to early 2000s
(Dlugokencky et al. 2003) and renewed growth rates
of ~10 ppb year
1 during the late 2000s (Rigby et al.
2008). This complex behaviour reflects short-term
source variability that has been variously ascribed to
decreased fossil fuel output following the economic
collapse of the former Soviet Union, volcanic activity,
wetland and rice paddy emissions, biomass burning,
changes in the global distributions of temperature
and precipitation, and reduced microbial sources in
the Northern Hemisphere (Denman et al. 2007;
Dlugokencky et al. 2009; Aydin et al. 2011; Kai et al.
2011).

117

A consequence of this ocean CO2 uptake is a
decrease in ocean pH, known as ocean acidification
(Sect. 3.5.2) (Feely et al. 2004; Raven et al. 2005). If
anthropogenic CO2 emissions were to cease now, the
oceans would eventually absorb 70–80 % of the
anthropogenic CO2 so far added to the troposphere,
but this would take several hundred years (Archer et al.
1997; Watson and Orr 2003). Dissolution of calcium
carbonate (CaCO3) in deep ocean sediments and on
land would further reduce tropospheric CO2 to within
8 % of its pre-industrial level over thousands of years
(Archer et al. 1997). Given the importance of the
oceans in moderating human-induced climate change,
quantifying net oceanic CO2 uptake and estimating
its long-term evolution are of critical importance.
Although much progress has been made in quantifying
CO2 air-sea fluxes over the past decade, considerable
uncertainties remain, in particular relating to interannual variability and long-term trends. The current
state of knowledge is discussed here for the open ocean
(Sect. 3.2.3) and for coastal seas (Sect. 3.2.4), with
emphasis on the principal uncertainties (Sect. 3.6).

3.2.2

3.2

Surface Ocean Distribution
and Air-Sea Exchange of CO2

3.2.1

Global Tropospheric CO2 Budget

In 2010 alone the tropospheric CO2 increase was
equivalent to 5.0  0.2 Pg C (Box 3.1), principally
due to the release of 9.1  0.5 Pg C from fossil fuel
burning and cement manufacture and 0.9  0.7 Pg C
from land use change (Fig. 3.2) (Global Carbon Project
2011; Peters et al. 2012). The ocean absorbs a substantial fraction of CO2 emissions to the troposphere. From
pre-industrial times to 1994 the oceans are estimated to
have taken up 118  19 Pg C from the troposphere,
corresponding to roughly 50 % of fossil fuel CO2 or
about 30 % of the total anthropogenic emissions that
include CO2 emissions from land use change (Fig. 3.2;
Table 3.2) (Sabine et al. 2004). Scientists are debating
whether regional and global ocean CO2 uptake has
increased, remained constant or decreased in recent
decades (Le Quéré et al. 2007, 2010; Schuster and
Watson 2007; McKinley et al. 2011; Ballantyre et al.
2012).

Processes Controlling CO2
Dynamics in the Upper Water
Column

The air-sea exchange fluxes of CO2 show high spatial
and temporal variability, reflecting a complex interplay
between the biological and physical processes affecting
surface water fCO2 (Box 3.2) (Takahashi et al. 2002;
Sarmiento and Gruber 2006). In addition, observations
show surface water fCO2 to rarely be in equilibrium
with tropospheric fCO2 (see below and Box 3.3). Key
to understanding the behaviour of CO2 with regard to
equilibration is CO2 chemistry, which we briefly review
next. We also discuss the key processes controlling the
CO2 dynamics of the upper ocean.
Dissolved CO2 in seawater chemically equilibrates
with carbonic acid (H2CO3) and the bicarbonate
(HCO3
) and carbonate (CO32
) ions:
CO2 þ H2 O $ H2 CO3

(3.1)

H2 CO3 $ Hþ þ HCO3


(3.2)

HCO3
$ Hþ þ CO3 2


(3.3)

118

3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

Atmosphere

597 + 238

0.2

59.6

60
2.3

NPP &
Weathering Respiration

Land
sink

0.9

Land
Use
Change

7.7
70.6 70 22.3 20
Fossil Fuels

Vegetation
Soil & Detritus

3700 - 365

2300 + 124 - 151

0.4

0.8
Rivers
Surface Ocean

Weathering

0.2

900

+ 34

90.2

1.6

101

50
39

Marine Biota

3

11

Intermediate
& Deep Ocean

37,100 + 120

0.2
Reservoir sizes in PgC
Fluxes and Rates in PgC yr -1
Fig. 3.2 The global carbon cycle with annual fluxes (in Pg C
year
1) for the years 2000–2009. Pre-industrial, natural fluxes
are in black and anthropogenic fluxes are in red. Integrated
fluxes and standing stocks are from 1850 to 2011. NPP is annual

Surface sediment

150

net terrestrial primary production. Cumulative changes are for
end 2011 (The figure updates those in Sarmiento and Gruber
(2002) and Denman et al. (2007). Figure courtesy of N Gruber)

Box 3.2

Whereas the amount of CO2 dissolved in seawater is generally reported in terms of its partial pressure
pCO2 (unit: μatm or 0.101325 Pa) or fugacity fCO2 (unit: μatm), N2O and CH4 are more commonly
presented in concentration units (nM or nmol kg
1 seawater) or as percent (%) saturation. The latter is
calculated from the ratio of the measured concentration to the theoretical equilibrium concentration, as
determined by ambient water temperature, salinity and air pressure, and the atmospheric dry mole fraction
corresponding to the time of last atmospheric contact. A surface saturation of 100 % indicates a water
mass in equilibrium with overlying air, values below 100 % indicate undersaturation and values above
100 % indicate supersaturation. For all three gases, deviations from the air-sea equilibrium value are
expressed as a negative or positive partial pressure difference (i.e. ΔpCO2, ΔpN2O, ΔpCH4) or for N2O
and CH4, as a negative or positive “concentration anomaly”. In this chapter, positive values denote a
partial pressure that is higher in the water than in the overlying air.
The fugacity of a gas is its partial pressure after correcting for any non-ideal behaviour by applying a
fugacity coefficient γ (Weiss 1974). The equation for CO2 is:
fCO2 ¼ γ pCO2

(3.4)

In practice the fugacity and partial pressure of CO2 differ by only about 0.4 %. In this chaper we refer to
fCO2 throughout.

3.2

Surface Ocean Distribution and Air-Sea Exchange of CO2

119

Box 3.3

Air-sea fluxes (F) can be quantified as the product of a gas transfer velocity (k), the gas solubility (K0) and
the difference in the gas fugacity (e.g. fCO2) across the air-sea interface. For N2O and CH4 the difference
in the partial pressure is usually applied.
FCO2 ¼ kCO2 K0;CO2 ðfCO2water
fCO2air Þ

(3.5)

FX ¼ kX K0;X ðpXwater
pXair Þ ðwith X ¼ N2 O or CH4 Þ

(3.6)

By convention, positive flux values indicate emission from the ocean and negative flux values indicate
uptake by the ocean. For CO2 these simplified equations neglect its possible chemical enhancement,
although this effect is thought to be small (Wanninkhof 1992; Matthews 1999). They further assume that
away from the air-sea interface both the lower troposphere and upper ocean are well mixed, so that bulk
measurements within them can be used to define a gas concentration gradient at the interface. A final
assumption is that the water temperature at the interface (the skin temperature) is the same as that of the
well-mixed upper ocean (Robertson and Watson 1992; Van Scoy et al. 1995).
Atmospheric CO2 needs to equilibrate with the large pool of dissolved inorganic carbon in seawater,
resulting in an equilibration time-scale of the surface ocean for gas exchange of nearly 1 year (Broecker
and Peng 1982), i.e. much longer than the time-scale typically associated with upper ocean perturbations
(such as by the seasonal cycle). The equilibration time-scale for CH4 and N2O is about 10 times shorter
than for CO2 so that the deviations of these gases from equilibrium are generally smaller unless strong
sources are present.
The gas transfer velocity (k) is a function of turbulence at the sea surface and is often parameterised
as a function of wind speed, as discussed in detail in Chap. 2. Several parameterisations of k as a
function of wind speed have been proposed (e.g. Liss and Merlivat 1986; Wanninkhof 1992;
Wanninkhof and McGillis 1999; Nightingale et al. 2000; Ho et al. 2006; Sweeney et al. 2007; Prytherch
et al. 2010). The uncertainty in k, which has been estimated at 30 % (Sweeney et al. 2007), adds further
uncertainty to estimates of net gas uptake and/or emission determined from surface water measurements
(Sect. 3.6).

On average surface seawater dissolved inorganic carbon (DIC) (alternatively referred to as total CO2,
ΣCO2 and CT) comprises about 90 % HCO3
, 9 %
CO32
, 1 % dissolved CO2 and 0.001 % H2CO3. Thus,
in order to equilibrate across the air-sea interface, CO2
needs to equilibrate not only with the dissolved CO2
pool, but with all chemical species making up DIC,
explaining the long equilibration time scale. The dominant presence of HCO3
and CO32
are also key to
explaining the large uptake capacity of the ocean with
regard to the anthropogenic perturbation of tropospheric CO2, as it is the reaction of CO32
with the
dissolved CO2 taken up to form two HCO3
ions that
gives seawater its large capacity to take up CO2 and
that will enable the ocean to eventually take up nearly

80 % of total anthropogenic emissions. An important
metric for this reaction is the oceanic buffer (or
Revelle) factor, which is a measure of the degree to
which this titration reaction occurs. The larger the
concentration of the CO32
ion, the higher this factor
is, and thus the larger is the oceanic uptake capacity.
However, the current net rate of oceanic CO2 uptake
is overall set by its transport from the surface to the
deep oceans (Fig. 3.2), leading to the observation that
the current uptake fraction (about 30 %) is considerably
smaller than the long-term potential (about 80 %).
The anthropogenic perturbation occurs on top of an
intense but largely internal cycling of “natural” carbon,
which is the primary driver of the high spatio-temporal
variability of CO2 in the surface ocean. This natural

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3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

internal cycling is often conceptualised as a number of
“pumps”, namely the solubility pump, the soft tissue or
organic carbon pump and the carbonate or hard tissue
pump (Volk and Hoffert 1985; Heinze et al. 1991). The
reason for the pump analogy is that the associated
processes act as gradient makers in that they tend to
reduce the surface concentration of DIC and enhance
its concentration at depth, thereby acting against the
tendency for these gradients to be eliminated by transport and mixing. The net effect of these pumps on the
air-sea exchange of CO2 is controlled by the interaction and relative importance of the downward
pump component relative to upward mixing and transport (Gruber and Sarmiento 2002). Regions where the
downward component dominates over upward transport are sinks for tropospheric CO2, while regions
where upward transport dominates are CO2 sources.
Given the need to consider both the downward and
upward components, the concept of biogeochemical
loops has been proposed (Gruber and Sarmiento 2002).
The solubility pump is maximal at high-latitudes
during winter when cold surface water rich in DIC (due
to higher CO2 solubility at lower temperatures) sinks to
depth, resulting in a net downward transport of DIC
(Fig. 3.2) (Volk and Hoffert 1985; Heinze et al 1991).
In contrast, the solubility pump acts in quasi reverse
order, when colder waters rich in DIC are brought to
the surface and warm, giving rise to reduced CO2
solubility.
The soft tissue pump is initiated by the photosynthetic incorporation of CO2 as phytoplankton cellular
organic carbon. As this organic carbon travels up the
food chain, a fraction of it is “lost” at each trophic step
by respiration, excretion and the death of organisms
(Kaiser et al. 2011). Bacteria and other microorganisms are critical to the recycling of carbon in
the upper ocean (Fig. 3.2). Nevertheless, a significant
fraction of the photosynthetically fixed carbon leaves
the upper ocean as “export production” in the form of
sinking organic particles, by vertical migration of zooplankton or as dissolved organic carbon (DOC) in
sinking water (Volk and Hoffert 1985; Heinze et al.
1991; Sarmiento and Gruber 2006).
The carbonate or hard tissue pump involves the
biological formation of calcium carbonate (CaCO3)
in near surface waters, its downward export primarily
by sinking and its subsequent dissolution in deep
water. The initial step is incorporation of the CO32

ion into the shells of calcifying organisms:

Ca2þ þ CO3 2
$ CaCO3

(3.7)

Equation 3.7 describes CaCO3 precipitation at a physiological level; however, the uptake of CO32
leads to
a chemical re-adjustment of DIC species with the
overall equation:
Ca2þ þ 2HCO3
$ CaCO3 þ CO2 þ H2 O

(3.8)

The precipitation of CaCO3 leads to a shift from the
HCO3
pool to the CO2 pool and a release of CO2 to
the surrounding water. The “released” CO2 subsequently equilibrates with HCO3
, so that for each
mole of CaCO3 precipitated, less than one mole of
CO2 is “released”. The fraction for average surface
sea water is 0.6 (Frankignoulle et al. 1994). Consequently, where the ratio between net organic carbon
production or net community production (NCP) and
calcification is below 0.6, the waters are a CO2 source
and where this ratio exceeds 0.6, they are a CO2 sink
(Suzuki and Kawahata 2004).
In coral reefs NCP is close to zero (Gattuso et al.
1998). Hence the CO2 “released” by CaCO3 precipitation generally exceeds the CO2 drawdown by NCP and
coral reefs tend to act as CO2 sources to the troposphere
(Gattuso et al. 1993, 1997; Frankignoulle et al. 1996;
Ohde and van Woesik 1999; Bates et al. 2001). In the
pelagic realm, where the vast majority of calcification
is carried out by the coccolithophore component of the
phytoplankton (Buitenhuis et al. 1996; Harlay et al.
2010, 2011; Suykens et al. 2010), the average ratio of
NCP to net CaCO3 precipitation is between 11 and 16
(Sarmiento et al. 2002; Jin et al. 2006). The net consequence of biological production and the export of
organic carbon and CaCO3 from the pelagic realm is a
tendency towards CO2 uptake from the troposphere.
Mineral CaCO3 in seawater occurs in two forms:
calcite and the more soluble aragonite (Mucci 1983).
For both, solubility increases with increased pressure
(depth) and decreased temperature. The saturation
state Ω describes whether sea-water is supersaturated
(Ω > 1) or undersaturated (Ω < 1) with respect to the
solubility product, Ksp, of either of these two CaCO3
forms:
Ω ¼ ½Ca2þ  ½CO3 2
=Ksp

(3.9)

3.2

Surface Ocean Distribution and Air-Sea Exchange of CO2

Ksp ¼ ½Ca2þ sat ½CO3 2
sat

(3.10)

At present nearly the entire upper ocean is supersaturated with regard to both calcite and aragonite,
while most of the deep ocean is undersaturated.
Organisms that form CaCO3 shells and structures therefore do so largely in waters that are supersaturated, while
the exported CaCO3 eventually sinks into regions of
undersaturation and dissolves.
A reduction in ocean pH due to anthropogenic
activities (Feely et al. 2004; Orr et al. 2005; Raven
et al. 2005) is one consequence of increased tropospheric CO2 and its transfer to the ocean. While the
term “ocean acidification” (OA) describes a decrease
in ocean pH, this is not expected to fall below 7
(Kleypas et al. 2006). The uptake of CO2 since preindustrial times has led to a reduction in surface
seawater pH of 0.1 units relative to the pre-industrial
value of about 8.2 (Orr et al. 2005). This is equivalent
to a 30 % increase in the hydrogen ion (H+)
concentration.
In situ pH measurements at the European Station
for Time-series in the Ocean (ESTOC, 29 N 15 W)
show a progressive reduction of pH and other
changes in the carbonate chemistry of surface waters
since 1995 (González-Dávila et al. 2010; SantanaCasiano and González-Dávila 2011). Figure 3.3
highlights a decrease in surface water pHT (the pH
corrected to a constant temperature of 25 C) of
0.0019 pH units year
1 from 1995 to 2010,
accompanied by increases in salinity normalised
DIC (NCT) and fCO2. Similar trends in pH, DIC
and fCO2 have been observed at the Bermuda Atlantic Time-series Study, BATS (Gruber et al. 2002),
and the Hawaii Ocean Time-Series site, HOT (Brix
et al. 2004; Denman et al. 2007).
An important consequence of the net oceanic
uptake of anthropogenic CO2 from the troposphere is
a decrease in the saturation states with regard to calcite
and aragonite. This is due to the aforementioned titration of the CO32
ion by the CO2 taken up, which
leads to a fall in the CO32
concentration. These
chemical changes are accompanied by an increase in
the concentration of H+ and CO2 (Feely et al. 2004;
Raven et al. 2005). The ESTOC time series
demonstrates how the saturation states for calcite and
aragonite have decreased at rates of 0.018  0.006
units year
1 and 0.012  0.004 units year
1,

121

respectively, from 1995 to 2004 (Santana-Casiano
and González-Dávila 2011).
Ocean acidification is suspected to lead to a reduction
in calcification by calcifying organisms, such as coral
reefs, coccolithophores, foraminifera, pteropods and
shell fish (Sect. 3.5.2) (Raven et al. 2005). In addition,
diminishing calcification would reduce net CaCO3
transfer to the deep ocean (Feely et al. 2004; Denman
et al. 2007).

3.2.3

Surface Ocean fCO2 and Air-Sea CO2
Fluxes in the Open Ocean

3.2.3.1 Surface Ocean fCO2 Distribution
The seasonal cycle in surface water fCO2 is relatively
weak in tropical regions (14 S–14 N), which are
strong CO2 sources throughout the year (Takahashi
et al. 2009) (Box 3.3). Surface water fCO2 in temperate
ocean regions (14–50 N and 14–50 S) has a strong
seasonal cycle with high values in summer and low
values in winter, as the seasonal effects of warming
and cooling outweigh biological effects (Fig. 3.3)
(Bates et al. 1996a; Dore et al. 2003; González-Dávila
et al. 2003; Takahashi et al. 2009). The temperate
Indian Ocean north of 14 N also has high fCO2 in
summer, but here seasonal upwelling in the southwest
monsoon is the main driver (Takahashi et al. 2009).
High latitude northern hemisphere waters have strong
fCO2 undersaturation in spring and summer as a result
of biological CO2 drawdown in the upper ocean
(Takahashi et al. 2009). Biological activity equally
creates a CO2 sink in Southern Ocean waters from
50 S to 60 S during austral spring and summer
(Takahashi et al. 2009). Seasonally ice covered waters
south of ~60 S rapidly change from strong CO2 supersaturation below sea ice to strong undersaturation upon
ice melt, most likely driven by biological carbon
uptake (Bakker et al. 2008).
Surface water fCO2 data coverage has improved
greatly over the past decade (Takahashi et al. 2009;
Watson et al. 2009; Pfeil et al. 2013; Sabine et al.
2013). For example, a basin-wide network of fCO2
measurements on Voluntary Observing Ships (VOS)
and buoys has been operational in the North Atlantic
Ocean since 2004, which allows the creation of basinwide monthly fCO2 maps, annual flux estimates and
trend analyses (Schuster et al. 2009; Telszewski et al.
2009; Watson et al. 2009). Data coverage is similarly

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3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

Fig. 3.3 Changes in total pH at 25  C, salinity normalised
dissolved inorganic carbon (NCT) and fCO2 from 1995 to 2010
at the European Station for Time-series in the Ocean (ESTOC,
29 N 15 W) for the full set of surface data (upper 10 m). The

regression lines have slopes of
0.0019  0.0004 pH units
year
1 for pHT,25, of 0.94  0.14 μmol kg
1 year
1 for NCT
and of 1.8  0.4 μatm year
1 for fCO2. (Figure courtesy of
M González-Dávila and JM Santana-Casiano)

3.2

Surface Ocean Distribution and Air-Sea Exchange of CO2

123

Fig. 3.4 Number of months in each 4 latitude by 5 longitude box with at least one surface water fCO2 measurement between 1970
and 2007 (Reproduced from Takahashi et al. (2009) by permission of Elsevier)

high in the North Pacific (Feely et al. 2006; Ishii et al.
2009). Elsewhere data coverage has increased, but many
regions remain data sparse, e.g. the Indian Ocean,
the South Pacific Ocean, the South Atlantic Ocean and
the Southern Ocean, notably in autumn and winter
(Fig. 3.4) (Takahashi et al. 2009, 2011; Bakker et al.
2012; Pfeil et al. 2013; Sabine et al. 2013).
A variety of techniques have been applied to interpolate between surface ocean fCO2 data, including
a diffusion–advection based interpolation scheme
(Takahashi et al. 1997, 2009), (multiple) linear regression (Boutin et al. 1999; Rangama et al. 2005; Olsen
et al. 2008) and a neural network approach (Lefèvre
et al. 2005; Telszewski et al. 2009). The principle of
many of these methods is to correlate sparse fCO2
data with more widely available parameters such as
satellite-derived chlorophyll a concentrations, sea surface temperatures and mixed layer depths and then to
use these correlations to predict fCO2 where
measurements are lacking.
The ‘true’ spatial distributions of surface water
fCO2 and air-sea CO2 fluxes are unknown and the
above methods only deliver approximations of them.
Interestingly, however, Watson et al. (2009) derived
similar air-sea CO2 fluxes for the North Atlantic
Ocean (10–65 N) using multiple linear regression

and a neural network. For both the standard deviation
of the annual mean fCO2 was ~10 %. It was concluded
that if the flux uncertainty arising from uncertainty in k
is ignored, the overall air-sea CO2 flux in this region is
well constrained by fCO2 observations and is thus
relatively insensitive to the mapping technique used.
Further development and testing of interpolation
methods should be a priority.

3.2.3.2 Multi-Year Changes and Trends
Analysis of the decadal evolution of fCO2 provides
information on the evolution of the oceanic CO2 sink.
If the rate of increase of surface ocean fCO2 matches
the increase in tropospheric CO2 the oceanic CO2 sink
is at steady state, but if it is higher, then the oceanic CO2
sink is decreasing (Schuster et al. 2009). For example,
Fig. 3.3 shows fCO2 from 1995 to 2010 for the upper
10 m at ESTOC (29 N 15 W). Regression of the data
reveals an increase in fCO2 of 1.8  0.4 μatm year
1.
Globally, surface water fCO2 increased at a mean
rate of 1.5 μatm year
1 from 1970 to 2007, similar to
the pace of the tropospheric CO2 increase of 1.5 μatm
year
1 from 1972 to 2005 (Takahashi et al. 2009).
Relatively low rates of increase were found in the
Equatorial Pacific Ocean (1.26  0.55 μatm year
1)
and the North Pacific Ocean (1.28  0.46 μatm

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3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

year
1), while fCO2 increased more rapidly in the
North Atlantic Ocean (1.80  0.37 μatm year
1) and
between 50 S and 60 S (2.13  0.64 μatm year
1)
(Takahashi et al. 2009). Similarly, surface water
fCO2 in the Southern Indian Ocean (south of 20 S)
increased more rapidly (2.11  0.07 μatm year
1)
than did tropospheric CO2 (1.72 μatm year
1) between
1991 and 2007 (Metzl 2009).
Regional and temporal differences in the rate of
increase of surface water fCO2 are not well understood
but have been attributed to changes in seawater buffer
capacity (Thomas et al. 2007), mixing and stratification
(Schuster and Watson 2007), temperature (Corbière
et al. 2007), biological activity (Lefèvre et al. 2004)
and lateral and vertical water transport (Takahashi et al.
2009). The expanding database for fCO2 highlights
considerable year-to-year and multi-year variations in
ocean carbon cycling.
Theory and biogeochemical models predict an
increase in air-sea fCO2 disequilibrium over time in
high latitude regions. Here water from the interior
ocean reaches the surface. This water has a relatively
low DIC content, as it equilibrated with an atmospheric
CO2 mixing ratio below the present one, when the water
last was at the surface. One might expect that the
increase in surface water fCO2 of these waters lags the
increase in tropospheric CO2 (Takahashi et al. 1997,
2002), given the long equilibration time for CO2 of
almost a year. Such an increase in the air-sea fCO2
disequilibrium would be accompanied by an increase
in the net oceanic CO2 sink. However, the observation
that surface water fCO2 in some regions of the Southern
Ocean is currently increasing more rapidly than tropospheric CO2 (Metzl 2009; Takahashi et al. 2009) runs
counter to these predictions. Air-sea CO2 flux estimates
derived from the inversion of tropospheric CO2 data
suggest that this may be a more wide-spread phenomenon in the Southern Ocean, extending to the entire
region south of 45 S (Le Quéré et al. 2007). This hypothesis of a weakening relative sink strength in the Southern Ocean is supported by several ocean modelling
studies (Wetzel et al. 2005; Le Quéré et al. 2007;
Lovenduski et al. 2007) and is attributed to a trend of
increasing Southern Ocean wind speeds, which enhance
the upwelling of deeper waters with high concentrations
of “natural” DIC (Lovenduski et al. 2008). The changing
wind regime may be related to a strengthening of the
Southern Annular Mode in response to increasing greenhouse gases and the depletion of stratospheric ozone

(Lenton et al. 2009). These trends in Southern Ocean
fCO2, the strength of the oceanic CO2 sink and the
mechanisms responsible are currently topics of much
scientific debate.
Recent studies provide evidence of multi-annual
variation in surface water fCO2 growth rates and CO2
air-sea fluxes in other regions, notably the Pacific
Ocean and the North Atlantic Ocean (Corbière et al.
2007; Schuster and Watson 2007; Ishii et al. 2009;
Schuster et al. 2009; Watson et al. 2009). For example,
the growth rates of surface water fCO2 in the western
Equatorial Pacific were different from 1985–1990
(0.3  1.3 μatm year
1) to 1990–1999 (2.2  0.7 μatm
year
1) and 1999–2004 (
0.2  1.0 μatm year
1)
(Ishii et al. 2009). Annual CO2 uptake along a shipping
route between the United Kingdom and the Caribbean
strongly decreased from the early 1990s to 2002–2005
(Schuster and Watson 2007; Schuster et al. 2009).
Annual air-sea CO2 fluxes varied by more than a factor
two for the period 2002–2007, with values rising and
falling over several years (Fig. 3.5) (Watson et al.
2009). These gradual changes suggest multi-year or
possibly decadal variation that might be linked to the
North Atlantic Oscillation (Thomas et al. 2008).

3.2.3.3 Comparison of Air-Sea CO2 Flux
Estimates
Independent estimates of the global oceanic uptake of
anthropogenic CO2 for the 1990s and early 2000s
range from 1.8 to 2.4 Pg C year
1 with model-based
values often exceeding observation-based estimates
(Gruber et al. 2009). An uptake of 1.8  1.0 Pg C
year
1 has been obtained by inversion of tropospheric
CO2 (Gurney et al. 2004; adjusted by Gruber et al.
2009), while a net ocean sink of 1.9  0.7 Pg C year
1
has been estimated from a surface water CO2 climatology (Takahashi et al. 2009; adjusted by Gruber et al.
2009). Ocean inversion of DIC has given an oceanic
CO2 sink of 2.2  0.3 Pg C year
1, (Gruber et al.
2009) and 2.4  0.5 Pg C year
1 has been estimated
using ocean biogeochemical models (Watson and Orr
2003). Other methods give a similar range of estimates
(Joos et al. 1999; Gruber and Keeling 2001; Bender
et al. 2005; Manning and Keeling 2006; Jacobson et al.
2007; Gruber et al. 2009).
Measurement and modelling techniques vary in
whether they quantify anthropogenic CO2 fluxes or net
contemporary CO2 fluxes and a correction needs to be
made for the outgassing of carbon from rivers and for

3.2

Surface Ocean Distribution and Air-Sea Exchange of CO2

125

Fig. 3.5 Annual CO2 uptake for the pink shaded ocean area
between the UK and the Caribbean for 2002–2007. The size of
climatological fluxes for the years 1995 and 2000 (Takahashi

et al. 2002, 2009) is indicated on the left axis of the right figure.
(Reproduced from Watson et al. (2009) by permission of
Science)

other natural CO2 fluxes when comparing such flux
estimates (Gruber et al. 2009; Takahashi et al. 2009).
The open ocean source of natural CO2 arising from river
inputs has been estimated as 0.5  0.2 Pg C year
1
(Gruber et al. 2009, after Sarmiento and Sundquist
1992). However, this value could be too high by
~0.2 Pg C year
1 due to the substantial outgassing of
river inputs during estuarine mixing (Sect. 3.2.4).
Figures 3.6 and 3.7 show the spatial distribution of
net contemporary CO2 fluxes as determined from a
pCO2-based climatology (Takahashi et al. 2009),
ocean inversion (Gruber et al. 2009), atmospheric
inversion (Baker et al. 2006) and ocean biogeochemistry models (Watson and Orr 2003). The fluxes
from the four methods are in reasonable agreement
for most ocean regions. The notable exception is the
Southern Ocean (here south of 44 S), where marine
biogeochemistry models predict a much larger CO2
sink than the other methods, mainly as a result of a
weak outgassing of natural CO2 (Mikaloff Fletcher
et al. 2007). A comparison of the ocean inverse
results with the pCO2 climatology shows that while
both methods indicate a similar net contemporary
CO2 sink of 0.3 Pg C year
1 south of 44 S, the
estimates disagree in the spatial distribution of the
flux (Gruber et al. 2009). The climatology-derived
flux estimates indicate a Southern Ocean sink between
44 S and 58 S and a small source south of 58 S
(Takahashi et al. 2009), while the ocean inversion

suggests a more uniform CO2 sink south of 44 S
(Gruber et al. 2009). It is worth noting that the recent
addition of further surface water fCO2 data in the
Southern Ocean, and in particular in seasonally ice
covered waters, has led to a revision of air-sea CO2
flux estimates for 50–62 S (from
0.34 to
0.06 Pg
C year
1) and south of 62 S (from
0.04 to +0.01 Pg
C year
1) in successive climatologies (Takahashi
et al. 2002, 2009).
The separation of contemporary air-sea CO2 fluxes
into natural CO2 fluxes (here excluding river-induced
fluxes), river borne fluxes and anthropogenic CO2
fluxes, using an inversion of interior ocean inorganic
carbon data, is shown in Fig. 3.7 (Gruber et al. 2009).
Natural CO2 fluxes in this study vary from CO2
sources in the tropics and the Southern Ocean to CO2
sinks in global temperate regions and the high latitude
northern hemisphere (Gruber et al. 2009). On a global
scale these natural fluxes (excluding river borne
fluxes) cancel out. Anthropogenic CO2 is taken up by
all ocean regions, with the largest sinks in the tropics
and the Southern Ocean.

3.2.3.4 Sea Ice
Sea ice influences marine DIC cycling and the air-sea
exchange of CO2 through physical processes such as
brine rejection (e.g. Anderson et al. 2004; Omar et al.
2005; Rysgaard et al. 2011) and air-ice-sea exchange
(e.g. Miller et al. 2011) (Chap. 2), in addition to

126

3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

Fig. 3.6 Contemporary annual air-sea CO2 fluxes for the year 2000 from a pCO2 climatology (Reproduced from Takahashi et al. (2009)
by permission of Elsevier)

biological and chemical processes (Delille et al. 2007;
Bakker et al. 2008; Geibert et al. 2010). Although our
understanding of the underlying processes is limited
and quantitative estimates are scarce, the physical processes are thought to result in a net sink for tropospheric
CO2 during sea ice formation in the polar oceans.
Recently, Rysgaard et al. (2011) estimated the net
influx of CO2 into the polar oceans at 33 Tg C year
1,
a flux resulting from the rejection of carbon from the ice
crystal matrix during winter and subsequent formation
of a surface layer of melt-water, undersaturated in CO2
during summer. The sink would be much stronger
(83 Tg C year
1), if CaCO3 crystals form in the sea
ice. Omar et al. (2005) suggested a wintertime CO2 sink
of 5.2 g C m
2 associated with the formation of seasonal sea ice and brine rejection in the Arctic. With a
seasonal sea ice extent of 14  106 km2 (in 2005) this
translated into a wintertime sink of 36 Tg C year
1,
which is on the higher end of estimates of 14 Tg C
year
1 (no CaCO3 precipitation) and 31 Tg C year
1
(with CaCO3 precipitation) for the Arctic Ocean by
Rysgaard et al. (2011). Sea ice related tropospheric
CO2 uptake was estimated as 19 Tg C year
1 for the
Southern Ocean, which would increase to 52 Tg C
year
1, if CaCO3 crystals form in the ice (Rysgaard
et al. 2011). Oceanic CO2 uptake during the seasonal
cycle of sea ice growth and decay is thus equivalent to
17–42 % of net tropospheric CO2 uptake in ice-free
polar seas (Rysgaard et al. 2011).

3.2.3.5 Coastal to Open Ocean
Carbon Exchanges
Exchanges of organic and inorganic carbon between
coastal shelves and the deep ocean remain poorly
quantified (Biscaye et al. 1988; Monaco et al. 1990;
Biscaye and Anderson 1994; Wollast and Chou 2001),
even though such exchange may be an important conduit
for transferring tropospheric carbon to the interior ocean
(Tsunogai et al. 1999; Thomas et al. 2004). For example
an efficient ‘continental shelf carbon pump’, as
proposed for the East China Sea and the North Sea,
critically depends on the off-shelf transport of carbonrich subsurface water (Tsunogai et al. 1999; Thomas
et al. 2004) to below the permanent pycnocline of the
deep ocean (Holt et al. 2008; Huthnance et al. 2009;
Wakelin et al. 2012), but these carbon transports have
not been verified in situ.

3.2.4

Air-Sea CO2 Fluxes in Coastal Areas

3.2.4.1 Continental Shelves
Contemporary air-sea exchange fluxes of CO2 in the
coastal environment have been estimated by adjusting
local flux data to the global scale using procedures of
varying complexity (Box 3.4; Table 3.4). These range
from extrapolating a single flux estimate from a continental shelf to the global scale, such as in the East
China Sea (Tsunogai et al. 1999) or the North Sea

3.2

Surface Ocean Distribution and Air-Sea Exchange of CO2

CONTEMPORARY NET FLUXES

a

CO2 Flux (Pg C yr-1)

127

0.70

Ocean Inversion (this study)
pCO2 climatology

0.50

Atmospheric Inversion (TransCOm-3, L3)
Ocean Forward Models (OCMIP-2)

0.30
0.10

Atlantic

Pacific

Indian

Atlantic

Pacific

Atlantic &
Arctic

-0.10
-0.30
-0.50
Atlantic

Pacific

Indian

-0.70
-0.90

Natural CO2 Fluxes

b

Anthropogenic CO2 Fluxes (1995)

FLUX COMPONENTS

Riverine carbon Outgassing
Contemporary CO2 Fluxes (1995)

CO2 Flux (Pg C yr-1)

0.50
0.30

Atlantic

0.10

Atlantic

Pacific

Pacific

Atlantic &
Arctic

Indian

-0.10
-0.30
-0.50

Atlantic

Pacific

Indian

-0.70
-0.90
<44°S

44°S-18°S

18°S-18°N

Southern
Ocean

Temperate SH

Tropical

18°N-49°N

Temperate NH

>49°N

High Lat.

Fig. 3.7 Air-sea CO2 fluxes for 10 regions per latitude range and
per basin with positive fluxes for CO2 leaving the ocean. (a) Net
contemporary air-sea CO2 fluxes from ocean inversion estimates
(Gruber et al. 2009), a surface ocean pCO2 climatology (Takahashi
et al. 2009), mean estimates from 13 ocean biogeochemistry
models (Watson and Orr 2003) and mean estimates from atmospheric inversion of CO2 (Baker et al. 2006). Error bars for the
ocean biogeochemistry model estimates are the unweighted

standard deviation of the model outputs. The uncertainties in the
atmospheric inversion estimates are based on the square of the
errors within and between models. (b) Natural, anthropogenic,
river-induced and contemporary air-sea CO2 fluxes by ocean
inversion. Error bars indicate the cross-model weighted standard
deviation of the mean. Anthropogenic and contemporary fluxes are
for the nominal year 1995. (Reproduced from Gruber et al. (2009)
by permission of the American Geophysical Union)

(Thomas et al. 2004), to approaches that compiled flux
values for several continental shelf systems with scaling by surface area. Areas have been grouped by
latitudinal bands (Borges 2005; Borges et al. 2005),

oceanic provinces (Cai et al. 2006) and surface areas
derived from bathymetry (Chen and Borges 2009;
Laruelle et al. 2010). The estimate of Laruelle et al.
(2010) was based on a typological approach, whereby

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3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

Box 3.4

For the purpose of this work the coastal zone is defined to include near-shore systems, such as estuaries,
and the continental shelf as far offshore as the 200 m depth contour (Walsh 1988; Gattuso et al. 1998; Liu
et al. 2010). The continental margin consists of the coastal zone and the continental slope (from 200 to
2,000 m depth) (Liu et al. 2010). Inner estuaries are characterised by large salinity gradients, complex
mixing and varying anthropogenic inputs. Laruelle et al. (2010) give a global inner estuarine area of
~1  106 km2. Outer estuaries (estuarine plumes) have restricted salinity ranges and salinities typically
below 34 (Frankignoulle et al. 1998). Estuarine plumes may extend tens of km offshore and may account
for considerable estuarine mixing (Naudin et al. 1997). Barnes and Upstill-Goddard (2011) estimated that
European outer estuaries account for more than 75 % of the total European estuarine area, but there is no
comparable global estimate. Nonetheless, estuarine plumes are generally considered as part of the
continental shelf and there is a concern that the characteristic distributions of CO2, CH4 and N2O in
such plumes may not be sufficiently sampled.

Table 3.4 CO2 fluxes scaled globally for continental shelves and estuaries: n is the number of data points used in the up-scaling
CO2 flux (Pg C year
1)
Continental shelves globally

0.95

0.40

0.37

0.45

0.22

0.34

0.22
Estuaries globally
+0.60
+0.43
+0.32
+0.36
+0.27

CO2 flux (mol m
2 year
1)

Surface area (106 km2)

n

Reference


2.90

1.33

1.17

1.44

0.71

0.92

0.71

27.0
25.2
25.8
25.8
25.8
30.0
24.7

1
1
15
17
29
58
37

Tsunogai et al. (1999)
Thomas et al. (2004)
Borges (2005)
Borges et al. (2005)
Cai et al. (2006)
Chen and Borges (2009)
Laruelle et al. (2010)

13
16
16
32
62

Abril and Borges (2004)
Borges (2005)
Borges et al. (2005)
Chen and Borges (2009)
Laruelle et al. (2010)

+36.5
+38.2
+28.6
+32.1
+21.0

continental shelves were defined as one of three types:
enclosed, upwelling, and open. In these studies the
Arctic Ocean was included in estimates for the coastal
ocean, but other deep marginal seas were excluded.
The first global estimate of the continental shelf sink
for CO2, based on East China Sea data (Tsunogai et al.
1999) was 1.0 Pg C year
1, whereas most recent
estimates converge to a value ~0.3 Pg C year
1 (Chen
and Borges 2009; Laruelle et al. 2010; Cai 2011). While
continental shelves cover less than 10 % of the total ocean
surface area, their air-sea CO2 flux density is about twice
as large (Laruelle et al. 2010) as the global average for the
open oceans based on the most recent CO2 climatology
(Takahashi et al. 2009). This is consistent with higher
biogeochemical reaction rates on continental shelves;

1.40
0.94
0.94
0.94
1.10

rates of net primary production and export production
are twice as high as in the open ocean, for example
(Wollast 1998). Even so, the zonal variability in air-sea
CO2 fluxes over continental shelves (Borges 2005;
Borges et al. 2005; Laruelle et al. 2010) follows the
patterns of the open ocean (Takahashi et al. 2009), with
low latitude continental shelves being CO2 sources and
temperate and high latitude shelves being sinks for tropospheric CO2. This suggests that the direction of air-sea
CO2 fluxes on continental shelves is to some extent
dictated by a “background” signal of “incoming” open
ocean waters, and that the intensity of the flux is further
modulated (enhanced) by biogeochemical processes on
the continental shelf.

3.2

Surface Ocean Distribution and Air-Sea Exchange of CO2

Lee et al. (2011) recently evaluated the anthropogenic
carbon inventory in four marginal seas (Arctic Ocean,
Mediterranean Sea, Sea of Okhotsk, and East/Japan
Sea). These authors conclude that each of these marginal
seas stores proportionally more anthropogenic CO2 than
the global open ocean and they attribute this to a dynamic
over-turning circulation in these marginal seas.

3.2.4.2 Near-Shore Systems
Near-shore systems such as estuaries are known to significantly modify the fluxes of organic carbon from land
to sea (e.g. Smith and Hollibaugh 1993; Gattuso et al.
1998; Battin et al. 2008) and to also emit large quantities
of N2O and CH4 (Sects. 3.3.5 and 3.4.5) (Barnes et al.
2006; Denman et al. 2007; Upstill-Goddard 2011).
Estuaries are also characterised by a net annual emission
of CO2 to the troposphere with intense flux densities
(Frankignoulle et al. 1998). Various estimates of the
global emission of CO2 to the troposphere from inner
estuaries are based on scaling exercises (Table 3.4)
(Abril and Borges 2004; Borges 2005; Borges et al.
2005; Chen and Borges 2009; Laruelle et al. 2010). All
are based on the global surface area estimate of
Woodwell et al. (1973) with the exception of Laruelle
et al. (2010), which is based on the typology of estuaries
from Dürr et al. (2011). The first estimate by Abril and
Borges (2004) of the emission of CO2 to the troposphere
was 0.6 Pg C year
1 and the most recent estimates
converge to ~0.3 Pg C year
1 (Laruelle et al. 2010;
Cai 2011). The estimate of Laruelle et al. (2010) relies
on an estuarine typology with four types (small deltas
and small estuaries, tidal systems and embayments,
lagoons, fjords and fjärds (sea inlets, which have been
subject to glacial scouring, in a rocky area of low topography)). This is an important innovation relative to
previous scaling attempts, since estuarine morphology
and physical structure strongly modulate the exchange
of CO2 with the troposhere (Borges 2005; Koné et al.
2009; Borges and Abril 2011). Fjords and fjärds constitute the most abundant estuarine type (~43 %),
although CO2 flux data have been reported for only
one system. This highlights the limitation of using scaling approaches that are too complex with regards to the
available data, and the need to obtain further data in
near-shore systems to improve estimates of air-sea
exchange of CO2.
In most macro-tidal estuaries the river input of DIC
can only sustain a small fraction of the observed CO2
emission (Borges et al. 2006), implying that the bulk of
estuarine CO2 emission is sustained by the degradation

129

of allochthonous organic matter, in agreement with the
net heterotrophic nature of these systems established
from measurements of community metabolic rates
(Odum and Hoskin 1958; Odum and Wilson 1962;
Heip et al. 1995; Kemp et al. 1997; Gattuso et al.
1998; Gazeau et al. 2004; Hopkinson and Smith
2005). This implies that near-shore coastal environments are effective sites (or ‘bypasses’) for returning
to the troposphere as CO2, a fraction of the carbon
passing from continents (through rivers) to the ocean.
The removal of river borne organic carbon during estuarine transit can be roughly evaluated at ~60 % based
on the above, given a global CO2 emission of ~0.3 Pg C
year
1 from near shore waters (Laruelle et al. 2010; Cai
2011) and known global organic carbon river inputs of
~0.4 Pg C year
1 (Schlünz and Schneider 2000). This is
in general agreement with the analysis of organic carbon in estuaries (e.g. Abril et al. 2002). Such a bypass
of carbon has important consequences for understanding and quantifying the global carbon cycle. For
instance, the pre-industrial ocean is assumed to have
been a CO2 source driven by degradation of river borne
organic carbon (Smith and Mackenzie 1987; Sarmiento
and Sundquist 1992). In budget studies the contemporary ocean air-sea CO2 flux is typically corrected for the
pre-industrial air-sea CO2 flux of 0.5  0.2 Pg C
year
1, so as to derive the anthropogenic CO2 flux
(Sarmiento and Sundquist 1992; Gruber et al. 2009;
Takahashi et al. 2009). However, if most of the degradation of river borne organic carbon occurs in nearshore coastal environments rather than in the open
ocean, this correction may be overestimated by
~0.2 Pg C year
1, corresponding to much of the estuarine CO2 emissions of ~0.3 Pg C year
1 (Laruelle et al.
2010; Cai 2011).

3.2.4.3 Multi-Year Changes and Trends
Based on the decadal analysis of surface water fCO2 in
a very limited number of coastal regions, the coastal
CO2 sink could be increasing in some regions (Wong
et al. 2010), while decreasing elsewhere (Thomas et al.
2007). Gypens et al. (2009) used a model reconstruction of the biogeochemistry of the Southern North Sea
during the last 50 years to evaluate how the change of
river nutrient loads has affected the annual exchange
of CO2 with the troposphere. These authors concluded
that carbon sequestration in the southern North Sea
increased from the 1950s to the mid 1980s due to an
increase in primary production fuelled by eutrophication with an N to P (nitrogen to phosphorus) ratio close

130

3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

to Redfield of 16 to 1 (Redfield et al. 1963). In consequence, the system shifted from a source to a sink of
tropospheric CO2. During this period pH and calcite
saturation increased, rather than decreased as one
would have expected from ocean acidification alone
(Borges and Gypens 2010). During a period of eutrophication reversal from the mid 1980s onwards, in
which river borne nitrogen inputs continued to
increase but phosphorus inputs were reduced, primary
production in the southern North Sea decreased due to
phosphorus limitation and the system shifted back to
being a source of tropospheric CO2. During this
period, the carbonate chemistry changed faster than
that expected from ocean acidification alone, i.e.
ocean acidification was enhanced.

3.3

Marine Distribution and Air-Sea
Exchange of N2O

3.3.1

Global Tropospheric N2O Budget

N2O emissions from oceanic and coastal waters play a
major role in the tropospheric N2O budget (Table 3.5).
According to the IPCC 4th Assessment Report (Denman
et al. 2007) the oceans are a natural N2O source of 3.8 Tg
N year
1 (range 1.8–5.8 Tg year
1), while coastal
waters, estuaries, rivers and streams together are an
anthropogenic N2O source of 1.7 Tg N year
1 (range
0.5–2.9 Tg year
1). These sources thus contribute 20 %
and 10 % respectively, of total global N2O emissions
(Tables 3.2 and 3.5). Considerable uncertainties arise
over these emission estimates for reasons that are
discussed in Sects. 3.3.5 and 3.6. The quantification of
oceanic N2O emissions and the identification of the
marine pathways of N2O formation and consumption
have received increased attention in recent decades
(Bange 2008, 2010b).

3.3.2

Nitrous Oxide Formation Processes

Oceanic N2O is formed exclusively by prokaryotes
(bacteria and archaea) via two major processes: nitrification (i.e. oxidation of ammonium, NH4+, to nitrate,
NO3
) and denitrification (i.e. reduction of NO3
to
N2) (Fig. 3.8). Nitrification is the dominant N2O formation process whereas denitrification contributes about
7–35 % to the overall N2O budget of the oceans

Table 3.5 Anthropogenic and natural sources of N2O to the
troposphere with the range of estimates between brackets
(Denman et al. 2007)
N2O source
(Tg N year
1)
Anthropogenic sources
Fossil fuel combustion and industrial
processes
Agriculture
Biomass burning
Human excreta
Rivers, estuaries, coastal zones
Atmospheric deposition
Total anthropogenic sources
Natural sources
Soils under natural vegetation
Oceans
Atmospheric chemistry
Total natural sources
Total sources

0.7 (0.2–1.8)
2.8 (1.7–4.8)
0.7 (0.2–1.0)
0.2 (0.1–0.3)
1.7 (0.5–2.9)
0.6 (0.3–0.9)
6.7
6.6 (3.3–9.0)
3.8 (1.8–5.8)
0.6 (0.3–1.2)
11.0
17.7

(Bange and Andreae 1999; Freing et al. 2012). The
contributions to oceanic N2O production from other
microbial processes such as dissimilatory nitrate reduction to ammonia (DNRA) are largely unknown. In
general, biological N2O production strongly depends
on the availability of dissolved oxygen (O2). Under
oxic conditions, as found in the majority of oceanic
waters, N2O formation occurs via nitrification. Suboxic
to anoxic conditions, which occur in about 0.1–0.2 % of
the ocean volume, favour the net formation of N2O via
denitrification (Box 3.5) (Codispoti 2010).

3.3.2.1 Denitrification
During denitrification N2O occurs as an intermediate
which can be both produced and consumed. The denitrification pathway consists of the four step reduction of
NO3
to N2 (Fig. 3.8), thus it constitutes a net loss of bioavailable (or “fixed”) nitrogen (N). Denitrification is
catalysed by four independent metallo-enzymes
(Zumft 1997). Both bacterial and archaeal denitrifiers
(Philippot 2002; Cabello et al. 2004) are able to respire
NO3
when O2 becomes limiting. Denitrification may
therefore be considered the ancestor of aerobic respiration (Cabello et al. 2004). The O2 sensitivity of the
enzymes involved in denitrification increases step by
step along the reduction chain. The enzymes are induced
sequentially and a complete denitrification process can
only take place at O2 concentrations below 2–10 μM
(Fig. 3.9) (Codispoti et al. 2005). With the observed

3.3

Marine Distribution and Air-Sea Exchange of N2O

131

N2
nifH

PON

NH4+

amoA

NH2OH

hao

oxic

N2 O
NO2-

NO3-

?

NO3narG

DON

NO2

-

NO2nirS

NO
norB

N2 O

NH4+

hzo

nosZ

N2 H 4

N2

anoxic
Fig. 3.8 The nitrogen cycle in the oceanic water column along
a vertical oxygen gradient. Key functional genes are shown in
blue italic letters for the transformations, the oxycline is

indicated by a horizontal, white dashed line, and archaeal
ammonia-oxidation is indicated by a horizontal, thin, black
dashed-dotted line. (Modified from Francis et al. (2007))

Box 3.5

Dissolved oxygen concentrations play an important role in N2O and CH4 cycling. Here we define hypoxic
or low oxygen conditions as an O2 concentration below ~60 μM, suboxic conditions as an O2 concentration below 5 μM (Deutsch et al. 2011) and anoxic conditions where O2 is undetectable.

expansion of Oxygen Minimum Zones (OMZs) in the
open ocean (Stramma et al. 2010) and the ongoing
deoxygenation of highly productive eastern boundary
upwelling areas (Codispoti 2010), net N2O formation
by denitrification may increase in the future
(Sect. 3.5.3).

3.3.2.2 Nitrification
Under the oxic conditions present in more than 90 % of
the ocean, N2O is formed as a metabolic by-product
during nitrification, the stepwise oxidation of NH4+ to
nitrite (NO2
) by both ammonia-oxidising bacteria
(AOB) and archaea (AOA). Bacteria form N2O during
the oxidation of NH4+ via hydroxylamine (NH2OH) to
NO2
(Fig. 3.8). Alternatively, N2O can be formed during the reduction of NO2
via nitric oxide (NO) to N2O,
the so-called nitrifier-denitrification pathway (Cantera

and Stein 2007). However, the enzymes involved in the
nitrifier-denitrification pathway are different from those
involved in classical denitrification (Sect. 3.3.2.1). The
production of N2O during nitrification increases with
decreasing O2 concentrations (Goreau et al. 1980;
Codispoti et al. 1992). This implies that a significant in
situ N2O production in the upper mixed layer is unlikely,
as this layer tends to be well oxygenated.
Until recently the formation of N2O by nitrification
was regarded as an exclusive property of AOB. This
view has subsequently been revised in the light of recent
work showing that AOA are the key organisms for
oceanic nitrification (Wuchter et al. 2006, 2007) and
that AOA are able to produce N2O in large amounts
(Santoro et al. 2011; Löscher et al. 2012). Experiments
using AOA enriched cultures and pure cultures of
Nitrosopumilus maritimus, as well as onboard incubation

132

3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

a

b

Fig. 3.9 Vertical profiles of (a) dissolved oxygen (circles) and
nitrate (triangles), and (b) nitrite (triangles) and nitrous oxide
(circles) at 19 N 67 E in the Arabian Sea. Note the pronounced
minimum in nitrous oxide within the denitrifying zone,

characterised by a minimum in nitrate and a maximum in nitrite.
Maxima in nitrous oxide are found at the peripheries of this
zone. (Reproduced from Naqvi (2008) by permission of
Elsevier)

experiments with the archaea inhibitor GC7 (Jansson
et al. 2000), demonstrated that AOA are the key
organisms for N2O production (Löscher et al. 2012).
Nevertheless, the precise metabolic pathway remains
unknown. The high affinity of archaea for NH4+
indicates their potential to outcompete AOB even
under the nutrient depleted (oligotrophic) conditions
(Martens-Habbena et al. 2009) which characterise large
areas of the open (surface) ocean. It can thus be
hypothesised that archaeal NH4+ oxidation is the major
source of oceanic N2O formation.

out DNRA (Fazzolari et al. 1990a, b) and are widespread
in the ocean and in other environments. N2O is produced
during the second stage of DNRA, the reduction of
NO2
to NH4+ catalysed by NO2
reductase (Jackson
et al. 1991). Nevertheless, information on the biochemical regulation of DNRA in oceanic environments
remains sparse (Baggs and Philippot 2010).

3.3.2.3 N2O Formation by Dissimilatory
Nitrate Reduction to Ammonium
Dissimilatory nitrate reduction to ammonium (DNRA)
via NO2
is a known source of N2O (Cole 1988), but
was previously considered unimportant in the oceanic
water column. However, it was recently found to significantly impact nitrogen cycling in OMZs (Lam et al.
2009) and so may be a more important source of N2O
than previously thought. A variety of bacteria (Bacillus
sp., Clostridium sp., Enterobacter sp.) are able to carry

Global maps of N2O in the surface ocean have been
computed by Nevison et al. (1995) (N95) and by
Suntharalingam and Sarmiento (2000) (SS00)
(Fig. 3.10). The N95 map is based on more than
60,000 measurements mainly made by the Scripps
Institution of Oceanography between 1977 and 1993.
Oceanic regions with no measurements were filled with
a simple statistical routine. The SS00 map was derived
from the same N2O data set, but employed a multivariate adaptive regression spline method using mixed

3.3.3

Global Oceanic Distribution
of Nitrous Oxide

3.3

Marine Distribution and Air-Sea Exchange of N2O

133

Fig. 3.10 Maps of ΔpN2O (in natm) in the surface layer of the
world’s oceans: (a) map by Nevison et al. (1995) and (b) map by
Suntharalingam and Sarmiento (2000). Note that the colour

coding is non-linear and different for both maps. (Reproduced
from Nevison et al. (1995) and Suntharalingam and Sarmiento
(2000) by permission of the American Geophysical Union)

layer depth, O2, sea surface temperature and upwelling
rate as predictor variables. Differences in the two maps
result mainly from the different computation methods
but they share important common features: (i) enhanced
N2O anomalies (i.e. supersaturation of N2O) in the
equatorial upwelling regions of the eastern Pacific and
Atlantic Oceans and in coastal upwelling regions such
as along the west coasts of North and Central America,
off Peru, off Northwest Africa and in the northwestern
Indian Ocean (Arabian Sea); (ii) N2O anomalies close

to zero (i.e. near equilibrium) in the North and South
Atlantic Ocean, the South Indian Ocean and the central
gyres of the North and South Pacific Oceans. Both maps
are biased by insufficient data coverage in some ocean
regions (for example in the Indian and western Pacific
Oceans). Since the studies of N95 and SS00 the number
of available N2O measurements has been steadily
increasing. With this in mind the MEMENTO (MarinE
MethanE and NiTrous Oxide) initiative was launched
with the aim of collecting and archiving N2O (and CH4)

134

3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

data sets and to provide surface N2O (and CH4) concentration fields for use in deriving emissions estimates
(Bange et al. 2009) (Chap. 5).
Nevison et al. (1995) calculated a global mean N2O
surface saturation of 103.5 %, which indicates that the
global ocean is a net source of N2O to the troposphere.
Apart from the spatial variability of N2O surface
concentrations described above, considerable seasonal
variability has been observed in areas such as the
Greenland and Weddell Seas. This seasonality can be
caused by (i) rapid temperature shifts resulting in
pronounced changes in solubility at a faster rate than
N2O exchange across the air-sea interface (e.g. in the
Greenland Sea) and (ii) mixing of surface waters with
N2O enriched subsurface waters (e.g. in the Weddell
Sea) (Nevison et al. 1995). While a biological source
of N2O in the well-oxygenated mixed layer seems
unlikely, some studies suggested in situ mixed layer
production based on a mismatch between the N2O airsea flux and the diapycnal flux into the mixed layer
(Dore and Karl 1996; Morell et al. 2001). In a recent
study of N2O air-sea and diapycnal fluxes in the
eastern tropical North Atlantic (including the upwelling off Mauritania, NW Africa) the mean air-sea flux,
calculated using a common gas exchange approach,
was about three to four times larger than the mean
diapycnal flux into the mixed layer (Kock et al. 2012).
Neither vertical advection nor biological production
could explain this discrepancy. Kock et al. (2012)
speculated that surfactants may dampen air-sea gas
exchange of N2O and other gases such as CO2
(see for example Tsai and Liu 2003) in areas with a
high biological productivity.

3.3.4

Coastal Distribution
of Nitrous Oxide

During the last two decades coastal areas such as
estuaries, upwelling regions, and mangrove ecosystems
have received increased attention as sites of intense N2O
formation and release to the troposphere. Studies of the
N2O pathways in coastal regions have mostly been
undertaken in European and North American coastal
regions but the number of studies from other coastal
regions (e.g. from Asia and South America) has recently
been increasing. In general, strongly positive N2O
anomalies are found in nitrogen-rich estuaries (Zhang
et al. 2010; Barnes and Upstill-Goddard 2011) and in

coastal upwelling systems (Nevison et al. 2004). Coastal
N2O emissions contribute significantly to the overall
oceanic emission (Table 3.6).
Nitrous oxide saturations in estuaries are highly variable and can reach values up to 6,500 % (Zhang et al.
2010; Barnes and Upstill-Goddard 2011). N2O formation in estuaries heavily depends on the availability of
NH4+ fuelling nitrification in the water column and/or
sedimentary denitrification as major N2O formation
pathways (Bange 2006b; Barnes and Upstill-Goddard
2011). In nitrogen-rich estuarine systems, extremely
high N2O anomalies are usually only found in inner
estuaries, whereas outer estuaries and adjacent shelf
waters, which are not influenced by the river plumes,
are close to equilibrium with the troposphere (Barnes
and Upstill-Goddard 2011). In some European estuaries
maximum N2O concentrations are associated with the
turbidity maximum zone at low salinities (Barnes and
Upstill-Goddard 2011). The traditional view of a simple
relationship between river inputs of dissolved inorganic
nitrogen (the sum of NH4+ and NO3
) and estuarine
N2O formation has been challenged by recent findings
that resuspended NH4+ and/or NH4+ derived from
ammonification of particulate organic nitrogen in the
turbidity maximum zone might dominate N2O production (Barnes and Upstill-Goddard 2011). This implies
that N2O formation may not be related to river inputs of
dissolved inorganic nitrogen in any simple way (Barnes
and Upstill-Goddard 2011). High N2O saturations in
estuaries (and rivers) are also found at sites of sewage
and industrial effluents.
The narrow bands of coastal upwelling systems
such as those found in the northwestern Indian Ocean
(Arabian Sea) and in the southeastern Pacific Ocean
(off central Chile) have been identified as ‘hot spots’
of extremely high N2O concentrations with N2O
saturations of up to 8,250 % and 2,426 %, respectively
(Naqvi et al. 2005; Cornejo et al. 2007). The high N2O
saturations in coastal upwelling regions appear to be
caused by the upwelling of N2O enriched subsurface
waters (Naqvi et al. 2005; Cornejo et al. 2007).
Some coastal upwelling areas show a rapid seasonal
transition from oxic via suboxic to anoxic conditions and
vice versa. In these systems, significant amounts of N2O
(up to several hundred nM) (Fig. 3.11) can accumulate
temporarily during the short transition time, when the
system is changing its oxygen regime. This phenomenon
has been observed at different coastal time-series sites
associated with coastal upwelling, such as off central

3.3

Marine Distribution and Air-Sea Exchange of N2O

135

Table 3.6 N2O emissions from marine waters
Open ocean
Total open ocean
Coastal upwellings
Estuaries
Mangroves
Other coastal
regions
Total coastal regions (incl. upwelling, estuaries
and mangroves)
Total ocean (open ocean and coastal regions)

N2O emission (Tg N year
1)
1.8–5.8
0.6–1.1
0.6–5.8
0.05–0.2
0.0015–0.0035
0.25
0.1
0.08 ( >25 %)
0.4–0.9
0.4–1.45

Area (106 km2)
–
313
348a
1.75
0.4b
1.4c
0.2
10d
48b
13.35e

Reference
Denman et al. (2007)
Rhee et al. (2009)
Nevison et al. (2004)
Rhee et al. (2009)
Kroeze et al. (2005)
Barnes et al. (2006)
Nevison et al. (2004)
Rhee et al. (2009)

1.0–7.25f

361

Estimated as the difference between the total ocean area (361  10 km ) and the sum of the areas (in 106 km2) of coastal upwelling
(1.75), continental shelves with a depth <200 m (10), estuaries (1.4) and mangroves (0.2)
b
Source not given
c
As cited in Nevison et al. (2004)
d
Continental shelves with a depth <200 m
e
Estimated as the sum of the areas (in 106 km2) of coastal upwelling (1.75), continental shelves with a depth <200 m (10), estuaries
(1.4) and mangroves (0.2)
f
Estimated as the sum of the regional minimum and maximum values, respectively
a

6

Chile and off West India, and in the western Baltic Sea
(Naqvi et al. 2010). During the transition stages, the
accumulation of N2O does not occur in the anoxic
zones, but at the oxic to anoxic boundaries. The exact
cause of this extreme accumulation of N2O is not well
understood, although inhibition of the activity of N2O
reductase through frequent incursion of O2 into the
O2-deficient layer has been proposed as one possible
explanation. In anoxic zones, N2O is usually found at
very low or even undetectable concentrations.
Another intriguing feature is the much smaller N2O
accumulation at the upper boundary of the suboxic
zone of enclosed anoxic basins (Black Sea, Baltic
Proper) and some anthropogenically-formed anoxic
zones (Tokyo Bay and Chesapeake Bay). In the hypoxic bottom waters of the East China Sea and the Gulf
of Mexico, the observed N2O build-up is modest.
Overall, these results do not show comparable N2O
build-up in the anthropogenically-formed coastal hypoxic zones to those in naturally-formed, upwellingrelated coastal suboxic zones (Naqvi et al. 2010).
However, a large number of anthropogenicallyformed anoxic zones remain to be investigated.
Mangrove ecosystems cover ~75 % of tropical
coasts and are among the world’s most productive
ecosystems. Their open waters cover ~0.2  106 km2,

equivalent to ~20 % of the global estuarine area (Borges
et al. 2003). The ecosystems of mangrove forests have a
high potential of N2O formation and release to the
troposphere (Barnes et al. 2006). There seems to be
no dominant formation process: N2O in mangrove
sediments from Puerto Rico was mainly produced by
nitrification (Bauzá et al. 2002), whereas incubation
experiments with mangrove soils from the east coast
of Australia revealed denitrification to be the main N2O
formation pathway (Kreuzwieser et al. 2003). In a seasonal study of N2O emissions from a pristine mangrove
creek on South Andaman Island (Gulf of Bengal),
Barnes et al. (2006) found that N2O emissions were
negatively correlated with tidal height, indicating that
N2O (and CH4) is released from sediment pore waters
during “tidal pumping”, i.e. during cyclic decrease and
increase of the hydrostatic pressure between low and
high water (Fig. 3.12; Sect. 3.4.4).

2

3.3.5

Marine Emissions of Nitrous Oxide

The N2O emission estimates in Tables 3.5 and 3.6
imply that coastal areas contribute significantly to
total marine N2O emissions, which is in line with
previous emission estimates (Bange 2006a). The

136

3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

900

W India

800

max. N2O, nmol L-1

700

Chile

600
500

Peru

400

300

Open Ocean

200

Mauritania
Oman

100
0
0

50

100

150

O2, µmol L

Fig. 3.11 Maximum N2O concentrations and associated O2
concentrations in coastal upwelling regions. A typical N2O
surface concentration in the tropical open ocean is also shown.
(Data sources: W. India and Oman – S.W.A. Naqvi, pers. comm.;

a

150

14

800

Water Height, cm

8

-150
16:00 21:00 2:00 7:00

200

150

16:00 21:00 2:00 7:00

Water Height, cm

400

8

16:00 21:00 2:00 7:00

Time, hours

200

16:00 21:00 2:00 7:00

Time, hours

Fig. 3.12 Variation of CH4 (circles), N2O (triangles), tidal
height (squares), O2 saturation (stars) and salinity (diamonds)
in a tropical mangrove creek (Wright Myo, Andaman Island)

4

32

120

24

90
60

6
heavy rain

150

16:00 21:00 2:00 7:00

30

16

Salinity

-50

10

N2O, nmol l-1

0

CH4, nmol l-1

600

8

Time, hours

12

50

16

0

30

% O2 saturation

100

-150

16:00 21:00 2:00 7:00

14

800

24

60

4
Time, hours

Time, hours

120
90

6

-100

32

Salinity

400

-50

10

N2O, nmol l-1

0

CH4, nmol l-1

600

150
% O2 saturation

12

50

-100

250

Mauritania – A. Kock and H.W. Bange, unpublished; Chile –
Cornejo et al. (2006); Peru – C.R. Löscher and H.W. Bange,
unpublished)

100

b

200

-1

8
0

Time, hours

during (a) the dry season (January 2004) and (b) the wet season
(July 2004) (Reproduced from Barnes et al. (2006) by permission
of the American Geophysical Union)

3.4

Marine Distribution and Air-Sea Exchange of CH4

emission estimates for coastal areas (upwelling
regions, shelves, estuaries and mangroves) have a
large uncertainty because of the small number of
available measurements. In particular, natural coastal
suboxic zones are strong N2O sources to the troposphere. The total N2O emission from these areas could
be as high as 0.56 Tg N year
1 (Naqvi et al. 2010),
comparable to global emissions from estuaries
(0.25 Tg N year
1) and continental shelves
(0.4–1.45 Tg N year
1) (Table 3.6) (Seitzinger and
Kroeze 1998). As is the case for open ocean emissions
(see below), flux estimates from coastal areas need to
consider seasonal variability (Wittke et al. 2010;
Zhang et al. 2010; Barnes and Upstill-Goddard 2011).
The recent open ocean estimate by Rhee et al.
(2009) is considerably lower than that of the widely
used IPCC 4th Assessment Report (Table 3.6)
(Denman et al. 2007). However, because the estimate
by Rhee et al. (2009) is based on a single meridional
transect in the Atlantic Ocean, it almost certainly
includes an unquantified seasonal and regional bias.
Unfortunately, the seasonality of surface water
N2O concentrations over large regions of the ocean
remains unknown because ship campaigns are limited
in space and time and N2O sensors are not yet available on gliders, floats or moorings. Neglecting the
seasonality of surface N2O concentrations introduces
severe bias into the N2O flux estimates. Freing (2009)
demonstrated how both N2O concentrations in surface
water and N2O fluxes in the North Atlantic Ocean
(19–42 N) follow a seasonal cycle similar to that of
fCO2 (Fig. 3.13; Sect. 3.2.3). This seasonal cycle can
be described by a harmonic function and is mainly
controlled by temperature. The presence of such seasonal variation renders a mean flux, if calculated from
a seasonally-biased dataset, a potentially poor estimate
of the true annual net flux (Freing 2009). Integrating
the harmonic function over a full annual cycle gives a
better estimate of the net annual flux.
In ocean regions where the upper boundary of the
OMZ is shallow, minor changes in the hydrographic or
meteorological conditions can lead to entrainment of
N2O from the OMZ into the surface layer, thereby
enhancing N2O sea-to-air fluxes (Naik et al. 2008). As
a result of high N2O concentrations close to the sea
surface, N2O emissions in open-ocean regions with
substantial N2O accumulation at mid-depth (associated
with O2 depletion) (e.g. in the Eastern Tropical South
Pacific and the Arabian Sea), are quite high. The N2O
emissions from these regions (0.8–1.35 Tg N year
1)

137

(Naqvi et al. 2010) make up a significant fraction of the
overall N2O emission from the oceans (Table 3.6).

