Articles | Volume 16, issue 16
https://doi.org/10.5194/gmd-16-4883-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4883-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
30 Aug 2023
Model description paper |
| 30 Aug 2023
| 30 Aug 2023
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
Marine Biogeosciences, Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, 27570 Bremerhaven, Germany
Laurent Oziel
Marine Biogeosciences, Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, 27570 Bremerhaven, Germany
Onur Karakuş
Marine Biogeosciences, Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, 27570 Bremerhaven, Germany
Dmitry Sidorenko
Marine Biogeosciences, Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, 27570 Bremerhaven, Germany
Christoph Völker
Marine Biogeosciences, Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, 27570 Bremerhaven, Germany
Marine Biogeosciences, Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, 27570 Bremerhaven, Germany
Moritz Zeising
Marine Biogeosciences, Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, 27570 Bremerhaven, Germany
Martin Butzin
Marine Biogeosciences, Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, 27570 Bremerhaven, Germany
MARUM – Center for Marine Environmental Sciences, University of Bremen, 28334 Bremen, Germany
Judith Hauck
Marine Biogeosciences, Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, 27570 Bremerhaven, Germany
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Ali Aydoğdu, Nadia Pinardi, Emin Özsoy, Gokhan Danabasoglu, Özgür Gürses, and Alicia Karspeck
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A 6-year simulation of the Turkish Straits System is presented. The simulation is performed by a model using unstructured triangular mesh and realistic atmospheric forcing. The dynamics and circulation of the Marmara Sea are analysed and the mean state of the system is discussed on annual averages. Volume fluxes computed throughout the simulation are presented and the response of the model to severe storms is shown. Finally, it was possible to assess the kinetic energy budget in the Marmara Sea.
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
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Verena Haid, Ralph Timmermann, Özgür Gürses, and Hartmut H. Hellmer
Ocean Sci., 19, 1529–1544, https://doi.org/10.5194/os-19-1529-2023, https://doi.org/10.5194/os-19-1529-2023, 2023
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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
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Claudia Hinrichs, Peter Köhler, Christoph Völker, and Judith Hauck
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Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-181, https://doi.org/10.5194/gmd-2023-181, 2023
Preprint under review for GMD
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Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
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Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
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The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
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Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-123, https://doi.org/10.5194/gmd-2023-123, 2023
Revised manuscript accepted for GMD
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High resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least the same important for obtaining faithful simulations.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-37, https://doi.org/10.5194/gmd-2023-37, 2023
Preprint under review for GMD
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Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea-ice component of the Finite volumE Sea ice – Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Martine Lizotte, Bennet Juhls, Atsushi Matsuoka, Philippe Massicotte, Gaëlle Mével, David Obie James Anikina, Sofia Antonova, Guislain Bécu, Marine Béguin, Simon Bélanger, Thomas Bossé-Demers, Lisa Bröder, Flavienne Bruyant, Gwénaëlle Chaillou, Jérôme Comte, Raoul-Marie Couture, Emmanuel Devred, Gabrièle Deslongchamps, Thibaud Dezutter, Miles Dillon, David Doxaran, Aude Flamand, Frank Fell, Joannie Ferland, Marie-Hélène Forget, Michael Fritz, Thomas J. Gordon, Caroline Guilmette, Andrea Hilborn, Rachel Hussherr, Charlotte Irish, Fabien Joux, Lauren Kipp, Audrey Laberge-Carignan, Hugues Lantuit, Edouard Leymarie, Antonio Mannino, Juliette Maury, Paul Overduin, Laurent Oziel, Colin Stedmon, Crystal Thomas, Lucas Tisserand, Jean-Éric Tremblay, Jorien Vonk, Dustin Whalen, and Marcel Babin
Earth Syst. Sci. Data, 15, 1617–1653, https://doi.org/10.5194/essd-15-1617-2023, https://doi.org/10.5194/essd-15-1617-2023, 2023
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Permafrost thaw in the Mackenzie Delta region results in the release of organic matter into the coastal marine environment. What happens to this carbon-rich organic matter as it transits along the fresh to salty aquatic environments is still underdocumented. Four expeditions were conducted from April to September 2019 in the coastal area of the Beaufort Sea to study the fate of organic matter. This paper describes a rich set of data characterizing the composition and sources of organic matter.
