Global Change – the IGBP Series 3

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Global Change – The IGBP Series 3

Berlin Heidelberg New York Hong Kong London Milan Paris Tokyo Michael J. R. Fasham (Ed.)

Ocean Biogeochemistry

The Role of the Ocean Carbon Cycle in Global Change

With 130 Figures

123 Editor

Michael J. R. Fasham Southampton Oceanography Centre Waterfront Campus, Southampton, SO14 3ZH, UK [email protected]

ISSN 1619-2435 ISBN 3-540-42398-2 Springer-Verlag Berlin Heidelberg New York

Library of Congress Cataloging-in-Publication Data

Ocean biogeochemistry : a synthesis of the Joint Global Ocean Flux Study (JGOFS) / Michael J.R. Fasham (ed.). p.cm. -- (Global change--the IGBP series, ISSN 1619-2435) Includes bibliographical references. ISBN 3-540-42398-2 (alk. pagper) 1. Carbon cycle (Biogeochemistry) 2. Chemical oceanography. I. Fasham, M. J. R. II. Joint Global Ocean Flux Study. III. Series.

QH344.025 2003 551.46'01--dc21 2002044503

Bibliographic information published by Die Deutsche Bibliothek Die Deutsche Bibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data is available in the Internet at http://dnb.ddb.de

Photos courtesy of U.S. JGOFS Planning Office. RVIB Nathaniel B. Palmer docks at McMurdo Station, Antarctica, at start of final U.S. JGOFS cruise. Photo by Mardi Bowles. Launching moored instruments from RV Atlantis II. Photo by Craig Dickson. El Niño-Southern Oscillation effects on equatorial Pacific chlorophyll a concentrations during January (El Niño, left) and July (La Niña, right) of 1998. (Image courtesy of the SeaWiFS Project, NASA Goddard Space Flight Center and ORBIMAGE.) The copepod photo is courtesy of Southampton Oceanography Centre.

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Printed on acid-free paper – 3140 – 5 4 3 2 1 0 Foreword

Peter G. Brewer, Monterey Bay Aquarium Research Institute, Moss Landing, USA

Pre-History

The development of the field of ‘Ocean Biogeochemistry’ is a remarkable story, and one in which the JGOFS program, and the researchers whose work is presented here, have played critical leadership roles. The term ‘biogeochemical cycles’ became familiar in the mid 1980s when scientists first tried to describe to the policy world the complex set of interlocking processes involved in global change. Before then almost wholly physical descriptions were given, of radiative balances, heat fluxes, transport processes etc., and a few simple ocean or land CO2 terms were added. However when attempts were then made to add the real effects and feedback terms of land and oceans a view of amazing complexity appeared. The first attempts to communicate this are fondly remembered in the ‘horrendograms’ produced by Francis Bretherton – wiring diagrams of computer chip complexity showing simultaneous links between warming and respiration, photosynthesis and CO2, ocean circulation and productivity, energy balances and chlorophyll, ocean gas releases and clouds. The problem was that no one knew how to handle all this, and, since real knowledge was lacking, all kinds of claims were made for rates, reservoirs and mechanisms, with no idea as to which one was dominant or even important. It was messy, clamorous, essential, and wide open. Today it is a powerful discipline, with measured rates, innovative experiments, complex models, and vigorous testing of ideas. The use of biogeochemical cycles as a term to describe diagenetic reactions in sediments had arisen earlier, but it was a total shock to hear in about 1986 that NASA had formally reorganized its earth science programs to highlight the new discipline. No one really knew how to react, since well-entrenched physics, chemistry, and biology programs were suddenly cast adrift. I well recall a corridor conversation with a distinguished physical oceanographer the day the news broke. It seemed incompre- hensible. Today the AGU journal ‘Global Biogeochemical Cycles’ ranks third out of 117 titles in the Geosciences in the 2000 Journal Citation Reports. The JGOFS program, or more accurately the scientists whose energy, dedication, and creativity are represented by that acronym, arose almost simultaneously with this transition, and its success is unassailable. How that happened is a remarkable story, and one worth telling.

A U.S. Initiative

In the US in the early 1980s the set of ocean observing programs deriving from the large scale International Decade of Ocean Exploration programs were winding down. Only the geochemical tracer efforts were truly active on a global scale, and here the first powerful measurements of the chlorofluorocarbon tracers were made. In ocean biology a program examining the processes associated with warm core rings, spun off from the Gulf Stream, occupied center stage. The first glimpses of ocean color data from space appeared from the Coastal Zone Color Scanner (CZCS) on Nimbus 7, but this was a struggle compounded not only by the technical challenge, but also by problems within the NASA team responsible for the data. The sensor was exceeding its design life, and beginning to fail. Frankly, it wasn’t an impressive picture. And discussions of plans for more of the same for the future were received by NASA with little enthusiasm. VI Foreword

Yet there were opportunities. The unease over global change was being translated, by Presidential Science Advisor D. Allan Bromley, into a ‘U.S. Global Change Program’ that offered the promise of political support. There were advances being made in trace metal clean techniques that yielded new insights into ocean biogeochemical processes. Lively and assertive individuals outside NASA were pressing forward with innovative CZCS results, and extending observations into blue water far beyond the coastal zones. New sediment trap techniques were capturing rhythmic fluctuations in the rain of particles to the sea floor. And the first ice core records of large scale CO2 fluctuations associated with glaciations, and attributed to linked changes in ocean circulation and productivity, were appearing. The leadership to capture these opportunities came first undeniably from John Steele. John was frustrated by seeing plans emerge within the ocean physics community for a major observing program, allied to an altimetric satellite, without equivalent planning for biological and geochemical programs. His own background in marine ecosystem modeling had not previously exposed him to serious ocean geochemistry, but he sensed that alone the ocean biology community would not be able to seize the opportunity. A set of planning meetings of an ‘Ad Hoc Group on Ocean Flux Experiments’ (John Steele, Jim Baker, Wally Broecker, Jim McCarthy, and Carl Wunsch) took place in 1983–1984 under the auspices of the US National Academy Ocean Sciences Board on Ocean Sci- ence and Policy, and this led to a major workshop at the NAS Woods Hole Center in September 1984. The ‘Global Ocean Flux Study’ (GOFS) report from that workshop provided the impetus for what is now the JGOFS program. The preface for that report emphasized the need for study of “the physical, chemical, and biological processes governing the production and fate of biogenic materials in the sea … well enough to predict their influences on, and responses to, global scale perturbations, whether natural or anthropogenic …” It went on to draw analogies between the Pleistocene fluctuations in climate, and “the beginning of a fossil-fuel CO2-induced super-interglacial period.” I attended that meeting, and was soon perplexed. Few attendees knew anything of the assumed linkage to satellite ocean color, and widespread skepticism prevailed (the language alone of this community was foreign to most, and to me). The failing CZCS was apparently to be replaced by an Ocean Color Imager in 1985 or so – in practice it took 15 years of hard work for SeaWIFS to be launched! The busy sediment trap community were convinced that their technique was central; but trapping particles close to the euphotic zone was fraught with problems of technique, and mixed layer modeling was scarcely understood. The reference to paleo-climates ensured that the sediment record had to be included, but the scale mismatch of those studies with upper water column chemistry and biology was obvious. And persistent large discrepancies between productivity estimates from the oxygen balance and 14C uptake, were aggres- sively debated. It was an interesting mess. Ocean physics was tacitly assumed to be taken care of somewhere else, most likely through WOCE. And in spite of the strong reference to the anthropogenic CO2 signals, no one had thought to schedule any CO2 papers into the meeting.

Building a U.S. Program

The lack of CO2 papers in the GOFS meeting report was fixed very simply by a committee charged with editing the proceedings of the meeting. Some manuscripts were simply added. I was asked by Neil Andersen to join that editorial committee (with Ken Bruland, Peter Jumars, Jim McCarthy) and we met about a week later in Washington, D.C. It at once became clear that we were to be charged with not simply editing, but with program creation. We were briefed by Jim Baker that the most urgent item was a pitch to NASA headquarters for support of an ocean color satellite. None of us knew how to proceed with turning such a wide-ranging report into a viable and coherent research program. To break the impasse I suggested we go home, each with an editing Foreword VII assignment, and meet a week later to finish the report. We also had homework to do in the nature of drafting some outline or scheme for pulling the material together into a comprehensible program. This did not work as I had hoped, for when we reconvened I was the only one of the group to have prepared any semi-formal material. These were some results from a very crude North Atlantic mixed layer model comparing physical and biological forcing of CO2, and comments on extending a similar calculation to the basin scale with some sense of what could be measured and tested. This was enough, and I was asked to Chair the group. A very difficult period then followed. Since the constituency was broad a large committee was formed, few of whom had any prior collegial contact. But we worked at it, and I formed a ‘Planning Office’ of one. We had meetings and produced some reports. After some wrangling a broad plan to implement a strategy of carrying out a global survey of CO2 and related properties, implementing time series stations, and execut- ing a set of sophisticated process studies was set in place. It took, and it allowed for multiple roles for remote sensing on many space and time scales. In the fall of 1986 a set of key events took place in rapid succession. Planning for WOCE had reached the point where a large-scale hydrographic program was forming. A meeting at the U.S. National Academy framed the debate; I had independently discussed with Carl Wunsch the issue of CO 2 measurements, and he felt that these were not part of the WOCE observing package. But he asked if I would present the case at the NAS meeting. This went very well, and a compromise was proposed whereby ‘GOFS’ would provide funding, oversight, people and tools, and WOCE would provide bunks on their cruises and access to samples and supporting data. A handshake sealed the deal, which was proposed and accepted in about 30 seconds. We could go global. Secondly Gene Feldman, new at NASA had created the first basin scale chlorophyll image from CZCS data. It had flaws: dubbed by some as ‘the ocean on fire’ from the garish orange for high pseudo-chlorophyll levels, or the ‘Pac-Man’ image from an oddly shaped data gap in Hudson Bay, it nonetheless broke new ground. This image appeared on the cover of the November 4, 1986 issue of EOS, with the AGU Fall Meeting ab- stracts and brief papers by the GOFS committee, and the NASA team. It was a coveted slot, and it had great impact. And since the WOCE connection had been made, and WOCE was formally international, we had the impetus to move beyond the U.S. This had always been intended, but without a formal opening or partially defined plan to propose, real progress had not been made. Within an hour or two of the WOCE handshake deal a letter to SCOR was drafted, and hand carried to Tasmania the next day. The letter requested that SCOR take up the challenge of sponsoring a major new initiative on an Ocean Flux Program, and cited the progress made. It was well received, and work began at once on an enabling meeting.

Creation of JGOFS

The first SCOR-sponsored meeting was held in Paris, at ICSU House, February 17–20, 1987. Gerold Siedler, as President of SCOR, kept a careful eye on proceedings, Jim Baker acted as Chair, and Elizabeth Gross facilitated. Roger Chesselet helped secure the superb location. We had all learned some hard lessons about preparation: Gene Feldman had now created the first global chlorophyll image, and this was first shown to me in dim dawn light at the luggage carousel in De Gaulle airport by Jim Baker the day before the meeting. It was superb. I had written a discussion paper especially for the meeting on the comparative North Atlantic heat, CO2, and nutrient budgets, with David Dyrssen. It illustrated what we might gain from a survey, and an abbreviated version was later backed up by some measurements and published in Science. There was no real understanding of how international logistics might work, but as we went round the room it was clear that the desire to create a novel and important program was strong. Jim Baker proposed a ‘J’ for the program; everyone said yes. Gerold Siedler nominated Bernt Zeitschel as the Chair, and this was agreed upon. It all happened quickly, VIII Foreword and the real work began. Hugh Livingston had joined the U.S. Planning Office, and was superb in science and diplomacy in this role. Liz Gross guided the multi-national effort with grace and skill. Neil Andersen kept the thread of funding and agency sponsorship alive and well. The U.S. JGOFS Newsletter was born, and thrived. And a succession of excellent reports cataloged the evolution of scientific planning and understanding.

Fast Forward

History is fun, and important. But what did all this start up effort achieve? There are now volumes of papers to testify to this, but perhaps I can pick on a few highlights. The 1989 multi-national North Atlantic Bloom Experiment was put together with extraordinary speed, and it combined ships and aircraft observations in new ways. U.S., German, U.K., Canadian and Dutch ships and scientists co-operated in the field. This set the tone for a whole series of successful process studies. The 1990 Fasham- Ducklow-McKelvie paper on modeling upper ocean production and the microbial loop laid the ground for a decade long renaissance in ocean biogeochemistry. The 1988 establishment of the Hawaii and Bermuda Time series Stations was essential. Critics at the time pointed to two sub-tropical locations as a deficiency: the separate signals evolving so beautifully there answer the challenge. The global CO2 survey was a heroic effort. From that we now see the penetration of fossil fuel CO2 to well below 1 km throughout the ocean, and the detection of sea floor carbonate dissolution from the 20th century chemical invasion. There were problems. An ocean color satellite did not fly until 1997: a full decade late, and only after endless effort. The linkage of the oceanic CO2 problem with biogeochemical measurements and models had amazing birth pains. Most ocean chemists had no real knowledge of microbial processes; and all (so far as I could tell) ocean biologists were in disbelief that the fossil fuel CO2 invasion was a purely inorganic/physical phenomenon. Very few people looked far to the fu- st ture, say to an ocean at the end of the 21 century where the CO2 maximum may be at the surface, and essential biogeochemical cycles may be profoundly changed. The first US JGOFS response to John Martin’s proposal of an iron fertilization field experiment (at a meeting I did not attend) was to vote it down! However, funding was obtained from the DOE and the resulting iron fertilization experiments were a brilliant success. The papers in this volume show how far we have come. Satellite ocean color images pervade the literature. Sophisticated models of biogeochemical cycles are routinely used. All participants are fluent in the CO2 connection. Ready access to more than a decade of time series data is taken for granted. Synthesis and modeling efforts are supported, and are productive. And iron fertilization science has a strong international community. The goals of JGOFS were carefully negotiated, and they included the need “To determine on a global scale the processes controlling the time varying fluxes of carbon and associated biogenic elements in the ocean …”, and “To develop the capability to predict on a global scale the response of oceanic biochemical processes to anthropogenic perturbations, in particular those related to climate change.” This knowledge is urgently required, for mankind’s influence on the carbon cycle is proceeding far faster than we usually acknowledge. In 1984, at the time of the first U.S. ‘GOFS’ meeting, atmospheric CO2 levels were 344 ppm, or 64 ppm above the pre-industrial baseline. Today they are 372 ppm, or 92 ppm above the pre-industrial levels – a 43% increase while we have been planning and carrying out our research. Over these 18 years the ocean has taken up some 131 billion tons of CO2 gas. International ocean science of a new kind evolved with JGOFS. It is created by the efforts of individuals who do not see boundaries, only opportunities. A thriving community of students and Post Docs. emerges each year, and happily spreads across international borders seeking excellence. And they often find it in the laboratories of scientists whose work is represented here. Contents

Introduction ...... 1 Acknowledgements ...... 2 References ...... 2

1 Biogeochemical Provinces: Towards a JGOFS Synthesis ...... 3 1.1 Plankton Community Structure and Distribution ...... 3 1.2 Partitioning the Oceans ...... 5 1.3 Primary Production in Ocean Domains and Provinces ...... 8 1.3.1 Adding up Global PP Observations ...... 11 1.4 Bacterial Production and DOC Flux ...... 11 1.5 A Provincial Outlook ...... 14 Acknowledgements ...... 14 References ...... 14