3.4

Marine Distribution and Air-Sea
Exchange of CH4

3.4.1

Global Tropospheric CH4 Budget

In contrast to the situation for CO2 and N2O, the marine
system plays a relatively minor role in the global tropospheric CH4 budget, representing a small net natural
contribution (Tables 3.2 and 3.3). However, in common
with other global CH4 sources, marine-derived CH4 has
proven difficult to quantify with any great certainty
(Table 3.3). Although detailed CH4 surveys in specific
ocean basins have been available since the 1970s, they
are comparatively limited in number and many of the
early measurements were derived in the absence of
reliable solubility data (Reeburgh 2007). The global
marine CH4 dataset is thus rather limited in comparison
to CO2 or N2O. Detailed maps of the global surface
ocean distribution remain to be compiled, with the
recent MEMENTO initiative (Bange et al. 2009) working towards this goal (Chap. 5).

3.4.2

Formation and Removal Processes
for Methane

Methanogenesis is the final stage of organic matter
decomposition and is a form of anaerobic respiration
carried out exclusively by single celled archaea whose
growth is severely O2-limited. The terminal electron
acceptor is therefore not O2, but carbon from low
molecular weight compounds. Carbon dioxide and
acetic acid (CH3COOH) are the most familiar:
CO2 þ 4H2 ! CH4 þ 2H2 O

(3.11)

CH3 COOH ! CH4 þ CO2

(3.12)

Other low molecular weight compounds acting as
methanogenic substrates include formic acid (HCOOH),
methanol (CH3OH), methylamine (CH3NH2), dimethylsulphide (CH3SCH3) and methanethiol (CH3SH). Unsurprisingly, anoxic coastal marine sediments (Middelburg
et al. 1996) and strongly O2-deficient waters (Naqvi et al.

138

3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

20

15

2

ΔpN O [natm]

10

5

0

−5

−10

−15

Jan

Feb Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov Dec

Month
Fig. 3.13 ΔpN2O (in natm) in the North Atlantic Ocean
(19–42 N 10–66 W): Tropical (red triangles), western subtropical (green diamonds) and eastern subtropical (blue circles)
regions. The solid black line denotes a fitted harmonic function.

The dotted lines denote the respective annual mean for all
data points (black, middle line), the western (green, bottom
line) and the eastern (blue, top line) basin. (The figure is from
Freing (2009))

2005) are major sites of methanogenesis. The ability
of sulphate-reducing bacteria (SRB) to outcompete
methanogens (Capone and Kiene 1988) means that
CH4 concentrations are generally low in near-surface
sediment pore waters where sulphate reduction is active
and that they are maximal below the depth where
dissolved sulphate (SO42
) becomes fully depleted
(Blair and Aller 1995). This can be anywhere between
several centimeters and several meters below the sediment surface. The role of SRB is an important consideration in estuaries, where water column SO42

concentrations vary from micro- to milli-molar along
the salinity gradient. In these situations the depth of
SO42
depletion increases and maximal CH4 production
generally decreases seaward. Methanogenesis rates have
thus been shown to decrease by up to two orders of
magnitude seaward (Abril and Borges 2004). In addition
to SO42
availability and associated SRB activity, rates
of organic matter sedimentation and the availability
of alternative electron acceptors also influence
methanogenesis rates.

Despite inhibition of methanogenesis by both O2 and
SO42
supply, CH4 is typically supersaturated in the
open ocean mixed layer (Sect. 3.4.3). Until recently
the prevailing explanation for this so-called “marine
CH4 paradox” has been methanogenesis (Eqs. 3.11
and 3.12) within “anoxic microniches” inside zooplankton guts and suspended particles (Oremland 1979; De
Angelis and Lee 1994). This notion is supported by an
“oxic” methanogenic archaea isolated from coastal
waters (Cynar and Yayanos 1991), the identification of
methanogens in marine zooplankton guts and particles
(Marty et al. 1997) and CH4 release from sinking
particles inferred from δ13CH4 measurements
(Sasakawa et al. 2008). Even though significant CH4
release has been observed from mixed zooplanktonphytoplankton cultures (De Angelis and Lee 1994) and
correlations of CH4 with primary productivity
indicators, such as chlorophyll a, have been found
(Oudot et al. 2002), such correlations are weak
(Upstill-Goddard et al. 1999; Holmes et al. 2000;
Forster et al. 2009).

3.4

Marine Distribution and Air-Sea Exchange of CH4

Recent investigations propose two alternative CH4
production mechanisms, both implicating nutrient limitation in the control of mixed layer CH4 formation. In
the first hypothesis bacterioplankton successfully
exploit phosphate-depleted waters, where nitrate is in
excess, by deriving phosphorus from phosphonates such
as methyl phosphonate (Karl et al. 2008). Methane is
thus produced aerobically as a byproduct of methyl
phosphonate decomposition. The second hypothesis
proposes that certain microbes can catabolise dimethylsulphoniopropionate (DMSP) as a carbon source
through methylotrophic methanogenesis in NO3

depleted waters, where phosphate (PO43
) is plentiful
(Damm et al. 2010). Based on the conversion of DMSP
to hydrogen sulphide (H2S) and CH3SH by DMSPutilising bacteria (Kiene et al. 2000) and a recent proposal for the intermediate formation of CH3SH during
CH4 oxidation (Moran et al. 2008), Damm et al. (2010)
proposed a thermodynamically plausible reverse reaction for the aerobic production of CH4 from CH3SH.
The two mechanisms are entirely compatible and raise
the intriguing possibility that deviations from the
Redfield N:P ratio could be indirectly responsible for
the marine CH4 paradox through planktonic succession
favouring species able to exploit alternative marine
phosphorus and nitrogen stores.
Whatever the mechanisms responsible for upper
ocean marine CH4 production, emission of CH4 to
the troposphere is strongly moderated by aerobic and
anaerobic microbial oxidation (Boetius et al. 2000).
Aerobic CH4 oxidation occurs in oxygenated water
columns and oxic sediment pore waters:
CH4 þ 2O2 ! CO2 þ 2H2 O

(3.13)

Anaerobic oxidation of methane (AOM) occurs both in
anoxic sediment pore waters and in anoxic water
columns and is believed to involve consortia of archaea
and SRB:
CH4 þ SO4 2
! HCO3
þ HS
þ H2 O
SO42
–CH4

139

3.4.3

Global Oceanic Distribution
of Methane

Although CH4 concentrations in the open ocean are
generally rather low (a few nM), net mixed layer CH4
production means that O2-saturated near-surface waters
are generally also supersaturated in CH4, with typical
values of 130–160 % and maxima near the base of the
mixed layer (Oudot et al. 2002; Forster et al. 2009).
Considerably higher mixed layer CH4 supersaturation
is, however, not uncommon. Figure 3.14 illustrates this
for the upper 300 m of the water column on north–south
Atlantic Ocean transects. In addition to upper ocean
CH4 production, lateral supply from continental
margins (Sect. 3.4.4) has also been invoked to explain
high mixed layer CH4 concentrations (Reeburgh 2007).
Similar to N2O, elevated CH4 levels are also found
in intermediate waters (~500–1,000 m depth) of the
three major open ocean OMZs: the Eastern Tropical
North and South Pacific and the Arabian Sea (Naqvi
et al. 2010), and in the upwelling zones associated
with these OMZs (Sect. 3.3.5). In these regions seasonal upwelling of nutrient-rich waters fuels primary
productivity and enhances the downward flux of
biogenic particles (Rixen et al. 1996), leading to O2
consumption in the intermediate waters and
subsequent methanogenesis.
Below the ocean mixed layer and away from the
OMZ’s, CH4 concentrations progressively decrease
through oxidation, such that CH4 concentrations may
approach undetectable levels in the deep ocean basins
(Upstill-Goddard et al. 1999; Yoshida et al. 2011).
Occasionally this deep water CH4 signal impacts surface waters through oceanic upwelling, as is illustrated
by the bottom panel of Fig. 3.14 where the effect of

equatorial upwelling is evident as far north as ~15 N.
Overall, on an annual basis the open ocean CH4 budget
is considered to be in steady-state with in situ production and vertical transport balancing CH4 oxidation and
emissions to the troposphere.

(3.14)

In sediment pore waters the
transition
constrains upward CH4 diffusion and leads to pore
water CH4 profiles having a concave-upward shape
(Blair and Aller 1995).

3.4.4

Coastal Distribution of Methane

3.4.4.1 Coastal Sediments
The total mass of CH4 in shallow marine sediments
remains unquantified but it is nevertheless thought to
be substantial, with methanogenesis considered likely
in at least 30 % of the global continental shelf area (Judd

140

3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

Fig. 3.14 CH4 saturation in the upper 300 m of the Atlantic Ocean between 50 N and 52 S during 2003. Top panel: April–May;
bottom panel: September–October. Dots represent water samples. (Reproduced from Forster et al. (2009) by permission of Elsevier)

and Hovland 2007), i.e. at least 7.4  106 km2
(Laruelle et al. 2010). Although it is estimated that
more than 90 % of the sediment CH4 inventory may
be consumed by AOM (Sect. 3.4.2) prior to sedimentwater exchange (Dale et al. 2006), CH4 emissions from
individual sites can be very high per unit area
(Middelburg et al. 1996; Abril and Iversen 2002) and
can significantly impact the CH4 signal in the overlying
water. Figure 3.15 is an example from a UK estuary
(Tyne) where the broad CH4 maximum between
salinities 5 and 20 may reflect inputs from anoxic intertidal mudflats in mid-estuary (Upstill-Goddard et al.
2000 and discussion below).
In some circumstances organic carbon burial may
be sufficiently intense that the resulting rate of
methanogenesis contributes to raising the total pore

water gas concentration above the hydrostatic pressure
in the sediment, with the result that gas bubbles are
formed (Wever et al. 1998). It has been shown that
bubbles start to form at CH4 concentrations well below
its solubility (~1 mM) and that these may contain
~40–100 % CH4 (Chanton et al. 1989). This results
in a rapid, episodic release of CH4 enriched bubbles to
the water column (ebullition) and potentially directly
to the troposphere with minimal oxidation (Dimitrov
2002). CH4 ebullition may typically exceed the diffusional sediment CH4 flux by more than an order of
magnitude (Ostrovsky 2003; Barnes et al. 2006;
Nirmal Rajkumar et al. 2008). However, ebullition is
notoriously difficult to quantify because spatial and
temporal variability can confound attempts to accurately capture a representative sample. Perhaps

3.4

Marine Distribution and Air-Sea Exchange of CH4

141

Fig. 3.15 Dissolved CH4 in the Tyne estuary, UK. (a) CH4
versus salinity; (b) CH4 versus distance from the tidal limit
(positive is downstream; negative is upstream). Dotted line:

location of major tributary (Derwent); arrows: locations of additional freshwater discharges (Reproduced from Upstill-Goddard
et al. (2000) by permission of the American Geophysical Union)

unsurprisingly, few studies have directly studied sediment CH4 ebullition and hence the controlling processes have not been well quantified. Temperature is
clearly important through solubility effects and its
control of methanogenesis rates, as is water depth. In
coastal waters ~3 m deep, minimal bubble dissolution
was observed during migration to the air-sea surface
(Martens and Klump 1980), but in deeper waters complete bubble dissolution may occur, before the bubbles
reach the sea surface (Joyce and Jewell 2003). Contamination of CH4-enriched bubbles by surfactants can
also significantly reduce their rise velocities, thereby
increasing the potential for dissolution (Leifer and
Patro 2002). However, surfactants on the bubble
surfaces will also decrease the rate of gas exchange
between the bubbles and the water (e.g. Tsai and Liu
2003). Shakhova et al. (2010) reported bubbles of CH4
entrapped in fast sea ice in the East Siberian Arctic
Shelf, which they attributed to ebullition from underlying sediment. If sea ice acts to moderate the emission
of CH4 in this way, this has clear implications for
Arctic CH4 emissions as a consequence of sea ice
retreat.

Vegetation strongly impacts the distribution and
transport of CH4 in coastal sediments. It has been
proposed that plant-mediated CH4 transport and CH4
ebullition are mutually exclusive processes in tidal
marshes (Van der Nat and Middelburg 1998, 2000).
Vegetation impacts CH4 concentrations in coastal
sediments via the release of labile organic compounds
that may stimulate methanogenesis. Vegetation also
acts as a conduit for the transport of CH4 to the
troposphere and for transport of tropospheric O2 to
the rhizosphere favouring CH4 oxidation, both transport pathways reducing CH4 concentrations in nearsurface sediments (Van der Nat and Middelburg 1998,
2000; Biswas et al. 2006). In addition, plants promote
CH4 oxidation at depth, where methanotrophs occur
adjacent to or within macrophyte roots (Gerard and
Chanton 1993; King 1994). Plant-mediated CH4 transport is both passive via molecular diffusion and active
via convective flow due to pressure gradients and is
maximal during daylight hours in the growing season
(Van der Nat et al. 1998; Van der Nat and Middleburg
2000). In two Tanzanian mangrove systems sedimentto-air CH4 fluxes were enhanced up to fivefold in the

142

3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

presence of pneumatophores (above ground root
systems) and it was estimated that transport via this
pathway accounted for 38–64 % of the total sediment
CH4 source to air at low tide (Kristensen et al. 2008).
In some situations both diffusive CH4 exchange and
ebullition can be enhanced by “tidal pumping” related
to falling hydrostatic pressure (Sect. 3.3.4) (Barnes
et al. 2006), with pressure changes of only a few
percent having a large effect (Ostrovsky 2003).
Figure 3.12 shows an inverse relation of CH4
(and N2O) concentrations with tidal height in a tropical mangrove creek. This indicates tidal pumping with
CH4-rich sediment pore waters seeping into the
overlying creek waters at low tide, but remaining in
the sediment as the hydrostatic pressure rises again.
Barnes et al. (2006) suggested that tidal pumping is a
major control of CH4 and N2O emissions from mangrove systems (Sect. 3.3.4).
Ancient microbial, thermogenic, and abiogenic CH4
in coastal shelf sediments can become “geologically
focussed”, which may result in episodic CH4 ebullition
on a potentially large scale (Judd and Hovland 2007).
The ephemeral nature of these “seep fluxes” is well
illustrated by observations at a seep site in the
outer Firth of Forth (North Sea). Dissolved CH4
concentrations in the water column strongly increased
towards the shallow seabed (~1,500 % CH4 saturation at
90 m), but 1 year later the dissolved CH4 concentration
was only mildly supersaturated (Upstill-Goddard 2011).
Shallow seeps clearly influence surface water CH4
concentrations (Damm et al. 2005; Schmale et al.
2005). The North Sea has numerous well-documented
“pock marks” (Dando et al. 1991); evidence for significant past CH4 seepage.

3.4.4.2 Coastal Waters
Inland waters (lakes, reservoirs, streams, and rivers) can
be substantial CH4 sources to the coastal zone, although
they are currently not well integrated in global greenhouse gas budgets (Table 3.3) (Bastviken et al. 2011).
Most rivers studied to date are highly CH4 supersaturated, including pristine, well-oxygenated regimes with
minimal sediment cover or anthropogenic disturbance
(Upstill-Goddard et al. 2000). Large CH4 inputs from
adjacent forest and/or agricultural soils have been
suggested (Devol et al. 1990; Yavitt and Fahey 1991).
The CH4 concentration in river water is a complex
function of catchment hydrology, vegetation cover,
microbial activity, and re-aeration rates. Upstill-

Goddard et al. (2000) found a general decrease in
dissolved CH4 with increasing river discharge. Their
compilation of published CH4 saturations in
rivers worldwide revealed a typical range of
~1,000–40,000 %, with one extreme value for an
organic-rich Amazon tributary exceeding 400,000 %
(Upstill-Goddard et al. 2000). Data from 474 freshwater
ecosystems point to a major CH4 source in inland
waters (Bastviken et al. 2011). Part of this contribution
is almost certainly included in the source estimate for
global wetlands (Table 3.3). The CH4 source from
inland waters should perhaps be formally specified as
an important component in the global tropospheric CH4
budget.
Most estuarine CH4 data are for temperate systems.
Methane saturations of up to 8,000 % have been
reported for some shallow coastal embayments
(Ferrón et al. 2007; Kitidis et al. 2007). A CH4 saturation of up to 20,000 % may be typical of the mid- to
upper-inner estuary (Upstill-Goddard et al. 2000;
Abril and Iversen 2002; Middelburg et al. 2002). As
much as 158,000 % CH4 saturation has been reported
in the Sado estuary, Portugal (Middelburg et al. 2002).
The highest value recorded exceeds 3,000,000 % for
the small, polluted subtropical Aydar estuary in SE
India (Nirmal Rajkumar et al. 2008). This may be
considered exceptional, resulting from a high organic
carbon input in fresh domestic organic wastes
and intense sediment methanogenesis. Indeed,
methanogenesis rates were estimated to be close to
maximal for the ambient temperature (Nirmal
Rajkumar et al. 2008).
An important aspect of CH4 cycling in many wellmixed macrotidal estuaries is strong tidal asymmetry
with the velocity of the flood tide exceeding that of the
ebb tide. This gives rise to the net transport of
suspended particles upstream and retains river borne
particles in a well defined turbidity maximum zone at
low salinity (Uncles and Stephens 1993). High microorganism numbers associated with the suspended
particles (Plummer et al. 1987) and long particle
lifetimes promote enhanced biogeochemical cycling
in such regions and CH4 concentrations can significantly exceed those in the input rivers (Fig. 3.15)
(Upstill-Goddard et al. 2000). While this could result
from active CH4 release from the underlying
sediments during particle resuspension (as discussed
above), it could also reflect in situ water column production of CH4 through attachment of methanogenic

3.4

Marine Distribution and Air-Sea Exchange of CH4

archaea to tidally suspended particles in the turbid,
O2–poor waters, analogous to the “anoxic microniche”
hypothesis in the open ocean (Barnes and UpstillGoddard 2011). By contrast, Abril et al. (2007)
found significant CH4 oxidation in laboratory sediment suspensions, although at higher turbidity than
measured in situ by Upstill-Goddard et al. (2000).
The conflicting results may reflect competition
between methanogenesis and oxidation, implying
complexity in the relationship between CH4
concentrations and estuarine turbidity.
Mangrove ecosystems are significant contributors to
the marine source of tropospheric CH4 (Fig. 3.12;
Table 3.7). Typically one half of mangrove net primary
production is retained within the system (Dittmar et al.
2006; Bouillon et al. 2008) and this carbon is buried
and/or recycled, resulting in significant CH4 production
in heterotrophic sediments and in the overlying water.
The mangrove CH4 source appears rather constant
throughout the year, possibly as a result of small annual
temperature excursions in mangrove systems that give
minimal variability in methanogenesis rates (Barnes
et al. 2006; Ramesh et al. 2007). CH4 saturations in
waters surrounding mangroves are spatially and temporally variable. Up to ~30,000 % saturation was observed
in mangrove creek waters (Barnes et al. 2006), but
regionally ~2,000–3,000 % might be more typical
(Biswas et al. 2007).
Coastal shelf seas are almost always supersaturated
in CH4. Data for coastal shelf seas tend to be restricted
to the temperate northern hemisphere with hardly any
data available for coastal waters along much of the
Russian Arctic, South America, East and West Africa
and Antarctica. Bange (2006b) provides the most
extensive regional data compilation to date, for a
European shelf area estimated at ~3  106 km2 or
~12 % of the global shelf sea area (Table 3.8). The
variability between sites may partly reflect seasonal
variation, but it is not straightforward to assess the
extent of this seasonality due to a lack of seasonal
measurements at individual locations.
Marine sediments are a major CH4 source to coastal
waters, in spite of the inhibition of methanogenesis by
SRB and CH4 oxidation by O2 and SO42
. Indeed,
substantial accumulation of dissolved CH4 in welloxygenated bottom waters overlying organic-rich
coastal sediments is well documented (Martens
and Klump 1980). Order of magnitude higher
concentrations have been observed in anoxic bottom

143

waters overlying sediments with moderate organic carbon content in the Arabian Sea (Jayakumar et al. 2001).
Production of CH4 in sediments and its supply to bottom
waters is primarily linked to biological productivity in
the overlying waters, with hypoxia in the water exerting
a secondary effect (Bange et al. 2010a). Most regions
with high productivity are associated with coastal
upwellings, the extent of which is set by Laruelle et al.
(2010) at ~2.3  106 km2. While this is only ~0.6 % of
the total ocean area, these regions are “hot spots” of
CH4 emissions to the troposphere, with typical surface
CH4 saturations of ~150–250 % (Naqvi et al. 2005;
Kock et al. 2008). Maximal water column CH4 accumulation occurs in sulphidic deep waters within
enclosed basins, such as the Black Sea and the Cariaco
Basin, but this CH4 is largely of geological origin
(Kessler et al. 2005). By contrast, over the highly productive Namibian shelf, where sulphidic bottom water
is also characteristic, the large CH4 emission from
underlying organic-rich sediments is a consequence of
contemporary methanogenesis (Brüchert et al. 2009). In
coastal zones, where the hypoxic conditions are caused
by anthropogenic activities, the CH4 distribution
is highly variable. For example, maximum CH4
concentrations in the Gulf of Mexico (Kelley 2003)
are ~15 times higher than in the Changjiang Estuary
and the East China Sea (Zhang et al. 2008).

3.4.4.3 Methane Hydrates
Methane hydrate is a quasi-stable solid, resembling ice,
in which CH4 molecules are trapped within the crystalline structure of water. Hydrate stability decreases with
increasing temperature and decreasing pressure
(Kvenvolden 1993). Hydrate occurs extensively in
buried sediments and seabed outcrops along continental
margins where water depths exceed 500 m (Beauchamp
2004) and along gravitationally unstable regions of the
continental slope (Fig. 3.16). The distribution and
extent of stable CH4 hydrate can be predicted from in
situ temperature and pressure, such that a theoretical gas
hydrate stability zone (GHSZ) can be defined.
Figure 3.17 is a schematic of the GHSZs in sediments
at shallow and deep marine sites. The CH4 source may
be biogenic, volcanic, hydrothermal or thermogenic.
Biogenic CH4 from sediment methanogenesis tends to
dominate (Sloan 2003).
Methane hydrates can be categorised into two broad
types: structural and stratigraphic. The formation of
structural hydrates involves migration of CH4 along

144

3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

Table 3.7 CH4 emissions from marine waters
Open ocean
Coastal upwellings
Continental shelves
Estuaries
Mangroves
Continental margin seeps

Area (106 km2)
334
2.3
24.7
1.0
0.2
?

CH4 emission (Tg C year
1)
0.3
0.02–0.15
0.38–7.3
0.08–2.3
1.7
7.5–36

Total

362.2

10–48

Reference
Bates et al. (1996b)
Naqvi et al. (2005), Kock et al. (2008)
EPA (2010)
Upstill-Goddard (2011)
Barnes et al. (2006)
Hornafius et al. (1999)
Kvenvolden and Rogers (2005)

Table 3.8 CH4 saturations in European shelf surface waters (excluding estuaries). SD standard deviation (Modified from
Bange (2006b))
Region
Barents Sea
Baltic Sea
Southern N. Sea
German Bight
Southern Bight
Central N. Sea
UK East coast
Bay of Biscay
Adriatic Sea
E. Ionian Sea
N. Aegean Sea
NW Black Sea
Average

Date
Aug 1991
1992a
Nov 1980
Aug 1993
Sep 1991
Mar 1989
May 1994
1995–1999
Nov 1980
Aug 1996
Jul 1993
Jul 1993
Jul 1995

CH4 saturation (%) (range or SD)
120 (115–125)
254 (113–395)
140
338 (118–701)
126  8
113 (95–130)
215 (120–332)
129 (112–136)
100
425 (420–450)a
148  22
231  32
567
222  142

Reference
Lammers et al. (1995)
Bange et al. (1994)
Conrad and Seiler (1988)
Upstill-Goddard et al. (2000)
Bange et al. (1994)
Scranton and McShane (1991)
Rehder et al. (1998)
Upstill-Goddard et al. (2000)
Conrad and Seiler (1988)
Leip (1999)
Bange et al. (1996)
Bange et al. (1996)
Amouroux et al. (2002)

a

Includes seasonal/interannual sampling

geological faults from deep sources and subsequent
crystallisation on seawater contact (O’Connor et al.
2010). This can lead to accumulation of high CH4
concentrations in domes or underneath impermeable
sediments (Archer 2007). Structural CH4 hydrates
occur at relatively shallow depths and are typically
“massive”, i.e. they displace sediment to generate
large hydrate chunks potentially filling tens of percent
of the sediment volume (Tréhua et al. 2004). However,
the majority of hydrate deposits are stratigraphic.
These deposits are typically dilute, accounting for
only a few percent of the sediment volume and are
generally located some hundreds of metres below the
sea floor. Gornitz and Fung (1994) further drew a
distinction between marine hydrate synthesis in
“passive” and “active” margins. Local sediment accumulation constrains hydrate formation in passive
margins, whereas scavenging of organics deriving
from adjacent areas leads to higher hydrate abundance

in active margins. Hydrate formation is also sensitive
to the O2 concentration in the overlying water: a 40 μM
decrease in the deep water O2 concentration may
enhance the CH4 inventory twofold (Buffett and
Archer 2004).
Knowledge of the true extent of marine CH4 hydrate
remains incomplete. Direct observations from well logs
(e.g. Sloan and Koh 2008) are limited in number.
Interpretations based on sea floor organic carbon
derived from sea surface chlorophyll a can be unsuccessful (Gornitz and Fung 1994) and indirect hydrate
detection via seismic profiling may prove similarly
inconclusive (Sloan 2003). The perceived CH4 hydrate
inventory has been downscaled by 3–4 orders of magnitude since the 1970s due to improvements in hydrate
understanding and detection. An early, widely adopted
global estimate of ~104 Pg C in CH4 hydrate, including
a small contribution from terrestrial permafrost
(Kvenvolden 1999), was more than twice the known

3.4

Marine Distribution and Air-Sea Exchange of CH4

145

Fig. 3.16 (a) Distribution of known and inferred methane hydrate accumulations; (b) estimated thickness of the gas hydrate
stability zone (GHSZ) in seafloor sediments (Reproduced from Krey et al. (2009) by permission of IOP publishing Ltd.)

fossil fuel carbon inventory. The most recent data synthesis gives a much lower estimate (170–1,000 Pg C)
for marine CH4 hydrate and a similar amount in
associated free CH4 bubbles (Archer 2007; Archer
et al. 2009). O’Connor et al. (2010) set an order of
magnitude uncertainty on these figures.

In excess of 250 CH4 bubble plumes were recently
observed over the GHSZ west of Spitsbergen, an area
showing ~1  C warming of bottom waters during the
last 30 years (Westbrook et al. 2009). These bubble
plumes were interpreted as upward migrating free gas
formerly trapped below the GHSZ, implying a strong
link between current warming and hydrate dissociation,

146

3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

Fig. 3.17 The gas hydrate stability zone (GHSZ) associated
with (a) shallow water and (b) deep water marine sediments.
GHSZ volume is determined by the local geothermal gradient.

Globally this is ~ 25–30 K km
1, but it can show significant
regional variability. (Adapted from O’Connor et al. (2010) by
permission of the American Geophysical Union)

a conclusion later supported by modelling (Reagan and
Moridis 2009). Lamarque (2008) calculated a potential
CH4 release at the sea floor of 420–1,605 Tg C year
1
following hydrate destabilisation from a doubling of
tropospheric CO2. Adjusting for 1 % CH4 leakage as
observed at a large field site (Mau et al. 2007), this
value was reduced to 4–16 Tg C year
1. However these
estimates do not take account of subsequent CH4 oxidation in the water column, which can easily account
for 90 % of the CH4 released from the sea floor
(Dale et al. 2006). Consequently, estimating the current
tropospheric CH4 flux from hydrate sources involves
large uncertainty (Table 3.3).

influenced, the available information suggests that these
regions are currently only minor contributors to total
coastal CH4 emissions (less than 0.026 Tg C year
1),
although this estimate does not include a contribution
from ebullition (Naqvi et al. 2010).
It is noteworthy that the low end of the range of
total marine CH4 emissions in Table 3.7 is at the high
end of the range given in the IPCC 4th Assessment
Report, as shown in Table 3.3 (Denman et al. 2007).
The discrepancy can be largely accounted for by CH4
emissions from continental margin seeps, which are
not well characterised in the IPCC synthesis, but
which could be the dominant contributor to the marine
source of tropospheric CH4. Such a large seep source
is compatible with the notion of a “missing” fraction
of fossil CH4 (~56  11 Tg C year
1) deduced from
tropospheric 14C data (Crutzen 1991). The uncertainty
in the seep estimate is compounded by the episodic
nature of the seeps and by the migration of source
regions on the sea floor (Kvenvolden and Rogers
2005). There is also large uncertainty in the spectrum
of bubble sizes emitted and dissolution of the gas
phase into seawater (Judd and Hovland 2007). Moreover, most current estimates of seep CH4 emissions are
indirect and involve large extrapolations. Overall, CH4
emissions in seeps have a large uncertainty.

3.4.5

Marine Emissions of Methane

Marine CH4 emissions reflect the balance of rates of
formation, removal and transport. Table 3.7 emphasises
the dominance of coastal over open ocean waters in the
marine CH4 budget, reinforcing the conclusions of an
early synthesis in which more than 75 % of marine CH4
emissions were ascribed to coastal waters (Bange et al.
1994). The total CH4 emission from open ocean areas,
experiencing O2 depletion in the water column, is rather
small (0.3 Tg C year
1) (Bates et al. 1996b). Similarly,
in coastal zones where hypoxia is anthropogenically

3.5

Impact of Global Change

Estimating CH4 emissions from estuaries and mangrove sites (Table 3.7) is compounded by uncertainties
over ebullition rates. The importance of this mechanism is unique to CH4 among reactive trace gases, as it
results from intense methanogenesis in sediments.
Importantly, although ebullition can account for
more than 90 % of CH4 emissions at some locations
(Ostrovsky 2003; Barnes et al. 2006; Nirmal Rajkumar
et al. 2008), it is excluded from routine air-sea emission estimates based on gas exchange relations applied
to dissolved gas gradients (Upstill-Goddard 2006). In
tidal estuaries and mangrove settings some fraction of
the CH4 emission occurs directly from sediments during emersion. While some progress has been made
towards estimating the global surface area of estuarine
water bodies (Dürr et al. 2011), no robust estimate is
currently available for the global inter-tidal area.
Borges and Abril (2011) made a crude estimate of
estuarine inter-tidal areas and derived a global estuarine CH4 emission ~5 Tg C year
1, notably higher
than the estimate of 0.1–2.3 Tg C year
1 given in
Upstill-Goddard (2011). Constraining marine CH4
emissions more accurately clearly requires additional
detailed studies.

3.5

Impact of Global Change

3.5.1

Future Changes in the Physics of the
Oceanic Surface Layer

In order to assess the influence of global climate change
on the air-sea exchange of long-lived greenhouse gases
(Table 3.2), we need to separately discuss CO2 from
N2O and CH4, since the latter two gases have a fundamentally different global balance between the ocean
and troposphere. In the case of CO2, the ocean ultimately controls the tropospheric content of CO2, with
the CO2 concentration in the surface layer being the
immediate determinant of this ocean–atmosphere balance. This surface ocean concentration is controlled by
physical, chemical, and biological processes that also
create very important sources and sinks within the
surface layer, causing the response of CO2 to climate
change to be complex. For N2O and CH4, the ocean acts
as a net source of these gases to the troposphere, but
their ultimate concentrations in the troposphere are on
average controlled by other factors, such as their tropospheric lifetimes and terrestrial sources. Furthermore,

147

excluding coastal regions, N2O and CH4 in marine
waters are mostly produced away from the surface in
the ocean’s interior, so that the surface layer primarily
acts as a conduit between the net ocean source and the
net tropospheric sink.

3.5.1.1 Carbon Dioxide in the Open Ocean
For CO2, it is furthermore of considerable help to
distinguish clearly between the climate change processes acting upon the oceanic uptake of anthropogenic
CO2, and those that influence the cycling of natural
carbon (Fig. 3.18). For the former, it is essentially
sufficient to consider only CO2 induced changes in the
oceanic buffer capacity and in what way ocean circulation will change the net downward transport of anthropogenic CO2. Changes in wind regimes are essentially
irrelevant because air-sea exchange is not a rate limiting
step for the oceanic uptake of anthropogenic CO2
(Sarmiento et al. 1992). Changes in sea ice will locally
impact the uptake of anthropogenic CO2 significantly,
but have a relatively small effect globally. Changes in
temperature also have a negligible direct effect on the
uptake of anthropogenic CO2, since the buffer factor is
essentially independent of temperature (Sarmiento and
Gruber 2002).
Regarding the cycling of natural CO2, changes in
temperature are very important, as they directly impact
CO2 solubility in surface water. Changes in the ocean’s
biogeochemical loop are of fundamental importance.
This loop (Sect. 3.2.2) consists of the downward
(biological) component, often referred to as the
biological pump, and an upward component driven by
transport and mixing, which brings carbon-rich waters
from the sub-surface ocean back to the surface (Gruber
and Sarmiento 2002).
One of the most consistently predicted impacts of
climate change on the ocean is an increase in upper
ocean stratification (e.g. Bopp et al. 2002). This will
largely result from continued oceanic uptake of excess
heat from the troposphere that will warm the upper
ocean more than the deep ocean (at least during a
transient period of several hundred years). In addition,
many high-latitude regions are predicted to become
fresher in response to an acceleration of the hydrological cycle (Curry et al. 2003), increasing stratification
there as well. In the lower latitudes, the enhanced
evaporation will actually increase salinity, but this
effect is much smaller than that of the higher temperature, so that the surface ocean is predicted to become

148

3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate
Anthropogenic CO2 uptake

Natural carbon cycle
Warming

Chemical changes

Q

DIC
N

Circulation changes

DIC

Org.
matter

Circulation changes

Biological
changes

DIC

DIC

Fig. 3.18 Overview of the most important processes that affect
the natural carbon cycle (left, black) and anthropogenic CO2
uptake (right, red) in a changing world. At the center of the
natural carbon cycle is the net formation of organic carbon by
photosynthesis. Part of this organic matter is exported to depth,
where it remineralises back to dissolved inorganic carbon
(DIC). Circulation and mixing close this loop. Gas exchange
through the air-sea interface connects this loop to the atmosphere. The magnitude of this exchange is governed by the
balance of upward transport of DIC and downward transport
of organic matter, as well as warming and cooling at the sea
surface. The increase in atmospheric CO2 has caused an

additional flux across the air-sea interface, i.e. that of anthropogenic CO2, leading to an anthropogenic increase in DIC (red).
This DIC is then further transported to depth by mixing and
transport. To first order, the anthropogenic CO2 is not
interacting with the natural carbon cycle, namely it does not
affect the magnitude and pattern of the organic matter production and export (red cross). When looking closer, this is not
entirely correct, as there is increasing evidence that the chemical
changes associated with the uptake of anthropogenic CO2 affect
ocean biology. The grey ellipses indicate the processes that are
most vulnerable to climate change, leading to a change in the net
ocean–atmosphere balance of CO2

more stably stratified nearly everywhere. Important
exceptions might be coastal upwelling regions, and
perhaps the Southern Ocean, where increased winds
may cause enhanced upwelling, partially compensating for the increased stratification.
An increase in stratification and consequently in the
surface-to-depth transport will have a strongly reducing impact on the uptake of anthropogenic CO2. In
contrast, increased stratification will produce a much
more complex response of the ocean’s natural carbon
cycle. On the one hand, the circulation (upward) component of the biogeochemical loop will be reduced,
but the net effect of this will depend on how ocean
biology responds. If the net export of organic matter
remains unchanged, the reduction in the upward transport will lead to a substantial increase in the rate of
CO2 uptake from the troposphere. This is because the
soft-tissue part of the biological pump will continue to
reduce surface CO2 by fixing it into organic matter and
exporting it to depth, while the re-supply by mixing
from depth will be greatly reduced. Instead, the lost
CO2 will be replaced by uptake from the troposphere,

constituting a negative feedback with regard to tropospheric CO2. If the net export of organic matter
increases, so will the uptake of CO2 from the troposphere, but if it decreases then the net uptake of CO2
from the troposphere will also decrease. The overall
effect of expected changes in the biogeochemical loop
on tropospheric CO2 can be summarised by the efficiency of the soft-tissue pump, which describes the
relative balance between the amount of (inorganic)
carbon that is brought to the near-surface by mixing/
transport and the amount of (organic) carbon that is
exported to depth. It is currently expected that globally
the efficiency of the soft-tissue pump will increase,
such that the overall effect of the biogeochemical loop
will be to enhance CO2 uptake from the troposphere
(Fig. 3.19). However, this conclusion critically
depends on the biological response, which at present
remains poorly understood.
At the same time, warming of the surface ocean
will lead to a loss of CO2 from the surface, constituting
a clear positive feedback. The magnitude of this
solubility-driven response is relatively well

Impact of Global Change

HEAT GAIN/STRATIFIED

3.5

149
Thermal

Biological/Circulation

Q
DIC
N2O/CH4

DIC

N2O/CH4

Efficient
biological
pump

Effect of

Global Warming

Thermal
HEAT LOSS/MIXING

Org.
matter

N

Biological/Circulation

Q
DIC
N2O/CH4

DIC

Org.
matter

N

N2O/CH4

Inefficient
biological
pump

Fig. 3.19 Schematic diagram highlighting two typical situations
and how they influence the exchange of natural CO2, N2O, and CH4
across the air-sea interface. The upper panel shows a typical lowlatitude situation characterised by a net heat gain from the

atmosphere and stably stratified conditions. The lower panel
represents a typical high latitude situation with a net loss of heat
and weakly stratified conditions. Global warming will tend to make
more regions of the global ocean behave like the upper panel

understood, and will largely depend on the magnitude
of the ocean’s future heat uptake. Changes in wind and
sea ice extent will influence the net exchange of CO2
across the air-sea interface as well. However this will
be of secondary importance compared to the direct
temperature and circulation effects, except in specific
local regions where large changes in sea ice and/or
winds might occur.
Based on an annual sea ice loss of 36,000 km2
(mostly summer ice; Cavalieri et al. 2003), the air-sea
CO2 influx would increase by 2.0  0.3 Tg C year
1
for the Arctic Ocean (Bates et al. 2006). Bates et al.
(2006) estimated that the Arctic Ocean sink for CO2
increased from 24 to 66 Tg year
1 over the past three
decades due to sea ice retreat and that future sea ice
melting will enhance this CO2 uptake by 20 Tg year
1
by 2012. Therefore, if one considers only the physical
processes through which sea ice directly impacts air-sea
CO2 exchange, the decrease of summertime sea ice
extent is expected to increase CO2 uptake in the coming
decades.
Summing up, the expected changes in surface temperature and upper ocean stratification will lead to a

strong reduction of the uptake of anthropogenic CO2,
and a mixed response on the natural carbon side with a
loss of carbon driven by solubility, and perhaps a gain
from the biogeochemical loop (Table 3.9). The net
balance is likely to be a decrease in the net uptake of
CO2 from the troposphere in response to climate
change, with current models suggesting a reduction
in the net ocean uptake of about
16 to
33 Pg  C
1
warming until 2100 (Table 3.2) (Roy et al. 2011).

3.5.1.2 Carbon Dioxide in Coastal Seas
The potential feedbacks on increasing tropospheric CO2
from changes in carbon flows in the coastal ocean could
be disproportionately higher than in the open ocean.
According to Borges (2011), the changes in carbon
flows and related potential feedbacks in the coastal
ocean could be driven by four main processes
(Table 3.10): (i) changes in coastal physics (This
section); (ii) changes in seawater carbonate chemistry
(ocean acidification) (Sect. 3.5.2); (iii) changes in land
use, waste water inputs and the use of agricultural
fertilisers; (iv) changes in the hydrological cycle.
These potential feedbacks remain largely unquantified

150

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Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

Table 3.9 Environmental influences on the future net ocean uptake of tropospheric CO2. BGC biogeochemical
Anthropogenic CO2
Chemical changes (buffer capacity)
Circulation/stratification changes
Wind/sea ice changes
Natural CO2
Temperature changes
Circulation changes affecting the BGC loop
Biological changes affecting the BGC loop
Wind/sea ice changes

Net ocean uptake

Magnitude

Understanding

Reduced
Reduced
Increased/reduced

Large
Medium
Small

High
Medium
Low

Reduced
Reduced
Increased/reduced
Increased/reduced

Medium
Medium
Unknown
Low

Medium
Low-medium
Very low
Low

due to a poor understanding of the underlying
mechanisms, and/or a lack of data and models with
which to evaluate them.
Feedbacks on increasing tropospheric CO2 due to
effects of carbon cycling in continental shelf seas
related to changes in circulation or stratification
could be important, but remain to be quantified
(Table 3.10). The effect of changes of stratification
on air-sea CO2 fluxes are not straightforward to
quantify since enhancement of stratification will
depress primary production and export production
due to the decrease of nutrient inputs, but at the
same time will also decrease the vertical inputs of
DIC and hence release of CO2 to the troposphere. For
example, on the Tasman shelf and in the adjacent
open ocean the overall effect of enhanced stratification seems to be an enhancement of the CO2 sink
(Borges et al. 2008).

3.5.1.3 Nitrous Oxide and Methane
For N2O and CH4 the upper ocean acts mostly as a
transport conduit from the ocean to the troposphere,
where they are ultimately decomposed by chemical
reactions. Changes in solubility are likely to exert a
major effect on the exchange of these two gases in the
open ocean, since their degree of supersaturation in
the surface ocean is mostly determined by temperature and only to a smaller degree by the magnitude of
the transport of supersaturated waters from below to
the surface. Therefore, the increase in stratification
induced by surface warming (and also by changes in
salinity) is likely to lead to only a modest reduction in
the ocean source strength of these two gases, provided
that their rates of production remain about the same.

Of course, if ocean warming and enhanced stratification lead to a strong loss of O2, then N2O and CH4
production in the ocean might increase substantially,
which could more than offset the reduction in the
deep-to-surface transport induced by stratification
(Table 3.2).

3.5.2

Ocean Acidification

3.5.2.1 Carbon Dioxide
Models predict that by the end of the twenty-first
century the pH of seawater will decrease by another
0.3–0.4 units (Caldeira and Wickett 2003). This
decrease will be accompanied by a reduction in the
CO32
concentration and in the saturation state for
calcite and aragonite (Sect. 3.2.2). Surface waters in
the Southern Ocean and the Subarctic Pacific Ocean
are expected to become undersaturated with respect to
aragonite by the year 2100 (Orr et al. 2005).
Ocean acidification will affect organic carbon production and calcification, but exactly how it will do this
is uncertain and is a topic of ongoing research in ocean
acidification programmes around the world. For example, ocean acidification may impact the shells of pelagic
calcifiers by reducing the ability of organisms to calcify
(Comeau et al. 2011) and by dissolving calcareous
shells (Orr et al. 2005). However, a few studies have
found an increase in calcification on ocean acidification
(e.g. Iglesias-Rodriguez et al. 2008). In particular, coral
reefs are highly susceptible to increases in temperature
and CO2 concentration (Kleypas et al. 1999). Coral
reefs globally may have ceased to grow and started to
dissolve by the time the atmospheric mole fraction of

3.5

Impact of Global Change

151

Table 3.10 Global change forcing of carbon cycling in the coastal ocean and associated feedback from increasing tropospheric
CO2 to the year 2100 (Adapted from Borges 2011)
Changes in CO2 sources and sinks
Global change forcings
Feedback (Pg C year
1)
Changes in coastal physics
Enhanced stratification

?
?
Enhanced coastal upwelling
+?
?
Impact of expanding OMZ in coastal upwelling regions
+
?
Enhancement of air-sea CO2 fluxes related to Arctic sea-ice retreat

0.002
Changes in land use, waste water inputs, agricultural fertilisers and changes in the hydrological cycle
Increase of river organic carbon delivery to the Arctic Ocean
+
?
Increase of river nutrients delivery to the Arctic Ocean


?
Global increase in river nutrient and organic matter delivery


0.200
Global increase in nutrient atmospheric deposition


?
Expanding hypoxic and anoxic zones
+
?
Changes in seawater carbonate chemistry (ocean acidification)
Decrease of benthic calcification
Coral reefs


0.015–0.026
Other benthic environments


0.025–0.046
Decrease of pelagic calcification
Coccolithophorids


0.013–0.019
Other pelagic calcifiers


?
Dissolution of metastable CaCO3 in sediment porewaters


0.022
Enhancement of primary production and export production due to


0.108–0.216
increasing [CO2]

Comment
1
2
3
4

5
6
3

7
8

1 Negative feedback only reported in Tasman shelf (Borges et al. 2008) assuming pCO2 behaviour during warm years is
representative of response to global warming, if extrapolated globally would produce a negative feed-back of the order of
0.1 PgC year
1.
2 Assuming exact opposite response of model output (Plattner et al. 2004) with decreasing upwelling favourable winds
3 Assuming enhanced denitrification leading to decreased primary production
4 Feedback computed for the next decade and not until 2100 (Bates et al. 2006)
5 The enhancement of primary production by nutrient inputs balances the additional CO2 production by organic matter inputs
(Mackenzie et al. 2004).
6 Not taking into account enhancement of acidification of surface waters by sulphur atmospheric deposition (Doney et al. 2007)
7 Based on Andersson et al. (2003)
8 Based on a single mesocosm experiment with mixed diatom and coccolithophorid assemblage (Riebesell et al. 2007)

CO2 reaches 560 ppm (Silverman et al. 2009). Ocean
acidification may well increase primary production by
some phytoplankton species (Rost and Riebesell 2004)
and may change ratios of carbon to nitrogen uptake by
phytoplankton and subsequent carbon export (Riebesell
et al. 2007).
Ocean acidification will also impact marine biogeochemical processes. These include changes in the
availability of essential trace metals like iron, which
are modified in speciation by pH change (SantanaCasiano et al. 2006). A decrease in pH may affect
the solubility of some minerals (Liu and Millero
2002) and also the distribution of chemical species,

favouring the free dissolved forms of metals, and
exerting significant physiological, ecological and toxicological effects on organisms.
On timescales of a few 100 years, a change in the
hard tissue CaCO3 pump is estimated to have a small
impact on global CO2 uptake by the oceans (Table 3.2)
(Denman et al. 2007), although the effect could be very
substantial on glacial to interglacial timescales (e.g.
Archer et al. 2000; Matsumoto et al. 2002). The effects
of ocean acidification have been estimated for coastal
waters (Table 3.10). Overall, ocean acidification is
likely to decrease pelagic and benthic calcification in
coastal waters and to result in the dissolution of CaCO3

152

3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

in coastal sediments. These combined effects have been
estimated to increase the uptake of tropospheric CO2 by
~0.02 Pg C year
1, an amount equivalent to ~10 % of
the modern-day CO2 sink in coastal seas (Tables 3.2
and 3.10). The increase in export production upon
ocean acidification could also provide a significant negative feedback to increasing tropospheric CO2,
although the conclusions are based on a single perturbation experiment (Table 3.10) (Riebesell et al. 2007).
Denman et al. (2007) concluded that the combined
effects of climate change and ocean acidification on the
biological carbon pump are not clear and could either
increase or decrease the uptake of tropospheric CO2.
What is more clear is that ocean acidification will likely
result in the dissolution of CaCO3 sediments in the
interior ocean on a time scale of 40 kyear, thus creating
a negative feedback on the increase in tropospheric CO2.

3.5.2.2 Nitrous Oxide and Methane
One other important consequence of ocean acidification
is a shift of the NH3/NH4+ equilibrium towards NH4+
(e.g. Bange 2008). Beman et al. (2011) recently showed
that nitrification rates decreased significantly when the
pH was lowered to values expected to occur in the
future ocean. One explanation for the pH sensitivity of
nitrification rates is that the ammonia mono-oxygenase
enzyme uses NH3 and not NH4+ as substrate in the first
step of the nitrification sequence. On the basis of these
results Beman et al. (2011) suggested that future oceanic N2O production via nitrification might decrease by
up to 44 %, although Freing et al. (2012) contend that
this scenario could be an over simplification. Nitrification is part of organic matter remineralisation (i.e. oxidation of organic matter with O2 to CO2) and it leads to
decreases in both pH and O2 (Sect. 3.3.2). In general
decreasing O2 concentrations lead to increasing N2O
production during nitrification, such that there seems to
be only a minor effect of decreasing pH on N2O production via nitrification as part of the organic matter
remineralisation process. Laboratory experiments to
verify the effect of ocean acidification on N2O production in the ocean are yet to be carried out (Table 3.2).
Similarly, the potential effect of ocean acidification on
oceanic CH4 production remains to be investigated
(Table 3.2).

3.5.3

Deoxygenation and Suboxia in the
Open Ocean

One of the most important effects of global change on
the oceans will be the deoxygenation of seawater arising from surface water warming and increased stratification of the upper ocean. These processes will lead to a
decrease in O2 solubility and its supply to subsurface
waters, respectively (Keeling et al. 2010). There is
compelling evidence to show that this may already be
happening (Joos et al. 2003; Stramma et al. 2008;
Keeling et al. 2010; Helm et al. 2011). The modelpredicted decrease in the oceanic O2 inventory ranges
from 1 % to 7 % by the year 2100 (Keeling et al. 2010).
The effects of such a decrease are expected to be
greatest in the oceanic OMZs. For example, while a
1  C warming of the upper ocean, which would lower
the O2 solubility by ~5 μM, may lead to an increase in
the volume of hypoxic waters by 10 %, the volume of
suboxic waters may increase by a factor of 3 (Deutsch
et al. 2011). The ongoing expansion and intensification
of the OMZs (Stramma et al. 2008) is expected to
profoundly impact the biogeochemical cycling of
redox-sensitive elements, especially nitrogen, and will,
in conjunction with ocean acidification, adversely affect
marine life (Brewer and Peltzer 2009). This may also
involve modification of the oceanic source terms for
climatically important gases that are sensitive to O2
concentrations, such as N2O and CH4 (Sects. 3.3.2
and 3.4.2).
N2O formation by nitrification is enhanced when O2
concentrations are lowered (Sect. 3.3.2). Stramma et al.
(2008) showed that intermediate ocean waters
(300–700 m water depth) have been losing O2 at rates
ranging from 0.09  0.21 μmol kg
1 year
1 in the
eastern equatorial Indian Ocean to 0.34  0.13 μmol
kg
1 year
1 in the eastern tropical Atlantic Ocean during the last 50 years. Assuming a mean Δ[N2O]/AOU
(apparent oxygen utilisation) ratio of 10
4 (Walter et al.
2006), Bange et al. (2010) computed a maximum additional N2O contribution from deoxygenation of 6 %
above the mean N2O background concentration in the
intermediate waters of the tropical North Atlantic
Ocean. It therefore seems reasonable to conclude that
ongoing open ocean deoxygenation will have a minor
effect on oceanic N2O production and emissions
(Table 3.2).

3.5

Impact of Global Change

The total CH4 emission from open ocean areas
experiencing O2 depletion in the water column is quite
small (0.3 Tg C year
1) (Table 3.7) (Bates et al. 1996b)
and is unlikely to increase significantly due to the future
expansion of open ocean OMZs (Table 3.2) (Naqvi
et al. 2010).

3.5.4

Coastal Euthrophication
and Hypoxia

Human activities related to the production of food and
energy are causing the release of large quantities of
nutrients, such as nitrogen and phosphorus, to the
environment, a substantial fraction of which gets
transported to coastal waters (Seitzinger et al. 2002;
Smith et al. 2003). By 2100 changes in biological
activity due to the increased nutrient delivery in rivers
might cause a negative feedback on increasing tropospheric CO2 similar in magnitude to the present-day
CO2 sink in coastal seas (0.2 Pg C year
1) (Tables 3.2
and 3.10).
Stimulation of primary productivity and the degradation of photosynthesised organic matter due to nutrient over-enrichment (eutrophication) often results in
dissolved O2 depletion in the bottom waters of seasonally stratified shelf waters. Thus, over 400 hypoxic
zones have developed in coastal areas all over the
world in the last few decades (Diaz and Rosenberg
2008). Due to their severely detrimental ecological
effects, such as the exclusion of higher animals,
these hypoxic zones are often popularly referred to
as “dead zones”. Although these “dead zones” differ
from the naturally-formed O2-deficient zones that
occur on the continental shelves in eastern-boundary
upwelling regions, there is now evidence that the latter
are also intensifying as a result of anthropogenic nutrient loading and/or changes in circulation (Naqvi et al.
2000; Chan et al. 2008).
The expansion and intensification of O2 deficiency
in coastal areas is expected to affect the future cycling
of N2O and CH4 in these regions. Currently, anthropogenic “dead zones” make a relatively insignificant
(less than 0.043 Tg N year
1) contribution to the
total marine source of tropospheric N2O (Naqvi et al.
2010). Nevertheless, it is likely that the comparatively
large N2O emissions from natural hypoxic zones

153

include an anthropogenically-enhanced component.
Taken together with the sensitivity of N2O cycling in
aquatic systems to minor changes in already low O2
concentrations, this implies that further expansion and
intensification of coastal hypoxia may significantly
impact the global tropospheric N2O budget
(Table 3.2).
From the limited information available on CH4
emissions from “dead zones”, it would appear that
these regions do not contribute much (less than
0.026 Tg C year
1) to the total marine source of
tropospheric CH4 (Naqvi et al. 2010), although it
should be noted that this estimate does not include
bubble ebullition. Given the lack of evidence for a
primary control of bottom water hypoxia on sedimentary CH4 production it would appear that future intensification and/or expansion of coastal hypoxia is
unlikely to significantly increase total marine
emissions of CH4 to the troposphere (Table 3.2).

3.5.5

Changes in Methane Hydrates

There is substantial debate over the role of CH4 hydrates
in potential future climate change (O’Connor et al.
2010). What is clear is that the currently estimated
CH4 hydrate inventory (Sect. 3.4.4) is sufficiently
large that the release of even a modest fraction to the
troposphere over a 12-year period, the tropospheric
lifetime of CH4 (Table 3.1), could enhance greenhouse
forcing by an amount equivalent to increasing the tropospheric CO2 concentration by a factor larger than 10
(Archer 2007). The global CH4 hydrate reservoir thus
has the potential to promote substantial global warming
(Table 3.2).
One region in which the effects of CH4 hydrate
destabilisation are likely to be most clearly manifested
is the Arctic Ocean; a marine ecosystem that is highly
susceptible to global change (Doney et al. 2012).
Romanovski et al. (2005) modelled the extent and temporal evolution of submarine permafrost on the shelves
of the Laptev Sea and East Siberian Sea and deduced
that the entire Arctic shelf is underlain by relic permafrost stable enough to support CH4 hydrate. Much of
this Arctic CH4 hydrate is comparatively shallow structural hydrate (Sect. 3.4.4) and the gas hydrate stability
zone is ~200 m below the sea surface over much of the

154

3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

region. If dislodged from the sediment, for example
during a submarine landslide (Brewer et al. 2002;
Paull et al. 2003) or by erosion, large amounts of
hydrate could potentially survive largely intact during
ascent to the ocean surface. Contemporary hydrate
melting is indeed apparent along the Siberian margin,
due to rapid coastline recession that is exposing sub-sea
floor deposits to overlying seawater at rates of
~10–15 km year
1. The hydrate melting collapses further land into the sea, increasing the exposure of
hydrates and leading to further melting, a process
which is thought to have occurred continually over the
past 7,500 years, resulting in ~100–500 km recession of
the coastline (Hubberten and Romanovski 2001). As
such these CH4 releases are not abrupt, but rather tend
to modestly increase the CH4 background over time.
For example, contemporary dissolved CH4 saturations
on the East-Siberian and Laptev Sea shelves are
~2,500 % in surface waters and in excess of 4,000 %
in bottom waters, consistent with such seabed release
(Shakhova and Semiletov 2007; Shakhova et al. 2007).
Similarly, surface waters over the North Slope of
Alaska are highly supersaturated in CH4 (Kvenvolden
1999) and the release of hydrate-derived CH4 has been
clearly observed in the Beaufort Sea (Paull et al. 2008).
Notwithstanding such evidence for the ongoing
release of hydrate-derived CH4, Archer and Buffett
(2005) and Archer (2007) argue that any significant
climate induced hydrate melting response is likely to
be on the time scale of millennia or longer, because the
vast majority of CH4 hydrate is of the stratigraphic
type (Sect. 3.4.4) and is sufficiently insulated from the
sediment surface by many hundreds of metres of
overlying sediment. They also argue that any hydrate
melting will occur below the gas hydrate stability
zone, forming CH4 bubbles whose fate is uncertain.
Possibilities include their retention in the sediment,
upward escape through the stability zone, or the initiation of submarine landslides through sediment column destabilisation. Although such a landslide would
potentially cause abrupt CH4 release, it is estimated
that a landslide the size of the Storegga slide off
Norway would typically release CH4 sufficient only
to affect climate on a scale comparable to a large
volcanic eruption for ~10 years (Archer 2007).
Estimates of the rate at which melting hydrates are
likely to increase the tropospheric CH4 inventory over
the timescale of decades (Table 3.2) are much less
well constrained than changes in other CH4 sources

such as peat decomposition in thawing permafrost,
fossil fuels and agriculture, although the potential
rates may be comparable (Archer 2007). Major
uncertainties exist over the rate and extent of CH4
escape to the overlying water and troposphere, which
is related to sediment stability and permeability and
the ability of the gas hydrate stability zone to trap CH4
bubbles (Archer 2007). On geologic timescales, due to
the relative tropospheric lifetimes of CH4 and CO2
(Table 3.1), the largest climate impact will likely be
from CO2 deriving from CH4 oxidation (Schmidt
and Shindell 2003; Archer and Buffett 2005; Archer
2007). Following the cessation of hydrate CH4 release,
the enhanced CO2 concentration will persist, while
tropospheric CH4 will recover relatively rapidly to
a lower steady state (Schmidt and Shindell 2003).
Significant oxidation of hydrate-derived CH4 to CO2
in the oceans would reduce the climate impact over
several decades, but on timescales of millennia or more
the climate impact might be significant, because of
equilibration of this oceanic CO2 with the troposphere
over several hundred years. Indeed, there may well be
positive climate feedback linking tropospheric CO2,
deep ocean temperature and CO2 production from
hydrate-derived CH4 (Archer and Buffett 2005).
These authors propose that in a worst case scenario,
on the timescale of millennia to hundreds of millennia,
the total global CH4 hydrate source of tropospheric
CO2 could equal that from fossil fuels.

3.6

Key Uncertainties in the Air-Sea
Transfer of CO2, N2O and CH4

3.6.1

Outgassing of Riverine
Carbon Inputs

The error bars on carbon inputs by rivers and estuarine
outgassing create considerable uncertainty when
attempting to convert from net contemporary fluxes to
anthropogenic fluxes (Gruber et al. 2009; Takahashi
et al. 2009). If the outgassing of river borne CO2 by
the open ocean has indeed been overestimated by
~0.2 Pg C year
1 as argued in Sect. 3.2.4, the anthropogenic CO2 sink derived from CO2 climatologies (e.g.
Takahashi et al. 2009) would have been over-estimated
by the same amount.

3.6

Key Uncertainties in the Air-Sea Transfer of CO2, N2O and CH4

3.6.2

Heterogeneity in Coastal Systems

Obtaining meaningful air-sea gas transfer estimates for
CO2, CH4 and N2O in coastal systems is a substantial
challenge and the ranges in Tables 3.4, 3.5 and 3.7 have
large inherent uncertainties. These uncertainties reflect
the heterogeneity and biogeochemical complexity of
coastal systems and include: (i) gross scaling errors
arising from the degree to which study sites are representative globally of each “compartment”; (ii) bias in
the CO2, N2O and CH4 values reflecting incomplete
spatial and temporal data resolution; (iii) uncertainties
in gas exchange rates arising from selected gas transfer
relations and representative wind speeds (Chap. 2)
(Upstill-Goddard 2006; Wanninkhof et al. 2009).
Minimising gross scaling errors largely relies on the
availability of accurate area determinations. General
problems of defining the extent of oceanic upwellings
are well documented (Nevison et al. 2004; Naqvi et al.
2005), as are the difficulties of defining representative
estuarine areas (Barnes and Upstill-Goddard 2011).
Minimising measurement bias has a clear seasonal
aspect; production and consumption of CO2, N2O and
CH4 are biologically driven and as such have strong
temperature dependence. Seasonality also affects the
intensity of upwelling and river runoff, which affects
nutrient and carbon supply. Unfortunately, most sampling campaigns take place during summer. There is
also a regional aspect; most coastal regions outside the
northern hemisphere are either undersampled or are not
sampled at all.
Given the likelihood that coastal regions will play an
important role in future trace gas budgets due to
increased economic and population pressures, additional studies of CO2, N2O and CH4 fluxes from key
coastal regions will be required. Sampling campaigns
should be spatially and temporally focused and ideally
coordinated internationally. In particular CH4 emissions
by ebullition and from seeps and inland waters need
more to be more accurately quantified.

3.6.3

Sea Ice

The role of sea ice in air-sea gas exchange remains
poorly understood (Chap. 2). Until recently sea ice was
regarded as a lid that effectively precluded air-sea gas
exchange, as evidenced by under ice concentrations of
dissolved gases (CFCs, O2, CO2) far from equilibrium

155

with their tropospheric contents (Gordon et al. 1984;
Weiss et al. 1992; Klatt et al. 2002; Bakker et al. 2008).
However, recent evidence now points to significant
gas exchange between sea ice and the troposphere
(Delille et al. 2007; Geilfus et al. 2012), highlighting
a need for more detailed research in this area.

3.6.4

Parameterising Air-Sea
Gas Transfer

The choice of turbulence-driven air-sea gas exchange
relations may introduce significant bias (Chap. 2),
especially where bottom-driven turbulence is a major
contributor to gas exchange in shallow systems
(Upstill-Goddard 2006, 2011). In addition, wind speeds
in coastal systems have short spatio-temporal variability
and often the appropriate wind speed distributions
required for gas exchange relations are not available.
Similarly, current speeds and water depths required for
non-wind speed driven gas exchange relations may also
be lacking (Upstill-Goddard 2006).

3.6.5

Data Collection, Data Quality
and Data Synthesis

The detailed study of interannual and decadal variations
and trends in regional surface water fCO2 and air-sea
CO2 fluxes has only recently become possible. Our
future understanding of these trends and of the underlying mechanisms responsible is expected to improve due
to more extensive data coverage and as longer observational records become available. Future long term data
collection and data synthesis will require the development of instrumentation that is more reliable and accurate while being less labour intensive. In addition the
rigorous standardisation of data collection and quality
control procedures will be essential. Such technical
developments will require substantial and sustained
funding (Borges et al. 2010; Byrne et al. 2010; Feely
et al. 2010; Gruber et al. 2010; Monteiro et al. 2010).
Encouragingly, the marine CO2 community is
already making good progress towards coordinated
data collection and synthesis. Notable is the agreed
use of certified reference materials for the analysis of
DIC and total alkalinity and traceable calibration gases
for fCO2 analysis (DOE 1994; Dickson et al. 2007).
There is also agreement on recommendations for

156

3

Air-Sea Interactions of Natural Long-Lived Greenhouse Gases (CO2, N2O, CH4) in a Changing Climate

reporting fCO2 measurements (IOCCP 2004). The
International Ocean Carbon Coordination Project
(IOCCP) plays the key role in coordinating the marine
carbon community and in ensuring the implementation
of international agreements. By contrast, the status of
marine N2O and CH4 research is far less mature in this
regard. The N2O and CH4 communities have yet to
progress towards discussing the adoption of either internationally agreed analytical standards or recommended
analytical protocols.
Recent synthesis products for ocean carbon enable
the intercomparison of model-data, the analysis of variation and trends in CO2 air-sea fluxes, and the processes
driving these. A vast and expanding global surface
water fCO2 database is now available for assessing
CO2 air-sea climatologies (Takahashi et al. 1997,
2002, 2009) and since 2011 the Surface Ocean CO2
Atlas (SOCAT; www.socat.info) enables public access
to a large CO2 data archive for the global oceans and
coastal seas (Bakker et al. 2012; Pfeil et al. 2013; Sabine
et al. 2013) (Chap. 5). Other data synthesis efforts are
also underway, notably MEMENTO for surface ocean
N2O and CH4 concentrations (Chap. 5) and syntheses of
carbon in the ocean interior: GLODAP (Global Ocean
Data Analysis Project), CARINA (CARbon IN the
Atlantic Ocean), PACIFICA (PACIFic ocean Interior
Carbon) and GLODAP-2 (Key et al. 2004; Bange et al.
2009; Tanhua et al. 2010; Suzuki et al. 2013). Such data
synthesis efforts, along with modelling and data-model
intercomparisons (Gruber et al. 2009) are critical to
improving our current understanding of the exchanges
of CO2, N2O and CH4 between the troposphere, coastal
seas, the surface open ocean and the ocean interior.

