Articles | Volume 21, issue 1
https://doi.org/10.5194/os-21-437-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-21-437-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Feb 2025
Research article |
| 13 Feb 2025
| 13 Feb 2025
Ocean carbon sink assessment via temperature and salinity data assimilation into a global ocean biogeochemistry model
Alfred-Wegener-Institut, Helmholtz Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
Judith Hauck
Alfred-Wegener-Institut, Helmholtz Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
Sinhué Torres-Valdés
Alfred-Wegener-Institut, Helmholtz Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
Lars Nerger
Alfred-Wegener-Institut, Helmholtz Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
Related authors
No articles found.
Lars Nerger, Yumeng Chen, Armin Corbin, and Johannes Keller
EGUsphere, https://doi.org/10.5194/egusphere-2026-2412, https://doi.org/10.5194/egusphere-2026-2412, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Data Assimilation combines information from numerical models and real-world observations. The complexity of models and observations necessitates advanced software to apply data assimilation with good efficiency. We discuss the relevant features of one such software, the open-source framework PDAF, which is already a widely used software for data assimilation. PDAF allows users to create their own efficient data assimilation system for a model of their choice as we demonstrate with an example.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Kjetil Aas, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Nicolas Bellouin, Alice Benoit-Cattin, Carla F. Berghoff, Raffaele Bernardello, Laurent Bopp, Ida Bagus Mandhara Brasika, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Nathan O. Collier, Thomas H. Colligan, Margot Cronin, Laique M. Djeutchouang, Xinyu Dou, Matt P. Enright, Kazutaka Enyo, Michael Erb, Wiley Evans, Richard A. Feely, Liang Feng, Daniel J. Ford, Adrianna Foster, Filippa Fransner, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Jefferson Goncalves De Souza, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Bertrand Guenet, Özgür Gürses, Kirsty Harrington, Ian Harris, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Akihiko Ito, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Steve D. Jones, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Yawen Kong, Jan Ivar Korsbakken, Charles Koven, Taro Kunimitsu, Xin Lan, Junjie Liu, Zhiqiang Liu, Zhu Liu, Claire Lo Monaco, Lei Ma, Gregg Marland, Patrick C. McGuire, Galen A. McKinley, Joe R. Melton, Natalie Monacci, Erwan Monier, Eric J. Morgan, David R. Munro, Jens D. Müller, Shin-Ichiro Nakaoka, Lorna R. Nayagam, Yosuke Niwa, Tobias Nutzel, Are Olsen, Abdirahman M. Omar, Naiqing Pan, Sudhanshu Pandey, Denis Pierrot, Zhangcai Qin, Pierre Regnier, Gregor Rehder, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, Ingunn Skjelvan, T. Luke Smallman, Victoria Spada, Mohanan G. Sreeush, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Didier Swingedouw, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Xiangjun Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Erik van Ooijen, Guido R. van der Werf, Sebastiaan J. van de Velde, Anthony P. Walker, Rik Wanninkhof, Xiaojuan Yang, Wenping Yuan, Xu Yue, and Jiye Zeng
Earth Syst. Sci. Data, 18, 3211–3288, https://doi.org/10.5194/essd-18-3211-2026, https://doi.org/10.5194/essd-18-3211-2026, 2026
Short summary
Short summary
The Global Carbon Budget 2025 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2025). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Moritz Zeising, Laurent Oziel, Silke Thoms, Özgür Gürses, Judith Hauck, Bernd Heinold, Svetlana N. Losa, Manuela van Pinxteren, Christoph Völker, Sebastian Zeppenfeld, and Astrid Bracher
Geosci. Model Dev., 19, 2077–2109, https://doi.org/10.5194/gmd-19-2077-2026, https://doi.org/10.5194/gmd-19-2077-2026, 2026
Short summary
Short summary
We assess the implementation of additional organic carbon pathways into a global setup of a numerical model, which simulates the ocean circulation, sea ice, and biogeochemical processes. With a focus on the Arctic Ocean, this model tracks the temporal and spatial dynamics of phytoplankton, exudation of organic carbon, and its aggregation to so-called transparent exopolymer particles. We evaluate the simulation using measurements from ship-based and remote-sensing campaigns in the Arctic Ocean.
Yumeng Chen, Dale Partridge, and Lars Nerger
EGUsphere, https://doi.org/10.5194/egusphere-2025-5851, https://doi.org/10.5194/egusphere-2025-5851, 2026
Short summary
Short summary
Operational marine ecosystem forecasts traditionally rely on combining phytoplankton chlorophyll observations with model forecasts. However, using our newly developed ensemble data assimilation system, we demonstrate that assimilating phytoplankton carbon data leads to more accurate phytoplankton estimates and improves estimates of global ocean carbon.
Li-Qing Jiang, Amanda Fay, Jens Daniel Müller, Luke Gregor, Alizée Roobaert, Lydia Keppler, Dustin Carroll, Siv K. Lauvset, Tim DeVries, Judith Hauck, Christian Rödenbeck, Nicolas Metzl, Andrea J. Fassbender, Jean-Pierre Gattuso, Peter Landschützer, Rik Wanninkhof, Christopher Sabine, Simone R. Alin, Mario Hoppema, Are Olsen, Matthew P. Humphreys, Kunal Chakraborty, Ana C. Franco, Kumiko Azetsu-Scott, Dorothee C. E. Bakker, Leticia Barbero, Nicholas R. Bates, Nicole Besemer, Henry C. Bittig, Albert E. Boyd, Daniel Broullón, Wei-Jun Cai, Brendan R. Carter, Thi-Tuyet-Trang Chau, Chen-Tung Arthur Chen, Frédéric Cyr, John E. Dore, Ian Enochs, Richard A. Feely, Hernan E. Garcia, Marion Gehlen, Prasanna Kanti Ghoshal, Lucas Gloege, Melchor González-Dávila, Nicolas Gruber, Debby Ianson, Yosuke Iida, Masao Ishii, Apurva Padamnabh Joshi, Esther Kennedy, Alex Kozyr, Nico Lange, Claire Lo Monaco, Derek P. Manzello, Galen A. McKinley, Natalie M. Monacci, Xose A. Padin, Ana M. Palacio-Castro, Fiz F. Pérez, J. Magdalena Santana-Casiano, Jonathan Sharp, Adrienne Sutton, Jim Swift, Toste Tanhua, Maciej Telszewski, Jens Terhaar, Ruben van Hooidonk, Anton Velo, Andrew J. Watson, Angelicque E. White, Zelun Wu, Liang Xue, Hyelim Yoo, Jiye Zeng, and Guorong Zhong
Earth Syst. Sci. Data, 18, 1405–1462, https://doi.org/10.5194/essd-18-1405-2026, https://doi.org/10.5194/essd-18-1405-2026, 2026
Short summary
Short summary
This review article provides an overview of 68 existing ocean carbonate chemistry data products and data product sets, encompassing a broad range of types, including compilations of cruise datasets, gap-filled observational products, model simulations, and more. It is designed to help researchers identify and access the data products that best support their scientific objectives, thereby facilitating progress in understanding the ocean's changing carbonate chemistry.
Nicholas Williams, Nicholas Byrne, Daniel Feltham, Peter Jan Van Leeuwen, Ross Bannister, David Schroeder, Isobel Lawrence, Lars Nerger, Jack Landy, and Geoffrey Dawson
EGUsphere, https://doi.org/10.5194/egusphere-2026-742, https://doi.org/10.5194/egusphere-2026-742, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
In this study we present three satellite era Arctic sea ice reanalyses, each assimilating different combinations of sea ice concentration and thickness observations. Results show that using year-round thickness observations substantially improves reanalysis compared to winter-only data. The best-performing reanalysis reveals a seasonally compensating bias cycle, suggesting errors in ice growth, leads, refreezing, marginal ice zone dynamics, redistribution, and melt timing mask model errors.
Alejandra Quintanilla-Zurita, Benjamin Rabe, Claudia Wekerle, Torsten Kanzow, Ivan Kuznetsov, Sinhue Torres-Valdes, Enric Pallàs-Sanz, and Ying-Chih Fang
Ocean Sci., 22, 305–328, https://doi.org/10.5194/os-22-305-2026, https://doi.org/10.5194/os-22-305-2026, 2026
Short summary
Short summary
During a year-long Arctic expedition, we discovered nine underwater eddies beneath the sea ice in the central Arctic Ocean. These hidden structures form within a layered part of the ocean just below the surface and may reshape water layers and transport heat, freshwater, and nutrients. Using drifting ice platforms, we measured their size, depth, and motion to understand how they form.