2 Physical Transport of Nutrients and the Maintenance of Biological Production ...... 19 2.1 Introduction ...... 19 2.2 Global Overturning Circulation and Nutrient Transport ...... 21 2.2.1 Overturning Circulation and Water-Mass Distributions ...... 21 2.2.2 Southern Ocean ...... 22 2.2.3 Nutrient Supply to the Northern Basins ...... 24 2.2.4 Summary ...... 25 2.3 Convection ...... 25 2.3.1 Vertical Transfer of Nutrients ...... 25 2.3.2 Biophysical Interactions and Convection ...... 25 2.3.3 Limited Role of Convection ...... 28 2.3.4 Summary ...... 28 2.4 Wind-Driven Circulations: Gyres and Boundary Currents ...... 29 2.4.1 Wind-Induced Upwelling and Gyre Circulations ...... 29 2.4.2 Gyre-Scale Circulations ...... 29 2.4.3 Subduction and Fluid Transfer into the Seasonal Boundary Layer . . . . 30 2.4.4 Oligotrophic Subtropical Gyres ...... 31 2.4.5 Western Boundary Transport of Nutrients ...... 33 2.4.6 Summary ...... 35 2.5 Smaller-Scale Circulations: Mesoscale Eddies, Waves and Sub-Mesoscale Fronts ...... 35 2.5.1 Formation of Mesoscale Eddies and Sub-Mesoscale Fronts ...... 35 2.5.2 Local Response to Planetary Waves, Eddies and Fronts ...... 36 2.5.3 Far Field Effects: Eddy Transport and Diffusion ...... 40 2.5.4 Summary ...... 41 2.6 Interannual and Long-Term Variability ...... 42 2.6.1 Coupled Atmosphere-Ocean Changes: ENSO ...... 42 2.6.2 North Atlantic Oscillation ...... 43 2.6.3 Changes in Overturning Circulation ...... 45 2.6.4 Summary ...... 46 X Contents

2.7 Conclusions ...... 46 Acknowledgements ...... 47 Notes ...... 47 References ...... 49

3 Continental Margin Exchanges ...... 53 3.1 Introduction ...... 53 3.2 Recycling Systems ...... 56 3.3 Export Systems ...... 59 3.4 Coastal Upwelling Systems ...... 60 3.5 California Current System ...... 61 3.6 Humboldt Current System ...... 62 3.7 Benguela Current System ...... 63 3.8 Monsoonal Upwelling Systems ...... 63 3.9 Biogeochemical Budgeting ...... 64 3.10 The Arctic Shelves ...... 67 3.10.1 Introduction ...... 67 3.10.2 The Arctic Ocean As a Mediterranean, Shelf-Dominated Sea ...... 69 3.10.3 The Shelves of the Arctic Ocean ...... 71 3.10.4 Barents Shelf ...... 73 3.10.5 Kara Shelf ...... 73 3.10.6 Laptev Shelf ...... 74 3.10.7 East Siberian and Chukchi Shelves ...... 74 3.10.8 Beaufort Shelf ...... 74 3.10.9 The Mackenzie Shelf of the Beaufort Sea as a Case Study ...... 75 3.10.10Shelf to Basin Sediment Transport in the Arctic ...... 76 3.10.11 CH4, DMS (Dimethyl-Sulphide) Production in the Arctic ...... 77 3.10.12 A Budget for the Arctic Shelves ...... 77 3.10.13 Global Change; Speculation on Consequences for Arctic Shelves . . . . . 78 3.11 Marginal Seas ...... 79 3.11.1 High Latitude Marginal Seas ...... 79 3.11.2 Semi-Enclosed Marginal Seas ...... 80 3.11.3 Initial Synthesis ...... 82 3.11.4 Future Research ...... 84 3.11.5 Summary ...... 88 Acknowledgments ...... 89 References ...... 89 Apendix 3.1 – Continental Margins: Site Descriptions ...... 95

4 Phytoplankton and Their Role in Primary, New, and Export Production . . . . 99 4.1 Introduction ...... 99 4.1.1 A Brief Introduction to Phytoplankton ...... 99 4.1.2 Photosynthesis and Primary Production ...... 101 4.1.3 Measuring Photosynthesis and Net Primary Production in the Sea ...... 102 4.1.4 A Brief History of the Measurement of Primary Productivity in the Oceans ...... 102 4.1.5 Quantifying Global Net Primary Productivity in the Oceans ...... 103 4.1.6 Export, New and ‘True New’ Production ...... 106 4.1.7 Elemental Ratios and Constraints on New Production ...... 107 4.1.8 New Production, Export Production, and Net Community Production ...... 107 4.1.9 Measurement of New Production ...... 108 4.1.10 Measurement of Net Community Production ...... 109 4.1.11 Measurement of Export Production ...... 109 4.1.12 Summary of Methods ...... 111 Contents XI

4.2 Synthesis ...... 112 4.2.1 Physical Controls of Export Fluxes: the Importance of Functional Groups ...... 115 4.2.2 Calcium Carbonate Precipitation ...... 116 4.2.3 Primary, New and Export Production and the Global Carbon Cycle on Longer Time Scales ...... 116 References ...... 118

5 Carbon Dioxide Fluxes in the Global Ocean ...... 123 5.1 Introduction ...... 123 5.2 The Oceans’ Influence on Atmospheric CO2 ...... 123 5.2.1 The Ocean Sets the Steady-State Atmospheric CO2 Concentration . . 123 5.2.2 The Pre-Industrial Steady State ...... 125 5.2.3 Pre-Industrial North-South Transports ...... 126 5.3 How Big is the Global Ocean Sink? ...... 127 5.3.1 1-D Models Calibrated with 14C ...... 127 5.3.2 3-D Models of the Ocean Carbon Cycle ...... 127 5.3.3 13C Changes with Time in the Ocean ...... 128 5.3.4 Atmospheric Observations ...... 128 5.3.5 Observations of the Air-Sea Flux ...... 129 5.3.6 Preformed Total Carbon Methods and the Ocean Inventory of CO2 . . . . 130 5.3.7 Summary of Recent Estimates of the Ocean Sink ...... 132 5.4 What Processes Control Air-Sea CO2 Flux? ...... 132 5.4.1 Patterns in the Global Survey ...... 132 5.4.2 Comparison Using Models ...... 133 5.4.3 Modelled Future Uptake of Anthropogenic CO2 ...... 138 5.5 Variability in the CO2 Signal ...... 138 5.5.1 Seasonal Variation ...... 138 5.5.2 Inter-Annual Variation ...... 138 5.6 The Gas Transfer Velocity ...... 140 5.7 Conclusion: the Next Ten Years ...... 141 Acknowledgements ...... 141 References ...... 141

6 Water Column Biogeochemistry below the Euphotic Zone ...... 145 6.1 Introduction ...... 145 6.2 The Twilight Zone: Biology, Biogeochemical Processes and Fluxes ...... 146 6.2.1 Biology of the Twilight Zone ...... 146 6.2.2 Nature of the Exported Material and Processes ...... 147 6.2.3 Microbial Production of Nitrous Oxide ...... 148 6.3 The Fluxes of Biogenic Matter versus Depth ...... 149 6.3.1 The Export Flux out of the Euphotic Zone ...... 149 6.3.2 The Export Flux towards the Ocean’s Interior (>1 000 m) ...... 150 6.4 The Variable Composition of the World Ocean Waters along the Conveyor Belt . 151 6.5 Conclusions and Perspectives ...... 152 6.5.1 The Ventilation Depth and the v-Ratio ...... 153 6.5.2 The Role of Mineral Ballasts in the Export of Carbon to the Ocean Interior ...... 153 References ...... 153

7 The Impact of Climate Change and Feedback Processes on the Ocean Carbon Cycle ...... 157 7.1 Introduction ...... 157 7.1.1 Climate and Change – Present Status ...... 157 7.1.2 Examples of Feedbacks in the Present and the Geological Past ...... 159 7.2 Feedbacks ...... 159 XII Contents

7.2.1 Definition ...... 159 7.2.2 Identification ...... 159 7.2.3 Classification ...... 160 7.2.4 Magnitude ...... 160 7.2.5 Evolution ...... 161 7.2.6 Interactions between Feedbacks ...... 161 7.2.7 Scales and Response Times ...... 161 7.2.8 Degree of Confidence – Understanding Feedbacks ...... 162 7.3 What do Current Models Predict? ...... 162 7.4 Status of Our Understanding of Feedbacks ...... 164 7.5 Nutrient Dynamics ...... 164 7.6 Phytoplankton and Carbon Limitation ...... 164 7.6.1 Atmospheric Supply of Nutrients ...... 164 7.6.2 Nitrogen Fixation ...... 165 7.6.3 Changes in Nutrient Uptake Stoichiometry – the Redfield Ratio . . . . 166 7.6.4 Export Production and Remineralisation in the Deep Ocean ...... 167 7.7 The Calcifiers ...... 167 7.7.1 Biogeochemistry and Feedbacks ...... 168 7.7.2 Global Distributions ...... 168 7.7.3 Controlling Factors, Forcing and Modelling ...... 169 7.7.4 A Case Study – the Bering Sea ...... 169 7.8 Iron Supply to the Oceans ...... 170 7.8.1 How Much of the Ocean Is Iron-Poor? ...... 170 7.8.2 The Supply of Iron to the Ocean ...... 170 7.8.3 Atmospheric Deposition of Iron versus Upwelling Supply ...... 170 7.8.4 Dust Supply – Global Maps and Fluxes ...... 171 7.8.5 Dust Transport – from Soil to Phytoplankton ...... 171 7.8.6 Response by the Biota – Detection ...... 171 7.8.7 The Future – Climate Change and Dust Deposition ...... 172 7.8.8 A Case Study – Uncertainties in Projection ...... 174 7.9 Dimethyl Sulphide and the Biota ...... 174 7.9.1 The CLAW Hypothesis ...... 174 7.9.2 What Produces DMSP/DMS? ...... 175 7.9.3 Global Distributions of DMS ...... 175 7.9.4 The Haptophyte Connection ...... 176 7.10 UV-B and Ozone Depletion ...... 176 7.10.1 Present Status of Ozone Depletion ...... 176 7.10.2 Phytoplankton and Primary Production ...... 177 7.10.3 Dissolved Organic Matter and Heterotrophic Bacteria ...... 177 7.10.4 Pelagic Community Response ...... 178 7.10.5 The Future ...... 178 7.11 Summary of Biotic Feedbacks ...... 178 7.12 Climate – Variability versus Change ...... 179 7.12.1 Climate Change ...... 179 7.12.2 Climate Variability ...... 180 7.12.3 Regime Shifts ...... 181 7.12.4 Unexpected Biological Responses to Climate Change ...... 183 7.13 Modeling – Future Goals ...... 183 7.14 The Future ...... 185 7.14.1 Detection and Projection ...... 185 7.14.2 Does the ‘Initial’ Condition Still Exist? ...... 185 7.14.3 The Need for a Regional Approach ...... 185 7.14.4 A New Definition of Biogeochemical Provinces? ...... 186 7.15 Summary ...... 186 Acknowledgements ...... 187 References ...... 187 Contents XIII

8 Benthic Processes and the Burial of Carbon ...... 195 8.1 Introduction ...... 195 8.2 Processes of Transport and Turnover of Material in the Deep Ocean ...... 196 8.2.1 Transfer of Organic Material from the Surface to the Deep Ocean . . 196 8.2.2 Benthic Carbon Turnover Processes ...... 197 8.3 Quantitative Estimates of Carbon Deposition and Carbon Turnover ...... 199 8.3.1 Strategies for Quantification of Benthic Fluxes ...... 199 8.3.2 Regional Assessments of Deep-Ocean Fluxes ...... 200 8.3.3 Global Estimates of Deep Ocean Carbon Deposition and Remineralization ...... 201 8.4 Proxy Indicators of Paleoproductivity ...... 204 8.4.1 Estimates Based on Organic Carbon Burial Rates ...... 204 8.4.2 Estimates Based on Biomarker Accumulation Rates ...... 205 8.4.3 Estimates Based on Barium Accumulation Rates ...... 205 8.4.4 Estimates Based on Radionuclide Ratios ...... 206 8.4.5 Estimates Based on Redox-Sensitive Trace Elements ...... 208 8.4.6 Estimates Based on Benthic and Planktonic Foraminifera ...... 208 8.4.7 Estimates Based on Coccolithophorids and Diatoms ...... 209 8.4.8 Proxies of Surface Nutrient Concentration ...... 209 8.4.9 Proxies of Surface Nutrient Utilization Efficiency ...... 210 8.5 Conclusions ...... 211 References ...... 212

9 Global Ocean Carbon Cycle Modeling ...... 217 9.1 Introduction ...... 217 9.2 Anthropogenic Carbon Uptake, Transient Tracers, and Physics ...... 218 9.3 Global Biogeochemical Cycles ...... 222 9.4 Ecosystem Dynamics ...... 225 9.5 Other Topics ...... 231 9.5.1 Mesoscale Physics ...... 232 9.5.2 Climate Variability and Secular Change ...... 232 9.5.3 Land, Coastal Ocean, and Sediment Interactions ...... 233 9.5.4 Inverse Modeling and Data Assimilation ...... 234 9.6 Summary ...... 234 Acknowledgements ...... 235 References ...... 235

10 Temporal Studies of Biogeochemical Processes Determined from Ocean Time-Series Observations During the JGOFS Era ...... 239 10.1 Introduction ...... 239 10.2 The Oceanic Carbon Cycle and the Biological Carbon Pump ...... 240 10.3 Global Inventory of JGOFS Time-Series Programs ...... 244 10.3.1 Bermuda Atlantic Time-Series Study (BATS) ...... 245 10.3.2 Dynamique des Flux Atmosphérique en Méditerranée (DYFAMED) . . 246 10.3.3 European Station for Time-Series in the Ocean Canary Islands (ESTOC) ...... 247 10.3.4 Hawaii Ocean Time-Series (HOT) ...... 247 10.3.5 Kerguelen Point Fixe (KERFIX) ...... 248 10.3.6 Kyodo Northwest Pacific Ocean Time-Series (KNOT) ...... 248 10.3.7 Ocean Station Papa (OSP or Sta. P) ...... 249 10.3.8 South East Asia Time-Series Station (SEATS) ...... 249 10.4 Some Practical Lessons Learned from the JGOFS Time-Series Programs . . . 250 10.5 Cross Ecosystem Habitat Comparisons: Nutrient, Chlorophyll and Production-Export Relationships ...... 251 10.5.1 Case Study 1: Estimates of the Biological Carbon Pump at Ocean Times Series Sites ...... 255 XIV Contents

10.5.2 Case Study 2: A ‘Bermuda Triangle’ Carbon Mystery with Global Implications ...... 257 10.5.3 Case Study 3: Decade-Scale, Climate-Driven Changes in the N2-Primed Prokaryote Carbon Pump ...... 258 10.5.4 Case Study 4: OSP Ecosytem Dynamics and the Role of Iron ...... 262 10.6 Beyond JGOFS: a Prospectus ...... 263 Acknowledgements ...... 264 References ...... 265

11 JGOFS: a Retrospective View ...... 269 11.1 The JGOFS Science Plan ...... 269 11.2 The Process Studies ...... 269 11.3 Iron Fertilisation Experiments ...... 271 11.4 The Time Series Stations ...... 271 11.5 The Global Survey ...... 272 11.6 Remote Sensing ...... 272 11.7 Benthic Studies ...... 273 11.8 Continental Margins ...... 273 11.9 Data Archiving ...... 273 11.10 Models and Synthesis ...... 274 11.11 Overall Conclusions ...... 274 References ...... 275

Index ...... 279 Contributors

Anderson, Robert Lamont-Doherty Earth Observatory Columbia University P.O. Box 1000, Palisades, NY 10964, USA

Barber, Richard T. Nicholas School of the Environment and Earth Sciences Duke University 135 Duke Marine Lab Road, Beaufort, NC 28516-9721, USA

Bates, Nicholas R. Bermuda Biological Station for Research, Inc. Ferry Reach, St. George’s GE01 Bermuda

Boyd, Philip W. National Institute of Water and Atmosphere Centre for Chemical and Physical Oceanography University of Otago Dunedin, New Zealand [email protected]

Brewer, Peter G. Monterey Bay Aquarium Research Institute Moss Landing CA 93923, USA

Chen, Chen-Tung Arthur Institute of Marine Geology and Chemistry National Sun Yat-sen University Kaohsiung 804, Taiwan, China (Taipei) [email protected]

Dittert, Nicolas Université de Bretagne Occidentale Institut Universitaire Européen de la Mer UMR CNRS 6539, Brest, France

Doney, Scott C. Climate and Global Dynamics National Center for Atmospheric Research P.O. Box 3000, Boulder, CO 80307, USA

Ducklow, Hugh W. School of Marine Science The College of William & Mary Box 1346, Gloucester Point, VA 23062-1346, USA [email protected]