3.7

Conclusions and Outlook

3.7.1

Carbon Dioxide

In general, the enhanced rate of change in tropospheric
CO2 observed today (Global Carbon Project 2011) will
have a strong impact on future climate and environmental change. Presently it appears that neither the oceans
nor terrestrial systems will absorb CO2 as efficiently in
the future as they do today. In addition, ocean acidification will become, and probably is already, an issue for
ecosystems in the ocean. Both the climate and environmental effects and feedbacks make it very difficult to
firmly predict future changes in ocean sources and sinks
for CO2. From time-series we can clearly identify the

rate of change in CO2 uptake and in ocean acidification.
From surface water CO2 measurements on Voluntary
Observing Ships we can make CO2 air-sea flux maps
and assess temporal and spatial variation in the oceanic
uptake of CO2. Finally, repeat hydrography elucidates
the storage of carbon in the interior ocean. More important will be to predict the effect of changes in processes
controlling oceanic uptake and release of carbon, notably of changes in ocean circulation, the magnitude of
the biological pumps and carbon storage. This will
require a comprehensive measuring system for future
observations and improved modelling tools. Today,
there is a clear lack of the observations required to
reduce the uncertainties in air-sea CO2 exchange and
to predict its future behaviour. This in turn is very
important for predicting the occurrence of levels of
ocean acidification that are harmful to ecosystems.
The latter also requires results from biological perturbation experiments under varying CO2 scenarios. The
future vision is:
– The design of a comprehensive network of VOS,
repeat hydrographic sections and time-series.
Optimalisation might be obtained through modelling,
statistical analysis and experience, based upon the
existing network;
– The development of automated ocean stations.
Cable-based systems might be applied along coastal
areas, and moored systems and buoys in open ocean
situations. Remotely controlled floats and other
moving platforms, such as gliders, would provide
additional process information;
– The development of data storage, data reduction,
data synthesis, data assimilation and visualisation
techniques, as well as continuation, automation and
expansion of ongoing data synthesis efforts;
– The development of models that can be validated
by data;
(Borges et al. 2010; Byrne et al. 2010; Feely et al.
2010; Gruber et al. 2010; Monteiro et al. 2010).

3.7.2

Nitrous Oxide and Methane

While our knowledge of the oceanic distribution, the
formation pathways and the oceanic emissions of N2O
and CH4 has increased considerably during the last
four decades, we are far from having a comprehensive
picture. Major questions and technical challenges
remain to be solved:

References

– Reliable and fast, high-precision N2O and CH4
sensors for use at open ocean and coastal time-series
stations and on ships of opportunity should be developed in order to expand the spatial and temporal
coverage of oceanic N2O and CH4 measurements.
Recently developed OA-ICOS (Off-Axis Integrated
Cavity Output Spectrometer) instruments coupled to
a continuously working equilibration device are a
promising technology for use on VOS lines (Gülzow
et al. 2011).
– We still only have a rudimentary understanding of
N2O and CH4 cycling in coastal areas. We need to
know more about seasonality in the major formation,
consumption and transport pathways and the driving
forces behind these. In this context, the ongoing
dramatic increase in the number of coastal “dead
zones” is a critical consideration because ongoing
coastal eutrophication may well modify greatly, current emissions of N2O and CH4 from coastal areas.
Open Access This chapter is distributed under the terms
of the Creative Commons Attribution Noncommercial
License, which permits any noncommercial use,
distribution, and reproduction in any medium, provided
the original author(s) and source are credited.

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4

Ocean–Atmosphere Interactions of Particles
Gerrit de Leeuw, Cécile Guieu, Almuth Arneth, Nicolas Bellouin,
Laurent Bopp, Philip W. Boyd, Hugo A.C. Denier van der Gon,
Karine V. Desboeufs, François Dulac, M. Cristina Facchini,
Brett Gantt, Baerbel Langmann, Natalie M. Mahowald,
Emilio Marañón, Colin O’Dowd, Nazli Olgun, Elvira Pulido-Villena,
Matteo Rinaldi, Euripides G. Stephanou, and Thibaut Wagener

Abstract

This chapter provides an overview of the current knowledge on aerosols in
the marine atmosphere and the effects of aerosols on climate and on processes
in the oceanic surface layer. Aerosol particles in the marine atmosphere originate
predominantly from direct production at the sea surface due to the interaction
between wind and waves (sea spray aerosol, or SSA) and indirect production
by gas to particle conversion. These aerosols are supplemented by aerosols
produced over the continents, as well as aerosols emitted by volcanoes and ship
traffic, a large part of it being deposited to the ocean surface by dry and wet
deposition. The SSA sources, chemical composition and ensuing physical and
optical effects, are discussed. An overview is presented of continental sources and
their ageing and mixing processes during transport. The current status of our
knowledge on effects of marine aerosols on the Earth radiative balance, both
direct by their interaction with solar radiation and indirect through their effects on
cloud properties, is discussed. The deposition on the ocean surface of some key
species, such as nutrients, their bioavailability and how they impact biogeochemical cycles are shown and discussed through different time and space scales
approaches.

4.1

Introduction

An aerosol consists of a suspension of particles and its
surrounding medium. For atmospheric aerosols the
surrounding medium is the air in which the aerosol
particles (or droplets) are suspended. Often the term
aerosol is used to refer to only the particles or droplets.

G. de Leeuw (*)
e-mail: gerrit.leeuw@fmi.fi
C. Guieu (*)
e-mail: guieu@obs-vlfr.fr

In this contribution we follow this convention and
refer to aerosol particles whether they occur as
particles or as droplets, i.e. chemicals in their liquid
phase, or dissolved in a liquid, are also referred to as
particles. Aerosol sources are numerous and can be of
natural or anthropogenic origin, they can enter the
atmosphere directly as particles (primary aerosol) or
form in the atmosphere from their precursors in the gas
phase through physical and chemical reactions (secondary aerosol formation).
Aerosols are an important constituent of the atmospheric boundary layer. Aerosol particles provide
surfaces for heterogeneous chemical processes, they
also act as a condensation sink for atmospheric trace

P.S. Liss and M.T. Johnson (eds.), Ocean-Atmosphere Interactions of Gases and Particles,
Springer Earth System Sciences, DOI 10.1007/978-3-642-25643-1_4, # The Author(s) 2014

171

172

gases. Hygroscopic particles serve as cloud condensation nuclei. The chemical and physical properties of
aerosol particles are very variable in both space and
time and depend on the proximity of sources and sinks
and the chemical and physical transformation during
their atmospheric lifetime. Particle sizes may vary
from a few nanometres to some tens of micrometres.
Particles at the high end of this size range are sufficiently heavy that their atmospheric residence time is
very short and hence their concentrations are negligible, although in hurricane conditions such large sea
spray particles may be important in the transfer of
ocean–atmosphere transfer of heat and water vapour
(e.g., Andreas et al. 2008). Physical processes, in
particular the vertical transport and removal of
particles by dry deposition to the surface, depend on
particle size. Very small particles are subject to growth
by condensation and coagulation. Transport is determined by turbulence and Brownian diffusion. Very
large particles having sufficient mass are subject
to gravitational forces resulting in rapid sedimentation. These processes, in addition to formation by
direct emission and secondary processes, chemical
transformations and in-cloud processing, determine
the number concentrations of aerosol particles which
may vary by 10 orders of magnitude depending on
size.
The particle size distribution describes the variation
of the aerosol number concentration as function of
particle size (radius or diameter, specified for a certain
relative humidity to account for differences due to
hygroscopic growth). Figure 4.1 shows a schematic
representation of a hypothetical particle size distribution, presented as number size distribution (bottom)
or mass size distribution (top) with five different
modes (cluster, nucleation, Aitken, accumulation and
coarse modes). The predominant origin of particles
contributing to a certain size range is indicated and
will be further discussed in Sect. 4.2. As a result of
various interacting processes, the most abundant aerosol particles in the atmosphere are those with a radius
of a few tenths of microns. Particles in this size range
experience a minimum in the efficiency of dry deposition and they are often referred to as accumulation
mode particles. The atmospheric lifetime of these
particles is relatively long, on the order of a few days
to a few weeks depending on the surface roughness
and related deposition velocity, and their main
removal mechanism is wet deposition.

4

Ocean–Atmosphere Interactions of Particles

Aerosols in the marine atmosphere originate from a
variety of production and transformation processes.
Sea spray aerosol is directly produced at the sea
surface through the interactions between wind and
surface waves. Ship emissions and volcanoes also
contribute to primary aerosol in the marine atmosphere. Secondary aerosol formation from gases
released from the sea surface also contribute significantly to marine atmosphere aerosol loading. In
addition, aerosols formed over land by either primary
or secondary formation processes are transported over
the oceans and contribute substantially to the aerosol
concentrations over most of the world’s oceans.
Estimates of the mass concentrations show that the
largest aerosol contributions on a global scale are
from sea spray aerosol and desert dust (Andreae and
Rosenfeld 2008; Jickells et al. 2005). A comprehensive review of marine aerosols was published by
Lewis and Schwartz (2004). An update on the status
of sea spray aerosol production was published by de
Leeuw et al. (2011).
Aerosol particles are important both because they
affect atmospheric processes and, after deposition to
the sea surface, because they affect processes in sea
water. Their effects in the atmosphere are very diverse
and depend on the chemical and physical properties of
the aerosol particles. Aerosols have a strong impact on
climate both due to scattering and absorption of
incoming solar radiation (direct effect) and through
their effects on cloud properties and associated cloud
albedo (first indirect effect) and precipitation (second
indirect effect). Optical properties of aerosol particles
are determined by their size relative to the wavelength
of incident light and their chemical composition
(which determines their complex refractive index).
The optical properties such as angular scattering (the
aerosol phase function) and absorption for a given
aerosol size distribution and chemical composition
can be computed using a Mie code (Mie 1908). For
non-spherical particles more sophisticated codes need
to be applied. The scattering and absorption efficiency
are near zero for very small particles and near two for
very large particles, where small and large are relative
to the wavelength, λ, of the incident radiation,
i.e. determined by the size parameter 2πr/λ (r is particle in situ radius). Therefore, the particles most important for climate, as determined by the product of the
particle size distribution and the scattering efficiency,
are in the accumulation mode.

Introduction

173

Mainly secondary particles,
e.g., sulfates, nitrates, ammonium,
but also primary, e.g., BC

log n (arbitrary units)

log m (arbitrary units)

4.1

Mass size distribution

Mainly primary particles,
e.g., sea salt, soil dust

Cluster
mode

0.001

Aitken
mode

Accumulaon
mode
Coarse parcle
mode

Number size distribution

Nucleaon
mode

0.01

0.1

1

10

100

Dp (μm)
Fig. 4.1 Schematic representation of a characteristic particle
size distribution showing the different modes and the origin of
particles which predominantly contribute to a certain size range
(see text for further explanation of sources). The bottom figure
shows a number size distribution and the top figure shows the

associated mass size distributions. Mass is related to number by
m ¼ πDp3ρ/6, where Dp is the dry particle diameter (m) and ρ is
the particle density (g m-3) (Figure courtesy of Dr. Aki Virkkula,
Finnish Meteorological Institute, Helsinki, Finland)

When aerosols are deposited at the ocean surface, a
number of processes between dissolved forms and
particles (such as dissolution, adsorption, and aggregation) take place on various timescales. Many of
these processes impact surface ocean chemistry and
in particular the cycles of elements of biogeochemical
interest (macro and micronutrients such as nitrogen,
phosphorus, iron etc.). New atmospheric nutrients
entering the surface layer of the ocean may fertilise
phytoplankton growth and as such may affect CO2
draw-down and production of other gases such as
DMS, that may modify global climate on long time
scales. The complex interactions between atmospheric
deposition, marine biogeochemistry, carbon export
and sequestration in the deep ocean and their
feedbacks on climate have been recognised as an
important field of research over the past 20 years

and a synthesis of current knowledge is presented
here.
In Sect. 4.2 we discuss the occurrence of aerosol
particles in the marine atmosphere through their
production, transport and transformation and deposition. The discussion on production includes the direct
production of sea spray aerosol, its organic enrichment
and indirect production of aerosols in the marine
atmosphere. Non-marine sources are discussed as
well as their transport and transformation in the marine
atmosphere and removal by deposition. In Sects. 4.3
and 4.4 effects of aerosols in the marine atmosphere,
i.e. direct and indirect effects on climate and effects
of atmospheric aerosol deposition on marine biology
are discussed. Section 4.5 discusses atmospheric
deposition and its biogeochemical impacts into the
ocean.

174

4.2

4

Aerosol Production and Transport
in the Marine Atmosphere

In this section an overview of marine sources of aerosol is presented, starting with the direct production of
sea spray aerosol and the enrichment of sea spray
aerosol in organic material, followed by secondary
production in the marine atmosphere. Subsequently
non-marine aerosol sources are discussed.

4.2.1

Sources of Aerosol in the Marine
Atmosphere

Aerosols are produced directly at the sea surface from
the interaction between wind and waves, or indirectly
through secondary processes involving gases
exchanged between ocean and atmosphere or gases
produced from anthropogenic activities over the
ocean (for instance, shipping producing extensive
amounts of NOx). The aerosol load in the marine
atmosphere is further augmented by the direct production of aerosols over land and their subsequent transport over the ocean. Most particles in the marine
atmosphere are deposited to the ocean where they
may affect biological processes. Particles which are
produced from the ocean and transported over land
may affect regional air quality (visibility and corrosion) and climate, and they may play a role in cycling
from the marine to the terrestrial environments.

4.2.1.1 Sea Spray Aerosol Production
Sea spray aerosol (SSA) consists of a suspension, in air,
of particles that are directly produced at the sea surface
(de Leeuw et al. 2011a). Sea spray aerosols may be
enriched in certain substances, i.e. their chemical composition may deviate from that of sea salt. These
particles exist mainly in the liquid phase (i.e. as
drops). The radii of these particles vary from around
10 nm to at least several millimeters. The atmospheric
residence times of the particles vary from seconds to
minutes for larger particles, for which gravitational
sedimentation is the principal removal mechanism, to
days for smaller particles, for which removal is primarily by precipitation. The size of an SSA particle is

Ocean–Atmosphere Interactions of Particles

commonly specified by its equilibrium radius at a relative humidity (RH) of 80 %, r80.
Many measurements indicate that the relative
concentrations of the major solutes in sea spray
particles are similar to their relative concentrations in
bulk seawater, although this may not be the situation
for some substances as a consequence of the formation
process, or of exchange with the atmosphere subsequent to formation. SSA particles are said to be
enriched or depleted in such substances, and the
enrichment factor, defined as the ratio of the concentration of a substance to the concentration of one of the
major constituents of bulk seawater (typically sodium)
in the particle to the same ratio for bulk seawater, may
be less (depletion) or greater than unity (enrichment).
The aerosol consisting of sea spray particles in the
atmosphere has traditionally been termed “sea salt aerosol”, but in de Leeuw et al. (2011a) it was denoted “sea
spray aerosol” in recognition that the composition of the
particles may differ from that of bulk seawater. One
consequence of this difference is that the hygroscopic
and cloud droplet activation properties of sea spray
particles may differ from those calculated under the
assumption that the particles are composed only of sea
salt. In particular, in biologically active waters, sea
spray aerosol has been observed to be enriched in
organic matter (OM) and the contribution of OM to
sea spray aerosol has been an important area of recent
research, as discussed in Sect. 4.2.2.2.
The production of sea spray aerosol (SSA) was
recently reviewed by de Leeuw et al. (2011a) who
critically examined laboratory and field experimental
results on sea spray production, on the enrichment
in organic matter, and on the measurement and
parameterisation of whitecap coverage, and placed it
in the context of previous understanding which was
comprehensively reviewed by Lewis and Schwartz
(2004). The review by de Leeuw et al. (2011a)
included material published in the peer-reviewed literature until early 2010. These authors considered 13
production flux formulations, as well as fluxes
measured by Norris et al. (2008) using the eddy correlation method, and these formulations are provided in
Fig. 4.1 and in the Appendix of de Leeuw et al.
(2011a). Below we briefly summarise some of that
material to provide background and context for an
overview of more recent work.
SSA particles are formed at the sea surface mainly
by breaking waves via bubble bursting and by the

4.2

Aerosol Production and Transport in the Marine Atmosphere

tearing off of wave crests at elevated wind speeds
(>9 ms1). When a wave breaks, air is entrained into
the water and dispersed into a cloud of bubbles
(Thorpe 1992). The resulting white coloured area of
the sea surface is often denoted a “whitecap” on
account of enhanced, wavelength-independent scattering of visible radiation by the interfaces between
water and bubbles. The fraction of the sea surface
covered by white area is defined as the whitecap
fraction, W.
The bubbles rise to the surface and float, where
water drains off. The film cap of the bubble becomes
unstable and when it opens it fragments into many
small droplets, the so-called film droplets, and the
water jet rising in the remaining cavity breaks up
into a stream of 1–6 ‘jet’ droplets.
The production flux of SSA particles can be
specified as either the interfacial flux, i.e. the flux of
those particles leaving the sea surface, or as the effective flux, which is defined as the flux of those particles
produced at the sea surface that attain a given height,
typically 10 m, above mean sea level. They thus
remain in the atmosphere for a sufficiently long time
to participate in processes such as the scattering and
absorption of solar radiation, cloud formation and
atmospheric chemistry. For small SSA particles
(i.e. those with r80 smaller than about 1 μm), the
effective flux can, for all practical purposes, be considered to be the same as the interfacial flux. For
medium SSA particles (those with r80 between about
1 and 25 μm), the effective flux becomes increasingly
less than the interfacial flux with increasing r80. For
larger SSA particles, which have short atmospheric
residence times and typically do not attain heights
more than a few meters above the sea surface, the
effective flux is essentially zero.
The SSSF (Sea Spray aerosol Source Function) is a
numerical representation of the size-dependent production flux of SSA particles:
f ðr80 Þ ¼ dFðr80 Þ=dlog10 r80

(4.1)

where f(r80) denotes the number of particles in a given
infinitesimal range of the common logarithm of r80,
dlog10r80, introduced into the atmosphere per unit area
per unit time, and F(r80) is the total number flux of
particles of size less than r80.
An expression for the SSSF required as input to
models would represent the size-dependent production

175

flux expressed by Eq. 4.1 as a function of the
controlling ambient variables a, b, . . .; i.e. f(r80;
a, b,. . .). The near-surface wind speed, commonly
measured and expressed at a reference height of 10 m,
U10, is thought to be the dominant factor affecting sea
spray production. Other factors that are expected to
affect the SSA production flux are those affecting sea
state, such as fetch (the upwind distance over the water
of nearly constant wind velocity) and atmospheric stability (often parameterised by the air-sea temperature
difference), which also affects vertical transport; seawater temperature and salinity; and the presence,
amount, and nature of surface-active substances.
As discussed in de Leeuw et al. (2011a), the effect
of water temperature on the resultant size distribution
was investigated by Mårtensson et al. (2003) (at 2  C,
5  C, 15  C, and 25  C) and by Sellegri et al. (2006) (at
4  C and 23  C), while effects of salinity were
investigated by Mårtensson et al. (2003) and Tyree
et al. (2007). Nilsson et al. (2007) compared the
Mårtensson et al. (2003) parameterisation with production fluxes derived from eddy covariance
measurements at Mace Head (assumed water temperature of 12  C) and the Clarke et al. (2006)
parameterisation derived from profile measurements
at the coast of Hawaii (water temperature ca. 25  C).
Both comparisons provided favourable results thus
confirming the effect of water temperature on the
SSA source flux from two independent types of
measurements.
The Mårtensson et al. (2003) experimental data was
used by Sofiev et al. (2011) to derive a modification of
the Monahan et al. (1986) SSSF formulation which
resulted in a temperature and salinity dependent SSSF.
This modified SSSF was implemented in the dispersion model SILAM (Sofiev et al. 2006) and applied to
compute the distribution of sea salt over the North
Atlantic and Western Europe, as well as globally.
The influence of sea surface temperature and salinity
were evaluated using data from several campaigns,
long-term in situ and satellite data (MODIS AOD).
An approach combining satellite observations, in
situ data from six cruises and model results was
presented by Jaeglé et al. (2011). These authors compared model results (GEOS-Chem, with the Gong
(2003) formulation for the SSSF) with MODIS and
AERONET AOD observations and in situ aerosol
measurements. Modelled mass concentrations of
coarse mode sea salt aerosol (SS) were overestimated

176

at high wind speeds over the Southern, North Pacific
and North Atlantic Oceans, but underestimated over
warm tropical waters of the Central Pacific, Atlantic
and Indian Oceans. The in situ observations were used
to derive an empirical SS source function depending on
both wind speed and SST. This resulted in a correction
to the Gong (2003) source function. Using Gong
(2003) with this correction, the model results for
AOD agree significantly better with the MODIS and
AERONET observations and provide an explanation
for the high AOD observed over the tropical oceans.
In contrast, Witek et al. (2007a, b) did not find a
water temperature dependence of the difference
between modelled and measured mass concentrations,
where the NAAPS model was compared with
measurements from five open-ocean shipboard
campaigns covering a range of water temperatures
from less than 10  C to about 30  C. The Mårtensson
et al. (2003) data show a size dependent effect of water
temperature which crosses over at r80 of about
30–40 nm. One may argue that in the mass concentration the water temperature effect would cancel out, but
because the mass is dominated by larger particles one
would expect that the mass increases with increasing
water temperature. Witek et al. included particles with
aerodynamic radius at 55 % RH of up to 5 μm in their
calculation of the sea salt aerosol mass. In NAAPS the
sea salt dry mass emission flux is simply parameterised
as a function of wind speed only (F ¼ 1.37  1013
U103.41 [kg m2 s1]) and has no SST dependence.
Hultin et al. (2011) conducted wave tank
experiments using fresh Baltic Sea water with a salinity of 6–7, much lower than oceanic sea water (salinity
~33). These authors observed a clear dependence
of aerosol production (r80 between 0.01 and 0.9 μm)
on water temperature, i.e. a distinct decrease for all
particle sizes with increasing water temperature
accompanied by a decrease of dissolved oxygen. As
discussed by Hultin et al. (2011) several authors have
studied the effect of dissolved gases on the production
of sea salt aerosol. For instance, Stramska et al. (1990)
observed that more sea salt aerosol particles are
produced in water in which dissolved oxygen is
super-saturated than in water where dissolved oxygen
is sub-saturated. Dissolved oxygen affects the bubble
size distribution and thus also the resulting aerosol
spectral flux (Lewis and Schwartz 2004). However,
Hultin et al. (2011) conclude that the range of
dissolved oxygen encountered during their

4

Ocean–Atmosphere Interactions of Particles

measurements is too small to significantly affect the
bubble size distributions in the size range (>2 mm) of
importance for their measurements and speculate that
the biological activity responsible for the decreased
dissolved oxygen concentrations also alters the surface
chemistry and the surfactant concentrations which in
turn reduce particle production.
Norris et al. (2012) used micrometeorological
measurements of SSA fluxes at the open North Atlantic
to formulate a source function in terms of only U10.
This source function lies within the range of earlier
formulations for particles with r80 < 1 μm but
decreases more rapidly for larger particles.

4.2.1.2 Organic Enrichment of Particulate
Organic Matter in Sea Spray Aerosol
In biologically rich seawater, accumulation of organic
substances at the sea surface can result in enrichment
of organic matter in sea spray particles, especially for
submicron particles (Blanchard 1964; Middlebrook
et al. 1998; O’Dowd et al. 2004). As far back as
1948, Woodcock (1948) showed that drops produced
by bubbles bursting in areas with high concentrations
of plankton could carry irritants across the air-sea
interface. Blanchard (1963, 1964) extended research
into enrichment of organic matter in sea spray and its
subsequent transfer into the atmosphere.
Surface-active OM of biogenic origin (such as
lipidic and proteinaceous material and humic
substances), enriched in the oceanic surface layer and
transferred to the atmosphere by bubble-bursting processes, are the most likely candidates to contribute to
the observed organic fraction in marine aerosol
(Gershey 1983; Mochida et al. 2002).
The observed organic aerosol characteristics are
consistent with laboratory studies on aerosol generated
from Atlantic sea water (Gershey 1983) that showed a
peak in organic aerosol concentration, and a concomitant increase in WIOC (water insoluble organic
carbon) and high-molecular-mass surface-active
fractions, during periods of phytoplankton blooming.
Moreover, the increasing enrichment of the aerosol
organic fraction with decreasing size is consistent
with thermodynamic predictions (Oppo et al. 1999)
of bubble-bursting processes under conditions in
which the ocean surface layer becomes concentrated
with surfactant material that can be incorporated into
sea spray drops in addition to inorganic salts.

4.2

Aerosol Production and Transport in the Marine Atmosphere

177

Fig. 4.2 Average mass concentration of total particulate matter
(white line, right axis) and mass fraction (colours, left axis) of sea
salt, NH4, nss-SO4, NO3, water-soluble organic matter (WSOM),
water-insoluble organic matter (WIOM), and black carbon (BC)
in several size ranges for North Atlantic marine aerosol sampled
at Mace Head, Ireland, in clean marine air during periods of (a)
low biological activity, November (2002) January (2003) and
February (2003); and (b) high biological activity, MarchOctober, (2002). Radius corresponds to relative humidity of

approximately 70 %. For low biological activity mass
concentrations of aerosol constituents other than sea salt were
below detection limits for the size range 0.03–0.06 μm. Oceanic
chlorophyll a concentrations over the North Atlantic for periods
of (c) low and (d) high biological activity are 5 year averages
(1998–2002) over the same months as for the composition
measurements, based on satellite measurements of ocean colour
(Courtesy of SeaWiFS Project, NASA/Goddard Space Flight
Center and ORBIMAGE) (Adapted from O’Dowd et al. (2004))

In 2004, O’Dowd et al. (2004) and Cavalli et al.
(2004) reported significant organic mass enrichment in
submicron aerosol (Fig. 4.2) that possessed a strong
seasonality following the chlorophyll a seasonal pattern. The organic matter comprised both water soluble
and water insoluble organic matter (WSOM/WIOM).
These studies, and a more extended study by Yoon
et al. (2007) suggested that the WIOM was primary in
origin. This suggestion was corroborated by gradient
flux measurements (Ceburnis et al. 2008) at Mace
Head which demonstrated that the WIOM had a
gradient similar to sea salt, indicating a surface
(i.e. primary) source while WSOM possessed a gradient identical to non-sea-salt (nss) sulphate, indicating
transfer from gas phase to aerosol surfaces (i.e. secondary aerosol production).

These N.E. Atlantic results were corroborated
by measurements in the South Atlantic at Amsterdam
Island (Sciare et al. 2009). Since 2008, an Aerodyne
high resolution Time of Flight aerosol mass spectrometer has been continuously deployed at Mace Head
and this continuous database of real-time chemical
composition has led to further elucidation of sea
spray aerosol chemical properties. In particular,
Ovadnevaite et al. (2011a) report the regular occurrence of significant primary organic aerosol plumes at
concentrations often exceeding those reported in
heavily polluted air (e.g. 4 μg m3) and extending
for periods exceeding 24 h. They also reported a
unique primary organic mass spectral fingerprint
hitherto unreported. In that study, it was also reported
that the organic aerosol was 55 % oxygenated and

178

4

Ocean–Atmosphere Interactions of Particles

Fig. 4.3 (a and b) CCN 0.75 % activity (CCN/CN) as a
function of GF (at 90 % RH), chemical composition (colour
scale) and weighted average particle size (size of the circle). CN
is the total particle number above 20 nm in diameter; the colour
scale represents the dominance of a given chemical species.
Measurement periods: 02nd – 27th May 2009, 11th – 28th
August 2009 and 14th July – 12th August 2010. Note, the
measurement periods cover periods much longer than individual plume events. In Figure (a), the boxed region highlights
particles dominated by primary organic matter while Figure

(b) highlights the particles dominated by sulphate. Particles to
the extreme right of both figures are dominated by sea salt mass.
(c) Two organic-dominated size distributions (on 00:00 UTC –
22:00 UTC 16th August 2009 and 13:30 UTC-16:30 UTC 05th
August 2010) and their resultant weighted diameters and (d) the
same for sulphate-dominated distributions (on 21:00 UTC –
22:00 UTC 02nd August 2010 and 06:00 UTC – 10:00 UTC
09th August 2010) (Reproduced from Ovadnevaite et al.
(2011b) by permission of the American Geophysical Union)

45 % hydrocarbon – like. The oxygenated component
must have a very low solubility to be consistent with
the previous off-line WSOM/WIOM ratios reported.
The correlation coefficient between the AMS
hydrocarbon-like and oxygenated organics was 0.97,
pointing to a common source, and a degree of chemical ageing of sea spray. The large enrichment of
hydrocarbon-like organics suggest low water uptake,
as shown in Fig. 4.3 (Ovadnevaite et al. 2011b) where
hygroscopic growth factors for highly enriched

organic particles have a growth factor of 1.3;
however, these aerosols have almost a 100 % CCN
activation efficiency. So, while water uptake is low at
subsaturated conditions (negatively influencing the
direct radiative effect), it can be high in supersaturated
conditions (positively influencing the indirect aerosol
radiative effect).
It has also been speculated that the observed
properties of marine primary organic aerosols may
be driven by the peculiar physico-chemical properties

4.2

Aerosol Production and Transport in the Marine Atmosphere

of marine hydrogels transferred into the atmosphere
through the bubble bursting process. Evidence of the
transfer of biogenic mucus-like exopolymers in sea
spray aerosol have been provided, for instance, by
Leck and Bigg (2005) from transmission electron
microscopy analyses.
Russell et al. (2010) observed an ocean derived
primary organic aerosol component in marine aerosol dominated by carbohydrate-like material, based
on FTIR measurements of submicron marine aerosol
over the North Atlantic and Arctic Oceans and
on Positive Matrix Factorisation data elaboration.
According to the authors, the primary marine signal
in submicron marine aerosol is made on average
for 88 % of hydroxyl groups. The apparent solubility of the carbohydrate-like components in an aqueous phase suggests that DOC provides the source
of most primary organics, although the authors
recognise that in bloom conditions in productive
waters POC could also contribute, as shown by
Facchini et al. (2008).
More recently, Decesari at al. (2011) presented the
results of a multi-technique investigation of the chemical properties of marine organic aerosol collected
during a cruise in the NE Atlantic Ocean that
downsized the role of hydroxyl groups in favour of
carboxyls and carbonyls. Moreover, the work of
Decesari et al. (2011) pointed out that both primary
and secondary processes contribute to the observed
organic aerosol load over remote oceanic regions,
with secondary products comprising both the atmospheric evolution of primary organics and the gas-toparticle conversion processes of volatile organic
precursors emitted by marine biota.
Laboratory Studies
Laboratory studies have partially elucidated the
properties of organic enriched sea spray. For example,
Sellegri et al. (2006) investigated the impact of the
artificial surfactant sodium dodecyl sulphate (SDS) on
bubble-mediated spray production. In particular, they
examined the physical size distribution using online
spectrometers and found that the sub-micron sea salt
distribution was tri-modal with modal diameters of 50,
110 and 350 nm resulting in a distribution which peaks
at a (dry) diameter of 100 nm. With the addition of
SDS, however, the peak diameter reduced to
50–80 nm, depending on the type of bubbling. The
mode at 350 nm became more prominent when SDS

179

was introduced and when the foam was artificially
burst by blowing air over the foam, this mode
dominated. The prominence of the large-diameter
mode in the presence of a surfactant is consistent
with the suggested increase in mean spray size when
enriched in organics (O’Dowd et al. 2004; Yoon et al.
2007). In contrast, Tyree et al. (2007) investigated
spray size distributions produced from natural sea
water for winter and summer DOC concentrations
and found little difference regardless of whether or
not artificial, filtered or unfiltered sea water was
used. Changes were seen, however, in number concentration as 20–40 % more spray droplets were observed
for the winter sample compared to that of the summer.
More recent studies by Fuentes et al. (2011) focussed
on evaluating the impact of nanogel and DOC plankton exudates both in the laboratory and during research
cruises. They found an increase in the production of
particles smaller than 100 nm for organic carbon
concentrations >175 μM. The sea spray produced
contained a volume fraction of organic carbon
8–37 % which was somewhat lower than the maximum enrichment fraction observed in the field.
Fuentes et al. (2011) suggest that the observed shift
to larger mean sizes for enriched sea spray aerosol
observed by Yoon et al. (2007) is inconsistent with
their results. However, Fuentes et al. (2011) conducted
experiments in natural seawater enriched with
organics released by algal laboratory cultures which
were subjected to 0.2 μm filtration in order to remove
bacteria and avoid biodegradation of the organic matter, which may have caused the discrepancy.
Keene et al. (2007) conducted bubble bursting
experiments using highly oligotrophic seawater from
near the Bermuda coast and found enrichment at all
sizes with the enrichment factor increasing with reducing particle size. The most detailed off-line chemical
laboratory study was conducted by Facchini et al.
(2008a) who produced sea spray in plankton rich
North East Atlantic waters amidst a large bloom.
They found that the mass fraction of organic matter
approached 75 % (Fig. 4.4) for the smallest sizes
(down to 0.062 μm diameter) to 20 % at sizes less
than 1 μm. Supermicron particles contained less than a
few percent organic mass fraction. The majority of the
enriched organic matter was water insoluble organic
matter (WIOM) and the mass fraction of WIOM and
sea salt, as a function of size, replicated very closely
the mass fraction observed in air (during the same

180

4

Ocean–Atmosphere Interactions of Particles

Fig. 4.4 (a) Mass fraction of sea salt, water-soluble organic
matter (WSOM), and water-insoluble organic matter (WIOM) as
a function of particle radius sampled at approximately 70 % RH,
(a) for seawater bubble-bursting chamber experiments with fresh
seawater, conducted in a shipboard laboratory in a plankton

bloom over the N.E. Atlantic (May–June 2006), (b) for clean
marine air at Mace Head, Ireland, May-June 2006, and (c) for
clean marine air 200–300 km offshore west–northwest of Mace
Head in a plankton bloom coincident in time with aforementioned
samples (Adapted from Facchini et al. (2008a))

cruise) and that sampled previously at Mace Head.
This comparison suggests that the vast majority of
the WIOM observed in clean air samples are primary
in origin. Facchini et al. (2008a) reported that the
WIOM consisted of colloids and aggregates exuded
by phytoplankton.

Figure 4.5 illustrates the global distribution of submicron sea salt and water insoluble organic matter using
the parameterisation of Vignati et al. (2010) with the
TM5 (Tracer Model 5; Krol et al. 2005) chemical
transport model.
The studies by Lapina et al. (2011) and Gantt et al.
(2011) are more advanced in that Lapina et al. (2011)
apply a water temperature dependent source function,
while Gantt et al. (2011) extended the scheme to
include a wind speed dependency of organic
enrichment – that is, the OM enrichment decreases
with wind speed, but still the net OM increased with
wind speed. Using this new scheme, Gantt et al. (2011)
improved significantly the agreement between measured
and predicted OM mass, as illustrated in Fig. 4.6.
Meskhidze et al. (2011) applied the Gantt et al.
(2011) scheme and the Vignati et al. (2010)
parameterisation, modified using Facchini et al. (2008a)
to include size dependence, with the NCAR Community Atmosphere Model CAM5. The findings of
Meskhidze et al. (2011) are that different mechanisms
contribute to the marine OM fluxes, with a major
contribution of marine organic aerosols to the submicron organic aerosol mass over the tropical and midlatitudes, while methane sulphonate dominates at high
latitudes. The Gantt et al. (2011) parameterisation
yields a more accurate representation of the seasonal
cycle of marine organic aerosol mass concentrations
than Vignati et al. (2010).
One question, however, continues to arise given
that the OM enrichment scheme is based on
chlorophyll a concentration fields and that is: “Is

Global Distribution of Organic Enrichment
A number of international studies have expanded the
measurement picture emerging from NE Atlantic
waters, corroborating the findings of OM enrichment
in seaspray. Namely, the following cruises: MAP
(Marine Aerosol Production, e.g., Facchini et al.
2008a), OOMPH (Organics over the Ocean Modifying
Particles; Zorn et al. 2008), ICEALOT (International
Chemistry Experiment in the Arctic LOwer Troposphere; Russell et al. 2010; Frossard et al. 2011), and
RHaMBLe (Reactive Halogens in the Marine Boundary Layer; Lee et al. 2010), all demonstrating the
enrichment of OM in sea spray, albeit to different
degrees. O’Dowd et al. (2008) integrated the studies
of O’Dowd et al. (2004), Cavalli et al. (2004) and
Yoon et al. (2007) with the eddy correlation microphysical flux measurements of Geever et al. (2005) to
produce the first combined organic–inorganic sea
spray source function and applied it to the REMOTE
(REgional MOdel with Tracer Extension) regional
climate model. This “chemical” parameterisation for
organic enrichment could be applied to any sea spray
physical source function and was indeed applied to
global budgets by Langmann et al. (2008), Vignati
et al. (2010) and Myriokefalitakis et al. (2010).

4.2

Aerosol Production and Transport in the Marine Atmosphere

0.0

0.1

0.00

0.2

0.01

0.5

0.02

1.0

2.0

0.50

Fig. 4.5 Global distribution of mass flux of sea salt (upper
panel) and water-insoluble organic matter WIOM (lower
panel) in sea spray, with 0.1 μm < r80 < 1 μm averaged over

181

-2

5.0 10.0 20.0 50.0 g m

0.10

0.20

-2

0.50 g m

yr

yr

-1

-1

a 1-year period in 2002–2003 using the TM5 chemical transport
model as described in Vignati et al. (2010) (E. Vignati, private
communication 2010)

182

4

0.8

Ocean–Atmosphere Interactions of Particles

chlorophyll a the best surrogate for OM enrichment?”
Russell et al. (2010) suggest there was little or no
relationship. A similar conclusion was reached by
Lapina et al. (2011). However, Gantt et al. (2011)
found that the OM enrichment was best correlated to
chlorophyll a rather than to DOM or POM. The original parameterisation was based on correlating
satellite-derived chlorophyll a concentrations, in a
box 1,000 km  1,000 km west of Mace Head, with
the measured OM enrichment fraction. While the correlation coefficient was significant, it was still quite
low (r ¼ 0.3). A sensitivity study shows the effect of
the formulation of this parameterisation and various
modifications thereof (Albert et al. 2012). Rinaldi et al.
(2013) reassessed the relationship between OM enrichment and chlorophyll a using analysis chlorophyll a
data from the ESA Data Users Element (DUE)
project GLOBCOLOUR (http://www.globcolour.info/).
The reanalysis combined SeaWifs, MODIS and
MERIS platforms, interpolated to reduce data loss
due to clouds. The results are impressive with r
increasing from 0.3 to 0.75–0.8. This study also
reports that chlorophyll a is the best surrogate for
OM enrichment in sea spray.

Secondary Inorganic Aerosol Formation
In a cloudy marine boundary layer, the submicron
marine aerosol size distribution is generally bimodal
with an Aitken mode at sizes less than 100 nm and an
accumulation mode at sizes larger than 100 nm
(cf. Fig. 4.1). This bimodality has been shown to result
from chemical processing (aqueous phase oxidation of
SO2) in non-precipitating clouds (Hoppel et al. 1986).
This was corroborated by O’Dowd et al. (1999a, b)
who illustrated through airborne measurements of progressive cloud cycling in marine stratocumulus that
the accumulation mode modal diameter increased
from ~158 to ~194 nm. This growth was observed to
occur over four cloud cycles, each taking approximately 40 min. During simulations of this case study,
30 % nss-sulphate production occurred in droplets
activated on sulphate nuclei, with the remainder
being produced on droplets activated on sea salt.
Detailed cloud parcel modelling studies of the heterogeneous oxidation of SO2 to aerosol sulphate were
conducted by O’Dowd et al. (2000) both for activated
droplets and un-activated haze particles. The
simulations showed that dissolved ozone and hydrogen peroxide were the dominant oxidants, with the
ozone oxidation pathway dominant on sea salt

Predicted Organic Mass Fracon (OMSSA)

Fig. 4.6 Scatterplot of predicted versus observed organic mass
fraction of sea spray aerosol < 1.5 μm in diameter for the Mace
Head Atmospheric Research Station (53.33 N, 9.90 W) in red
and of sea spray aerosol < 2.5 μm in diameter for the Point
Reyes National Seashore IMPROVE site (38.12 N, 122.91 W)
in blue, with the 1:1 line as a black dotted line (Adapted from
Gantt et al. 2011)

4.2.1.3 Secondary Aerosol Formation in the
Marine Atmospheric Boundary Layer
Secondary aerosol formation is the production of
aerosol via gas-to-particle conversion processes.
Such processes include homogeneous nucleation of
stable clusters, condensation processes, aqueous
phase chemical reactions converting dissolved gasses
into aerosol mass, and heterogeneous chemical
reactions on the surface of particles. Historically,
nss-sulphate (i.e. the sulphate not associated with
primary sea spray) has been considered the dominant
secondary marine aerosol species (Shaw 1983;
Charlson et al. 1987) and is one of the main oxidation
products of dimethylsulphide (DMS), a plankton
waste gas. Ammonium can form a significant contribution to marine secondary aerosol; however, it does
not form aerosol on its own, more so, it requires the
presence of an acid aerosol which it can neutralise.
Typically, the most abundant acid is sulphuric acid;
however, a range of organic acids, including methane
sulphonic acid (MSA), also an oxidation product of
DMS, can also be present in significant amounts, as
will be discussed later.

Mace Head
Point Reyes

1:1 Line

0.6

0.4

0.2

0

0

0.2

0.4

0.6

0.8

Observed Organic Mass Fracon (OMSSA)

4.2

Aerosol Production and Transport in the Marine Atmosphere

activated droplets. The production of sulphate across
the aerosol size distribution is non-linear and a significant amount of sulphate production (75–90 %)
occurred in sea salt based droplets. The number concentration of activated salt nuclei also significantly
influences the total amount of sulphate produced.
Below cloud, the amount of sulphate produced in sea
salt aerosol is limited by the carbonate buffering
capacity (Sievering et al. 1992). However, once
activated, in-cloud production can exceed by many
times the cloud-free production due to the transient
buffering capacity of activated droplets (O’Dowd et al.
2000). Up to 2 μg m3 nss-sulphate could be produced
at SO2 concentrations of the order of 500 ppt. Organic
acids and nitric acid tend to be more associated with
the sea salt modes and reduces alkalinity and consequently the amount of sulphate produced.
In terms of sulphate aerosol, nss-sulphate is typically
partially neutralised by ammonia to different degrees,
leading to sulphuric acid, ammonium bisulphate or
ammonium sulphate. In Polar Regions, nss-sulphate is
typically in the form of sulphuric acid (O’Dowd et al.
1997) with the ammonium to sulphate molar ratio
increasing at lower latitudes.

Secondary Organic Marine Aerosol
The second most abundant aerosol sulphur species is
methane sulphonic acid (MSA), an organic acid which
is also an oxidation product of DMS. It is the single
most dominant secondary organic species (Facchini
et al. 2008b). MSA in the aerosol phase results from
condensation of gas phase MSA; however, it appears
to be semi-volatile as gas phase concentrations have
been observed to be inversely correlated with dew
point, reflecting a sensitive equilibrium partitioning
(Berresheim et al. 2002).
Over the past few years studies over marine
remote regions have allowed identification of typical
marine SOA components, other than MSA and DMS
oxidation products. The presence of monomethylammonium (MMA+), dimethylammonium (DMA+)
and trimethylammonium (TMA+) salts in marine aerosol particles was reported for the first time by Gibb
et al. (1999). Their presence was attributed to secondary production, suggesting the condensation of volatile alkyl amines, degassed from the sea, through
acid–base reactions, in analogy with NH4+. This
hypothesis has been strengthened, in recent years, by

183

the observation that alkyl amines participate in SOA
formation in different environments reacting with
acids (Murphy et al. 2007; Angelino et al. 2001; Tan
et al. 2002).
Facchini et al. (2008b) report DMA+ and diethylammonium (DEA+) salt concentrations ranging,
together between <0.4 and 56 ng m3 in submicron
marine aerosol particles collected over the North
Atlantic Ocean during Spring and Summer. The
authors highlight the importance of alkylammonium
salts in marine aerosols, observing that they are the
most abundant organic species, after MSA, in submicron marine particles. Alkyl-ammonium salts represent on average 11 % of the marine SOA and 35 % of
the aerosol water soluble organic nitrogen (WSON).
Facchini et al. (2008b) present also considerable evidence that DMA+ and DEA+ are secondary aerosol
components, originating from biogenic precursors
emitted by the ocean. The maxima in the accumulation
mode, as is the case for other well-known secondary
components (nssSO4, NH4, MSA), supports the
hypothesis of a gas-to-particle conversion process
responsible for the accumulation of alkyl-ammonium
salts in the fine aerosol fraction. Moreover, DMA+ and
DEA+ concentrations measured at Mace Head were
always higher in clean marine samples than in polluted
air masses, as for MSA, therefore a natural biogenic
source is very likely.
Confirming the findings of Facchini et al. (2008b),
Müller et al. (2009) reported non-negligible
monomethylammonium (MA+), DMA+ and DEA+ in
submicrometer particles at Cape Verde, during the
season of enhanced oceanic biological activity. Also,
Sorooshian et al. (2009) observed DEA+ in submicron
particles over the North Pacific Ocean, reporting a
certain correlation with chlorophyll a sea surface
concentrations, further supporting the hypothesis of a
biogenic origin of marine aerosol amines.
Besides alkylammonium salts and MSA, carboxylic and di-carboxylic acids are commonly reported in
marine aerosol (Kawamura and Sakaguchi 1999;
Claeys et al. 2010; Crahan et al. 2004; Sorooshian
et al. 2007; Aggarwal and Kawamura 2008), accounting for less than 10 % of total particulate organic
carbon. Usually, a secondary origin is attributed to
the detected di-carboxylic acids (Kawamura et al.
2010), of which oxalic acid is often reported as the
most abundant (Kawamura et al. 1996a, b). However,
oxidised organics, such as C5–C10 carboxylic or

184

di-carboxylic acids, can also be produced by the
oxidative degradation of primary particles generated
by sea spray and rich in fatty acids (Kawamura
and Sakaguchi 1999). Recently, Rinaldi et al. (2011)
presented convincing evidence that an important fraction of marine aerosol oxalic acid may derive from the
oxidation, in clouds, of glyoxal.
Recent instrumental advances have allowed a
deeper insight into the chemical composition of
marine organic aerosols. Using liquid chromatography/negative ion electrospray ionisation mass spectrometry, Claeys et al. (2010) investigated marine
organic aerosol chemical composition at Amsterdam
Island (Southern Indian Ocean). They managed to
characterise about 25 % of the analysed marine aerosol
WSOC, which is a remarkable result. MSA (17–21 %),
oxalate (5  2 %), malonate (1.8  0.9 %) and
organosulphates (0.8  1.5 %) were the major
identified components. The organosulphates characterised in Claeys et al. (2010) can be considered tracers
for an SOA formation process that is specific to the
marine environment, that is, oxidation of marine biomass. More specifically, the organosulphates correspond to sulphate esters of C9–C13 hydroxyl
carboxylic acids, which are attributed to oxidation of
unsaturated fatty acid of phytoplanktonic origin.
In addition, Decesari et al. (2011) demonstrated,
through an ensemble of NMR and LC-MS analyses,
that marine aerosol WSOC, the fraction traditionally
associated to SOA, is the combination of a less hydrophilic fraction, consisting of a distribution of C8–C9
alkanoic acids and diacids, and of a more oxidised,
more hydrophilic fraction, where sulphate esters of
C6–C11 hydroxycarboxylic acids were found together
with MSA and low-molecular weight amines and
acids. These results highlight the complexity of the
chemical composition of marine SOA and provide
evidence for the coexistence in marine SOA of the
products of the atmospheric oxidation of primary biogenic materials emitted within sea spray and of
compounds deriving from the gas-to-particle conversion of volatile organic compounds emitted by marine
biota.
Further, Zhou et al. (2008) found evidence that
primary organic matter emitted within sea spray is a
dominant sink for the OH radical, with its consequent
degradation and the likely production of a series of
low-molecular weight organic compounds. These can

4

Ocean–Atmosphere Interactions of Particles
Ox

SOA

Volatilisation/
Condensation

Ox

POA

VOCs

Ox

Ox

OVOCs

SEA
Fig. 4.7 Conceptual picture of the interactions of organic aerosol sources within the marine boundary layer

partition into the gas phase and contribute to SOA
formation.
The picture emerging from these results is a complex one, in which primary and secondary aerosol
sources interact to generate the observed organic
aerosol burden in the MBL. These interactions are
schematically shown in Fig. 4.7. Notwithstanding
recent improvements, current knowledge of the chemical composition of marine SOA and on their formation mechanisms remains limited and further research
is required to address the many unresolved issues.
New Particle Formation in the Marine Boundary
Layer?
While the majority of the secondary marine aerosol
mass is thought to be produced via heterogeneous
and/or condensation processes, the number concentration of secondary marine aerosol is determined by
homogeneous nucleation, or new particle formation.
New particle production may occur in the marine
boundary layer (Russell et al. 1994; Pandis et al.
1994) or in the free troposphere after which it can be
entrained into the marine boundary layer (Raes 1995),
although the relative importance of these two sources
has been an issue of debate. Over the open oceans,
there have only been a few recorded observations of
new particle formation (Covert et al. 1992, 1996a, b;
Clarke et al. 1998; Hoppel et al. 1994; Ehn et al. 2010).
Observations of a significant nucleation event by
Kollias et al. (2004) in the southeastern Pacific appear
to be due to emissions from South America (Tomlinson
et al. 2007).

4.2

Aerosol Production and Transport in the Marine Atmosphere

185

Fig. 4.8 (Top) N3–20 concentrations in clean marine air at Mace
Head for a 12 day period in August 2009, illustrating clean
nucleation mode event occurrence. (Middle) Combined
nSMPS and standard SMPS-derived aerosol size distributions
(3–500 nm diameter) corresponding to selected aerosol growth

event on JD 236–237 and (bottom) on JD 240–241. A coastal
event is evident and distinct from the open ocean event on
JD240.6-JD240.8 (Reproduced from O’Dowd et al. (2010) by
permission of the American Geophysical Union)

Examination of some of the experimental data
indicates that under typical marine conditions, nucleation does not occur. Ultrafine particles observed over
the Pacific by Covert et al. (1992) could be explained
by entrainment from the free troposphere, while ultrafine particle concentrations observed in the tropics
were considered to result from in situ particle nucleation relating to the natural DMS cycle (Clarke et al.
1998). However, ultra-fine particles appear to occur in
polar marine air masses in the Antarctic region
(O’Dowd et al. 1997) and in a very recent analysis
of nucleation and Aitkin mode aerosol in North East
Atlantic air sampled at Mace Head, O’Dowd et al.
(2010) report the regular appearance of a recently
formed nucleation mode (D ~ 10–15 nm) followed

by subsequent growth to sizes of 50 nm over
24–48 h timescales (O’Dowd et al. 2010), as shown
in Fig. 4.8.
A detailed aerosol nucleation and dynamics parcel
modelling study, with monte carlo simulations, by
Pirjola et al. (2000) evaluated the likelihood of new
particle formation occurring in the marine boundary
layer, taking DMS-derived sulphuric acid as the main
nucleating candidate. These authors considered both
binary nucleation of sulphuric acid and water and
ternary nucleation of sulphuric acid, water and ammonia under a range of realistic aerosol regimes. They
concluded that the occurrence of new particles in the
unperturbed marine boundary could not be explained
by known natural sources of sulphur species or DMS;

186

and the occurrence of new or ultra-fine particles could
only be explained by the presence of additional condensible species, which are required to grow newly
formed clusters to detectable sizes before they are
scavenged by the pre-existing aerosols through coagulation processes.
Iodine oxides have also been implicated in marine
new particle formation events, particularly in coastal
air (O’Dowd et al. 1998, 1999a, b, 2002). Laboratory
experiments (Hoffmann et al. 2001; Jimenez et al.
2003; Burkholder et al. 2004) have pointed to the
rapid photolysis and subsequent oxidation by ozone
of CH2I2, released from macro-algae. McFiggans et al.
(2004), Saiz-Lopez et al. (2005) and Sellegri et al.
(2005) demonstrated that I2 was the dominant precursor with Sellegri et al. (2005) reporting a linear
relationship between I2 concentrations and 3.0–3.4 nm
sized particle concentrations. However, some differences in the exact form of iodine oxides is still an
issue of debate (Hoffmann et al. 2001; Jimenez et al.
2003; Saunders and Plane 2005).
Such new particle formation events are related to
strong coastal emissions of halogen precursors (Mäkelä
et al. 2002), resulting in concentrations of the order of
107 cm3. O’Dowd et al. (2002b) suggested that significantly lower concentrations of iodine oxide precursor
condensible vapours (i.e. away from strong coastal
sources) could provide either the nucleation mechanism
for embryo formation (typically <1 nm in size) and/or
the additional condensible vapours required to grow
stable sulphate clusters into stable aerosol sizes of a few
nanometers (>5–10 nm). If a particle grows to a size of
6 nm, it has a 100 times greater likelihood to survive
coagulation loss as compared to a 1–2 nm sized particle.
While iodine oxides may be involved in open ocean
nucleation and/or growth, due to the very short
lifetimes of these species, it has proved very difficult
to specify their role. For that matter, it has also been
impossible to elucidate, from experimental field studies, what species are actually involved in the nucleation process and if additional species are required to
explain growth of clusters into aerosol particles. This
is because of the inability, until recently, to measure
particles in the nucleation size range.
However, recent deployment of mass spectrometers
has provided realtime information on condensing
vapours as detected in conjunction with the appearance
and growth of a nucleation mode (Dall’Osto et al. 2012).
While not direct evidence of participation of either

4

Ocean–Atmosphere Interactions of Particles

nucleation or growth of clusters into sizes larger than
3 nm, the quantification of additional condensible aerosol mass during such an event strongly suggests that the
same condensible vapours are also responsible for the
initial stages of growth. This study pointed to the coincidence of nitrogenated and aliphatic condensible vapours
as being responsible for the observed nucleation modes
and subsequent growth. In the same study it was found
using quantum-chemistry calculations that nucleation of
sulphuric acid, dimethylamine and subsequent condensation of MSA decreases cluster evaporation rates (and
hence promotes cluster-to-aerosol formation rates).

4.2.2

Non-Marine Sources

4.2.2.1 Desert Dust
Desert aerosol is composed of mineral crustal particles
suspended from surface soils by aeolian erosion. Desert and semi-arid areas are a major source of particles
in the global atmosphere (e.g. Prospero et al. 2002)
and large oceanic areas are regularly under the direct
influence of turbid air masses transported from deserts
or semi-arid areas, as shown by Fig. 4.9. These dust
transport events cause long-range transfers from continent to the remote surface ocean of huge amounts of
matter. It has long been recognised that mineral dust
deposition to the remote surface ocean significantly
influences trace element biogeochemistry (BuatMénard and Chesselet 1979; Graham and Duce
1979), marine productivity (Martin and Fitzwater
1988), and deep-sea sedimentation (Venkatarathnam
and Ryan 1971; Loÿe-Pilot et al. 1986). More recently,
it has been suggested that suspended dust particles
affect the optical properties of clear surface waters
(Claustre et al. 2002).
Due to the trade wind regimes, dust from the Sahara
and Sahel is encountered all year long over the tropical
Atlantic (Moulin et al. 1997) and as a consequence
dominates the solar extinction by aerosol particles in
this region on a yearly time scale (Mahowald et al.
2009). Other regions of the world ocean are subject to
dust transport and deposition from more seasonal or
episodic dust events. Whereas the Sahara-Sahel region
dominates dust deposition in the Mediterranean, in most
of the North Atlantic and even in regions of the North
tropical Indian and Pacific Oceans, source regions in
China dominate dust deposition in the North Pacific
and Arctic. Middle East sources are most important for

4.2

Aerosol Production and Transport in the Marine Atmosphere

Fig. 4.9 Desert dust on the move over the tropical Atlantic.
This colour combination of visible and infrared Meteosat images
shows a huge desert dust plume transported from Africa. The
total mass of dust transported was several millions of tons
(Figure courtesy of X. Schneider, CEA)

dust deposition in the Arabian Sea and Bay of Bengal.
Sources in North America provide dust deposited in the
North tropical Pacific and northwestern Atlantic while
those in South America dominate dust deposition in the
South tropical Pacific, the southern Atlantic and the
Indian Ocean. Sources in South Africa contribute dust
to the South tropical Atlantic and Indian Oceans and
Australian sources dominate dust deposition in the southern Pacific and subtropical South Indian Ocean,
(Grousset and Biscaye 2005; Mahowald 2007, Fig. 4.10).
Fine dust particles in the long-range transported
aerosol size range (diameter smaller than ~20 μm)
are strongly bound and not easily mobilised in arid
soils, but tend to form large aggregates of fine particles
(e.g. clays) or stick to larger particles (e.g. sand grains)
which compose the particle size modes found in arid
soils (Chatenet et al. 1996). The production of soil dust
aerosols is a two-step process (Gomes et al. 1990)
firstly resulting from the mobilisation of those large
loose soil grains when the surface wind reaches a
sufficient velocity (>5–15 m s1 depending on surface
characteristics; Goudie and Middleton 2006) to lift
them and produce the so-called saltation of particles,
bouncing downstream as commonly seen on sand
beaches. Secondly saltating grains then produce

187

small aerosol particles by a sandblasting effect when
they settle out and impact the surface soil,
disintegrating aggregates or producing small debris.
The most productive source areas combine the presence of fine sand grains of ~60–100 μm in diameter
that require the minimum threshold friction velocity
for being lifted (Iversen and White 1982) and alluvial
deposits of aggregated fine clay materials (Prospero
et al. 2002) that have the best potential to produce fine
aerosol particles (Marticorena et al. 1997).
According to Alfaro et al. (1998) the particle
size distribution of aeolian dust aerosol can be
approximated by a sum of three lognormal modes
with respective mass-median diameters of the order
of 1.5, 6.7, and 14.2 μm. (a similar particle size distribution is also produced when clay aggregates from
arid soils are crushed and sieved for a long time,
allowing the production of a dust aerosol model for
use in experiments; Guieu et al. 2010). The relative
proportions of the modes are a function of surface
wind speed, with larger wind velocities producing
more fine particles (Alfaro et al. 1997). Following
settling of the largest particles during transport, the
mass-median diameter of desert dust over ocean is
generally a few microns (e.g. Arimoto et al. 1985,
1997; Dulac et al. 1989; Dubovik et al. 2002; Reid
et al. 2003a). However, a number of authors report
observations of “giant” sand-sized (>62.5 μm in diameter) dust particles of aeolian origin at very long
distances from sources (e.g. Betzer et al. 1988, see
also Goudie and Middleton 2006, p. 31). Compared
to other super-micron sized aerosol particles such as sea
salt aerosol particles which adsorb water, desert dust
aerosol particles are characterised by a non-spherical
shape, which can be explained by the laminar structure
of the clay minerals that are abundant in desert dust
aerosols. This irregular shape results in optical
properties, i.e. the angular scattering of solar light,
which complicate the inversion of dust properties
from passive remote sensing data in the solar spectrum
(Mishchenko et al. 1995; Dubovik et al. 2002). The
irregular particles cause a relatively high rate of depolarization of the scattered light which is useful for
identifying dust layers in aerosol lidar remote sensing
(Sassen 2000). Another specific optical property of
desert dust, responsible for their colour, is their ability
to absorb the shortest (UV-blue) solar wavelengths
(Moulin et al. 2001). This absorption is controlled
by the presence of iron oxides (Alfaro et al. 2004).

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4

Ocean–Atmosphere Interactions of Particles

Dominant Source

Asia

South America

North Africa

Australia

North America

Central Asia and
South Africa

Fig. 4.10 A model view of source regions dominating the
distribution of dust deposition fluxes (From Mahowald et al.
2009). Models generally reproduce data on dust provenance

(Grousset and Biscaye 2005) with the exception of the
Greenland region which is dominated by Asian dust (Bory
et al. 2003)

It causes radiative heating of the atmospheric turbid
layers (Alpert et al. 1998) and reinforces a decrease in
incoming UV radiation that impacts dissolved organic
matter and phytoplankton organisms in surface waters
(Tedetti and Sempéré 2006).
Due to the minimum threshold surface wind speed
required for aeolian erosion, and to the fact that the
dust emission flux varies with the cube of the surface
wind speed (Greeley and Iversen 1985), dust events
are episodic and show a very high variability in
strength. In terms of desert dust atmospheric concentrations or deposition at a given place, the intensity
distribution at yearly or longer time scales generally
shows a tail towards infrequent high values that control long-term means (Loye-Pilot and Martin 1996;
Mahowald et al. 2009 and references therein on
observations series).
Desert dust transport is generally characterised by
maximum concentrations of aerosol particles in turbid
air layers lifted above the marine atmospheric boundary layer, as was observed in the North Pacific (Kritz
et al. 1990), the tropical (Carlson and Prospero 1972;
Karyampudi et al. 1999; Dulac et al. 2001) and

subtropical (Chazette et al. 2001) Atlantic Ocean and
the Mediterranean Sea (Dulac et al. 1996; Hamonou
et al. 1999). Desert dust settles from the atmosphere to
the surface both through gravitational settling and wet
deposition, with precipitation either within or below
the clouds. Wet deposition of Saharan dust often
produces highly concentrated red rains (Avila et al.
1998); mentioned as ‘blood’ rain in Homer’s Illiad.
Dry deposition is generally dominant only close to
source regions and is controlled by the small fraction
of the largest dust particles (Dulac et al. 1989; Arimoto
et al. 1997). It seems that atmospheric processes such
as the large-scale vertical upward movement of dustloaded air masses counteract the gravitational settling
velocity of dust particles (Dulac et al. 1992; Maring
et al. 2003).
The chemical composition of desert dust aerosol
particles reflects that of the average Earth surface
rocks with a dominance of SiO2 (~60 %) and Al2O3
(10–16 %) resulting from the dominance of quartz and
clay minerals (Goudie and Middleton 2006). Either Si
or Al are considered as chemical tracers of the soil
dust fraction in aerosols. The relative contributions of

4.2

Aerosol Production and Transport in the Marine Atmosphere

various clay minerals or minor constituents such as Fe
and Ca can generally be used to identify various dust
source regions (Bergametti et al. 1989, Goudie and
Middleton 2006). The high carbonate content of desert
dust is principally responsible for relatively high pH
values of rainfall affected by dust particles, with
values sometimes larger than six (Loÿe-Pilot et al.
1986; Avila and Rodà 2002). The deposition of desert
dust may be a significant source of limiting nutrients
such as Fe (Jickells et al. 2005; Mahowald et al. 2009)
to remote surface waters (e.g. Bergametti et al. 1992;
Ridame and Guieu 2002), despite the relatively low
solubility as compared to that of anthropogenic
aerosols (Bonnet and Guieu 2004; Baker et al.
2006a). However, solubility is likely to be enhanced
by the condensation/evaporation cycle encountered in
cloud formation (Desboeufs et al. 2001). Contrary to
previous hypotheses, it has been recently shown that
the solubility of iron from desert dust is controlled by
the iron content in clay minerals rather than by the
abundance of iron oxides (Journet et al. 2008).
The interested reader is referred to Goudie and
Middleton (2006) for a more comprehensive review
of the literature on desert dust related questions.