Yumeng Chen, Lars Nerger, and Amos S. Lawless
Geosci. Model Dev., 18, 8235–8252, https://doi.org/10.5194/gmd-18-8235-2025, https://doi.org/10.5194/gmd-18-8235-2025, 2025
Short summary
Short summary
In this paper, we present pyPDAF, a Python interface to the parallel data assimilation framework (PDAF) allowing for coupling with Python-based models. We demonstrate the capability and efficiency of pyPDAF under a coupled data assimilation setup.
Miriam Seifert, Christopher Danek, Christoph Völker, and Judith Hauck
Biogeosciences, 22, 5897–5919, https://doi.org/10.5194/bg-22-5897-2025, https://doi.org/10.5194/bg-22-5897-2025, 2025
Short summary
Short summary
Ocean alkalinity enhancement (OAE) can help to remove CO2 from the atmosphere. Yet, our study is the first to investigate the link between OAE and phytoplankton in an Earth system model. We show that OAE can indirectly decrease primary production and that biological feedbacks can modify the efficiency of OAE. Our study provides hints for ecological risks of OAE and the quantification of carbon drawdown and can serve as a kick start for other models to consider OAE–ecosystem interactions.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
Short summary
Short summary
The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
Short summary
Short summary
Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
Short summary
Short summary
To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024, https://doi.org/10.5194/gmd-17-4433-2024, 2024
Short summary
Short summary
This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
Short summary
Short summary
We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
Short summary
Short summary
In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
Short summary
Short summary
Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Christoph Heinze, Thorsten Blenckner, Peter Brown, Friederike Fröb, Anne Morée, Adrian L. New, Cara Nissen, Stefanie Rynders, Isabel Seguro, Yevgeny Aksenov, Yuri Artioli, Timothée Bourgeois, Friedrich Burger, Jonathan Buzan, B. B. Cael, Veli Çağlar Yumruktepe, Melissa Chierici, Christopher Danek, Ulf Dieckmann, Agneta Fransson, Thomas Frölicher, Giovanni Galli, Marion Gehlen, Aridane G. González, Melchor Gonzalez-Davila, Nicolas Gruber, Örjan Gustafsson, Judith Hauck, Mikko Heino, Stephanie Henson, Jenny Hieronymus, I. Emma Huertas, Fatma Jebri, Aurich Jeltsch-Thömmes, Fortunat Joos, Jaideep Joshi, Stephen Kelly, Nandini Menon, Precious Mongwe, Laurent Oziel, Sólveig Ólafsdottir, Julien Palmieri, Fiz F. Pérez, Rajamohanan Pillai Ranith, Juliano Ramanantsoa, Tilla Roy, Dagmara Rusiecka, J. Magdalena Santana Casiano, Yeray Santana-Falcón, Jörg Schwinger, Roland Séférian, Miriam Seifert, Anna Shchiptsova, Bablu Sinha, Christopher Somes, Reiner Steinfeldt, Dandan Tao, Jerry Tjiputra, Adam Ulfsbo, Christoph Völker, Tsuyoshi Wakamatsu, and Ying Ye
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-182, https://doi.org/10.5194/bg-2023-182, 2023
Revised manuscript not accepted
Short summary
Short summary
For assessing the consequences of human-induced climate change for the marine realm, it is necessary to not only look at gradual changes but also at abrupt changes of environmental conditions. We summarise abrupt changes in ocean warming, acidification, and oxygen concentration as the key environmental factors for ecosystems. Taking these abrupt changes into account requires greenhouse gas emissions to be reduced to a larger extent than previously thought to limit respective damage.
Claudia Hinrichs, Peter Köhler, Christoph Völker, and Judith Hauck
Biogeosciences, 20, 3717–3735, https://doi.org/10.5194/bg-20-3717-2023, https://doi.org/10.5194/bg-20-3717-2023, 2023
Short summary
Short summary
This study evaluated the alkalinity distribution in 14 climate models and found that most models underestimate alkalinity at the surface and overestimate it in the deeper ocean. It highlights the need for better understanding and quantification of processes driving alkalinity distribution and calcium carbonate dissolution and the importance of accounting for biases in model results when evaluating potential ocean alkalinity enhancement experiments.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
Short summary
Short summary
This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Nicholas Williams, Nicholas Byrne, Daniel Feltham, Peter Jan Van Leeuwen, Ross Bannister, David Schroeder, Andrew Ridout, and Lars Nerger
The Cryosphere, 17, 2509–2532, https://doi.org/10.5194/tc-17-2509-2023, https://doi.org/10.5194/tc-17-2509-2023, 2023
Short summary
Short summary
Observations show that the Arctic sea ice cover has reduced over the last 40 years. This study uses ensemble-based data assimilation in a stand-alone sea ice model to investigate the impacts of assimilating three different kinds of sea ice observation, including the novel assimilation of sea ice thickness distribution. We show that assimilating ice thickness distribution has a positive impact on thickness and volume estimates within the ice pack, especially for very thick ice.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Christian Rödenbeck, Tim DeVries, Judith Hauck, Corinne Le Quéré, and Ralph F. Keeling
Biogeosciences, 19, 2627–2652, https://doi.org/10.5194/bg-19-2627-2022, https://doi.org/10.5194/bg-19-2627-2022, 2022
Short summary
Short summary
The ocean is an important part of the global carbon cycle, taking up about a quarter of the anthropogenic CO2 emitted by burning of fossil fuels and thus slowing down climate change. However, the CO2 uptake by the ocean is, in turn, affected by variability and trends in climate. Here we use carbon measurements in the surface ocean to quantify the response of the oceanic CO2 exchange to environmental conditions and discuss possible mechanisms underlying this response.
Hao-Cheng Yu, Yinglong Joseph Zhang, Lars Nerger, Carsten Lemmen, Jason C. S. Yu, Tzu-Yin Chou, Chi-Hao Chu, and Chuen-Teyr Terng
EGUsphere, https://doi.org/10.5194/egusphere-2022-114, https://doi.org/10.5194/egusphere-2022-114, 2022
Preprint archived
Short summary
Short summary
We develop a new data assimilative approach by combining two parallel frameworks: PDAF and ESMF. This allows maximum flexibility and easy implementation of data assimilation for fully coupled earth system model applications. It is also validated by using a simple benchmark and applied to a realistic case simulation around Taiwan. The real case test shows significant improvement for temperature, velocity and surface elevation before, during and after typhoon events.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, https://doi.org/10.5194/essd-14-1917-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Cited articles