Pengyang Song, Dmitry Sidorenko, Patrick Scholz, Maik Thomas, and Gerrit Lohmann
Geosci. Model Dev., 16, 383–405, https://doi.org/10.5194/gmd-16-383-2023, https://doi.org/10.5194/gmd-16-383-2023, 2023
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Tides have essential effects on the ocean and climate. Most previous research applies parameterised tidal mixing to discuss their effects in models. By comparing the effect of a tidal mixing parameterisation and tidal forcing on the ocean state, we assess the advantages and disadvantages of the two methods. Our results show that tidal mixing in the North Pacific Ocean strongly affects the global thermohaline circulation. We also list some effects that are not considered in the parameterisation.
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
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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.
Jan Streffing, Dmitry Sidorenko, Tido Semmler, Lorenzo Zampieri, Patrick Scholz, Miguel Andrés-Martínez, Nikolay Koldunov, Thomas Rackow, Joakim Kjellsson, Helge Goessling, Marylou Athanase, Qiang Wang, Jan Hegewald, Dmitry V. Sein, Longjiang Mu, Uwe Fladrich, Dirk Barbi, Paul Gierz, Sergey Danilov, Stephan Juricke, Gerrit Lohmann, and Thomas Jung
Geosci. Model Dev., 15, 6399–6427, https://doi.org/10.5194/gmd-15-6399-2022, https://doi.org/10.5194/gmd-15-6399-2022, 2022
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We developed a new atmosphere–ocean coupled climate model, AWI-CM3. Our model is significantly more computationally efficient than its predecessors AWI-CM1 and AWI-CM2. We show that the model, although cheaper to run, provides results of similar quality when modeling the historic period from 1850 to 2014. We identify the remaining weaknesses to outline future work. Finally we preview an improved simulation where the reduction in computational cost has to be invested in higher model resolution.
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
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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.
Xiaoxu Shi, Martin Werner, Carolin Krug, Chris M. Brierley, Anni Zhao, Endurance Igbinosa, Pascale Braconnot, Esther Brady, Jian Cao, Roberta D'Agostino, Johann Jungclaus, Xingxing Liu, Bette Otto-Bliesner, Dmitry Sidorenko, Robert Tomas, Evgeny M. Volodin, Hu Yang, Qiong Zhang, Weipeng Zheng, and Gerrit Lohmann
Clim. Past, 18, 1047–1070, https://doi.org/10.5194/cp-18-1047-2022, https://doi.org/10.5194/cp-18-1047-2022, 2022
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Since the orbital parameters of the past are different from today, applying the modern calendar to the past climate can lead to an artificial bias in seasonal cycles. With the use of multiple model outputs, we found that such a bias is non-ignorable and should be corrected to ensure an accurate comparison between modeled results and observational records, as well as between simulated past and modern climates, especially for the Last Interglacial.
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
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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.
Stephan Krätschmer, Michèlle van der Does, Frank Lamy, Gerrit Lohmann, Christoph Völker, and Martin Werner
Clim. Past, 18, 67–87, https://doi.org/10.5194/cp-18-67-2022, https://doi.org/10.5194/cp-18-67-2022, 2022
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We use an atmospheric model coupled to an aerosol model to investigate the global mineral dust cycle with a focus on the Southern Hemisphere for warmer and colder climate states and compare our results to observational data. Our findings suggest that Australia is the predominant source of dust deposited over Antarctica during the last glacial maximum. In addition, we find that the southward transport of dust from all sources to Antarctica happens at lower altitudes in colder climates.
Patrick Scholz, Dmitry Sidorenko, Sergey Danilov, Qiang Wang, Nikolay Koldunov, Dmitry Sein, and Thomas Jung
Geosci. Model Dev., 15, 335–363, https://doi.org/10.5194/gmd-15-335-2022, https://doi.org/10.5194/gmd-15-335-2022, 2022
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Structured-mesh ocean models are still the most mature in terms of functionality due to their long development history. However, unstructured-mesh ocean models have acquired new features and caught up in their functionality. This paper continues the work by Scholz et al. (2019) of documenting the features available in FESOM2.0. It focuses on the following two aspects: (i) partial bottom cells and embedded sea ice and (ii) dealing with mixing parameterisations enabled by using the CVMix package.
Vera Fofonova, Tuomas Kärnä, Knut Klingbeil, Alexey Androsov, Ivan Kuznetsov, Dmitry Sidorenko, Sergey Danilov, Hans Burchard, and Karen Helen Wiltshire
Geosci. Model Dev., 14, 6945–6975, https://doi.org/10.5194/gmd-14-6945-2021, https://doi.org/10.5194/gmd-14-6945-2021, 2021
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We present a test case of river plume spreading to evaluate coastal ocean models. Our test case reveals the level of numerical mixing (due to parameterizations used and numerical treatment of processes in the model) and the ability of models to reproduce complex dynamics. The major result of our comparative study is that accuracy in reproducing the analytical solution depends less on the type of applied model architecture or numerical grid than it does on the type of advection scheme.