4.2.2.2 Volcanic Gases, Aerosols and Ash
Volcanic emissions are important sources of atmospheric gases (e.g. Bardintzeff and McBirney 2000),
aerosols and ash (e.g. Mastin et al. 2009). Volcanic
gas emissions consist primarily of H2O, followed by
CO2, SO2, HCl, HF and other compounds. These gases
and their oxidation products (in particular sulphate
aerosols) may play an important role in the tropospheric and stratospheric chemistry and can impact
terrestrial and oceanic ecosystems and human health.
H2O and CO2 are important greenhouse gases, but their
atmospheric concentrations are so large that volcanic
eruptions have only a negligible effect on their
concentrations (Robock 2000), although locally the
release of CO2 might have important environment
effects.
Volcanic ash is a size class referring to fragmented
fine-grained particles with diameters of submicron to
less than 2 mm. Tephra is the general term for
fragmented volcanic material produced during volcanic eruptions that includes ash particles (<2 mm),
lapilli (2–64 mm), and bombs and blocks (>64 mm)
(Fisher and Schmincke 1984; Schmincke 2004).
Volcanic emissions can be released continuously
by passive degassing or diffusive (soil) degassing into

189

the troposphere. Most of the volcanic SO2 in the atmosphere is released from relatively less explosive continuous volcanic activity compared to episodic large
scale eruptions (Andres and Kasgnoc 1998). About
99 % of volcanic SO2 is released continuously, while
only 1 % is released during sporadic eruptions (Andres
and Kasgnoc 1998) (see Table 4.1 for the frequency of
eruptions based on the eruption magnitude).
One of the most important climatic effects of explosive volcanic eruptions is through their emission of
sulphur species to the stratosphere, mainly in the form
of SO2 which reacts with OH and H2O to form sulfate
aerosols on a timescale of weeks, producing one of the
dominant radiative effects from volcanic eruptions
(Robock 2000). Volcanic SO2 release into the atmosphere on a 100 year scale (between 1900 and 2000) is
estimated to be 8–11  1012 g S year1, contributing
8–11 % of the total global sulphur emissions of
100  1012 g S year1, which includes emissions
from biomass burning, other anthropogenic sources
and the marine-derived dimethylsulphide (Halmer
et al. 2002).
The global annual direct radiative forcing of
sulphate aerosols at the top of the atmosphere by
volcanic sulphate is estimated to make up 33 % of
the total sulphate forcing (Graf et al. 1997) thereby
exceeding the percentage contribution of volcanic
SO2 emissions by a factor of about three. Even the
silent degassing volcanoes release their emissions
into higher atmospheric levels compared to most
anthropogenic sulphur emissions, and therefore provide longer atmospheric lifetime of volcanic sulfur
species. In particular, volcanic sulphate aerosols
from plinian eruptions, like the Pinatubo June 1991
eruption, may influence solar radiation reaching the
Earth surface for years, as indicated by the enhanced
aerosol optical depth (AOD) after the eruption
(Fig. 4.11). Similarly, the Mt. Hudson (Chile) August
1991 eruption may have contributed considerably to
the Southern Hemisphere AOD. Reduced solar radiation also affects marine primary productivity (MPP).
It should be also considered in satellite retrieval
algorithms e.g. for surface ocean chlorophyll a
concentration.
Volcanic ash and aerosols can be transported over
long distances to remote parts of the ocean (Fig. 4.12).
Upon deposition in the ocean, volcanic ash can release
nutrients as well as toxic substances into the seawater
(Frogner et al. 2001; Duggen et al. 2007; Jones and
Gislason 2008; Hamme et al. 2010; Langmann et al.

190

4

Ocean–Atmosphere Interactions of Particles

Table 4.1 The type and frequency of volcanic activity based on Volcanic Explosivity Index (VEI) (Newhall and Self 1982; Simkin
and Siebert 1994)
VEI
0
1
2
3
4
5
6
7
8

Plume height (km)
0.1
0.1–1
1–5
3–15
10–25
>25
>25
>25
>25

Eruptive volume (km3)
106
105
103
102
101
1
10
100
1,000

Eruption type
Hawaiian
Hawaiian/Strombolian
Strombolian/Vulcanian
Vulcanian
Vulcanian/Plinian
Plinian
Plinian/Ultra-Plinian
Ultra-Plinian
Ultra-Plinian

Frequency
Continuous
Months
Months/year
Year/few years
Year/few years
5–10 years
1,000 years
10,000 years
100,000 years

Example
Kilauea
Stromboli
Galeras
Puyehue (2011)
Eyjafjallaökull (2010)
Pinatubo (1991)
Krakatoa (1883)
Tambora (1815)
Toba (74 ka)

Fig. 4.11 Optical depth of stratospheric aerosol during four
periods between April 1991 and January 1994 (http://wwwsage2.larc.nasa.gov/Introduction.html). The eruption of

Pinatubo took place in June 1991, Mt. Hudson erupted in August
1991 (Figure courtesy of NASA)

2010; Lin et al. 2011; Olgun et al. 2011). Therefore,
volcanic ash may affect marine primary productivity,
phytoplankton community structure, atmospheric
CO2 concentrations and can eventually (directly or
indirectly) impact higher trophic levels the oceanic
food-web (e.g. of zooplankton, fish). For the marine

ecosystem response related to volcanic eruptions
(Chap. 5: Sect. 5.2.2).
The importance of volcanic eruptions for the biogeochemistry of the surface ocean, however, has gained
limited attention compared to the much better
investigated effects of mineral dust. This is despite the

4.2

Aerosol Production and Transport in the Marine Atmosphere

(MODIS, NASA)

Ash plume

Puyeheu
(Chile)

Atlantic
Ocean

Fig. 4.12 Satellite image shows the long distance transport of
volcanic eruption plumes that are ejected to high altitudes in the
atmosphere as illustrated by the Puyehue eruption in Chile on 6
June 2011 with the white ash plumes reaching more than 10 km
altitude and transported across Argentina towards the Atlantic
Ocean (Captured by MODIS, NASA)

fact that an average of about 20 volcanoes erupt at any
given time, 50–70 volcanoes erupt every year, and at
least one large eruption occurs every year (e.g. Puyehue
(Chile) and Grimsvötn (Iceland) in 2011, Table 4.1).
Recent estimates based on marine sediment core data
show that about 128–221  1015 g ka1 (ka ¼ 1,000
years) of volcanic ash has been deposited into the Pacific
Ocean, the largest ocean basin covering 70 % of the ironlimited ocean regions (Olgun et al. 2011). The flux of
volcanic ash is of the same order of magnitude as that of
mineral dust, which is around 39–519  1015 g ka1
(Rea 1994; Mahowald et al. 2005). On longer timescales (e.g. during Holocene) the amount of volcanic
ash deposition is comparable to that of mineral dust
(Olgun et al. 2011), although marine biogeochemical
impacts probably differ. Ocean regions with higher likelihood of volcanic ash deposition are shown in
Chap. 5: Sect. 5.2.2.
The amount of volcanic ash and bio-available iron
attached to the ash surface deposited into the ocean
during large episodic volcanic eruptions may exceed
the annual dust flux significantly. For example, iron
input during the large eruption of Mount Hudson
(Chile) between 12th and 15th August 1991 has been
found to be equivalent to ~500 years of Patagonian iron
dust fallout (Gaiero et al. 2003). Re-mobilization of
well-preserved tephra deposits in dry regions can also
impact the marine ecosystems after the eruptions (posteruption impacts). The eruption of Mount Hudson, for

191

example, created several volcanic ash clouds (ash
storms) which would have different chemical behaviour
compared to mineral dust (Wilson et al. 2011).

4.2.2.3 Global Emissions of Biogenic Volatile
Organis Compounds (BVOC’s) from
Terrestrial Ecosystems
The term biogenic volatile organic compounds
summarises a large number of compounds emitted from
terrestrial biota comprising in total an estimated >
1,000 Tg C year1 (Guenther et al. 1995). From an
atmospheric chemistry and climate perspective, the
isoprenoids (isoprene C5H8, and its monoterpenes and
sesquiterpenes derivatives) have been the main focus
of attention, reflecting the large mass emitted
(isoprene), and/or fast atmospheric reactivity (isoprene,
monoterpenes, sesquiterpenes) and related importance
for the atmospheric burdens of O3, SOA, OH and CH4
(Atkinson 2000; Atkinson and Arey 2003a, b).
For BVOC emissions no regional or global scale
observations exist to provide estimates of their past or
present emission strength, distribution and seasonality.
Global scale analyses thus have to depend on
modelling studies, and these are to date only extensively published and evaluated for isoprene, and to a
lesser degree, monoterpenes (Arneth et al. 2008).
Current estimates of global isoprene emissions range
between approximately 400 and 600 Tg C year1,
while variability in monoterpene emission estimates
is larger at ca. 30–130 Tg C year1 (Arneth et al.
2008). The bottom-up model experiments are
complemented by top-down approaches that seek to
infer regional emissions of isoprene from remotely
sensed formaldehyde column signals (Chance et al.
2000; Palmer et al. 2003; Barkley et al. 2008;
Stavrakou et al. 2009), since formaldehyde is one of
the chief isoprene oxidation products in the atmosphere. However, linking the formaldehyde retrievals
directly to isoprene emissions is hampered by the need
to use a chemistry transport model to account for
atmospheric isoprene oxidations which are incompletely understood, adding considerable uncertainty
to these types of analyses (Barkley et al. 2011, 2012).
Typically, global scale model experiments rely on
algorithms that vary diurnal emissions in response to
temperature and light (Guenther et al. 1995) that have
been found to be the main drivers underlying emission
observations in the short-term. Other attempts seek to
link emissions and their variability to photosynthetic

192

electron transport rate, reflecting the chloproplastic
metabolic pathway of isoprene and monoterpene
production and presence or absence of tissue storage
of some BVOC, which underlies the observed light
and temperature sensitivity (Niinemets et al. 1999;
Arneth et al. 2007). Both approaches have to rely on
specifying a leaf-level emission capacity which is
defined for standard light and temperature conditions.
More recently, the use of a canopy-scale emission
capacity has been proposed (Guenther et al. 2006) as
a model product that relies on the combination of
leaf-level measurements and a canopy transfer
model. Field observations demonstrate that emission
capacities are not constant, but vary strongly during
the year and over longer periods, for instance in
response to leaf development, previous weather
conditions or atmospheric CO2 levels. In a set of
recent reviews, emission capacities have been identified as the largest uncertainty in global BVOC emission models (Niinemets et al. 2010a, b, 2011).
Emission capacities are species-specific and need to
be set for larger plant functional units to be applicable
in large-scale models. Emission algorithms are therefore either linked to global vegetation distribution
classes derived from remote sensing information, or
to dynamic global vegetation models. Only the latter
are capable of estimating changes in emissions in
future or past environments (see e.g. Arneth et al.
2008 and references therein).
Current models agree on emissions from tropical
ecosystems dominating the global totals of isoprene
emissions, a combination of tropical vegetation having
high emission potential as well as the warm temperatures and high light conditions throughout the year.
By contrast, the global monoterpene emission estimates assume also a substantial contribution from
the coniferous and evergreen deciduous forests of
mid- to high latitudes, even though the period of high
emissions in these regions is restricted to few months
of favourable weather (Arneth et al. 2011; Guenther
et al. 2006; Lathière et al. 2005). While regional
differences in emission seasonal patterns and overall
strength for BVOC emissions appear to be large, probably much larger than for emission and uptake of CO2,
their interannual variability seems small, around
5–10 % of the mean (Arneth et al. 2011).
In the absence of regional to global observational
data, model outputs from BVOC simulation experiments are severely hampered by lack of evaluation

4

Ocean–Atmosphere Interactions of Particles

possibilities. At present, canopy-atmosphere flux
measurements by micrometeorological techniques
are the sole possibility to provide constraints and
reality-checks on the scale of the ecosystem and with
the potential to cover a period of months to years. Flux
measurements from aircraft could be used to extrapolate to larger regions, although these are restricted to
short-term campaigns. On canopy scale, only for isoprene are sensors sufficiently robust to allow longterm observations. But still, only one study location
reported measurements covering several years
(Pressley et al. 2005). Hence questions on the magnitude of interanual variability essentially remain unanswered. For other BVOC species, flux measurements
rely on techniques using gas chromatography or proton transfer reaction spectrometers as sensors. A few
short-term campaign studies from a very limited number of ecosystems have been published to-date (a list
of example data are provided in Rinne et al. 2009,
Pacifico et al. 2011, Arneth et al. 2007, Lathière
et al. 2006) which is insufficient for model evaluation.
For future emission estimates large uncertainties
exist with respect to how changes in climate, atmospheric CO2, N deposition, tropospheric O3 and
human land use/land cover changes interact to alter
BVOC emissions in direct and indirect ways (Arneth
et al. 2010; Niinemets et al. 2010a, b). In addition to
improved understanding of the processes of BVOC
production and emissions in response to these various
environmental changes, observation and modelling
efforts clearly also have to move towards substances
beyond isoprene and monoterpenes to reflect the multiple open questions on BVOC-atmosphere and climate interactions (Goldstein and Galbally 2007;
Holzinger et al. 2005; Lelieveld et al. 2008).
BVOC directly or via their atmospheric oxidation
products, contribute to the formation and growth of
secondary organic aerosol (Kulmala et al. 2004). Similar to BVOC emissions, the total mass and number
concentration of SOA particles that are formed from
BVOCs are highly uncertain. Current estimates are
around 12–70 Tg year1, but these numbers have
also been challenged as being too small, perhaps by
up to a factor of 10 (Kanakidou et al. 2004; Carslaw
et al. 2010; Hallquist et al. 2009). Most modelling
work to-date has estimated SOA based on monoterpene emissions only using a constant mass yield of
typically 10 % of emissions. Yet, both sesquiterepenes
(Bonn and Moortgat 2003) and isoprene (Claeys et al.

4.2

Aerosol Production and Transport in the Marine Atmosphere

2004) have been identified as important precursor
sources. While the SOA yield from isoprene may
be low, its source strength and the gas-particle
partitioning characteristics of its oxidation products
are efficient to the point where it may promote SOA
growth at higher altitudes and enhance the SOA formation from other sources (Claeys et al. 2004; Henze
and Seinfeld 2006).
Quantification of the future direct climate impact of
SOA in terms of radiative forcing have so far only
considered the case of increasing BVOC emissions
over the twenty-first century; these model scenarios
result in a substantial cooling (up to 24 W m2;
Carslaw et al. 2010) due to the increased scattering
and reflection of radiation by the larger SOA mass. In
addition, SOA can grow to particle size classes that act
as cloud condensation nuclei, with associated indirect
climate effects if cloud albedo and lifetime are
affected (Lohmann and Feichter 2005). The full
chain of processes from emissions, particle nucleation
and subsequent growth to aerosol direct and indirect
effects is only just now beginning to be included in
global climate models. Initial results indicate large
differences between past, present-day and future
SOA number concentration and SOA radiative forcing
if these processes are treated explicitly (Makkonen
et al. 2012).

4.2.2.4 Anthropogenic Emissions
Emission inventories of reactive gases and aerosols are
needed as input for climate and atmospheric chemistry
and transport models (CTMs) to be able to assess and
predict the climate impacts, air pollution concentrations
or deposition of elements to ecosystems (Sect. 4.5.1).
Emissions can be separated into natural emissions and
anthropogenic emissions. The latter implies that the
emissions are produced as a result of human activities
and can be influenced by changes in technologies
and/or emission reduction policies. Emissions need to
be spatially distributed, e.g. on a grid, to be suitable as
(deposition) model input because the location of
emissions is important for their impact, especially for
reactive and/or short-lived species. Examples of such
gridded emissions can be found at the GEIA/ACCENT
emissions portal (http://geiacenter.org).
The most important precursors of secondary
anthropogenic aerosol are NOx, SOx, NH3 and NonMethane Volatile Organic Compounds (NMVOC).
For a detailed review of aerosol formation we refer

193

to Seinfeld and Pandis (2006). To quantify the impact
of anthropogenic emissions on the ocean (through
deposition of nutrients and/or particles) it is necessary
to quantify both the primary aerosol emissions and the
aerosol precursor emissions. In this section we distinguish between land-based anthropogenic emissions,
biomass burning emissions and emissions from international shipping. The latter will in almost all
circumstances directly influence the marine ecosystem, although a substantial part of the emissions may
also deposit on land surfaces.
Anthropogenic Land-Based Emissions
Recently Lamarque et al. (2010) developed a new
emissions dataset covering the 1850–2000 period,
based on the combination and harmonisation of
published and publicly available datasets, in support
of the Intergovernmental Panel on Climate Change
(IPCC) Fifth Assessment Report (AR5). This so-called
ACCMIP emission data set also acts as a starting point
for the emission projections up to the year 2100 given
by different representative concentrations pathways
(RCPs) as used in the AR5 (Van Vuuren et al. 2011).
The ACCMIP output is the most recent and widely
used global emission data. It is distributed on a
0.5  0.5 degree grid and made available through
ftp://ftp-ipcc.fz-juelich.de/pub/emissions/. Lamarque
et al. (2010) provide emission estimates for the
precursors of secondary anthropogenic aerosol and
for primary carbonaceous particulate matter (BC and
OC) (Table 4.2) but not for total PM10.
Uncertainty in Global Anthropogenic Emissions
Antropogenic uncertainty in emission estimates arise
from uncertainty in the activity data, the fuel composition and the emission factors for all individual
sources. The resulting uncertainty leads to a range of
possible emissions for a given process and base year
that varies strongly between regions, sectors, and
pollutants. A consistent uncertainty analysis for all
pollutants in Table 4.2 is a complex and laborious
task, as it would have to be done for all air pollutant/
source/technology/country combinations separately
and has only been performed for a few species e.g.
BC and OC (Bond et al. 2004)/SO2 (Smith et al. 2011).
To get an impression of the uncertainties Granier et al.
(2011) compiled all currently available consistent
global and regional emission inventories and calculated the ratio between the lowest and highest

194

4

Table 4.2 Global anthropogenic land-based emissions
(Tg year1,) for the year 2000 (Lamarque et al. 2010; Granier
et al. 2011)
Anthropogenic
land-based
Shipping
Biomass Burning

NOxa
57
12
9.7

VOC
130
3
78

NH3
37

SO2
93

BCb
5.0

OCb
13

0
11

11
3.8

0.13
2.6

0.14
23

NOx in Tg NO year1
BC particulate black carbon, OC particulate organic carbon
(Tg C year1)

a

b

emissions for each species and each region for selected
base years. The ratios for the global year 2000
estimates for NOx, SO2, and BC were 1.17, 1.40 and
1.13, respectively (Granier et al. 2011). The ratios for
the global inventories were small compared to the
variation in the regional inventories which usually
ranged between 1.5 and 3.0. The spread gives an
impression of consensus but is not an uncertainty analysis. For example, Bond et al. (2004) estimated global
fossil black carbon emissions in 1996 as 3.0 Tg C, with
an uncertainty range of 2.0–7.4 Tg C, or +150 % and
30 %; this is in strong contrast with the ratio of 1.13
between highest and lowest global inventory for this
pollutant. Smith et al. (2011) performed an uncertainty
analysis for global and regional sulphur dioxide
emissions and concluded that the overall global uncertainty is relatively small: 6–10 % over the twentieth
century, but regional uncertainties ranged up to 30 %.
For the SO2 year 2000 and values presented in
Table 4.2, the uncertainty, based on Smith et al.
(2011), would be ~10 % and ~30 % for anthropogenic
land-based sources and shipping, respectively. The
calculated global SO2 uncertainty bounds are relatively
small: the low value is due to cancellation between
source categories and regions. This uncertainty level
would appear to be unrealistically low given that a
number of previous global sulphur dioxide emissions
estimates do not fall within this estimated uncertainty
bound (the ratio high-low inventories was 1.40; see
Granier et al. (2011) for a compilation). The reason is
that additional, essentially correlated uncertainties are
present that add to the uncertainty value estimated
above. Examples include reporting or other biases in
global data sets for energy, sulfur removal, and other
driver data, methodological assumptions, and the use
of common default assumptions for sources where
little data exists. Sulphur emissions are less uncertain
than emissions of the other air pollutants listed in

Ocean–Atmosphere Interactions of Particles

Table 4.2 because emissions depend largely on sulphur
contents in fuels. Uncertainty for other air pollutants is
controlled more by combustion conditions and
installed technologies and their uncertainty is considerably larger, as quoted above for BC. A qualitative
indication is that for VOC, NH3 and OC the uncertainty
range will be similar to the BC ranges of Bond et al.
(2004) while NOx will be in between the SO2 and BC
ranges.
Global Biomass Burning Emissions
Biomass burning emissions are highly variable from
year to year as a result of different environmental and
human factors. Schultz et al. (2008) provided a detailed
literature review on continental scale estimates of biomass burning emissions and constructed a global
emissions data set with monthly time resolution for the
period 1960–2000. The previously discussed ACCMIP
historical dataset (Lamarque et al. 2010) provides
decadal monthly mean biomass burning emissions,
mostly based on Schulz et al. (2008). As can be seen
from Table 4.2, biomass burning emissions contribute
significantly to total global emissions of aerosols and
their precursors but its relevance differs by substance,
from being quite modest for SO2 to dominant for OC.
Over the past 20 years estimates of biomass burning
emissions, including their spatial location, have greatly
improved due to the availability of earth observation
data from satellites. The activity data detected from
space (burned area or fire radiative power) includes all
major grassland, savanna, and forest fires (including
deforestation fires) (e.g. Van der Werf et al. 2006,
2010; Kloster et al. 2010). To estimate emissions,
satellite-derived burned areas (Giglio et al. 2010) drive
the fire module of a biogeochemical model that
calculates fuel loads for each month and grid cell,
which are then combined with emission factors
(Andreae and Merlet 2001). A good example of this
methodology is the Global Fire Emissions Database
(GFED) which contains emissions from open fires for
the 1997–2004 period (van der Werf et al. 2006). A new
version of the inventory that covers the 1997–2009
period, called GFED-v3, was made available at the
beginning of 2010 (van der Werf et al. 2010).
International Shipping Emissions
The ACCMIP emissions dataset (Table 4.2) provides
ship emissions including international shipping,
domestic shipping and fishing, but excluding military

4.2

Aerosol Production and Transport in the Marine Atmosphere

195

Fig. 4.13 Historic development of yearly anthropogenic NOx emissions for the years 1970–2000 (data courtesy of Schultz et al.,
2008; Granier et al. 2011)

vessels, based on Eyring et al. (2009). These authors
used data from the International Maritime Organization (IMO) study discussed in Buhaug et al.
(2008), while non-CO2 emission totals were derived
as a mean of previous studies (Corbett and Köhler
2003; Eyring et al. 2005; Endresen et al. 2003, 2007).
Ship emissions are distributed over the globe using
the International Comprehensive Ocean–atmosphere
Data Set (ICOADS; Wang et al. 2008), which
provides changing shipping patterns on a monthly
basis. Eyring et al. (2009) estimated that in 2000,
the emissions released by the ocean-going registered
fleet for nitrogen oxides (NOx), sulphur oxides (SOx),
and particulate matter (PM) were 11.6 Tg NO,
11 Tg SO2, and 1.4 Tg PM, respectively, within a
bounded range of 6–21 Tg NO, 6–20 Tg SO2 and
0.4–3.4 Tg PM.
Comparison and Evaluation of Different
Emission Datasets
A detailed comparison and evaluation of different
emission datasets of anthropogenic and biomass burning emissions for the 1980–2010 period was made by
Granier et al. (2011). They identified large discrepancies between the global and regional emission
data sets, showing that there is still no consensus
on the best estimates for surface emissions of

atmospheric compounds. Hence the data presented in
Table 4.2 have a substantial uncertainty; about
10–30 % for land-based anthropogenic emissions and
shipping and 50–80 % for biomass burning (Granier
et al. 2011). For a full description of the various global
and regional emission datasets currently available we
refer to Granier et al. (2011). To understand the impact
of anthropogenic emissions on marine ecosystems it
is important to know not only the absolute emission
in a certain base year but also the trend over time. As
an example we show the historic development of NOx
emissions for the period 1970–2000 in Fig. 4.13.
Clearly all emissions have increased over the past
decades, with the land-based emission slowing down
somewhat in recent years but shipping emissions
showing a steep increase. The significant increase in
the emissions from wildfires throughout the period
from 1960 to 2000 due to the increasing importance
of forest and peat soil burning is remarkable (Schultz
et al. 2008). Annual global carbon emissions averaged
at 1,660 Tg C year1 during the 1960s and rose to an
average of 2,560 Tg C year1 during the 1990s. The
most important contribution to the trend comes from
enhanced deforestation in the tropical regions. The
implication of Fig. 4.13 is that the input from the
atmosphere to the ocean will have significantly
increased over the past decades and that this is bound

196

4

to have influenced the cycling of elements and
nutrients as well as other processes. Trends in other
substances may vary somewhat from the NOx trend
depicted here but overall the pattern is consistent. For
a full description of the various global and regional
emission datasets currently available and emission
trends for 1980–2010 we refer to Granier et al.
(2011); historic emission trends since 1850 are
provided by Lamarque et al. (2010).

4.2.3

Ageing and Mixing of Aerosols
During Transport

Aerosol particles produced over the continents are
present in remote oceanic regions as a result of
long-range transport from high-emission regions.
From the moment an aerosol particle forms or is
emitted until its removal from the atmosphere by
deposition, it has undergone various physical and
chemical transformations. This aerosol ageing is due
to coagulation, condensation/evaporation of semivolatile components, adsorption/absorption of volatile components and chemical reactions. The dynamic
exchange of semi-volatile substances facilitates the
exchange of molecules between particles of different
origin. As a consequence, ambient aerosols are often
complex internal and external mixtures of both inorganic and organic components. The degree to which
aerosol particles are transformed controls their chemical composition, size, shape, and surface area and
hence determines the aerosol chemical evolution
as well as the particles hygroscopic and optical
properties.
Recent studies on aerosol ageing have been
focussed on dust and organic aerosol (OA). Current
understanding of secondary organic aerosol (SOA)
formation is incomplete (e.g. de Gouw et al. 2005;
Heald et al. 2005, O’Dowd and de Leeuw 2007) and
models underpredict SOA concentrations, notably in
aging plumes (Alvarado and Prinn 2009; Grieshop
et al. 2009). Moreover, despite extensive work on
dust evolution during transport, in particular on Asian
dust, the chemical mechanisms involved in dust aging
are still in question, implying large uncertainties in
estimation of dust cycling and impact (Jickells et al.
2005; Su et al. 2008; Formenti et al. 2011).
Recent studies show an explosion of identified
organic species in the aerosol phase, notably due to

Ocean–Atmosphere Interactions of Particles

the improvement of analytical techniques. In contrast
to the clear evolution of sulphate which is irreversibly
oxidised and condensed, the OA presents versatile
transformation highly dependent on meteorological
and chemical conditions. The chemistry of these species is not well-known and there is a large need for
further studies.

4.2.3.1 Chemical Ageing of Organic Aerosols
The organic composition of aerosols continues to
change long after initial particle formation as a result
of chemical reactions between particle constituents,
reactive uptake of gaseous semivolatile molecules,
direct photochemical processes inside the particle,
and condensation and evaporation of water on the
particle (Pöschl 2005; Robinson et al. 2007; Rinaldi
et al. 2010). The additional gas-phase oxidation during
transport appears to be principally responsible for
most of the aging of organic aerosol particles (e.g.
Lambe et al. 2009). However, other dominant ageing
mechanisms such as condensational ageing (uptake of
oxidised organic vapors) or homogeneous ageing
(condensed-phase chemistry such as oligomerisation)
has also been identified (Volkamer et al. 2007; Jimenez
et al. 2009). These processes are schematically shown
in Fig. 4.14.
The ageing takes place on a time scale ranging from
minutes to days (e.g. Dunlea et al. 2009), and may
significantly change the molecular composition of
particles before their removal from the atmosphere.
For example, in biomass burning plumes, the
unreacted primary organic aerosol, traced by
levoglucosan concentrations, represents only 17 % of
the OA mass after 3.5 h (Hennigan et al. 2011),
suggesting that the majority of OA is transformed via
photo-oxidation. Fu et al. (2011) show that over tropical regions, atmospheric oxidation products can
account for 47–59 % of the total organics. Typically,
field and laboratory measurements indicate that
organic material changes its carbon number and
becomes increasingly oxidised, less volatile, and
more hygroscopic as a result of continuous ageing in
the atmosphere (Zhang et al. 2007; Capes et al. 2008;
DeCarlo et al. 2008; Jimenez et al. 2009; Morgan et al.
2010, Ng et al. 2010; Kroll et al. 2011). The extent of
oxidation of OA, generally estimated from Aerosol
Mass Spectrometer (AMS) measurements, increases
as the aerosol is exposed to atmospheric oxidants,
notably OH radicals (Grieshop et al. 2009; Sage

4.2

Aerosol Production and Transport in the Marine Atmosphere

VOCs
Volalizaon
/Condensaon

POA/ SOA
Oxidaon

OOAi
Oxidaon
Photolysis
Hydrolysis

OOAii

OOAj
Oligomerizaon

OOAjj

Fig. 4.14 Main pathways of OA aging. OOA corresponds to
oxygenated organic aerosol

et al. 2008). Thus, diurnal oxidant cycling involves a
significant daily variation of OA composition with
more oxidized OA in the afternoon (Aiken et al.
2008; Hildebrandt et al. 2010; Adler et al. 2011).
As a result of the large number of reaction
pathways, intermediates and products, aerosol
particles sampled from ambient air contain thousands
of chemically distinct organic compounds (e.g. Fu
et al. 2011). Ageing processes in the atmosphere,
resulting in further oxidation and oligo-polymerisation
of the organic compounds, ultimately transform them
to highly oxidised macromolecular material such as
humic-like substances (HULIS) (Holmes and Petrucci
2006; Hallquist et al. 2009). Moreover, due to their
semi-volatile properties and high atmospheric reactivity, organic species are often observed in internal
mixing with inorganic particles. Thus, inorganic
sulphate or nitrate aerosols are usually coated or
aggregated with organic particles, forming organic
sulphate or organic nitrate aerosols (Surratt et al.
2007; Froyd et al. 2010; Hawkins et al. 2010; Li and
Shao 2010). The detailed characterisation of the huge
set of OA compounds is beyond the capabilities of
most analytical techniques (Kroll et al. 2011). As a
consequence, several recent studies aim to identify
organic species or find a metric which can be used as
a tracer of organic ageing, such as the determination of
molecular markers (e.g. Claeys et al. 2010), the mass

197

spectral diagnostic (e.g. via f44 increase, Ng et al.
2010) or the use of the average carbon oxidation
state coupled with carbon number (Kroll et al. 2011).
Despite this extreme chemical complexity, it seems
that organic matter produced from pollutants in urban
atmospheres, from biogenic emissions, or from terpenes
exposed to photochemical reactions in smog chambers,
is processed to particulate organic matter of similar
oxidation state, hygroscopicity, volatility, and molecular mass (Jimenez et al. 2009). In other words, organic
aerosols from very diverse origin are observed to evolve
in the atmosphere to particles with similar chemical and
physical properties (Jimenez et al. 2009; Ng et al. 2010).

4.2.3.2 Internal Mixing
Dust/Inorganic Species
The transformation of dust during atmospheric transport leads mainly to coating by sulphates or nitrates
(Formenti et al. 2011). First observations of internal
mixing have been made in Asia where desert sources
are located close to highly polluted areas (e.g. Iwasaka
et al. 1988; Okada et al. 1990; Yamato and Tanaka
1994; Zhou et al. 1996; Fan et al. 1996). Strong internal mixing of dust and sulphate or nitrate was also
reported for the eastern Mediterranean (e.g. Falkovich
et al. 2001; Sobanska et al. 2003; Putaud et al. 2004;
Koçak et al. 2007; Coz et al. 2009) and over the
Atlantic Ocean (Kandler et al. 2007; Dall’Osto et al.
2010) in polluted European and North African air
masses. The sulphate/nitrate coating is the result of
different heterogeneous chemical processes, such as
the uptake of gaseous SO2 or HNO3 (and/or NOx) onto
the particle surface and their subsequent conversion to
SO42 and NO3 (Dentener et al. 1996; Kim and Park
2001; Usher et al. 2002) and collision/coalescence
between dust and aerosol ammonium sulphate or
nitrate (Mori et al. 1998; Sullivan et al. 2007; Suzuki
et al. 2010). The heterogeneous processes responsible
for sulphate mixing with dust may be season-dependent
with predominance of coagulation process in summer
and SO2 uptake in spring (Suzuki et al. 2010). Recent
observations of strong internal mixing of dust with
nitrate or sulphate in mesoscale convective clouds during the AMMA campaign (Crumeyrolle et al. 2008;
Matsuki et al. 2010a) suggest in-cloud processing
involving aqueous phase oxidation of NOx/SO2, as
previously assumed by Levin and Ganor (1996) and
Liu et al. (2005). Carbonate minerals in dust particles,
notably calcite (CaCO3), have been identified as the

198

main substrate for adsorbing sulfur and nitrogen gaseous species (Dentener et al. 1996; Krueger et al.
2004). Thus, the heterogeneous reactions involve the
conversion of calcium carbonate to other calcium salts
such as calcium nitrate (Ca(NO3)2), calcium sulphate
(gypsum, CaSO4) or their ammonium salts ((NH4)NO3
and (NH4)HSO4 or (NH4)2SO4) as first demonstrated
in laboratory experiments (Krueger et al. 2004) and
then observed in recent field studies (Laskin et al.
2005; Matsuki et al. 2005, 2010b; Sullivan and
Prather 2007; Shi et al. 2008; Huang et al. 2010;
Suzuki et al. 2010).
The reactivity of inorganic acids with dust is determined by several factors: chemical mineralogy of dust
(Sullivan et al. 2007), transport pathways, the extent to
which dust is transported across polluted sources
(Sullivan et al. 2007; McKendry et al. 2008) and meteorological and chemical processing. In particular,
sulphate and nitrate coating on dust surfaces is
favoured in the marine atmosphere where the relative
humidity is high (Hanisch and Crowley 2001; Usher
et al. 2002; Trochkine et al. 2003; Zhang et al. 2003a;
Okada and Kai 2004; Ooki and Uematsu 2005; Matsuki
et al. 2005). Dall’osto et al. (2010), who compare
African dust mixing state at different distances from
the emission source, demonstrate a continuous chemical evolution of dust particle composition during atmospheric transport, consistent with the relatively slow
atmospheric oxidation of sulphur dioxide. Moreover,
measurements on individual particles show evidence
of the mineralogy-dependent formation of sulphate/
nitrate on dust particles (Matsuki et al. 2005; Laskin
et al. 2005; Sullivan and Prather 2007). Sulphate
formation is favoured on aluminosilicate-rich particles
(Laskin et al. 2005; Shi et al. 2008) while preferential
nitrate formation on carbonate-rich dust is observed
(Ro et al. 2005; Sullivan et al. 2007; Matsuki et al.
2010a; Fairlie et al. 2010). It was proposed that the
preferential association of sulphate with Al-rich dust is
partly due to the oxidation of SO2 to H2SO4 catalysed
by transition-metals, mainly iron, present in
aluminosilica minerals (Sullivan et al. 2007; Sullivan
and Prather 2007). Yet, the opposite behavior of
sulphate and nitrate formation on carbonates could be
explained by their difference of hygroscopicity
(Sullivan et al. 2009; Formenti et al. 2011). Calcium
sulphate is poorly water soluble, preventing further
uptake of water and other gaseous species, and hence
suppressing the transformation of sulphur dioxide to

4

Ocean–Atmosphere Interactions of Particles

sulphate in these particles. In contrast, calcium nitrate
is highly hydrophilic, enhancing uptake of water
and resulting in a positive feedback, transforming all
calcium in the particles to calcium nitrate. The
calcium-rich spherical particles observed in Asian
dust plumes in Japan and in polluted urban air masses
in China (Fig. 4.15; Matsuki et al. 2005; Okada et al.
2005), as well as in the Eastern Mediterranean (Laskin
et al. 2005) or in convective systems over the Sahel
during the monsoon period (Matsuki et al. 2010a)
provide field evidence of this feedback process. This
mineralogy-dependent salt formation could imply dust
source dependence.
In addition, mixing between dust and chloride has
also been observed close to the Asian coast, over the
North Pacific (Zhang and Iwasaka 2001; Murphy et al.
2006; Sullivan et al. 2007; Tobo et al. 2009, 2010) and
over the North Atlantic (Sullivan et al. 2007). The
absorption of hydrogen chloride (HCl) seems to be
responsible for the chloride coating, forming calcium
chloride CaCl2 (Kelly and Wexler 2005; Tobo et al.
2009). This heterogeneous pathway could be predominant in the remote marine boundary layer with respect
to sulphate and nitrate formation (Ma and Choi 2007;
Tobo et al. 2009, 2010). The main source of gaseous
HCl is volatilization from sea salt particles during
heterogeneous reaction of sea salt with HNO3 or
H2SO4 (Tobo et al. 2009).
The mixing of dust in polluted air masses also
favors mixing with other anthropogenic compounds,
such as metals (Cu, As, Ni, Cd, Zn, Pb) (Sun et al.
2005; Zhang et al. 2005b; Erel et al. 2006; Huang et al.
2010; Wang et al. 2011), probably playing a role on
the atmospheric deposition of nutrients to the marine
biosphere (Sun et al. 2005).
Dust/Organic Species
Internal mixing between dust and organic carbon has
been observed in African biomass burning plumes
(Hand et al. 2010) and in polluted Asian air masses
(Fig. 4.15, Leaitch et al. 2009; Geng et al. 2009; Li and
Shao 2010; Stone et al. 2011). Recent studies show
that ATOFMS (Aerosol Time of Flight Mass Spectrometry) in providing aerosol particles composition
as a function of particle size, is a pertinent technology
to identify organic species mixed with dust (Sullivan
and Prather 2007; Yang et al. 2009; Dall’Osto et al.
2010). Thus, Sullivan and Prather (2007) found from
their experiments during ACE-Asia that oxalic acid

4.2

Aerosol Production and Transport in the Marine Atmosphere

199

Fig. 4.15 Illustration of different mixing states between dust
(Si-rich and K-rich), organic species and soot particles by
Transmission Electron Microscopy (TEM) images and
Energy-Dispersive X-ray (EDX) spectra. The circles show the
sites of EDX measurements. The carbon peaks from EDX
spectra were compared between the background (grey spectrum
from an area without particles) and the particle (black

spectrum). (a) S-rich particle with organic coating aggregated
with an organic particle. (b) K-rich particle aggregated with an
organic particle and soot, including a fine Fe-rich particle.
(c) S-rich particle with organic inclusions. (d) S-rich particles
with organic coating (Re-produced from Li and Shao (2010) by
permission of the American Geophysical Union)

and malonic acid were predominantly internally
mixed with mineral dust and aged sea salt particles.
They also observed a diurnal enrichment of oxalic acid
in mineral dust, indicating a probable gas-phase photochemical production of dicarboxylic acid followed
by partitioning into the particle phase (Sullivan et al.
2007). Yang et al. (2009) also observed oxalatecontaining dust and sea salt particles in Shangai but
they associated oxalate formation to oxalic acid production by heterogeneous reaction occurring in
hydrated/deliquesced aerosol. Leaitch et al. (2009)
reported enrichment of dust particles by various

organic species, notably formate and acetate in Asian
dust collected on the North American coast, probably
due to uptake of organic particle precursors by dust
nearer Asian anthropogenic sources. Internal mixing
between dust and carboxylic acid has also been
suggested for African dust in source areas during
monsoon periods by in-cloud processes in convective
systems (Desboeufs et al. 2010) and in zones of transport in Israel (Falkovich et al. 2001, 2004) and over the
French Alps (Aymoz et al. 2004). Due to the numerous
observations of mixing between dust and carboxylic
acids, which are tracers of SOA, dust is suspected to

200

play a role in SOA formation (Mochida et al. 2007;
Duvall et al. 2008; Wang et al. 2009; Stone et al.
2011). Nevertheless, there is no clear evidence of the
formation of secondary organic aerosol on dust in
these studies. If carboxylic acids are the most common
identified organic species in mixing with dust, internal
mixing of dust has been observed with MSA
(Dall’Osto et al. 2010); PAH (Falkovich et al. 2004;
Stone et al. 2011); pesticides (Falkovich et al. 2004);
fatty acids (Mochida et al. 2007); hopane and
levoglucosan (Stone et al. 2011) and organic nitrogen
(Dall’Osto et al. 2010). The abundant organic matter
found on dust surfaces suggests that dust is an excellent medium for long-range transport of pollution in
the troposphere (Falkovich et al. 2004). The extent
organic compounds coat onto dust depends on the
transport pathway, reactivity of organic species, ambient concentration and ambient humidity, notably for
high water-soluble species like carboxylic acids
(Falkovich et al. 2004; Dall’Osto et al. 2010).
Sea Salt
Sea salt aerosols can also participate in heterogeneous
reactions with nitric and sulphuric acids, leading to
chloride (and also other halogens like Br) depletion
(notably Cl-depletion) through HCl volatilisation and
the production of halogen radicals, particularly in relatively polluted marine air (Sturges and Shaw 1993;
Johansen et al. 1999; Kumar et al. 2008). The release
of reactive chloride is considered to be an important
intermediate in the oxidation reactions associated with
the removal of light hydrocarbons and ozone in the
marine atmosphere (Singh and Kasting 1988; Vogt
et al. 1996). The magnitude of Cl-depletion of marine
aerosols has been demonstrated to usually increase
with decreasing sea salt particle size (Mouri and
Okada 1993; Kerminen et al. 1998; Yao et al. 2003;
Hsu et al. 2007). This reaction produces sea salt
particles coated with sulphate and nitrate over Asian
and Pacific areas (Matsumoto et al. 2004; Matsuki et al.
2005; Yang et al. 2009) and in the Mediterranean
region (Tursic et al. 2006). In the clean atmosphere,
methanesulphonate is the major species involved in
chloride depletion as observed in Finland (Kerminen
et al. 1998) and in the Arctic (Maskey et al. 2011). The
S-containing sea salt particles are generated by
reactions of the sea salt particles with MSA and/or
H2SO4 from biogenic sources rather than anthropogenic ones. Mixing between sea salt and oxalate is

4

Ocean–Atmosphere Interactions of Particles

also observed (Kerminen et al. 1998; Yang et al.
2009). A number of studies have reported large
variations in the magnitude of Cl-depletion (ranging
from few percent to 100 %) over different oceanic
regions (Graedel and Keene 1995; Song and
Carmichael 1999; Maxwell-Meier et al. 2004; Quinn
and Bates 2005; Hsu et al. 2007). This variability has
been attributed to the complex interplay of several
factors, including variable wind-field, turbulence in
the vicinity of the sampling site and the influence of
anthropogenic components. Thus, the primary
mechanisms governing differential Cl loss from sea
salt particles in diverse oceanic regions are now well
understood (e.g. Song and Carmichael 1999; MaxwellMeier et al. 2004; Quinn and Bates 2005).
Sea salt mixing with dust has also been reported:
more than 60 % of the total particle population collected
in Japan was found to be modified by sea salt as
concluded from individual particle analysis (Okada
et al. 1990; Niimura et al. 1998; Zhang et al. 2003b;
Ma et al. 2005) and the interaction of dust with sea salt
was likely an important process in size and composition
changes of dust aerosols during their long-range transport (Zhang and Iwasaka 2004, 2006). In contrast,
mixtures of dust and sea salt were reported to occur
only to a minor degree in the African dust plume outflow
(e.g. Reid et al. 2003b; Niemi et al. 2005; Kandler et al.
2007). The mechanisms responsible for the mixing of
dust particles and sea salt have not been elucidated in
detail (Andreae et al. 1986; Zhang et al. 2005a; Andreae
and Rosenfeld 2008), while in-cloud processing was
suggested as a major route for agglomerate formation
(Andreae et al. 1986; Niimura et al. 1998).

Future Directions
Since the first models of aerosol ageing (e.g. Song and
Carmichael 1999), progress in the understanding of
chemical multiphase processes enabled, at best, taking
into account the chemical reactivity of aerosols. However, recent studies show an explosion of identified
organic species in the aerosol phase, notably due to the
improvement in analytical techniques. In contrast to
the clear evolution of sulphate which irreversibily
oxidised and condensed, the OA presents versatile
transformations which are highly dependent on meteorological and chemical conditions. The chemistry of
the OA species is not well-known and there is a large
need for further studies.

4.2

Aerosol Production and Transport in the Marine Atmosphere

While the convergence of physico-chemical
properties with ageing is observed for OA, this is not
the case for dust and sea salt particles. Thus, even
while numerous studies focussed on the chemical evolution of these aerosol particles, the challenge remains
to improve our understanding of the link between
aerosol ageing and their properties, notably size, ability to release nutrients and hygroscopicity. This point
also needs to extend to measurements, up to now
largely situated in Asian regions and the Pacific, to
other oceanic regions, notably regions where high
climate change is expected such as the Mediterranean
basin (Giorgi 2006; IPCC 2007).

4.2.4

Dust-Mediated Transport of Living
Organisms and Pollutants

A major argument for studying inter-regional and
inter-continental transport of dust and its impacts is
the recognition of ecological and human health damage risks. Owing to advances in meteorology, analytical instrumentation, satellite technology and image
interpretation, more precise information on source
areas, transport patterns and depositional zones for
dust can now be obtained.
As an example, fungi transported by Saharan dust
have been found to damage Caribbean corals (Colarco
2003a, b). Several health studies in urban and suburban environments have shown a higher risk of mortality from high exposure to fine (particles with diameter
<10 μm or PM10) particulate matter (e.g. Lippmann
2007). Airborne particles emitted from geological
strata also pose threats to human health and the environment worldwide due to expansion of infrastructure
development to serve the increasing population. After
generation, dust can be carried by wind into sensitive
environments and adverse health effects of respiratory
“dust” on human health are well documented (Love
et al. 1997).
Two misconceptions exist: desert soils are too
inhospitable to accommodate a diverse microbial
community and if present, the microorganisms will
not be able to withstand the physical stresses (UV,
desiccation, temperature) of atmospheric transport.
However, dust events have been shown to introduce
a significant pulse of microorganisms (Griffin 2007;
Schlesinger et al. 2006; Polymenakou et al. 2008)
and other microbiological materials (e.g. cellular

201

fragments, fungal spores) into the atmosphere
(Jaenicke 2005). Dust-borne transport of microorganisms, mainly over the marine environment,
should be enhanced due to tolerable humidity levels
and attenuation of UV by the particle load of the
various dust clouds. Polymenakou et al. (2008) examined the microbial quality of aerosols over the Eastern
Mediterranean region (Island of Crete, Greece) during
an African dust storm. Bacterial communities associated with aerosol particles of six different size ranges
were characterised using molecular cultureindependent methods (analysis of 16S rRNA genes).
Spore forming bacteria such as Firmicutes were found
to be present in all aerosol particles and dominated the
large particle sizes. Besides the dominance of
Firmicutes in dust particles, phylogenetic neighbours
to human pathogens associated with the respirable
particles were also detected. These pathogens have
been linked to several diseases such as pneumonia,
meningitis, and bacteremia or suspected to
induce pathologic reactions such as endocarditis (i.e.
S. pneumoniae, S. mitis, S. gordonii, H. parainfluenzae,
A. lwoffi, A. johnsonii, P. acnes).
The amount of toxic waste stemming from obsolete
pesticides in Africa is higher than previously
estimated (Mandavilli 2006). In Africa alone the
amounts of toxic wastes are estimated at ca. 120,000
tonnes, with more than 500,000 tonnes worldwide.
An estimated 30 % of the waste is believed to be
persistent organic pollutants (POPs). The POPs family
includes dioxins (polychlorinated dibenzodioxins and
dibenzofurans), polychlorinated biphenyls (PCBs) and
several organochlorine pesticides such as dichlorodiphenyltrichloroethane (DDT) and hexachlorobenzene.
It should be pointed out that the POPs composition
of dust in air masses changes in relation to modifications in land use, intensity of pesticide use, and
burning of synthetic materials and biomass in the
dust source regions and in areas swept by dust
air masses (Garrison et al. 2003). There are few studies of the transport of POPs from Africa to
the Mediterranean. It has been shown that the air
masses arriving in the Eastern Mediterranean from
Africa contained levels of polychlorinated biphenyls
(PCBs) as high (>100 pg m3) as those in the air
masses coming from industrialised Western Europe
(Mandalakis and Stephanou 2002). Concurrently, in
another study (Garrison et al. 2006) aimed at
elucidating the potential role that Saharan dust might

202

4

play in the degradation of Caribbean ecosystems, a
series of persistent organic pollutants (POPs), trace
metals and viable microorganisms were identified in
the atmosphere over dust source areas of West Africa
and in the Caribbean.
Microbes and fungi present on dust can survive a
transcontinental journey and stay alive for centuries
(Gorbushina et al. 2007). It was shown that Saharan
dust collected over the Atlantic by Charles Darwin in
the nineteenth century contained members of the sporeforming bacteria Bacillales (Firmicutes) and fungi
attached to the particles. By combining geochemical,
microbiological and microscopic methods to analyse
their almost 200-year-old samples, Gorbushina et al.
(2007) were able to show beyond doubt that dust,
which clearly originated from West Africa, transported
viable microorganisms across the Atlantic Ocean.

4.3

Direct Radiative Effects (DRE)

The direct radiative effect (DRE) is the change in net
downward radiative flux, measured in Wm2, due to
aerosol scattering and absorption of radiation (Forster
et al. 2007). The DRE exerted by marine aerosols has
received less attention that the anthropogenic DRE,
which is a climate forcing. However, there are good
reasons for quantifying the marine DRE. Firstly, it
gives insight into pre-industrial radiative effects of
aerosols since the distinction between natural and
anthropogenic aerosols is important in the study of the
climate system. Secondly, the decrease in sea-ice extent
due to climate change leads to an increase in sea salt
generation (Bellouin et al. 2011). Climate change also
impacts the activity of ocean biogeochemistry and the
associated DMS emissions into the atmosphere
(Halloran et al. 2010). These changes impact the DRE
of marine aerosols and the Earth’s radiative budget.
In the shortwave spectrum and at the top of the
atmosphere, the sign of the DRE depends on the balance between the decrease in net flux due to aerosol
scattering and the increase due to aerosol absorption.
Marine aerosols lack elemental carbon and iron oxides
and are therefore weakly absorbing in the shortwave
spectrum (Irshad et al. 2009). Consequently, their shortwave DRE at the top of the atmosphere is negative, and
more negative per unit mass than absorbing aerosols of
similar sizes. The marine aerosol DRE is exerted
predominantly in cloud-free conditions, except where

Ocean–Atmosphere Interactions of Particles

clouds are too thin to mask it. Aerosols are more efficient at exerting a DRE when their radius is comparable
to the wavelength of the radiation. The size distribution
of marine aerosols, especially sea salt, covers both the
sub- and super-micron ranges (Dubovik et al. 2002).
Their DRE therefore covers both the shortwave and
longwave spectra. In the longwave spectrum, marine
DRE is positive at the top of the atmosphere because the
aerosol layer is typically colder than the surface (Reddy
et al. 2005). In addition, since aerosol size is a key
parameter for the DRE, hygroscopic growth is important, especially at the high relative humidity experienced by marine aerosols, and is a strong function of
chemical composition (Randles et al. 2004).
Four aerosol characteristics are needed to quantify
the DRE: optical depth, single-scattering albedo, size
distribution, and vertical profile (Yu et al. 2006).
Among those parameters, aerosol optical depth is
better constrained, with dedicated ground-based and
satellite instruments providing routine retrievals
(Holben et al. 2001; Remer et al. 2008; Kokhanovsky
and de Leeuw 2009; de Leeuw et al. 2011b). Retrievals
are however limited to cloud-free conditions, a restriction detrimental to the sampling of marine aerosols,
especially at high latitudes of the southern hemisphere
where near-surface wind speeds, cloud cover, and sea
salt optical depths are large. Satellite retrievals are
more accurate over dark ocean surfaces, where marine
aerosols are located, than over the relatively brighter
land surfaces (King et al. 1999). Satellite products also
include information on the aerosol size distribution,
through the Ångström coefficient or optical depth of
the fine mode (Anderson et al. 2005). Ground-based
sun-photometer networks provide retrievals of the
single-scattering albedo (Schuster et al. 2005) and
size distribution (Dubovik et al. 2002). Coastal and
island sites and ship cruises are useful to characterise
marine aerosols locally (Smirnov et al. 2011). The
aerosol vertical profile is available from groundbased and spaceborne lidars (Winker et al. 2010).
DRE estimates from observations use retrieved
aerosol properties coupled to radiative transfer
calculations (e.g. Bellouin et al. 2008) or attribute a
fraction of broadband radiative fluxes measured by
radiometers to aerosols (e.g. Loeb and Manalo-Smith
2005). The shortwave cloud-free top-of-atmosphere
total (natural and anthropogenic) DRE is estimated to
be in the range 4 to 6 Wm–2 over global oceans and
around 5 Wm–2 over land (Yu et al. 2006). In the

4.3

Direct Radiative Effects (DRE)

203

a

Mean: -3.9 Wm-2
-10 -8

-6

-4

-3

-2

-1

0

1

2

3

4

6

8 10

3

4

6

8

b

Mean: -1.7 Wm-2
-10 -8

-6

-4

-3

-2

-1

0

1

2

10

Fig. 4.16 (a) Shortwave direct radiative effect, in Wm2, of
marine aerosols at the cloud-free top of atmosphere as estimated
from MODIS collection five aerosol retrievals by Bellouin et al.

(2008) for the year 2006. (b) Same but for sea salt aerosols
modelled in the Hadley Centre climate model HadGEM2 for
present-day conditions

southern ocean where marine aerosols are expected to
contribute most to the total cloud-free DRE, the latter
is estimated at 4 to 6 Wm–2 (Yu et al. 2006).
Aerosol observations characterise the ambient aerosol.
The contribution of different aerosol types to the
total DRE is therefore difficult to quantify from
observations alone. Aerosol size retrievals have been
used as a useful but imperfect proxy for this task

(Kaufman et al. 2005; Bellouin et al. 2008). Marine
aerosols are found to be the dominant contributor to
the global direct effect. Bellouin et al. (2008)
estimated that the sea salt shortwave DRE for the
year 2006 is 3.9 Wm2 (Fig. 4.16a) at the cloudfree top of the atmosphere, half of the total estimated
DRE. Zhao et al. (2011) mix observation with
modelling to attribute the total DRE to individual

204

species. They find a global-averaged sea salt shortwave DRE at the cloud-free top of the atmosphere of
2.2  0.6 Wm2, one third of the total DRE. Further
speciation into the DRE exerted by nss- sulphate from
ocean DMS, and marine organics has not been
attempted.
Modelling estimates of the DRE involve the conversion of simulated dry masses of various aerosol types
into optical properties. A realistic conversion requires a
good simulation of size distribution (Vignati et al.
2004), mixing state (Stier et al. 2007), and hygroscopic
growth. In a comparison of global numerical aerosol
modelling (Kinne et al. 2006), the median sea salt
optical depth is 0.030 at 0.55 μm on a global average,
24 % of the total aerosol optical depth. Sea salt is the
second largest contributor after sulphate, which itself
includes an unspecified marine component. Diversity
among participating models is large and the sea salt
fraction of the optical depth varies between 18 % and
50 %. Reddy et al. (2005) report a sea salt and natural
sulphate (mostly contributed by the ocean) optical
depth at 0.55 μm of 0.040, a third of the global total
aerosol optical depth. In their model, marine aerosols
exert a shortwave DRE of 0.9 Wm2 in cloud-free
conditions and 0.5 Wm2 in all-sky conditions. Those
numbers represent nearly half the total shortwave DRE.
In the longwave spectrum and cloud-free sky, the
simulated marine DRE is +0.2 Wm2, 40 % of the
total DRE in that spectrum, the remainder being exerted
by mineral dust. The sea salt contribution is larger in
simulations with the Hadley Centre climate model
described by Bellouin et al. (2011). Sea salt optical
depth at 0.55 μm is 0.052, 44 % of the total optical
depth. Its shortwave DRE in cloud-free sky at the top of
the atmosphere is 1.7 Wm2 (Fig. 4.16b) on a global
average, 40 % of the total DRE. In all-sky, the sea salt
DRE is estimated at 0.8 Wm2 for a total DRE of
2.2 Wm2. In cloud-free longwave, sea salt DRE is
+0.4 Wm2, half of the total DRE in that spectrum.
Modelled estimates are typically weaker than observational estimates, although the incomplete coverage of
the Earth’s surface by satellite aerosol retrievals
complicates the comparison.
The results above show that observation-based and
modelling estimates of the total and marine DRE
remain associated with large ranges of values.
Uncertainties in the aerosol optical properties, mixing,
and identification of different components need to be
reduced in order to improve our knowledge of the total

4

Ocean–Atmosphere Interactions of Particles

and marine DRE. Progress is being made in aerosol
remote-sensing techniques (Dubovik et al. 2011), with
remote-sensing of aerosols in cloudy sky becoming
possible (Waquet et al. 2009; Omar et al. 2009).
Improving the sampling of remote oceanic regions
through a synergy of satellite instruments would be
beneficial to observations of marine aerosols. On the
modelling side, the diversity in the simulations of
aerosol dry mass and optical properties contributes to
the large range of simulated DRE. There is a factor 5
among model simulations of sea salt dry mass (Kinne
et al. 2006), and improvements in the parameterisation
of sea salt production as a function of wind speed
could reduce that range, although differences in
simulated wind speed will certainly remain. Sea salt
and sulphate from DMS oxidation are the only
aerosols produced at the ocean–atmosphere interface
in current atmosphere–ocean general circulation
models. However, evidence for a major role of organic
compounds in the atmospheric aerosol cycle is growing (Kirkby et al. 2011). Parameterisations should be
developed and included in global numerical models in
order to follow on recent observational evidence of the
importance of marine organic emissions (O’Dowd
et al. 2004).

4.4

Effects on Cloud Formation and
Indirect Radiative Effects

Marine clouds, particularly stratiform clouds, contribute significantly to the global albedo for two reasons:
firstly, they contribute a large fraction of global cloud
coverage and secondly, they comprise reflecting
layers over the dark, absorbing, ocean surface. Furthermore, given that marine clouds are typically clean
clouds (i.e. low cloud droplet number concentration),
they are far more susceptible to perturbations in cloud
nuclei availability compared to continental clouds. For
example, Slingo (1990) estimated that a 10–15 %
decrease in effective radius, corresponding to increase
in cloud droplet concentration of 30–45 % would be
sufficient to off-set global warming increase relating
to a doubling of CO2.
Although in the early days of cloud physics, sea salt
was considered the dominant marine cloud condensation species (Mason 1957), by the 1980s, sea salt was
more or less dismissed as having little or no role in
cloud formation (Charlson et al. 1987) on the basis that

4.4

Effects on Cloud Formation and Indirect Radiative Effects

the number concentration of sea salt CCN rarely
exceeded 1 cm3 and was typically too large to mix
up to cloud base. Despite this, Latham and Smith
(1990) postulated that sea salt could be involved in a
wind-speed related negative feedback system whereby
with increasing global temperatures, zonal wind speeds
would increase, leading to increased sea salt CCN
generation, increased cloud droplet concentrations
and ultimately increased cloud albedo. Their
measurements were based on the existence of sea salt
CCN down to sizes of ~0.2 μm radius. O’Dowd and
Smith (1993) later demonstrated that sea salt nuclei
extended into sizes as small as 0.05 μm radius and,
under moderately high wind speeds, could account for
~70 % of the nuclei larger than this size. Furthermore,
O’Dowd et al. (1993) demonstrated through combined
measurement and modelling studies that there was
significant competition between sea salt and nsssulphate nuclei and that under certain conditions, the
addition of a small number of sea salt nuclei under
moderately low updraft velocity conditions could
lead to a reduction in cloud droplet concentrations as
these nuclei cloud be preferentially activated over
sulphate nuclei, leading to a suppression of the peak
supersaturation achieved in clouds.
Korhonen et al. (2010a), taking observations of the
accelerating tropospheric westerly jet, estimated an
increase in wind speed of 0.45 m s1 decade1 at
50–65 S since 1980 and that this wind speed increase
has produced an increase in cloud condensation nuclei
of 22 % on average and up to 85 % in some regions,
leading to increased cloud albedo. The recognition of
the importance of sea salt nuclei in marine cloud processes has led to the interesting suggestion that global
warming could be ameliorated somewhat through controlled enhancement of albedo and lifetime in low level
marine clouds (Latham 2002), in other words, geoengineering, whereby artificial floating sea spray
generators are deployed in regions of persistent stratocumulus clouds. While intriguing as a geo-engineering
solution to global warming, Korhonen et al. (2010b),
using a global model, and simulating emissions from a
fleet of spray-emitting vessels in four regions of persistent stratocumulus fields found increases in cloud droplet concentration of maximum 20 %, and even a
reduction was predicted in one region.
The above studies focussed on sea salt as opposed
to sea spray nuclei, the latter which can comprise
varying degrees of organic matter enrichment. As

205

discuseed in Sect. 4.2.1.2, O’Dowd et al. (2004)
quantified the relative contributions of marine aerosol
in terms of sea salt, sulphate, WSOM and WIOM during
seasons of low and high biological activity over the
North East Atlantic. They found that organic matter
dominated the sub-micron mass fraction during the
high biological activity period and suggested that a
significant fraction of the organic matter was of primary, sea spray, origin. This was later corroborated
through laboratory and gradient flux studies which
demonstrated that the WIOM was almost exclusively
produced from bubble bursting at the ocean surface.
Ovadnevaite et al. (2011a) further identified, using aerosol mass spectrometry, significant primary organic aerosol plumes approaching 4 μg m3 for extended periods,
although the mass spectrometry revealed significant
oxygenated organic matter in the plume. It should be
noted that solubility is a relative definition and the
detection of oxygenated organic matter in the plumes
is not inconsistent with the previous identification of
almost exclusively WIOM. The question is whether or
not this primary organic matter which is apparently
water insoluble, will have a negative or positive impact
on cloud droplet concentration? In a follow-on study,
Ovadnevaite et al. (2011b) found that the aerosol
particles dominating these so-called primary organic
plumes have a low hygroscopicity (~1.2–1.25), they
have almost a 100 % activation efficiency at 0.25 %
supersaturation even for Aitken mode particles.
Ovadnevaite et al. (2011b) calculated the weighted
number of (organically-enriched) sea spray and nsssulphate nuclei and compared the number concentration of these nuclei with CCN and cloud droplet concentration (Fig. 4.17) for the cloud forming on the
plume. The total (combined sulphate and sea spray)
calculated nuclei concentration agreed almost perfectly with the CCN concentration at 0.75 % supersaturation, while the sea spray concentration agreed very
closely to the cloud droplet concentration, leading to a
correlation coefficient of r ¼ 0.76, while nss-sulphate
was anti-correlated to the cloud droplet concentration.
Not only were these results surprising in that what was
apparently non- or very low-solubility organic spray
aerosol acting as highly efficient cloud nuclei, but the
number concentration and resultant cloud droplet concentration exceeded 350 cm3 which can be regarded
as a very high droplet concentration for a maritime
stratiform cloud, and more typical of polluted or continental clouds. It was postulated that the organic

206

4

Ocean–Atmosphere Interactions of Particles

Fig. 4.17 (a) CDNC, measured CCN0.75 %, calculated sea
spray, sulphate and total nuclei concentration. (b) CDNC as a
function of sea spray particle concentration (Reproduced from

Ovadnevaite et al. (2011b) by permission of the American
Geophysical Union)

matter in the sea spray could be a marine hydrogel
resulting in such behaviour. To summarise, sea salt
aerosol is likely to have an important impact on the
indirect effect, and organic enrichment, in certain
regions, may have an even more important impact.

section showing that we are beginning to understand
the links between atmospheric deposition and global/
regional biogeochemical cycles.

4.5.1

4.5

Deposition of Aerosol Particles to
the Ocean Surface and Impacts

Studying the key interactions between the atmosphere
and the ocean is essential to understand the present
functioning of biogeochemical cycles in the ocean and
to predict their evolution in the future. Over the past
two decades a considerable effort has been made to
improve our understanding of the relevant processes
involved in the delivery of bioavailable atmospheric
nutrients to the surface of the ocean, their impact on
marine biogeochemical cycles, biota response and
carbon export. As shown in the following sections,
different temporal and spatial scales have been
explored from the large-scale experiments to the
microcosm studies and through modelling approaches,
in both high-nutrient low-chlorophyll (HNLC) and
low-nutrient-low chlorophyll (LNLC) areas of the
ocean. Recent progress is presented in the following

Deposition

Atmospheric deposition of aerosols can impact the
biogeochemical cycles of several important nutrients
in the ocean, especially iron, nitrogen and phosphorus
(Martin et al. 1991) (Duce 1986; Callaghan et al. 2008;
Falkowski et al. 1998; Fung et al. 2000) (Krishnamurthy
et al. 2010), although deposition of other species may
also be important (e.g. Nozaki 1997). Some atmospheric
inputs (e.g. toxic metals or dissociation products of
strong acids and bases) may actually reduce ocean productivity (Paytan et al. 2009; Doney et al. 2007). The
most important constituent for ocean biogeochemistry is
likely iron, with nitrogen, phosphorus, toxic metals and
acidic species also important in some regions (Doney
et al. 2007; Hunter et al. 2011; Krishnamurthy et al.
2010; Okin et al. 2011; Paytan et al. 2009).

4.5.1.1 Iron
Iron is a micronutrient, and required in small quantities
by biota. In some regions there is insufficient iron, and

4.5

Deposition of Aerosol Particles to the Ocean Surface and Impacts

thus iron limitations occurs (Martin et al. 1991). Therefore, atmospheric deposition can play a critical role in
supplying new iron to the surface ocean (Fung et al.
2000). Most of the iron deposited onto the ocean surface comes from atmospheric mineral aerosols which is
approximately 3.5 % iron, with small contributions
from combustion sources (Luo et al. 2008); because of
the dominance of mineral aerosols to the iron budget,
the largest deposition of iron to the oceans occurs
downwind of the large desert regions (Fig. 4.18a).
Not all forms of iron are thought to be equally
bioavailable (Jickells and Spokes 2001; Mahowald
et al. 2009). Most soils have relatively insoluble iron
forms, while atmospheric aerosols appear to be more
soluble, arguing that atmospheric processing may be
occurring (Jickells and Spokes 2001) and plausible
mechanisms based on acidity have been proposed
(e.g. Jickells and Spokes 2001; Meskhidze et al.
2005; Zhu et al. 1997), although this can be very
sensitive to the mineralogical composition of aerosol
(Journet et al. 2008). Smaller particles are slightly
more soluble than larger particles (e.g. Baker and
Jickells 2006; Chen and Siefert 2004; Hand et al.
2004), which can largely be explained by the longer
residence time of smaller particles (Hand et al. 2004).
Recent studies have suggested a role for combustion
sources of soluble iron (Chuang et al. 2005; Guieu
et al. 2005; Sedwick et al. 2007), however estimates
from observations and models still suggest that the
largest deposition of soluble iron occurs downwind
of the main desert dust source areas (Fig. 4.18b).
Extrapolations of the limited data on soluble iron
from combustion and atmospheric processing suggest
that soluble iron deposition may have doubled over the
last century, assuming constant mineral aerosol composition because of increased pollution (Luo et al.
2008; Mahowald et al. 2009). More recent estimates
of changes in desert dust over the past century based
on observations suggest there may have been almost a
doubling in desert dust between 1900 and 2000
(Mahowald et al. 2010), suggesting almost a
quadrupling of soluble iron inputs to the oceans over
this time period.
The importance of atmospheric iron was probably
initially overestimated (e.g. Ridgwell and Watson
2002), because of an underestimate of the ocean sediment sources of iron (Lam and Bishop 2008) and a
lack of understanding of the role of colloids in the
ocean in maintaining iron supply (Parekh et al.