Bakker, D. C. E., Pfeil, B., Landa, C. S., Metzl, N., O'Brien, K. M., Olsen, A., Smith, K., Cosca, C., Harasawa, S., Jones, S. D., Nakaoka, S., Nojiri, Y., Schuster, U., Steinhoff, T., Sweeney, C., Takahashi, T., Tilbrook, B., Wada, C., Wanninkhof, R., Alin, S. R., Balestrini, C. F., Barbero, L., Bates, N. R., Bianchi, A. A., Bonou, F., Boutin, J., Bozec, Y., Burger, E. F., Cai, W.-J., Castle, R. D., Chen, L., Chierici, M., Currie, K., Evans, W., Featherstone, C., Feely, R. A., Fransson, A., Goyet, C., Greenwood, N., Gregor, L., Hankin, S., Hardman-Mountford, N. J., Harlay, J., Hauck, J., Hoppema, M., Humphreys, M. P., Hunt, C. W., Huss, B., Ibánhez, J. S. P., Johannessen, T., Keeling, R., Kitidis, V., Körtzinger, A., Kozyr, A., Krasakopoulou, E., Kuwata, A., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lo Monaco, C., Manke, A., Mathis, J. T., Merlivat, L., Millero, F. J., Monteiro, P. M. S., Munro, D. R., Murata, A., Newberger, T., Omar, A. M., Ono, T., Paterson, K., Pearce, D., Pierrot, D., Robbins, L. L., Saito, S., Salisbury, J., Schlitzer, R., Schneider, B., Schweitzer, R., Sieger, R., Skjelvan, I., Sullivan, K. F., Sutherland, S. C., Sutton, A. J., Tadokoro, K., Telszewski, M., Tuma, M., van Heuven, S. M. A. C., Vandemark, D., Ward, B., Watson, A. J., and Xu, S.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383–413, https://doi.org/10.5194/essd-8-383-2016, 2016. a, b, c
Bakker, D. C. E., Alin, S. R., Bates, N., Becker, M., Feely, R. A., Gkritzalis, T., Jones, S. D., Kozyr, A., Lauvset, S. K., Metzl, N., Munro, D. R., Nakaoka, S.-i., Nojiri, Y., O'Brien, K. M., Olsen, A., Pierrot, D., Rehder, G., Steinhoff, T., Sutton, A. J., Sweeney, C., Tilbrook, B., Wada, C., Wanninkhof, R., Akl, J., Barbero, L., Beatty, C. M., Berghoff, C. F., Bittig, H. C., Bott, R., Burger, E. F., Cai, W.-J., Castaño Primo, R., Corredor, J. E., Cronin, M., De Carlo, E. H., DeGrandpre, M. D., Dietrich, C., Drennan, W. M., Emerson, S. R., Enochs, I. C., Enyo, K., Epherra, L., Evans, W., Fiedler, B., Fontela, M., Frangoulis, C., Gehrung, M., Giannoudi, L., Glockzin, M., Hales, B., Howden, S. D., Ibánhez, J. S. P., Kamb, L., Körtzinger, A., Lefèvre, N., Lo Monaco, C., Lutz, V. A., Macovei, V. A., Maenner Jones, S., Manalang, D., Manzello, D. P., Metzl, N., Mickett, J., Millero, F. J., Monacci, N. M., Morell, J. M., Musielewicz, S., Neill, C., Newberger, T., Newton, J., Noakes, S., Ólafsdóttir, S. R., Ono, T., Osborne, J., Padín, X. A., Paulsen, M., Perivoliotis, L., Petersen, W., Petihakis, G., Plueddemann, A. J., Rodriguez, C., Rutgersson, A., Sabine, C. L., Salisbury, J. E., Schlitzer, R., Skjelvan, I., Stamataki, N., Sullivan, K. F., Sutherland, S. C., T'Jampens, M., Tadokoro, K., Tanhua, T., Telszewski, M., Theetaert, H., Tomlinson, M., Vandemark, D., Velo, A., Voynova, Y. G., Weller, R. A., Whitehead, C., and Wimart-Rousseau, C.: Surface Ocean CO2 Atlas Database Version 2023 (SOCATv2023) (NCEI Accession 0278913), NOAA National Centers for Environmental Information [data set], https://doi.org/10.25921/r7xa-bt92, 2023. a, b, c
Ballantyne, A. P., Alden, C. B., Miller, J. B., Tans, P. P., and White, J. W. C.: Increase in Observed Net Carbon Dioxide Uptake by Land and Oceans during the Past 50 Years, Nature, 488, 70–72, https://doi.org/10.1038/nature11299, 2012. a
Bernardello, R., Sicardi, V., Lapin, V., Ortega, P., Ruprich-Robert, Y., Tourigny, E., and Ferrer, E.: Ocean biogeochemical reconstructions to estimate historical ocean CO2 uptake, Earth Syst. Dynam., 15, 1255–1275, https://doi.org/10.5194/esd-15-1255-2024, 2024. a
Boutin, J., Vergely, J.-L., Reul, N., Catany, R., Koehler, J., Martin, A., Rouffi, F., Arias, M., Chakroun, M., Corato, G., Estella-Perez, V., Guimbard, S., Hasson, A., Josey, S., Khvorostyanov, D., Kolodziejczyk, N., Mignot, J., Olivier, L., Reverdin, G., Stammer, D., Supply, A., Thouvenin-Masson, C., Turiel, A., Vialard, J., Cipollini, P., and Donlon, C.: ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_CCI): Weekly and Monthly Sea Surface Salinity Product, v03.21, from 2010 to 2020, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/5920a2c77e3c45339477acd31ce62c3c, 2021. a, b
Brix, H., Menemenlis, D., Hill, C., Dutkiewicz, S., Jahn, O., Wang, D., Bowman, K., and Zhang, H.: Using Green's Functions to Initialize and Adjust a Global, Eddying Ocean Biogeochemistry General Circulation Model, Ocean Model., 95, 1–14, https://doi.org/10.1016/j.ocemod.2015.07.008, 2015. a
Buchanan, P. J., Matear, R. J., Chase, Z., Phipps, S. J., and Bindoff, N. L.: Dynamic Biological Functioning Important for Simulating and Stabilizing Ocean Biogeochemistry, Global Biogeochem. Cy., 32, 565–593, https://doi.org/10.1002/2017GB005753, 2018. a
Bunsen, F.: Assessing the ocean carbon sink: Assimilation of temperature and salinity into a global ocean biogeochemistry model, Zenodo [code], https://doi.org/10.5281/zenodo.11495274, 2024a. a
Bunsen, F.: Assessing the ocean carbon sink: Assimilation of temperature and salinity into a global ocean biogeochemistry model, Zenodo [data set], https://doi.org/10.5281/zenodo.11495081, 2024b. a
Bunsen, F., Nissen, C., and Hauck, J.: The Impact of Recent Climate Change on the Global Ocean Carbon Sink, Geophys. Res. Lett., 51, e2023GL107030, https://doi.org/10.1029/2023GL107030, 2024. a
Bushinsky, S. M., Landschützer, P., Rödenbeck, C., Gray, A. R., Baker, D., Mazloff, M. R., Resplandy, L., Johnson, K. S., and Sarmiento, J. L.: Reassessing Southern Ocean Air-Sea CO2 Flux Estimates With the Addition of Biogeochemical Float Observations, Global Biogeochem. Cy., 33, 1370–1388, https://doi.org/10.1029/2019GB006176, 2019. a
Cao, L., Eby, M., Ridgwell, A., Caldeira, K., Archer, D., Ishida, A., Joos, F., Matsumoto, K., Mikolajewicz, U., Mouchet, A., Orr, J. C., Plattner, G.-K., Schlitzer, R., Tokos, K., Totterdell, I., Tschumi, T., Yamanaka, Y., and Yool, A.: The role of ocean transport in the uptake of anthropogenic CO2, Biogeosciences, 6, 375–390, https://doi.org/10.5194/bg-6-375-2009, 2009. a
Carroll, D., Menemenlis, D., Adkins, J. F., Bowman, K. W., Brix, H., Dutkiewicz, S., Fenty, I., Gierach, M. M., Hill, C., Jahn, O., Landschützer, P., Lauderdale, J. M., Liu, J., Manizza, M., Naviaux, J. D., Rödenbeck, C., Schimel, D. S., Van der Stocken, T., and Zhang, H.: The ECCO-Darwin Data-Assimilative Global Ocean Biogeochemistry Model: Estimates of Seasonal to Multidecadal Surface Ocean pCO2 and Air-Sea CO2 Flux, J. Adv. Model. Earth Syst., 12, e2019MS001888, https://doi.org/10.1029/2019MS001888, 2020. a, b, c
Chapman, C. C., Lea, M.-A., Meyer, A., Sallée, J.-B., and Hindell, M.: Defining Southern Ocean fronts and their influence on Biological and Physical processes in a changing Climate, Nat. Clim. Change, 10, 209–219, https://doi.org/10.1038/s41558-020-0705-4, 2020. a