Emerson, Steven Department of Oceanography University of Washington Seattle, WA 98195, USA XVI Contributors

Falkowski, Paul G. Institute of Marine and Coastal Science and Department of Geology Rutgers, The State University of New Jersey New Brunswick, New Jersey 08901, USA

Fasham, Michael J. R. Southampton Oceanography Centre Waterfront Campus, Southampton, SO14 3ZH, UK [email protected]

Follows, Michael J. Program in Atmospheres, Oceans and Climate Department of Earth, Atmospheric and Planetary Sciences Massachusetts Institute of Technology Cambridge MA 02139, USA [email protected] or [email protected]

Francois, Roger Woods Hole Oceanographic Institution Woods Hole, MA 02543, USA

Harrison, Paul J. Department of Earth and Ocean Sciences (Oceanography) University of British Columbia Vancouver, BC V6T 1Z4, Canada

Jahnke, Richard A. Skidaway Institute of Oceanography 10 Ocean Science Circle, Savannah, Georgia 31411, USA

Jeandel, Catherine Observatoire Midi-Pyrenées 14 Ave E. Belin, 31400 Toulouse, France

Karl, David M. Department of Oceanography, SOEST University of Hawaii, Honolulu, HI 96822, USA [email protected]

Laws, Edward A. Department of Oceanography University of Hawaii at Manoa Honolulu, Hawaii 96822

Legendre, Louis Laboratoire d’Océanographie CNRS, Villefranche-sur-mer, France

Lindsay, Keith National Center for Atmospheric Research Boulder, CO 80307, USA

Liu, Kon-Kee Institute of Oceanography National Taiwan University Taipei 100, Taiwan 106, ROC

Llinás, Octavio Instituto Canario de Ciencias Marinas Telde, Gran Canaria, Spain Contributors XVII

Lochte, Karin Institut für Meereskunde Düsternbrooker Weg 20 24105 Kiel, Germany [email protected]

Macdonald, Robie Institute of Ocean Sciences Sidney, B.C. V8L 4B2, Canada

Marty, Jean-Claude Laboratoire d’Océanographie de Villefranche BP 08, F-06 238 Villefranche sur mer Cedex, France

Michaels, Anthony F. Wrigley Institute for Environmental Studies University of Southern California Los Angeles, CA 90089-0371, USA

Miquel, Jean C. Marine Environment Laboratory International Atomic Energy Agency 4 Quai Antoine ler, BP 800, MC98000 Monaco

Moore, J. Keith National Center for Atmospheric Research Boulder, CO 80307, USA

Murray, James W. School of Oceanography University of Washington Box 355351, Seattle, Washington, 98195-5351, USA

Neuer, Susanne Department of Biology Arizona State University Tempe, AZ 85287, USA

Nojiri, Y. National Institute for Environmental Studies Tsukuba, Ibaraki 305-0053, Japan

Orr, James C. Laboratoire des Sciences du Climat et de l’Environnement Unite Mixte de Recherche CEA-CNRS, CEA Saclay F-91191 Gif-sur-Yvette Cedex, France

Ragueneau, Olivier Université de Bretagne Occidentale Institut Universitaire Européen de la Mer UMR CNRS 6539, Brest, France

Rivkin, Richard T. Ocean Sciences Centre Memorial University of Newfoundland St. John’s, Canada

Shimmield, Graham Scottish Association for Marine Science Dunstaffnage Marine Laboratory Oban, Argyll, PA37 1QA, Scotland, UK XVIII Contributors

Tréguer, Paul Université de Bretagne Occidentale Institut Universitaire Européen de la Mer UMR CNRS 6539, Brest, France [email protected]

Vetrov, Alexander P. P. S h i r s hov I ns t i t ute of O ce an o l o g y Russian Academy of Sciences Krasikova 23, 117218 Moscow, Russia

Watson, Andrew J. School of Environmental Sciences University of East Anglia Norwich NR4 7TJ, UK [email protected]

Williams, Richard G. Oceanography Laboratories Department of Earth Sciences, University of Liverpool Liverpool L69 7ZL, UK

Wong, Chi Shing Climate Chemistry Laboratory, OSAP Institute of Ocean Sciences P.O. Box 6000, Sidney, B.C., Canada V8L 4B2 Chapter 9 Global Ocean Carbon Cycle Modeling

Scott C. Doney · Keith Lindsay · J. Keith Moore

solved inorganic carbon). The second and third terms 9.1 Introduction on the left hand side of the equation describe the physical processes of advection and mixing, respectively. All One of the central objectives of the Joint Global Ocean of the chemical and biological interactions are sub- Flux Study (JGOFS) is to use data from the extensive field sumed into the final source/sink term(s) on the right effort to improve and evaluate numerical ocean carbon hand side, which often involve complex interactions cycle models. Substantial improvements are required in among a number of prognostic variables. In addition, the current suite of numerical models if we are to un- the model may require external boundary conditions derstand better the present ocean biogeochemical state, (e.g., solar radiation, wind stress, dust deposition) and, hindcast historical and paleoclimate variability, and pre- for time varying problems, initial conditions. The model dict potential future responses to anthropogenic per- equations are then solved numerically by integrating turbations. Significant progress has been made in this forward in time for X. regard, and even greater strides are expected over the Numerical models cannot capture all of the complex- next decade as the synthesis of the JGOFS data sets are ity of the real world. Part of the art of modeling is to completed and disseminated to the scientific commu- abstract the essence of a particular problem, balancing nity. The goals of this chapter are to outline the role of model complexity with insight. Many processes must modeling in ocean carbon cycle research, review the sta- be either parameterized in a simple fashion or neglected tus of basin to global-scale modeling, and highlight ma- altogether. For example, the biophysical details of pho- jor problems, challenges, and future directions. tosynthesis, though quite well known, may not neces- Marine biogeochemical models are quite diverse, cov- sarily be crucial and certainly not sufficient for simu- ering a wide range of complexities and applications from lating the seasonal bloom in the North Atlantic. On the simple box models to global 4-D (space and time) cou- other hand, a number of key processes (e.g., phytoplank- pled physical-biogeochemical simulations, and from strict ton mortality, the controls on community structure) are research tools to climate change projections with direct not well characterized and are often used as model tun- societal implications. Model development and usage are ing parameters. strongly shaped by the motivating scientific or policy prob- As opposed to much of ocean physics, fundamental lems as well as the dynamics and time/space scales con- relationships either are not known or may not exist at all sidered. A common theme, however, is that models allow for much of marine biogeochemistry. Therefore, ocean us to ask questions about the ocean we could not address biogeochemical modeling is inherently data driven. The using data alone. In particular, models help researchers JGOFS field data are invaluable in this regard, providing quantify the interactions among multiple processes, syn- the basis for highlighting model deficiencies, develop- thesize diverse observations, test hypotheses, extrapolate ing improved parameterizations, and evaluating overall across time and space scales, and predict future behavior. model performance. The desire for increasing model re- A well posed model encapsulates our understanding alism and sophistication must be tempered by the reali- of the ocean in a mathematically consistent form. Many, zation that models can quickly outstrip the ability to though not all, models can be cast in general form as a parameterize the appropriate processes or evaluate the coupled set of time-dependent advection, diffusion, re- overall simulation. Inverse methods and data assimila- action equations: tion will certainly help in this regard, but the true ben- efits will only be gained when the underlying models rest on a sound, mechanistic basis. (9.1) Broadly speaking, much of current ocean carbon cycle modeling can be condensed into a few overarching where X refers to a set of prognostic or predicted vari- scientific questions that match well with the other indi- ables (e.g., temperature, phytoplankton biomass, dis- vidual chapters of this book. These include: What are 218 Scott C. Doney · Keith Lindsay · J. Keith Moore the physical and biological controls on primary, new and tions) and their responses to climate variability and secu- export production? What are the roles of multiple lim- lar change (e.g., glacial-interglacial transition and green- iting nutrients, mesoscale variability and trophic struc- house warming). The treatment of biological processes ture? How are organic and inorganic carbon trans- in this class of models has been rather rudimentary in ported, transformed and remineralized below the sur- most cases. Third, marine ecosystem models have been face layer? How much anthropogenic carbon does the focused much more on the details of biological interac- ocean take up and where? How does ocean biogeochem- tions within the upper ocean, tracking the controls on istry respond to climate variability and are there feed- upper ocean primary and export production as well as backs on climate change? Ocean carbon modeling is a the flow of mass and energy though the marine food web. diverse and growing field and can not be covered com- These models often are created for specific biogeographi- prehensively in a single chapter. Rather, we present cal regions commonly based on local surface or 1-D time- an overview of the current state and major issues in- series data sets. More recently, ecosystem models have volving ocean biogeochemical and ecosystem modeling been extended to basin and global scale. One of the most drawing mostly on specific examples from the NCAR important trends in the field is the unification of these modeling program. three approaches, leading ultimately to a coherent Historically, global ocean biological and chemical modeling framework linking ocean physics, biology and modeling has evolved along three related, though often chemistry over a range of time and space scales. distinct, paths. First, a number of early efforts were di- rected toward improving oceanic anthropogenic carbon uptake estimates, building on simple box models and 9.2 Anthropogenic Carbon Uptake, coarse resolution ocean physical general circulation Transient Tracers, and Physics models (GCMs). Transient tracer simulations (radiocarbon, tritium, chlorofluorocarbons) developed in con- An initial and ongoing focus of ocean biogeochemical junction as a way to assess model physical circulation modeling research is to quantify the rate at which the and mixing. Second, biogeochemical carbon cycle mod- ocean takes up transient tracers and excess anthropo- els, while often relying on the same physical model genic CO2. The water column and upper few meters of frameworks, were developed to improve our under- marine sediments contain the largest mobile, natural standing of the dynamics controlling large-scale bio- reservoir of carbon on time-scales of 102 to 105 years. geochemical fields (e.g., surface pCO2, subsurface nu- With about 50 times more carbon than that stored in trient, oxygen and dissolved inorganic carbon distribu- the atmosphere (Fig. 9.1) (Sarmiento and Sundquist

Fig. 9.1. Schematic of present global carbon cycle budget. The budget includes the natural background cycle as well as anthropogenic perturbations. Reservoir sizes are given in units of Pg C (1 Pg equals 1015 g), while fluxes are given in Pg C yr–1 (adapted from Schimel et al. (1995) and US CCSP (1999)) CHAPTER 9 · Global Ocean Carbon Cycle Modeling 219

1992; Siegenthaler and Sarmiento 1993), the ocean will 2001). More recently, empirically based methods have serve as the ultimate sink for about 90% of human fos- been developed for estimating anthropogenic carbon sil fuel emissions (Archer et al. 1998). Anthropogenic distributions directly from ocean carbon and hydro- carbon uptake is often computed as a passive perturba- graphic observations (Gruber et al. 1996; Gruber 1998; tion to the natural dissolved inorganic carbon (DIC) Wanninkhof et al. 1999; Watson this volume). With the field (Sarmiento et al. 1992), a fairly reasonable assump- completion of the high quality, JGOFS/WOCE global CO2 tion for the pre-industrial to the present time period. survey (Wallace 1995, 2001), a baseline can be con- Under these conditions (i.e., fixed circulation and back- structed for the world ocean for the pre-industrial DIC ground biogeochemical cycles), net carbon uptake is field and the anthropogenic carbon perturbation as of simply a matter of ocean physics, primarily determined the mid-1990s, an invaluable measure for testing nu- by the ventilation time-scales exposing deep water to merical model skill and monitoring future evolution. the surface and, to a much lesser degree, air-sea gas ex- As an example of this class of carbon uptake simu- change. The invasion into the ocean of transient tracers lations, the large-scale patterns of anthropogenic CO2 such as radiocarbon, tritium, and the chlorofluorocar- air-sea flux and integrated water column inventory from bons provides a direct, often quite dramatic illustration the NCAR CSM Ocean Model (Large et al. 1997; Gent of ocean ventilation and is commonly used either to et al. 1998) are shown in Fig. 9.2. The regions of highest calibrate/evaluate ocean physical models or as proxies anthropogenic carbon uptake – equatorial upwelling for anthropogenic CO2 uptake. bands, western boundary currents, high latitude inter- Early attempts to calculate ocean CO2 uptake in the mediate and deep water formation regions – are associ- 1970s and 1980s relied heavily on ocean box and 1-D ad- ated with the transport of older subsurface waters to vection diffusion models of varying complexity (Oeschger the air-sea interface (Doney 1999). Although the maxi- et al. 1975; Siegenthaler and Oeschger 1978; Siegenthaler mum specific uptake rates are found in the subpolar and Joos 1992). This class of models represents, in a fairly North Atlantic, the area is relatively small, and the inte- crude, schematic form, the basics of ocean thermocline grated uptake of the Southern Ocean and Equatorial ventilation and thermohaline circulation. The crucial band are larger. The anthropogenic DIC water column model advection and mixing parameters are typically anomaly is stored primarily in the thermocline and in- set by calibrating simulated transient tracer distribu- termediate waters of the subtropical convergence zones tions (tritium, natural and bomb radiocarbon) to ob- and the lower limb of the North Atlantic thermohaline servations. More recently, such models have mostly been circulation as illustrated by the second panel of Fig. 9.2 supplanted by full 3-D general circulation models for the and Fig. 9.3, a depth vs. latitude comparison of field anthropogenic CO2 question. But because they are sim- data derived and model simulated anthropogenic DIC. ple to construct (and interpret) and computationally in- The two meridional sections follow the thermohaline expensive, box models and a related derivative the 2-D, overturning circulation from the northern North At- zonally averaged basin model (Stocker et al. 1994) con- lantic to the Southern Ocean and then back to the tinue to be used today for a number of applications re- northern North Pacific. The model simulates in a rea- quiring long temporal integrations including paleoce- sonable fashion the patterns from empirical estimates anography (Toggweiler 1999; Stephens and Keeling except perhaps in the subpolar and intermediate depth 2000) and climate change (Joos et al. 1999). Some cau- North Atlantic, which may reflect problems with the tion is advised, however, as recent studies (Broecker et al. model formation of North Atlantic Deep Water (Large 1999; Archer et al. 2000) clearly demonstrate that box et al. 1997). model predictions for key carbon cycle attributes can At present, most numerical models predict a similar differ considerably from the corresponding GCM results. net uptake of anthropogenic CO2 for the 1990s of ap- Ocean general circulation model studies of anthro- proximately 2 Pg C yr–1 (1 Pg C equals 1015 g C) (Orr pogenic carbon uptake date back to the work of Maier- et al. 2001), a result supported by atmospheric biogeo- Reimer and Hasselmann (1987) and Sarmiento et al. (1992), chemical monitoring and a variety of other techniques and the number of model estimates (and modeling (Schimel et al. 1995; Keeling et al. 1996; Rayner et al. groups) for CO2 uptake has increased significantly over 1999). The models, however, show considerable regional the 1990s. For example, more than a dozen international differences, particularly in the Southern Ocean (Orr groups are participating in the IGBP/GAIM Ocean Car- et al. 2001). The agreement of the NCAR model with em- bon Model Intercomparison Project (OCMIP; http:// pirical basin inventories is quite good (Table 9.1), sug- www.ipsl.jussieu.fr/OCMIP/). These numerical experi- gesting that at least at this scale the NCAR model trans- ments are closely tied to and greatly benefit from ef- port is relatively skillful. forts to evaluate ocean GCMs using hydrographic (Large While based on a more complete description of ocean et al. 1997; Gent et al. 1998) and transient tracer data physics, the coarse resolution, global GCMs used for (Toggweiler et al. 1989a,b; Maier-Reimer 1993; England these carbon studies still require significant parameteri- 1995; Heinze et al. 1998; England and Maier-Reimer zation of sub-gridscale phenomenon such as deep wa- 220 Scott C. Doney · Keith Lindsay · J. Keith Moore