207

2004). However, current understanding suggests that
changes in iron between glacial and interglacial times
can play an important, if secondary, role in facilitating
the drawdown of atmospheric carbon dioxide
(Kohfeld et al. 2005), and changes in soluble iron
deposition over the anthropocene may have driven
small (5 ppm) changes in atmospheric carbon dioxide
(Mahowald et al. 2010).

4.5.1.2 Phosphorus
On longer time scales, ocean productivity is limited by
phosphorus (Falkowski et al. 1998), and in some
regions on shorter time scales (Mills et al. 2004;
Moore et al. 2006; Wu et al. 2000). While the source
of phosphorus to the ocean from rivers is thought to be
much larger than from atmospheric deposition
(11 Tg P year1 vs. 0.6 Tg P year1) (Seitzinger
et al. 2005; Mahowald et al. 2008), much of the riverine inputs may be sequestered in estuaries, and not be
available to open ocean biota. Thus, atmospheric
deposition of phosphorus can be important. Similar
to iron, most atmospheric phosphorus is thought to
be in the form of aerosols (Graham and Duce 1979),
predominately mineral aerosol particles (83 %)
(Mahowald et al. 2008), since crustal material is on
average 700 ppm phosphorus. Thus, atmospheric
phosphorus deposition is similar to iron in being largest downwind of desert regions (Fig. 4.18e). Other
sources of phosphorus include primary biogenic emission of aerosol particles and aerosol emission from
biomass burning, fossil and bio-fuel burning,
volcanoes, as well as sea salt aerosol (Mahowald
et al. 2008). Not all phosphorus is likely to be soluble
in the oceanic mixed layer; soluble P or phosphate is
measured to be between 7 % and 100 % of total
phosphorus in aerosols (e.g. Graham and Duce 1979;
Mahowald et al. 2008). It is likely that mineral
aerosols are less soluble than other sources of phosphorus (e.g. Mahowald et al. 2008), but there may be
atmospheric processing by acids of phosphorus to
make it more soluble (Baker et al. 2006b; Nenes
et al. 2011). Estimates of phosphate deposition are
limited by shortage of measurements of phosphorus
and phosphate, as well as limitations in the understanding of the atmospheric phosphorus cycling (e.g.
Mahowald et al. 2008). Because of the large reservoir
of phosphorus in the oceans, atmospheric deposition is
not thought to be a dominant control on global ocean
productivity (Krishnamurthy et al. 2010).

208

4

Ocean–Atmosphere Interactions of Particles

Fig. 4.18 Deposition maps of Fe (a) (Mahowald et al. 2005), soluble Fe (b) (Okin et al. 2011), P (c) (Okin et al. 2011) soluble P
(d) (Okin et al. 2011) NHx (e) (Dentener et al. 2006), NOy (f) (Dentener et al. 2006), Total N (g) (Dentener et al. 2006), SOx
(h) (Dentener et al. 2006)

4.5

Deposition of Aerosol Particles to the Ocean Surface and Impacts

4.5.1.3 Nitrogen
The two known sources of new nitrogen to the ocean are
biological N2 fixation and atmospheric deposition.
Although molecular nitrogen gas (N2) is only available
for diazotrophs organisms, nitrogen fixation can be
an important process induced by atmospheric deposition of other limiting nutrient and is discussed elsewhere (see Sect. 4.5.4.2). Nitrogen aerosol deposition
comes predominately from combustion sources (NOx)
and agricultural sources (NH4). The atmospheric
reactive nitrogen flux (NOx, NH4, organic nitrogen
compounds) has dramatically increased over the last
century (Duce et al. 2008; Galloway et al. 2008) and
most of it is bioavailable (Duce et al. 2008). While
released into the atmosphere as a gas, about half of the
reactive nitrogen is deposited as aerosols (Adams et al.
1999). Nitrogen aerosols tend to have a relatively short
lifetime in the atmosphere (few days), which is seen in
the estimated distribution of reactive nitrogen deposition (Fig. 4.18c, d). Because the pool of nitrogen in the
ocean is so large, it is unlikely that anthropogenic new
nitrogen is important to net ocean uptake of carbon
(Krishnamurthy et al. 2010; Okin et al. 2011). However,
large parts of the ocean are thought to be nitrogen
limited, additional N deposition could locally lead to
additional productivity, especially because the effects
of increasing atmospheric nitrogen deposition are
expected to continue to grow in the future (Duce et al.
2008; Krishnamurthy et al. 2010).
4.5.1.4 Deposition of Other Species
While iron, nitrogen and phosphorus deposition are
thought to provide nutrients which increase ocean productivity, deposition of toxic metals (e.g. Cu) or acidic
species are thought to reduce ocean productivity. While
Cu is deposited into the ocean predominately by mineral
aerosols, deposition is thought to be enhanced by
anthropogenic activity (Paytan et al. 2009). Atmospheric acids are deposited to the oceans as acid rain
in the form of sulphates and nitrates occurring in
aerosols: thus nitrate aerosols both increase productivity
by adding nitrogen, and potentially reduce productivity
by increasing acidity (Doney et al. 2007) (Fig. 4.18g).

4.5.2

Elements of Biogeochemical
Interest and Their Chemical Forms

As shown in the previous section, atmospheric
particles from both natural and anthropogenic sources

209

contain chemical elements which participate to marine
biogeochemistry when deposited to the surface ocean.
Among them, iron (Fe) has received particular attention, especially when associated with mineral dust
which dominates the external input of this key element
to the surface open ocean (Jickells et al. 2005). Dustderived Fe has been proposed to be responsible of the
glacial-interglacial differences in atmospheric CO2
due to the strong Fe limitation of biological activity
in high-nutrient low chlorophyll oceanic regions
(Martin 1990). This has boosted the literature on the
chemical forms of dust-derived Fe and the multiple
factors controlling its solubility both in the atmosphere
and in the marine environment (see Sect. 4.5.3). Other
sources of atmospheric Fe such as biomass burning
(Guieu et al. 2005) or volcanic ash (Sect. 4.2.2.2) can
partially contribute to the marine Fe pool at a regional
scale although their overall contribution remains low
compared to aeolian dust (Mahowald et al. 2005).
As opposed to HNLC oceanic regions, in LNLC
areas biological activity is often limited or co-limited
by phosphorus and/or nitrogen. This has motivated
interest in atmospheric sources of these two elements
and their contribution to the marine pool, particularly in
two LNLC areas: the Mediterranean Sea (e.g.Markaki
et al. 2010) and the Atlantic Ocean (e.g. Baker et al.
2010). At a global scale, mineral dust appears as the
major source of P followed by primary biogenic
particles and combustion sources (Mahowald et al.
2008). Indeed, dust deposition events can transiently
increase the concentration of dissolved inorganic P in
the surface waters of the LNLC Mediterranean Sea
(Pulido-Villena et al. 2010). The partitioning between
dissolved and total P in atmospheric deposition varies
widely between 7 % and 100 % (Migon and Sandroni
1999; Mahowald et al. 2008). As for Fe, numerous
factors such as the aerosol source and physico-chemical
transformation during transport control the dissolved
fraction of atmospheric P deposition. Recent results
highlight the importance of the atmospheric acidification of aerosols resulting from the mixing of polluted
and dust-laden air masses as a source of dissolved P to
the oceans (Nenes et al. 2011).
As opposed to Fe and P, N in the atmosphere is
mainly present in gaseous form which implies a different behaviour. N deposition is dominated at a global
scale by anthropogenic emissions which have significantly increased since the mid-1800s and for which
future increases are expected (Dentener et al. 2006;
Duce et al. 2008). Atmospheric fluxes of N are also

210

Ocean–Atmosphere Interactions of Particles

Carbon flux (Gt C yr-1)

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PO PO in
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very important in remote dusty regions. Modelling and
laboratory studies have indicated that mineral dust
particles can take up acids resulting in increased coarse
mode nitrate (NO3) (Usher et al. 2003 and references
therein). When secondary NO3 is accumulated in the
coarse mode, it can be removed more rapidly by dry or
wet deposition (Dentener et al. 1996).
The bioavailable fraction of nutrients has been traditionally associated with the inorganic forms. This
may explain the scarcity of data concerning atmospheric deposition of organic nutrients. Today, we are
aware that not only heterotrophic organisms but also
autotrophic phytoplankton is able to take up organic
forms of either phosphorus or nitrogen. Organic N and
P contained in atmospheric deposition can thus exert a
biogeochemical effect on the surface ocean the magnitude of which remains poorly explored. Although
organic nitrogen has been measured in atmospheric
deposition (e.g. Cornell et al. 1995), it is only recently
that a real interest on its contribution to biogeochemical cycles at a global scale has begun to emerge
(Cornell 2011; Lesworth et al. 2010). Indeed, organic
nitrogen can constitute 30 % of total N deposition and
an important fraction is of anthropogenic origin (Duce
et al. 2008 and references therein).
Virtually nothing is known about the significance
of organic P in total P deposition. A few studies have
been conducted in the Mediterranean Sea showing a
contribution of organic P to total P deposition of 34 %
and 38 % in the western and eastern basins respectively (Migon and Sandroni 1999; Markaki et al.
2010). These values are in good agreement with
the results of Chen et al. (2007) who estimated that,
on average, organic P constituted 31 % of total P
deposition in the Gulf of Aqaba. These few reported
values suggest that atmospheric inputs can be a significant source of organic phosphorus and highlight the
need of evaluating the role of this external source in
marine biogeochemistry.
Current models of the carbon cycle, either at
regional or global scale, do not account for
atmosphere–ocean exchanges of organic carbon, particularly, atmospheric inputs of organic carbon to the
surface ocean. And yet, the few existing data indicate
that the magnitude of these deposition processes are
far from being negligible (Fig. 4.19). Willey et al.
(2000) estimated the input of dissolved organic carbon
(DOC) to the ocean to be 90 Tg C year1, equivalent
to the magnitude of riverine input to the open ocean.

4

Fig. 4.19 Comparison between the only existing estimate of
the global atmospheric flux of organic carbon to the ocean and
selected global oceanic carbon fluxes typically considered in
carbon cycle models. The aim of this figure is to represent
visually the relative magnitudes, rough values are given and
no error bars are included. (1) Global atmospheric deposition
of organic carbon (Jurado et al. 2008). (2) Global oceanic
downward flux of particulate organic carbon of which 45 %
actually reaches the deep sea floor (Jahnke 1996). (3) Global
oceanic downward flux of dissolved organic matter of which
10 % survives to depths >500 m (Hansell et al. 2009). (4)
Current estimate of the oceanic sink of anthropogenic carbon
(Denman et al. 2007). (5) Global riverine input of organic
carbon to the ocean (Seitzinger et al. 2010). The atmospheric
input of organic carbon is of the same order of magnitude as the
POC and DOC deep downward flux and represents more than
10 % of the oceanic carbon sink. Moreover, it is similar to the
riverine input, which has been so far the main allochthonous
source of organic carbon represented in carbon cycle models

Accounting not only for rain, but also for dry deposition, Jurado et al. (2008) derived a global estimate of
245  1012 Tg C year1, a value largely dominated
(76 %) by wet deposition of gaseous species (not
including CO2). This atmospheric source may be particularly important for the open ocean areas less
affected by riverine influences.
In summary, our current knowledge of the atmospheric fluxes of nutrients to the surface ocean suffers
from a disequilibrium between the organic and inorganic fractions. Further data on atmospheric deposition of organic phosphorus, nitrogen and carbon will
certainly constitute a step forward towards the

4.5

Deposition of Aerosol Particles to the Ocean Surface and Impacts

quantification and prediction of the impact of atmospheric deposition on marine biogeochemistry.

4.5.3

Dissolution- Scavenging Processes

In recent years, an abundant literature has been
published on the effect of dust deposition on the biogeochemical functioning of the surface ocean (e.g.
Landing and Paytan 2010). Even if the assumption is
rough, it can be considered that microorganisms preferentially uptake elements in the dissolved form.
Therefore, from a (bio)-geochemical point of view,
for a given chemical element, a key issue is to assess
the impact of atmospheric particle deposition on the
dissolved pool of such elements in the ocean. Atmospheric deposition can transport a large amount of
essential macro and micro-nutrients to the ocean (see
Sect. 4.5.1). Due to the large scientific debate on the
role of atmospheric dust iron deposition to the surface
ocean on the climate variability at millennium time
scales (Martin 1990), an extensive set of studies have
been produced on the impact of dust deposition on iron
stocks. Due to its importance in oligotrophic oceanic
areas, an abundant literature has also been produced
for phosphorus in the last two decades. However,
some recent studies suggest a potential control of
biological activity by other trace elements of atmospheric origin (Paytan et al. 2009; Ridame et al. 2011)
in large oceanic areas. It is therefore important to
emphasise that, even if most of the examples hereafter
are based on iron or phosphorus chemistry, the
concepts introduced have a wider importance than
just for these elements.
This section will focus on the “post-depositional”
processes, which include all processes that occur at the
surface of aerosol particles once they have deposited at
the ocean surface. The impact of atmospheric particles
on elemental stocks in the ocean will be controlled by
the balance between dissolution and scavenging at the
surface of the atmospheric particles which settle
through the water column. For a given elemental
stock, dissolution will constitute a source whereas
scavenging will be a sink.
A large set of literature exists on the dissolution of
nutrients or trace metals from atmospheric particles,
with an important emphases on iron (Baker and Croot
2010). A number of these studies focus on atmospheric
processes. They have demonstrated that the solubility of

211

different elements from atmospheric particles is
controlled by the nature, origin, size and mineralogy of
the particles and by the complex processes that affect
particles during atmospheric transport (see Sect. 4.2.3).
Once atmospheric particles deposit in seawater, the
solvent where the solubility processes occur changes
drastically from atmospheric conditions, shifting from
low pH value and low ionic strength in cloud droplets
to high ionic strength and a slightly basic pH value for
seawater. Recent studies have demonstrated that solubility of different trace elements is lower in seawater
than in ultrapure deionized water (Buck et al. 2010;
Chen et al. 2006). The main driving force for these
differences is pH.
In the ocean, the thermodynamic solubility of ionic
species can be very low. For example, the solubility of
inorganic iron in oxic seawater is between 100 and
200 pM (Liu and Millero 2002). Higher concentrations
of dissolved iron are possible in seawater through
complexation by organic iron binding ligands. Indeed,
more than 99 % of dissolved iron is in the form of
organic complexes (Hunter and Boyd 2007). In this
case, the seawater binding ligand concentration may
control the amount of a specific element that can
dissolve in seawater. For iron, this control by specific
binding ligand has been demonstrated in batch dissolution experiments with dust end-member particles
(Wagener et al. 2008, 2010). Moreover, the potential
of free iron binding ligand to keep iron from dust
particles dissolved was demonstrated in situ during a
cruise in the tropical Atlantic after a Saharan dust
event (Rijkenberg et al. 2008). Other elements, such
as cobalt (Saito and Moffet 2001) and copper (Buck
and Bruland 2005) are also found predominantly in the
form of organic complexes in seawater and the same
control mechanism on dissolution as for iron could
occur.
The importance of the particulate concentration in
seawater on the relative solubility has been discussed
in earlier studies. Bonnet and Guieu (2004) have
demonstrated that the relative solubility of iron
decreases when the particulate concentration
increases. This trend might be explained by the iron
binding capacity of seawater which limits the iron that
can dissolve and therefore decreases the relative
amount of dissolved iron for higher particulate
concentrations (Baker and Croot 2010). However,
similar trends have been demonstrated for phosphorus
dissolution (Ridame and Guieu 2002) whereas

212

dissolved phosphorus concentrations are not
demonstrated to be controlled by the solubility capacity of seawater.
Scavenging of dissolved elements on particles in
the water column has been extensively studied on
settling particles because this process is essential to
explain the deep concentrations of different chemical
elements. For some elements, scavenging on particles
is even used to trace fluxes of biological particles in
the water column: thorium-234 deficit in the water
column is used to assess the export of organic carbon
(Buesseler et al. 1992). Moreover, in the field of geochemistry, a rich literature exists on rare earth
elements scavenging on pure mineral phases because
of the importance of this geochemical process during
sediment formation.
Only few studies exist on scavenging processes for
atmospheric particles in the ocean. Zhuang and Duce
(1993) performed a set of scavenging experiments with
radiolabelled 59Fe on natural aerosols in seawater and
demonstrated significant scavenging on dust particles.
A similar type of experiments with radiolabelled 33P
demonstrated scavenging of phosphorus on Saharan
dust end-member particles (Ridame et al. 2003). In
earlier studies, based on pure haematite particles,
Honeyman and Sanchi (1991) demonstrated the
scavenging of colloidal iron when freshly produced
haematite particles coagulate to form larger aggregates. This points to the importance of the particle
size (and certainly chemical composition) for scavenging processes.
Even fewer studies have taken into account the
balance between scavenging and dissolution processes
when atmospheric particles deposit to the surface
ocean. In a set of batch reactor iron dissolution
experiments in seawater, Bonnet and Guieu (2004)
considered the importance of scavenging to suggest
that the net result of dust dissolution is lower than the
effective dissolution from dust particles. In a recent
artificial dust seeding experiment in a large mesocosm,
it was demonstrated that the net effect of dust addition
was a decrease of the dissolved iron stock in the first
10 m of the water column due to scavenging on the
settling dust particles (Wagener et al. 2010). The
mesoscoms experiment was simulated with a 1D
model which takes into account dissolution and scavenging of iron on dust particles. The equilibrium
between both processes allowed definition of a critical
Fe concentration above which the balance between

4

Ocean–Atmosphere Interactions of Particles

dissolution and scavenging is in favour of scavenging,
resulting in a net sink of dissolved iron (Ye et al.
2011).
One of the major challenges in future studies on
abiotic processes occurring when atmospheric
particles deposit at the ocean surface will be to investigate the above process in a more realistic way. This
implies studying the processes involved (scavenging
and dissolution) at a higher temporal and vertical
resolution. So far, most of the studies on the biogeochemical impact of atmospheric deposition, have
assumed that once they deposit at the ocean surface,
atmospheric particles are perfectly mixed through the
surface mixed layer (SML) (Fig. 4.20 – Left panel). It
is considered that after a certain time, a fraction of a
soluble element is dissolved (or scavenged) and will
increase (or decrease) the dissolved stock of the element. Based on this postulate, and in order to asses the
impact of atmospheric particles, batch reactor
experiments need to be conducted where atmospheric
particles are in contact with seawater at the expected
concentration in the SML. The net effect of atmospheric deposition for an element would then be
assessed by measuring the increase or decrease of the
element after a certain amount of time. However, in
order to understand the atmospheric particle deposition process in a more realistic way, two main issues
(among others) need to be considered:
1. When they deposit at the surface of the ocean,
particles will first encounter the sea surface microlayer. This micro-layer is enriched in organic matter
with a specific chemical composition (Frew 1997),
which might greatly impact processes at the surface
of atmospheric particles directly after deposition.
2. Once particles cross the micro-layer, the physicochemical processes that occur during settling
through the water column must be considered.
Through the formation of aggregates (between
atmospheric particles but also with organic
particles), the size distribution of the particles will
change when they settle through the water column.
Moreover the influence of organic matter, which
may play the role of a glue during these coagulation
and aggregation processes, will also change the
chemical nature of the particles and in particular
the surface properties (Verdugo et al. 2004). These
changes will influence the settling rate of the
particles, but also the scavenging or dissolution
processes that may occur at the particle surface.

4.5

Deposition of Aerosol Particles to the Ocean Surface and Impacts

213

Fig. 4.20 Conceptual view of the fate of atmospheric particles
after their deposition. Left panel: “Batch reactor” point of view
with atmospheric particles perfectly mixed over the SML. Right
panel: Hypothetical dynamics of atmospheric particles in the

water column. At T0 particles are in the surface microlayer.
After T0, bigger particles (orange) settle faster than smaller
particles (black) which form aggregates with organic matter
(green) at an intermediate depth

One challenge for future research is thus to take
into account the dynamics of the particles which settle
through the water column. As illustrated in Fig. 4.20
(right panel), this new approach might lead to a much
more complex image of the impact of atmospheric
particles in the ocean. However, to tackle these
concepts, new tools able to investigate at such scales
need to be developed. In particular, two major technological issues have to be solved:
1. The settling of atmospheric particles through the
water column and their change in size and composition must be followed in situ with high temporal
resolution. Promising results have been recently
obtained in a mesocosm experiment with a combination of optical in situ measurements (Bressac
et al. 2012).
2. Chemical techniques must be fast and reliable
enough to investigate at these scales. Even though
the recent development of new sensors for biogeochemical parameters on board new platforms
(gliders, profilers,. . .) is promising, there is a crucial need of chemical sensors for relevant micro
and macro nutrients.
In conclusion, a major perspective in assessing the
impact of atmospheric deposition on the ocean is to
investigate the competition between scavenging and
dissolution within a dynamical process of the settling

of the particles through the water column. The debate
is largely open on the relevant scales that need to be
investigated.

4.5.4

Atmospheric Impacts in HNLC
and LNLC Areas

4.5.4.1 Experimental: Large Scale
Fertilisation Experiments (Fe, P)
Direct assessment of the fate of atmospheric dust
deposition and the subsequent biogeochemical impact
on HNLC or LNLC waters has been extremely limited
due to the episodic nature of such events and the need
for researchers to ‘be in the right place at the right
time’. Such assessments have therefore been indirect,
and mainly confined to a comparison of remotelysensed datasets from different satellite sensors (Boyd
et al. 2010). The use of such indirect approaches has in
many cases led to mis-attribution of cause (aerosol
deposition) and effect (altered ocean biogeochemistry
via an aerosol-iron mediated phytoplankton bloom)
(Boyd et al. 2010).
Due to the lack of direct information on the ‘dustnutrients-biota-biogeochemistry’ linkages, we have
had to rely on experimental approaches in which a
known quantity of nutrients (either iron or phosphate,

214

and in a few cases iron and phosphate, Boyd et al.
2007) are added to an area (generally 50–100 km2) of
the surface ocean. Such experiments, referred to as in
situ mesoscale ocean enrichment, provide valuable
insights into the wide range of processes that are
influenced by an episodic nutrient addition, and as
such provide a ‘biogeochemical timeline’ of how the
nutrients released from dust deposition may have
myriad effects on both surface and subsequently
subsurface or atmospheric processes.
Trends from over ten of these experiments reveal
convergences but also differences, which in many
cases are due to regional differences between the environmental characteristics of HNLC waters in tropical
to polar waters (de Baar et al. 2005). Convergent
results provide confidence in compiling a biogeochemical timeline of the many processes that may be
altered by aerosol deposition. In the case of iron
enrichment, rapid initial changes (i.e. hours to days)
include altered iron chemistry, up-regulation of cellular machinery in pelagic microbes such as photosynthetic competence (Fv/Fm), increased growth rates by
all phytoplankton groups which is first manifested as
an increase in stocks of cyanobacteria followed by
those of nanophytoplankton (such as haptophytes).
This initial increase in cyanobacterial abundances is
truncated within a day or so as they are grazed down to
initial levels as nano-grazers respond to higher picoprey concentrations. A similar increase in haptophyte
abundances lags the cyanobacteria, and then they are
also brought under grazer control. In contrast the
diatom stocks take longer to reveal an increase in
abundances, and in many cases they escape grazing
pressure resulting in an iron-mediated bloom (de Baar
et al. 2005; Boyd et al. 2007).
This biological activity results in distinct biogeochemical signatures that produce feedbacks that are
important for ocean–atmosphere interactions. For
example, at some but not all experimental sites, the
grazing of the haptophytes by microzooplankton may
result in increased ocean DMSP inventories (Boyd
et al. 2007) that consequently may in some cases result
in elevated DMS concentrations in the upper ocean.
However, in other studies such as SERIES in the NE
subarctic Pacific no such increase in DMS was
observed by the end of the bloom (Boyd et al. 2007).
Concurrently there is widespread evidence of significant decreases in oceanic CO2 concentrations due to
enhanced C fixation by the blooming diatoms (de Baar

4

Ocean–Atmosphere Interactions of Particles

et al. 2005) which may result, in time, in a drawdown
of atmospheric CO2. There are also knock-on effects
on ocean physics (warming due to more absorption of
incoming solar radiation by the higher phytoplankton
stocks) and chemistry (uptake of added iron, and
decrease of much of the inventories (with in some
cases alteration of the stoichiometry of nutrient stocks
– Boyd et al. 2004) of other plant nutrients such as
silicate and nitrate). In some studies, the imprint of
the iron-mediated blooms is eventually recorded
(1–2 weeks) in the waters underlying the surface
mixed layer, such varying degrees of enhancement of
downward export of algal carbon from the bloom
(Boyd et al. 2004; c.f. Smetacek et al. 2012), and
elevated concentrations of other greenhouse gases
such as N2O and CH4 probably due to remineralisation
of the sinking particles (Boyd et al. 2007). However, in
many experiments there were major uncertainties in
estimating the ratio of iron added to additional carbon
exported to depth from the base of the surface mixed
layer.
Although fewer in situ nutrient enrichment studies
have taken place in LNLC waters, relative to those in
HNLC waters, they too provide evidence of the many
effects of episodic nutrient enhancement, for example
resulting in widespread foodweb effects such as
the putative mechanisms of ‘ecological tunnelling’
reported for the CYCLOPS experiment in the E. Mediterranean by Thingstad et al. (2005). As there have
been so few studies in LNLC waters, it is premature to
comment on whether more effects are observed when
an episodic pulse of nutrients is added to either HNLC
or LNLC waters.
The benefits of what we have learned from these in
situ mesoscale experiments for better understanding
the biogeochemical effects of aerosol nutrient deposition into the ocean are twofold. Firstly, they provide us
with a timeline of conspicuous changes in upper ocean
properties that may help detection and attribution of
the effects of episodic dust deposition events on the
upper ocean using a combination of shipboard and/or
satellite observations. Second, they have increased our
conceptual understanding of how atmospheric processes influence those in the ocean, and how in turn
those in the ocean may feedback on the lower atmosphere. However, when the magnitude, resulting
elemental stoichiometry of the upper ocean, and
chemistry of added nutrients of mesoscale ocean nutrient enrichment is compared with that resulting from an

4.5

Deposition of Aerosol Particles to the Ocean Surface and Impacts

aerosol deposition event it is clear that the former is
much greater than the latter (see Fig. 4.21). This
prevents the ready extrapolation of the results from
such experiments to the natural world, and suggests
that some cautious downscaling – via modelling
simulations – of the observed biogeochemical
signatures might provide some bounds on how an
aerosol deposition event might influence the upper
ocean.

4.5.4.2 Experimental: Microcosms
Microcosm experiments involve the enclosure of a
water sample in a container, typically 1–10 l in volume, and its incubation under simulated in situ
conditions. Microcosm experiments are often used in
the study of the effects of atmospheric nutrient deposition on natural plankton communities, as they represent an easy experimental approach to monitor a
wide range of chemical and biological properties
and their response to controlled perturbations. Microcosm experiments have the same limitations as any
approach that involves in vitro confinement of water
samples, including sampling bias and the difficulty of
precisely simulating in situ conditions. The composition of the community can change markedly after
bottle enclosure (Massana et al. 2001; Calvo-Dı́az
et al. 2011) and therefore most bioassay experiments
have a duration that does not exceed 3–4 days (e.g.
Bonnet et al. 2005; Mills et al. 2004; Moore et al.
2008), which means the recorded changes represent
short-term responses to the simulated perturbation. For
all these reasons, the results obtained from bioassay
experiments are best interpreted in conjunction with in
situ observations collected during oceanographic
cruises and time-series monitoring programmes.
Published reports show a large degree of variability
in the responses of surface plankton communities during microcosm fertilisation experiments intended to
simulate the effects of atmospheric deposition. Part
of this variability stems from the fact that the materials
used to amend the water samples differ widely among
studies, as they may consist of collected aerosols
(Herut et al. 2005), collected rainwater (Klein et al.
1997), unprocessed desert soils (Mills et al. 2004), and
atmospherically processed soils (Ternon et al. 2011). In
addition to the geographical variability in the composition and solubility of natural aerosols, the relative
abundance of anthropogenic particles varies widely in
both space and time, and plays a crucial role in

215

determining the biological effects of atmospheric
deposition. An additional source of variability is the
fact that different communities inhabiting the surface
ocean have distinct community structures and experience different types and degrees of nutrient limitation
(Moore et al. 2013), all of which result in diverging
responses to a given perturbation. In spite of all these
sources of variability, the microcosm experiments
conducted during the last decade have yielded consistent patterns regarding the responses of surface plankton to atmospheric inputs in terms of biomass and
abundance, community structure, and metabolic
activity.
Main Results Obtained from the Microcosm
Approach
Phytoplankton biomass, estimated from chlorophyll a
concentration, typically increases after the addition of
aerosols, although the reported increases are usually
modest, e.g. around 50 % or less (Mills et al. 2004;
Bonnet et al. 2005; Marañón et al. 2010). In LNLC
waters, the potential of atmospheric deposition to
cause phytoplankton blooms (e.g. resulting in chlorophyll a concentrations of 1 μg L1 or more) is limited
because, after taking into account nutrient content and
solubility as well as dilution over the upper mixed
layer, even large deposition events such as strong
dust storms are unlikely to increase nitrate and phosphate concentrations by more than approximately
0.2 μM (Guieu et al. 2002; Ridame and Guieu 2002;
Mills et al. 2004). As an example, if in a nitrogenlimited system nitrate concentration is increased by
0.2 μM and one assumes a molar C:N ratio of 6.6 for
phytoplankton biomass production and a carbon to
chlorophyll a ratio (g:g) of 100, the resulting maximum increase in chlorophyll a concentration will be
only 0.16 μg L1. It must be taken into account,
however, that LNLC waters cover vast expanses
of the ocean, such as the subtropical gyres, which
means that even small areal increases in biomass and
productivity can have significant global impacts. The
situation is different in the case of HNLC regions,
where iron limits both phytoplankton standing stocks
and primary production rates, and an excess of
macronutrients is available. A strong dust deposition
event (delivering 2 mg L1 of aerosols to the upper
mixed layer) can release around 2 nM Fe (Bonnet and
Guieu 2004; Mills et al. 2004). If we assume that 10 %
of this dissolved iron is bioavailable (Wu et al. 2001)

216

Fig. 4.21 A comparison for Southern Ocean waters of
mechanisms responsible for perturbations in Fe supply. Numbers
in each panel: (1) Fe*, the relative magnitude of Fe supply relative
to macronutrient supply (Parekh et al. 2005); (2) the mode of Fe
supply; (3) the time scale over which surface waters receive
increased Fe supply; and (4) the length scales of Fe supply events.
(a) Satellite image of a purposeful in situ Southern Ocean FeAX
(Mesoscale iron addition experiments) [SOIREE (Boyd et al.
2000)]. (b) An FeNX (Fe natural enrichment experiments) near
Crozet within the HNLC Southern Ocean, where naturally occurring blooms are evident from remote sensing (Boyd et al. 2007
and references given therein). (c) An atmospheric dust deposition
event (dust units are g m–2 year–1) in the modern Southern Ocean
[e.g. from Patagonia (Jickells et al. 2005)]. (d) Fe supply to
the Southern Ocean during the last glacial maxima from direct

4

Ocean–Atmosphere Interactions of Particles

[i.e. higher dust deposition (Martin 1990; Wolff et al. 2006)] and/
or indirect [i.e. upwelling of waters with higher Fe concentrations
(Lefevre and Watson 1999)] sources. The magnitude of this
supply is unknown; hence, Fe* is expressed as < 0. Fe* is
defined as Fe* ¼ [Fe]–{(Fe/P) algal uptake ratio  [PO43–]}
(Parekh et al. 2005). If Fe* > 0, primary production is ultimately
macronutrient-limited; if Fe* < 0, production is ultimately Felimited. The width of red arrows denotes the relative magnitude
of changes in Fe supply; the hatched arrows in (d) denote
uncertainties about whether Fe supply in the geological past was
episodic or sustained. In (b) to (d), downward- and upwardpointing arrows represent atmospheric and oceanic (upwelling)
supply, respectively (Reproduced from Boyd et al. 2007 by permission of American Association for the Advancement of
Science)

4.5

Deposition of Aerosol Particles to the Ocean Surface and Impacts

217

Fig. 4.22 Relative change (%) of different variables in
response to dust addition during eight bioassay experiments in
the tropical Atlatic Ocean. Relative change was calculated as
100  (D–C)/C, where D and C are the mean value of the
variable in the dust and the control treatments, respectively.
Boxes and bars enclose the 25th–75th and 5th–95th percentiles,
respectively, the vertical dotted line is the mean, and the vertical
continuous line is the mode (n ¼ 8). Variables shown are total

chlorophyll a concentration; the abundance of Synechococcus,
Prochlorococcus, picoeukaryotes and total heterotrophic bacteria; the abundance of bacteria belonging to the groups
Roseobacter, SAR11, bacteroidetes and gammaproteobacteria;
and the metabolic rates primary production, N2 fixation, bacterial production, and community respiration (Figure modified
from Marañón et al. (2010))

and take a molar C:Fe ratio of 105 for surface phytoplankton (Boyd et al. 2007), the resulting estimated
chlorophyll a increase would be 2.4 μg L1. Thus,
stoichiometric constraints and the different nutrient
limitation conditions dictate that dust deposition
events have a much larger potential for stimulation
of phytoplankton biomass and production in HNLC
regions than they do in LNLC regions.
Bacterial abundance and biomass have also been
reported to increase after the addition of atmospheric
aerosols (Herut et al. 2005; Pulido-Villena et al. 2008;
Marañón et al. 2010). It is now well established that
bacterial metabolism can be limited by inorganic
nutrients in very oligotrophic regions (Rivkin and
Anderson 1997; Mills et al. 2008) and atmospheric
inputs therefore have the potential to stimulate
bacterial growth. In most cases, however, the observed
increases of bacterial abundance in the amended
treatments, compared with the controls, are modest
(<20 %). A recurrent pattern in microcosm experiments is that the response of phytoplankton and
bacterioplankton to the addition of aerosols or individual nutrients is much stronger in terms of metabolic
activity that in terms of abundance or biomass (see

Fig. 4.22). In oligotrophic environments, a tight trophic coupling exists between picoplankton and their
unicellular grazers, which means that a stimulation of
production rates translates only partially, if at all, into
an increase of population abundance.
In addition to considering the bulk biomass of
bacterioplankton and phytoplankton, it is also important to take into account community structure and
how the relative abundance of different taxonomic
groups changes in response to atmospheric deposition.
There is growing evidence that within both the
phytoplankton and the bacterioplankton different
taxonomic groups respond differently to a given
input. In particular, the abundance of the cyanobacterium Prochlorococcus has been shown to
decrease in response to the addition of Saharan soils
and collected aerosols (Hill et al. 2010; Marañón et al.
2010) and also during natural deposition events (Herut
et al. 2005). Adverse effects of some aerosols have
also been reported for Synechococcus and the
picoeukaryotes (Paytan et al. 2009; Marañón et al.
2010). The toxicity of some aerosols for certain groups
of phytoplankton may be related to the presence of
high levels of copper (Paytan et al. 2009). A negative

218

response by some groups can be compensated by an
increase in other groups, such that the bulk biomass
does not change markedly. For instance, during a set
of eight dust addition experiments conducted in the
Atlantic Ocean it was found that both Prochlorococcus
and Synechococcus decreased in the presence of
dust, whereas the opposite was true for the
picoeukaryotes (Marañón et al. 2010) (Fig. 4.22).
Less is known about the response of bacterioplankton community structure to atmospheric deposition, but again contrasting changes in different
groups have been observed. In the Atlantic Ocean,
the abundance of the SAR11 lineage, which is dominant in ultraoligotrophic environments (Morris et al.
2002), tends to decrease in treatments amended with
Saharan dust (Hill et al. 2010; Marañón et al. 2010),
whereas the opposite is true for gammaproteobacteria, a group known to respond to increased
nutrient concentrations (Horňák et al. 2006).
Metabolic rates are usually more responsive than
standing stocks to aerosol additions and nutrient
amendments. Phytoplankton primary production and
N2 fixation can show marked increases, by a factor of
2 or more, which are due to the fertilising effect of N,
P and Fe (Mills et al. 2004; Bonnet et al. 2005; Herut
et al. 2005; Blain et al. 2004; Marañón et al. 2010;
Law et al. 2011; Ternon et al. 2011). Factorial nutrient
addition experiments have demonstrated that N is the
primary limiting nutrient for carbon fixation in LNLC
waters (Moore et al. 2008), whereas P and Fe co-limit
N2 fixation in the Atlantic Ocean (Mills et al. 2008).
Thus, desert dust has the potential to stimulate autotrophic productivity in the ocean through an increase
in both carbon and N2 fixation, therefore favouring
CO2 sequestration by the biological pump. However,
recent evidence indicates that heterotrophic processes
such as bacterial production and bacterial respiration
are in fact stimulated by atmospheric deposition to
a larger degree than autotrophic processes. In the
NW Mediterranean Sea, it has been reported that bacterial respiration can increase by a factor of 3 in
response to Saharan dust inputs (Pulido-Villena et al.
2008). Similarly, bacterial production was shown to
increase by a factor of 8 during microcosms dust
addition experiments in the Eastern Mediterranean
Sea (Herut et al. 2005). In a set of eight dust addition
experiments conducted in the Atlantic Ocean, the largest increases in metabolic rates (up to 700 %) were
reported for bacterial production and community

4

Ocean–Atmosphere Interactions of Particles

respiration (Marañón et al. 2010) (Fig. 4.22). Whether
phytoplankton or bacterioplankton dominate the metabolic response to atmospheric inputs has important
biogeochemical implications. If bacteria outcompete
phytoplankton in using the newly supplied nutrients,
and increase their production and respiration rates, the
net result may be the remineralisation of dissolved
organic carbon, the release of CO2 and a weakening
of the potential for biological CO2 sequestration. This
is a paradoxical outcome if we consider that atmospheric deposition is commonly regarded as a process
that fertilises the open ocean. However, only a handful
of studies have addressed concurrently the metabolic
rates of both phytoplankton and bacteria to atmospheric inputs, and therefore more studies are needed
in order to understand how competitive interactions
within the planktonic community modulate the overall
response in terms of the balance between CO2 fixation
and respiration. Another priority for future studies is to
conduct repeated microcosm addition experiments
over time and in different locations, in order to obtain
general patterns that relate the initial characteristics of
the community, in terms of taxonomic composition
and type and degree of limitation by different
nutrients, to its response to a given perturbation.

4.5.4.3 Experimental: In Situ Mesocosms
Although in situ mesocosms have been widely used to
study biological responses to changes in nutrient
conditions (e.g. Duarte et al. 2000) or environmental
conditions such as increasing levels of atmospheric
carbon dioxide on carbon uptake import or export
from the upper ocean (eg Riebesell et al. 2007), impacts
of actual atmospheric deposition on ecosystems was
only performed recently (Guieu et al. 2010). In situ
marine manipulation, although being a technical challenge, represents a significant progress compared to the
approach using microcosms. Indeed, mesocosms have
the potential to allow controlling physical and chemical
forcing, including atmospheric deposition, and the
advantage of following simultaneously the whole ecosystem and the export of matter (Fig. 4.23) the biogeochemical modelling in a 1-D model taking into account
the role of atmospheric inputs on the biological carbon
pump can thus be done. Such experiments remain challenging first because of the difficulty to reproduce an
actual atmospheric deposition event, meaning that
enough representative atmospheric material has to be
produced and second because contamination has to be

4.5

Deposition of Aerosol Particles to the Ocean Surface and Impacts

219

Fig. 4.23 Stocks (in green) and fluxes (in blue) that can be
measured in a mesocosm experiment after the simulation of a
realistic atmospheric input in a water body large enough to be
representative of natural processes. As the particles are naturally
sinking, those changes are closer to the ‘real’ processes occurring in the open ocean, compared to microcosms approaches

where the particles are homogenised and not allowed to sink.
Sampling in quick succession at different depths of the
mesocosms allows for parameterising the complex processes
involved. Sedimentation, including carbon export, can be
quantified and this represents a major step forward of that
methodology

avoided during the different steps of the experiments.
As a matter of fact, the water tested for their response to
atmospheric inputs has usually low concentrations of
(micro)nutrients (typically iron and phosphorus are at
nanomolar levels) and the induced changes in the biogeochemical cycle of those elements is one of
the objectives of such large mesocosm experiments. In
the recent experiment reported by Guieu et al. 2010,
large (52 m3) mesocosms containing no metallic part
and with as little as possible induced perturbation during the sampling sequence have been used and the
required conditions for biogeochemical studies in oligotrophic environments have been reproduced
(Fig. 4.24). Such large clean mesocosms have been
thus proven to be highly relevant tools to study in situ
the response of an oligotrophic ecosystem to atmospheric deposition.
Mesocosms present the advantage of enabling studies of processes both as a function of time and depth
while the atmospheric particles are sinking. According
to what was pointed out in Sect. 4.5.3, this is a very
important consideration, in particular regarding
adsorption/desorption processes and aggregation
mediated by the introduction of lithogenic particles

(Bressac et al. 2012 and Sect. 4.5.5). Mesocosms are
thus much more representative of the reality of the
atmospheric deposition to the surface of the ocean and
results obtained can be more easily extrapolated than
results obtained in microcosms that rely on a fixed and
homogeneously-distributed concentration of particles
that can alter the dust dissolution kinetics and does not
account for particle migration through the water column, precluding any estimation of C export induced
by atmospheric deposition.
Those recent mesocosm experiments confirm that
significant changes in the cycling of chemical elements
are induced by the deposition of dust particles, and that
there are strong responses of the ecosystem at different
trophic levels. Pulido-Villena et al. (2010) provided a
quantitative confirmation of the fertilising potential of
mineral dust as a significant increase in phosphate
concentration was observed soon after seeding. The
dissolved inorganic phosphorus (DIP) released was
completely lost after 24 h and no further increase was
observed during the experiment. DIP loss might thus
have been dominated by biological uptake, in agreement with the observed rapid response of both bacteria
and phytoplankton. The iron cycle was also profoundly

220

4

Ocean–Atmosphere Interactions of Particles

Fig. 4.24 The different steps to reproduce a realistic atmospheric
dust deposition and subsequently follow the biogeochemical
changes induced near the surface of the oligotrophic ocean
illustrated by experiment 2 (2010) of the DUNE project: (a) sampling soils in a region of South Tunisia where the Saharan aerosol
is produced and is typical of inputs to the North-Western Mediterranean, (b) Soil collected on the three stages of the dry sieving
column at the end of the fine particle production process.

(c) Artificial ageing of the atmospheric particles in a clean room
to reproduce the processes taking place during aerosols transport
(d) a group of three mesocosms from below, (e) a view of the seven
mesocosms during the DUNE2 campaign in June 2010 (f) a view
above the surface of a group of three mesocosms showing the
artificial seeding. (Photos a, b : F. Dulac, CEA; photo c: LISA;
photos d, e, f : D. Luquet, OOV). Methodology fully described in
Guieu et al. 2010

affected by the dust input to the surface seawater, as a
dissolved iron (DFe) scavenging was observed rapidly
after the seeding, withdrawing almost 1 nM DFe from
the whole water column inside the three mesocosms
amended with dust while the atmospheric particles are
settling (Wagener et al. 2010). Those results were satisfactorily simulated with a one-dimensional model of
the Fe biogeochemical cycle, coupled with a simple
ecosystem model (Ye et al. 2011). When a second
dust wet deposition was simulated, iron dissolution
from the dust particles was then evidenced due to the
excess Fe binding ligand concentrations produced by
the enhanced biological activity (Wuttig et al. 2013).
When simulating dust wet deposition at least a doubling of Chla (mainly attributed to small phytoplankton
(<3 μm); Giovagnetti et al. 2013) was observed but the
Chla concentrations remained very low (maximum
values 0.22 μg L-1) maintaining the oligotrophic status
of the tested waters (Guieu et al. 2013). Laghdass et al.
(2011) investigated the effect of dust deposition on the

diversity of the heterotrophic bacterial community.
Combining several molecular biology approaches, the
results indicate that besides a natural temporal trend
observed inside and outside the mesocosms, dust deposition affected the particle-attached bacterial community. In particular, Alteromomas macleodii made a
higher contribution to the active particle-attached bacterial community in the dust-amended than in the control mesocosms. Bacterial respiration and primary
production both increased very rapidly after the seeding
(by a factor of 2 and 3, respectively), showing a competition for the new nutrients between heterotrophic
bacteria and phytoplankton (Guieu et al. 2012).
Among phytoplankton, diazotrophs – although only
responsible for few percent of the induced new production – were strongly stimulated by the atmospheric
input (Ridame et al. 2013). Coupling geochemical and
optical measurements have demonstrated that rapid
particulate carbon export was in part due to aggregation
processes between organic matter and lithogenic

4.5

Deposition of Aerosol Particles to the Ocean Surface and Impacts

particles (Bressac et al. 2012). Results from mesocosm
approaches indicate that the role of atmospheric deposition on oligotrophic areas cannot be seen solely as a
simple fertilisation effect because (1) both autotrophs
and heterotrophs are stimulated (confirming on longer
time scales the findings from microcosm experiments
described in Sect. 4.5.4.2) and (2) a significant export
of particulate organic carbon to the deep ocean is
attributed to aggregation processes (Bressac et al.
2013).
Although they represent a powerful tool to handle the
complex question of the role of atmospheric deposition
on ecosystem functioning, the use of such large, clean
and pelagic mesocosm in the open ocean remains a
difficult task for obvious logistical reasons. So far, the
only two experiments conducted have been done in
coastal areas chosen for being representative of waters
with comparable chemical and biological characteristic
to open waters. Future development could be the feasibility of identical experiments in the open ocean.
Another disadvantage of the mesocosm approach is
that only few treatments can be handled (during the
DUNE (A DUst experiment in a low Nutrient, low
chlorophyll Ecosystem) experiment, triplicates of
controls and triplicates of dust deposition were the only
treatments considered), contrary to microcosm approach
where a lot of different treatments can easily be used.

4.5.4.4 Modelling
In the past two decades, ocean biogeochemical
models (e.g. Maier-Reimer 1993) have been used to
study the response of marine biogeochemistry to a
variety of forcings. Many studies have focussed in
particular on the response of ocean carbon storage
(Sarmiento and Le Quéré 1996) and marine productivity (Bopp et al. 2001) to anthropogenic climate
change. Among the various forcings employed, the
impact of changes in atmospheric deposition on
marine biota and marine biogeochemistry has
received much less attention. One reason for this
relative lack of attention is linked to the very large
uncertainties in future changes of atmospheric nutrient
deposition to the ocean due to climate change, as
exemplified by simulated future changes in dust deposition (e.g. Mahowald and Luo 2003; Mahowald et al.
2006; Tegen et al. 2004).
Early studies have focussed on the role of changes
in dust deposition on the marine carbon pump and
atmospheric CO2 during glacial times. Watson et al.

221

(2000) used a box model of the ocean carbon cycle
forced with atmospheric iron fluxes derived from the
Vostok ice-core dust record (Petit et al. 1999). They
showed that changes in the Southern Ocean biota
caused by iron deposition could explain only a maximum of 40 ppmv drawdown of atmospheric CO2 at the
glacial maximum, half of the observed glacialinterglacial CO2 change. Other modelling studies,
however, using general circulation models and more
complex biogeochemistry (including a explicit representation of the ocean iron cycle) converge towards a
more moderate effect; the impact of changes in dust
deposition on marine biota at glacial maximum could
drive a ~15 ppm reduction in atmospheric CO2 (e.g.
Archer et al. 2000; Bopp et al. 2003).
Similar models have been used to quantify or predict the effect of changing dust deposition on marine
biota and the global carbon cycle in the recent past and
in the near future. Over the twentieth century, increase
in dust deposition inferred from observations has been
estimated to be responsible for an increase in marine
productivity and an additional drawdown of carbon
into the ocean (Mahowald et al. 2010). This additional
drawdown could amount to 8 Pg C, more than 5 % of
total anthropogenic carbon stored in the ocean over the
industrial period.
While future projections of desert dust deposition
over the ocean are still largely uncertain, even as
regards the sign of changes (Tegen et al. 2004;
Mahowald et al. 2009), several ocean modelling studies have addressed its potential impact on marine biogeochemistry. Changes in dust deposition may force
changes in ocean productivity, as large as the changes
in productivity forced by CO2 increases and the
resulting climate change (Mahowald et al. 2011). But
there is only relatively little impact of varying aeolian
Fe input on cumulative ocean CO2 fluxes and atmospheric pCO2 over 2000–2100 (Tagliabue et al. 2008).
The simulated sensitivity of marine biota to iron
deposition is, however, largely dependent on processes that are or not explicitly included in models.
Complex iron chemistry in the ocean (Tagliabue et al.
2009), adsorptive scavenging of dissolved iron and
solubilisation of particulate iron (Ye et al. 2011),
varying iron content of dust particles as well as varying iron solubility in seawater (Luo et al. 2006) have
been investigated in individual modelling studies but
an explicit representation of these processes is still
lacking in most of the biogeochemical models that

222

4

Ocean–Atmosphere Interactions of Particles

Fig. 4.25 Simulated ratios between atmospheric inputs of N, Fe,
Si, and P and corresponding sinking particulate export for modern
era climate conditions. Note the change in scale between upper

and lower panels (Reproduced from Krishnamurthy et al. (2010)
by permission of the American Geophysical Union)

do include an explicit representation of the iron cycle.
In addition, Krishnamurthy et al. (2009) have shown
that increasing iron deposition could not only lead to
increasing marine productivity in HNLC regions, but
also significantly enhance N-fixation in subtropical
regions if iron limitation of N-fixation is included.
Finally, iron mobilisation from sediments has been
suggested to be of the same magnitude as dust deposition and its inclusion in models significantly reduces
the response to changes in atmospheric iron deposition
(Moore and Braucher 2008).
Apart from the effect of iron deposition, few studies
have focussed on the role of atmospheric deposition of
other elements on marine biota and ocean biogeochemical cycles. Using a coupled ocean general circulation/
biogeochemical model, Krishnamurthy et al. (2010)
have shown that atmospheric Si and P depositions
have only a weak effect on marine productivity and
biogeochemical cycles, as these depositions are small
relative to the flux of these nutrients from below. They
would contribute only to a very small fraction of export
production (Fig. 4.25). Even if atmospheric nitrogen
inputs have only a weak effect on marine biota and

productivity at the global scale, they can contribute
significantly (up to 25 %) to export production at the
regional scale, especially in subtropical oligotrophic
gyres (Krishnamurthy et al. 2009, 2010). The impacts
of changes in N, P and Si deposition in the near future
on marine biota are largely unknown.
In addition to the direct effect of nutrient deposition
on the marine biota through a fertilisation effect, Doney
et al. (2007) have shown that atmospheric inputs of
reactive sulphur and nitrogen could potentially alter
seawater alkalinity and pH. They show that inputs of
strong acids such as HNO3 and H2SO4 could lead to a
decrease in pH. The alterations in surface water chemistry however are only a few percent of the acidification
due to the oceanic uptake of anthropogenic CO2. The
impacts could be more substantial in coastal waters.

4.5.5

Particulate Matter and Carbon
Export

Depending on the source of the aerosols, atmospheric
deposition to the ocean is of very variable intensity, the

4.5

Deposition of Aerosol Particles to the Ocean Surface and Impacts

a

223

b
Atmospheric sampling site
(Cap Ferrat)

Marine lithogenic
flux, mg.m-2

Atmospheric
deposition, mg.m-2

Marine POC flux, mg.m-2

14000
DYFAMED me series
(sedimen raps)

20000

500

15000

400

12000
10000
8000
6000

300
10000
200

4000

8000
100

2000
1/24 2/3 2/13 2/23 3/4 3/14 3/24

4/3

dates
Fig. 4.26 Saharan dust event in February 2004 and its
consequences for marine lithogenic material and particulate
organic carbon exports in the water column. (a) Satellite image
showing the transport of Saharan dust across the Mediterranean
Sea (SeaWiFS, NASA). (b) Total atmospheric deposition mass
flux measured at the Cap Ferrat sampling site (French Riviera)

along with fluxes from sediment traps at 200 m at the nearby
DYFAMED time series station (43 250 N 07 520 E; http://www.
obs-vlfr.fr/sodyf/) showing the huge increase in both lithogenic
and POC flux following the event (Adapted from Ternon et al.
2010)

most spectacular atmospheric fluxes being associated
with lithogenic particles from the different deserts of
the continents. Indeed, strong dust events can bring
large amounts of particulate matter to the ocean surface
in a few hours and fluxes as high as 25 g m2 per event
have been reported in the Mediterranean Sea (Ternon
et al. 2010), see Fig. 4.26. This lithogenic material is
transferred to the deep ocean and contributes significantly to sedimentation in areas such as the tropical
North Pacific, the North Atlantic and the Mediterranean
Sea, which are well impacted by dust deposition
(Uematsu et al. 1985; Prospero et al. 2002; Loÿe-Pilot
et al. 1986). But not only lithogenic export is associated
with those important atmospheric depositions.
As shown in previous sections, aerosols from different sources do bring to the ocean a significant
amount of elements of biogeochemical interest that
feed the pool of nutrients and micronutrients in the
ocean surface layer. When the body of water impacted
by atmospheric deposition is depleted in those (micro)
nutrient(s) brought by atmospheric deposition, (micro)
nutrient(s) limited organisms such as bacteria and
phytoplankton will feed on this new resource. In
such conditions, atmospheric deposition has a
‘fertilising’ effect (see references in Sect. 4.5.4) and

carbon fixation by autotrophic organisms should be
followed by particulate organic carbon fluxes to the
deep ocean. Although the link between addition of
aerosol iron and export production was emphasised
by Cassar et al. 2007, the quantification of such
induced export of particulate carbon was difficult to
assess in the numerous iron fertilisation experiments
reported in de Baar et al. (2005) where it was only
firmly proven in two of the reported experiments for
which it was shown to be quite modest. Differently,
Smetacek et al. (2012) concluded that following the
EIFEX ocean iron fertilisation experiment a substantial portion of the induced bloom likely reached the sea
floor. According to Blain et al. (2007) carbon export
from such artificially induced short-term blooms are at
least an order of magnitude lower than carbon export
induced by natural iron fertilisation in the Southern
Ocean.
On the other hand, several studies have shown the
importance of lithogenic particles for the export of
organic matter through an aggregation process
(Hamm 2002; Passow and De la Rocha 2006; Ploug
et al. 2008) and the export in this case is not related to a
fertilisation effect. During 4 years of simultaneous
measurements of atmospheric deposition and export

224

of material in the water column in the NW Mediterranean Sea, Ternon et al. 2010 found a series of
“lithogenic events” corresponding to both high POC
and high lithogenic marine fluxes (originating mainly
from either recent Saharan fallout events, or from ‘old’
Saharan dust ‘stored’ in the upper water column layer).
Such “lithogenic events”, are believed to result in part
from organic-dust aggregation inducing a ballast
effect. The most remarkable event was in February
2004 when an extreme Saharan event exported ~45 %
of the total annual POC (Fig. 4.26), compared to an
average of ~25 % for the bloom period, at a time of the
year when the organisms are not limited by the
concentrations of nutrient in the upper ocean and thus
such high POC flux couldn’t be the consequence of a
fertilisation by the dust. Zooplankton activity and the
incorporation of mineral particles into faecal pellets
have also been proposed as a mechanism to explain
such lithogenic events (e.g. Fowler et al. 1987; BuatMenard et al. 1989). Ternon et al. also found another
‘lithogenic event’, in summer when the surface mixed
layer is depleted in nutrients, where only 20 % of the
POC exported could be explained by fixation of carbon
induced by atmospheric fertilisation, the rest being
assumed to be due to the incorporation of mineral
particles acting as ballast in organic aggregates formed
by the dissolved and colloidal organic matter present in
the water column prior to the event.
From those few examples, we can stipulate that
dust deposition can have two main effects: a POC
export directly linked to the carbon fixation induced
by the fertilisation via atmospheric nutrients and
the POC export ‘mediated’ by the introduction of
lithogenic particles with a ballast effect on organic
matter, leading to the formation of large aggregates.
As mentioned above, both effects can be combined
and this was recently observed by Bressac et al. (2012)
following an artificial dust addition in a large clean
mesocosm: it was observed, with optical measurements, that particulate export following a strong dust
deposition (10 g m2) was a nonlinear multi-step
process composed of particle populations with different size distributions and composition as a function
of time and depth. Interestingly, it was shown that the
pattern of the size distribution and composition of
particles in the mesocosm was strongly influenced by
the formation of organic-mineral aggregates and that
the fresh organic matter produced by the fertilisation
induced by the dust deposition lead to organic-mineral

4

Ocean–Atmosphere Interactions of Particles

aggregates with the fastest settling velocities compared to the other particles populations. Bressac et al.
measured organic-mineral aggregate populations
(>61.2 μm) having settling velocities of the order of
24~86 m d–1. Such rapid transfer following a dust
event with settling velocity of at least 100 m d–1 has
been observed for ‘lithogenic events’ from sediment
trap data (Ternon et al. 2010). Such fast sinking
particles would be less prone to remineralisation
while transferring to the deeper layers (Hedges et al.
2000) and this may have important consequences
regarding carbon sequestration. Those different recent
studies have shown that lithogenic particles do not
sink following Stokesian settling calculations and
that atmospheric particles may have a very important
role in transferring rapidly particulate organic matter
to the deep ocean but, so far, no quantification of this
role in carbon export and budget has been performed.
The parameterisation of the complex processes
involved in the sinking of both lithogenic and organic
particles following an intense dust event will only
be possible by realising a combination of optical,
biological and chemical measurements in the
upper water column when dust deposition occurs.
This could be done by a combination of in situ
measurements (including in situ mesocosms) and in
large tanks in the laboratory (both in biotic and abiotic
conditions) with approaches allowing the actual
sinking of the particles. The characterisation of
organic matter is very much needed in such combined
approaches as organic-mineral aggregation depends
mainly on the quantity and quality of the organic
matter (Passow and De La Rocha 2006) meaning that
the biogeochemical conditions of the water body in
which the deposition occurs may be a crucial factor for
the fate of particulate matter following a dust event.

4.6

Summary and Outlook

Aerosol particles in the marine atmosphere are produced either directly at the sea surface, as sea spray
aerosol particles, or over land from where they can be
subsequently transported over the ocean. Secondary
production takes place in the atmosphere from precursor gases which are emitted either from the sea surface
or from a variety of sources over land. The direct
production of sea spray aerosol was recently reviewed
by de Leeuw et al. (2011a) and conclusions presented

4.6

Summary and Outlook

there still hold. Results in the last decade show the
large contribution of organic substances to SSA
particles especially in locations of high biological
activity, which dominates the chemical composition
for particles with r80 < 0.25 μm. The consequences
for hygroscopic properties of SSA particles and thus
their effect on climate via light scattering and their
influence on cloud properties has been recognised and
is still an important topic for research. Meskhidze and
co-workers (Gantt et al. 2011; Meskhidze et al. 2011)
have shown the effect of wind speed on the fraction of
organic matter in SSA particles in addition to the
dependence on particle size (Facchini et al. 2008a).
Ovadnevaite et al. (2011) show the importance of
organic matter for the ability of SSA particles to act
as CCN.
Production fluxes have been determined for
particles with r80 as small as 0.01 μm, which is also
important for the assessment of the role of SSA
particles on climate using large-scale models. However, de Leeuw et al. (2011a) concluded that the
uncertainties in the production fluxes are still very
large. For particles with r80 > 1 μm the uncertainty
is a multiplicative factor of 4 (Lewis and Schwartz
2004). Recent research by Jaeglé et al. (2011) confirms
the large importance of the effect of SST (Mårtensson
et al. 2003) on the production fluxes of coarse particles
(those with r80 > 1 μm), with consequences for the
effect of SSA particles on the direct radiative effect on
climate.
For particles with 0.3 < r80 < 1 μm and smaller all
flux estimates published after Lewis and Schwartz
(2004) appear to be higher than the range of values
discussed by these authors. These high production
fluxes would result in unrealistically large number
concentrations of SSA particles in the marine atmospheric boundary layer (de Leeuw et al. 2011a) but the
reason for these high production fluxes is not clear.
There is no convincing reason to discard the Lewis and
Schartz (2004) production flux estimates and their
uncertainties, which are based on numerous
observations, but the more recent formulations not
only produce larger production fluxes but they also
seem to converge to values which differ by about a
factor of 2–3 (Clarke et al. 2006).
For SSA particles with r80 < 0.3 μm the production
flux estimates rely on the whitecap method, i.e.
estimates of the whitecap fraction and on the particle
production flux per unit area of whitecap. The latter is

225

mainly obtained from laboratory experiments and
comparisons by de Leeuw et al. (2011a) show the
large difference in the shape and magnitude of the
SSA particle size spectra produced in such
experiments, with a large dependence on experimental
conditions (e.g. Fuentes et al. 2010). This puts into
question the validity of one of the underlying
assumptions of the whitecap method, i.e. that the production per unit whitecap area can be independently
determined and that the total production flux is given by
the product of the production per unit whitecap area and
the whitecap fraction. The whitecap fraction in turn
varies over a very wide range between different
observations (Lewis and Schwartz 2004; Anguelova
and Webster 2006) and all the most recent determinations, using novel methods, appear to result in
smaller whitecap fractions than the most commonly
used function by Monahan and O’Muirchaertaigh
(1986) which is based on numerous previous
observations. Furthermore the whitecap fraction
appears not to be unambiguously determined by wind
speed and depends on wind speed history (Callaghan
et al. 2008).
Most of these studies use a combination of different
techniques and novel technology developments. These
techniques include combinations of advanced
modelling using different information sources based
on results from laboratory and field experiments, long
term observations or intensive field campaigns at
coastal locations or open ocean, using advanced
techniques such as AMS, HTDMA, CCNC and shipborne eddy covariance measurements. Many of the
recent studies rely on the use of earth observation
(EO), i.e. satellite-based instruments, to obtain information on aerosol concentrations, forcing parameters
and aerosol effects. EO is increasingly used in studies
on sea spray aerosols and their effects. Nevertheless,
remote sensing results also show rather large
ambiguities such as in the determination of the aerosol
optical depth (AOD) as determined by different
remote sensing methods (Smirnov et al. 2012). The
use of EO in air-sea interaction studies and effects on
SSA production and their effects on climate and biogeochemical cycles has an important place in the
SOLAS community.
In spite of recent progress based on combinations of
modelling, EO and in situ observations, a large drawback put forward in the literature is the lack of reliable
experimental data for a wide variety of conditions.

226

Another question posed in the literature concerns the
reliability and accuracy of EO products which are
further developed based on new insights and improved
EO instrumentation leading to improvement of data
quality and development of new products. However,
satellite data alone cannot be used to assess SSA
production and needs to be used in combination with
other techniques such as modelling and in situ or
ground-based remote sensing.
The contribution of land-based aerosol particles
and precursor gases is another issue which is
extremely important for the assessment of atmospheric
inputs into the ocean and their biogeochemical effects
(see below). These studies rely to a large extent on
models to determine aerosol transport. The models in
turn utilise emission data bases as input and they are
often based on bottom-up emission inventories for a
certain period or year and therefore represent mean
emissions. Often the inventories lack detail on seasonal or diurnal variations and do not include actual
episodic emission strenghts of species which are
important for biogeochemical cycles such as dust,
volcanic ash and biomass burning aerosol. Earth
Observation (satellites) may contribute to improvement of emission estimates and several studies have
recently been published on the use of EO data with
advanced modelling techniques to provide this information for emissions of e.g. volcanic ash, VOCs and
NO2.
Numerous studies have been conducted to establish
links between atmospheric deposition and biogeochemical cycles in the past 20 years, increasing
exponentially with recent developments of new
sophisticated methodologies (in particular the use of
clean sampling techniques in the field and the
generalisation of expertise in low levels determination
of chemicals in seawater).
Studies on effects of atmospheric iron on biogeochemical processes have been an important research
activity for more than 20 years because of, its
recognised importance as a limiting nutrient in the
large HNLC ocean. These studies no longer focus
only on iron in dust but also the biogeochemical impact
of other recently recognised important sources – such
as volcanic ash – are now considered. Other studies
have recently focussed on LNLC areas where microbes
can be limited or co-limited by deposition of atmospheric inorganic nitrogen and phosphorus. In terms
of biotic response to episodic atmospheric nutrient

4

Ocean–Atmosphere Interactions of Particles

inputs, the large scale experiments conducted over
the past 20 years have mainly focussed on the role of
iron in HNLC areas. Although responses differ due to
regional differences between the environmental
characteristics of HNLC waters, such experiments
have allowed simultaneous observation of the wide
range of processes involved by such inputs of new
nutrients, how these change the upper ocean properties
and how the ocean feedbacks on the lower atmosphere.
Microcosm experiments show the response of microorganisms to deposition of aerosol particles to LNLC
areas. Such experiments also show consistent patterns
in terms of biomass and abundance, community structure, and metabolic activity revealing that both
autotrophs (including diazotrophs) and heterotrophs
can be significantly stimulated. This is confirmed by
mesocosm experiments where both chemical and
biological processes can be studied simultaneously
over longer time scales while accounting for the vertical dimension as well. In situ observations and
mesocosm approaches indicate that atmospheric deposition, in particular dust, can result in carbon export
through two types of processes: export directly linked
to carbon fixation induced by the fertilization via atmospheric nutrients and export by carbon ballasting by the
lithogenic particles. Ocean biogeochemical models to
study the response of marine biota and biogeochemistry to atmospheric deposition have mainly focussed
so far on dust (iron) deposition over glacial times,
recent past and near future, showing moderate effects
on reduction of atmospheric CO2. The few modelling
studies which recently focussed on the role of atmospheric deposition of other elements than iron have
shown that atmospheric N, P and Si deposition have a
weak effect on marine productivity and biogeochemical cycles contributing only to a very small fraction of
export production.
All those different approaches indicate that extrapolation of the results to the ‘real world’ is not easy and
that further studies using a combination of the different approaches are necessary to quantify the present
picture and how it will change in the future. A number
of research directions have been recommended, such
as specific studies to understand the role of the
microlayer, to establish the partitioning between
organic and inorganic forms of atmospheric nutrients
and their link with bioavailability, to consider the
balance between scavenging/dissolution taking into
account the dynamics of the particles while they settle

References

through the water column (Law et al. 2013). Studies
considering a wide range of in situ conditions should
be conducted in order to understand the competition
for the new nutrient resources among planktonic
organisms and how the balance between CO2 fixation
and respiration impact the carbon budget. The balance
between POC export linked to carbon fixation induced
by the atmospheric nutrient fertilisation, and the POC
export mediated by the introduction of atmospheric
particles through aggregation processes, have to be
better constrained. Another challenge is to correctly
model the complex biogeochemical processes
involved in order to quantify the impacts on biota
and global carbon cycle and to predict the changes
induced by the evolution of emission/deposition in the
future.
Open Access This chapter is distributed under the terms
of the Creative Commons Attribution Noncommercial
License, which permits any noncommercial use,
distribution, and reproduction in any medium, provided
the original author(s) and source are credited.