Chen, H. W., Alley, R. B., and Zhang, F.: Interannual Arctic Sea Ice Variability and Associated Winter Weather Patterns: A Regional Perspective for 1979–2014, J. Geophys. Res.-Atmos., 121, 14433–14455, 2016. a
Ciavatta, S., Kay, S., Saux-Picart, S., Butenschön, M., and Allen, J. I.: Decadal Reanalysis of Biogeochemical Indicators and Fluxes in the North West European Shelf-sea Ecosystem, J. Geophys. Res.-Oceans, 121, 1824–1845, https://doi.org/10.1002/2015JC011496, 2016. a
Ciavatta, S., Brewin, R. J. W., Skákala, J., Polimene, L., de Mora, L., Artioli, Y., and Allen, J. I.: Assimilation of Ocean-Color Plankton Functional Types to Improve Marine Ecosystem Simulations, J. Geophys. Res.-Oceans, 123, 834–854, https://doi.org/10.1002/2017JC013490, 2018. a
Crisp, D., Dolman, H., Tanhua, T., McKinley, G. A., Hauck, J., Bastos, A., Sitch, S., Eggleston, S., and Aich, V.: How Well Do We Understand the Land-Ocean-Atmosphere Carbon Cycle?, Rev. Geophys., 60, e2021RG000736, https://doi.org/10.1029/2021RG000736, 2022. a, b
Danilov, S., Sidorenko, D., Wang, Q., and Jung, T.: The Finite-volumE Sea ice–Ocean Model (FESOM2), Geosci. Model Dev., 10, 765–789, https://doi.org/10.5194/gmd-10-765-2017, 2017. a, b
Davila, X., Gebbie, G., Brakstad, A., Lauvset, S. K., McDonagh, E. L., Schwinger, J., and Olsen, A.: How Is the Ocean Anthropogenic Carbon Reservoir Filled?, Global Biogeochem. Cy., 36, e2021GB007055, https://doi.org/10.1029/2021GB007055, 2022. a
de Boyer Montégut, C.: Mixed layer depth climatology computed with a density threshold criterion of 0.03kg/m3 from 10 m depth value, SEANOE [data set], https://doi.org/10.17882/91774, 2023. a
de Boyer Montégut, C., Madec, G., Fischer, A. S., Lazar, A., and Iudicone, D.: Mixed Layer Depth over the Global Ocean: An Examination of Profile Data and a Profile-based Climatology, J. Geophys. Res.-Oceans, 109, C12003, https://doi.org/10.1029/2004JC002378, 2004. a, b, c, d
Denvil-Sommer, A., Gehlen, M., and Vrac, M.: Observation system simulation experiments in the Atlantic Ocean for enhanced surface ocean pCO2 reconstructions, Ocean Sci., 17, 1011–1030, https://doi.org/10.5194/os-17-1011-2021, 2021. a
DeVries, T.: The Ocean Carbon Cycle, Annu. Rev. Env. Resour., 47, 317–341, https://doi.org/10.1146/annurev-environ-120920-111307, 2022. a
DeVries, T., Holzer, M., and Primeau, F.: Recent Increase in Oceanic Carbon Uptake Driven by Weaker Upper-ocean Overturning, Nature, 542, 215–218, https://doi.org/10.1038/nature21068, 2017. a
DeVries, T., Yamamoto, K., Wanninkhof, R., Gruber, N., Hauck, J., Müller, J. D., Bopp, L., Carroll, D., Carter, B., Chau, T.-T.-T., Doney, S. C., Gehlen, M., Gloege, L., Gregor, L., Henson, S., Kim, J. H., Iida, Y., Ilyina, T., Landschützer, P., Le Quéré, C., Munro, D., Nissen, C., Patara, L., Pérez, F. F., Resplandy, L., Rodgers, K. B., Schwinger, J., Séférian, R., Sicardi, V., Terhaar, J., Triñanes, J., Tsujino, H., Watson, A., Yasunaka, S., and Zeng, J.: Magnitude, Trends, and Variability of the Global Ocean Carbon Sink From 1985 to 2018, Global Biogeochem. Cy., 37, e2023GB007780, https://doi.org/10.1029/2023GB007780, 2023. a, b, c, d, e, f, g
Doney, S. C., Lindsay, K., Caldeira, K., Campin, J.-M., Drange, H., Dutay, J.-C., Follows, M., Gao, Y., Gnanadesikan, A., Gruber, N., Ishida, A., Joos, F., Madec, G., Maier-Reimer, E., Marshall, J. C., Matear, R. J., Monfray, P., Mouchet, A., Najjar, R., Orr, J. C., Plattner, G.-K., Sarmiento, J., Schlitzer, R., Slater, R., Totterdell, I. J., Weirig, M.-F., Yamanaka, Y., and Yool, A.: Evaluating Global Ocean Carbon Models: The Importance of Realistic Physics, Global Biogeochem. Cy., 18, GB3017, https://doi.org/10.1029/2003GB002150, 2004. a
Doney, S. C., Lima, I., Feely, R. A., Glover, D. M., Lindsay, K., Mahowald, N., Moore, J. K., and Wanninkhof, R.: Mechanisms Governing Interannual Variability in Upper-ocean Inorganic Carbon System and Air–sea CO2 Fluxes: Physical Climate and Atmospheric Dust, Deep-Sea Res. Pt. II, 56, 640–655, https://doi.org/10.1016/j.dsr2.2008.12.006, 2009. a
Egleston, E. S., Sabine, C. L., and Morel, F. M. M.: Revelle Revisited: Buffer Factors that Quantify the Response of Ocean Chemistry to Changes in DIC and Alkalinity, Global Biogeochem. Cy., 24, GB1002, https://doi.org/10.1029/2008GB003407, 2010. a
EU Copernicus Marine Service Information (CMEMS): Global Ocean OSTIA Sea Surface Temperature and Sea Ice Analysis, Marine Data Store [data set], https://doi.org/10.48670/moi-00165, 2023. a
EUMETSAT: OSI SAF Global Medium Resolution Sea Ice Concentration Climate Data Record 2002–2020 (v3.0), Ocean and Sea Ice Satellite Application Facility [data set], https://doi.org/10.15770/EUM_SAF_OSI_0015, 2022. a, b
Evensen, G.: The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation, Ocean Dynam., 53, 343–367, https://doi.org/10.1007/s10236-003-0036-9, 2003. a
Fay, A. R. and McKinley, G. A.: Global open-ocean biomes: mean and temporal variability, Earth Syst. Sci. Data, 6, 273–284, https://doi.org/10.5194/essd-6-273-2014, 2014. a
Fennel, K., Mattern, J. P., Doney, S. C., Bopp, L., Moore, A. M., Wang, B., and Yu, L.: Ocean Biogeochemical Modelling, Nature Reviews Methods Primers, 2, 1–21, https://doi.org/10.1038/s43586-022-00154-2, 2022. a
Ford, D. and Barciela, R.: Global Marine Biogeochemical Reanalyses Assimilating Two Different Sets of Merged Ocean Colour Products, Remote Sens. Environ., 203, 40–54, https://doi.org/10.1016/j.rse.2017.03.040, 2017. a, b
Fransner, F., Counillon, F., Bethke, I., Tjiputra, J., Samuelsen, A., Nummelin, A., and Olsen, A.: Ocean Biogeochemical Predictions – Initialization and Limits of Predictability, Front. Mar. Sci., 7, 508048, https://doi.org/10.3389/fmars.2020.00386, 2020. a
Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Bakker, D. C. E., Hauck, J., Landschützer, P., Le Quéré, C., Luijkx, I. T., Peters, G. P., Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Barbero, L., Bates, N. R., Becker, M., Bellouin, N., Decharme, B., Bopp, L., Brasika, I. B. M., Cadule, P., Chamberlain, M. A., Chandra, N., Chau, T.-T.-T., Chevallier, F., Chini, L. P., Cronin, M., Dou, X., Enyo, K., Evans, W., Falk, S., Feely, R. A., Feng, L., Ford, D. J., Gasser, T., Ghattas, J., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., Gürses, Ö., Harris, I., Hefner, M., Heinke, J., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Jacobson, A. R., Jain, A., Jarníková, T., Jersild, A., Jiang, F., Jin, Z., Joos, F., Kato, E., Keeling, R. F., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., Körtzinger, A., Lan, X., Lefèvre, N., Li, H., Liu, J., Liu, Z., Ma, L., Marland, G., Mayot, N., McGuire, P. C., McKinley, G. A., Meyer, G., Morgan, E. J., Munro, D. R., Nakaoka, S.-I., Niwa, Y., O'Brien, K. M., Olsen, A., Omar, A. M., Ono, T., Paulsen, M., Pierrot, D., Pocock, K., Poulter, B., Powis, C. M., Rehder, G., Resplandy, L., Robertson, E., Rödenbeck, C., Rosan, T. M., Schwinger, J., Séférian, R., Smallman, T. L., Smith, S. M., Sospedra-Alfonso, R., Sun, Q., Sutton, A. J., Sweeney, C., Takao, S., Tans, P. P., Tian, H., Tilbrook, B., Tsujino, H., Tubiello, F., van der Werf, G. R., van Ooijen, E., Wanninkhof, R., Watanabe, M., Wimart-Rousseau, C., Yang, D., Yang, X., Yuan, W., Yue, X., Zaehle, S., Zeng, J., and Zheng, B.: Global Carbon Budget 2023, Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, 2023. a, b, c, d, e, f, g, h, i, j, k, l