Fig. 9.2. Spatial distributions of model simulated ocean anthropogenic (perturbation) carbon. Simu- lated fields are shown for (top) air-sea flux (mol C m–2 yr–1) and (bottom) water column inventory (mol C m–2) for 1990 from the NCAR CSM Ocean Model. The two lines indicate the Atlantic and Pacific tran- sects used for the horizontal sections in Fig. 9.3 and 9.7

ter formation, surface and bottom boundary layer phys- ocean models without active sea ice components appear ics, and mixing rates along and across density surfaces to have weak AABW formation while many of the inter- (isopycnal and diapycnal diffusion). The ongoing active ocean-sea ice models tend to have way too much OCMIP effort is comparing about a dozen current gen- bottom water production. eration global ocean carbon models among themselves These known deficiencies in ocean GCM physics and against ocean observations. Completed analysis of hamper quantitative model-data comparisons of bio- OCMIP Phase 1 and early results from Phase 2 demon- geochemical and ecosystem dynamical models as well. strate significant differences among the models in the Uncertainties in the physical flow field, particularly ver- physical circulation and simulated chlorofluorocarbon tical velocity (Harrison 1996), mixing and convection, (Dutay et al. 2001), radiocarbon, and current and pro- affect a variety of biogeochemical processes – nutrient jected future anthropogenic CO2 fields (Orr et al. 2001). supply, boundary layer stability and mean light levels, The largest model-model differences and model-data downward transport of transient tracers, anthropogenic discrepancies are found in the Southern Ocean, reflect- carbon and semi-labile dissolved organic matter – and ing differences in the relative strength and spatial pat- thus obscure the validation of tracer and biogeochemical terns of Antarctic Mode (Intermediate) Waters and Ant- components. The refinement of global ocean GCMs is arctic Bottom Water (AABW) (Dutay et al. 2001). Mod- an on-going process, and substantial progress will likely els using horizontal mixing rather than an isopycnal arise from improved treatments of surface boundary scheme (Gent and McWilliams 1990) tend to overesti- forcing and subgrid-scale physics (McWilliams 1996; mate convective mixing in the region of the Antarctic Haidvogel and Beckmann 1999; Griffes et al. 2000). Tran- Circumpolar Current (Danabasoglu et al. 1994). Not sur- sient tracers and biogeochemistry can contribute in this prisingly, the formation of AABW appears quite sensi- regard by providing additional, often orthogonal, con- tive to the under-ice, surface freshwater fluxes in the straints on model performance to traditional physical deep water formation zones (Doney and Hecht 2002); measures (Gnanadesikan 1999; Gnanadesikan and CHAPTER 9 · Global Ocean Carbon Cycle Modeling 221

–3 Fig. 9.3. Depth vs. latitude contour plots of anthropogenic CO2 (mmol C m ). The panels show the simulated results from the NCAR CSM ocean model and the empirical, observation based estimates (N. Gruber 2000, pers. comm.) each for an Atlantic and Pacific section along the main path of the thermohaline circulation (see Fig. 9.2). Note that depth is limited to 3000 m

Table 9.1. Estimated basin inventories of Ocean NCAR CSM ocean model Data-based C* estimates Data reference anthropogenic DIC (Pg C) Indian 22.1 20 ±3 Sabine et al. (1999) Atlantic 39.5 40 ±6 Gruber (1998) Pacific 46.746 ±5? Feely and Sabine (pers. comm.) Total 108.4 106 ±8? Feely and Sabine (pers. comm.)

Toggweiler 1999). The incorporation of active biology two increase in horizontal resolution, the integration tests new facets of the physical solutions, especially the time goes up by roughly a factor of 8. Basin-scale, eddy- surface air-sea fluxes and boundary layer dynamics resolving biological simulations at such resolution are (Large et al. 1994; Doney 1996; Doney et al. 1998) and only now becoming computationally feasible and only their interaction with the interior mesoscale field (Gent for short integrations. Second, very high resolution on and McWilliams 1990). the order of 1/10° appears to be required to correctly cap- The desired horizontal resolution for ocean carbon ture the dynamics (not just presence) of the mesoscale cycle models is often a contentious issue, involving eddies (e.g., eddy kinetic energy; eddy-mean flow in- tradeoffs between model fidelity/realism and computa- teractions) (Smith et al. 2000), and some numerical er- tional constraints. Most global climate models used for rors persist even as resolution is decreased (Roberts and long integrations (i.e., the multi-decade to centennial Marshall 1998). One solution is to incorporate the effect and longer timescales often of interest to the ocean car- of the unresolved processes using more sophisticated bon community) have relatively coarse horizontal reso- sub-grid scale parameterizations. For example, the Gent lution of one to a few degrees and thus do not explicitly and McWilliams (1990) isopycnal mixing scheme tends represent key processes such as deep-water overflows to greatly reduce the resolution dependence and im- and mesoscale eddies. Increasing the resolution of this proves, in both eddy permitting and non-eddy resolv- class of models is an important objective but is not a ing solutions, the simulated meridional heat transport, general panacea for a number of reasons. First, compu- an important physical diagnostic likely relevant for nu- tational costs increase dramatically; for every factor of trients and carbon as well as heat. 222 Scott C. Doney · Keith Lindsay · J. Keith Moore

Another important, and often overlooked, numerical issue is the tracer advection scheme (Haidvogel and Beckmann 1999; Griffes et al. 2000). The centered difference schemes used in most 3-D ocean general circulation models, while conserving first and second mo- ments of the tracer distribution, tend to produce dis- persive errors (e.g., under and overshoots, ripples, non- positive definite tracer fields), which can be particularly troubling for biogeochemical and biological properties that have sharp vertical gradients (Oschlies and Garçon 1999). Oschlies (2000), for example, demonstrates that the common problem of equatorial nutrient trapping (Najjar et al. 1992) is primarily numerical and can be solved by increasing vertical resolution and/or implementing more sophisticated advection methods. The wide range of alternative advection schemes (e.g., third order upwinding, flux corrected transport) mostly use some amount of diffusion (only first order accurate) to suppress the dispersion errors. The main differences in the methods are the magnitude of the dissipation, whether it is applied uniformly or selectively in space and time, and the exact numerical implementation (Webb et al. 1998; Hecht et al. 2000). Fig. 9.4. Schematic of OCMIP global ocean carbon biogeochemical model. For more details see text and (http://www.ipsl.jussieu.fr/OCMIP) 9.3 Global Biogeochemical Cycles dissolved inorganic carbon DIC, total alkalinity TALK, The net anthropogenic ocean carbon uptake occurs on semi-labile dissolved organic matter DOM, and dis- top of the large background DIC inventory and ocean solved oxygen. Upper ocean production (0–75 m) is cal- gradients, air-sea fluxes, biological transformations, and culated by restoring excess model PO4 toward a monthly internal transports driven by the natural carbon cycle nutrient climatology (Louanchi and Najjar 2000). The (Fig. 9.1). Beginning with a series of global biogeo- production is split with 1/3 going into rapidly sinking chemical simulations in the early 1990s (Bacastow and particles and the remainder into the DOM pool. The Maier-Reimer 1990; Najjar et al. 1992; Maier-Reimer sinking particles are remineralized in the subsurface 1993), numerical models have played key roles in esti- consumption zone (>75 m) using an empirical particle mating basin and global-scale patterns and rates of bio- flux depth curve similar in form (though with different geochemical processes (e.g., export production, remin- numerical parameters; Yamanaka and Tajika 1996) to eralization). The primary measure for evaluating such that found by Martin et al. (1987) from sediment trap models has been the large-scale fields of inorganic nu- data. The DOM decays back to phosphate and DIC us- trients, oxygen, and DIC (Levitus et al. 1993; Conkright ing first order kinetics with a 6 month time-scale et al. 1998; Wallace 2001). As more robust global esti- throughout the water column. Most of the DOM is re- mates of biogeochemical rates (e.g., new production, mineralized within the surface production zone but a Laws et al. 2000) are developed from the JGOFS field fraction is mixed or subducted downward prior to de- data and satellite remote sensing, they too are being in- cay and thus contributes to overall export production. cluded in model-data comparisons (Gnanadesikan et al. Surface CaCO3 production is set at a uniform 7% of 2001). Numerical biogeochemical models are also valu- particulate organic matter production, and all of the able tools for exploring specific hypotheses (e.g., iron CaCO3 is export as sinking particles which are remin- fertilization; Joos et al. 1991), estimating interannual eralized with a deeper length-scale relative to organic variability (Le Quéré et al. 2000), and projecting future matter. The relative uptake and release rates of PO4, DIC, responses to climate change (Sarmiento et al. 1998). and O2 from the organic pools are set by fixed, so-called With a few exceptions (Six and Maier-Reimer 1996), Redfield elemental ratios, and CO2 and O2 are exchanged the treatment of biology in these global biogeochemical with the atmosphere via surface air-sea gas fluxes com- models to date has been rather rudimentary. This is ex- puted using the quadratic wind-speed gas exchange re- hibited in Fig. 9.4 by a schematic of the biotic carbon lationship of Wanninkhof (1992). model from OCMIP Phase 2. The OCMIP model con- Despite its simplicity, the OCMIP model captures to a sists of five prognostic variables, a limiting nutrient PO4, degree many of the large-scale ocean biogeochemical CHAPTER 9 · Global Ocean Carbon Cycle Modeling 223

Fig. 9.5. a Annual averaged new production estimates. In the upper panel (a) the NCAR model total production (particle plus net semi-labile DOM creation) and net DOM creation computed to 150 m are compared against recent new/export production estimates from Laws et al. (2000) (satellite primary production and ecosystem model based f-ratios) and Moore et al. (2002a) (global ecosystem model including DOM loss from downwelling and seasonal mixed layer shoaling). In the lower panel (b), the NCAR model global integral total, particle and DOM new production rates are shown as a func- b tion of the bottom limit of the depth integration

patterns found in nature. The model, zonally averaged, tionship of f-ratio to temperature and primary produc- total new production (particle export plus net DOM pro- tion from an ecosystem model. As discussed by duction) is compared in Fig. 9.5a with recent new/export Gnanadesikan et al. (2001), the Laws et al. (2000) values production estimates from Laws et al. (2000) (satellite in the equatorial region are sensitive to assumptions primary production and ecosystem model based f-ra- about the maximum growth rate as a function of tem- tios) and Moore et al. (2002a,b) (global ecosystem model; perature (and implicitly nutrients), and alternative for- see below for more details). The NCAR model estimate mulations can give higher values. has been recomputed at 150 m rather than 75 m as speci- A significant fraction of the GCM export production fied in the OCMIP formulation to be more consistent at mid- to high latitudes is driven by net DOM produc- with data based and the other model estimates. The glo- tion followed by downward transport (global integral bal integrated new production estimates from the GCM at 150 m of 2.4 Pg C) (Fig. 9.5a and 9.5b). This has been (9.6 Pg C at 150 m), satellite diagnostic calculation observed in the field at a number of locations (Carlson (12.6 Pg C), and ecosystem model (11.9 Pg C) are com- et al. 1994; Hansell and Carlson 1998), and is thought to parable but with significant regional differences. The be an important mechanism north of the Antarctic Po- Moore et al. and Laws et al. curves have similar patterns lar Front supporting a significant fraction of the organic with high values in the Northern Hemisphere temper- matter remineralization in the upper thermocline ate and subpolar latitudes, low levels in the tropics and (Doval and Hansell 2000). Because the semi-labile DOM subtropics and slightly elevated rates in the Southern in the model is advected by the horizontal currents, the Ocean around 40° S. The GCM results are considerably local sum of new production and remineralization do larger in the equatorial upwelling band and lower in the not always balance leading to regional net convergence/ subtropics, reflecting in part net production, horizon- divergence of nutrients and DIC. Some ocean inversion tal export and subsequent remineralization of organic transport estimates, for example, suggest that there are matter. The Laws et al. (2000) estimates are based on net horizontal inputs of organic nutrients into subtropi- two components: satellite derived primary production cal areas from remote sources (Rintoul and Wunsch rates from CZCS ocean color data and the Behrenfeld 1991). Another factor to consider when looking at the and Falkowski (1997) algorithm, and a functional rela- model production estimates and model-data compari- 224 Scott C. Doney · Keith Lindsay · J. Keith Moore

Fig. 9.6. Spatial distributions of present (1990), annual mean surface sea-air pCO2 difference (µatm) from (top) the NCAR CSM Ocean Model and (bottom) the Takahashi et al. (1997) climatology. The two lines indicate the Atlantic and Pacific tran- sects used for the horizontal sections in Fig. 9.3 and 9.7

sons is the sensitivity of new production to the depth CO2 outgassing from the equatorial regions, where cold surface chosen for the vertical integral. The cumulative DIC rich water is brought to the surface by upwelling, (surface to depth) new production drops off significantly and CO2 uptake in the western boundary currents, Ant- with depth below 75 m in the model because of the as- arctic Circumpolar Current, and North Atlantic deep sumed rapid decrease in the sinking particle flux and water formation zones. The most striking regional relatively shallow penetration of DOM governed mostly model-data difference is the predicted larger (smaller) by seasonal convection (Fig. 9.5b). For most field stud- model uptake in the Southern Ocean (North Atlantic), ies, the vertical mixing and advection terms are diffi- compared to the Takahashi et al. (1997) climatology, and cult to quantify, and the new production is computed the indication of net outgassing right along the Antarc- typical at either the base of the euphotic zone (100 m to tic coast in the observations. Interestingly, the model 125 m) or the shallowest sediment trap (~150 m). Southern Ocean results are more in line with recent at- Another important measure of model skill is the sur- mospheric inversion results from the IGBP/GAIM atmos- face water pCO2 field (Sarmiento et al. 2000), which can pheric transport model intercomparison, TRANSCOM be compared to extensive underway pCO2 observations (S. Denning, per. comm. 2000). All three approaches (Takahashi et al. 1997, 1999) and atmospheric CO2 data (ocean model, pCO2 data climatology, and atmospheric sets (Keeling et al. 1996; Rayner et al. 1999). The model inversion) have their own unique uncertainties and po- surface water pCO2 field is the thermodynamic driving tential biases, and more effort should be given to re- force for air-sea gas exchange and is governed by bio- solving these apparent discrepancies using a combina- logical DIC drawdown, physical transport, surface tem- tion of improved numerical models and enhanced field perature (and salinity), and air-sea fluxes. Figure 9.6 data collection. ∆ shows the annual mean air-sea pCO2 field from the The model subsurface nutrient, DIC and oxygen fields model for 1990 (pre-industrial equilibrium plus anthro- can also be compared with observations, in this case pogenic perturbation) and the Takahashi et al. (1997) historical hydrographic data sets and the JGOFS/WOCE climatology. The large-scale patterns are similar with global CO2 survey. The preindustrial DIC results are CHAPTER 9 · Global Ocean Carbon Cycle Modeling 225

Fig. 9.7. Depth vs. latitude contour plots of pre-industrial DIC (mmol C m–3). The panels show the simulated results from the NCAR CSM ocean model and observed DIC fields with the anthropogenic DIC component removed using the C* technique (N. Gruber 2000, pers. comm.) for an Atlantic and Pacific section along the main path of the thermohaline circulation (see Fig. 9.6) shown in the same format as for anthropogenic DIC 9.4 Ecosystem Dynamics (Fig. 9.3), i.e., meridional sections in the Atlantic and Pacific (Fig. 9.7). The model surface to deep water DIC vertical gradient, which is comparable to the observa- If the simple OCMIP biogeochemical model captures tions, results from contributions of about 2/3 from the the zeroth-order state of the ocean carbon cycle then biological export (‘biological pump’) and 1/3 from the what are the important areas for progress? An obvious physics (‘solubility pump’). The horizontal gradients in deficiency of the OCMIP straw man is the lack of ex- the deep-water are determined by a mix of the thermo- plicit, prognostic biological dynamics to drive surface haline circulation and the subsurface particle remin- production, export and remineralization. By linking to eralization rate, and the NCAR-OCMIP model captures a fixed surface nutrient climatology, we have avoided most of the broad features. Several of the key model-data specifying the details of how the surface nutrient field differences can be ascribed, at least partly, to problems is controlled (e.g., grazing, iron fertilization, mesoscale with the model physics (e.g., too shallow outflow of North eddies) or how it might evolve under altered forcing. Atlantic Deep Water, Large et al. 1997; overly weak pro- While useful for the purposes of OCMIP, clearly a more duction of Antarctic bottom water, Doney and Hecht 2002). mechanistic approach is desired for many applications. The WOCE/JGOFS carbon survey and historical data sets For example, looking toward the next several centuries, can also be used to estimate the horizontal transport of future changes in ocean circulation and biogeochemis- biogeochemical species within the ocean (e.g., Brewer try may lead to large alterations in the background car- et al. 1989; Rintoul and Wunsch 1991; Broecker and Peng bon cycle that could strongly impact projected ocean 1992; Holfort et al. 1998; Wallace 2001), providing another carbon sequestration (Denman et al. 1996; Sarmiento constraint for ocean biogeochemical models (Murnane et al. 1998; Doney and Sarmiento 1999; Boyd and Doney et al. 1999; Sarmiento et al. 2000; Gruber et al. 2001). 2003). Realistic projections will require coupled ecosys- 226 Scott C. Doney · Keith Lindsay · J. Keith Moore