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Perspectives and Integration in SOLAS Science
Véronique C. Garçon, Thomas G. Bell, Douglas Wallace,
Steve R. Arnold, Alex Baker, Dorothee C.E. Bakker,
Hermann W. Bange, Nicholas R. Bates, Laurent Bopp,
Jacqueline Boutin, Philip W. Boyd, Astrid Bracher, John P. Burrows,
Lucy J. Carpenter, Gerrit de Leeuw, Katja Fennel, Jordi Font,
Tobias Friedrich, Christoph S. Garbe, Nicolas Gruber, Lyatt Jaeglé,
Arancha Lana, James D. Lee, Peter S. Liss, Lisa A. Miller,
Nazli Olgun, Are Olsen, Benjamin Pfeil, Birgit Quack, Katie A. Read,
Nicolas Reul, Christian Rödenbeck, Shital S. Rohekar,
Alfonso Saiz-Lopez, Eric S. Saltzman, Oliver Schneising,
Ute Schuster, Roland Seferian, Tobias Steinhoff,
Pierre-Yves Le Traon, and Franziska Ziska
Abstract

Why a chapter on Perspectives and Integration in SOLAS Science in this book?
SOLAS science by its nature deals with interactions that occur: across a wide
spectrum of time and space scales, involve gases and particles, between the ocean
and the atmosphere, across many disciplines including chemistry, biology, optics,
physics, mathematics, computing, socio-economics and consequently interactions
between many different scientists and across scientific generations. This chapter
provides a guide through the remarkable diversity of cross-cutting approaches and
tools in the gigantic puzzle of the SOLAS realm.
Here we overview the existing prime components of atmospheric and oceanic
observing systems, with the acquisition of ocean–atmosphere observables either
from in situ or from satellites, the rich hierarchy of models to test our knowledge
of Earth System functioning, and the tremendous efforts accomplished over
the last decade within the COST Action 735 and SOLAS Integration project
frameworks to understand, as best we can, the current physical and biogeochemical state of the atmosphere and ocean commons. A few SOLAS integrative studies
illustrate the full meaning of interactions, paving the way for even tighter
connections between thematic fields. Ultimately, SOLAS research will also
develop with an enhanced consideration of societal demand while preserving
fundamental research coherency.

V.C. Garçon (*)
e-mail: veronique.garcon@legos.obs-mip.fr
T.G. Bell (*)
e-mail: tbe@pml.ac.uk
D. Wallace (*)
e-mail: douglas.wallace@dal.ca
P.S. Liss and M.T. Johnson (eds.), Ocean-Atmosphere Interactions of Gases and Particles,
Springer Earth System Sciences, DOI 10.1007/978-3-642-25643-1_5, # The Author(s) 2014

247

5

248

5 Perspectives and Integration in SOLAS Science

The exchange of energy, gases and particles across the air-sea interface is
controlled by a variety of biological, chemical and physical processes that operate
across broad spatial and temporal scales. These processes influence the composition, biogeochemical and chemical properties of both the oceanic and atmospheric
boundary layers and ultimately shape the Earth system response to climate and
environmental change, as detailed in the previous four chapters. In this crosscutting chapter we present some of the SOLAS achievements over the last decade
in terms of integration, upscaling observational information from processoriented studies and expeditionary research with key tools such as remote sensing
and modelling.
Here we do not pretend to encompass the entire legacy of SOLAS efforts but
rather offer a selective view of some of the major integrative SOLAS studies that
combined available pieces of the immense jigsaw puzzle. These include, for
instance, COST efforts to build up global climatologies of SOLAS relevant
parameters such as dimethyl sulphide, interconnection between volcanic ash and
ecosystem response in the eastern subarctic North Pacific, optimal strategy to
derive basin-scale CO2 uptake with good precision, or significant reduction of the
uncertainties in sea-salt aerosol source functions. Predicting the future trajectory
of Earth’s climate and habitability is the main task ahead. Some possible routes
for the SOLAS scientific community to reach this overarching goal conclude the
chapter.

5.1

Perspectives: In Situ Observations,
Remote Sensing, Modelling and
Synthesis

The scope of SOLAS science depends on multidisciplinary and multi-scale approaches being applied to
the complex problems and challenges within the field.
Laboratory process studies and in situ lagrangian field
experiments make substantial contributions to our
understanding of the various biogeochemical processes and their feedbacks. Models are almost the
only way to assess what are often complex problems
and they rely on input from such studies. To truly
represent the domain of SOLAS within Earth System
models requires global-scale datasets of accurate
measurements of relevant parameters. The most accurate data-based estimates of air-sea exchange processes require as much data as possible at large
spatial and temporal scales in order to be able to
validate and calibrate both model outputs and satellite
data. Maintaining and further expanding existing
global arrays of autonomous instrumented platforms,
as well as oceanic and atmospheric fixed observatories, is a modern-day challenge. Producing
integrated, quality and potential bias-controlled global

datasets from the collection of these measurements is
our ongoing responsibility. This section of Chap. 5
considers a variety of data collation and synthesis
projects relevant to SOLAS science presented in the
previous four chapters. The following subsections
briefly introduce each project.

5.1.1

In Situ Observations

5.1.1.1 ARGO (T, S, O2)
In November 2007, the international Argo programme
reached its initial target of 3,000 profiling floats
(http://www.argo.ucsd.edu/). Every 10 days these floats
measure temperature and salinity throughout the global
ocean, diving down to 2,000 m and delivering data both
in real time for operational users and, after careful
scientific quality control, for climate change research
and monitoring. Argo results from an outstanding
international cooperation. More than 30 countries are
involved in the development and maintenance of the
array. Argo is the major systematic source of data
about the interior of the ocean. Argo aims to maintain
a global array of in situ measurements integrated with

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis

3516 Active Floats
May 2012

249

ARGENTINA (7)

CHINA (88)

GABON (1)

JAPAN (255)

NETHERLANDS (34)

SPAIN (29)

AUSTRALIA (382)

ECUADOR (3)

GERMANY (168)

KENYA (3)

NEW ZEALAND (10)

UNITED KINGDOM (123)

BRAZIL (8)

EUROPEAN UNION (3)

INDIA (90)

SOUTH KOREA (78)

NORWAY (3)

UNITED STATES (1895)

BULGARIA (3)

FINLAND (5)

IRELAND (11)

MAURITIUS (4)

RUSSIAN FEDERATION (2)

CANADA (91)

FRANCE (209)

ITALY (9)

MEXICO (1)

SOUTH AFRICA (1)

Fig. 5.1 Position of active Argo floats (Figure courtesy of JCOMMOPS, Argo Information Centre, http://argo.jcommops.org)

other elements of the climate observing system (in particular satellite observations) to:
• Detect climate variability from seasonal to decadal
scales and provide long-term observations of climate change in the oceans. This includes regional
and global changes in temperature and ocean heat
content, salinity and freshwater content, steric
height and large-scale ocean circulation.
• Provide data to constrain global and regional ocean
analysis and forecasting models, to initialise seasonal and decadal forecasting ocean/atmosphere
coupled models and to validate climate models.
• Provide information necessary for the calibration
and validation of satellite data.
An overview of the achievements of the first decade
of Argo is given in Freeland et al. (2010). Argo data
(Fig. 5.1) have been used to dramatically improve
estimation of heat stored by the ocean, to better understand global sea level rise, to analyse large-scale ocean
circulation variations and deep convection areas. Argo
has also brought remarkable advances in ocean analysis and forecasting capability. Argo data can be
accessed at http://www.nodc.noaa.gov/argo/.
The last OceanObs09 conference discussed the
main priorities of the international community for

Argo (Roemmich et al. 2009; Freeland et al. 2010;
Claustre et al. 2010). Based on the assessment that
climate change research requires long-term, sustained,
high quality and global observations, the leading priority and challenge for Argo must be to complete and
sustain the global array. This requires deploying
between 800 and 900 new floats every year. Several
developments of the Argo core mission have also been
proposed. Minor changes include the extension of the
array into seasonal ice zones and marginal seas. Major
expansions of Argo will include monitoring the deep
ocean below 2,000 m and marine ecosystems. Deeper
measurements are needed to constrain the deep ocean
property fields for climate monitoring and long-term
prediction. Recent technological advances in biogeochemical sensors will permit the acquisition of new
observations of the ocean interior (e.g. Claustre et al.
2010; Adornato et al. 2010). The main parameters that
are considered for initial implementation are oxygen,
nitrate, chlorophyll a and particulate carbon. Pilot
experiments have already begun, in particular for
dissolved oxygen (almost 200 Argo floats are today
equipped with an oxygen sensor). Potential systematic
errors due to different measurement techniques or
sensors need to be analysed and further corrected to

250

ensure delivery of quality-flagged controlled data sets.
These evolutions will require new resources and careful progressive implementation so that the core of
Argo is not diminished. If they are managed carefully,
however, Argo’s second decade will be even more
transformative than the first.

5.1.1.2 Ocean Observatories
Ocean observatories provide a view of how the oceans
are changing with time and in relation to depth. The
spectrum of ocean observations includes data collection from moorings, AUV surveys (e.g. gliders,
submersibles), ARGO floats (see Sect. 5.1.1.1) and
repeat observations at select time-series sites in the
global ocean. The growing network of OceanSites
moorings (http://www.whoi.edu/virtual/oceansites/)
consists of about 30 surface and 30 subsurface arrays
primarily collecting physical data as part of the Global
Ocean Observing System. Ocean time-series sites are
fewer in number but they allow collection of critically
needed data that illustrates temporal variability on
ocean–atmosphere exchange and water-column processes over seasonal to multi-decadal timescales.
Four of the longest ocean time-series stations include:
(1) Hydrostation S, (32
500 N, 64
100 W; 1954-present)
located near Bermuda in the NW Atlantic Ocean
(Steinberg et al. 2001); (2) BATS (Bermuda Atlantic
Time-series Study), located near Bermuda (32
100 N,
64
300 W; 1988 – present) in the NW Atlantic Ocean
(Steinberg et al. 2001; Bates 2007); (3) ALOHA (A
Long-term Oligotrophic Habitat Assessment) or HOT
site, located near Hawaii (22
450 N, 158
W; 1988 –
present) in the North Pacific Ocean (Dore et al.
2009); and; (4) ESTOC; European Station for Timeseries in the Ocean Canary Islands (29
100 N, 15
,300 W
1994-present) (González-Dávila et al. 2010).
The monthly BATS and biweekly Hydrostation ‘S’
programmes play a pivotal role in better understanding of the seasonality and long-term changes in
ocean–atmosphere exchange of gases and particles.
Both sites serve as important frameworks for largerscale field and modelling studies in the subtropical
gyre of the North Atlantic Ocean (or Sargasso Sea).
Over the last 50 years, the surface ocean has warmed
by ~ 0.3–0.5
C while salinity has increased by ~ 0.15.
The BATS site exhibits strong seasonality in the
ocean–atmosphere exchange of gases such as oxygen
(e.g. Ono et al. 2001) and carbon dioxide (CO2 e.g.

5 Perspectives and Integration in SOLAS Science

Bates et al. 1996, 1998; Bates 2001) with the subtropical
gyre of the North Atlantic a sink for atmospheric CO2.
The BATS record shows that ocean CO2 content has
kept pace with atmospheric CO2 changes and
demonstrates the change in ocean pH due to ocean
acidification (Bates 2007). In the Sargasso Sea, seasonal
measurements of oceanic dimethyl sulphide (DMS) and
DMSP (dimethylsulphoniopropionate) have provided
one of the only long-term time series for DMS in the
open ocean (e.g. Dacey et al. 1998; Toole et al. 2008).
The observed decoupling of DMS concentration from
its precursors (i.e. DMSP) in the Sargasso Sea is the
basis for the ‘DMS summer paradox’ hypothesis (Simó
and Pedrós-Alió 1999).
The challenges that have faced ocean time-series
programmes have been both practical and scientific.
Sustaining ocean time-series requires the provision of
suitable platforms for observation (i.e. research ships,
moorings) and creates logistical demands if the continuity of funding and frequency of occupation are to be
maintained. Scientific questions include reconciling
time-variations within the context of the four dimensional state (space and time) of the ocean, which
includes substantial mesoscale and sub-mesoscale
variability (e.g. McGillicuddy et al. 1999, 2007). In
the future, ocean time-series programmes will be
integrated with the Global Ocean Observing System
(http://www.ioc-goos.org/), with these sites acting as
important nodes for new observing technologies such
as gliders and AUV platforms (Dickey et al. 2009).
Understanding ocean and climate relevant processes
that influence the ocean–atmosphere exchange of
gases and particles requires an improved synergy
between (sustained) observation and hypothesis testing over a variety of scales, both spatial and temporal.

5.1.1.3 Atmospheric Observatories
The relative homogeneity of marine vs terrestrial air
provides an exceptional opportunity to test aspects of
surface atmospheric photochemistry. There is a paucity
of long-term measurements of reactive trace gases
and aerosols in clean marine environments, but the
reported studies have revealed important insights into
ocean–atmosphere interactions and their consequences
for atmospheric composition and climate. Seasonal
observations of sulphur-containing gases and aerosols
at Cape Grim atmospheric observatory, Tasmania
(40.7
S, 144.7
E) showed summer maxima and winter
minima in dimethyl sulphide (DMS), methanesulphonic

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis

acid (MSA, a unique product of DMS oxidation), nonsea-salt(nss) sulphate aerosol, and the concentration of
cloud condensation nuclei (CCN) (Ayers and Gras 1991;
Ayers et al. 1997), supporting proposed mechanisms for
DMS oxidation to particulate sulphate. DMS emissions
are believed to contribute to a significant fraction of
remote marine CCN concentrations, up to 46 % in
summer over the Southern Ocean between 30
S and
45
S (Korhonen et al. 2008). Measurements of sea-salt
aerosol composition at Cape Grim confirmed that bromine deficits (a decrease of the bromine to sodium ratio
of sea- salt aerosol compared to sea water) were linked to
the availability of sulphate acidity in the aerosol (Ayers
et al. 1992), as proposed by modelling studies which
suggest the importance of acid catalysis in the dehalogenation process (Sander and Crutzen 1996; Vogt et al.
1996). This provided experimental evidence for the net
transfer of bromine from sea salt aerosol to the gas
phase, where it catalyses photochemical ozone destruction and modifies the concentrations of many important
tropospheric gases (Sander and Crutzen 1996; Vogt et al.
1996; von Glasow et al. 2004).
Aspects of O3 photochemistry have also been confirmed by long-term marine observations; at Cape
Grim, diurnal cycling of hydrogen peroxide, one of
the major products of HOx radical (OH and HO2)
recombination reactions, is in opposite phase to that of
O3 (Ayers and Gras 1991), as expected in clean
low-NOx air. The observed relationships between free
radical levels and the O3 photolysis rate (jO1D) change
according to NOx levels, indicating the critical NO
concentration required to switch from O3 destruction
to O3 production (Carpenter et al. 1997). In the northern
hemisphere, a longer than 20-year record in baseline O3
at the coastal Irish station at Mace Head (53.28
N,
9.02
W) showed that mixing ratios rose steadily during
the 1980s and 1990s, probably due to increased tropospheric ozone production from methane oxidation in
the presence of nitrogen oxides (NOx), and stabilised
during the 2000s (Derwent et al. 2007) with sporadic
increases over the period due to boreal biomass burning
events. Background O3 can be an important contributor
to the levels experienced in urban regions, which are
not declining in developed regions including Europe
despite decreasing precursor emissions.
The northern tropical Atlantic ocean is subject to
sporadic but significant dust deposition originating in
the African Sahara and Sahel regions. Dust emission
has immediate impacts on humans, plus widespread

251

influence on the radiative balance and on marine
biological production and biogeochemical cycles,
and is believed to have increased due to changes in
land use practices (Jickells et al. 2005). A wealth of
information comes from the long-term record (since
1965) of airborne desert dust measured in Barbados
(13.17
N, 59.43
W). At this site, mineral dust
concentrations are correlated with rainfall deficits in
the sub-Saharan region (Prospero and Nees 1986;
Prospero and Lamb 2003), and the net light scattering
of dust exceeds that of nss-sulphate aerosol by about a
factor of 4 (Li et al. 1996). There are still, however,
considerable uncertainties associated with the global
radiative forcing of mineral dust, given the high
variability of dust loadings and limited knowledge of
dust optical properties (Andreae et al. 2002). In the
tropical North Atlantic, Saharan dust has been shown
to stimulate nitrogen fixation, which is co-limited by
iron and phosphorus (Mills et al. 2004 and references
therein; Moore et al. 2009; Rijkenberg et al. 2011), and
microbial species diversity (Hill et al. 2010, 2012).
Recently, Okin et al. (2011) show that atmospheric
deposition of iron can potentially contribute considerably to rates of marine productivity in high-nutrientlow-chlorophyll (HNLC) regions, and that iron is
likely to be much more important than nitrogen in
supporting net primary productivity globally.

5.1.1.4 Monitoring Reactive Trace Species in
the Marine Atmosphere: Highlights
from the Cape Verde Observatory
The tropics are particularly under-populated with
marine reactive gas measurements, and this was a
major motivation for setting up the Cape Verde Atmospheric Observatory (CVAO) in 2005. The location of
the site (Fig. 5.2) about 800 km off the northwest coast
of Africa in the tropical east Atlantic allows the study
of clean marine air from diverse origins including
North America, the Atlantic, Arctic, Europe and
Africa. The station, now part of the global WMOGAW long-term observing network, has been developed jointly by UK and German scientists at the
Universities of York (UK), MPI-Jena (Germany) and
IfT-Leipzig (Germany), in collaboration with the
Cape Verdean meteorological service (INMG). Atmospheric measurements focus on reactive trace gases,
greenhouse gases and aerosols (Fig. 5.3). The CVAO
links with a sister oceanographic station, the Cape

252

5 Perspectives and Integration in SOLAS Science

Fig. 5.2 The Cape Verde Islands and location of the CVAO, marked on São Vicente with a red cross. Prevailing trade winds are
from the north-east (Map from http://commons.wikimedia.org/wiki/File:Cape_Verde_Map.jpg)

Verde Oceanic Observatory, located upwind of the
CVAO at 17.59
N, 24.25
W. This was set-up by
IFM-Geomar (Germany) in collaboration with the
Cape Verde Fisheries Institute (INDP).
Similarly to Barbados, the Cape Verde archipelago
is a region subject to very large depositions of dust,
originating from the Sahel region, as well as from
north-western Saharan sources and as far east as the
Bodélé depression in Chad. In this region, dust is
deposited mainly by dry deposition, peaking in the
winter months when African desert dust is exported
across the Atlantic within the lower troposphere. The
ionic composition of the aerosol at Cape Verde is
dominated by sea salt, but in Saharan dust episodes
iron typically constitutes ca. 3.8 % of the total aerosol
mass, with aluminium, a tracer of mineral dust, at a
slightly higher concentration of ca. 7 % (Trapp et al.

2010; Carpenter et al. 2010). Total iron content
reaches up to 33 μg m3 in winter, and the soluble
Fe content is between 0.1 % and 15.7 % (Carpenter
et al. 2010). Higher solubilities are measured at lower
atmospheric dust concentrations, a ubiquitous feature
among aerosol solubility datasets.
Although the total aerosol mass is dominated by sea
salt, in aerosol particles < 0.14 μm diameter, non-seasalt components contribute about 80 % of the mass.
These components include low-molecular-weight
dicarboxylic acids (DCAs) and hydroxylated DCAs,
methanesulphonic acid (MSA) and aliphatic amines.
A bimodal size distribution for the DCA oxalic acid
and coarse mode concentration maxima for the other
DCAs are observed, as is typical for marine aerosols.
The MSA concentration closely follows that of nonsea-salt-sulphate and the size distribution shows a

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis

140

50

40

120

30

ppbv

ppbv

253

100

20

10

80

0
2007−01 2007−07 2008−01 2008−07 2009−01 2009−07

2007−01 2007−07 2008−01 2008−07 2009−01 2009−07

O3

CO

100
1000

60

pptv

pptv

80

500

40

20

0

0
2007−01 2007−07 2008−01 2008−07 2009−01 2009−07

2007−01 2007−07 2008−01 2008−07 2009−01 2009−07

NOy

NOX

4000

pptv (pptv x10 for DMS)

2000

pptv

1500

1000

500

3000

2000

1000

0

0
2007−01 2007−07 2008−01 2008−07 2009−01 2009−07

propane
ethane

acetylene

2007−01 2007−07 2008−01 2008−07 2009−01 2009−07

methanol
acetone

DMS

Fig. 5.3 Three-year time series (Oct 2006–Sept 2009) of daily averaged O3, CO, NOx, NOy and VOC (propane, ethane, acetylene,
acetone, methanol and DMS) mixing ratios measured at the Cape Verde Atmospheric Observatory (From Carpenter et al. 2010)

254

maximum mean concentration in the accumulation
mode and in sea-salt particles. Aliphatic amines,
assumed to be important in the growing process of
sulphuric acid clusters, are correlated with phytoplankton activity in the subtropical North Atlantic,
especially during an unexpected winter algal bloom
(Müller et al. 2009).
The CVAO is one of the few global GAW stations that
measures nitrogen oxides (NOx) (as well as NOy – both
with low pptv detection limits) and VOCs (including
oxygenated (O)VOCs). There is very little information
on the abundance and distribution of these gases in the
marine boundary layer (MBL) in part due to an inability
to observe some of these compounds at the very
low concentrations characteristic of this environment.
Nitrogen oxides act as a catalyst for O3 production and
cycle HO2 to OH, so are central to determining both the
concentration of O3 and CH4. Observations show that in
this region NOx levels peak in winter (at 35–45 pptv),
when air masses from Africa and Europe prevail
(Lee et al. 2009a; Carpenter et al. 2010). This seasonality
is attributed mainly to increased NOx transported from the
West African continent from e.g. soils, particularly after
rainfall events over the Sahel region during the summer
monsoon (Jaeglé et al. 2005; Stewart et al. 2008) or from
anthropogenic sources, either directly as NOx or locally
produced from transported reservoir species (e.g. peroxy
acetyl nitrate (PAN) or nitric acid (HNO3)). These
reservoir species may undergo decomposition within
long-range plumes re-releasing NOx, particularly for
PAN as air masses descend and reach higher temperatures. Averaged NO mixing ratios at Cape Verde (daily
averages are between 2 and 8 pptv) are negatively
correlated with observed photochemical O3 destruction;
these observations were reproduced using a simple box
model and together imply that the presence of 17–34 pptv
of NO would be required to turn the tropical North Atlantic from an O3 destroying to an O3 producing regime (Lee
et al. 2009a). Since NOx emissions from shipping (e.g.
Dalsøren et al. 2010) and African anthropogenic sources
(Clarke et al. 2007) are believed to be increasing, future
trends of background O3 in this region could be of major
concern for air quality and climate (Lelieveld et al. 2004).
OVOCs are generally present in higher concentrations in the lower atmosphere than non-methane
hydrocarbons (NMHC) and have a comparable if not
greater effect on oxidising capacity through reaction
with hydroxyl radical (OH). Upon photodecomposition,
they produce organic radicals that can form organic

5 Perspectives and Integration in SOLAS Science

nitrate compounds such as PAN, sequestering NOx and
transporting it to remote regions of the atmosphere, thus
affecting the tropospheric ozone budget and concentrations of OH (Singh et al. 1995; Tie et al. 2003). In
the remote marine atmosphere, oceanic sources and
sinks are expected to play a significant role in controlling
OVOC concentrations, however both the magnitude
and direction of OVOC fluxes are a matter of debate
(Heikes et al. 2002; Carpenter et al. 2004; Williams et al.
2004; Jacob et al. 2005). Five years of acetone, methanol
and acetaldehyde data from the CVAO (October
2006–September 2011) have recently been analysed
using the CAM-Chem chemistry-transport model
(Read et al. 2012). Observed annual mean mixing ratios
of acetone, methanol and acetaldehyde were 763  126
pptv, 1,029  151 pptv and 511  106 pptv, respectively. All three OVOCs show a similar cycle with
maxima in spring (March) and autumn (between July
and September with particularly high peaks in some
years in September), lower levels in summer and generally the lowest levels in winter (Nov-Jan). The model
reproduced the acetone concentrations fairly well in
magnitude (annual average 670  41 pptv) although
underestimating the measured autumn peak, possibly
due to underestimation of African biogenic sources.
The modeled methanol levels (annual average
355  17 pptv) were almost a factor of 3 lower than
the observations, showed considerably less variability,
and did not capture the pronounced peaks in spring
and in summer – autumn. Possible reasons for the
discrepancies include an underestimate by the model
of methane concentrations and of terrestrial biogenic
sources of methanol, and/or that the tropical North
Atlantic is a significant net source of methanol, as
suggested by recent data (Beale et al. 2013). Indeed,
including estimates of the sea-air flux of methanol
based upon these measurements led to an increase in
the simulated levels by a factor of ~ 2.5 (new modeled
annual average 879  84 pptv) (Read et al. 2012).
Of the three OVOCs, the most pronounced modelmeasured discrepancy was for acetaldehyde, with a
model underestimation of over a factor of 10 (modelled
annual mean mixing ratio 38  7pptv) and a predicted
strong seasonal minimum in summer, contrary to the
observations. Acetaldehyde is produced in the ocean
through photodegradation of coloured dissolved organic
matter and has a strong dependence on sunlight (Kieber
and Mopper 1990). Including estimates of the sea-air flux
of acetaldehyde from the measurements of Beale et al.

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis

(2013) led to a significant increase in concentrations
of acetaldehyde (annual average 139 pptv  46). The
model thus still falls short of the observations (especially
in September through to December – a period of high
coastal and continental African influence) for reasons
that are currently unknown.
Reactive marine-derived halogens have been proposed to exert a globally significant effect on the
concentration and lifetimes of climatically active
gases through gas and aerosol phases of the marine
boundary layer (Vogt et al. 1996; von Glasow et al.
2002a, b). Bromine and iodine-containing reactive
halogen species can influence tropospheric oxidation
capacity via a number of reaction cycles including
catalytic O3 destruction, modification of NOx and
HOx cycles with resulting effects on the lifetimes of
other climatically important trace gases (Keene et al.
2009), oxidation of DMS (von Glasow et al. 2004);
and oxidation of sulphur(IV) in acidified sea-salt aerosol and cloud droplets (Vogt et al. 1996). Observations
of halogen oxide radicals, ozone, and supporting data
at Cape Verde made in 2007 provided the first direct
experimental evidence for halogen-catalysed tropospheric ozone destruction (Read et al. 2008). More
recently, the presence of such halogens at only at a
few pptv has been shown to constitute nearly 20 % of
the instantaneous sink of HO2 in this region (Whalley
et al. 2010). Iodine monoxide (IO) radicals are
believed to be produced mainly via photolysis of
iodine-containing halocarbons volatilised from the
ocean, yet recent data shows that the sea-air flux of
these compounds is sufficient to explain only ~ 20–25 %
of the levels of IO observed at Cape Verde (Jones et al.
2010; Mahajan et al. 2010). Recent research (Martino
et al. 2009; Jammoul et al. 2009; Carpenter et al. 2013)
suggests a role for sea surface chemistry in producing
additional halogens, however the significance of such
mechanisms remains an open question.
Cape Verde researchers aim to build on these first
few years of measurements at the CVAO over the next
decade by, for example, quantifying the nitrogen
oxides budget, elucidating the nature and magnitude
of oceanic iodine emissions, evaluating the influence
of dust on the ocean heat budget, understanding oceanic nitrogen fixation, quantifying air-sea exchange
fluxes of important gases in the west African upwelling area, and analysing long-term trends in trace gases
and aerosols in the context of environmental and
climate change. CVAO data can be accessed at

255

http://badc.nerc.ac.uk/data/solas/projects/capeverde.
html and http://gosic.org/gcos/GAW-data-access.
htm.

5.1.1.5 Conclusions
Oceanic and atmospheric time-series sites started in the
1950s, and their contribution has been invaluable. Some
have been in continuous operation without any interruption, some have stopped temporarily and some indefinitely. Since the early 2000s, the launch of the Argo
programme with the target of 3,000 floats per year
cruising the global ocean has brought a new perspective
since it provides a unique and systematic source of
information about the interior of the ocean. Only the
combination of eulerian and lagrangian observatories in
an integrated framework will allow a four dimensional
vision of the state of the ocean. One basic key to success
of these networks is the constant quest for the best
procedures for quality checking, intercomparability
and treatment of the data collected. Another key is for
the data to be archived in a responsible manner, meaning
ensuring proper software developments and addressing
management challenges of really huge datasets to
secure online delivery and long-term security.

5.1.2

Earth Observation Products

In 1957, the Sputnik was successfully launched and
the space age initiated, heralding the evolution of
Earth Observation, i.e. the scientific study of the Earth’s
surface and atmospheric composition from space.
Since the early 1970s, satellite oceanography has made
huge progress and global satellite observations are now
crucial elements of the global climate observing systems
(GCOS). Remotely sensed data are also basic ingredients of any oceanic and atmospheric process study. Sea
surface temperature, sea level, wave height, winds,
sea surface salinity, sea ice and ocean colour are
ocean–atmosphere observables monitored with near
global coverage on a daily to monthly basis. Satellite
measurements of concentrations of trace gases and long
lived greenhouse gases, when combined appropriately
with atmospheric chemistry models, have over the past
30 years provided a continuously improving picture of
the distribution of the surface fluxes of these gases at the
air-sea interface. Satellite observations are instrumental
tools of SOLAS science to address seasonal to multidecadal time scale variability in the ocean–atmosphere

256

exchange of gases and aerosol-borne chemicals. In situ
observations, both oceanic and atmospheric, presented
in Sect. 5.1.1, provide groundtruthing for the calibration
algorithms necessary for deriving these oceanographic
and atmospheric properties from space. Remotely
sensed data provide global-scale data sets at an unprecedented spatio-temporal resolution. One major challenge
ahead is to avoid any discontinuity of operating satellites
for the long-term archive and to minimise calibration
drift for performing proper climate studies. Earth observations represent a unique observational capability to
detect changes in the ocean–atmosphere system and
to better understand how planet Earth functions as a
complex adaptive system. The following subsections
briefly introduce each type of ocean–atmosphere
observation.

5.1.2.1 Altimetry, SST, Winds, Sea State
The advent of satellite altimetry has given oceanographers a unique tool for studying oceanic circulation
and its changes with time. From the vantage point of
space, a radar altimeter is able to measure the shape of
the sea surface globally and frequently. Due to three
decades of international effort, satellite altimetry has
benefited from a series of missions, leading to an
improvement in measurement accuracy by three orders
of magnitude, from tens of meters to a few centimeters
(Fu and Cazenave 2001). The evolution from Seasat
(1978), Geosat (1985–1989), ERS-1/2 (1991–2011),
TOPEX/POSEIDON (1992–2008), Jason1 (2001 to
present), OSTM/Jason2 (2008 to present), ENVISAT
(2000–2012) to Cryosat-2 (2010 to present) has produced and will produce a wealth of data of progressively improving quality (http://www.aviso.oceanobs.
com/, http://sealevel.jpl.nasa.gov). The Saral/Altikal
mission, launched in 2012, will ensure, in association
with Jason-2, the continuity of the service currently
provided by the altimeters onboard Envisat and
Jason-1, and will contribute to building a global
ocean observing system. The Surface Water and
Ocean Topography (SWOT, http://swot.jpl.nasa.gov/
mission) mission to be launched in 2020 will revolutionise our conceptual view of ocean dynamics since it
will characterise mesoscale and submesoscale circulation with a 10 km space resolution or better. In order
to meet the long wavelength calibration accuracy
requirements, topography profile measurements will
be available with an accuracy equal to or better than
the Jason series of altimeters and radiometers (see

5 Perspectives and Integration in SOLAS Science

Fig. 5.4 The hypothetical vertical profiles of temperature for
the upper 10 m of the ocean surface in high wind speed
conditions or during the night (red) and for low wind speed
during the day (black) (https://www.ghrsst.org/ghrsst-science/
sst-definitions) (# American Meteorological Society. From
Donlon et al. 2007. Used with permission)

SWOT Science Requirements Document, Version
1.1, 2012). Satellite altimetry observations, often
assimilated by global ocean circulation and coupled
numerical models, constitute the first global synoptic
data sets for the study of the following topics: large
scale circulation, mesoscale eddies, boundary currents,
tropical circulation, large-scale variability on time
scales from intraseasonal to interannual in relation to
forcing mechanisms, El Niño and La Niña, planetary
wave dynamics, eddy dynamics, to list a small sample
of topics.
Sea Surface Temperature (SST) is a difficult parameter to define exactly because the upper ocean (~ 10 m)
has a complex and variable vertical temperature structure
that is related to ocean turbulence and the air-sea fluxes
of heat, moisture and momentum. Figure 5.4 presents a
schematic diagram that summarises the definition of SST
in the upper 10 m of the ocean. The skin temperature
(SSTskin) is defined as the temperature measured by an
infrared radiometer typically operating at wavelengths
3.7–12 μm (chosen for consistency with the majority
of infrared satellite measurements) that represents the
temperature within the conductive diffusion-dominated
sub-layer at a depth of ~ 10–20 μm. Merging measurements of SST made by different satellite and in situ
instruments on drifting or moored buoys requires a
proper framework to understand the information content
and relationships between these measurements.

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis

The latest reprocessing (Pathfinder Version 5.2) is a
new reanalysis of the Advanced Very High Resolution
Radiometer (AVHRR) data stream developed by the
University of Miami RSMAS and the NOAA National
Oceanographic Data Center. It uses an improved version
of the Pathfinder algorithm and processing steps to produce twice-daily SST and related parameters dating back
to 1981, at an areal resolution of approximately 4 km,
the highest possible for a global AVHRR (see http://
www.nodc.noaa.gov/SatelliteData/pathfinder4km/). The
through-cloud capabilities of microwave radiometers
(AMSR-E, TMI) provide a global daily SST map without missing data due to orbital gaps or environmental
conditions precluding SST retrieval. Microwave
optimally-interpolated products have been proposed at
¼
resolution, and by blending with infrared SSTs from
MODIS at 0.09
resolution.
Under the Global Ocean Data Assimilation Experiment (GODAE) umbrella, the Group for High Resolution
SST (GHRSST) aims at providing the best quality sea
surface temperature data, without missing data, for
applications in short, medium and decadal/climate time
scales in the most cost effective and efficient manner.
Each day the GHRSST Multi-product Ensemble (GMPE)
experiment produces a median SST map and associated
standard deviation map using SST analysis data collected
over the previous 24 h period (i.e. yesterday). Thus, the
nominal analysis time for the GMPE median ensemble
SST is 12:00 for the previous day (i.e. T-1). The GMPE
median ensemble SST is computed as a median average
using a variety of GHRSST L4 analysis products after
their differing analysis grids have been homogenised by
area averaging onto a standard ½
latitude longitude
grid (https://www.ghrsst.org/data/todays-global-sst/).
The median-ensemble SST coverage is restricted by
the use of the OSTIA analysis land mask. The GMPE
median ensemble SST is currently derived using the
following inputs: the Met Office OSTIA SST analysis
http://ghrsst-pp.metoffice.com/pages/latest_analysis/
ostia.html, the NCEP RTG_SST_HR SST analysis
http://polar.ncep.noaa.gov/sst/, the NAVOCEANO
NAVO K10 SST observations https://www.navo.navy.
mil/ops.htm, JMA MGDSST SST analysis http://goos.
kishou.go.jp/rrtdb-cgi/jma-analysis/jmaanalysis.cgi, the
RSS MW Fusion SST and MW + IR Fusion SST
analyses mentioned above http://www.remss.com/sst/
microwave_oi_sst_browse.html, the FNMOC GHRSSTPP SST analysis http://www.usgodae.org/cgi-bin/
datalist.pl?summary¼Go&dset¼fnmoc_ghrsst,
the

257

MERSEA ODYSSEA SST analysis http://www.mersea.
eu.org/Satellite/sst_validation.html, the NOAA AVHRR
OI (Reynolds) http://www.ncdc.noaa.gov/oa/climate/
research/sst/oi-daily.php, the Meteorological Service
of Canada (CMC) 1/3
SST analysis http://www.mscsmc.ec.gc.ca/contents_e.html, and the BMRC GAMSSA
SST analysis http://podaac.jpl.nasa.gov/dataset/ABOML4LRfnd-GLOB-GAMSSA_28km.
As an example central to ocean–atmosphere
interactions at the heart of SOLAS science, Fig. 5.5
presents the GMPE ensemble SST anomaly map for
January 18, 2012 showing clearly that La Niña
conditions are present across the Equatorial Pacific.
SSTs are at least 0.5
C below average across much of
the central and eastern equatorial Pacific ocean. A
horseshoe pattern of above-average SSTs extends
from the Maritime Continent into the mid-latitudes
of the Pacific Ocean. The sea surface height anomalies
from Jason-2 for January 20, 2012 confirm that La
Niña is peaking in intensity in the equatorial Pacific
(Fig. 5.6). This image is based on the average of 10
days of data centered on January 20, 2012. It depicts
places where the Pacific sea surface height is higher
than normal (due to warm water), and where the sea
surface is lower than normal (due to cool water).
Green colour indicates near-normal conditions. The
La Niña episode changes global weather patterns and
is associated with less moisture in the air over cooler
ocean waters. This results in less rain along the coasts
of North and South America and along the equator,
and more rain in the far Western Pacific.
Ocean surface winds are needed to estimate
momentum transfer (surface stress) and gas transfer
velocity between the atmosphere and the ocean, and
are instrumental for determining large-scale ocean
circulation and transport. Accurate wind speeds are
essential for ensuring reliable computations of air-sea
heat and mass fluxes making surface winds critically
important for budgeting energy, moisture, gases and
particles (Fairall et al. 2010). Several reviews of
space-based wind measurements and applications
have been published (i.e. Liu 2002; Liu et al. 2008;
Bourassa et al. 2010). The challenge is to continuously
improve the present ocean wind system by means of
better bias removal and calibration for low and very
high wind speeds, increased temporal sampling using
a constellation of instruments, finer spatial resolution
and improved methods of fusing observations from
multiple platforms.

258

5 Perspectives and Integration in SOLAS Science

Ensemble Median minus NCEP OIv2 climatology SST for 20120118
90N

45N

0

45S

Crown Copyright, Met Office 2012

90S
0

90E

-4

-3

-2

180

-1

0

90W

1

2

0

3

4

SST Difference, Degrees Celsius

Fig. 5.5 GMPE ensemble SST anomaly map for January 18,
2012; climatology is derived from NCEP/NOAA between
1985 and 2001 (https://www.ghrsst.org/data/todays-global-sst/;

Martin et al. 2012) (Figure provided by J. Roberts-Jones, Met
Office UK, Crown Copyright)

Instruments that are routinely used to measure
vector winds (speed and direction or two vector
components) include scatterometers, passive polarimetric sensors and Synthetic Aperture Radar (SAR),
and those measuring scalar winds (speed only) include
passive microwave radiometers and altimeters. The
SeaWinds scatterometer on the QuikSCAT satellite
measures surface winds with a resolution of ~ 25 km
across a swath width of ~ 1,600 km. The temporal
sampling is a function of the orbit and the swath
width. The main weaknesses of scatterometers are
rain contamination for some rain conditions and lack
of data near land (~ 15 km for QuikSCAT) (Weissman
et al. 2002; Draper and Long 2004; Nie and Long
2008). Fusion of data from multiple scatterometers
significantly improves the temporal coverage (Liu
et al. 2008). The assimilation of scatterometer winds
in Numerical Weather Prediction (NWP) models has
improved the quality of forecasts of tropical cyclones
(e.g. in wave forecasting and hurricane force warnings
for nowcasting applications). High winds play a large

role in Earth’s climate, dramatically enhancing gas
exchange of greenhouse and trace gases and marine
aerosols. However, validation under high winds is
difficult due to the scarcity of such events and their
tendency to occur in high latitude regions, together
with uncertainty in data from buoys and/or ships in
rough seas due to wave sheltering.
The root mean square (rms) difference between
remotely sensed and buoy wind speeds is generally
less than 1 m s1 in non-rainy regions, provided that
atmospheric stability and surface currents are taken
into account as satellite measurements are physically
more related to wind stress than to atmospheric wind
speed. The sensitivity of satellite microwave (synthetic aperture radar, scatterometer, altimeter and
microwave radiometer) measurements to the sea
state varies according to the instrument type and to
its operating frequencies.
In the future, the combination of multi-frequency,
multi-angular and multi-sensor measurements should
improve the characterisation of the sea state and

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis

259

Fig. 5.6 Jason-2 Sea surface
height anomalies centered on
January 20th, 2012 (Figure
courtesy of NASA/JPLCaltech)

spatial resolution, especially in rainy cases and in very
strong wind speed cases. Recent studies show that the
retrieval of wind speed in rainy regions, with a
degraded precision, is now possible using WindSat
radiometric measurements (Meissner and Wentz
2009), that ocean satellite altimetry can retrieve wind
speeds from the radar backscatter in gale to storm
conditions (Quilfen et al. 2011) and that the new
L-band radiometer measurements, much less affected
by rain than at higher frequencies, allow
measurements of winds in a cyclone up to 50 m s1
(Reul et al. 2012b). Apart from wind speed, the combination of existing and future instruments should also
help to retrieve wave parameters.
Altimeters enable the measurement of wave height
and mean square slope (mss) in various wavelength
ranges depending on the altimeter frequency. Until
now, altimeters (e.g. Jason, ENVISAT) have operated
at three frequencies (S, C, Ku bands), corresponding to
wavelengths of 2.2, 5.6 and 9.3 cm. The future Saral/
Altika altimeter operating in the Ka band (wavelength

of 8 mm) will complement existing measurements.
First retrievals of mean square slope using Global
Navigation Satellite System-Reflectometry (GNSSR) are encouraging (e.g. Clarizia et al. 2009).
Among the new planned missions, the FrenchChinese CFOSAT mission (see http://smsc.cnes.fr/
CFOSAT/index.htm) will, in addition to wind speed,
provide the directional wave spectrum.
Scatterometer observations together with microwave observations of SST have facilitated the collection of data that couple air-sea processes at scales
smaller than the regional, typically at the mesoscale.
Indeed Chelton et al. (2004) and Small et al. (2008)
have identified an intimate link between modification
of the dynamics of the atmospheric boundary layer by
the SST and the feedback of this modification on the
ocean through wind surface stress and heat flux. This
link has been observed between sharp SST gradients
and surface winds – the so called ‘Chelton effect’,
where winds tend to accelerate over warm and decelerate over cold waters in the frontal zone, resulting in a

260

5 Perspectives and Integration in SOLAS Science

Fig. 5.7 Frontal-scale SST effects on wind stress divergence
and curl. Shown are binned scatter plots of spatial high-pass
filtered fields of the wind stress divergence as a function of the
downwind SST gradient (top row) and the wind stress curl as a
function of the crosswind SST gradient (bottom row) for four
geographical regions: the Southern Ocean, the eastern tropical

Pacific, the Kuroshio Extension and the Gulf Stream. The points
in each panel are the means within each bin computed from 12
overlapping 6-week averages, and the error bars are  1 standard deviation over the 12 samples in each bin. The wind stress
divergence and curl are multiplied by 107 and the units are
Nm3 (From Chelton et al. 2004)

quasi-linear relationship between the curl (divergence)
of the wind and the SST gradient according to a perpendicular (parallel) direction to the wind (Fig. 5.7).
The remarkable 10-year QuikSCAT data record has
given some insight into the nature of this variability
and the dynamic and thermodynamic impacts of this
atmosphere–ocean coupling on ocean circulation and
atmospheric weather patterns. However, the intricacy
of interaction between the atmospheric and oceanic
boundary layers through the ‘Chelton effect’ has yet
to be merged with the still unresolved biogeochemical
impact of mesoscale eddies (Siegel et al. 2011).

until 2009 sea surface salinity (SSS) was not measured
from space. Salinity is recognised as an essential climate variable (GCOS 2010) and satellite SSS is
expected to be highly complementary to existing
in situ salinity measurements (Lagerloef et al. 2010).
The feasibility of measuring SSS from space was
first demonstrated in the frame of the Skylab mission
launched in 1973. However, at L-band frequencies
around 1.4 GHz, the sensitivity of radiometric
measurements to salinity is low and the radiometric
resolution of the instruments remained an obstacle
to the development of new satellite missions until
the 1990s. Since then, the development of new
technologies (Lagerloef et al. 1995) has contributed to
two satellite missions accepted by space agencies: the
Soil Moisture and Ocean Salinity (SMOS) mission of
the European Space Agency and the Aquarius/SAC-D

5.1.2.2 Sea Surface Salinity
While sea surface temperature, sea level, sea ice and sea
state are relatively well monitored as an intrinsic part of
the global climate observing system (GCOS 2009),

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis

a

261

b

Fig. 5.8 SMOS SSS maps derived at (CATDS-CEC-OS), after
applying a thorough filtering of the outliers. (a) global map in
April 2010; (b) in the Gulf of Guinea from 21 to 30 April 2010
(See http://www.catds.fr/ for CATDS processing activities;

N. Reul and J. Tenerelly, SMOS Level 3 SSS Research products
-Algorithm Theoretical Breadboard Document, 2011, available
on http://www.ifremer.fr/naiad/salinityremotesensing.ifremer.fr/
CATDS_CECOS_SMOS_Level3Products_ATBD.pdf)

mission of the NASA/CONAE agencies. SMOS uses a
new antenna concept (synthetic aperture) for
spaceborne radiometry applications and was launched
in November 2009; Aquarius uses a large size real
aperture antenna and was launched in June 2011. The
goal of these two missions is to achieve SSS accuracy
of ~ 0.2 or better when averaged over 150–200 km and
a monthly timescale.
An overview of the first retrievals of SMOS SSS is
given in Font et al. (2012) and is detailed in papers
submitted to the IEEE TGRS SMOS special issue
(May 2012). Recently, numerous improvements have
been made; below we present examples obtained from
recently reprocessed data (Fig. 5.8).
Data for the entire year 2010 has been reprocessed
at the Centre Aval de Traitement des Données SMOS
(CATDS-CEC-OS). In order to remove outliers linked
to radio-frequency interferences (RFI), consistency
checks based on yearly SMOS data have been
performed before retrieving SMOS SSS. When comparing monthly 1
SMOS SSS with in situ SSS data
from ships, ARGO and moorings at a global scale, the
error standard deviation is 0.6 globally and 0.4 in the
tropics (Reul et al. 2012a).
SSS derived in July 2010 with Version v5 of the
ESA processors (v5 will be used for the whole mission

reprocessing until the end of 2011), led to a precision
of 0.2 for SSS averaged over 10 days and 100 km in
the subtropical Atlantic. In the rainy tropical Pacific
Ocean between 5
N and 5
S, the SMOS-ARGO SSS
scattering is greater due to the SSS vertical gradient:
SMOS exhibits a mean freshening of 0.1 in the surface
water with respect to 5 m depth (Fig. 5.9 Boutin et al.
2012).
The new SMOS data processing demonstrates the
capability of retrieving SSS from satellites with a precision of about 0.3 or even better in warm areas. The
combination of experience from the SMOS and Aquarius
missions is expected to improve this precision.
New satellite SSS will be a key tool for studying
air-sea interactions, e.g. the spread of fresh river water
into the open ocean, rain surface freshening and the
detection of fronts (see BEC processings on http://
www.smos-bec.icm.csic.es/).

5.1.2.3 Marine Carbon Observations from
Satellite Data: Ocean Color/PIC/POC
Phytoplankton are the basis of marine food webs and
contribute up to about 50 % of global primary production. Phytoplankton play a role in the budgets of
both organic trace gases and aerosols as a marine
source: for example, dimethylsulphide production by

262

5 Perspectives and Integration in SOLAS Science

Fig. 5.9 SMOS SSS at 1 cm
depth minus ARGO SSS at
5 m depth in the tropical
Pacific Ocean (5–15
N;
180–110
W) versus SSMI
Rain Rate (Boutin et al. 2012)
(More details about alongtrack SMOS ESA processing
is available on www.argans.
co.uk/smos/ and about
LOCEAN/IPSL SMOS Cal/
Val activities on www.loceanipsl.upmc.fr/smos)

oceanic phytoplankton, leading to the formation of
sulphate aerosol and cloud condensation nuclei formation/growth in the marine atmosphere has been studied
extensively (e.g. Liss et al. 1997). A link between
oceanic chlorophyll a content (chl a), the indicator of
phytoplankton biomass, and cloud droplet numbers
over the Southern Ocean (Plass-Dülmer et al. 1995)
has been observed, as well as enhanced organic mass
in marine aerosols during periods of enhanced ocean
biological activity (Singh et al. 2003; O’Dowd et al.
2004). Bromoform observations in the tropical eastern
Atlantic Ocean (Quack et al. 2004, 2007) have
revealed a pronounced subsurface maximum at the
depth of the subsurface chl a maximum, suggesting a
phytoplanktonic source of bromoform. Ocean-emitted
volatile organic compounds also appear to be related
to phytoplankton activity (e.g. Gantt et al. 2009;
Yassaa et al. 2008). An improved quantification of
the dependence of atmospheric composition on marine
biological activity is important for studying the
ocean–atmosphere interactions of gases and particles
and understanding the Earth’s climate system and its
response to anthropogenic influence. In order to study
the dynamics of phytoplankton distribution over
longer timescales, optical remote sensing of ocean
phytoplankton (ocean colour) provides data on

phytoplankton distribution and related parameters
with near global coverage on a daily to monthly time
resolution. Using relationships derived from in situ
oceanic and atmospheric data at or just above the sea
surface, ocean colour data products can then be used to
infer emission rates of trace gases on longer time
scales with reasonable temporal and spatial resolution
(down to 1 km), e.g. Arnold et al. (2009, 2010).
Ocean colour has been focused since the 1980s on
the detection of chl a, due to its strong absorption
properties. Merged chl a and reflectance satellite data
products are available from the SeaWiFS, MODIS and
MERIS sensors (1997 until present), through NASA
and ESA efforts to produce essential climate variables
(Maritorena et al. 2010). However, chl a concentration
changes with species composition and physiological
state and cannot be converted directly into carbon
biomass, which is the currency used in ocean carbon
models. Therefore, recent advances in ocean colour
have focused on the quantification of carbon pools,
such as particulate organic carbon (POC), particulate
inorganic carbon (PIC) and dissolved organic carbon
(DOC), as well as on the assessment of different phytoplankton functional types (PFTs). Different PFTs
have distinct impacts on the marine food web and
biogeochemical cycling, e.g. variable relationships of

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis

different PFTs to isoprene production have been
observed in laboratory experiments (Bonsang et al.
2010). Ocean colour satellite observation is restricted
to the near-surface layer, which varies from meters to
about 60 m thick depending on the presence of
optically-significant water constituents and the wavelength considered (Smith and Baker 1978). Products
derived from ocean colour satellite data are integrated
over the first penetration depth.
POC algorithms are based on empirically-derived
relationships of POC to either inherent optical
properties (particle backscattering or attenuation
coefficients), which are related to reflectances,
measured by remote sensing at several wavelengths
(Stramski 1999, Loisel et al. 2001, 2002; Gardner
et al. 2006), or to the blue-to-green reflectance ratio
(Stramski et al. 2008). The latter algorithm seems less
sensitive to regional variability, but is closely related to
chl a as it uses the same input parameters (the blue
to green reflectance ratio). For biogeochemical studies,
satellite near-surface POC data are insufficient because
they correspond to the first attenuation layer only and
deep chl a maxima often exist. Moreover, biogenic
detrital particles, heterotrophic bacteria and viruses
also contribute to POC in variable proportions throughout the entire water column. Stramski et al. (2008)
provided quantitative estimates of the POC reservoir
in three oceanic layers: the attenuation, the mixedlayer (MLD) and the 200 m layer depth. In oligotrophic
waters, this approach may underestimate the POC
reservoir where high POC accompanies deep chl a
maxima, because it assumes that POC is uniform
throughout the MLD and equals the near-surface
POC concentration. This work has been improved by
the empirical algorithm of Duforêt-Gaurier et al.
(2010) who derive integrated euphotic zone POC
from the entire SeaWiFS satellite data set.
The PIC ocean colour product represents biogenic
particles composed of calcium carbonate which is
produced by several phytoplankton groups, mainly
coccolithophores. These create massive blooms in
the ocean, and their PIC, being white, strongly reflects
light, which imparts a turquoise-blue-white colour to
the ocean. The blooms are easily observed in the
pseudo-true-colour images from satellites and can be
monitored using ocean colour (Brown and Yoder
1994). Algorithms have been elaborated to quantitatively retrieve PIC at regional and global scales

263

(Gordon et al. 2001; Balch et al. 2005) and the
most recent have been used to process the whole
SeaWiFS and MODIS data set. An important constraint
of these algorithms is that, at typical non-bloom
concentrations, the PIC scattering represents only a
few percent of the total scattering. Thus, to maximise
the signal to noise ratio, satellite pixels must be
aggregated in space and time, in order to define accurate mean concentrations. Currently, more verification
with PIC field measurements is underway to optimise
the use of PIC ocean-colour data in models (Balch et al.
2011). PIC is also produced by certain zooplanktonic
organisms but these particles are too large to be
detected by ocean colour (Balch et al. 1996).
Different bio-optical and ecological methods have
been established that use ocean colour data to identify
and differentiate between PFTs or phytoplankton size
classes (PSCs) in the surface ocean. These can be
summarised into four main types: spectral-response
methods which are based on differences in the shape
of the light reflectance/absorption spectrum for different PFTs/PSCs (Sathyendranath et al. 2004; Alvain
et al. 2005, 2008; Ciotti and Bricaud 2006; Bracher
et al. 2009; Sadeghi et al. 2011; Brewin et al. 2010a;
Devred et al. 2011), methods which use information
on the magnitude of chlorophyll a biomass or light
absorption to distinguish between PFTs or PSCs
(Devred et al. 2006; Uitz et al. 2006; Hirata et al.
2008; Brewin et al. 2010b; Hirata et al. 2011; Mouw
and Yoder 2010), methods that retrieve the particle
size distribution from satellite-derived backscattering
signal and derive PSCs (Kostadinov et al. 2010), and
ecological-based approaches which use information
on environmental factors, such as temperature and
wind stress, to supplement the bio-optical data for
investigating PFTs (Raitsos et al. 2008). All methods
derive dominant phytoplankton groups, while Uitz
et al. (2006), Bracher et al. (2009, improved by
Sadeghi et al. 2011) and Hirata et al. (2011) also
give chl a for the different PFTs. Nearly all the PFT
methods mentioned use information from the multispectral ocean colour sensors SeaWiFS, MERIS or
MODIS and are based on the parameterisation of a
large global or regional in situ data set in order to yield
PFTs from satellite chl a or normalised water leaving
radiances. Unexpected changes in the relationships
between these parameters resulting from a regional
or temporal sampling bias leads to a bias in the

264

5 Perspectives and Integration in SOLAS Science

Fig. 5.10 Global climatology for April of coccolithophore
biomass (given as chlorophyll a concentration) derived from
the average of all SCIAMACHY data retrieved via PhytoDOAS

multitarget-fitting (according to Sadeghi et al. 2011, 2012)
(Source: T. Dinter, A. Bracher, Phytooptics, AWI-IUP)

detection of PFTs. In contrast, the PhytoDOAS
method of Bracher et al. (2009, improved by Sadeghi
et al. 2011) exploits the whole spectrum by using
hyperspectral data of the satellite sensor SCIAMACHY
and discriminates different PFTs by their characteristic
absorption. Diatoms, cyanobacteria, dinoflagellates and
coccolithophores are quantified (example in Fig. 5.10)
without assuming empirical relationships as in the case
of other PFT methods. Recent PFT algorithm intercomparison studies show the robustness of the abundancebased approaches at detecting dominant PSCs (Brewin
et al. 2011). A new PFT algorithm intercomparison has
been initiated where the quantitative assessment of PFT
distributions will be compared. PFT monthly resolved
products are available for the whole SeaWiFS or
SCIAMACHY missions, using the methods of Alvain
et al. (2008) and Hirata et al. (2011) covering
1998–2009 for the first data sets and the PhytoDOAS
method covering 2002–2011.

comprehensive reference list. Here we provide a brief
summary of the approaches most relevant to the
science of surface ocean-lower atmosphere exchanges.
In addition to satellite-borne remote sensing tools
(e.g. Massom 2009), automated in situ systems (see
Perovich 2009), mainly mounted on buoys, are proving to be a critical component of the global sea-ice
observing network. In contrast to open water, sea ice is
a very complex and variable surface, complicating
calibration of remote sensing signals, and information
from multiple sources is generally required to resolve
measurements. Therefore, both multiple satellite
sensors and widely distributed automatic measurement stations (also called ice-based observatories)
remain critical to interpreting Earth observation data
on sea ice (see Massom 2009 for a detailed
discussion).
Sea ice distribution and motion are fundamental
parameters needed for research on air-sea exchange
in polar oceans. Passive microwave data (e.g. from the
SSM/I series satellites) may be the most valuable tool
available for tracking sea ice. Not only does microwave radiation provide information in the dark and
through clouds, but in the Arctic, where summer brine

5.1.2.4 Sea Ice
Methods for studying sea ice, including Earth observation products, have recently been thoroughly
reviewed by Eicken et al. (2009), which includes a

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis

flushing is common, it can also distinguish between
first- and multi-year ice. While the relatively low
resolution of passive microwave data precludes their
use for studying landfast ice, synthetic aperture radar
(SAR; aboard, for example, the ERS series and
RADARSAT satellites) provides very high resolution
data, to as good as 1 m, which is adequate not only for
fast ice, but also for detailed ice edge information.
Visible and infrared sensors (e.g. MODIS and
AVHRR) also provide data on ice distribution,
although only far-infrared sensors are useful during
winter. Deriving ice motion from time series of satellite images carries a high uncertainty, and data from
drifting ice buoy arrays are a vital component of accurate ice motion estimates.
Beyond the simple presence or absence of sea ice,
ice thickness and the rates of freezing or melting
strongly impact air-ice-ocean exchanges. To date, ice
mass balance buoys (e.g. Metocean) and moored sonar
have been the most accurate tools available to determine sea ice thickness over distributed areas. Kwok
(2010) reviewed satellite remote sensing of sea-ice
thickness and concluded that at least in the Arctic,
ice thickness from radar and lidar altimetry is maturing and its shortcomings are relatively well understood. In contrast, Southern Ocean ice cover, in
which flooding and snow-ice formation cause substantial density variations and where there are fewer
observations to help address processing deficiencies,
sea-ice thickness retrieval has not achieved the same
level of accuracy as in the Arctic. The CryoSat-2satellite, launched in 2010, was designed to use SAR
to measure ice thickness very precisely, and appears to
be meeting expectations.
While visible-IR sensors can provide surface skin
temperatures, sea ice is often covered by a snow layer
of variable thickness, and the temperature at the icesnow interface is an important parameter in determining ice-atmosphere exchanges. Passive microwave has
shown potential for providing information on both the
snow thickness and snow-ice interface temperatures,
but these applications are still in development. The
impurity content of the snow (i.e. its salinity) can be
derived from visible-IR imagery, and the salinity of
sea ice substantially influences its radar transparency,
providing a potential tool for remotely sensing sea ice
bulk salinity that has not yet been fully developed.
Frost flowers, which form on the surface of new ice
under very cold and still conditions, appear to play

265

an important role in transferring sea-ice salts,
organohalides, and other organic matter to the atmosphere and can be identified with synthetic aperture
radar or with a combination of active and passive
microwave sensors (Kaleschke and Heygster 2004).
Regardless of which sensor data are used, classification of ice types, including leads or polynias,
from remote sensing involves refined analytical tools
(e.g. Soh et al. 2004; Qin and Clausi 2010). Most are
formulated in Bayesian frameworks, which require
substantial ground truth data to train the analytical
classifier. To help meet this need, Clausi et al. (2010)
developed a tool for generating high-resolution (pixelbased) maps for SAR images. They provide estimates
of ice concentrations, types, and floe sizes derived
from manually classified ice charts of low spatial
resolution, such as those produced by the Canadian
Ice Service. Similarly, Röhrs et al. (2012) used manual
observation of visible satellite images to test and
validate an algorithm for identifying leads in passive
microwave imagery. Note that although these
approaches provide a lot of data for training classification algorithms, the data are not truly ground-truthed,
which requires instruments at the surface.
The autonomous O-buoys developed by the
Ocean–Atmosphere-Sea
Ice-Snow
(OASIS)
programme during IPY (Fig. 5.11) have successfully
measured both ozone and CO2 over sea ice for at least 3
months during the winter-spring transition (Knepp
et al. 2010). As these buoys are further developed and
more are deployed, they will provide valuable groundtruthing data to supplement those from satellite-borne
absorption spectrometers (see Sects. 5.1.2.5 and
5.1.2.6) measuring aerosol and trace gas emissions
from sea ice.
As yet, surface waters below the ice are inaccessible
to satellites except through openings in the ice, such
as leads and polynias. Therefore, the only available
tools that potentially could provide operational information on ice-sea water exchanges are ice buoys,
such as the Autonomous Ocean Flux Buoys (AOFB),
and ice-tethered profilers (Krishfield et al. 2008).
Gliders also have the potential to provide surfaceocean information at higher temporal and spatial
resolution than ship-borne measurements, but their use
at shallow depths under sea ice can be complicated
by ice keels.
As noted before, interpreting remotely sensed data
on sea ice requires synthesising information from

266

5 Perspectives and Integration in SOLAS Science

Fig. 5.11 O-buoy OB-5
shortly after deployment on
August 5, 2011, at 78
0.40 N,
139
55.50 W in the Beaufort
Sea (Photo: John W. Halfacre)

numerous tools, including different satellite sensors
and buoy arrays. Recent initiatives to coordinate
observations in ice-covered seas should substantially
improve the utility of Earth observation products for
sea ice research. Notably, the International Polar Year
programme on integrated Arctic Ocean Observing
Systems initiated basin-wide research coordination in
the Arctic Ocean, including deployments of ice buoys
and ice-tethered profilers (e.g. Dickson 2009; Perovich
et al. 2012, IAOOS: Ice-Atmosphere- Arctic Ocean
Observing System see http://www.iaoos-equipex.upmc.
fr/en/index.html). A similar initiative in the Southern
Ocean (SOOS, the Southern Ocean Observing
System) is also now underway (Rintoul et al. 2012).