Gaspari, G. and Cohn, S. E.: Construction of Correlation Functions in Two and Three Dimensions, Q. J. Roy. Meteor. Soc., 125, 723–757, https://doi.org/10.1002/qj.49712555417, 1999. a
Gasparin, F., Cravatte, S., Greiner, E., Perruche, C., Hamon, M., Van Gennip, S., and Lellouche, J.-M.: Excessive Productivity and Heat Content in Tropical Pacific Analyses: Disentangling the Effects of In Situ and Altimetry Assimilation, Ocean Model., 160, 101768, https://doi.org/10.1016/j.ocemod.2021.101768, 2021. a
Gent, P. R. and Mcwilliams, J. C.: Isopycnal Mixing in Ocean Circulation Models, J. Phys. Oceanogr., 20, 150–155, https://doi.org/10.1175/1520-0485(1990)020<0150:IMIOCM>2.0.CO;2, 1990. a
Gerber, M., Joos, F., Vázquez-Rodríguez, M., Touratier, F., and Goyet, C.: Regional Air-sea Fluxes of Anthropogenic Carbon Inferred with an Ensemble Kalman Filter, Global Biogeochem. Cy., 23, GB1013, https://doi.org/10.1029/2008GB003247, 2009. a, b
Global Carbon Project: Supplemental data of Global Carbon Budget 2023 (Version 1.1), Global Carbon Project [data set], https://doi.org/10.18160/gcp-2023, 2023. a
Gloege, L., McKinley, G. A., Landschützer, P., Fay, A. R., Frölicher, T. L., Fyfe, J. C., Ilyina, T., Jones, S., Lovenduski, N. S., Rodgers, K. B., Schlunegger, S., and Takano, Y.: Quantifying Errors in Observationally Based Estimates of Ocean Carbon Sink Variability, Global Biogeochem. Cy., 35, e2020GB006788, https://doi.org/10.1029/2020GB006788, 2021. a, b, c, d
Good, S., Fiedler, E., Mao, C., Martin, M., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., and Worsfold, M.: The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses, Remote Sens., 12, 720, https://doi.org/10.3390/rs12040720, 2020. a, b
Good, S. A., Martin, M. J., and Rayner, N. A.: EN4: Quality Controlled Ocean Temperature and Salinity Profiles and Monthly Objective Analyses with Uncertainty Estimates, J. Geophys. Res.-Oceans, 118, 6704–6716, https://doi.org/10.1002/2013JC009067, 2013. a, b, c
Gray, A. R., Johnson, K. S., Bushinsky, S. M., Riser, S. C., Russell, J. L., Talley, L. D., Wanninkhof, R., Williams, N. L., and Sarmiento, J. L.: Autonomous Biogeochemical Floats Detect Significant Carbon Dioxide Outgassing in the High-Latitude Southern Ocean, Geophys. Res. Lett., 45, 9049–9057, https://doi.org/10.1029/2018GL078013, 2018. a, b
Gruber, N., Gloor, M., Fletcher, S. E. M., Doney, S. C., Dutkiewicz, S., Follows, M. J., Gerber, M., Jacobson, A. R., Joos, F., Lindsay, K., Menemenlis, D., Mouchet, A., Müller, S. A., Sarmiento, J. L., and Takahashi, T.: Oceanic Sources, Sinks, and Transport of Atmospheric CO2, Global Biogeochem. Cy., 23, GB1005, https://doi.org/10.1029/2008GB003349, 2009. a
Gürses, Ö., Oziel, L., Karakuş, O., Sidorenko, D., Völker, C., Ye, Y., Zeising, M., Butzin, M., and Hauck, J.: Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3, Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, 2023. a, b, c, d, e, f, g, h, i
Hauck, J., Völker, C., Wang, T., Hoppema, M., Losch, M., and Wolf-Gladrow, D. A.: Seasonally Different Carbon Flux Changes in the Southern Ocean in Response to the Southern Annular Mode, Global Biogeochem. Cy., 27, 1236–1245, https://doi.org/10.1002/2013GB004600, 2013. a, b
Hauck, J., Zeising, M., Le Quéré, C., Gruber, N., Bakker, D. C. E., Bopp, L., Chau, T. T. T., Gürses, Ö., Ilyina, T., Landschützer, P., Lenton, A., Resplandy, L., Rödenbeck, C., Schwinger, J., and Séférian, R.: Consistency and Challenges in the Ocean Carbon Sink Estimate for the Global Carbon Budget, Front. Mar. Sci., 7, 571720, https://doi.org/10.3389/fmars.2020.571720, 2020. a, b, c, d, e, f, g
Hauck, J., Gregor, L., Nissen, C., Patara, L., Hague, M., Mongwe, P., Bushinsky, S., Doney, S. C., Gruber, N., Le Quéré, C., Manizza, M., Mazloff, M., Monteiro, P. M. S., and Terhaar, J.: The Southern Ocean Carbon Cycle 1985–2018: Mean, Seasonal Cycle, Trends, and Storage, Global Biogeochem. Cy., 37, e2023GB007848, https://doi.org/10.1029/2023GB007848, 2023a. a, b, c
Hauck, J., Nissen, C., Landschützer, P., Rödenbeck, C., Bushinsky, S., and Olsen, A.: Sparse Observations Induce Large Biases in Estimates of the Global Ocean CO2 Sink: An Ocean Model Subsampling Experiment, Philos. T. Roy. Soc. A, 381, 20220063, https://doi.org/10.1098/rsta.2022.0063, 2023b. a, b, c, d
Hemmings, J. C. P., Barciela, R. M., and Bell, M. J.: Ocean Color Data Assimilation with Material Conservation for Improving Model Estimates of Air-Sea CO2 Flux, J. Mar. Res., 66, 87–126, https://doi.org/10.1357/002224008784815739, 2008. a
Hohn, S.: Coupling and Decoupling of Biogeochemical Cycles in Marine Ecosystems, PhD thesis, University of Bremen, http://nbn-resolving.de/urn:nbn:de:gbv:46-diss000112787 (last access: 28 January 2025), 2008. a
Johnson, K. S., Plant, J. N., Coletti, L. J., Jannasch, H. W., Sakamoto, C. M., Riser, S. C., Swift, D. D., Williams, N. L., Boss, E., Haëntjens, N., Talley, L. D., and Sarmiento, J. L.: Biogeochemical Sensor Performance in the SOCCOM Profiling Float Array, J. Geophys. Res.-Oceans, 122, 6416–6436, https://doi.org/10.1002/2017JC012838, 2017. a
Johnson, R., Strutton, P. G., Wright, S. W., McMinn, A., and Meiners, K. M.: SRS Satellite Contributed Ocean Colour – SeaWIFS Chlorophyll Concentration in the Southern Ocean: Monthly, Johnson et al 2013, Integrated Marine Observing System (IMOS), Australian Ocean Data Network (AODN) [data set], https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/10fcb776-d331-4a04-ac1f-4a48fa050385 (last access: 7 February 2025), 2014a. a
Johnson, R., Strutton, P. G., Wright, S. W., McMinn, A., and Meiners, K. M.: SRS Satellite Contributed Ocean Colour – MODIS – Aqua Chlorophyll Concentration in the Southern Ocean: Monthly, Johnson et al 2013, Integrated Marine Observing System (IMOS), Australian Ocean Data Network (AODN) [data set], https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/ce859887-bdf0-4eb0-acf2-80d38af4c85c (last access: 7 February 2025), 2014b. a
Jones, S. D., Le Quéré, C., and Rödenbeck, C.: Autocorrelation Characteristics of Surface Ocean pCO2 and Air-sea CO2 Fluxes, Global Biogeochem. Cy., 26, GB2042, https://doi.org/10.1029/2010GB004017, 2012. a