Fig. 9.8. Schematic of a simple marine ecosystem model originally developed for the Bermuda Atlantic Time-Series Study site (Doney et al. 1996; Doney et al., pers. comm.) and (in red) the recent extension by Moore et al. (2001a)

tem-biogeochemical models that include the main proc- ratory experiments, the latter often with species and con- esses thought to be sensitive to climate change (e.g., at- ditions of limited relevance to the actual ocean (Fasham mospheric dust, nitrogen fixation, community structure). 1993; Evans and Fasham 1993; Evans and Garçon 1997). As an example of a typical marine ecosystem, con- This type of compartment ecosystem model has been sider the schematic shown in Fig. 9.8. The model devel- used extensively in oceanography (Steele 1974) and theo- oped for vertical 1-D simulations of the Sargasso Sea by retical ecology (May 1973; Case 2000) since the early 1970s Doney et al. (1996) incorporates five prognostic vari- but has roots much further back in the literature (e.g., ables: phytoplankton, zooplankton, nutrient, detritus Riley 1946; Steele 1958). The area was revitalized about and chlorophyll (a so-called PZND model). As is com- the time of the inception of JGOFS by the seminal work mon, the model aggregates populations and species of of Evans and Parslow (1985), Frost (1987), Fasham et al. organisms into broadly defined trophic compartments. (1990), and Moloney and Field (1991). The equations are based on the flow of a single limiting Despite its simplifications, the PZND model (Fig. 9.8) currency, in this case the concentration of nitrogen does an adequate job capturing the vertical structure and (mol N m–3), among compartments rather than indi- broad seasonal patterns of bulk biogeochemical proper- vidual organisms. The various source/sink terms (e.g., ties in Bermuda field data (e.g., chlorophyll – Fig. 9.9; ni- photosynthesis, zooplankton grazing, detrital reminerali- trate; Doney et al. 1996). Specific features include: a winter zation) are calculated using standard, though not always phytoplankton bloom following nutrient injection via deep well agreed upon, sets of empirical functional forms and convection; low surface nutrients and chlorophyll during parameters derived either from limited field data or labo- the stratified summer period; and the formation of a sub- CHAPTER 9 · Global Ocean Carbon Cycle Modeling 227

Fig. 9.9. A comparison of the modeled and observed time-depth chlorophyll distribution for the Bermuda Atlantic Time- Series Study site in the western subtropical North Atlantic. The 1-D coupled biological-physical model is based on Doney et al. (1996) and Doney (1996)

surface chlorophyll maximum at the top of the nutricline. 1999a; Bissett et al. 1999), iron limitation (Armstrong The 1-D coupled biological-physical model, based on 1999b; Leonard et al. 1999; Denman and Pena 1999; surface forcing and physics described by Doney (1996), Pondaven et al. 2000), and nitrogen fixation (Hood et al. also reproduces aspects of the observed interannual vari- 2001; Fennel et al. 2002). One problem, however, is that ability driven by the depth of the winter convection. most 1-D coupled models are developed and evaluated Variants on the PZND theme have been successfully for a single site, and the generality of these models and applied in vertical 1-D form in a diverse range of bio- their derived parameter values for basin and global geographical regimes from oligotrophic subtropical simulations remains an open question. gyres (Bissett et al. 1994) to seasonal bloom regimes Early three-dimensional basin and global scale cal- (Fasham 1995) to subarctic high-nitrate, low chlorophyll culations (Sarmiento et al. 1993; Six and Maier-Reimer regions (McClain et al. 1996; Pondaven et al. 2000). The 1996) were conducted with single, uniform PZND eco- construction of the 1-D physical framework (vertical system models applied across the entire domain. These mixing, temperature etc.) requires explicit considera- experiments demonstrated that large-scale features such tion (Archer 1995; Doney 1996; Evans and Garçon 1997), as the contrast between the oligotrophic subtropical and but in general 1-D coupled models have resulted in use- eutrophic subpolar gyres could be simulated qualita- ful test-beds for exploring ecological processes and im- tively. Some problems arose, however, with the details. plementing biological data assimilation techniques. It For example, the incorporation of the Fasham et al. has been known for a while that the relatively simple (1990) model into a North Atlantic circulation model PZND dynamics belie the ecological complexity of the by Sarmiento et al. (1993) showed too low production real system, and recent idealized and local 1-D coupled and biomass in the oligotrophic subtropics and too weak models include increasing levels of ecological sophisti- a spring bloom at high latitudes. The Six and Maier- cation. Models are incorporating a range of factors such Reimer (1996) result required careful tuning of the phy- as: size and community structure (Armstrong 1994, toplankton growth temperature sensitivity and zoo- 228 Scott C. Doney · Keith Lindsay · J. Keith Moore plankton grazing in order to control biomass in the databases (Levitus et al. 1994; Conkright et al. 1998) and Southern Ocean HNLC (high nitrate-low chlorophyll) the NCAR CSM Ocean Model (NCOM) (Large et al. 1997). regions. A number of coupled 3-D ecosystem models A preliminary version of the ecosystem model also has now exist for regional (Chai et al. 1996; McCreary et al. been tested in a fully coupled, 3-D North Atlantic Basin con- 1996; Ryabchenko et al. 1998; Dutkiewicz et al. 2001) and figuration (Lima et al. 1999), and the full ecosystem model global (Aumont et al., pers. comm.) applications, and is currently being implemented in the new global NCAR- these 3-D ecosystem models are beginning to include Los Alamos model. The mixed layer ecosystem model is many of the features already addressed in 1-D, including discussed in some detail to highlight new modeling direc- multiple nutrient limitation and community structure tions and approaches to model-data evaluation. (Christian et al. 2001a,b; Gregg et al. 2002). Often, how- The ecosystem model (Fig. 9.8) is adapted from Doney ever, these models are not used to fully explore the cou- et al. (1996) and consists of eleven main compartments, pling of upper ocean biology and subsurface carbon and small phytoplankton, diatoms, and diazotrophs; zoo- nutrient fields because of the short integration time (a plankton; sinking and non-sinking detrital classes; and few years) or limited horizontal/vertical domain. dissolved nitrate, ammonia, phosphorus, iron, and sili- The next step is to combine reasonably sophisticated cate. The small phytoplankton size class is meant to ge- components for both ecosystem and biogeochemical nerically represent nano- and pico-sized phytoplank- dynamics in a global modeling framework. The exact ton, with parameters designed to replicate the rapid and form of such a model is yet to be determined. Based on highly efficient nutrient recycling found in many sub- the new insights emerging from JGOFS and other re- tropical, oligotrophic (low nutrient) environments. The cent field studies, a minimal model can be envisioned small phytoplankton class may be iron, phosphorus, covering those basic processes that govern surface pro- nitrogen, and/or light-limited. The larger phytoplank- duction, export flux, subsurface remineralization, and ton class is explicitly modeled as diatoms and may be the (de)coupling of carbon from macronutrients (multi- limited by silica as well. Many of the biotic and detrital nutrient limitation; size structure and trophic dynam- compartments contain multiple elemental pools to track ics; plankton geochemical functional groups; microbial flows through the ecosystem. The model has one zoo- loop and dissolved organic matter cycling; particle plankton class which grazes the three phytoplankton transport and remineralization). groups and the large detritus. Phytoplankton growth As part of such a project, we have developed an in- rates are determined by available light and nutrients termediate complexity, ecosystem model incorporated using a modified form of the Geider et al. (1998) dy- within a global mixed layer framework (Moore et al. namic growth model. Carbon fixation rate is governed 2002a,b). The model biology is simulated independently by internal cell nutrient quotas (whichever nutrient is at each grid point and then composited to form global currently most-limiting), and the cell quotas computed fields. The model has a low computational overhead, and relative to carbon are allowed to vary dynamically as thus can be used for extensive model evaluation and ex- the phytoplankton adapt to changing light levels and ploration. Sub-surface nutrient fields are from climato- nutrient availability. There is good laboratory evidence logical databases, and the mixed layer model captures for a relationship between cell quotas (measured as nu- the local processes of turbulent mixing, vertical trient/C ratios) and specific growth rates (Sunda and advection at the base of the mixed layer, seasonal mixed Huntsman 1995; Geider et al. 1998). Photoadaptation is layer entrainment/detrainment, but not horizontal modeled according to Geider et al. (1996, 1998) with a advection. Other forcings include sea surface tempera- dynamically adaptive Chl/C ratio. The diazotrophs are ture, percent sea ice cover, surface radiation, and the at- assumed to fix all required nitrogen from N2 gas fol- mospheric deposition of iron (Fung et al. 2000; Fig. 9.10). lowing Fennel et al. (2002) and are limited by iron, phos- The physical forcings are prescribed from climatological phorus, light or temperature. Calcification is para-

Fig. 9.10. Annual mean map of atmospheric iron deposition to the ocean adapted from Tegen and Fung (1995) model estimates (reprinted from Deep-Sea Res II 49, Moore et al. (2002) Iron cycling and nutrient limitation patterns in surface waters of the world ocean. pp 463–507, © 2002, with permission from Elsevier Science) CHAPTER 9 · Global Ocean Carbon Cycle Modeling 229 meterized as a time-varying fraction of the small (pico/ eters that are typically sampled too sparsely to construct nano) plankton production as a function of ambient global data sets. The variables that are available from temperature and nutrient concentrations. Based on observations on a global scale are more limited, includ- Harris (1994) and Milliman et al. (1999) we assume that ing seasonal (now monthly) nutrient fields (Conkright grazing processes result in substantial dissolution of et al. 1998), satellite remotely sensed surface chlorophyll CaCO3 in the upper water column. (McClain et al. 1998) (Fig. 9.12) and diagnostic model The model output is in generally good agreement derived products such as satellite based integrated pri- with the bulk ecosystem observations (e.g., total bio- mary production (Behrenfeld and Falkowski 1997) and mass; productivity; nutrients) across diverse ecosystems f-ratio (Laws et al. 2000) estimates. Compared with the that include both macro-nutrient and iron-limited re- satellite estimates, the model produces realistic global gimes and very different physical environments from patterns of both primary and export production. high latitude sites to the mid-ocean gyres. The detailed, The incorporation of iron limitation plays a critical local data sets from JGOFS and historical time-series part in the model skill of reproducing the observed high stations (Kleypas and Doney 2001) have been impor- nitrate and low phytoplankton biomass conditions in tant for developing parameterizations, testing hypoth- the Southern Ocean and the subarctic and equatorial eses, and evaluating model performance. As an exam- Pacific regions. A small number of desert regions (e.g., ple, a comparison of model simulated and observed China, Sahel), mostly in the Northern Hemisphere, pro- mixed layer seasonal cycle for nitrate is shown in Fig. 9.11 vide the main sources of atmospheric dust (and thus for nine locations across the globe. The time-series sta- iron) to the ocean, and the estimated iron deposition tions and regional JGOFS process studies (e.g., EqPAC, rate to oceanic HNLC environments can be orders of Arabian Sea) often provide invaluable constraints on magnitude lower than other locations (Fig. 9.10). At such biological fluxes (primary productivity profiles, export low deposition rates, upwelling of subsurface iron likely flux, zooplankton grazing, not shown) as well, param- contributes a significant fraction of the total bioavailable

Fig. 9.11. Comparison of simulated and observed seasonal nitrate cycle at nine JGOFS time-series stations across a range of biogeo- graphical regimes (Kleypas and Doney 2001). The model results are from a global mixed layer ecosystem model with uniform biological coefficients (reprinted from Deep-Sea Res II 49, Moore et al. (2002) An intermediate complexity marine ecosystem model for the global domain. pp 403–462, © 2002, with permission from Elsevier Science) 230 Scott C. Doney · Keith Lindsay · J. Keith Moore

Fig. 9.12. Global field of monthly mean surface chlorophyll concentration for January from SeaWiFS and a global mixed layer ecosystem model (reprinted from Deep-Sea Res II 49, Moore et al. (2002) Iron cycling and nutrient limitation patterns in surface waters of the world ocean. pp 463–507, © 2002, with permission from Elsevier Science)

Fig. 9.13. Ecosystem model simulated nutrient limitation patterns during summer months in each hemisphere (June–August in the Northern Hemisphere, December–February in the Southern Hemisphere) for diatoms. The global fractional area limited by each nutrient is listed below the plot. Nutrient replete areas (here arbitrarily defined as areas where all nutrient cell quotas are >90% of their maximum values) are largely restricted to areas of extreme light-limitation under permanently ice-covered regions (reprinted from Deep-Sea Res II 49, Moore et al. (2002) Iron cycling and nutrient limitation patterns in surface waters of the world ocean. pp 463–507, © 2002, with permission from Elsevier Science) iron. In the model, these regions are characterized by limitation. Not too surprisingly, the model suggests that strong iron limitation of diatom growth and modest iron both small phytoplankton and diatoms are iron limited limitation and strong grazing pressure on the small phy- in the classic HNLC regions (40% and 52% of the global toplankton. The observed low chlorophyll and low ni- surface area, respectively for the two phytoplankton trate levels in oligotrophic gyres are also simulated well, groups), while the mid-ocean subtropical gyres are typi- but the model does not fully capture the strong blooms cally nitrogen or, to much smaller degree, phosphorus in some of the coastal upwelling regions, most likely a limited (Fig. 9.13). Diatom silica limitation is exhibited result of the weak vertical velocities input from the in the subantarctic and North Atlantic waters with bands coarse resolution physics model. of silica-iron co-limitation along the edges of the trop- Models should allow us to do more than simply rep- ics. The variable cell quota approach allows for easy di- licate what is already known, by posing new (and test- agnosis of varying degrees of nutrient stress, which can able) hypotheses of how the ocean functions at the sys- be compared in the near future with global nutrient tem level. As an example, the global mixed layer model stress fields to be derived from the MODIS natural fluo- predicts the degree and time/space patterns of nutrient rescence measurements (Letelier and Abbott 1996). CHAPTER 9 · Global Ocean Carbon Cycle Modeling 231

Fig. 9.14. Model simulated annual mean nitrogen fixation and calcification fields (reprinted from Deep-Sea Res II 49, Moore et al. (2002) Iron cycling and nutrient limitation patterns in surface waters of the world ocean. pp 463–507, © 2002, with permission from Elsevier Science)