5.1.2.5 Aerosols
Properties of aerosols in the marine atmosphere are
extensively described in Chap. 4 of this book (see also
Sects. 5.1.1.3 and 5.1.1.4), including the use of satellite remote sensing for the determination of the
organic mass fraction in sea spray aerosol. In this
section we further elaborate on the use of satellite
remote sensing for the determination of aerosol
properties, which started some three decades ago
with the retrieval of the aerosol optical depth (AOD,
often also called aerosol optical thickness or AOT)
over the ocean. The retrieval of aerosol properties
over the oceans can be achieved due to the relatively
small reflection of solar radiation by the ocean surface,
as compared to the reflection by aerosol particles, at
wavelengths in the visible and near infrared part of the

electromagnetic spectrum. Over land the surface
reflection at these wavelengths is much larger and
over bright surfaces this overwhelms the aerosol
signal.
A brief description of the history of aerosol
observations from space was presented by Lee et al.
(2009b) and Kokhanovsky and de Leeuw (2009),
including an overview of instruments used for this
purpose (see also de Leeuw et al. 2011a). Currently,
AOD observations are available from several
instruments, including operational products such as
MODIS, which provides AOD with a validated accuracy over the ocean of  (0.03 + 0.05AOD) (Remer
et al. 2008). Other examples of instruments providing
aerosol products are: MISR, PARASOL, AATSR,
MERIS, OMI, SCIAMACHY, GOME-2, MSG
and CALIOP. These products include a variety of
parameters, in addition to AOD at one or more
wavelengths, such as fine mode fraction, absorption
aerosol index (AAI) and an indication of aerosol
chemical composition. Products, accuracy and spatial
and temporal coverage depend on the instrument
characteristics. The results are validated by comparison
with independent ground-based sun photometer data.
Results from a comparison of MISR and MODIS
aerosol products by Kahn et al. (2009) show good
correlations between the AOD products (correlation
coefficient 0.9 over ocean and 0.7 over land) and the
Ångström exponent (correlation coefficient 0.67 over
ocean when MISR AOD values > 0.2 are considered).
Kahn et al. emphasise the necessity for proper
interpretation of the satellite products. In particular

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis

data-quality statements should be followed to ensure
proper interpretation and use of the satellite aerosol
products. Other intercomparisons of satellite products
also reveal significant discrepancies between AOD
(order of 0.1) from different instruments, even over
the ocean (Myhre et al. 2004, 2005).
The AOD is important for the measurement-based
assessment of aerosol effects on climate and chemical
processes in the atmosphere. Satellite data also show
the spatial distribution of aerosols over the ocean which
reveals, e.g. transport patterns of dust, biomass burning
aerosols and anthropogenic pollution, all of which play
a role in the atmospheric input of nutrients into the
ocean and their biogeochemical effects (see Chap. 4
for a description of current knowledge on this subject).
Measurements of AOD over the ocean clearly show
the effect of wind speed on SSA production, as
reported by e.g. Mulcahy et al. (2008); O’Dowd et al.
(2010), both using local sun photometer and wind
speed measurements at Mace Head, Glantz et al.
(2009) using SeaWifs data and ECMWF wind speeds,
Lehahn et al. (2010) using MODIS AOD and
QuickSCAT, AMSR-E and SSM/I data, Huang et al.
(2010) using AATSR AOD and ECMWF wind speed
data, Kiliyanpilakkil and Meskhidze (2011) using
CALIOP and AMSR-E data and Smirnov et al.
(2011) using data from the Marine Aerosol Network
(MAN, Smirnov et al. 2011). Smirnov et al. (2012)
compare various AOD versus wind speed relationships
showing large differences in the AOD at the same
wind speed. Most of these relationships show a similar
change in AOD over the wind speed range of
0–10 ms1, with the exception of the relations of
Mulcahy et al. (2008) and O’Dowd et al. (2010). The
latter relations show exponential dependence of AOD
on wind speed whereas Smirnov et al. (2012) found a
linear dependence.
AOD has been used by several authors to evaluate or
improve their model results. Sofiev et al. (2011) used
MODIS AOD data to evaluate their model results over
the ocean. Jaeglé et al. (2011) used AOD observations
to include the effect of SST on the production flux of
coarse mode sea-salt aerosol in their model through a
correction to the sea salt aerosol source function (see
Chap. 4 and Sect. 5.2.4). Lapina et al. (2011) used
MODIS and MAN AOD data for comparison with
GEOS-Chem model results that use the Jaeglé et al.
(2011) correction for coarse mode sea-salt aerosol production fluxes as well as an emission scheme for

267

organic matter (see Chap. 4). These authors found that
the model AOD is lower than the mean MODIS AOD
value but agrees well with MAN AOD for the studied
regions. Lapina et al. (2011) argue that this may be
partially explained by uncertainties in the satellite
retrieval and that uncertainties in the marine OM emission scheme cannot account for the AOD estimate.
They conclude that only a sea spray aerosol emission
parameterisation resulting in a very different spatial
distribution of sea salt could resolve this discrepancy,
which may suggest that either some additional marine
source of aerosol has not been accounted for or that
observations used in the study are insufficient to close
the marine aerosol budget.
Sea spray aerosol is principally produced from
waves breaking under the action of the wind. The
area of the ocean covered with whitecaps is expressed
in the whitecap fraction. The retrieval of whitecap
fraction using satellite data was explored by
Anguelova and Webster (2006). A review of this subject can be found in Lewis and Schwartz (2004), see de
Leeuw et al. (2011b) for the current status in this area
and a comparison of different methods.

5.1.2.6 Satellite Measurements of Trace
Gases Over the Oceans
Carbon dioxide (CO2) and methane (CH4,) are the two
most important greenhouse gases (GHG) being
modified directly by anthropogenic activity, primarily
fossil fuel combustion, biomass burning and land use
change. In 1957, during the International Geophysical
Year, IGY, accurate measurements of the mixing ratio
of CO2 at the Mauna Loa Observatory, led by C.D.
Keeling, were initiated. These revealed the growth of
CO2, attributed to fossil fuel combustion, and the
annual biogeochemical seasonal cycling of CO2.
Measurements were extended to other sites and
to include CH4 and other relevant gases, resulting
in a global but sparse network. However global
measurements at high spatial resolution are needed to
identify and assess the local and regional response of
CO2 and CH4 surface fluxes in a warming world and
for the verification of national inventories of GHG.
During the 1980s, the retrieval of the total dry
atmospheric columns of CO2 and CH4 from space
was proposed as part of the SCIAMACHY (SCanning
Imaging Absorption spectroMeter for Atmospheric
CartograpHY, Burrows et al. 1995 and Bovensmann
et al. 1999) mission. This is achieved by the remote

268

5 Perspectives and Integration in SOLAS Science

Fig. 5.12 The average global
distribution of the dry column
of methane retrieved from
SCIAMACHY, showing the
source regions such as natural
wetlands and rice paddies and
the hemispheric gradient

XCH4 [ppb]
1655 1674 1693 1712 1731 1750 1769 1788 1807 1826 1845

sounding in the short wave infrared spectral region and
the retrieved CO2/CH4 data products, provided they
have been adequately sampled with sufficient accuracy and are coupled with models, constrain local and
regional surface fluxes. SCIAMACHY was selected in
1989, as a national contribution by Germany to the
ESA ENVISAT, with The Netherlands and Belgium
joining the funding consortium in Phase A and B,
respectively. ENVISAT flies in a sun-synchronous
orbit in descending node, having an equator crossing
time of 10.00 a.m. and was launched on the 28th of
February 2002. SCIAMACHY measures contiguously
in eight channels scattering, reflecting and transmitting solar electromagnetic radiation upwelling from
the earth’s atmosphere between 214 and 2,380 nm at
a channel dependent spectral resolution between 0.2
and 1.4 nm. Measurements are made alternately in
limb and nadir viewing geometry and for solar and
lunar occultation. Mathematical inversion of the nadir
measurements of the absorptions of CO2, CH4 around
1.6 μ and molecular oxygen, O2 around 0.76 μ, yields
the total dry columns of CO2 and CH4.
The map of the average dry total column of methane
is shown in Fig. 5.12. The source regions in the northern hemisphere, such as natural wetlands, rice paddies
and anthropogenic regions are readily identified along
with the hemispheric gradients. In Fig. 5.13 the dry
column of CO2 and CH4 are plotted against time and
sin(latitude) to show the latitudinal distribution of
increase from 2003 to 2012. Combining these
measurements appropriately with atmospheric models
yields and constrains the surface fluxes of CH4 and
CO2. There is now a growing body of literature

combining these data and an accurate network of
measurements with which to assess our understanding
of surface flux distributions. SCIAMACHY has
demonstrated the feasibility, but higher spatial resolution measurements and improved sampling are needed
to unambiguously measure point sources and sinks of
CO2 and CH4.
The GOSAT (Greenhouse gases Observing SATellite) was launched on 23rd of January 2009. The instrument TANSO-FTS (Thermal And Near infrared Sensor
for carbon Observations - Fourier Transform Spectrometer) has now made over four years of measurements in
space (Hamazaki et al. 2004). The measurements have
higher spectral resolution and contain potentially more
information than those of SCIAMACHY but have
poorer sampling. The OCO (Orbiting Carbon Observatory) was selected by NASA but the OCO-1 launch
vehicle failed in February 2009 (Crisp et al. 2004).
OCO aims to make high spatial resolution
measurements of the dry column of CO2 and OCO2 is now planned for launch in 2014. Based on the
success of SCIAMACHY, the MaMap instrument was
developed in 2006 to demonstrate that high spatially
resolved measurements, e.g. 50 m, and high signal to
noise of the total dry mole fraction of CO2 and CH4 are
feasible from aircraft (Gerilowski et al. 2011). The
results of MaMap have been used to retrieve surface
fluxes of CO2 and CH4 from point sources. This demonstration was an essential prerequisite for the development of the CarbonSat and CarbonSat Constellation
concepts. A single CarbonSat was proposed for the
ESA earth explorer opportunity mission 8, and selected
in November 2010 by ESA for Phase A, B1 studies,

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis
Fig. 5.13 Plots of the dry
mole fraction of CO2 and CH4
versus sin (latitude) from 2003
to 2012 retrieved from
SCIAMACHY. These show
the latitudinal increase of CO2
and the changes in CH4. The
rate of increase accelerates in
2008 and is not yet
unambiguously explained
(Figures courtesy of
O. Schneising, M. Buchwitz
and J. P. Burrows University
of Bremen)

269

60º N
30º N
0º N
30º S
60º S
2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

XCO2 [ppm]

366 370 374 378 382 386 390 394 398 402

60º N
30º N
0º N
30º S
60º S

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

XCH4 [ppb]

1640 1660 1680 1700 1720 1740 1760 1780 1800 1820

yielding 5 day global coverage. A constellation yields
the daily measurements at high spatial resolution
measurements required for the verification of CO2 and
CH4 emissions in the post Kyoto era and the observation of the response of the ecosystem in a warming
world.
The background concentration of most trace gases in
the lower marine atmosphere is difficult to quantify
from space, mainly due to low atmospheric
concentrations and the low reflectance of the
ocean surface. This is particularly true for ozone as its
concentration in the lower troposphere is considerably
lower than that throughout the rest of the ozone column.
However, emissions from volcanic eruptions (e.g.
SO2), shipping routes (NO2) and long-range transport
plumes can be monitored over the oceans.
International shipping routes are a significant
source of pollution in the marine boundary layer.
Emissions of traces gases such as NO2 have been
measured from different satellite instruments

(e.g. Richter et al. 2004; Beirle et al. 2004). For
instance, Franke et al. (2009) compared modelled
and satellite-observed NO2 for the shipping lane
between India and Indonesia using GOME,
SCIAMACHY, OMI and GOME-2 data, finding
indications of an upward trend in shipping emissions
over recent years.
The background concentration of SO2 is difficult to
measure by instruments onboard satellites, although
volcanic eruptions and their emission plumes can be
monitored over several days. The first observation of a
volcanic eruption from satellite SO2 measurements
was made using data from the Total Ozone Mapping
Spectrometer (TOMS) during the El Chichón eruption
in 1982 by Krueger (1983). Since then, several authors
have reported satellite SO2 observations from volcanic
ash plumes across the world (e.g. Heue et al. 2010;
Nowlan et al. 2011).
Reactive halogen species, such as BrO and IO, lead
to ozone destruction in the lower atmosphere,

270

especially during the polar spring. Measurements from
satellite instruments such as GOME, GOME-2,
SCIAMACHY and OMI have delivered detailed
maps of BrO (e.g. Chance 1998; Wagner and Platt
1998; Richter et al. 1998). The detection of IO over
Antarctica has also been demonstrated using
SCIAMACHY data (Saiz-Lopez et al. 2007;
Schönhardt et al. 2008).
Oxygenated volatile organic compounds such as
HCHO and (CHO)2 are key intermediate species
produced during the oxidation of precursor
hydrocarbons. Their short lifetime of a few hours
in the lower troposphere links them to emission
sources and makes them useful tracers of photochemical activity. Vrekoussis et al. (2009) and
Lerot et al. (2010) observed high values of glyoxal
over the oceans, mainly in the tropics close to the
upwelling areas and regions having significant
amounts of phytoplankton, implying oceanic biogenic activity as a possible source of glyoxal
precursors. Marbach et al. (2009) have reported the
detection and quantification of HCHO linked to
shipping emissions from GOME data.

5.1.2.7 Conclusions
The success of integration of Earth Observations
products in SOLAS Science depends on the efforts
of the international atmospheric and marine science
communities. They need to maintain a continuous
series of sensors on satellite missions, without interruption, with a constant aim to optimise the quality
of the retrieved observables. This required quality
implies continuous and rigorous calibration with in
situ measurements in the atmosphere and in the
ocean including coastal regions. The European
Space Agency has launched in 2011 a Support to
Science Element Project on SOLAS Science entitled
Oceanflux (http://due.esrin.esa.int/stse/projects.php)
comprising three themes: Upwellings, Sea spray
aerosols and Greenhouse Gases to foster the use of
EO data in addressing these SOLAS science
questions. Ultimately, it is up to SOLAS scientists
together with Earth Observation experts to imagine
and develop novel sensors, products, algorithms and
methodologies to provide the long duration records
of all relevant parameters of the ocean–atmosphere
coupled system. Together with models, Earth Observation products constitute the key sentinels for
predicting the future trajectory of Earth’s climate.

5 Perspectives and Integration in SOLAS Science

5.1.3

Modelling

Modeling has emerged as a critical and successful
method to answer scientific questions, to test
hypotheses and to make predictions (Gruber and
Doney 2008). Models range from conceptual (essentially ideas about the functioning of a system or
process) to complex realistic models that push the
boundaries of computational capabilities. We restrict
our discussion here to mathematical models and their
numerical implementations unless explicitly stated.
But in all cases, models are designed for a particular
purpose and therefore their transferability is often
strongly limited.
Modeling has become the third pillar of the scientific method. In particular, the development of models
can be thought of as an iterative process in that
observations and experiments stimulate the formulation of conceptual models and hypotheses, which are
then translated into mathematical models. The models
can then produce predictions, which when confronted
with new observations permit the developer to evaluate the models and to either corroborate their underlying hypotheses or to reject them. This leads to an
iterative process of modification and improvement.
The predictions do not necessarily have to lie in the
future (forecast mode), since models can also make
“predictions” for the past, i.e. when the models are run
in hindcast mode. Finally, models can also be run in
assimilation mode, where the model’s parameters or
its initial or boundary conditions are modified in order
to optimally fit a given set of observations.
Physical models of oceanic and atmospheric circulation are used routinely and several mature coupled
climate modelling systems exist (e.g. Gent et al. 2012;
Roeckner et al. 2006; Collins et al. 2006). Ocean
biogeochemical models, obtained by coupling ocean
circulation models with mathematical representations
of biogeochemical processes, are less mature partly
because the quantitative understanding of biogeochemical processes is relatively patchy and mostly
empirically-based. This is aggravated by the relative
lack of critical biological and chemical observations.
As a result, ocean biogeochemical/ecological models
differ widely in terms of their complexity (e.g. number
of functional plankton groups/state variables) and process parameterisations used (see e.g. Le Quéré et al.
2005). Arguably this is less due to differences in
scientific objectives and more to the fact that an

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis

optimal compromise between realism and feasibility
has not yet emerged (Anderson 2005). Also, biogeochemical predictions depend on many parameterisations and parameter choices that are not well
constrained – a direct reflection of the relative paucity
of biogeochemical observations and experiments.
Ocean and atmospheric models are often run
independently of each other, where atmospheric
model output is used as boundary information for
ocean models (e.g. atmospheric temperature, humidity and carbon dioxide concentration) and vice versa.
An interactive coupling between ocean and atmosphere is needed when feedbacks are of interest
that lead to changes in the oceanic or atmospheric
mean state, e.g. in climate models and Earth System
models such as those used in the IPCC assessments.
Global models are constantly being pushed to finer
spatial resolutions. However, for reasons of computational efficiency and convenience models are often
run at regional and local scales (as three-dimensional
or one-dimensional, vertical models). Regional
models allow for higher spatial resolution and more
complex representation of biogeochemical processes
than global models. They can thus be targeted to better
resolve, for example, the scales of open ocean process
studies or processes in coastal regions and on continental shelves (e.g. Gruber et al. 2011). These regional
models require boundary information either from
climatological observations or larger scale models.
Model simulation results are always approximations
of reality. They come with uncertainties resulting from
inadequacies in process resolution and parameterisations, numerical approximations and imperfections
in initial and boundary information. Some of these
uncertainties are not well understood or quantified.
Data assimilation and inverse modeling, which encompass a variety of statistically based techniques for
blending observations and models, are a way to reduce
uncertainty in model simulations. Data assimilation
has been used routinely for many years in numerical
weather prediction for short-term forecasting and, more
recently, for ocean models that are run operationally (in
forecast mode) or for improving state estimates in
hindcasts (e.g. Wunsch and Heimbach 2007; Brasseur
et al. 2009).
For model development and validation in general
and data assimilation and inverse modeling in particular, the availability of high-quality observations is
essential. For example, the Argo array has led to

271

remarkable improvements in our ability to characterise the physical state of the ocean (see Sect. 5.1.1.1).
Expansion of this initiative aimed at including chemical and bio-optical measurements will likely lead to
tremendous improvements in our ability to characterise the biogeochemical state of the ocean (Johnson
et al. 2009; Brasseur et al. 2009).
Perhaps the most pressing modeling challenge is to
provide estimates of how the mean physical and biogeochemical state of ocean and atmosphere and
variations around this mean will change in the future
on time scales of a century or longer. Such simulations
are referred to as projections. An obvious difficulty is
that these future states are outside the envelope of
historical observations against which models can be
validated. One strategy is to run climate models for
periods in the geological past, where some information
is available from palaeo-oceanographic proxies,
although this solution is imperfect in that no direct
and well-understood analogue to the future exists and
that palaeo-proxies provide only an incomplete
characterisation of the coupled ocean–atmosphere
system. In IPCC assessments, attempts are made to
address uncertainty in future projections by using
multi-model ensembles. Experience has shown that
the means of such ensembles frequently perform better
than any single model (e.g. Tebaldi and Knutti 2007).
In summary, modeling is an integral part of SOLAS
science. Different modeling approaches and
techniques are used for different purposes depending
on the scientific objective. Key examples are given in
the following sections.

5.1.3.1 Global Perspective, Prognostic IPCC
and Hindcast
Over the past two centuries, the ocean has taken up
about 30 % of total anthropogenic CO2 emissions,
which include the emissions from the burning of fossil
fuel and from land use change (Sabine et al. 2004).
Although this uptake came at the cost of ocean acidification, it helped considerably to mitigate the accumulation of this anthropogenic CO2 in the atmosphere. It
is thus of great importance to determine whether the
ocean will continue to provide this service to mankind,
or whether feedbacks between global climate change
and the ocean carbon cycle will reduce the uptake of
CO2 from the atmosphere (Sarmiento et al. 1998; Joos
et al. 1999; Gruber et al. 2004). Increases in ocean

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5 Perspectives and Integration in SOLAS Science

stratification and marine oxygen levels can also lead to
the enhanced production of marine N2O, which would
further accelerate global warming (see Chap. 3).
Global prognostic models are the only means to
provide answers to such questions. In order to assess
all aspects of such climate-ocean-biogeochemical
feedbacks, coupled Earth system models need to be
employed. In such models, all components of the system, i.e. atmosphere, ocean, land surface and sea-ice
are fully prognostic, and include not only descriptions
of how energy, momentum, and water are cycled
between these reservoirs, but also how carbon and
other important biogeochemical constituents, such as
nitrogen, are transported and transformed. In the last
10 years, several such models have been developed,
and they have formed an important contribution to the
4th assessment report of IPCC (Denman et al. 2007).
Such models are typically forced with a prescribed set
of CO2 emission scenarios, and the model itself then
determines what fraction of the emitted CO2 stays
in the atmosphere, and what amount of warming
corresponds to the resulting increase in this greenhouse
gas (Friedlingstein et al. 2006).
Roy et al. (2011) recently conducted an intercomparison of four such Earth system models and
investigated how the net ocean CO2 uptake was altered
in response to increases in atmospheric CO2 and
global warming. Specifically, a linear sensitivity analysis was performed, where they represented the net
oceanic CO2 uptake as a sum of a CO2-driven part, and
of a temperature-driven part, i.e.,
Int ðFas net Þ dt ¼ γ ΔT þ β ΔCO2
where Fasnet is the net ocean CO2 uptake, ΔT is the
change in global mean temperature and ΔCO2 is the
change in atmospheric CO2, and where γ is the temperature sensitivity and β that for CO2 (Friedlingstein
et al. 2006). Figure 5.14 reveals that all values of β are
negative, i.e. that the oceanic CO2 uptake increases as
atmospheric CO2 increases. Small differences in the
responses are related to differences in the buffer factor
and differences in the age structure of the water that
resides at the surface (Gruber et al. 2009; Roy et al.
2011). Regions where waters upwell that have not been
in contact with the atmosphere for several decades and
more have a large relative deficit with regard to anthropogenic CO2 and hence have a high tendency to take it
up from the atmosphere. Figure 5.14 also shows that

most regions have a positive γ, i.e. that climate change
decreases the uptake of atmospheric CO2. An important
exception are the high-latitude oceans, especially the
Southern Ocean and the Arctic, where global warming
tends to increase the uptake. These differential patterns
are a result of the complex interactions occurring
between ocean physics (primarily warming and vertical
stratification) and ocean biology working primarily on
the natural carbon cycle. Although these results represent multi-model means, the robustness of the details in
these results is not yet well established. Nevertheless,
the general tendency is clear. Climate change will tend
to make the ocean carbon sink weaker (Gruber et al.
2004; Denman et al. 2007).

5.1.3.2 Regional Perspectives from
High-Resolution Modeling
A disproportionate fraction of the air-sea fluxes of
climatically relevant gases (e.g. CO2, N2O) is thought
to occur in coastal and continental shelf regions even
though they cover only about 7 % of the global ocean
surface area. However, processes on continental shelves
are not well described by global and basin-wide models,
primarily because these models do not resolve the
smaller scales relevant for shelf and coastal processes.
Instead high-resolution regional models are nested
within larger scale, state-of-the-art operational models
such as MERCATOR (Bahurel et al. 2006) and
HYCOM (Chassignet et al. 2007) and are used to
quantify air-sea fluxes in these regions and to improve
our understanding of the underlying mechanisms. For
example, drivers of air-sea CO2 fluxes for the wide,
passive-margin shelves of the western North Atlantic
were studied by Fennel et al. (2008), Fennel and
Wilkin (2009) and Previdi et al. (2009) and for the
semi-enclosed North Sea, a marginal sea in the eastern
North Atlantic, by Prowe et al. (2009) and Kühn et al.
(2010). Lachkar and Gruber (2013) recently investigated the air-sea CO2 fluxes in the Canary and
California Current Systems.
On the western North Atlantic shelves, where
exchange between shelf and open ocean waters is
restricted by a pronounced shelf-break front, coupling
between biogeochemical processes in sediments and
in the overlying water column was found to be highly
relevant for air-sea fluxes of CO2 (snapshots of the airsea gradient in partial pressure are shown in Fig. 5.15).
For example, sediment denitrification (the anaerobic
remineralisation of organic matter which produces

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis

273

Fig. 5.14 Separation of the
regional response of
integrated air-sea CO2 fluxes
into a CO2 (a) and a
temperature (b) driven
component. Data are for the
period from 2010 until 2100
and represent the multi-model
mean (From Roy et al. (2011)
# American Meteorological
Society. Used with
permission)

nitrogen gas, N2) leads to decreases in primary production and increases in alkalinity, both of which alter
air-sea fluxes of CO2 (Fennel et al. 2008). Furthermore, the restricted exchange of shelf water with the
open ocean prevents an efficient export of shelfgenerated organic carbon to the deep ocean (Fennel
and Wilkin 2009). Previdi et al. (2009) investigated
interannual variations in air-sea fluxes of CO2 and
found differences in the Middle Atlantic Bight to be
driven mostly by changes in wind stress while
differences in the Gulf of Maine were due to changes
in sea surface temperature and new production.
In the North Sea, which is seasonally stratified in its
northern part and year-round tidally mixed in its southern part, pronounced spatial differences exist in terms
of air-sea fluxes of CO2. In the stratified northern part
there appears to be net uptake of CO2 from the atmosphere driven in large part by biological processes
including the overflow production of semi-labile

dissolved organic matter in summer. In contrast, the
tidally mixed southern part is a weak source of CO2 to
the atmosphere (Prowe et al. 2009). Interannual
variability of air-sea CO2 fluxes in the North Sea
appears to be driven by variability in atmospheric
forcing and river inputs (Kühn et al. 2010).
Continental shelf regions are heterogeneous with
respect to air-sea fluxes and regional differences result
from a diversity of characteristics and mechanisms.
This makes it hard to scale up from individual regions
to global estimates. Since coastal regions are of most
direct relevance for human activities and most directly
subjected to many human perturbations (e.g. riverine
and atmospheric inputs of nitrogen), regional modeling
studies will continue to play an important role.
In eastern boundary upwelling regions, the net airsea CO2 exchange is governed by a zone of intense
outgassing in the nearshore region, and a region of
marginal outgassing to actual uptake further offshore,

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5 Perspectives and Integration in SOLAS Science

Fig. 5.15 Snapshots of simulated pCO2 difference between
atmosphere and surface ocean (positive values indicate uptake
of atmospheric CO2) for the western North American shelves

(Reproduced from Fennel and Wilkin 2009 by permission of the
American Geophysical Union)

making the entire region a small sink to a small source
(Lachkar and Gruber 2013; Turi et al. 2013). The zone
of intense outgassing is a consequence of the nearshore
upwelling of waters rich in respired CO2, creating
strongly supersaturated conditions when these waters
reach the surface. As these waters are rich in nutrients
as well, the upwelling stimulates a strong growth by
phytoplankton, creating a strong drawdown of DIC and
consequently also of CO2. Overall there appears to be a
balance between upwelling-driven outgassing and biologically driven uptake, with preformed concentrations
also playing a role (Hales et al. 2005).

or empirical model of oceanic/atmospheric transport,
matches as closely as possible the tracer observations.
Mathematically, the match is most often quantified by
a ‘least squares’ cost function minimisation.
The oceanic transport inversion was introduced by
Gloor et al. (2001) and Gruber et al. (2001). Based on
inorganic carbon observations from throughout the
ocean, the first set of CO2 fluxes was estimated by
Gloor et al. (2003). As inorganic carbon is not only
changed by air-sea exchange but also by marine photosynthesis and remineralisation, nutrient data are
needed to remove these biological influences from
the data (C* method, based on Redfield ratios between
biological carbon and nutrient changes). Moreover,
the portion of carbon recently injected into the ocean
following the anthropogenic CO2 rise can be split off
from the data (e.g. using the ΔC* technique, Gruber
et al. 1996), allowing separate estimates of natural and
anthropogenic air-sea CO2 exchange.
As oceanic transport proceeds on long time scales,
the ocean inversion can estimate the mean spatial
pattern of air-sea exchange, but not its seasonal or
interannual variability. Results are also affected by
errors in the modelled transport, though this can partially be taken into account by considering ensembles
of transport models (Mikaloff Fletcher et al. 2006,
2007; Gruber et al. 2009).
The atmospheric transport inversion was pioneered
by Bolin and Keeling (1963), and later formalised by

5.1.3.3 Inverse Modelling
The aim of inverse modelling is to estimate the air-sea
exchange fluxes of CO2 (or other trace gases) on the
basis of suitable data sets. This requires some model
to quantitatively link the measured quantity and the
CO2 fluxes. Then, state variables or parameters of the
model can be fit to the data by ‘inverse methods’.
A prototypical example uses measurements of
tracer abundance within the ocean or the atmosphere,
respectively. This ‘transport inversion’ method is
based on the fact that the spatial and temporal patterns
of air-sea exchange, being transported and mixed
away, lead to spatial gradients and temporal changes
in the oceanic/atmospheric tracer field. Air-sea fluxes
can thus be estimated from the condition that their
corresponding tracer field, as simulated by a numerical

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis

Newsam and Enting (1988). Presently, more than 100
atmospheric measurement stations have been operated
by many institutions, providing weekly or even hourly
CO2 mixing ratio time series, some of which extend
for several decades. These data have been used to
estimate the spatial patterns of CO2 exchange (e.g.
Gurney et al. 2002) or their seasonal and interannual
variability (e.g. Bousquet et al. 2000; Rödenbeck et al.
2003; Baker et al. 2006, and many others).
However, the atmospheric CO2 mixing ratios do
not only reflect ocean–atmosphere exchanges, but
also terrestrial and anthropogenic fluxes. Due to the
diffusive nature of the atmospheric flow, the CO2 data
from the relatively few locations do not provide
enough information to fully separate land and ocean
fluxes. The dominance of the terrestrial signals in the
atmospheric records thus largely obstructs the estimation of ocean–atmosphere exchange. Several studies
therefore rely on Bayesian prior estimates of ocean
fluxes, most often based on the flux climatology of
Takahashi et al. (2009) calculated from measurements
of CO2 partial pressure (pCO2) and a gas exchange
parameterisation. In some cases, Bayesian priors are
derived from results of ocean transport inversions;
Jacobson et al. (2007) formalised this into a joint
ocean–atmosphere inversion. In all these cases, however, air-sea fluxes are mainly constrained by the oceanic data from most of the globe. An exception is the
Southern Ocean, where multi-decadal trends in the
ocean–atmosphere CO2 exchange have tentatively
been detected from atmospheric data (Le Quéré et al.
2007).
Besides oceanic or atmospheric transport
inversions, recent studies use measurements of CO2
partial pressure (pCO2) in an inverse context. Valsala
and Maksyutov (2010) employed a tracer transport and
biogeochemical model, and inversely adjusted the
inorganic carbon concentration to match the pCO2
observations. Ongoing studies involve neural
networks, using inverse methods to ‘learn’ the
relationships between pCO2 and oceanic state
variables available from ocean reanalysis projects;
these relationships can then be applied to calculate
the pCO2 field at any location and time. Other ongoing
studies employ simple diagnostic models of the biogeochemistry in the oceanic mixed layer, estimating
ocean-internal carbon sources and sinks.
A common challenge of all these pCO2-based studies is the need to parameterise air-sea gas exchange,

275

involving substantial uncertainties in its formulation
as well as in the driving wind fields (see Chap. 2). This
problem may be solved by combining the pCO2
approach with transport inversions, which do not
depend on gas exchange parameterisations.
Further information on air-sea CO2 fluxes may be
obtained from measurements of other tracer species
that share source and sink processes. For example,
biological processes do not only involve uptake or
release of carbon, but also of oxygen. Measurements
of the small changes in the atmospheric oxygen
content have been used in an atmospheric transport
inversion (Rödenbeck et al. 2008) to infer interannual
variations in the biogeochemistry of the tropical
oceans. Also remote-sensing data offer potential to
constrain air-sea CO2 fluxes. On the one hand,
measurements of the atmospheric CO2 column
mixing ratios (see Sect. 5.1.2.6) with much higher
spatial density (albeit lower precision) than the surface stations, may allow atmospheric transport
inversions to better separate land and ocean fluxes.
Satellite observations of ocean surface properties,
such as ocean colour (see Sect. 5.1.2.3) reflecting
chlorophyll a content, can also constrain inverse
models of ocean productivity as part of the CO2
exchange.

5.1.3.4 Conclusions
The scope and ambition of modeling is rapidly
expanding. For example, exponentially increasing
computational power now permits scientists to
simulate global and regional models at ever increasing
resolution and over longer periods. The complexity of
the models and of their coupling is rapidly increasing
as well. While these two developments provide many
fascinating new opportunities, they also come with
certain risks. Our level of understanding of the results
of high-resolution complex models tends to develop
less rapidly, creating increasing gaps between our
ability to model a system and our ability to fully
decipher why a particular model produces a particular
result (e.g. Anderson 2005). A second risk is that
the observations and experiments that challenge the
models are also not increasing as rapidly as the model
development is being pushed forward. This requires
sustained efforts, also in the interests of the modeling
community, to maintain and expand oceanographic
observations.

276

5.1.4

5 Perspectives and Integration in SOLAS Science

SOLAS/COST Data Synthesis Efforts

The very nature of data collection at sea leads to
relatively small-scale cruises of necessarily limited
spatial/temporal scope. At an international level, the
elevated resource requirements and organisational
difficulties associated with large-scale research
campaigns further impede coordinated data collection
efforts. Considering this, it is unsurprising that oceanic
data coverage is fragmentary. As set out within the
Memorandum of Understanding for COST Action 735
and the aims of SOLAS Project Integration, a major
Earth System Science challenge is to translate the
findings of these research campaigns into large scale
datasets and climatologies that help improve our
understanding of global concentrations and air/sea
fluxes. Data paucity calls for centralising all concentration measurements of relevant SOLAS parameters
into one common database to secure their longevity.
The tasks seemed dantean because of two major
issues, central to the usability of a database, to be
solved as a pre-requisite. One is intercomparing
measurements originating from different cruises/
instruments/scientists, and another is establishing
rigorous quality control and flagging of the data.
To get global concentration fields, methods to inter/
extrapolate in space and time are used introducing
uncertainties in the resulting concentrations. Including
the uncertainty in gas exchange velocity parameterisation will yield substantial uncertainties in air/sea flux
calculations. Any progress in the future in this velocity
parameterisation will allow recomputation of fluxes
from the existing global concentrations data sets.
This section presents some specific initiatives carried out within the COST Action 735 and SOLAS
Project Integration framework trying to highlight in
each case the difficulties encountered, the solutions
found and the recommendations proposed.

5.1.4.1 MEMENTO (MarinE MethanE and
NiTrous Oxide) Database
The assessments of radiative forcing from long-lived
greenhouse gases such as nitrous oxide (N2O) and
methane (CH4) depend on an accurate synthesis of
the global distribution and magnitudes of N2O and
CH4 sources and sinks (see Chap. 3 of this book).
Atmospheric dry mole fractions of N2O and CH4
have been routinely available since the late 1970s
and they benefit from a highly coordinated global

monitoring network (see e.g. Prinn et al. 2000). In
stark contrast, although measurements of marine
N2O and CH4 date back over almost four decades,
they lack the temporal continuity and spatial coverage
of their atmospheric counterparts. This is because
almost all of them relate to single cruises or at best
coordinated cruise programmes of rather limited
scope. In large part this reflects the high costs and
organisational difficulties of mounting large coordinated oceanographic expeditions, especially at the
international level. Not surprisingly, oceanic data
coverage remains fragmentary.
Seasonal and interannual variability, spatial heterogeneity in coastal areas and gradients between coastal
and open ocean areas all impact the quality of marine
emission estimates for N2O and CH4, compounded by
the limited overall data coverage (Bange et al. 2009).
A cost effective way of using all existing N2O and
CH4 measurements, despite the data limitations, is
to establish a global database to improve the value
of marine emissions estimates. To this end the
MEMENTO (MarinE MethanE and NiTrous Oxide)
database has been launched as a joint initiative between
SOLAS and COST Action 735. MEMENTO’s major
aims are to:
• Collect available N2O and CH4 data (i.e. underway
and depth profile data) from the global ocean
including coastal areas. To date 129,012 N2O and
21,003 CH4 measurements have been collated;
• Archive the data in a database with open access for
the scientific community;
• Compute global fields of dissolved N2O/CH4
concentrations as well as air-sea fluxes in both the
open and coastal ocean, and;
• Publish the database and the derived flux data.
Once all existing datasets have been incorporated
into MEMENTO it will rapidly become a valuable tool
for identifying regions of the world ocean that should
be targeted in future work to improve the quality of the
emissions estimates. The locations of the data archived
so far in MEMENTO are shown in Fig. 5.16. Further
information on MEMENTO can be obtained from
https://memento.ifm-geomar.de/.

5.1.4.2 HalOcAt (Halocarbons in the Ocean
and Atmosphere)
Compilation of existing air and seawater
measurements of halogenated hydrocarbons into
the project HalOcAt (https://halocat.geomar.de/) was

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis

N2O

CH4

b

80ºN

80ºN

40ºN

40ºN

Latitude

Latitude

a

0º

0º

40ºS

40ºS

80ºS

80ºS

180ºW

120ºW

60ºW

0º

60ºE

120ºE

Longitude

277

120ºW

60ºW

0º

60ºE

120ºE

180ºW

Longitude

Fig. 5.16 Locations of N2O (a) and CH4 (b) measurements archived in MEMENTO (Version as of October 2012)

initiated through SOLAS/COST Action 735 in May
2009 and is still ongoing. Global oceanic and atmospheric halocarbon data with an emphasis on shortlived brominated and iodinated trace gases from the
surface ocean and lower atmosphere have been collated in order to obtain concentration fields and air-sea
fluxes. At the time of writing, the database contains
191 contributions, comprising roughly 55,400 oceanic
and 476,000 atmospheric data points from depth
profiles in the surface ocean and a range of heights in
the lower atmosphere. Although predominantly
bromoform measurements, these data represent 19
different halocarbon compounds and were collected
between 1989 and 2011 during research cruises, aircraft missions and coastal studies from all over the
globe. The database stems from active submission of
data, literature review and publicly available data.
Quality checks as well as precision and error
estimates were often missing from the contributions,
coinciding with a large range in reported concentrations. Based on the current available scientific knowledge it is unfortunately not possible to retrospectively
identify which data is ‘correct’ or ‘incorrect’. Especially for oceanic data, recalibration is highly unlikely
and the database is too small to perform a statistical
evaluation of the data quality or to take advantage of
utilising datasets that physically crossed paths for
inter-calibration. There is some hope that it might
be possible to check the consistency of atmospheric

data from time series stations, and the community
is already assessing inter-calibrations for some
compounds (Jones et al. 2011). This information will
be incorporated into the HalOcAt data analysis.
In spite of the challenges described above and the
paucity of data on a global scale it has been possible to
construct meaningful global concentration fields on a
1
 1
grid. Data interpolation, statistical regression,
analysis of published distributions and information
about coastal and biological sources including relation
to physical and biogeochemical constraints has
enabled the calculation of plausible compound
distributions (Ziska et al. 2013). From these data,
global climatological air-sea fluxes have been calculated for certain halocarbons such as CHBr3 (see
Fig. 5.17) and these fall within the published ranges
of top down and bottom up emission approaches
(Montzka and Reimann 2011). This novel data set
now facilitates the use of more realistic data based
emission fields to drive atmospheric chemistry models
and enables the community to revise current emission
scenarios.
Future work on and with the database will focus on
its further enlargement, quality control and publication
of the data products (database and climatologies
of concentrations and ocean/atmosphere exchange).
Although simple correlations between oceanic halocarbon concentrations and biological, physical and
chemical parameters have not yielded noteworthy

278

5 Perspectives and Integration in SOLAS Science

Fig. 5.17 Global air-sea flux
climatology of bromoform in
pmol m2 h1 including
surface oceanic bromoform
data points (white points)
extracted from the HalOcAt
database

results to date, the construction and evaluation of
proxies and parameterisations for concentrations and
fluxes will continue to be explored.

5.1.4.3 DMS-GO (DMS in the Global Ocean)
In 1999 Jamie Kettle and Meinrat Andreae (Kettle et al.
1999) initiated a collation of available seawater DMS
measurements into a global database and subsequently
created a climatology of surface ocean concentrations
based on the data available. However, this important
contribution to the literature (over 285 citations to date)
was limited by the number of data points (15,617) of
surface seawater DMS concentration and the large
areas of the global ocean that lacked data, requiring
crude estimates to be made. In the years since, many
more DMS measurements have been made and in
2010 the DMS-GO (DMS in the Global Ocean) team
initiated a call to update the database and climatology.
In April 2010 the DMS database had grown to approximately 47,000 data points (Fig. 5.18), and these were
used to construct an updated surface ocean DMS concentration climatology.
The construction of the climatology involved
data mapping, extrapolation and interpolation onto
biogeochemical provinces, smoothing, and data
re-assimilation (see Lana et al. 2011 for details). The
first step was the transformation of DMS concentration
data into a mean value using a delineation of 1
 1

grid squares. The second step was also based exclusively on the DMS measurements – a background
DMS field was created using biogeochemical ocean
provinces (Longhurst 2007), which have been defined
using spatial similarities in the chemistry, physics and

biology of the world ocean. The background field was
created for each province for each month using an
average DMS concentration. If there was a temporal
paucity of data, the gaps were filled by applying an
interpolation to the time series of the province, or in
some cases by substituting with a scaled pattern of
another province with similar biogeochemical
characteristics. After these two steps, the construction
of the climatology was based on the combination of
1
 1
pixels and the background field. The new
DMS climatology along with an estimate of the uncertainty (upper and lower concentration bounds) is
available online for free download on the SOLASBODC server (http://www.bodc.ac.uk/solas_integration/
implementation_products/group1/dms/).
The global sea-to-air DMS flux estimate (between
17.6 and 34.4 Tg S year1) using the new climatology
has improved understanding of ocean–atmosphere
DMS emissions in the global sulphur cycle and
suggests an approximate 17 % increase in flux c.f.
the previous climatology, mainly due to the inclusion
of data from new areas of the ocean (e.g. the Indian
Ocean). The updated distribution of global DMS
measurements and the new DMS climatology will
be useful for the validation of future ocean biogeochemical models. In addition, the continuous climatological surface ocean DMS field will likely be used
as an input variable for atmospheric chemistry
models. However, the updated DMS database has
also highlighted areas of the global ocean that require
more measurements and this is powerful information
in the context of recent developments in automatic
and semi-automatic DMS analysis systems. As new

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis

279

Fig. 5.18 Locations of global
surface ocean DMS
measurements (approx.
47,000 as of April 2010) for
all months of the year (white)
plotted over the mean
climatological DMS
concentration (in nM) from
the same data (Data is
available for download at
http://saga.pmel.noaa.gov/
dms/)

techniques become more widely-used the challenge
will be ensuring data inter-comparability as the database swells in size (see Bell et al. 2011).

5.1.4.4 The Surface Ocean CO2 ATlas (SOCAT)
The Surface Ocean CO2 ATlas (SOCAT) is a global
synthesis of surface ocean carbon dioxide (CO2)
measurements collected on research vessels, voluntary
observing ships and moored as well as drifting
platforms (http://www.socat.info/). The first public
release of SOCAT (version 1.5) took place at
UNESCO in September 2011. Version 1.5 consists of
6.3 million quality controlled, uniform format and
recalculated surface water fCO2 (fugacity of CO2)
data from 1851 voyages in the global ocean between
1968 and 2007 (Fig. 5.19).
SOCAT was initiated at the Surface Ocean CO2
Variability and Vulnerabilities (SOCOVV) workshop
in 2007 (IOCCP 2007). At that time surface ocean
CO2 data were archived in a wide range of formats
and at numerous sites around the world, each with its
own rules for access, and documentation of the data
was frequently poor. This made it virtually impossible
to generate comprehensive data synthesis products for
large scale or long-term studies. To alleviate this situation the international ocean carbon community

decided to initiate the Surface Ocean CO2 Atlas as a
community driven effort to assemble, harmonise,
quality control and document the surface ocean CO2
data into one open access database. While the outlook
of the SOCAT products in version 1.5 has closely
followed this original vision, as documented in the
2007 meeting report, the community underestimated
the amount of work involved in the first release of
SOCAT. It took 4 years of hard work by marine carbon
scientists around the world to assemble and quality
control the first version of SOCAT.
Two SOCAT products are available:
1. A global data set of recalculated surface water
fCO2 values in a uniform format, which has
undergone 2nd level quality control;
2. A global, gridded product of monthly mean surface
water fCO2, with no temporal or spatial
interpolation.
The above SOCAT products can be accessed at
the Carbon Dioxide Information Analysis Center
(CDIAC, http://cdiac.ornl.gov/oceans/) on a global
and basin wide level. In addition to the concatenated
data, products are all recalculated and available at
the ICSU World Data Centre PANGAEA – Data
Publisher for Earth & Environmental Science (http://
www.pangaea.de/) as individual cruise files. Those files

280

5 Perspectives and Integration in SOLAS Science

Fig. 5.19 Spatial distribution
of recalculated fCO2
measurements of SOCAT
Version 1.5 (Courtesy of
NOAA/PMEL), http://www.
socat.info/

are in a uniform format and give access to the detailed
metadata, input data and recalculated fCO2 values.
SOCAT tools include an online data visualisation and
analysis tool (PMEL’s Live Access Server) and desktop
tools like an Ocean Data View Collection. All can be
accessed via http://www.socat.info/.
The methods in SOCAT are fully documented
(Pfeil et al. 2013; Sabine et al. 2013). The products
and individual cruises are citable through Digital
Object Identifiers (DOI-s). Cruises in SOCAT have
an Expocode (a code containing Cruise ID, Year,
Month and Day of Cruise), a DOI and detailed information on the data, so-called metadata (e.g. investigator name, vessel, methods, calibrations). Every data
point in SOCAT has a link to its cruise file as archived
at PANGAEA via the DOI string in the data file.
Preparations for the second version of SOCAT are
underway. Regular releases of SOCAT are planned,
e.g. every 1–2 years from the 3rd release onwards.
Data can be submitted to CDIAC (http://cdiac.ornl.
gov/oceans/submit.html) for inclusion in future
SOCAT releases. Prompt data and metadata submission as well as citation of SOCAT in publications are
deemed essential for the future existence of SOCAT.
Automation of data and metadata submission and quality control as well as inclusion of additional variables
(e.g. atmospheric CO2 and calculated fCO2 from discrete measurements) is currently being discussed.
SOCAT meets the needs of the global carbon
community by making high quality surface water fCO2
data available for addressing major scientific questions
in the field of global change. The large number of
visitors to the SOCAT website (> 1,000 hits/month)
demonstrates the intense interest in these carbon

synthesis products. It is anticipated that SOCAT will
be used in high profile scientific analyses informing
policy decisions by governments and intergovernmental organisations. We kindly ask colleagues to inform
SOCAT (submit@socat.info) of recommendations for
and publications that use SOCAT data products.
Updates on SOCAT will be posted on http://www.
socat.info/.

5.1.4.5 Aerosol and Rainwater Chemistry
Database
The development of the aerosol and rainwater chemistry database is motivated by the desire to provide a
repository for datasets collected from ships at sea
because there had previously been no facility for
collecting such data, while databases for land-based
measurements are already well established (e.g. the
World Data Centre for Aerosols; http://wdca.jrc.it/
data/parameters/data_chem.html). The database has a
deliberately broad focus in terms of the chemical species accepted, with nutrients, trace metals and organics
being primary targets, but other data equally welcome.
So, for instance, iron data may be a key interest for the
database, but submitters of iron data are encouraged to
also supply any other chemical data associated with
those measurements. This ancillary data may be of use
to the iron community as well as in other, unrelated
research fields.
Data has been collated at BODC since 2007 and a
data portal was added to the site in 2011 (http://www.
bodc.ac.uk/solas_integration/implementation_products/
group1/aerosol_rain/). At the time of writing the
database has ~ 1,300 aerosol and ~ 80 rainfall data
points, most directly downloadable from the website

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis

281

80
60
40

Latitude

20
0
−20
−40
−60
−80
−150

−100

−50

0

50

100

150

Longitude

Fig. 5.20 Distribution of aerosol sample locations contained in the database as of November 2011

(Fig. 5.20). The site also contains links to other on-line
databases holding related data and seven datasets of
aerosol chemistry obtained from island sites which are
not housed in any other publicly available database.
Baker et al.’s study (2010) is a good example of the
potential benefits attainable from large ship-based
datasets. Aerosol and rain data obtained during 12
cruises was used to estimate atmospheric nitrogen
inputs to the Atlantic Ocean. This was achieved partly
through a process of classifying the aerosol samples
in their database according to which source regions
they had recently passed over, and marrying these
classifications, and their chemical characteristics, to
an air mass climatology for the Atlantic basin. The
applicability of this approach is strongly determined
by the availability of data, and its spatial and seasonal
distribution, as well as the seasonal variation in atmospheric source strength. For example, a subtly different approach has been required for recent attempts to
estimate atmospheric iron inputs to the Atlantic
(Powell et al. in preparation).
To date, the COST Action 735 database contains
only chemical and very limited metadata such as sample positions, but no other potentially valuable
resources such as air mass back trajectories. All of
the studies discussed above used data obtained from
only one research group. Similar efforts using data

from multiple sources will encounter problems
associated with lack of inter-comparability, particularly in ‘historical’ datasets. The marine aerosol community is only just starting to address these problems
through an inter-comparison exercise led by the
GEOTRACES programme, with active participation
from within SOLAS (see http://www.geotraces.org/
for more information).

5.1.4.6 A Data Compilation of Iron Addition
Experiments
In the last 3 years datasets from ten mesoscale iron
enrichment experiments have been brought together
through a SCOR working group (WG 131) entitled
‘The Legacy of mesoscale ocean enrichment
experiments’. This data collation has then been
transformed into a relational database (the Iron Synthesis Database) by the database management team
(Cyndy Chandler & Steve Gegg at BCO-DMO) based
at Woods Hole Oceanographic Institution (WHOI) in
the USA (http://bcodmo.org/data). The database has
been structured using project and data directories,
metadata, the status of data rescue for each project,
and tools that are available to discover and download
data of interest (see Fig. 5.21).
Datasets from the following experiments reside
at the BCO-DMO site: IronEX I, IronEX II,

282

5 Perspectives and Integration in SOLAS Science

Fig. 5.21 Annual surface
mixed-layer nitrate
concentrations in units of
μmol liter1 with approximate
site locations of iron addition
experiments. Fe addition
experiments: white crosses,
Fe natural enrichment
experiments: red crosses, and
joint Fe and P enrichment
study of the subtropical LNLC
Atlantic Ocean: green cross
(Figure reproduced with kind
permission from Science. See
Boyd et al (2007) for full
details)

SOIREE, SAGE, SEEDS, SEEDS II, SOFEX North,
SOFEX South and SERIES. The dataset from the
Eisenex Southern Ocean study is held at the German
PANGEA database (http://www.pangaea.de/). No
comprehensive datasets are presently available from
the EifEX Southern Ocean experiment, although some
papers have been published (e.g. Hoffmann et al.
2006). The datasets within the relational database
consist of physical (e.g. mixed layer depth), chemical
(e.g. dissolved iron concentrations), biological
(e.g. net primary production rates), optical (e.g. incident irradiance) and in some cases meteorological
(e.g. wind speed) parameters.
The ten experiments all straddle High Nitrate Low
Chlorophyll (HNLC) regions from polar to tropical
HNLC waters and hence provide a robust test for
modeling studies and comprehensive details for synthetic studies. For example, datasets concerning the
production of DMSP and its subsequent transformation to DMS are available for multiple experiments
including SOIREE and SERIES. Such datasets can be
readily related to a wide range of environmental
properties such as mixed layer depth, incident irradiance, and microbial rate processes. The interplay of
these factors has been proposed as important in setting
DMS concentrations following iron enrichment
(Le Clainche et al. 2006). Data are also available
concerning changes in the concentration of other biogenic gases following iron enrichment, and in some
cases their efflux and fate.

The main challenge in setting up this relational
database was obtaining datasets from some of the first
in situ experiments – such as IronEX I and II – and
from some of the more recent experiments. In the case
of the early studies from almost two decades ago,
some of the data was on very old laptops. Alternatively, for the most recent experiments, manuscripts
are still being written and published and at this point
there was an understandable reluctance to contribute
to the public domain.
So what is the future for large-scale Fe addition
experiments and what else would we really wish to
better understand? Some scientists have called for
larger experiments (100 km length scale) to overcome some of the artefacts such as dilution with
surrounding HNLC waters, but these bring enormous
logistical challenges with them. In the conventional
10 km length scale experiments conducted so far, it
takes one ship 24 h to add the iron and SF6 (tracer) to
the 100 km2 area of ocean in a manner that results in
a coherent patch of tracer/iron. So how many ships
would be required to enrich 10,000 km2? Using
planes to ‘top-dress’ the ocean would be equally
problematic (Boyd 2008). The most promising
research areas for the future are the study of naturally
high iron regions where phytoplankton blooms occur,
such as around the Crozet Islands in the Southern
Ocean, and conducting medium scale in situ
enrichments, or using mesocosms, in the generally
quiescent oligotrophic waters of the lower-latitude

5.1 Perspectives: In Situ Observations, Remote Sensing, Modelling and Synthesis

ocean to study the environmental controls on different groups of diazotrophs.

5.1.4.7 Conclusions
The initiatives discussed in this section represent the
spectrum of integration activity within SOLAS.
MEMENTO and the aerosol and rainwater chemistry
databases are still in relative infancy, just beginning
to collate data. In contrast, the relatively mature
DMS database has recently been substantially updated,
tripling the number of data points and re-estimating the
climatological DMS flux to the atmosphere (Lana et al.
2011). Ultimately, the SOCAT project is leading the
field, with a lot of data points collated from the global
ocean and well established procedures for treating the
data and archiving it in a responsible manner. Its progress and willingness to continue pushing forward the
boundaries of data collation and synthesis are admirable. Meanwhile, the synthesis of data and information
from the various large-scale iron addition experiments
has been invaluable.
Past experience suggests that such data collation
exercises often prove their worth many times over,
informing the scientific community in ways that were
previously unforeseen. For example, the original DMS
database attempted to find correlations between in situ
concentrations and other ancillary parameters such as
chlorophyll a (Kettle et al. 1999). Despite a relative
lack of success at the time, understanding of the
reduced sulphur cycle has benefitted enormously
from this database – much of the work carried out
after the initial data collation period has arguably
proven to be much more fruitful. Without attempting
to predict the future, a similar scenario appears to exist
with the HalOcAt database, with initial examinations
of the data suggesting no obvious correlating factors;
only time will tell. . .
What challenges remain for the integration of
SOLAS data? Success depends in part on the scope
of the project. Based on the projects outlined within
this chapter, data collation initiatives must overcome
the following issues common to many of the
databases:
Community engagement. The number of scientists
willing to be a part of any initiative and who
actively engage with the process can have a significant impact on the scale of data collation and the
degree of community involvement. Engaging the
community to deliver to data bases is an extremely

283

time consuming effort which requires strong motivation and determination.
Support for the project. This can take the form of a
website, advice on data management and/or project
personnel, often data managers. Existing projects
that have had resources for a data manager have
benefitted enormously from this. Data management
should be an intrinsic part of any project.
Approaches to data paucity. Whether extrapolating
to biogeochemical provinces (e.g. DMS-GO),
performing deep ocean cross over checks (e.g.
SOCAT) or using aerosol chemical composition
data to characterise different air masses within an
Atlantic-scale dataset, these techniques are very
important for ‘filling’ data gaps and estimating
global fluxes. The techniques need to be adequately
described stating clearly the assumptions being
made and resulting uncertainties.
Data intercomparability. Arguably, this is one of the
major challenges facing many datasets within
SOLAS science. Collating a large-scale dataset is
almost useless if the data are subsequently shown
to be incomparable. An obvious way forward is
to carry out regularly, at the international level,
measurement intercalibration exercises. Some
work has begun on the halocarbons (e.g. Butler
et al. 2010; Jones et al. 2011) and for DMS (Bell
et al. 2011), while GEOTRACES has plans to
address this important issue for trace metal aerosol
and rainwater chemical composition. SOCAT in
particular has spent a lot of time on such issues,
and it was noteworthy that in their 2007 meeting
report the community recognised that they had
probably underestimated the amount of work
involved for the first release of SOCAT.
Data legacy. Managing large data sets is a challenging
task. It takes dedicated efforts to set up efficient online
processed data delivery (e.g. metadata added and formatted in an internationally recognised format), to
mount them on an open access portal, to provide
archiving and data enhancement (e.g. post processing,
climatology enhancement), and to provide a data portal for archived data. International and national data
centres should be the natural repositories to ensure
long-term security of our community efforts.
The success of SOLAS Integration and the production
of global databases depends on a concerted effort from
the international atmospheric and marine science
communities to not only collect data through extensive

284

5 Perspectives and Integration in SOLAS Science

field campaigns, but also to engage with and support
projects such as those outlined above.

5.2

Examples of SOLAS Integrative
Studies

This section of the chapter offers a selection of
SOLAS integrative studies. Integration implies a
synergistic use of cross-cutting tools and eclectic
data in both the atmosphere and ocean to address
specific SOLAS science questions. These studies all
contribute to an improved understanding of biogeochemical cycling, the establishment of present-day
climatologies or the closure of global budgets.

5.2.1

DMS Ocean Climatology and DMS
Marine Modelling

5.2.1.1 Global Climatologies Based
on Observations
The need for a global climatology of DMS sea-surface
concentrations was identified more than a decade ago.
The first global database of DMS measurements was
put together by Kettle et al. (1999) and Kettle and
Andreae (2000). At the time, the database compiled of
the order of 15,000 data points, unevenly distributed in
time and space. Since then, the original database has
been growing extensively and has now reached more
than 45,000 data points (see: http://saga.pmel.noaa.
gov/dms/). This on-going extension has been made
possible thanks to the scientific community feeding
the database with new measurements, but also thanks
to the DMS-GO initiative (see Sect. 5.1.4.3) and
SOLAS integration (Surface Ocean Lower Atmosphere
Study, http://www.bodc.ac.uk/solas_integration/).
As discussed in Sect. 5.1.4.3, Lana et al. (2011)
have used this updated database to obtain a new global
climatology of sea surface DMS concentrations
following a modified interpolation approach to the
one used by Kettle et al. (1999) (Fig. 5.22). They
obtained a global annual sea-to-air DMS flux,
estimated at 28.1 Tg S year1 (17.6–34.4), which
represents a global emission increase of 17 % with
respect to previous calculations. Regionally, annuallyaveraged concentrations show rather homogeneous
values, most of them between 1 and 5 nM.

The characteristics of the seasonal cycle, already
shown by Kettle et al. (1999), are mostly confirmed:
maximum concentrations (up to 15–20 nM) are
obtained in summer at high latitudes, in phase with
chlorophyll a. Between 40
S and 40
N, however,
DMS and chlorophyll a do not seem to be in phase,
with high summer DMS concentrations associated with
low chlorophyll a levels, a part of the DMS cycle
which has been referred to as the “summer paradox”.

5.2.1.2 Diagnostic Approaches: Based
on Empirical Correlations
In addition to the climatology described above, several
other approaches have been proposed to estimate
DMS concentrations (Anderson et al. 2001; Simó
and Dachs 2002; Belviso et al. 2004a). These diagnostic approaches are based on empirical relationships
between DMS sea-surface concentrations and some
other variables (e.g. SST, chl a, MLD), derived at a
local scale, and then extrapolated to the global scale.
Anderson et al. (2001) generated monthly global DMS
fields using a relationship between DMS and the product of chlorophyll a, light and nutrient concentration.
Simó and Dachs (2002) proposed a double-equation
algorithm in which chlorophyll a and mixed layer
depth are used to derive DMS sea-surface concentration. Belviso et al. (2004a) subsequently proposed a
non-linear parameterisation to relate DMS concentration to chlorophyll a and an index of the community
structure of marine phytoplankton.
These approaches offer some additional advantages
compared to the use of DMS climatologies solely
derived from in situ DMS measurements. They
make use of additional information (ocean physics and
biogeochemistry) in regions where no DMS
measurements are available. They can be used to study
the variability in time (e.g. interannual variability) of
DMS sea-surface concentrations and emissions if information on ocean physics and biogeochemistry are available. These approaches have been compared in Belviso
et al. (2004b) and their use in Earth System Models in
the context of anthropogenic climate change has been
described in Halloran et al. (2010).
5.2.1.3 Prognostic Modelling: From 1D to 3D
In addition to the diagnostic approaches described
above, a number of prognostic DMS models have
been developed in the last decade. Contrary to diagnostic approaches, these models include an explicit

5.2 Examples of SOLAS Integrative Studies
Fig. 5.22 (a) Annuallyaveraged DMS concentrations
(nM), and (b) latitude-time
(Hovmoller) diagram of DMS
concentrations (nM)
(Modified from Lana et al.
(2011))

285

a

b

description of processes leading to DMS production
and emission.
These models are coupled to marine biogeochemical models that represent plankton ecosystem
dynamics. They have been applied at specific onedimensional vertical column sites (1-D), but also at
the global scale coupled to Ocean General Circulation
Models (3-D OGCM).
Le Clainche et al. (2010) conducted a model
inter-comparison exercise, CODiM, which stands

for Comparison of Ocean Dimethylsulfide Models,
in the frame of the SOLAS science plan and
implementation strategy. They compared nine
process-based models, both local one-dimensional
(1-D) or global three-dimensional (3-D). From that
comparison, their major point is the divergence
among models related to their (in)ability to reproduce
the summer peak in DMS concentrations usually
observed at low- to mid- latitudes. This deficiency
in simulating the summer mismatch between chl

286

5 Perspectives and Integration in SOLAS Science

Fig. 5.23 Changes in DMS emissions at 2  CO2 (in micromol m2 d1) as simulated by a coupled climate – ocean DMS model
(Adapted from Bopp et al. 2003)

a and DMS at low- to mid-latitudes could have
significant implications by reducing simulated global
DMS emissions by up to 15 %. They point towards
processes related to particulate DMSP production
and release as critical pathways not well represented
in prognostic DMS models, e.g. increased DMSP
synthesis in phytoplankton, increased DMS
release from phytoplankton under stress conditions,
light-dependent DMSP to DMS conversion by
bacteria.

5.2.1.4 Examples of Applications
Climate Change
Several diagnostic (Bopp et al. 2003; Gabric et al.
2004) and prognostic (Kloster et al. 2007) DMS
models have been coupled to climate models to
predict future DMS emissions and/or sea-surface
concentrations. Due to the large variety of approaches

used to model DMS emissions, there is still no
consensus among the different models, even for the
sign of evolution of DMS emissions with anthropogenic climate change: in summary, models tend to
simulate either a slight increase or a slight decrease in
global DMS emissions in a warming world. Gabric
et al. (2004) for example predict an increase of DMS
emissions of 14 % for a tripling of pre-industrial atmospheric CO2. Models agree however in projecting large
spatial heterogeneities in future DMS emissions. Bopp
et al. (2003) for example predict an increase in the seato-air DMS flux of 3 % for doubled atmospheric CO2
(Fig. 5.23), but with large spatial heterogeneities (up
to + 30 % in the Southern mid-latitudes).
Iron Fertilisation
Large increases (of up to 400 %) in DMS
concentrations have been documented in some

5.2 Examples of SOLAS Integrative Studies
Volcanic ash deposition

0°

20°

287

40° 60°

80° 100°

120° 140° 160°

180° 160° 140° 120° 100°

80°

60°

40° 20°

0°

Global
low-latitude
wind directions

80°N

higher likelihood

Kasatochi

lower likelihood

60°N

Volcanoes in
Subduction zones
Other tectonic
settings
(e.g. hot spots)

40°N

Miyake-Jima
20°N

Anatahan

0°

Fe-limited regions

20°S

40°S

Tephra frequency
frequent
moderate
minor
none

60°S

80°S

Fig. 5.24 World-map illustrating the distribution of subaerial
(Holocene-) active volcanoes indicated as triangles (highlighted
are the volcanoes cited in the text). Oceanic regions with higher
likelihood of volcanic ash deposition are shown as yellow
regions (Olgun et al. 2011), based on marine sediment core
data, the frequency of tephra layers found in the ocean

sediments (coloured circles) and global wind directions. Volcanic ash deposition can affect marine primary production in much
of the Fe-limited areas that are found in the Subarctic Pacific,
the Eastern Equatorial Pacific and the Southern Ocean (shown
as areas with oblique lines)

Southern Ocean artificial iron addition experiments
(Turner et al. 2004). These results have been used to
suggest that iron fertilisation of the Southern Ocean
could act to increase DMS fluxes from the ocean and
hence amplify the potential cooling due to increased
uptake of carbon (Wingenter et al 2007). Bopp et al.
(2008) used a global biogeochemical model including
a DMS process-based scheme. They showed that
whereas patchy iron fertilisation may stimulate a
short-term increase in DMS due to the initial increase
in productivity, a long-term and large-scale iron
fertilisation could indeed lead to reduced DMS seasurface concentrations and emissions because of an
increase in bacterial consumption rates of DMS.