Joos, F. and Spahni, R.: Rates of Change in Natural and Anthropogenic Radiative Forcing Over the Past 20,000 Years, P. Natl. Acad. Sci. USA, 105, 1425–1430, https://doi.org/10.1073/pnas.0707386105, 2008. a
Karakuş, O., Völker, C., Iversen, M., Hagen, W., Wolf-Gladrow, D., Fach, B., and Hauck, J.: Modeling the Impact of Macrozooplankton on Carbon Export Production in the Southern Ocean, J. Geophys. Res.-Oceans, 126, e2021JC017315, https://doi.org/10.1029/2021JC017315, 2021. a, b
Keppler, L. and Landschützer, P.: Regional Wind Variability Modulates the Southern Ocean Carbon Sink, Sci. Rep., 9, 7384, https://doi.org/10.1038/s41598-019-43826-y, 2019. a
Kriest, I., Kähler, P., Koeve, W., Kvale, K., Sauerland, V., and Oschlies, A.: One size fits all? Calibrating an ocean biogeochemistry model for different circulations, Biogeosciences, 17, 3057–3082, https://doi.org/10.5194/bg-17-3057-2020, 2020. a, b
Large, W. G., McWilliams, J. C., and Doney, S. C.: Oceanic Vertical Mixing: A Review and a Model with a Nonlocal Boundary Layer Parameterization, Rev. Geophys., 32, 363–403, https://doi.org/10.1029/94RG01872, 1994. a
Laurindo, L. C., Mariano, A. J., and Lumpkin, R.: An improved near-surface velocity Climatology for the Global Ocean from drifter Observations, Deep-Sea Res. Pt. I, 124, 73–92, https://doi.org/10.1016/j.dsr.2017.04.009, 2017a. a, b, c, d
Laurindo, L. C., Mariano, A. J., and Lumpkin, R.: Near-surface velocity climatology for the global ocean from drifter observations, Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC) [data set], https://doi.org/10.7266/N7SJ1HN5, 2017b. a
Lauvset, S. K., Key, R. M., Olsen, A., van Heuven, S., Velo, A., Lin, X., Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S., Steinfeldt, R., Jeansson, E., Ishii, M., Perez, F. F., Suzuki, T., and Watelet, S.: A new global interior ocean mapped climatology: the 1° × 1° GLODAP version 2, Earth Syst. Sci. Data, 8, 325–340, https://doi.org/10.5194/essd-8-325-2016, 2016. a, b, c
Lauvset, S. K., Key, R. M., Olsen, A., van Heuven, S. M. A. C., Velo, A., Lin, X., Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S., Steinfeldt, R., Jeansson, E., Ishii, M., Pérez, F. F., Suzuki, T., and Watelet, S.: A new global interior ocean mapped climatology: the 1° × 1° GLODAP version 2 from 1972-01-01 to 2013-12-31 (NCEI Accession 0286118), NOAA National Centers for Environmental Information [data set], https://doi.org/10.3334/cdiac/otg.ndp093_glodapv2, 2023a. a
Lauvset, S. K., Lange, N., Tanhua, T., Bittig, H. C., Olsen, A., Kozyr, A., Álvarez, M., Azetsu-Scott, K., Becker, S., Brown, P. J., Carter, B. R., Cotrim da Cunha, L., Feely, R. A., Hoppema, M., Humphreys, M. P., Ishii, M., Jeansson, E., Jones, S. D., Lo Monaco, C., Murata, A., Müller, J. D., Pérez, F. F., Schirnick, C., Steinfeldt, R., Suzuki, T., Tilbrook, B., Ulfsbo, A., Velo, A., Woosley, R. J., and Key, R. M.: Global Ocean Data Analysis Project version 2.2023 (GLODAPv2.2023) (NCEI Accession 0283442), NOAA National Centers for Environmental Information [data set], https://doi.org/10.25921/zyrq-ht66, 2023b. a
Lauvset, S. K., Lange, N., Tanhua, T., Bittig, H. C., Olsen, A., Kozyr, A., Álvarez, M., Azetsu-Scott, K., Brown, P. J., Carter, B. R., Cotrim da Cunha, L., Hoppema, M., Humphreys, M. P., Ishii, M., Jeansson, E., Murata, A., Müller, J. D., Pérez, F. F., Schirnick, C., Steinfeldt, R., Suzuki, T., Ulfsbo, A., Velo, A., Woosley, R. J., and Key, R. M.: The annual update GLODAPv2.2023: the global interior ocean biogeochemical data product, Earth Syst. Sci. Data, 16, 2047–2072, https://doi.org/10.5194/essd-16-2047-2024, 2024. a, b, c
Lebehot, A. D., Halloran, P. R., Watson, A. J., McNeall, D., Ford, D. A., Landschützer, P., Lauvset, S. K., and Schuster, U.: Reconciling Observation and Model Trends in North Atlantic Surface CO2, Global Biogeochem. Cy., 33, 1204–1222, https://doi.org/10.1029/2019GB006186, 2019. a, b
Le Quéré, C., Raupach, M. R., Canadell, J. G., Marland, G., Bopp, L., Ciais, P., Conway, T. J., Doney, S. C., Feely, R. A., Foster, P., Friedlingstein, P., Gurney, K., Houghton, R. A., House, J. I., Huntingford, C., Levy, P. E., Lomas, M. R., Majkut, J., Metzl, N., Ometto, J. P., Peters, G. P., Prentice, I. C., Randerson, J. T., Running, S. W., Sarmiento, J. L., Schuster, U., Sitch, S., Takahashi, T., Viovy, N., Van Der Werf, G. R., and Woodward, F. I.: Trends in the sources and Sinks of Carbon Dioxide, Nat. Geosci., 2, 831–836, https://doi.org/10.1038/ngeo689, 2009. a
Li, H., Ilyina, T., Müller, W. A., and Sienz, F.: Decadal Predictions of the North Atlantic CO2 Uptake, Nat. Commun., 7, 1–7, https://doi.org/10.1038/ncomms11076, 2016. a
Liao, E., Resplandy, L., Liu, J., and Bowman, K. W.: Amplification of the Ocean Carbon Sink during El Niños: Role of Poleward Ekman Transport and Influence on Atmospheric CO2, Global Biogeochem. Cy., 34, e2020GB006574, https://doi.org/10.1029/2020GB006574, 2020. a
Long, M. C., Stephens, B. B., McKain, K., Sweeney, C., Keeling, R. F., Kort, E. A., Morgan, E. J., Bent, J. D., Chandra, N., Chevallier, F., Commane, R., Daube, B. C., Krummel, P. B., Loh, Z., Luijkx, I. T., Munro, D., Patra, P., Peters, W., Ramonet, M., Rödenbeck, C., Stavert, A., Tans, P., and Wofsy, S. C.: Strong Southern Ocean Carbon Uptake Evident in Airborne Observations, Science, 374, 1275–1280, https://doi.org/10.1126/science.abi4355, 2021. a
Löptien, U. and Dietze, H.: Reciprocal bias compensation and ensuing uncertainties in model-based climate projections: pelagic biogeochemistry versus ocean mixing, Biogeosciences, 16, 1865–1881, https://doi.org/10.5194/bg-16-1865-2019, 2019. a
Mamnun, N., Völker, C., Krumscheid, S., Vrekoussis, M., and Nerger, L.: Global Sensitivity Analysis of a One-Dimensional Ocean Biogeochemical Model, Socio-Environmental Systems Modelling, 5, 18613, https://doi.org/10.18174/sesmo.18613, 2023. a
Mayot, N., Le Quéré, C., Rödenbeck, C., Bernardello, R., Bopp, L., Djeutchouang, L. M., Gehlen, M., Gregor, L., Gruber, N., Hauck, J., Iida, Y., Ilyina, T., Keeling, R. F., Landschü”tzer, P., Manning, A. C., Patara, L., Resplandy, L., Schwinger, J., Séférian, R., Watson, A. J., Wright, R. M., and Zeng, J.: Climate-driven Variability of the Southern Ocean CO2 Sink, Philos. T. Roy. Soc. A, 381, 20220055, https://doi.org/10.1098/rsta.2022.0055, 2023. a
Mayot, N., Buitenhuis, E. T., Wright, R. M., Hauck, J., Bakker, D. C. E., and Le Quéré, C.: Constraining the Trend in the Ocean CO2 Sink during 2000–2022, Nat. Commun., 15, 1–11, https://doi.org/10.1038/s41467-024-52641-7, 2024. a, b, c
Menemenlis, D., Fukumori, I., and Lee, T.: Using Green's Functions to Calibrate an Ocean General Circulation Model, Mon. Weather Rev., 133, 1224–1240, https://doi.org/10.1175/MWR2912.1, 2005. a