The other new aspect of the global model is the in- parisons (Abbott 1995). The phytoplankton iron limita- clusion of community structure through planktonic geo- tion story is an illuminating example. Atmospheric dust/ chemical functional groups, namely diatoms (export iron deposition flux estimates vary considerably (per- flux and silica ballast), diazotrophs (nitrogen fixation), haps as large as a factor of ten or more in some areas) and calcifiers (alkalinity and ballast). The model spa- and the bioavailable fraction of the dust iron is not well tial patterns of annual nitrogen fixation (Fig. 9.14) agree known. Surface and subsurface ocean iron measure- well with the limited information known from in situ ments are limited (particularly from a global modeler’s work (Capone et al. 1997), high trichodesmium biomass perspective), and there remain serious analytical and and/or nitrogen fixation rates reported in the Caribbean standardization issues. Organic ligands may play a role Sea and eastern tropical North Atlantic (Lipschulz and in governing both bioavailability and subsurface iron Owens 1996) as well as in the subtropical North Pacific concentrations. Not enough is known about the effect (Letelier and Karl 1996, 1998; Karl et al. 1997). The total of iron limitation and variability on species competi- model nitrogen fixation of 58 Tg N, which accounts only tion at ambient low iron levels. A host of other proc- for the mixed layer production, is somewhat less than, esses may be relevant, but are currently poorly charac- though of comparable magnitude to, the 80 Tg N esti- terized, including: iron release by photochemistry and mate of Capone et al. (1997) and the Gruber and Sarmiento zooplankton grazing, release of iron from ocean mar- (1997) geochemical estimates of >100 Tg N. gin sediments, and iron remineralization from sinking The parameterization of phytoplankton calcification particles. is an active research topic, but the spatial patterns shown in Fig. 9.14 are generally similar to those estimated by Milliman (1993) and Milliman et al. (1999). CaCO3 pro- 9.5 Other Topics duction/export is lower in the mid-ocean gyres and higher in the North Atlantic, coastal upwelling zones and In a recent review paper, Doney (1999) described a set mid-latitude Southern Ocean waters. The high latitude of key marine ecological and biogeochemical modeling North Atlantic in particular is known to be a region with issues to be addressed in the next generation of numeri- frequent coccolithophore blooms (Holligan et al. 1993). cal models: multi-element limitation and community The model production/export is lower in the equatorial structure; large-scale physical circulation; mesoscale Pacific and Indian ocean compared with Milliman et al. space and time variability; land, coastal, and sediment (1999), but the global sinking export of 0.46 Gt C is in exchange with the ocean; and model-data evaluation and good agreement with their integrated estimate. data assimilation. In the preceding three sections we Two main factors limiting progress on ecosystem have presented in some detail the nature of several of modeling are the conceptualization of key processes at these challenges and specific initial progress made by a mechanistic level and the ability to verify model our group. Below we more briefly outline some of the behavior through robust and thorough model-data com- remaining items. 232 Scott C. Doney · Keith Lindsay · J. Keith Moore

equilibrium and climate simulations will be limited pri- 9.5.1 Mesoscale Physics marily to non-eddy resolving models in which submesoscale and mesoscale eddy effects will have to be incor- The ocean is a turbulent medium, and mesoscale vari- porated via subgrid-scale parameterizations (Levy et al. ability (scales of 10 to 200 km in space and a few days to 1999; Lima et al. 2002). weeks in time) is a ubiquitous feature of ocean biological fields such as remotely sensed ocean color. Based on new in situ measurement technologies (Dickey 9.5.2 Climate Variability and Secular Change et al. 1998) and mesoscale biogeochemical models (McGillicuddy and Robinson 1997; Oschlies and Garçon A key measure for the skill of numerical models is their 1998; Spall and Richards 2000; Lima et al. 2002) it has ability to accurately hind-cast oceanic responses to natu- become clear that mesoscale variability is not simply ral climate variability on timescales from the seasonal noise to be averaged over, but rather a crucial factor cycle to multiple decades. Large-scale modeling stud- governing the nature of pelagic ecosystems. The eco- ies, with some exceptions, have tended to focus prima- logical impacts of disturbance are diverse, and the ini- rily on the mean state of the ocean. Biological oceano- tial research emphasis on the eddy enhancement of new graphic time series exhibit significant variability on nutrient fluxes to the euphotic zone (McGillicuddy et al. interannual to interdecadal scales associated with physi- 1998; Fig. 9.15) is broadening to include light limitation, cal climate phenomenon such as the El Niño-Southern community structure, organic matter export, and Oscillation (ENSO) and the Pacific Decadal Oscillation subsurface horizontal transport effects as well (Garçon (PDO) (Venrick et al. 1987; Karl et al. 1995; McGowan et al. 2001). et al. 1998; Karl 1999). The ecosystem response to physi- Quantifying the large-scale effect of such variability cal forcing may be quite nonlinear, manifesting in the will require concerted observational, remote sensing and North Pacific, for example, as a major biological regime numerical modeling programs with likely heavy reliance shift in the mid-1970s due to the PDO (Francis and Hare on data assimilation. The computational demands of 1994). Comparable climate related biological shifts are truly eddy resolving basin to global calculations are sig- also inferred for the North Atlantic (Reid et al. 1998). nificant, however. Recent high resolution physical simu- Retrospective models can help explain the underlying lations of the North Atlantic show that dramatic im- mechanisms of such phenomena (Polovina et al. 1995). provement in eddy statistics and western boundary cur- Because of an interest in separating terrestrial and oce- rent dynamics is reached only at 1/10 of a degree reso- anic signals in the atmospheric CO2 network, there is lution (Smith et al. 2000), and even higher resolution also a growing effort to model the oceanic contribution may be needed for biology if submesoscale (0.5–10 km) to atmospheric variability, which appears to be small processes are as important as suggested by preliminary except for the tropical ENSO signal (Rayner et al. 1999; results (Levy et al. 1999; Mahadevan and Archer 2000; Le Quéré et al. 2000). Lima et al. 2002). Over the near term, long time-scale Numerical models are also being used to project the potential marine biogeochemical responses to anthropogenic climate change (Sarmiento et al. 1998; Matear and Hirst 1999). Coupled ocean-atmosphere model simulations differ considerably in their details, but most models suggest general warming of the upper ocean and thermocline, increased vertical stratification in both the low latitude (warming) and high latitude (freshening) surface waters, and weakening of the thermohaline circulation. Combined, the physical effects lead to a 30–40% drop in the cumulative anthropogenic CO2 uptake over the next century partly compensated by changes in the strength of the natural biological carbon pump. Given the low level of biological sophistication used in these early simulations, such projections must be considered preliminary, demonstrating the potential sensitivity of the system and posing important questions to be addressed through future research. Preliminary ecosystem simulations (Bopp et al. 2001) Fig. 9.15. Daily snapshot of new production from a Los Alamos- POP 1/10° mesoscale simulation of the North Atlantic (Dennis show different regional climate change responses to McGillicuddy, pers. comm.) enhanced stratification with decreased subtropical pro- CHAPTER 9 · Global Ocean Carbon Cycle Modeling 233 ductivity (nutrient limited) and increased subpolar pro- denitrification, trace metal reduction and mobilization), ductivity (light limited) reminiscent of the PDO signal with implications for the global carbon, nitrogen, phos- (Polovina et al. 1995). Other environmental factors to phorus and iron cycles. Finally, the direct and indirect consider include alterations of aeolian trace metal depo- human perturbations to the coastal environment (e.g., sition due to changing land-use and hydrological cycle, pollution, nutrient eutrophication, fisheries) are large, variations in cloud cover and solar and UV irradiance, with important impacts on marine ecosystems (harm- coastal eutrophication, and lower surface water pH and ful algal blooms, coral reefs, spawning grounds) and carbonate ion concentrations due to anthropogenic CO2 society (e.g., commercial fisheries, tourism, and human uptake (Kleypas et al. 1999). The decadal time-scale bio- health and aesthetics). geochemical and ecological responses to such physical Because of the topographic complexity, smaller time/ and chemical forcings are not well understood in detail, space scales, and specific regional character of coastal and prognostic numerical models will be relied on heav- environments, basin to global scale models typically do ily along with historical and paleoceanographic climate not fully account for biogeochemical fluxes and dynam- variability reconstructions (Doney and Sarmiento 1999; ics on continental margins and in the coastal ocean. Thus Boyd and Doney 2003). coastal/open-ocean exchange and the large-scale influence on the ocean are not well quantified except in a few locations (Falkowski et al. 1994; Liu et al. 2000). Re- 9.5.3 Land, Coastal Ocean, and Sediment Interactions gional coastal ecosystem models have been moderately successful (Robinson et al. 2001; Fig. 9.16), and an obvi- The coastal/margins zone interacts strongly and com- ous next step is to meld open ocean and coastal domains plexly with the land, adjacent atmosphere, continental through more adaptable grid geometries such as unstruc- shelves and slopes, and open-ocean. The specific rates of tured (spectral) finite element grids (Haidvogel et al. productivity, biogeochemical cycling, and organic/inor- 1997) or by embedding regional domain, higher-resolu- ganic matter sequestration are higher than those in the tion models (Spall and Holland 1991). Dynamic marine open ocean, with about half of the global integrated new sediment geochemistry models (Heinze et al. 1999) are production occurring over the continental shelves and needed both for the coastal problem and for large-scale slopes (Walsh 1991; Smith and Hollibaugh 1993). The paleoceanographic applications, an example being the high organic matter deposition to, and close proximity compensation of the sediment CaCO3 to changes in ocean of the water column to, the sediments raises the impor- carbon chemistry on millennial time-scales (Archer and tance of sedimentary chemical redox reactions (e.g., Maier-Reimer 1994; Archer et al. 2000).

Fig. 9.16. Annual mean chlorophyll for the California Current coastal region from SeaWiFs and the UCLA regional coastal ecosystem model ROMS (James McWilliams, pers. comm.) 234 Scott C. Doney · Keith Lindsay · J. Keith Moore

(e.g., nitrogen fixers, calcifiers), key to addressing hypoth- 9.5.4 Inverse Modeling and Data Assimilation eses of how the ocean might alter or drive long term changes in atmospheric CO2. Growing utilization of retro- The emerging techniques of inverse modeling and data spective or hindcasting experiments will be used to evalu- assimilation, which more formally compare and meld ate model skill relative to historical interannual and model results and data, are becoming essential in model paleoclimate variability. Significant progress is also being development and evaluation (U.S. JGOFS 1992; Kasibhatla made in process and regional models on issues such as et al. 2000). In theory data assimilation provides a solu- biological-physical sub- and mesoscale interactions as well tion, if it exists, that is dynamically consistent with both as coastal ecosystem and biogeochemical dynamics. the observations and model equations within the esti- A number of major challenges remain for the next mated uncertainties. Much of the art of data assimila- decade(s): tion lies in assigning relative error weights to different data types and to the model equations themselves, the Ecological sophistication. Ocean models must be so-called cost function problem (U.S. JGOFS 1992). A grounded at a more fundamental level by ecological and number of recent studies have used this approach to evolutionary hypotheses. The current emphasis is of- better constrain or optimize parameters for marine bio- ten on simulating chemical and biochemical analyses: geochemical box and one-dimensional models, particu- phytoplankton treated simply as concentrations of pig- larly with time series data (Matear 1995; Fasham and ments and organic carbon, zooplankton as grazers, and Evans 1995; Hurtt and Armstrong 1996; Spitz et al. 1998; physics as a mechanism for providing nutrients. A more Fennel et al. 2001). Applications to three-dimensional mechanistic understanding is needed of how individual models are more limited but include efforts to assimi- organisms and species interact to form pelagic ecosys- late satellite ocean color data into ecosystem models tems, how food webs affect biogeochemical fluxes, and (Ishizaka 1990) or to estimate poorly measured fluxes how the structure of food webs and corresponding bio- such as dissolved organic phosphorus transport/remin- geochemical fluxes will change in the coming decades. eralization (Matear and Holloway 1995), surface export production (Schlitzer 2000), and air-sea oxygen fluxes High frequency variability. The importance of high fre- (Gruber et al. 2001) from the large-scale nutrient distri- quency spatial and temporal variability (e.g., fronts, butions and physical circulation flow fields. The utility mesoscale eddies) on the large-scale carbon cycle needs of data assimilation will continue to grow with the im- to be better characterized. This will require a combina- port and refinement of numerical methods from mete- tion of subgrid-scale parameterizations, nested mod- orology and physical oceanography to interdisciplinary els, and dedicated very high-resolution computations. problems (Robinson 1996) and with the availability of automated software systems for generating the required Land-ocean-sediment interactions. Explicit treatment model adjoints (Giering and Kaminski 1998). of the biologically and biochemically active regions along continents needs to be incorporated. At present, coastal modeling is often ‘parochial’ in the sense that 9.6 Summary each region is treated as unique both physically and eco- logically. The computational approaches will be similar Numerical models are essential tools for understanding to those outlined for mesoscale dynamics. the complex physical, biological and chemical interactions that govern the ocean carbon cycle. They are also crucial Model-data fusion. Models must be confronted more for extrapolating local/regional relationships to the global directly with data using a hierarchy of diagnostic, in- scale and for projecting the effects and feedbacks on the verse, and data assimilation methods. While technically ocean carbon cycle of past and potential future climate challenging, data assimilation holds the promise of cre- change. As outlined in this chapter, the field of marine bio- ating evolving, 4-D ‘state estimates’ for the ocean car- geochemical modeling is alive and vigorous, benefiting bon cycle. Further, assimilation methods (e.g., param- greatly from the surge of new data and concepts arising eter optimization) can be used to demonstrate that some from the decade long international JGOFS field effort. The models or functional forms are simply incompatible boundaries of the three quasi-independent lines of re- with observations, thus offering some hope for focus- search (i.e., anthropogenic CO2 transient tracer uptake; ing the current and growing model plethora. biogeochemical cycling; and ecosystem dynamics) that characterized numerical modeling historically are being Global carbon cycle. The ocean is only one component blurred, and integrated regional and global 3-D eco-bio- of the global carbon cycle, and independent and often geochemical models are emerging. These models are complementary estimates of key measures of ocean car- based on the new paradigms of multi-element cycling, bon dynamics are being developed by scientists work- community structure and geochemical functional groups ing in other disciplines. Examples include air-sea CO2 CHAPTER 9 · Global Ocean Carbon Cycle Modeling 235 fluxes based on atmospheric inversions and seasonal Bissett WP, Walsh JJ, Carder KL (1999) Carbon cycling in the upper waters of the Sargasso Sea: I. Numerical simulation of differen- marine net community production based on atmos- tial carbon and nitrogen fluxes. Deep-Sea Res Pt I 46:205–269 pheric O2/N2 ratios. Similar to progress made in ocean- Bopp L, Monfray P, Aumont O, Dufresne J-L, Le Treut H, Madec G, atmosphere modeling, one solution is to emphasize and Terray L, Orr JC (2001) Potential impact of climate change on marine export production. Global Biogeochem Cy 15:81–99 attempt to reconcile model fluxes that occur between Boyd P, Doney S (2003) The impact of climate change and feed- the ocean-atmosphere and land-ocean. Another is to ac- back process on the ocean carbon cycle. Chap. 7. Springer- tively pursue adding integrated carbon cycle dynamics Verlag, (this volume) Brewer PG, Goyet C, Dyrssen D (1989) Carbon dioxide transport into coupled (ocean-atmosphere-land) climate models. by ocean currents at 25° N latitude in the Atlantic Ocean. Sci- ence 246:477–479 Broecker WS, Peng T-H (1992) Interhemispheric transport of car- Acknowledgements bon dioxide by ocean circulation. Nature 356:587–589 Broecker W, Lynch-Stieglitz J, Archer D, Hofmann M, Maier-Reimer E, Marchal O, Stocker T, Gruber N (1999) How strong is the We would like to thank our numerous colleagues and Harvardton-Bear constraint? Global Biogeochem Cy 13:817–820 collaborators who have contributed to this chapter Capone DG, Zehr JP, Paerl HW, Bergman B, Carpenter EJ (1997) Trichodesmium: a globally significant cyanobacterium. Science through formal and informal discussions, comments, 276:1221–1229 and research material. We especially thank P. Falkowski, Carlson CA, Ducklow HW, Michaels AF (1994) Annual flux of dis- D. Feely, I. Fung, D. Glover, N. Gruber, J. Kleypas, I. Lima, solved organic carbon from the euphotic zone in the northwest Sargasso Sea. Nature 371:405–408 F. Mackenzie, D. McGillicuddy, J. McWilliams, C. Sabine, Case TJ (2000) An illustrated guide to theoretical ecology. Oxford and S. Smith. We also thank the editor of this volume, University Press 4, 49 pp M. Fasham, and an anonymous reviewer for providing Chai F, Lindley ST, Barber RT (1996) Origin and maintenance of high nutrient condition in the equatorial Pacific. Deep-Sea Res guidance and suggestions on the text. This work is sup- Pt II 42:1031–1064 ported in part by a NASA US Ocean Carbon Model Christian JR, Verschell MA, Murtugudde R, Busalacchi AJ, McClain Intercomparison Project (OCMIP Phase II) grant (NASA CR (2001a) Biogeochemical modelling of the tropical Pacific Ocean. I. Seasonal and interannual variability. Deep-Sea Res W-19,274), a NOAA-OGP Global Carbon Cycle grant Pt II 49:509–543 (NOAA-NA96GP0360), and the NSF US JGOFS Synthe- Christian JR, Verschell MA, Murtugudde R, Busalacchi AJ, McClain sis and Modeling Project management grant (NSF 97308). CR (2001b) Biogeochemical modelling of the tropical Pacific Ocean. II. Iron biogeochemistry. Deep-Sea Res Pt II 49:545–565 The National Center for Atmospheric Research is spon- Conkright ME, Levitus S, O’Brien T, Boyer TP, Stephens C, Johnson sored by the US National Science Foundation. D, Stathoplos L, Baranova O, Antonov J, Gelfeld R, Burney J, Rochester J, Forgy C (1998) World ocean atlas database 1998; CD-ROM data set documentation. National Oceanographic Data Center, Silver Spring, MD References Danabasoglu G, McWilliams JC, Gent PR (1994) The role of mesoscale tracer transports in the global ocean circulation. Science 264:1123–1126 Abbott MR (1995) Modeling the southern ocean ecosystem. Denman K, Hofmann E, Marchant H (1996) Marine biotic re- GLOBEC Planning Report, 18, U.S. Global Ocean Ecosystem sponses to environmental change and feedbacks to climate. In: Dynamics (GLOBEC) Program, Berkeley, California, 63 pp Houghton JT, Meira LG Filho, Callander BA, Harris N, Katten- Archer D (1995) Upper ocean physics as relevant to ecosystem berg A, Maskell K (eds) Climate change 1995. IPCC, Cambridge dynamics: a tutorial. Ecol Appl 5:724–739 University Press, pp 487–516 Archer D, Maier-Raimer E (1994) Effect of deep-sea sedimentary Denman KL, Pena MA (1999) A coupled 1-D biological/physical calcite preservation on atmospheric CO2 concentration. Na- model of the northeast subarctic Pacific Ocean with iron limi- ture 367:260–263 tation. Deep-Sea Res Pt II 46:2877–2908 Archer D, Kheshgi H, Maier-Reimer E (1998) Dynamics of fossil Dickey T, Frye D, Jannasch H, Boyle E, Manov D, Sigurdson D, fuel CO2 neutralization by marine CaCO3. Global Biogeochem McNeil J, Stramska M, Michaels A, Nelson N, Siegel D, Chang Cy 12:259–276 G, Wu J, Knap A (1998) Initial results from the Bermuda testbed Archer D, Eshel G, Winguth A, Broecker W (2000) Atmospheric mooring program. Deep-Sea Res Pt I 45:771–794 CO2 sensitivity to the biological pump in the ocean. Global Bio- Doney SC (1996) A synoptic atmospheric surface forcing data set and geochem Cy 14:1219–1230 physical upper ocean model for the U.S. JGOFS Bermuda Atlantic Armstrong RA (1994) Grazing limitation and nutrient limitation Time-Series Study (BATS) site. J Geophys Res 101: 25615–25634 in marine ecosystems: steady state solutions of an ecosystem Doney SC (1999) Major challenges confronting marine biogeo- model with multiple food chains. Limnol Oceanogr 39:597–608 chemical modeling. Global Biogeochem Cy 13:705–714 Armstrong RA (1999a) Stable model structures for representing Doney SC, Hecht MW (2002) Antarctic bottom water formation biogeochemical diversity and size spectra in plankton com- and deep water chlorofluorocarbon distributions in a global munities. J Plankton Res 21:445–464 ocean climate model. J Phys Oceanogr 32:1642–1666 Armstrong RA (1999b) An optimization-based model of iron-light Doney SC, Sarmiento JL (eds) (1999) Synthesis and modeling ammonium colimitation of nitrate uptake and phytoplankton project; ocean biogeochemical response to climate change. U.S. growth. Limnol Oceanogr 44:1436–1446 JGOFS Planning Report 22, U.S. JGOFS Planning Office, Woods Bacastow R, Maier-Reimer E (1990) Ocean-circulation model of Hole, MA, 105 pp the carbon cycle. Clim Dynam 4:95–125 Doney SC, Glover DM, Najjar RG (1996) A new coupled, one-di- Behrenfeld MJ, Falkowski PG (1997) Photosynthetic rates derived mensional biological-physical model for the upper ocean: ap- from satellite-based chlorophyll concentration. Limnol plications to the JGOFS Bermuda Atlantic Time Series (BATS) Oceanogr 42:1–20 site. Deep-Sea Res Pt II 43:591–624 Bissett WP, Meyers MB, Walsh JJ (1994) The effects of temporal Doney SC, Large WG, Bryan FO (1998) Surface ocean fluxes and variability of mixed layer depth on primary productivity water-mass transformation rates in the coupled NCAR Climate around Bermuda. J Geophys Res 99:7539–7553 System Model. J Climate 11:1422–1443 236 Scott C. Doney · Keith Lindsay · J. Keith Moore