Qiang Wang, Sergey Danilov, Longjiang Mu, Dmitry Sidorenko, and Claudia Wekerle
The Cryosphere, 15, 4703–4725, https://doi.org/10.5194/tc-15-4703-2021, https://doi.org/10.5194/tc-15-4703-2021, 2021
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Using simulations, we found that changes in ocean freshwater content induced by wind perturbations can significantly affect the Arctic sea ice drift, thickness, concentration and deformation rates years after the wind perturbations. The impact is through changes in sea surface height and surface geostrophic currents and the most pronounced in warm seasons. Such a lasting impact might become stronger in a warming climate and implies the importance of ocean initialization in sea ice prediction.
Tobias R. Vonnahme, Martial Leroy, Silke Thoms, Dick van Oevelen, H. Rodger Harvey, Svein Kristiansen, Rolf Gradinger, Ulrike Dietrich, and Christoph Völker
Biogeosciences, 18, 1719–1747, https://doi.org/10.5194/bg-18-1719-2021, https://doi.org/10.5194/bg-18-1719-2021, 2021
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Diatoms are crucial for Arctic coastal spring blooms, and their growth is controlled by nutrients and light. At the end of the bloom, inorganic nitrogen or silicon can be limiting, but nitrogen can be regenerated by bacteria, extending the algal growth phase. Modeling these multi-nutrient dynamics and the role of bacteria is challenging yet crucial for accurate modeling. We recreated spring bloom dynamics in a cultivation experiment and developed a representative dynamic model.
Christian B. Rodehacke, Madlene Pfeiffer, Tido Semmler, Özgür Gurses, and Thomas Kleiner
Earth Syst. Dynam., 11, 1153–1194, https://doi.org/10.5194/esd-11-1153-2020, https://doi.org/10.5194/esd-11-1153-2020, 2020
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In the warmer future, Antarctica's ice sheet will lose more ice due to enhanced iceberg calving and a warming ocean that melts more floating ice from below. However, the hydrological cycle is also stronger in a warmer world. Hence, more snowfall will precipitate on Antarctica and may balance the amplified ice loss. We have used future climate scenarios from various global climate models to perform numerous ice sheet simulations to show that precipitation may counteract mass loss.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone Alin, Luiz E. O. C. Aragão, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp, Selma Bultan, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Wiley Evans, Liesbeth Florentie, Piers M. Forster, Thomas Gasser, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Luke Gregor, Nicolas Gruber, Ian Harris, Kerstin Hartung, Vanessa Haverd, Richard A. Houghton, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Koji Kadono, Etsushi Kato, Vassilis Kitidis, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Gregg Marland, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Adam J. P. Smith, Adrienne J. Sutton, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Guido van der Werf, Nicolas Vuichard, Anthony P. Walker, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Xu Yue, and Sönke Zaehle
Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, https://doi.org/10.5194/essd-12-3269-2020, 2020
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The Global Carbon Budget 2020 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.
Eric P. Chassignet, Stephen G. Yeager, Baylor Fox-Kemper, Alexandra Bozec, Frederic Castruccio, Gokhan Danabasoglu, Christopher Horvat, Who M. Kim, Nikolay Koldunov, Yiwen Li, Pengfei Lin, Hailong Liu, Dmitry V. Sein, Dmitry Sidorenko, Qiang Wang, and Xiaobiao Xu
Geosci. Model Dev., 13, 4595–4637, https://doi.org/10.5194/gmd-13-4595-2020, https://doi.org/10.5194/gmd-13-4595-2020, 2020
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This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea ice simulations to assess the robustness of climate-relevant improvements in ocean simulations associated with moving from coarse (∼1°) to eddy-resolving (∼0.1°) horizontal resolutions. Despite significant improvements, greatly enhanced horizontal resolution does not deliver unambiguous bias reduction in all regions for all models.