Met Office: EN4 quality controlled subsurface ocean temperature and salinity profiles and objective analyses (EN.4.2.2), Met Office Hadley Centre for Climate Change [data set], https://www.metoffice.gov.uk/hadobs/en4/ (last access: 7 February 2025), 2023. a
Mu, L., Nerger, L., Streffing, J., Tang, Q., Niraula, B., Zampieri, L., Loza, S. N., and Goessling, H. F.: Sea-Ice Forecasts With an Upgraded AWI Coupled Prediction System, J. Adv. Model. Earth Syst., 14, e2022MS003176, https://doi.org/10.1029/2022MS003176, 2022. a, b
Müller, J. D.: RECCAP2-ocean data collection, Zenodo [data set], https://doi.org/10.5281/zenodo.7990823, 2023. a
Müller, J. D., Gruber, N., Carter, B., Feely, R., Ishii, M., Lange, N., Lauvset, S. K., Murata, A., Olsen, A., Pérez, F. F., Sabine, C., Tanhua, T., Wanninkhof, R., and Zhu, D.: Decadal Trends in the Oceanic Storage of Anthropogenic Carbon From 1994 to 2014, AGU Advances, 4, e2023AV000875, https://doi.org/10.1029/2023AV000875, 2023. a
Nerger, L., Hiller, W., and Schröter, J.: PDAF – The Parallel Data Assimilation Framework: Experiences with Kalman Filtering, in: Use of High Performance Computing in Meteorology, World Scientific, Singapore, 63–83, ISBN 978-981-256-354-5, https://doi.org/10.1142/9789812701831_0006, 2005. a
Nerger, L., Janjić, T., Schröter, J., and Hiller, W.: A Unification of Ensemble Square Root Kalman Filters, Mon. Weather Rev., 140, 2335–2345, https://doi.org/10.1175/MWR-D-11-00102.1, 2012. a, b
Nerger, L., Tang, Q., and Mu, L.: Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0), Geosci. Model Dev., 13, 4305–4321, https://doi.org/10.5194/gmd-13-4305-2020, 2020. a, b
Nerger, L., Tang, Q., and Mu, L.: The PDAF Model Binding for AWI-CM (AWI-CM-PDAF version 1.0 update 1), Zenodo [code], https://doi.org/10.5281/zenodo.3822030, 2024. a
Olsen, A., Key, R. M., van Heuven, S., Lauvset, S. K., Velo, A., Lin, X., Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S., Steinfeldt, R., Jeansson, E., Ishii, M., Pérez, F. F., and Suzuki, T.: The Global Ocean Data Analysis Project version 2 (GLODAPv2) – an internally consistent data product for the world ocean, Earth Syst. Sci. Data, 8, 297–323, https://doi.org/10.5194/essd-8-297-2016, 2016. a
Orr, J. C. and Epitalon, J.-M.: Improved routines to model the ocean carbonate system: mocsy 2.0, Geosci. Model Dev., 8, 485–499, https://doi.org/10.5194/gmd-8-485-2015, 2015. a
Orr, J. C., Najjar, R. G., Aumont, O., Bopp, L., Bullister, J. L., Danabasoglu, G., Doney, S. C., Dunne, J. P., Dutay, J.-C., Graven, H., Griffies, S. M., John, J. G., Joos, F., Levin, I., Lindsay, K., Matear, R. J., McKinley, G. A., Mouchet, A., Oschlies, A., Romanou, A., Schlitzer, R., Tagliabue, A., Tanhua, T., and Yool, A.: Biogeochemical protocols and diagnostics for the CMIP6 Ocean Model Intercomparison Project (OMIP), Geosci. Model Dev., 10, 2169–2199, https://doi.org/10.5194/gmd-10-2169-2017, 2017. a
Park, J.-Y., Stock, C. A., Yang, X., Dunne, J. P., Rosati, A., John, J., and Zhang, S.: Modeling Global Ocean Biogeochemistry with Physical Data Assimilation: a Pragmatic Solution to the Equatorial Instability, J. Adv. Model. Earth Syst., 10, 891–906, https://doi.org/10.1002/2017MS001223, 2018. a, b
Pérez, F. F., Becker, M., Goris, N., Gehlen, M., López-Mozos, M., Tjiputra, J., Olsen, A., Müller, J. D., Huertas, I. E., Chau, T. T. T., Cainzos, V., Velo, A., Benard, G., Hauck, J., Gruber, N., and Wanninkhof, R.: An Assessment of CO2 Storage and Sea-Air Fluxes for the Atlantic Ocean and Mediterranean Sea Between 1985 and 2018, Global Biogeochem. Cy., 38, e2023GB007862, https://doi.org/10.1029/2023GB007862, 2024. a, b
Peylin, P., Law, R. M., Gurney, K. R., Chevallier, F., Jacobson, A. R., Maki, T., Niwa, Y., Patra, P. K., Peters, W., Rayner, P. J., Rödenbeck, C., van der Laan-Luijkx, I. T., and Zhang, X.: Global atmospheric carbon budget: results from an ensemble of atmospheric CO2 inversions, Biogeosciences, 10, 6699–6720, https://doi.org/10.5194/bg-10-6699-2013, 2013. a
Pham, D. T.: Stochastic Methods for Sequential Data Assimilation in Strongly Nonlinear Systems, Mon. Weather Rev., 129, 1194–1207, https://doi.org/10.1175/1520-0493(2001)129<1194:SMFSDA>2.0.CO;2, 2001. a
Pham, D. T., Verron, J., and Roubaud, M. C.: A Singular Evolutive Extended Kalman Filter for Data Assimilation in Oceanography, J. Marine Syst., 16, 323–340, https://doi.org/10.1016/S0924-7963(97)00109-7, 1998. a
Primeau, F. and Deleersnijder, E.: On the time to tracer equilibrium in the global ocean, Ocean Sci., 5, 13–28, https://doi.org/10.5194/os-5-13-2009, 2009. a
Raghukumar, K., Edwards, C. A., Goebel, N. L., Broquet, G., Veneziani, M., Moore, A. M., and Zehr, J. P.: Impact of Assimilating Physical Oceanographic Data on Modeled ecosystem dynamics in the California Current System, Prog. Oceanogr., 138, 546–558, https://doi.org/10.1016/j.pOcean.2015.01.004, 2015. a
Regnier, P., Resplandy, L., Najjar, R. G., and Ciais, P.: The Land-to-Ocean Loops of the Global Carbon Cycle, Nature, 603, 401–410, https://doi.org/10.1038/s41586-021-04339-9, 2022. a
Rödenbeck, C., Bakker, D. C. E., Gruber, N., Iida, Y., Jacobson, A. R., Jones, S., Landschützer, P., Metzl, N., Nakaoka, S., Olsen, A., Park, G.-H., Peylin, P., Rodgers, K. B., Sasse, T. P., Schuster, U., Shutler, J. D., Valsala, V., Wanninkhof, R., and Zeng, J.: Data-based estimates of the ocean carbon sink variability – first results of the Surface Ocean pCO2 Mapping intercomparison (SOCOM), Biogeosciences, 12, 7251–7278, https://doi.org/10.5194/bg-12-7251-2015, 2015. a
Sathyendranath, S., Jackson, T., Brockmann, C., Brotas, V., Calton, B., Chuprin, A., Clements, O., Cipollini, P., Danne, O., Dingle, J., Donlon, C., Grant, M., Groom, S., Krasemann, H., Lavender, S., Mazeran, C., Mélin, F., Müller, D., Steinmetz, F., Valente, A., Zühlke, M., Feldman, G., Franz, B., Frouin, R., Werdell, J., and Platt, T.: ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Version 5.0 Data, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/1dbe7a109c0244aaad713e078fd3059a, 2021. a, b
Schartau, M., Engel, A., Schröter, J., Thoms, S., Völker, C., and Wolf-Gladrow, D.: Modelling carbon overconsumption and the formation of extracellular particulate organic carbon, Biogeosciences, 4, 433–454, https://doi.org/10.5194/bg-4-433-2007, 2007. a
Scholz, P., Sidorenko, D., Gurses, O., Danilov, S., Koldunov, N., Wang, Q., Sein, D., Smolentseva, M., Rakowsky, N., and Jung, T.: Assessment of the Finite-volumE Sea ice-Ocean Model (FESOM2.0) – Part 1: Description of selected key model elements and comparison to its predecessor version, Geosci. Model Dev., 12, 4875–4899, https://doi.org/10.5194/gmd-12-4875-2019, 2019. a, b
Scholz, P., Sidorenko, D., Danilov, S., Wang, Q., Koldunov, N., Sein, D., and Jung, T.: Assessment of the Finite-VolumE Sea ice–Ocean Model (FESOM2.0) – Part 2: Partial bottom cells, embedded sea ice and vertical mixing library CVMix, Geosci. Model Dev., 15, 335–363, https://doi.org/10.5194/gmd-15-335-2022, 2022. a