Doval M, Hansell DA (2000) Organic carbon and apparent oxy- Gnanadesikan A, Toggweiler JR (1999) Constraints placed by sili- gen utilization in the western South Pacific and central Indian con cycling on vertical exchange in general circulation mod- Oceans. Mar Chem 68:249–264 els. Geophys Res Lett 26:1865–1868 Dutay J-C, Bullister JL, Doney SC, Orr JC, Najjar R, Caldeira K, Gnanadesikan A, Slater R, Gruber N, Sarmiento JL (2001) Oceanic Champin J-M, Drange H, Follows M, Gao Y, Gruber N, Hecht vertical exchange and new production: a comparison between MW, Ishida A, Joos F, Lindsay K, Madec G, Maier-Reimer E, models and observations. Deep-Sea Res Pt II 49:363–401 Marshall JC, Matear RJ, Monfray P, Plattner G-K, Sarmiento J, Gregg WW (2002) Tracking the SeaWiFS record with a coupled Schlitzer R, Slater R, Totterdell IJ, Weirig M-F, Yamanaka Y, Yool physical/biogeochemical/radiative model of the global oceans. A (2001) Evaluation of ocean model ventilation with CFC-11: Deep-Sea Res Pt II 49:81–105 comparison of 13 global ocean models. Ocean Modelling 4: Griffes SM, Böning C, Bryan FO, Chassignet EP, Gerdes R, Hasumi 89–120 H, Hirst A, Treguer A-M, Webb D (2000) Developments in ocean Dutkiewicz S, Follows M, Marshall J, Gregg WW (2001) Interannual climate modelling, vol. 2. pp 123–192 variability of phytoplankton abundances in the North Atlan- Gruber N (1998) Anthropogenic CO2 in the Atlantic Ocean. Global tic. Deep-Sea Res Pt II 48:2323–2344 Biogeochem Cy 12:165–191 England MH (1995) Using chlorofluorocarbons to assess ocean Gruber N, Sarmiento JL (1997) Global patterns of marine nitro- climate models. Geophys Res Lett 22:3051–3054 gen fixation and denitrification. Global Biogeochem Cy 11: England MH, Maier-Reimer E (2001) Using chemical tracers to 235–266 assess ocean models. Rev Geophys 39:29–70 Gruber N, Sarmiento JL, Stocker TF (1996) An improved method Evans GT, Fasham MJR (ed) (1993) Towards a model of ocean bio- for detecting anthropogenic CO2 in the oceans. Global Biogeo- geochemical processes. Springer-Verlag, New York chem Cy 10:809–837 Evans GT, Garçon VC (ed) (1997) One-dimensional models of wa- Gruber N, Gloor M, Fan SM, Sarmiento JL (2001) Air-sea flux of ter column biogeochemistry. JGOFS Report 23/97, 85 pp., JGOFS, oxygen estimated from bulk data: implications for the marine Bergen, Norway and atmospheric oxygen cycle. Global Biogeochem Cy 15(4): Evans GT, Parslow JS (1985) A model of annual plankton cycles. 783–803 Biolo Oceanogr 3:327–347 Haidvogel DB, Beckmann A (1999) Numerical ocean circulation Falkowski PG, Biscaye PE, Sancetta C (1994) The lateral flux of modeling. Imperial College Press, London, 318 pp biogenic particles from the eastern North American continen- Haidvogel DB, Curchitser E, Iskandarani M, Hughes R, Taylor M tal margin to the North Atlantic Ocean. Deep-Sea Res Pt II 41: (1997) Global modeling of the ocean and atmosphere using 583–601 the spectral element method. Atmos Ocean 35:505–531 Fasham MJR (1993) Modelling the marine biota. In: Heimann M (ed) Hansell DA, Carlson CA (1998) Net community production of dis- The global carbon cycle. Springer-Verlag, Heidelberg, pp 457–504 solved organic carbon. Global Biogeochem Cy 12:443–453 Fasham MJR (1995) Variations in the seasonal cycle of biological Harris RP (1994) Zooplankton grazing on the coccolithophore production in subarctic oceans: a model sensitivity analysis. Emiliania huxleyi and its role in inorganic carbon flux. Mar Deep-Sea Res Pt I 42:1111–1149 Biol 119:431–439 Fasham MJR, Evans GT (1995) The use of optimisation techniques Harrison DE (1996) Vertical velocity variability in the tropical to model marine ecosystem dynamics at the JGOFS station at Pacific: a circulation model perspective for JGOFS. Deep-Sea 47° N and 20° W. Philos T Roy Soc B 348:206–209 Res Pt II 43:687–705 Fasham MJR, Ducklow HW, McKelvie SM (1990) A nitrogen-based Hecht MW, Wingate BA, Kassis P (2000) A better, more discrimi- model of plankton dynamics in the oceanic mixed layer. J Mar nating test problem for ocean tracer transport. Ocean Model- Res 48:591–639 ling 2:1–15 Fennel K, Losch M, Schröter J, Wenzel M (2001) Testing a marine Heinze C, Maier-Reimer E, Schlosser P (1998) Transient tracers in ecosystem model: sensitivity analysis and parameter optimi- a global OGCM: source functions and simulated distributions. zation. J Marine Syst 28:45–63 J Geophys Res 103:15903–15922 Fennel K, Spitz YH, Letelier RM, Abbott MR, Karl DM (2002) A Heinze C, Maier-Reimer E, Winguth AME, Archer D (1999) A glo- deterministic model for N2-fixation at the HOT site in the sub- bal oceanic sediment model for long-term climate studies. Glo- tropical North Pacific. Deep-Sea Res II 49:149–174 bal Biogeochem Cy 13:221–250 Francis RC, Hare SR (1994) Decadal-scale regime shifts in the large Holfort J, Johnson KM, Wallace DWR (1998) Meridional transport marine ecosystems of the North-east Pacific: a case for his- of dissolved inorganic carbon in the South Atlantic Ocean. Glo- torical science. Fish Oceanogr 3:279–291 bal Biogeochem Cy 12:479–499 Frost BW (1987) Grazing control of phytoplankton stock in the Holligan PM, Fernandez E, Aiken J, Balch WM, Boyd P, Burkill PH, subarctic Pacific: a model assessing the role of mesozooplank- Finch M, Groom SB, Malin O, Muller K, Purdie DA, Robinson ton, particularly the large calanoid copepods, Neocalanus spp. C, Trees CC, Turner SM, van del Wal P (1993) A biogeochemical Mar Ecol Prog Ser 39:49–68 study of the coccolithophore Emiliania huxleyi in the North Fung IY, Meyn SK, Tegen I, Doney SC, John JG, Bishop JKB (2000) Atlantic. Global Biogeochem Cy 7:879–900 Iron supply and demand in the upper ocean. Global Biogeo- Hood RR, Bates NR, Olson DB (2001) Modeling the seasonal to chem Cy 14:281–295 interannual biogeochemical and N2 fixation cycles at BATS. Garçon VC, Oschlies A, Doney SC, McGillicuddy D, Waniek J (2001) Deep-Sea Res Pt II 48:1609–1648 The role of mesoscale variability on plankton dynamics. Deep- Hurtt GC, Armstrong RA (1996) A pelagic ecosystem model cali- Sea Res Pt II 48:2199–2226 brated with BATS data. Deep-Sea Res Pt II 43:653–683 Geider RJ, MacIntyre HL, Kana TM (1996) A dynamic model of Ishizaka J (1990) Coupling of Coastal Zone Color Scanner data to photoadaptation in phytoplankton. Limnol Oceanogr 41:1–15 a physical-biological model of the Southeastern U.S. continen- Geider RJ, MacIntyre HL, Kana TM (1998) A dynamic regulatory tal shelf ecosystem, 3, nutrient and phytoplankton fluxes and model of phytoplankton acclimation to light, nutrients, and CZCS data assimilation. J Geophys Res 95:20201–20212 temperature. Limnol Oceanogr 43:679–694 Joos F, Siegenthaler U, Sarmiento JL (1991) Possible effects of iron Gent PR, McWilliams JC (1990) Isopycnal mixing in ocean circu- fertilization in the Southern Ocean on atmospheric CO2 con- lation models. J Phys Oceanogr 20:150–155 centration. Global Biogeochem Cy 5:135–150 Gent PR, Bryan FO, Danabasoglu G, Doney SC, Holland WR, Large Joos F, Plattner O-K, Schmittner A (1999) Global warming and WG, McWilliams JC (1998) The NCAR Climate System Model marine carbon cycle feedbacks on future atmospheric CO2. global ocean component. J Climate 11:1287–1306 Science 284:464–467 Giering R, Kaminski T (1998) Recipes for adjoint code construc- Karl DM (1999) A sea of change: biogeochemical variability in the tion. Acm t math software 24:437–474 North Pacific subtropical gyre. Ecosystems 2:181–214 Gnanadesikan A (1999) A global model of silicon cycling: sensi- Karl DM, Letelier R, Hebel D, Tupas L, Dore J, Christian J, Winn C tivity to eddy parameterization and dissolution. Global Bio- (1995) Ecosystem changes in the North Pacific subtropical gyre geochem Cy 13:199–220 attributed to the 1991–1992 El Niño. Nature 378:230–234 CHAPTER 9 · Global Ocean Carbon Cycle Modeling 237