Hiroyuki Tsujino, L. Shogo Urakawa, Stephen M. Griffies, Gokhan Danabasoglu, Alistair J. Adcroft, Arthur E. Amaral, Thomas Arsouze, Mats Bentsen, Raffaele Bernardello, Claus W. Böning, Alexandra Bozec, Eric P. Chassignet, Sergey Danilov, Raphael Dussin, Eleftheria Exarchou, Pier Giuseppe Fogli, Baylor Fox-Kemper, Chuncheng Guo, Mehmet Ilicak, Doroteaciro Iovino, Who M. Kim, Nikolay Koldunov, Vladimir Lapin, Yiwen Li, Pengfei Lin, Keith Lindsay, Hailong Liu, Matthew C. Long, Yoshiki Komuro, Simon J. Marsland, Simona Masina, Aleksi Nummelin, Jan Klaus Rieck, Yohan Ruprich-Robert, Markus Scheinert, Valentina Sicardi, Dmitry Sidorenko, Tatsuo Suzuki, Hiroaki Tatebe, Qiang Wang, Stephen G. Yeager, and Zipeng Yu
Geosci. Model Dev., 13, 3643–3708, https://doi.org/10.5194/gmd-13-3643-2020, https://doi.org/10.5194/gmd-13-3643-2020, 2020
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The OMIP-2 framework for global ocean–sea-ice model simulations is assessed by comparing multi-model means from 11 CMIP6-class global ocean–sea-ice models calculated separately for the OMIP-1 and OMIP-2 simulations. Many features are very similar between OMIP-1 and OMIP-2 simulations, and yet key improvements in transitioning from OMIP-1 to OMIP-2 are also identified. Thus, the present assessment justifies that future ocean–sea-ice model development and analysis studies use the OMIP-2 framework.
Dmitry Sidorenko, Sergey Danilov, Nikolay Koldunov, Patrick Scholz, and Qiang Wang
Geosci. Model Dev., 13, 3337–3345, https://doi.org/10.5194/gmd-13-3337-2020, https://doi.org/10.5194/gmd-13-3337-2020, 2020
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Computation of barotropic and meridional overturning streamfunctions for models formulated on unstructured meshes is commonly preceded by interpolation to a regular mesh. This operation destroys the original conservation, which can be then be artificially imposed to make the computation possible. An elementary method is proposed that avoids interpolation and preserves conservation in a strict model sense.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Judith Hauck, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Dorothee C. E. Bakker, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Peter Anthoni, Leticia Barbero, Ana Bastos, Vladislav Bastrikov, Meike Becker, Laurent Bopp, Erik Buitenhuis, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Kim I. Currie, Richard A. Feely, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Daniel S. Goll, Nicolas Gruber, Sören Gutekunst, Ian Harris, Vanessa Haverd, Richard A. Houghton, George Hurtt, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Jed O. Kaplan, Etsushi Kato, Kees Klein Goldewijk, Jan Ivar Korsbakken, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Danica Lombardozzi, Gregg Marland, Patrick C. McGuire, Joe R. Melton, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Craig Neill, Abdirahman M. Omar, Tsuneo Ono, Anna Peregon, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Roland Séférian, Jörg Schwinger, Naomi Smith, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Guido R. van der Werf, Andrew J. Wiltshire, and Sönke Zaehle
Earth Syst. Sci. Data, 11, 1783–1838, https://doi.org/10.5194/essd-11-1783-2019, https://doi.org/10.5194/essd-11-1783-2019, 2019
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The Global Carbon Budget 2019 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.
Patrick Scholz, Dmitry Sidorenko, Ozgur Gurses, Sergey Danilov, Nikolay Koldunov, Qiang Wang, Dmitry Sein, Margarita Smolentseva, Natalja Rakowsky, and Thomas Jung
Geosci. Model Dev., 12, 4875–4899, https://doi.org/10.5194/gmd-12-4875-2019, https://doi.org/10.5194/gmd-12-4875-2019, 2019
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This paper is the first in a series documenting and assessing important key components of the Finite-volumE Sea ice-Ocean Model version 2.0 (FESOM2.0). We assess the hydrographic biases, large-scale circulation, numerical performance and scalability of FESOM2.0 compared with its predecessor, FESOM1.4. The main conclusion is that the results of FESOM2.0 compare well to FESOM1.4 in terms of model biases but with a remarkable performance speedup with a 3 times higher throughput.
Nikolay V. Koldunov, Vadym Aizinger, Natalja Rakowsky, Patrick Scholz, Dmitry Sidorenko, Sergey Danilov, and Thomas Jung
Geosci. Model Dev., 12, 3991–4012, https://doi.org/10.5194/gmd-12-3991-2019, https://doi.org/10.5194/gmd-12-3991-2019, 2019
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We measure how computational performance of the global FESOM2 ocean model (formulated on an unstructured mesh) changes with the increase in the number of computational cores. We find that for many components of the model the performance increases linearly but we also identify two bottlenecks: sea ice and ssh submodules. We show that FESOM2 is on par with the state-of-the-art ocean models in terms of throughput that reach 16 simulated years per day for eddy resolving configuration (1/10°).