(Fig. 5.24), which create at least ten explosive volcanic eruptions each year (http://www.volcano.si.edu/).
Recent geochemical and bio-incubation experiments
have shown that volcanic ash rapidly releases sufficient amounts of nutrients into seawater that are potentially bio-available for phytoplankton production and
growth (Frogner et al. 2001; Duggen et al. 2007; Jones
and Gislason 2008; Hamme et al. 2010; Langmann
et al. 2010; Lin et al. 2011; Olgun et al. 2011). Atmospheric impacts of other volcanic products such as
the sulphate aerosols are discussed in Sect. 4.2.3.5 of
Chap. 4.
The North Pacific, especially the subarctic region, is
a high-nutrient, low-chlorophyll (HNLC) ocean region
where phytoplankton growth is known to be limited by
iron (Martin and Fitzwater 1988; Boyd et al. 1996;
Boyd and Harrison 1999) and sporadically by silicate
(Wong and Matear 1999; Whitney et al. 2005). Episodic increases of Asian mineral dust input have been
observed to increase primary production in the North
Pacific (Young et al. 1991). The causal connection
between aeolian iron input and diatom production
over longer time-scales (e.g. Quaternary) was also
indicated by sediment core studies (McDonald and
Pedersen 1999). In the North Pacific, volcanic ash is
one of the major iron sources, due to the high flux of ash

5.2.2

North Pacific Volcanic Ash
and Ecosystem Response

The Pacific Ocean, the largest of the ocean basins, is
encircled by a ring of numerous explosive volcanoes
(the Pacific Ring of Fire) and hosts several hot spot
volcanic islands (e.g. Hawaii) (Fig. 5.24). The North
Pacific is one of the most active volcanic belts in the
Pacific, surrounded by more than 150 active volcanoes
in Kamchatka, the Aleutians and mainland Alaska

288
Fig. 5.25 MODIS-Aqua
image showing the diatom
bloom that has been related to
the volcanic ash-fall during
the Kasatochi eruption on 7th
and 8th of August 2008, with
the volcanic ash plume that
has been transported over the
Gulf of Alaska (coloured
areas show the position of the
plume) (Langmann et al.
2010). The relative increases
in chlorophyll a
concentrations in August 2008
are based on monthly mean
August 2008 minus the
monthly mean in the years
between August 2002 and
2007 (Langmann et al. 2010).
Also shown is the location of
the Fraser River watershed
area related to the discussion
of increased salmon
populations that are likely to
have been positively affected
by ocean fertilisation by the
Kasatochi eruption during
summer 2008

5 Perspectives and Integration in SOLAS Science

60°

- 180°

- 160°

- 140°

- 120°

Fraser River
watershed

Kasatochi
2008
eruption

50°

40°

Gulf of Alaska

500 km

Chlorophyll a difference (mg/m3) (August 2008) - (August 2002-2007)
no data -1 -0.5 -0.3 -0.1 0.1 0.3 0.5 1.0
Position of the ash cloud between 8-11 August 2008 (hours after the erution).
1

15

28

released by active volcanism in the region (Olgun et al.
2011). Deposition of volcanic ash during explosive
eruptions can therefore impact phytoplankton and
marine foodwebs in the North Pacific by releasing
especially iron and other nutrients into seawater.
The first evidence of volcanic enhancement of
marine primary production was related to the eruption
of Miyake-Jima volcano (Japan, Fig. 5.24) in 2000
(Uematsu et al. 2004). The powerful Miyake-Jima
2000 eruption spread out a volcanic plume that formed
ammonium-sulphate aerosols, more than 200 km away
in the oligotrophic western North Pacific, resulting in
an increase in chlorophyll a levels (Uematsu et al.
2004). Similarly, the eruption of Anahatan Volcano
(in the Mariana Islands, Fig. 5.24) in 2003, produced a
bloom-like patch in the western North Pacific a week
after the eruption, evidenced by the MODIS images
(Lin et al. 2011).
Volcanic ash fall during a recent eruption of
Kasatochi volcano (a remote Aleution island,
Fig. 5.25) in August 2008, has also been speculated
to generate a massive diatom bloom in the Fe-limited
eastern subarctic North Pacific (Fig. 5.25). The unusual

38

52

62

76

86

phytoplankton bloom in the Gulf of Alaska started a
few days after the eruption (Hamme et al. 2010;
Langmann et al. 2010). Geochemical experiments
also confirmed that iron released from the Kasatochi
volcanic ash is sufficient to iron-fertilise the surface
North Pacific (Olgun et al. 2013). The bloom area in
the eastern subarctic North Pacific (Fig. 5.25) was
dominated by large diatoms, and a notably high abundance of large copepods (Hamme et al. 2010),
providing a good-quality food-source for the young
salmon in the ocean. Lindenthal (2011) provided
another source of evidence for the fertilisation of the
NE Pacific Ocean by volcanic ash fertilisation from the
eruption of the Kasatochi volcano. Indeed, by using an
ocean biogeochemical model study and this iron
source, he found a good agreement between model
outputs and measured chlorophyll a, nutrient
concentrations, pH and surface ocean pCO2.
Volcanic eruptions are likely to have impacted the
marine foodweb in the North Pacific many times in
Earth’s recent history (Duggen et al. 2010). The 1980
eruption of Mount St. Helens, for example, was
suggested to have fertilised rivers and lakes and

5.2 Examples of SOLAS Integrative Studies

led to an increase in populations of golden algae
(chrysophytes) and diatoms (Smith and White 1985).
Notably, the Kasatochi 2008 eruption has gained further public attention because of its potential impact on
fish populations (Jones 2010; Parsons and Whitney
2012; Olgun et al. 2013).
In late summer 2010, record numbers of sockeye
salmon (estimated 35 million fish) returned back
to Fraser River (Fig. 5.25) 2 years after the large
Kasatochi eruption (Jones 2010). It has been speculated
that the Kasatochi eruption provided rich food
conditions (zooplankton) by enhancing marine primary
production (Hamme et al. 2010; Langmann et al. 2010),
and increased the marine survival of sockeye during
their most critical marine life stage – the first months
after they migrate into the ocean (July-October)
(Parsons and Whitney 2012; Olgun et al. 2013).
Similarly, one of the largest salmon runs in 1958
followed the large eruption of Bensiammny volcano in
Kamchatka. The eruption in 1912 of the Katmai volcano (Alaska) was also suggested to have fertilised the
neighbouring lakes by input of phosphorus (Eicher and
Rousefell 1957). High ash-loads during Katmai eruption, however, caused initial mortality of pre-smolts
due to gill damage (Eicher and Rousefell 1957;
Duggen et al. 2010). Despite the smaller number of
spawners after the Katmai eruption, the numbers that
returned in the following 4 years were as large as
the pre-eruption, indicating a rapid recovery and
favourable survival conditions in the ocean (Eicher
and Rousefell 1957).
Further surveys of the Kasatochi 2008 eruption
suggest that the local wildlife was also (directly or
indirectly) affected by the eruption (del Moral 2010;
Drew et al. 2010; Williams et al. 2010). The largest
direct impact was probably the mortality of about
20,000–40,000 young birds (Williams et al. 2010).
Most of the seabirds were displaced and the terrestrial
birds did not return in 2009 (Williams et al. 2010).
Marine mammals (e.g. seals) probably suffered less,
but there was a clear disruption or loss of breeding
habitat on the land due to meter-scale ash layers on the
island (Drew et al. 2010).
In summary, the long-term response (months to
years) of the ecology after major eruptions may provide new insights into how marine ecosystems are
affected. Recent observations indicate that ocean
fertilisation by volcanic eruptions may affect marine
biomass within the ash-fall and neighbouring areas.

289

5.2.3

CO2 in the North Atlantic

The North Atlantic Ocean is a major sink for atmospheric carbon dioxide (CO2), and is therefore significant in slowing the global increase of atmospheric
CO2 caused by human activity. Until 1994, the North
Atlantic stored approximately 23 % of anthropogenic
carbon whilst covering only about 15 % of the world’s
ocean surface area (Sabine et al. 2004). Due to its
significance, the North Atlantic uptake of CO2 is
continuing to be studied intensively, in order to determine the long-term trends of the uptake and its
variability.
In the North Atlantic, the high-latitudes are a net
sink of atmospheric CO2 whilst the low-latitudes are a
net annual source (Fig. 5.26). The mean seasonal cycle
is small near the equator, a winter sink and summer
source in the Subtropics, and a small winter source and
strong summer sink in the subpolar region.
The air-sea flux of CO2 is commonly estimated by
the difference of CO2 partial pressure (pCO2) between
the ocean and atmosphere combined with the transfer
velocity k, which can be derived from wind speed by
various parameterisations e.g. (Nightingale et al.
2000; Wanninkhof and McGillis 1999; Wanninkhof
1992, discussed in detail in Chaps. 2 and 3). The
atmospheric pCO2 is calculated from the molar fraction of CO2 in the atmosphere (e.g. from Globalview
(2011)) and atmospheric pressure. In contrast to the
well-mixed atmosphere, surface seawater pCO2 concentration is subject to considerable variability in
space and time, mainly driven by temperature and
biological activity. Automated measurements of
pCO2 and related parameters onboard Voluntary
Observing Ships (VOS) have provided a valuable
monitoring network in the North Atlantic for more
than a decade. The pCO2 of seawater is commonly
determined by equilibrating a volume of air with seawater and measuring the CO2 concentration in the gas
phase (Pierrot et al. 2009). Alternatively, membranebased systems are used, where CO2 equilibrates (by
diffusion) through a membrane and is measured within
the sensor using an infra-red detector (Hales et al.
2004). An international effort to rigorously quality
control such measurements has led to the creation of
a global sea surface pCO2 data set, the Surface Ocean
CO2 Atlas, SOCAT (see Sect. 5.1.4.4 of this chapter,
http://www.socat.info/ and Pfeil et al. 2013). Additionally, pCO2 can be calculated from dissolved

b
3
80
2
60
1

0

20

0

−1

−20

−2

−40
Jan

inorganic carbon (DIC), total alkalinity (TA), temperature, salinity, and sea-surface pressure. This opens up
the possibility of computing or estimating pCO2 from
parameters that can be monitored on a basin- or
near basin-scale. Sea surface temperature (SST) is
remotely-sensed by satellites (see Sect. 5.1.2.1) and
measured by floats. Sea surface salinity (SSS) is monitored by floats and satellite data will be available
soon through the Aquarius (Le Vine et al. 2007,
http://aquarius.nasa.gov/) and SMOS projects (see
Sect. 5.1.2.2). DIC and TA cannot yet be remotelysensed. But for any individual ocean basin, TA can be
estimated from SSS using a non-linear fit (Eden and
Oschlies 2006). In addition, the SeaWiFS project
provides satellite-derived estimates of the chlorophyll
a concentration (see Sect. 5.1.2.3).
The sea surface pCO2 and the air-sea CO2 flux vary
over time. A synthesis study by Watson et al. (2009)
presented annual CO2 flux estimates for the North
Atlantic for the years 2002–2007. It demonstrated
that the CO2 uptake is subject to large interannual
variability. Bates (2007) reported high observed
variability of CO2 fluxes due to increased wind speed
over a 20 year period (1984–2005) at the Bermuda
Atlantic Time Series (BATS) site. The occurrence of
hurricanes in this region accounts for up to 29 % of the
variability of summertime CO2 fluxes (Bates 2007)
and the increased frequency of hurricanes can potentially impact by approximately 16 % the interannual
variability of CO2 fluxes in the North Atlantic Ocean.
In this context the North Atlantic Oscillation (NAO) is
also a driver of variable wind speeds as it drives large
scale climate variability over the North Atlantic
(Hurrell 1995) and increasing values of NAO index
correspond to increased CO2 fluxes in the northern

yr

−1 o

−1

lat ]

40

−2

a

[mol Cm

Fig. 5.26 (a) Geographical
distribution of the mean airsea CO2 flux and (b) the zonal
mean seasonal cycle of the
flux in the North Atlantic
(Adapted from Schuster et al.
2012)

5 Perspectives and Integration in SOLAS Science

Latitude [ o N]

290

−3
Apr

Aug

Nov

part of the North Atlantic (Olsen et al. 2003). However, Schuster et al. (2009) and Thomas et al. (2008)
reported a link between the variability of NAO and
CO2 fluxes for the rest of the North Atlantic, although
the heterogeneity makes the nature of the linkage
difficult to identify. Modelling studies (Le Quéré
et al. 2003; McKinley et al. 2004; Friedrich et al.
2006) show that SST is the primary control of North
Atlantic subtropical surface pCO2.
On a basin-scale, however, CO2 fluxes simulated in
the above model studies are not significantly correlated
with the NAO index. Chemical buffering of changes in
the CO2 concentrations by the large pool of carbonate
and bicarbonate ions (Broecker and Peng 1974) has
been suggested as one reason for this decoupling.
Seawater pCO2 itself is driven by sea surface temperature (SST), mixing (advective and convective)
and biology (production/respiration). In the tropical
North Atlantic NA, the seasonal pCO2 cycle is strongly
coupled to the SST variability. These oligotrophic
waters do not support strong biological productivity
due to the lack of nutrients (Longhurst 2007). The
subtropical regions demonstrate low biological productivity and the seasonal cycle of pCO2 is dominated
by temperature changes. The annual amplitude of
approximately 40 μatm peaks during the summer
months, which corresponds to the temperature peak
(Takahashi et al. 2009; Telszewski et al. 2009) but is
reduced to a certain extent by low primary production.
The pCO2 in the subpolar NA is driven by high temperature variability (ΔSST ~ 8
C) and high biological
productivity. Here the biological productivity during
spring and summer has a strong counteracting effect on
the temperature driven pCO2 increase during this
period (Körtzinger et al. 2008; Lüger et al. 2004).

5.2 Examples of SOLAS Integrative Studies

Steinhoff et al. (2010) have shown that the interannual
variability of convective mixing of the surface layer
may influence the wintertime budget of carbon and
nutrients which directly impacts the observed seawater
pCO2 and thus the ΔpCO2. Furthermore, advective
mixing in the northwest Atlantic has a strong influence.
The Labrador Current transports nutrient rich, colder,
and fresher water with a different CO2 signature into
the North Atlantic Drift region. On a decadal timescale,
increased SST and thus increased primary production
may explain the observed long-term changes of seawater pCO2 (Corbière et al. 2007; Takahashi et al. 2009).
The North Atlantic air-sea CO2 flux is, however,
not constant over time. Based on observations and
sinusoidal curve fitting, Schuster et al. (2009) found
that between 45
N and 65
N, pCO2 in the sea surface
increased faster than that in the atmosphere between
1990 and 2006 (3 and 1.8 μatm year1, respectively),
resulting in a decreasing sink from 0.20 to
0.09 Pg C year1, whilst winter-time observations in
the Subpolar North Atlantic showed that surface pCO2
in winter increased even faster at 3.7 μatm year1
between 1993 and 2003 (Corbière et al. 2007).
Several problems arise when using related
parameters to gain an insight into basin-wide surface
pCO2. First of all, a direct calculation is not possible
with the data currently available. One needs to estimate surface pCO2 from e.g. remote sensing data
using empirical fitting functions. Thus, errors in the
initial observations and data gaps will impair the accuracy of the pCO2 estimate or result in missing data.
The search for a fitting function can be challenging.
For example, Watson et al. (1991) and Lefèvre and
Taylor (2002) reported robust linear relationships
between SST and pCO2 in the subpolar and subtropical North Atlantic, respectively. The regression
coefficients in both studies were similar in magnitude,
yet of opposite sign. The study by Watson et al. (1991)
also documented a promising covariation of chl a and
pCO2. On the other hand, Lüger et al. (2004) found no
significant correlation between chl a and pCO2 based
on measurements covering an entire seasonal cycle in
the midlatitude North Atlantic.
The pioneering study by Lefèvre et al. (2005) was
the first to derive maps of North Atlantic in situ pCO2.
Using mainly VOS data of the years 1995–1997, multiple regression techniques and self-organising maps
(Kohonen 1982), monthly mean surface pCO2 fields
were constructed from temperature and position in

291

time and space on a 1
 1
grid between 50
N and
70
N. The authors determined the Subpolar Gyre
uptake to be 0.13–0.15 Pg C year1 based on their
pCO2 estimates and NCEP monthly wind speed. For
the year 2000, the climatological mean North Atlantic
sink was approximately 0.44 Pg C year1 (equator to
80
N, Takahashi et al. 2009), whilst an ocean inversion
model showed the mean uptake to be 0.41 Pg C year1
(equator to 90
N, Gruber et al. 2009). A recent study of
the air-sea CO2 fluxes in the Atlantic gives a best
estimate of the long-term mean flux for the North
Atlantic of 0.46 Pg C year1 for the time period of
1990–2009 (Schuster et al. 2012); this study is based
on observations, atmospheric inversions, ocean inversions, and ocean biogeochemical models. The availability of satellite-derived chl a and mixed layer depth
(MLD) allowed for an improvement of regression
techniques as demonstrated by Jamet et al. (2007).
The strongly enhanced VOS coverage achieved by
the CAVASSOO and CARBOOCEAN projects and
their US-American partners enabled Telszewski et al.
(2009) to provide seasonal pCO2 estimates for the
years 2004–2006 in the North Atlantic between 10
N
and 70
N.
The accuracy of pCO2 mapping is a major source of
uncertainty to this day. Common techniques such as a
validation against the data used for deriving the
mapping function or against an independent data set
(e.g. additional VOS data) were used in the studies
mentioned above. To what degree, however, errors
derived from individual VOS were representative of
the basin-wide mapping error remains unclear. The
study by Friedrich and Oschlies (2009a) addressed
this challenge through combining observational and
modelling approaches. The authors simulated the
procedure of VOS monitoring and remote sensing in
an eddy-resolving biogeochemical ocean model. Subsequently, the modelled pCO2 output was used as a
ground truth to analyse the accuracy of the pCO2 maps
generated from simulated SST and chlorophyll a fields.
One major finding of this study was that the conventional validation techniques underestimate the basinscale mapping error significantly, raising questions
about the reliability of error estimates in earlier studies.
However, it was also shown that despite large mapping
errors in some regions, the basin-wide CO2 uptake can
be determined with promising precision and that the
accuracy can be improved when ARGO float data are
used (Friedrich and Oschlies 2009b).

292

5 Perspectives and Integration in SOLAS Science

Fig. 5.27 Global burden of SSA (in mg m2) calculated with
the GEOS-Chem CTM for the year 2008. Left panel: SSA
burden calculated using the Gong (2003) source function
(MODEL-STD). Central panel: SSA burden with the

empirically derived source function including a sea surface
temperature dependence (MODEL-SST). Right panel: Zonal
average of SSA burden over the oceans (Adapted from Jaeglé
et al. (2011))

In summary, the network of Voluntary Observing
Ships is an efficient way to monitor the evolution of
surface pCO2 in the North Atlantic and elsewhere.
Provided that sufficient coverage is maintained, VOS
measurements can be combined with data from
satellites and ARGO floats to generate basin-scale
maps of surface pCO2 and to estimate air-sea carbon
fluxes with good precision. Future instrumentation such
as floats that measure pCO2 will be a significant asset
and will help increase the accuracy of such estimates.

same CTM (e.g. Guelle et al. 2001; Pierce and Adams
2006). Most source functions are a function of whitecap
coverage, with the frequently used 10 m wind speed
dependence of u103.41 (Mohanan and O’Muircheartaigh
1980). Thus, small biases in calculated wind speeds are
amplified in the resulting SSA emissions. Even when
SSA emissions are specified, differences in transport
and deposition can result in a factor of 2 variation in
predicted global SSA burdens for different models (Liu
et al. 2007; Textor et al. 2007).
Despite these significant differences in the absolute
concentrations of SSA, models generally display similar features in the global distribution of SSA following the spatial and seasonal distribution of surface
wind speed: year-round very high SSA concentrations
over the Southern Ocean, winter maximum over the
Northern mid latitude storm tracks, and a minimum in
the tropics and subtropics (Fig. 5.27, left panel).
This view has been challenged by three independent studies using satellite and in situ observations as
top-down constraints on the global distribution of
SSA. Haywood et al. (1999) examined the clear-sky
solar irradiance measured by the CERES satellite and
found a strong signature of SSA over the oceans away
from pollution-influenced regions. The geographical
pattern of SSA reflectance displayed maxima over
both the Southern Ocean and the tropical oceans in
the 10–20
latitude band. Satellite measurements of
brightness temperature have been used to infer a first
estimate of global whitecap coverage (Anguelova and
Webster 2006). The resulting whitecap coverage
showed a fairly uniform distribution with latitude,
contrary to the expectation of strong enhancements
over high latitude regions with high wind speeds and
very low concentrations in the tropics.

5.2.4

Global Distribution of Sea Salt
Aerosols

The global distribution of sea salt aerosols (SSA)
is generally estimated using chemical transport
models (CTMs) or General Circulation Models
(GCMs). Emissions are calculated by integrating a
size-dependent source function of SSA at each time
step over several size bins. Simultaneously, transport
and depositional loss of SSA are also calculated.
Models then solve the continuity equation for mass
conservation in each size bin.
The resulting global distribution of SSA is highly
dependent not only on the assumed sea-salt source
function (see reviews by Lewis and Schwartz 2004;
O’Dowd and de Leeuw 2007; de Leeuw et al. 2011b)
but also on the upper size range of particles, and meteorological fields, as demonstrated in the model intercomparison study of Textor et al. (2006). These authors
found a large inter-model range of 3–18 Tg for the
global SSA burdens calculated in 16 different CTMs.
Calculated SSA concentrations can differ by factors
of 2–3 when different source functions are used in the

5.3 Perspectives for the Future

Jaeglé et al. (2011) analysed cruise observations
of supermicron SSA mass concentrations (1–10 μm
diameter) from six Pacific Marine Environmental
Laboratory (PMEL) cruises (Quinn and Bates 2005)
with a CTM. They inferred that in addition to the
well-known wind speed dependence, SSA emissions
appear to depend on sea surface temperature. This is
consistent with laboratory experiments reporting
increased production of SSA with increasing water
temperature for coarse SSA particles (Bowyer 1984,
1990; Woolf et al. 1987; Mårtensson et al. 2003).
The temperature dependence leads to a decrease in
the predicted SSA burden over the cold high latitude
oceans, but enhanced SSA burden over the warm
tropical oceans, especially in the trade wind regions.
Implementing an empirical temperature- and wind
speed-dependent SSA source function in the CTM
leads to a more uniform global distribution of SSA
(Fig. 5.27 central and right panels) and also improves
agreement with in situ SSA observations and
remotely sensing aerosol optical depth observations
(Jaeglé et al. 2011).
These recent studies illustrate that one way to
reduce the very high uncertainties associated with
SSA source functions determined from laboratory
and field measurements is via integrative studies
that combine these source functions in CTMs and
GCMs and use top-down constraints based on in situ
and satellite observations. Compilations of aerosol
observations such as the SOLAS aerosol database
(Sect. 5.1.4.5), with a wide distribution over most of
the ocean basins are extremely useful in this endeavour.

5.3

Perspectives for the Future

In this chapter we have summarised some of the major
integrative SOLAS activities carried over the past
decade. While there is a remarkable diversity in the
scope, goals, approach, and tools associated with these
efforts, they all fall into one or more of the following
broad categories:
1. Understanding biogeochemical cycles: Compilation of geospatial data sets for the purpose of understanding biogeochemical cycles and discovering
the physical and chemical controls on biogeochemistry. Such data sets permit hypothesis testing and

293

provide a common basis for comparison of global
models.
2. Establishing current climatologies: Establishing a
baseline describing the current physical and biogeochemical state of the oceans and atmosphere.
For most parameters we are far from an adequate
description of the modern Earth, and as a result, we
are ill prepared to detect changes.
3. Closing global budgets: Time series measurements
of the composition of the atmosphere, such as
the Mauna Loa CO2 record, provide a unique
integrated view of the global accumulation of
greenhouse gases and other pollutants. Detecting
change in the oceanic inventory of anthropogenic
emissions is an equally critical step towards closing
global budgets, but is considerably more logistically challenging.
The SOLAS integrative studies are examples
of activities that could contribute to an Earth Observing System. Such a system spans a wide range of
activities including process-scale laboratory and
expeditionary research, development of new observational capabilities, and operational data streams from
satellites, buoys, drifters and models. To be successful,
an Earth Observing system will require contributions
from many nations and a workforce of highly skilled
Earth Scientists and engineers who operate as a global
community. One of the most important and exciting
aspects of the integrated SOLAS science described
here is the extent to which it reflects the work of
young scientists from many nations, and the development of a common vision for future priorities in global
environmental science.
With this in mind, the International SOLAS Steering
Committee identified five unresolved issues of significance to the global climate system that required
improved international cooperation and networking
(Law et al. 2013). These novel cross-cutting areas, or
Mid Term Strategy Initiatives (http://www.solas-int.
org/about/mid-term-strategy.html), i.e. Upwellings and
associated Oxygen Minimum Zones, Sea ice biogeochemistry, Marine Aerosols, Atmospheric nutrient
inputs to the oceans, and Ship Emissions, will benefit
from enhanced community engagement to deliver
major advances.
Looking forward, societal needs for large-scale
integrated data and models are increasing. The
human biogeochemical footprint on the planet is now

294

so large that the future quality and sustainability of
environmental resources will be determined by societal choices rather than natural variability. At the same
time, it is critical to understand how natural Earth
systems will respond to this forcing. Large scale
Earth Observations and Earth System models will be
essential tools underpinning societal decision-making.
The future challenges include:
1. Discovery and detection: The pace of environmental change is increasing rapidly as a result of
increasing industrialisation, population growth,
and technological development. The ability to
detect large-scale changes in the environment at
an early stage is essential in order to allow society
adequate time to avoid or mitigate the deterioration
of environmental resources and services.
2. Attribution: When environmental changes occur, it
is critical to understand the underlying causes and
the extent to which they can be attributed to natural
or anthropogenic causes.
3. Prediction and uncertainties: Prediction of future
environmental change is an essential tool for the
development of effective environmental policy.
Almost every aspect of human society and its
infrastructure is sensitive to climate and other
environmental conditions. Science-based models
are the principle tool used to assess the likely
outcomes of current trends, the effectiveness
of proposed policy options and/or geoengineering
strategies, and the environmental impacts of
new technologies. Quantitative assessment of
uncertainties inherent in predictions of future environmental change is one of the most important
products of Earth System Science models. These
uncertainties contribute greatly to risk assessments
of policy options.
There is enormous potential benefit to society in
understanding the environmental consequences of
societal trends and policies. These benefits include
long term planning to take advantage of new
opportunities (e.g. the melting of sea ice and the opening of the Arctic Ocean to exploration and navigation),
or to avoid potential costs (i.e. minimising sea level sea
level rise by mitigating greenhouse gas emissions). For
the SOLAS realm, providing the types of information
needed for decision-making will require improved
process-level understanding of biogeochemistry, and
much better observational capability for remote
regions of the atmosphere and oceans. Significant

5 Perspectives and Integration in SOLAS Science

investment will be required in order to maintain
existing observing systems, develop and deploy new
sensors for in situ and remote observations, and
improve infrastructure for the archiving and distribution of data for both research and operational products.
The importance of the SOLAS realm to the future
trajectory of Earth’s climate and habitability is very
clear. Our ability to manage and improve the quality
of both natural and human systems will ultimately
depend on our understanding of these interactions.
The coastal zone is heavily populated and most people are well aware of the impact that coastal water
quality can have on their lives and local economy.
Although the open oceans cover most of Earth’s
surface, they are largely uninhabited, and there is a
tendency for the average person to see them as
remote, unchanging, and disconnected from their
daily lives. The reality is far from that, and societal
decision-making must take into account the myriad
interactions that link us with the surface ocean and
lower atmosphere. The challenges are to identify and
understand these linkages, to inform the public about
them, and to develop tools for integrating scientific
knowledge into societal decision-making. The
research summarised in this volume demonstrates
the remarkable progress of SOLAS science over the
past decade, and shows the capacity of the SOLAS
scientific community to meet the broader challenges
ahead.
Open Access This chapter is distributed under the
terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial
use, distribution, and reproduction in any medium,
provided the original author(s) and source are credited.

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sea-to-air flux climatology for bromoform, dibromomethane
and methyl iodide. Atmos. Chem. Phys. Discuss.,
13:5601–5648, 2013 www.atmos-chem-phys-discuss.net/
13/5601/2013/ doi:10.5194/acpd-13-5601-2013

Index

A
Absorption aerosol index (AAI), 266
Acetaldehyde, 26–29, 254, 255
Acetone, 26–29, 253, 254
Acetylene, 253
Acidification, 250
Activation, activated, 174, 178, 182, 205
Advanced Very High Resolution Radiometer (AVHRR), 257, 265
Advective mixing, 290
Aerobic, 18, 37
Aerosol(s), 250–252, 255, 258, 261, 262, 266–267, 270,
280–281, 283, 287, 288, 292–293
concentration, 176, 225
mass, 176, 177, 180, 205
production, 174–202
size distribution, 172, 182, 183, 185, 202
sources, 171, 174, 184, 209
Aerosol (sea salt aerosol), 9, 10, 22, 23
Aerosol mass spectrometer (AMS), 196
Aerosol optical depth (AOD), 175, 190, 204, 225, 266, 267
Aerosol optical thickness (AOT), 266
Aerosol production, 61, 62, 64, 79, 80
Ageing, 178, 196–201, 220
Aggregation processes (aggregates), 180, 187, 197, 199,
212, 213, 219–221, 223, 224, 227
Aircraft, 27, 39
Air mass climatology, 281
Air quality, 174
Air-sea flux
of CO2, 272, 273, 290
of heat, 257
Air-sea gas exchange, 115, 134, 155
Alcohols, 26, 29, 30
Aldehydes, 26, 27, 35
Aliphatic amines, 252
Alkane, 31
Alkene, 31
Alkyl amines, 37
Alkyl nitrates, 31–33
Alkyl peroxy radical (RO2), 27, 33
A Long-term Oligotrophic Habitat Assessment (ALOHA), 250
Altimeter, 90, 99–101
Altimetry, 256–260, 265
Amines (RxNHy), 35–37
Ammonia (NH3), 25, 35–37, 183
Ammonium (NH4), 20, 35, 36, 182, 197
Ammonium monooxygenase (AMO), 20

AMS. See Aerosol mass spectrometer (AMS)
Anaerobic, 18, 37
Anammox, 37
Ångström exponent/coefficient, 202, 266
Anoxic, 18
Anthropogenic, 113, 115–119, 121, 124–130, 135,
142, 146–150, 153, 154
Anthropogenic aerosol, 189, 193, 202
Anthropogenic carbon, 289
AOD. See Aerosol optical depth (AOD)
Apparent quantum yield (AQY), 29
Aquarius/SAC-D mission, 260–261
Aqueous phase, 28, 32, 33, 35
ARGO, 248–250, 255, 261, 262, 271, 291, 292
Artificial surfactant, 68
Asian mineral dust, 287
Atlantic Ocean, 176, 179, 180, 183, 187, 188, 191, 197,
202, 209, 218
Atmospheric aerosol, 171, 204, 206, 217
Atmospheric deposition, 251
Atmospheric nitrogen inputs, 281
Atmospheric nutrient inputs, 293
Atmospheric observatories, 250–251
Atmospheric pCO2, 221
Atmospheric pressure chemical ionization tandem mass
spectrometry, 21
Atmospheric processes, 172, 188, 214
Atmospheric stability, 71, 82
Atmospheric stratification, 65, 71, 76
Atomic iodine (I), 21
Attenuation coefficients, 263
Autonomous Ocean Flux Buoys (AOFB), 265
Autonomous underwater vehicle (AUV), 250
Autotroph, 221, 226
AVHRR. See Advanced Very High Resolution Radiometer
(AVHRR)

B
Bacteria, bacterioplankton, 179, 201, 217–220, 223
Bacterial abundance, 217
Bacterial production, 218
Bacterial respiration, 218
Base, 35
Batch, 211, 212
BATS. See Bermuda Atlantic Time-series Study (BATS)
BC. See Black carbon (BC)

P.S. Liss and M.T. Johnson (eds.), Ocean-Atmosphere Interactions of Gases and Particles,
Springer Earth System Sciences, DOI 10.1007/978-3-642-25643-1, # The Author(s) 2014

307

308
Bermuda Atlantic Time-series Study (BATS), 250, 290
Bioassay experiments, 215, 217
Bioavailable, 206, 207, 209, 210, 215
Biogenic detrital particles, 263
Biogenic volatile organic compounds (BVOC), 191–192
Biogeochemical sensors, 249
Biogeochemistry, 125, 127, 129, 173, 186, 202, 206, 209–211,
213, 221
Biological activity, 176, 177, 183, 205, 209, 211, 214, 225
Biological carbon pump, 214
Biological production, 120, 134, 143
Biomass, 201, 217, 226
burning, 189, 193, 194, 198, 209, 210, 226
burning aerosols, 267
Biota, 179, 184, 191, 206, 207, 221, 222, 226
Black carbon (BC), 177, 194
Bloom, 15, 38, 179, 180, 213–216, 223, 224
Bodélé depression, 252
Boundary currents, 256
Brine, 95, 96
Brine flushing, 264–265
Bromine (Br), 10, 13, 16–18, 21–24, 251, 255
(bromo-)Chloroperoxidase, 18
Bromodichloromethane (CH2BrCl2), 17, 18
Bromoform (CHBr3), 17–19, 262, 277
Bromoiodomethane (CH2BrI), 20, 22
Bubble, 174–176, 179, 180, 205
generation, 62
scavenging, 62, 68
Bubble-mediated transfer, 62–64, 66, 85, 88, 91, 94, 97, 101
Budgets, 2, 7, 11, 14, 26–29, 34, 38
Buoyancy, 66, 67, 76, 90, 91
Butane, 31
Butyraldehyde, 29
BVOC. See Biogenic volatile organic compounds (BVOC)

C
Calcium carbonate (CaCO3), 117, 120, 121, 126, 151, 152, 263
Calibration algorithms, 256
Calibration drift, 256
CAM-Chem chemistry-transport model, 254
Canary and California Current System, 272
Cape Grim, 251
Cape Verde Atmospheric Observatory (CVAO), 251–255
Carbon, 173, 179, 183, 194–199, 202, 206, 209, 210, 212, 214,
215, 218–224, 226
Carbonate and bicarbonate ions, 290
Carbon cycle, 116, 118, 129, 148
Carbon dioxide (CO2), 113–157
Carbon Dioxide Information Analysis Center (CDIAC), 279, 280
Carbon disulphide (CS2), 6, 7
Carbon monoxide (CO), 33, 38–39
CarbonSat and CarbonSat Constellation concepts, 268
Carbon tetrachloride (CCl4), 14
Carbonyl, 29
Carbonyl sulfide (COS), 6, 7
CARBOOCEAN, 291
Carboxylic acids, 26, 27
Catalytic O3 destruction, 260

Index
CAVASSOO, 296
Cavity ring down, 21
CCN. See Cloud condensation nuclei (CCN)
CDIAC. See Carbon Dioxide Information Analysis Center
(CDIAC)
CDOM. See Coloured dissolved organic matter (CDOM)
CERES satellite, 292
Chapman reactions, 26
Chelton effect, 259, 260
Chemical enhancement, 34
Chemical transport models (CTMs), 292, 293
Chemistry, 173, 175, 176, 180, 189, 191, 193, 196,
200, 210, 211, 213, 214, 221
chl-a maximum, 262, 263
Chlorine, 13–14, 21–23
Chloroform (CHCl3), 14, 16, 19
Chloroiodomethane (CH2ClI), 15, 20, 21
Chloromethane (CH3Cl), 15
Chloroperoxidase, 16, 18
Chlorophyll, 262–264, 275, 282, 284, 288, 291
Chlorophyll (maximum), 17
Chlorophyll-a, 177, 180, 182, 183, 215, 217, 249, 264, 288, 290
CLAW, 2, 6, 11–13, 39
Cleavage, 3
Climate, 172, 191–193, 201, 202, 204, 211, 221, 222, 225
change, 113–157
feedback, 10, 11
model, 180, 193, 203
observing systems, 249
variability, 249, 290
Climatology, 79, 97
Cloud, 2, 9–13, 24, 27, 28, 31, 35
albedo, 172, 193, 205
cycling, 182
formation, 175, 204–206
Cloud condensation nuclei (CCN), 12, 13, 24–27, 39, 178, 204,
205, 225, 251
formation/growth, 262
Cloud droplets, 255, 262
C* method, 274
Coagulation, 172, 186, 196, 197, 212
CO2 air-sea climatologies, 156
COARE algorithm, 87, 90
Coarse mode, 172, 175, 210
Coastal ocean, 6, 17, 149, 151
Coastal sediments, 141–143, 152
Coccolithophores, 263, 264
CO2 emission scenarios, 276
Coherent structure, 56, 58, 60, 61, 66, 74
Coloured dissolved organic matter (CDOM), 6, 15, 30, 37–38, 254
Community structure, 190, 215, 217, 226
Concentration, 114, 118–121, 123, 124, 130, 131, 134–144, 147,
148, 150–156
Concentration gradient, 28–29
Condensible vapour, 186
Conditions, 56, 59, 60, 62, 67, 68, 70, 71, 74, 76–79, 84–88, 93, 96
neutral conditions, 76
Continental shelves, 126–129, 135, 137, 139–140, 144, 150, 153
Convection
water-side convection, 66

Index

309

Convective mixing, 291
Convective regions, 16, 26, 27
CO2 partial pressure (pCO2), 274, 275, 289–292
Copper (Cu), 198, 209, 211, 217
CO2 uptake, 271, 272, 290, 291
Cryosat-2, 256, 265
CTMs. See Chemical transport models (CTMs)
CVAO. See Cape Verde Atmospheric Observatory (CVAO)
Cyanobacteria, 18, 19, 31, 37, 38, 214, 364

DMSO. See Dimethylsulphoxide (DMSO)
DMSP. See Dimethylsulphoniopropionate (DMSP)
DMSP lyase, 2
DOC. See Dissolved organic carbon (DOC)
DOM. See Dissolved organic matter (DOM)
DRE. See Direct radiative effect (DRE)
Dry deposition, 172, 188, 210
Dry mole fraction, 113, 114, 116–118
Dust deposition, 251

D
Dark production, 38
Dead zones, 153, 157
Degradation rate, 6, 17
Dehalogenation, 251
Demethylation, 3
Denitrification, 130–131, 134, 135, 151, 272
Density stratification, 67
Deposition, 172, 173, 186–193, 196, 198, 206–224
deposition velocity, 11, 34, 35, 78–80, 92
direct deposition, 78, 80
dry deposition, 62, 74, 78–79
particle deposition, 78
wet deposition, 79–80
Depth profiles, 32
Desert dust, 172, 186–189, 207, 218, 221
Detrital aggregates, 19, 20
Diatoms, 3, 18, 31, 38, 264, 287, 288
Diazotrophic bacteria, 37
Dibromomethane, 15, 17
Dibromochloromethane (CH2Br2Cl), 17, 18
Dibromomethane (CH2Br2), 17–19
DIC. See Dissolved inorganic carbon (DIC)
Dichloromethane (CH2Cl2), 14–16
Differential optical absorption spectroscopy (DOAS), 21
Di-iodomethane (CH2I2), 15, 21, 22, 24
Dimethylammonium ((CH2)2NH2), 35
Dimethyl selenide, 7
Dimethylsulphide (DMS), 2–13, 24, 28, 36, 39, 40, 173,
182, 183, 185, 194, 202, 204, 214
climatology, 278
summer paradox, 250
Dimethylsulphoniopropionate (DMSP), 2–7, 40, 250, 282, 286
Dimethylsulphoxide (DMSO), 2, 3, 9, 11
Dinoflagellates, 3, 264
Direct flux, 14, 28, 36, 39
Direct radiative effect (DRE), 178, 202–204, 225
Dispersion, 175
Dissipation rate, 58, 65, 73, 93
Dissociation constant (pKb), 35
Dissolution, 173, 211–213, 219, 226
Dissolved form, 173, 211
Dissolved inorganic carbon (DIC), 119–122, 124, 125, 129, 148,
150, 155, 274, 290
Dissolved organic carbon (DOC), 179, 210, 262
Dissolved organic matter (DOM), 182, 210
Diurnal variation, 14, 18
Diurnal warm layer, 85
DMS. See Dimethylsulphide (DMS)
DMS climatology, 3–5, 9

E
Earth observation (EO), 194, 225, 255–270, 294
Earth observing systems, 294
Earth system models, 271, 272, 284, 294
Earth system science, 2, 40
Eastern boundary upwelling regions, 273
Ebullition, 140–142, 146, 147, 153, 155
Ecosystem, 189, 191–193, 195, 202, 218–221
functioning, 221
response, 287–289
Eddy correlation, 28
Eddy dynamics, 256
Effective flux, 175
El Chichón eruption, 269
Electromagnetic spectrum, 266
El Niño, 256
Emission, 113–115, 117, 119, 127, 130, 134–137, 139–144,
146–147, 152, 153, 155–157, 172, 176, 184, 186,
188–196, 198, 202, 204, 205, 207, 209, 226
Emission inventory, 193, 226
Enhancement, 56, 58, 67, 69–70, 91, 94, 101
Enrichment, 173, 174, 176–179, 199, 205, 206, 214, 216
Environmental Satellite (ENVISAT), 256, 259, 268
ENVISAT. See Environmental Satellite (ENVISAT)
EO. See Earth observation (EO)
Estuaries, 128–130, 134, 135, 137–141, 143, 144, 147
Ethane, 31, 253
Ethanol, 26, 27, 29, 30
Ethyl nitrate (EtONO2), 31
Euphotic zone, 31, 38
European Iron Fertilization Experiment (EifEX), 282
European Remote Sensing (ERS) satellites, ERS-1 and-2, 256, 265
European Space Agency, 260–261, 270
European Station for Time-series in the Ocean Canary Islands
(ESTOC), 250
Evasion, 67, 84
Export of matter, 218
External mixing, 196

F
fCO2. See Surface water (fCO2)
Fertilisation, 213–215, 221–224, 227
Fine mode, 202
Fine particles, 24
Flow structures, 81
Flux, 2, 5–11, 13, 16, 17, 19–21, 28, 29, 32, 34–39
Flux gradient, 34
Footprint, 82, 86
Forecasting capability, 249

310
Forecast mode, 270, 271
Formaldehyde (HCHO), 27, 29, 270
Formic acid, 26, 27
Free troposphere, 34
Freons, 15
Friction velocity, 56, 58, 60, 61, 65, 66, 70, 71, 76, 93, 101
Frost flowers, 265
Fugacity, 118, 119

G
Gases
poorly soluble gases, 62
soluble gases, 56, 62, 63, 72, 74, 90, 92, 94, 97
Gas exchange parametrisation, 275
Gas phase, 9, 10, 12, 21–25, 28, 31, 32, 35, 36
Gas transfer velocity, 59, 64, 65, 73, 78, 80, 81, 83, 85, 87, 88,
91, 93, 97, 98, 119, 257
GCOS. See global climate observing systems (GCOS)
Geo-engineering, 205
Geosat, 256
GEOTRACES, 281, 283
GFED. See Global fire emissions database (GFED)
GHRSST Multi-product Ensemble (GMPE), 257, 258
Gliders, 250, 265
Global annual sea-to-air DMS flux, 285
Global array, 248
Global budgets, 113
Global burden of SSA, 292
Global change, 147–154
Global climate observing systems (GCOS), 255, 260
Global fire emissions database (GFED), 194
Global Ocean Data Assimilation Experiment
(GODAE), 262
Global Ocean Observing System, 255
Global prognostic models, 276
Global SSA burdens, 297
Global whitecap coverage, 297
Glyoxal, 274
GMPE. See GHRSST Multi-product Ensemble (GMPE)
Gravitational settling, 193
Grazing, grazers, 218, 219, 221
Greenhouse gas, 34, 38
Greenhouse gases Observing SATellite (GOSAT), 274
Groundtruthing, 261

H
Halocarbons, 14, 19
Halocarbons in the Ocean and Atmosphere (HalOcAt),
282–283, 288
HalOcAt. See Halocarbons in the Ocean and Atmosphere
(HalOcAt)
Halogen, 14, 15, 18, 19, 21–26, 40
Halogenated hydrocarbons, 282
Haloperoxidase, 16, 21
Haptophytes, 3
Hard tissue pump, 123, 124
HCN. See Hydrogen cyanide (HCN)
Heat flux, 60, 63, 68, 69, 71, 73, 75, 76, 79, 84, 93
Heterogeneous, 9, 10, 22

Index
Heterogeneous processes, 189, 202
Heterotrophic bacteria, 268
Heterotrophic organisms, 214
High-nutrient low-chlorophyll (HNLC), 211, 214, 218–227,
230, 231, 286
Hindcast mode, 275
HNLC. See High-nutrient low-chlorophyll (HNLC)
HOBr, 9, 22
HOCl, 9, 11, 17
HOI, 21–24, 26
Hot spot volcanic islands, 291
HOx, 21, 22, 27
HOx radical, 256
H2SO4, 9, 24, 25
Humic substances, 30
Hurricanes, 263, 295
HYbrid Coordinate Ocean Model (HYCOM), 278
Hydration, 72
Hydrazine (N2H4), 37
Hydrogen (H2), 27, 37–38
Hydrogen cyanide (HCN), 33–34
Hydrogenolysis, 19
Hydrogen peroxide (H2O2), 18, 21, 256
Hydrogen sulphide (H2S), 7, 9
Hydrolysis, 6, 7, 17, 19, 20, 33
Hydroperoxyl (HO2), 256, 259, 260
Hydrostation S, 255
Hydroxide (OH), 256, 259
Hygroscopic, 176, 177, 179, 182, 200, 201, 207, 229
Hypobromite (BrO), 274
Hypoxic zones, 155, 157

I
Ice-atmosphere exchanges, 270
Ice cores, 118–121
Indirect radiative effect, 209–211
Inductively coupled plasma mass spectrometry, 22
Infrared imaging, 76
Infrared radiometer, 262
Inorganic nutrients, 221
In situ mesocosms, 223–225, 229
In-situ observations, 253–288
Integrated Arctic Ocean Observing System (IAOOS), 271
Interannual variability, 279, 281, 290, 295
Interfacial flux, 179, 180
Internal mixing, 201–205
Inventory, 86, 90, 92, 96, 99, 102–103
Inverse modelling, 279–280
I2O5, 25, 26
IO3, 26
Iodine, 14, 19–26, 190, 260
Iodine dioxide (OIO), 22–25
Iodine monoxide (IO, iodine oxide), 22
Iodine monoxide (IO) radicals, 260
Iodobutanes (C4H9I), 20
Iodocarbon, 19–21, 24
Iodoethane (C2H5I), 20, 22
Iodoform (CHI3), 21
1-and 2-iodopropane (C3H7I), 20, 22
IONO2, 22–24

Index
Iron (Fe), 256, 257, 285–287, 291–293
binding capacity, 216
binding ligands, 216
Iron addition experiments, 287, 291
Iron fertilisation, 291
Isoprene, 16, 31, 32, 40, 268
Isopropyl nitrate, 32

K
Kelp, 21
Ketones, 26, 28, 35

L
Labrador current, 295
Langmuir circulation, 67, 69
Langmuir number, 69
La Niña, 261, 262
Laser speckle velocimetry (LSV), 83
Layer
amospheric boundary layer (ABL), 73, 74, 77, 89
atmospheric surface layer, 73
deposition layer, 81, 82
interfacial turbulent boundary-layer, 77
mass boundary layer (MBL), 60, 72, 74, 75, 79, 80
mixed layer, 68, 69, 77, 86, 87, 89
molecular diffusion layer, 73
sea surface microlayer, 65, 71
turbulent boundary layer, 77
turbulent shear layer, 63
Lidar altimetry, 270
Limitations, 86–87, 211, 213, 214, 219, 221, 223, 226
Lithogenic, 223, 224, 227–229, 231
LNLC. See Low-nutrient-low chlorophyll (LNLC)
Low-molecular-weight dicarboxylic acids (DCAs), 259
Low-nutrient-low chlorophyll (LNLC), 211, 214,
218–227, 231

M
Mace head, 180–182, 184, 185, 187–190, 256, 272
(Macro)algae, 14, 17, 18, 24, 38
Macrophyte, 17
Mangrove ecosystems, 137, 141, 147
Marine aerosols, 259, 263, 267, 272, 285, 298
Marine atmosphere, 177–206, 229
Marine boundary layer (MBL), 9–11, 14, 21–26, 31, 33–35,
185–191, 259, 274
MarinE MethanE and NiTrous Oxide (MEMENTO) database,
281, 287
Marine productivity, 191, 226, 231
Mass, 177, 178, 180–185, 187–189, 191, 193, 194, 196, 197,
201–204, 206, 207, 209, 214, 228
Mass median diameter, 192
Mauna Loa CO2 record, 298
Mauna Loa Observatory, 272
MBL. See Marine boundary layer (MBL)
Mean square slope (mss), 264
Mean-square wave slope, 60, 62, 72
Mechanisms, 6, 12, 17, 20, 21, 32, 33, 35, 37
Mediterranean Sea, 193, 214, 223, 227, 228

311
MEdium Resolution Imaging Spectrometer
(MERIS), 267, 269, 271
Medium scale in situ enrichments, 287
MEMENTO. See MarinE MethanE and NiTrous Oxide
(MEMENTO) database
MERCATOR, 278
MERIS. See MEdium Resolution Imaging Spectrometer
(MERIS)
Mesocosm, 217, 218, 223–225, 228, 229, 231
Mesoscale eddies, 261
Mesoscale ocean enrichment, 218
experiments, 286
Metabolic activity/rates, 221–223, 231
Methane (CH4), 2, 20, 28, 31, 37, 117–161, 256, 259,
272–274, 281
Methane hydrate, 120, 148–150, 158–159
Methane monooxygenase (MMO), 20
Methanesulphonic acid (MSA), 9, 11, 13, 256, 259
Methanethiol (CH3SH, methylmercaptan), 7
Methanogenesis, 142–148, 151
Methanol, 26–31, 258, 259
Methanotrophic/nitrifying bacteria, 20
Methylating agents, 20
Methyl bromide (CH3Br), 15, 17–18
Methyl cyanide (acetonitrile, CH3CN), 33–34
Methyl ethyl ketone, 28
Methyl halide, 20
Methyl iodide (CH3I, iodomethane), 15, 19, 20, 22
Methyl nitrate (MeONO2), 32
Methyltransferase, 20
Microbes, 206, 218, 231
Microcosm, 211, 219–225, 231
Microlayer, 14, 26, 35, 65, 71, 72, 217, 231
Micrometeorology (micrometeorological), 181, 197
Microorganism, 206, 215
Microscale wave breaking, 59–63, 67, 83
Mid Term Strategy Initiatives, 293
Mixed layer, 213, 217, 219, 221
Mixed-layer depth (MLD), 268
Mixing ratios, 256, 258–260, 272, 279, 280
Model(s), 3–5, 9–13, 17–20, 22, 24–27, 32, 35, 39, 180, 185,
186, 192, 196–199, 201, 205, 207–211, 215, 217,
223–226, 229–231
aerosol transport model, 82
surface penetration model, 76, 84
surface renewal model, 75, 76, 84
Moderate Resolution Imaging Spectroradiometer (MODIS),
262, 267–272, 292, 293
Molecular iodine (I2), 22, 25
Momentum, 63, 69, 70, 73, 74, 76, 78–80, 83, 84, 93, 94
Monin-Obukhov length, 73
Monin-Obukhov Similarity Theory (MOST), 73
Monoterpene, 28, 32, 196, 197
MSA. See Methanesulphonic acid (MSA)
Multispectral ocean colour sensors, 269

N
National Centers for Environmental Prediction (NCEP),
262, 263, 296
Natural aerosol, 216, 221
Naval Oceanographic Office (NAVOCEANO), 262

312
NCEP. See National Centers for Environmental Prediction
(NCEP)
Neutral stability, 81
New particle formation, 189–191
NHx, 36
Nitrate, 255, 259, 287
Nitric acid (HNO3), 187, 259
Nitric oxide (NO), 21, 22, 24, 33, 35
Nitrification, 135, 136, 138, 141, 156, 157
Nitrification/denitrification, 33
Nitrogen dioxide (NO2), 274
Nitrogen (N2) fixation, 213, 222, 226, 256, 260
Nitrous oxide (N2O), 117–161, 276, 277, 281
Nitryl chloride (ClNO2), 21
NMHC. See Non-methane hydrocarbons (NMHC)
NMVOC. See Non-methane volatile organic compounds
(NMVOC )
NO3, 9, 10, 23, 24, 30, 31
Non-methane hydrocarbons (NMHC), 26–34, 40, 259
Non-methane volatile organic compounds (NMVOC ), 198
Non-sea-salt-(nss) sulphate aerosol, 256
Normalised water leaving radiances, 269
North Atlantic, 3, 7, 8, 10, 20, 24, 29, 31, 32, 36
North Atlantic Oscillation (NAO), 295
NOx, 10, 21–25, 28, 31
n-propyl nitrate, 32
Nucleation, 177, 185, 189–191, 197
Nucleophilic substation, 20
Numerical Weather Prediction (NWP) models, 263
Nutrients, 178, 193, 194, 198, 200, 204, 205, 211, 213–216, 218,
219, 221–228, 230, 231

O
Ocean acidification, 115, 117, 121, 130, 149–152, 156
Ocean-atmosphere exchanges, 147, 148
Ocean-atmosphere interaction, 171–227
Ocean-atmosphere interface (air-sea interface), 1
Ocean-Atmosphere-Sea Ice-Snow (OASIS), 265
Ocean Carbon-cycle Model Intercomparison Project, 97
Ocean colour, 177, 255, 262, 263, 275
Oceanflux, 270
Oceanic fertilisation by volcanic eruptions, 289
Oceanic observatories, 248
Oceanic transport inversion, 274
Ocean inversion model, 291
Ocean surface winds, 257
Ocean turbulence, 256
OH, 9–11, 14, 21–23, 26–28, 31, 34, 35, 38
OIO. See Iodine dioxide (OIO)
Oligotrophic, 30, 38, 179, 211, 217, 219–222
Oligotrophic environments, 217, 219
Oligotrophic waters, 263, 282–283
OMI, 266, 269
OMZ. See Oxygen minimum zone (OMZ)
Open ocean, 115, 117, 121–126, 131, 135–139, 143, 144,
146–150, 152–157
O3 photochemistry, 251
O3 photolysis rate, 251
Optical measurements, 220–221, 224
Orbiting Carbon Observatory (OCO), 268

Index
Organic carbon, 179, 194, 198, 210, 212, 218, 221, 223
Organic carbon pump, 120
Organic matter (OM), 174, 176–182, 184, 197, 200, 205–206,
210, 212, 220–221, 223, 224
Organic nutrients, 210
Organic radicals, 254
Organohalides, 265
OSTIA, 257
OVOCs. See Oxygenated volatile organic compounds (OVOCs)
Oxidation, 2, 7, 9–13, 18, 20, 24, 27, 29, 30, 35–38
Oxidative stress, 20
Oxidising capacity, 254
Oxygen, 249, 268, 272, 275
Oxygenated volatile organic compounds (OVOCs), 26–31,
254, 270
Oxygen minimum zone (OMZ), 131, 132, 137, 139,
151–153, 293
Ozone (O3), 9, 17, 20–27, 31
Ozone destruction, 251, 255, 269–270

P
Palaeo-proxies, 271
Parameterisation, 56, 62, 64, 66, 70, 73, 75, 79, 87–95, 97–99
Parasitic capillary wave, 56, 74
Partial pressure, 118, 119
difference, 56, 85, 98
Particle
backscattering, 263
burst, 24
concentration, 185, 186, 206
fluxes, 78, 79
settling, 212, 213
size distribution, 172, 187, 263
Particle image velocimetry (PIV), 59, 80
Particles (IOP), 24
Particle tracking velocimetry (PTV), 80
Particulate carbon, 249
Particulate inorganic carbon (PIC), 261–264
Particulate organic carbon (POC), 179, 210, 224, 227, 261–264
Particulate organic matter (POM), 182, 197, 224
Partitioning, 183, 193, 199, 209, 226
Passive polarimetric sensors, 258
Pathfinder algorithm, 257
pCO2. See CO2 partial pressure (pCO2)
pCO2 mapping, 291
Péclet (Pe) number, 57
Perchloroethylene (PCE tetrachloroethylene), 14
Peroxyacetylnitrate (PAN), 27, 254
PFTs. See Phytoplankton functional types (PFTs)
pH, 117, 121, 122, 130, 150, 151, 189, 211, 222, 250, 288
Phaeocystis, 15, 17
Phosphorus, 173, 206–212, 219, 226, 251, 289
Photobleaching, 30
Photochemical/photochemistry, 7, 14, 19, 29, 30, 32, 33, 36–38
Photochemistry, 251
Photodecomposition, 254
PhytoDOAS, 264
Phytoplankton, 2, 3, 6, 14, 15, 17–20, 31–33, 36, 38, 120, 138,
151, 173, 176, 180, 188, 190, 210, 213–220, 223, 261,
263, 270, 274, 284, 287

Index
Phytoplankton functional types (PFTs), 263
Phytoplankton size classes (PSCs), 263, 264
PIC. See Particulate inorganic carbon (PIC)
Picoplankton, 217
PIV. See Particle image velocimetry (PIV)
Planetary wave dynamics, 256
Plankton, 176, 179, 182, 215, 218
PM10, 193, 201
POC. See Particulate organic carbon (POC)
Polybrominated methanes, 17–19
Polynias, 265
Precipitation, 62, 78–80, 83, 96
Precursor hydrocarbons, 270
Primary production, 17, 71, 96, 216–218, 220, 261, 287, 291
Prochlorococcus, 19, 217
Production, 172–202, 204, 215–223
Production flux, 174, 175, 225
Production rate, 19, 27, 31
Profile, 58, 61, 66, 70–72, 74, 75, 80, 84–86, 93
Profiling floats, 248
Propanal (propional), 27, 29
Propane, 31
Propanol, 29, 30
Propene, 31
PSCs. See Phytoplankton size classes (PSCs)
Pyruvate, 29
Q
Quality-flagged controlled data sets, 250
R
Radar altimeter, 256, 265
Radar backscatter, 259
Radiative balance, 251
Radiative budget, 202
Radiative forcing, 24
Radical recombination, 19
Radio-frequency interferences (RFI), 261
Radiometers, 256–258
Rain, 56, 67–68, 78, 79, 93, 101
Rainwater chemistry, 280–281, 283
Reactive bromine, 17
Reactive marine-derived halogens, 255
Reactive trace gases, 250, 251
Reactive trace species, 251–255
Relative humidity (RH), 172, 174, 176–180, 198, 202
Remineralisation, 214, 218, 224
Removal, 172–174, 194–196, 200
Resonance fluorescence, 21
RH. See Relative humidity (RH)
Rivers, 115, 125, 129, 130, 134, 142, 151, 153–155
Roughness length, 71, 82

S
S-adenosyl-L-methionine (SAM), 20
Sahel region, 251, 252, 254
Salinity, 175, 176, 248, 250, 255, 260–261, 265, 290

313
SAR. See Synthetic aperture radar (SAR)
Satellite, 21, 27, 33, 39, 175, 177, 182, 191, 194, 201, 202, 204,
213–216, 223, 225
measurement, 99
oceanography, 255
retrieval, 190, 202
wind speeds, 90, 99, 101
Saturation (state, super, under), 16–18, 30, 32, 37
Saturation anomaly, 32
SCanning Imaging Absorption spectroMeter for Atmospheric
CartograpHY (SCIAMACHY), 264, 266–270
Scatterometer, 90, 99–101, 258, 259
Scavenging, 211–213, 220, 221, 226
Schmidt number (Sc)
dependence, 63, 84, 100
exponent, 72, 78, 93
SCIAMACHY. See SCanning Imaging Absorption
spectroMeter for Atmospheric CartograpHY
(SCIAMACHY)
SDS. See Sodium dodecyl sulphate (SDS)
Sea ice, 95–96, 121, 125–126, 141, 147–151, 155
bulk salinity, 265
distribution and motion, 264
transfer estimates, 96
Sea-ice fraction, 97
Sea salt, 174–183, 200, 204–207
aerosol production, 61–62
source function, 292
Sea salt aerosols (SSA), 175, 176, 200, 203, 206, 207
emissions, 293
mass concentrations, 293
reflectance, 292
source functions, 293
Seasat, 256
Seasonal(ity, cycles), 3, 14, 16–18, 32, 33, 35, 39, 180
Seasonal ice zones, 249
Sea spray, 174, 176, 178–180, 184, 205, 206
particles, 62
Sea spray aerosol (SSA), 172–182, 224–226
Sea spray aerosol source function (SSSF), 175
Sea spray source function (SSSF), 62, 64
Sea state, 256–260
Sea surface salinity (SSS), 255, 260–261, 290
Sea surface temperature (SST), 14–17, 34, 176, 224, 256–260,
267, 273, 284, 290–292
SeaWiFS, 177, 182, 223, 262–264, 267, 290
Secondary organic aerosol (SOA), 37, 183, 184, 191, 193, 196,
199–200
Secondary production, 174, 183
Sediment core studies, 287
Self-organising maps, 291
Sensor
closed path sensors, 83
open path sensor, 83, 95
Sesquiterpene, 191
Shear
air-side, 67
wind shear, 76
Ship emissions, 172, 194–195, 293

314
Short-lived brominated and iodinated trace gases, 277
Short lived trace gases, 1–40
Silicate (Si), 188, 214, 222, 226
Simulation
direct numerical simulation (DNS), 74–76
large-eddy simulation (LES), 74–76
large-wave simulation (LWS), 75
Single scattering albedo, 202
Sink, 6, 17, 19, 27, 28, 30, 32–39
Sinking particles, 214, 224
Skin temperature (SSTskin), 256, 265
Slick, 68, 99
SMOS. See Soil Moisture and Ocean Salinity (SMOS)
SOA. See Secondary organic aerosol (SOA)
SOCAT. See Surface Ocean CO2 ATlas (SOCAT)
Sodium dodecyl sulphate (SDS), 179
Soil Moisture and Ocean Salinity (SMOS), 261, 262, 290
Solar radiation dose (SRD), 2, 12
Solubility, 56, 63, 64, 71, 76–77, 90–91, 95, 100, 101, 178, 189,
205, 209, 212, 215, 221
Solubility pump, 120
Soluble organic iodide (SOI), 26
Source, 2, 3, 6–9, 11, 13–23, 26–40
SRD. See Solar radiation dose (SRD)
SSA. See Sea salt aerosols (SSA); Sea spray aerosol (SSA)
SSM/I series satellites, 264
SSS. See Sea surface salinity (SSS)
SSS scattering, 261
SST. See Sea surface temperature (SST)
SSTskin. See Skin temperature (SSTskin)
Stoichiometry, 214
Stoke’s drift, 65, 66
Stratification, 65, 67, 70, 71, 76, 85, 93, 124, 147, 149, 150
atmospheric stratification, 65, 70, 71, 76
Stratosphere, 13, 17, 33
Stratosphere-troposphere exchange, 26
Stress
kinematic stress, 76
surface stress, 76, 99
Submersibles, 250
Sub-mesoscale variability, 250
Subpolar gyre, 291
Subtropical gyre, 250
Sulphate aerosols, 251, 262, 287
Sulphate/sulfate, 7, 9, 12, 36, 177, 178, 182–184, 186, 189, 190,
196–200, 204, 205, 209
Sulphur cycle, 4, 9, 11, 12
Sulphur dioxide (SO2), 9–11, 269
Sulphuric acid clusters, 254
Summer paradox (or DMS summer paradox), 2, 5, 250, 284
Sun synchronous orbit, 268
Supersaturation, 118, 133, 139, 150
Surface
divergence, 57, 58, 60
renewal, 57, 59, 68, 74, 81
roughness, 59, 82, 87, 90, 92
steepness, 74
Surface Ocean CO2 Atlas (SOCAT), 279–280, 283, 289
Surface ocean pCO2, 288

Index
Surface water (fCO2) (fugacity of CO2), 117–126, 129, 137,
156, 279, 280
Surface Water and Ocean Topography (SWOT), 256
Surfactants, 8, 34, 59, 65, 68–69, 82, 88, 90, 93
soluble surfactants, 68
Synechococcus, 217, 218
Synthetic aperture radar (SAR), 258, 265
T
TCE. See Trichloroethylene (TCE)
Technique
disjunct eddy covariance (DEC), 82, 83
dual tracer technique, 68, 84–86, 94, 99
eddy-covariance (EC), 63, 81–83, 85–88, 92–94, 101
flow-measurement technique, 80
mass balance technique, 80, 84, 85, 88, 89, 101
micrometeorological technique, 62, 81–83, 85
radon deficit, 84
small-scale technique, 80–87
thermographic technique, 81, 85
Ternary nucleation, 37
Terrestrial, 174, 189, 191–193
Tert-butyl alcohol, 29
Thermal structure, 58
Thermography, 85
Tidal cycles, 24
Tidal pumping, 135, 142
TOPEX/Poseidon, 256
Total Ozone Mapping Spectrometer (TOMS), 269
Trace gas cycling, 1
Trace gas exchange, 21
Transfer resistance, 21, 77, 78
Transfer velocity, 56, 57, 59, 62–65, 69–74, 76–81, 84, 87–92,
94, 97–99, 101, 257, 289
Transport, 9, 14, 18, 26, 33–36, 172, 174–202, 209–211, 220,
223, 226
Transport model, 180, 181, 191, 193
eddy renewal model, 73
surface penetration model, 73, 81
surface renewal model, 73, 81
thin film model, 72
Trichloroethylene (TCE), 14–16
Trichodesmium, 37
Tropical circulation, 256
Troposphere, 13, 17, 21, 22, 26–28, 31, 34, 35, 38, 39,
116–119, 121, 129, 130, 134, 137, 139–143,
147–150, 153, 154, 156
Tropospheric gases, 251
Tropospheric ozone, 251, 254, 255
Turbulence
coherent turbulence, 60, 61
Kolmogoroff scale turbulence, 74
near-surface turbulence, 57, 58, 60, 67
shear induced turbulence, 60, 65, 92
surface water turbulence, 93
Turbulent kinetic energy, 60, 65, 87
Turbulent kinetic energy dissipation, 58
Turbulent transport, 72
Turnover, 3, 36

Index
U
Ultrafine particles, 24, 25
Upper ocean dynamics, 66
Uptake, 2, 12, 15, 22, 23, 25, 30, 34–36, 178, 192, 196–199, 209,
211, 214, 216, 218
Upwelling, 7, 17, 30, 32, 38
UV radiation, 2, 3, 12, 30, 33, 38
V
Velocity parameterisation, 276
Volcanic ash, 190, 191, 209, 226, 287–289
Volcanic ash fertilisation, 288
Voluntary Observing Ships (VOS), 289, 291
VOS. See Voluntary Observing Ships (VOS)

W
Water insoluble organic carbon (WIOC), 176
Water insoluble organic matter (WIOM), 177, 180, 181, 205
Water soluble organic carbon (WSOC), 183
Water soluble organic matter (WSOM), 177, 178, 180, 205
Water temperature, 175, 176, 180
Wave
capillary wave, 56, 60, 87
gravity wave, 59, 60, 71, 74–76, 90

315
mean square slope, 90, 99, 101
wave age, 66
wind-generated surface wave, 77
wind-generated wave, 65–66, 74
Wave breaking
large wave breaking, 61
microscale wave breaking, 56–60, 65, 81
Wave field, 59, 60, 65, 72, 94
Wave height, 255, 259
Wet deposition, 172, 188, 211
Whitecap, 56, 62–64, 88, 91, 95, 174, 225, 267
whitecap coverage, 63, 64, 292
whitecap fraction, 175, 225, 267
Wind speed, 175, 176, 180, 187, 188, 202, 204, 205, 225
Wind stress divergence, 260
Wind surface stress, 259
WIOC. See Water insoluble organic carbon (WIOC)
WIOM. See Water insoluble organic matter (WIOM)
WSOC. See Water soluble organic carbon (WSOC)
WSOM. See Water soluble organic matter (WSOM)

Z
Zooplankton, 190, 224