Karl D, Letelier R, Tupas L, Dore J, Christian J, Hebel D (1997) The Maier-Reimer E, Hasselmann K (1987) Transport and storage in role of nitrogen fixation in biogeochemical cycling in the sub- the ocean – an inorganic ocean-circulation carbon cycle model. tropical North Pacific Ocean. Nature 388:533–538 Clim Dynam 2:63–90 Kasibhatla P, Heimann M, Rayner P, Mahowald N, Prinn RG, Martin JH, Knauer GA, Karl DM, Broenkow WW (1987) VERTEX: Hartley DE (ed) (2000) Inverse methods in global biogeo- carbon cycling in the northeast Pacific. Deep-Sea Res 34:267–285 chemical cycles. AGU Geophysical. Monograph Series, Ameri- Matear RJ (1995) Parameter optimization and analysis of ecosys- can Geophysical Union, Washington D.C., 324 pp tem models using simulated annealing: a case study at Station Keeling RF, Piper SC, Heimann M (1996) Global and hemispheric P. J Mar Res 53:571–607 CO2 sinks deduced from changes in atmospheric O2 concen- Matear RJ, Hirst AC (1999) Climate change feedback on the future tration. Nature 381:218–221 oceanic CO2 uptake. Tellus B 51:722–733 Kleypas JA, Doney SC (2001) Nutrients, chlorophyll, primary pro- Matear RJ, Holloway G (1995) Modeling the inorganic phospho- duction and related biogeochemical properties in the ocean rus cycle of the North Pacific using an adjoint data assimila- mixed layer – a compilation of data collected at nine JGOFS tion model to assess the role of dissolved organic phosphorus. sites. NCAR Technical Report, NCAR/TN–447+STR, 53 pp Global Biogeochem Cy 9:101–119 Kleypas JA, Buddemeier RW, Archer D, Gattuso J-P, Langdon C, May RM (1973) The stability and complexity of model ecosystems. Opdyke BN (1999) Geochemical consequences of increased at- Princeton University Press, Princeton New Jersey, 265 pp mospheric carbon dioxide on coral reefs. Science 284:118–120 McClain CR, Arrigo K, Turk D (1996) Observations and simulations Large WG, McWilliams JC, Doney SC (1994) Oceanic vertical mix- of physical and biological processes at ocean weather station ing: a review and a model with a nonlocal boundary layer P, 1951–1980. J Geophys Res 101:3697–3713 parameterization. Rev Geophys 32:363–403 McClain CR, Cleave ML, Feldman GC, Gregg WW, Hooker SB, Large WG, Danabasoglu G, Doney SC, McWilliams JC (1997) Sen- Kuring N (1998) Science quality SeaWiFS data for global bio- sitivity to surface forcing and boundary layer mixing in a glo- sphere research. Sea Technol 39:10–14 bal ocean model: annual-mean climatology. J Phys Oceanogr McCreary JP, Kohler KH, Hood RR, Olson DB (1996) A four com- 27:2418–2447 partment ecosystem model of biological activity in the Ara- Laws EA, Falkowski PG, Smith WO Jr., Ducklow H, McCarthy JJ bian Sea. Prog Oceanogr 37:193–240 (2000) Temperature effects on export production in the open McGillicuddy DJ Jr., Robinson AR (1997) Eddy-induced nutrient ocean. Global Biogeochem Cy 14:1231–1246 supply and new production. Deep-Sea Res Pt I 44:1427–1450 Le Quéré C, Orr JC, Monfray P, Aumont O, Madec G (2000) Inter- McGillicuddy DJ Jr., Robinson AR, Siegel DA, Jannasch HW, annual variability of the oceanic sink of CO2 from 1979 though Johnson R, Dickey TD, McNeil J, Michaels AF, Knap AH (1998) 1997. Global Biogeochem Cy 14:1247–1265 Influence of mesoscale eddies on new production in the Sar- Leonard CL, McClain CR, Murtugudee R, Hofmann EE, Harding gasso Sea. Nature 394:263–266 JLW (1999) An iron-based ecosystem model of the central equa- McGowan JA, Cayan DR, Dorman LM (1998) Climate-ocean vari- torial Pacific. J Geophys Res 104:1325–1341 ability and ecosystem response in the Northeast Pacific. Sci- Letelier RM, Abbott MR (1996) An analysis of chlorophyll fluores- ence 281:210–217 cence for the Moderate Resolution Imaging Spectrometer McWilliams JC (1996) Modeling the oceanic general circulation. (MODIS). Remote Sens Environ 58:215–223 Annu Rev Fluid Mech 28:215–248 Letelier R, Karl D (1996) Role of Trichodesmium spp. in the pro- Milliman JD (1993) Production and accumulation of calcium car- ductivity of the subtropical North Pacific Ocean. Mar Ecol Prog bonate in the ocean: budget of a nonsteady state. Global Ser 133:263–273 Biogeochem Cy 7:927–957 Letelier R, Karl D (1998) Trichodesmium spp. physiology and nu- Milliman JD, Troy PJ, Balch WM, Adams AK, Li YH, Mackenzie FT trient fluxes in the North Pacific subtropical gyre. Aquat Microb (1999) Biologically mediated dissolution of calcium carbonate Ecol 15:265–276 above the chemical lysocline? Deep-Sea Res Pt I 46:1653–1669 Levitus S, Conkright ME, Reid JL, Najjar RG, Mantilla A (1993) Moloney CL, Field JG (1991) The size-based dynamics of plankton Distribution of nitrate, phosphate and silicate in the world food webs. I. A simulation model of carbon and nitrogen flows. oceans. Prog Oceanogr 31:245–273 J Plankton Res 13:1003–1038 Levitus S, Burgett R, Boyer T (1994) World atlas 1994. NOAA Atlas Moore JK, Doney SC, Kleypas JA, Glover DM, Fung IY (2001a) An NESDIS, U.S. Dept. of Commerce, Washington D.C. intermediate complexity marine ecosystem model for the glo- Levy M, Memery L, Madec G (1999) Combined effects of mesoscale bal domain. Deep-Sea Res Pt II 49:403–462 processes and atmospheric high-frequency variability on the Moore JK, Doney SC, Kleypas JA, Glover DM, Fung IY (2001b) Iron spring bloom in the MEDOC area. Deep-Sea Res Pt I 47:27–53 cycling and nutrient limitation patterns in surfce waters of the Lima I, Doney S, Bryan F, McGillicuddy D, Anderson L, Maltrud M world ocean. Deep-Sea Res Pt II 49:463–507 (1999) Preliminary results from an eddy-resolving ecosystem Murnane RJ, Sarmiento JL, Le Quéré C (1999) Spatial distribution model for the North Atlantic. EOS, Transactions AGU, 80(49), of air-sea CO2 fluxes and the interhemispheric transport of Ocean Sciences Meeting Supplement, OS28 carbon by the oceans. Global Biogeochem Cy 13:287–305 Lima ID, Olson DB, Doney SC (2002) Biological response to fron- Najjar RG, Sarmiento JL, Toggweiler JR (1992) Downward trans- tal dynamics and mesoscale variability in oligotrophic envi- port and fate of organic matter in the ocean: simulations with ronments: a numerical modeling study. J Geophys Res (in press) a general circulation model. Global Biogeochem Cy 6:45–76 Lipschultz F, Owens NJP (1996) An assessment of nitrogen fixa- Oeschger H, Siegenthaler U, Guglemann A (1975) A box-diffusion model tion as a source of nitrogen to the North Atlantic. Biogeochem- to study the carbon dioxide exchange in nature. Tellus 27:168–192 istry 35:261–274 Orr JC, Maier-Reimer E, Mikolajewicz U, Monfray P, Sarmiento JL, Liu K-K, Atkinson L, Chen CTA, Gao S, Hall J, Macdonald RW, Toggweiler JR, Taylor NK, Palmer J, Gruber N, Sabine CL, Le Talaue McManus L, Quiñones R (2000) Exploring continental Quéré C, Key RM, Boutin J (2001) Estimates of anthropogenic margin carbon fluxes on a global scale. EOS, Transactions of carbon uptake from four three-dimensional global ocean mod- the American Geophysical Union 81:641–644 els. Global Biogeochem Cy 15:43–60 Louanchi F, Najjar RG (2000) A global monthly mean climatology Oschlies A (2000) Equatorial nutrient trapping in biogeochemical of phosphate, nitrate and silicate in the upper ocean: spring– ocean models: the role of advection numerics. Global summer production and shallow remineralization. Global Biogeochem Cy 14:655–667 Biogeochem Cy 14:957–977 Oschlies A, Garçon V (1998) Eddy-induced enhancement of pri- Mahadevan A, Archer D (2000) Modeling the impact of fronts and mary production in a model of the North Atlantic Ocean. Na- mesoscale circulation on the nutrient supply and biogeochem- ture 394:266–269 istry of the upper ocean. J Geophys Res 105:1209–1225 Oschlies A, Garçon V (1999) An eddy-permitting coupled physi- Maier-Reimer E (1993) Geochemical cycles in an ocean general cal-biological model of the North Atlantic-1. Sensitivity to circulation model. Preindustrial tracer distributions. Global advection numerics and mixed layer physics. Global Biogeo- Biogeochem Cy 7:645–677 chem Cy 13:135–160 238 Scott C. Doney · Keith Lindsay · J. Keith Moore

Polovina JJ, Mitchum GT, Evans GT (1995) Decadal and basin-scale Smith RD, Maltrud ME, Bryan FO, Hecht MW (2000) Numerical simu- variation in mixed layer depth and the impact on biological lation of the North Atlantic at 1/10°. J Phys Oceanogr 30:1532–1561 production in the Central and North Pacific, 1960–88. Deep- Smith SV, Hollibaugh JT (1993) Coastal metabolism and the oce- Sea Res Pt I 42:1701–1716 anic organic carbon balance. Rev Geophys 31:75–89 Pondaven P, Ruiz-Pino D, Jeandel C (2000) Interannual variabil- Spall MA, Holland WR (1991) A nested primitive equation model ity of Si and N cycles at the time-series station KERFIX be- for oceanic applications. J Phys Oceanogr 21:205–220 tween 1990 and 1995 – a 1-D modelling study. Deep-Sea Res Pt Spall SA, Richards KJ (2000) A numerical model of mesoscale fron- I 47:223–257 tal instabilities and plankton dynamics. I. Model formulation Rayner PJ, Enting IG, Francey RJ, Langenfelds R (1999) Recon- and initial experiments. Deep-Sea Res Pt I 47:1261–1301 structing the recent carbon cycle from atmospheric CO2, d13C Spitz YH, Moisan JR, Abbott MR, Richman JG (1998) Data assimi- and O2/N2 observations. Tellus B 51:213–232 lation and a pelagic ecosystem model: parameterization using Reid PC, Edwards M, Hunt HG, Warner AJ (1998) Phytoplankton time series observations. J Marine Syst 16:51–68 change in the North Atlantic. Nature 391:546 Steele JH (1958) Plant production in the northern North Sea. Mar Riley GA (1946) Factors controlling phytoplankton populations Res 7:1–36 on Georges Bank. J Mar Res 6:54–73 Steele JH (1974) The structure of marine ecosystems. Harvard Rintoul SR, Wunsch C (1991) Mass, heat, oxygen and nutrient fluxes University Press, Cambridge, MA, 128 pp and budgets in the North Atlantic Ocean. Deep-Sea Res 38 Stephens BB, Keeling RF (2000) The influence of Antarctic sea ice (suppl.) S355–S377 on glacial/interglacial CO2 variations. Nature 404:171–174 Roberts M, Marshall D (1998) Do we require adiabatic dissipation Stocker TF, Broecker WS, Wright DG (1994) Carbon uptake ex- schemes in eddy-resolving ocean models? J Phys Oceanogr periments with a zonally-averaged global ocean circulation 28:2050–2063 model. Tellus B 46:103–122 Robinson AR (1996) Physical processes, field estimation and an Sunda WG, Huntsman SA (1995) Iron uptake and growth limitation approach to interdisciplinary ocean modeling. Earth-Sci Rev in oceanic and coastal phytoplankton. Mar Chem 50:189–206 40:3–54 Takahashi T, Feely RA, Weiss RF, Wanninkhof RH, Chipman DW, Robinson AR, McCarthy JJ, Rothschild BJ (2001) The sea: biologi- Sutherland SC, Takahashi TT (1997) Global air-sea flux of CO2, cal-physical interactions in the ocean. John Wiley & Sons, New An estimate based on measurements of sea-air pCO2 differ- Yo r k ence. P Natl Acad Sci USA 94:8929–8299 Ryabchenko VA, Gorchakov VA, Fasham MJR (1998) Seasonal dy- Takahashi T, Wanninkhof RH, Feely RA, Weiss RF, Chipman DW, namics and biological productivity in the Arabian Sea euphotic Bates N, Olafson J, Sabine C, Sutherland SC (1999) Net air-sea zone as simulated by a three-dimensinoal ecosystem model. CO2 flux over the global oceans: an improved estimate based Global Biogeochem Cy 12:501–530 on the sea-air pCO2 difference. In: Center for Global Environ- Sabine CL, Key RM, Goyet C, Johnson KM, Millero FJ, Poisson A, mental Research, National Institute for Environmental Stud- Sarmiento JL, Wallace DWR, Winn CD (1999) Anthropogenic ies (ed) Proceedings of the 2nd International Symposium on CO2 inventory in the Indian Ocean. Global Biogeochem Cy CO2 in the Oceans. Tsukuba, Japan, pp 9–15 13:179–198 Tegen I, Fung I (1995) Contribution to the atmospheric mineral Sarmiento JL, Sundquist ET (1992) Revised budget for the oceanic aerosol load from land surface modification. J Geophys Res uptake of anthropogenic carbon dioxide. Nature 356:589–593 100:18,707–18,726 Sarmiento JL, Wofsy SC (1999) A U.S. Carbon Cycle Science Plan. Toggweiler JR (1999) Variation of atmospheric CO2 by ventilation (U.S. CCSP 1999), U.S. Global Change Research Program, Wash- of the ocean’s deepest water. Paleoceanography 14:571–588 ington DC, 69 pp Toggweiler JR, Dixon K, Bryan K (1989a) Simulations of radiocar- Sarmiento JL, Orr JC, Siegenthaler U (1992) A perturbation simu- bon in a coarse-resolution world ocean model; 1. Steady state lation of CO2 uptake in an ocean general circulation model. pre-bomb distribution. J Geophys Res 94:8217–8242 J Geophys Res 97:3621–3646 Toggweiler JR, Dixon K, Bryan K (1989b) Simulations of radiocar- Sarmiento JL, Slater RD, Fasham MJR (1993) A seasonal three-di- bon in a coarse-resolution world ocean model; 2. Distributions mensional ecosystem model of nitrogen cycling in the North of bomb-produced carbon-14. J Geophys Res 94:8243–8264 Atlantic euphotic zone. Global Biogeochem Cy 7:417–450 US Joint Global Ocean Flux Study (US JGOFS) (1992) Report of Sarmiento JL, Hughes TMC, Stouffer RJ, Manabe S (1998) Simu- the U.S. JGOFS Workshop on Modeling and Data Assimilation. lated response of the ocean carbon cycle to anthropogenic cli- Planning Report Number 14, U.S. JGOFS Planning Office, mate warming. Nature 393:245–249 Woods Hole, MA, 28 pp Sarmiento JL, Monfray P, Maier-Reimer E, Aumont O, Murnane Venrick EL, McGowan JA, Cayan DR, Hayward TL (1987) Climate RJ, Orr JC (2000) Sea-air CO2 fluxes and carbon transport: a and chlorophyll a: longterm trends in the central North Pa- comparison of three ocean general circulation models. Global cific Ocean. Science 238:70–72 Biogeochem Cy 14:1267–1281 Wallace DWR (1995) Monitoring global ocean carbon inventories. Schimel D, Enting IG, Heimann M, Wigley TML, Raynaud D, Alves Ocean Observing System Development Panel Background, D, Siegenthaler U (1995) CO2 and the carbon cycle. In: Texas A&M University, College Station, TX, 54 pp Houghton JT, Meira Filho LG, Bruce J, Lee H, Callander BA, Wallace DWR (2001) Storage and transport of excess CO2 in the Haites E, Harris N, Maskell K (eds) Climate change 1994. Inter- oceans: the JGOFS/WOCE Global CO2 survey. In: Siedler G, governmental Panel on Climate Change. Cambridge Univer- Gould J, Church J (eds) Ocean circulation and climate: observ- sity Press, pp 39–71 ing and modeling the global ocean. Academic Press, New York Schlitzer R (2000) Applying the adjoint method for global bio- Walsh JJ (1991) Importance of continental margins in the marine biogeochemical modeling. In: Kasibhatla P, et al. (eds) Inverse geochemical cycling of carbon and nitrogen. Nature 350:53–55 methods in global biogeochemical cycles. AGU Geophysical Wanninkhof R (1992) Relationship between wind speed and gas Monograph Series, American Geophysical Union, Washington exchange over the ocean. J Geophys Res 97:7373–7382 D.C., pp 107–124 Wanninkhof R, Doney SC, Peng T-H, Bullister J, Lee K, Feely RA Siegenthaler U, Joos F (1992) Use of a simple model for studying (1999) Comparison of methods to determine the anthropogenic oceanic tracer distributions and the global carbon cycle. Tellus CO2 invasion into the Atlantic Ocean. Tellus B, 51:511–530 B 44:186–207 Watson AJ, Orr JC (2003) Carbon dioxide fluxes in the global ocean. Siegenthaler U, Oeschger H (1978) Predicting future atmospheric Springer-Verlag, (this volume) carbon dioxide levels. Science 199:388–395 Webb DJ, deCuevas BA, Richmond CS (1998) Improved advection Siegenthaler U, Sarmiento JL (1993) Atmospheric carbon dioxide schemes for ocean models. J Atmos Ocean Tech 15:1171–1187 and the ocean. Nature 365:119–125 Yamanaka Y, Tajika E (1996) The role of the vertical fluxes of Six KD, Maier-Reimer E (1996) Effects of plankton dynamics on particulate organic matter and calcite in the oceanic carbon seasonal carbon fluxes in an ocean general circulation model. cycle: studies using an ocean biogeochemical general circula- Global Biogeochem Cy 10:559–583 tion model. Global Biogeochem Cy 10:361–382