Schourup-Kristensen, V., Sidorenko, D., Wolf-Gladrow, D. A., and Völker, C.: A skill assessment of the biogeochemical model REcoM2 coupled to the Finite Element Sea Ice–Ocean Model (FESOM 1.3), Geosci. Model Dev., 7, 2769–2802, https://doi.org/10.5194/gmd-7-2769-2014, 2014. a, b
Séférian, R., Bopp, L., Gehlen, M., Swingedouw, D., Mignot, J., Guilyardi, E., and Servonnat, J.: Multiyear Predictability of Tropical Marine Productivity, P. Natl. Acad. Sci. USA, 111, 11646–11651, https://doi.org/10.1073/pnas.1315855111, 2014. a
Sidorenko, D.: The North Atlantic Circulation derived from inverse Models, PhD thesis, University of Bremen, http://nbn-resolving.de/urn:nbn:de:gbv:46-diss000012023 (last access: 28 January 2025), 2004. a
Spring, A., Dunkl, I., Li, H., Brovkin, V., and Ilyina, T.: Trivial improvements in predictive skill due to direct reconstruction of the global carbon cycle, Earth Syst. Dynam., 12, 1139–1167, https://doi.org/10.5194/esd-12-1139-2021, 2021. a, b
Sursham, D.: Improving the Simulation and Understanding of Biologically Driven Carbon Pumps in Marine Ecosystems using an Ensemble-Based Data Assimilation Method, PhD thesis, University of Reading, https://centaur.reading.ac.uk/84860/ (last access: 28 January 2025), 2018. a
Sutton, A. J., Williams, N. L., and Tilbrook, B.: Constraining Southern Ocean CO2 Flux Uncertainty Using Uncrewed Surface Vehicle Observations, Geophys. Res. Lett., 48, e2020GL091748, https://doi.org/10.1029/2020GL091748, 2021. a
Takahashi, T., Olafsson, J., Goddard, J. G., Chipman, D. W., and Sutherland, S. C.: Seasonal Variation of CO2 and Nutrients in the High-latitude Surface Oceans: A Comparative Study, Global Biogeochem. Cy., 7, 843–878, https://doi.org/10.1029/93GB02263, 1993. a
Tang, Q., Mu, L., Sidorenko, D., Goessling, H., Semmler, T., and Nerger, L.: Improving the Ocean and Atmosphere in a Coupled Ocean–atmosphere Model by Assimilating Satellite Sea-Surface Temperature and Subsurface Profile Data, Q. J. Roy. Meteor. Soc., 146, 4014–4029, https://doi.org/10.1002/qj.3885, 2020. a, b, c, d, e, f
Terhaar, J., Frölicher, T. L., and Joos, F.: Observation-constrained estimates of the global ocean carbon sink from Earth system models, Biogeosciences, 19, 4431–4457, https://doi.org/10.5194/bg-19-4431-2022, 2022. a, b
Terhaar, J., Goris, N., Müller, J. D., DeVries, T., Gruber, N., Hauck, J., Perez, F. F., and Séférian, R.: Assessment of Global Ocean Biogeochemistry Models for Ocean Carbon Sink Estimates in RECCAP2 and Recommendations for Future Studies, J. Adv. Model. Earth Syst., 16, e2023MS003840, https://doi.org/10.1029/2023MS003840, 2024. a, b
Timmermann, R. and Beckmann, A.: Parameterization of Vertical Mixing in the Weddell Sea, Ocean Model., 6, 83–100, https://doi.org/10.1016/S1463-5003(02)00061-6, 2004. a
Tsujino, H., Urakawa, S., Nakano, H., Small, R. J., Kim, W. M., Yeager, S. G., Danabasoglu, G., Suzuki, T., Bamber, J. L., Bentsen, M., Böning, C. W., Bozec, A., Chassignet, E. P., Curchitser, E., Boeira Dias, F., Durack, P. J., Griffies, S. M., Harada, Y., Ilicak, M., Josey, S. A., Kobayashi, C., Kobayashi, S., Komuro, Y., Large, W. G., Le Sommer, J., Marsland, S. J., Masina, S., Scheinert, M., Tomita, H., Valdivieso, M., and Yamazaki, D.: JRA-55 based surface Dataset for driving Ocean–sea-ice Models (JRA55-do), Ocean Model., 130, 79–139, https://doi.org/10.1016/j.ocemod.2018.07.002, 2018a. a, b
Tsujino, H., Urakawa, S., Nakano, H., Small, R. J., Kim, Who M., Yeager, Stephen G., Danabasoglu, G., Suzuki, T., Bamber, J. L., Bentsen, M., Boening, C., Bozec, A., Chassignet, E., Curchitser, E., Dias, F. B., Durack, P. J., Griffies, S. M., Harada, Y., Ilicak, M., Josey, S., Kobayashi, C., Kobayashi, S., Komuro, Y., Large, W. G., Le Sommer, J., Marsland, S., Masina, S., Scheinert, M., Tomita, H., Valdivieso, M., and Yamazaki, D.: input4MIPs.CMIP6.OMIP.MRI.MRI-JRA55-do-1-3 Version v1.5.0.1, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/input4MIPs.2205, 2018b. a
Valsala, V. and Maksyutov, S.: Simulation and Assimilation of Global Ocean pCO2 and Air–sea CO2 Fluxes Using Ship Observations of Surface Ocean pCO2 in a Simplified Biogeochemical Offline Model, Tellus B, 62, 821–840, https://doi.org/10.1111/j.1600-0889.2010.00495.x, 2010. a
Vetra-Carvalho, S., van Leeuwen, P. J., Nerger, L., Barth, A., Altaf, M. U., Brasseur, P., Kirchgessner, P., and Beckers, J.-M.: State-of-the-art Stochastic Data Assimilation Methods for High-dimensional Non-Gaussian Problems, Tellus A, 70, 1–43, https://doi.org/10.1080/16000870.2018.1445364, 2018. a
Völker, C., Wallace, D. W. R., and Wolf-Gladrow, D. A.: On the Role of heat Fluxes in the Uptake of Anthropogenic Carbon in the North Atlantic, Global Biogeochem. Cy., 16, 85-1–85-9, https://doi.org/10.1029/2002GB001897, 2002. a, b
Wanninkhof, R.: Relationship between Wind Speed and Gas Exchange over the Ocean Revisited, Limnol. Oceanogr.-Meth., 12, 351–362, https://doi.org/10.4319/lom.2014.12.351, 2014. a, b
Wanninkhof, R., Park, G.-H., Takahashi, T., Sweeney, C., Feely, R., Nojiri, Y., Gruber, N., Doney, S. C., McKinley, G. A., Lenton, A., Le Quéré, C., Heinze, C., Schwinger, J., Graven, H., and Khatiwala, S.: Global ocean carbon uptake: magnitude, variability and trends, Biogeosciences, 10, 1983–2000, https://doi.org/10.5194/bg-10-1983-2013, 2013. a
While, J., Totterdell, I., and Martin, M.: Assimilation of pCO2 Data into a Global coupled Physical-Biogeochemical Ocean Model, J. Geophys. Res.-Oceans, 117, C03037, https://doi.org/10.1029/2010JC006815, 2012. a
Williams, N. L., Juranek, L. W., Feely, R. A., Johnson, K. S., Sarmiento, J. L., Talley, L. D., Dickson, A. G., Gray, A. R., Wanninkhof, R., Russell, J. L., Riser, S. C., and Takeshita, Y.: Calculating Surface Ocean pCO2 from Biogeochemical Argo Floats Equipped with pH: An Uncertainty Analysis, Global Biogeochem. Cy., 31, 591–604, https://doi.org/10.1002/2016GB005541, 2017. a, b
Wunsch, C.: The Ocean Circulation Inverse Problem, Cambridge University Press, Cambridge, England, UK, ISBN 978-0-52148090-1, https://doi.org/10.1017/CBO9780511629570, 1996. a
Short summary
Computer models are often used to estimate the ocean's CO2 uptake due to a lack of direct observations. Because such idealized models do not match precisely with the real world, we combine real-world observations of ocean temperature and salinity with a model and study the effect on the modeled air–sea CO2 flux (2010–2020). The corrections of temperature and salinity have their largest effect on regional CO2 fluxes in the Southern Ocean in winter and a small effect on the global CO2 uptake.
Computer models are often used to estimate the ocean's CO2 uptake due to a lack of direct...