Özgür Gürses, Vanessa Kolatschek, Qiang Wang, and Christian Bernd Rodehacke
The Cryosphere, 13, 2317–2324, https://doi.org/10.5194/tc-13-2317-2019, https://doi.org/10.5194/tc-13-2317-2019, 2019
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The warming of the Earth's climate system causes sea level rise. In Antarctica, ice streams flow into the sea and develop ice shelves. These are floating extensions of the ice streams. Ocean water melts these ice shelves. It has been proposed that a submarine wall could shield these ice shelves from the warm water. Our model simulation shows that the wall protects ice shelves. However, the warm water flows to neighboring ice shelves. There, enhanced melting reduces the effectiveness of the wall.
Thomas Rackow, Dmitry V. Sein, Tido Semmler, Sergey Danilov, Nikolay V. Koldunov, Dmitry Sidorenko, Qiang Wang, and Thomas Jung
Geosci. Model Dev., 12, 2635–2656, https://doi.org/10.5194/gmd-12-2635-2019, https://doi.org/10.5194/gmd-12-2635-2019, 2019
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Current climate models show errors in the deep ocean that are larger than the level of natural variability and the response to enhanced greenhouse gas concentrations. These errors are larger than the signals we aim to predict. With the AWI Climate Model, we show that increasing resolution to resolve eddies can lead to major reductions in deep ocean errors. AWI's next-generation (CMIP6) model configuration will thus use locally eddy-resolving computational grids for projecting climate change.
Corinne Le Quéré, Robbie M. Andrew, Pierre Friedlingstein, Stephen Sitch, Judith Hauck, Julia Pongratz, Penelope A. Pickers, Jan Ivar Korsbakken, Glen P. Peters, Josep G. Canadell, Almut Arneth, Vivek K. Arora, Leticia Barbero, Ana Bastos, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Scott C. Doney, Thanos Gkritzalis, Daniel S. Goll, Ian Harris, Vanessa Haverd, Forrest M. Hoffman, Mario Hoppema, Richard A. Houghton, George Hurtt, Tatiana Ilyina, Atul K. Jain, Truls Johannessen, Chris D. Jones, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Peter Landschützer, Nathalie Lefèvre, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Craig Neill, Are Olsen, Tsueno Ono, Prabir Patra, Anna Peregon, Wouter Peters, Philippe Peylin, Benjamin Pfeil, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Matthias Rocher, Christian Rödenbeck, Ute Schuster, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Tobias Steinhoff, Adrienne Sutton, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Nicolas Viovy, Anthony P. Walker, Andrew J. Wiltshire, Rebecca Wright, Sönke Zaehle, and Bo Zheng
Earth Syst. Sci. Data, 10, 2141–2194, https://doi.org/10.5194/essd-10-2141-2018, https://doi.org/10.5194/essd-10-2141-2018, 2018
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The Global Carbon Budget 2018 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.
Ali Aydoğdu, Nadia Pinardi, Emin Özsoy, Gokhan Danabasoglu, Özgür Gürses, and Alicia Karspeck
Ocean Sci., 14, 999–1019, https://doi.org/10.5194/os-14-999-2018, https://doi.org/10.5194/os-14-999-2018, 2018
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A 6-year simulation of the Turkish Straits System is presented. The simulation is performed by a model using unstructured triangular mesh and realistic atmospheric forcing. The dynamics and circulation of the Marmara Sea are analysed and the mean state of the system is discussed on annual averages. Volume fluxes computed throughout the simulation are presented and the response of the model to severe storms is shown. Finally, it was possible to assess the kinetic energy budget in the Marmara Sea.
Corinne Le Quéré, Robbie M. Andrew, Pierre Friedlingstein, Stephen Sitch, Julia Pongratz, Andrew C. Manning, Jan Ivar Korsbakken, Glen P. Peters, Josep G. Canadell, Robert B. Jackson, Thomas A. Boden, Pieter P. Tans, Oliver D. Andrews, Vivek K. Arora, Dorothee C. E. Bakker, Leticia Barbero, Meike Becker, Richard A. Betts, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Catherine E. Cosca, Jessica Cross, Kim Currie, Thomas Gasser, Ian Harris, Judith Hauck, Vanessa Haverd, Richard A. Houghton, Christopher W. Hunt, George Hurtt, Tatiana Ilyina, Atul K. Jain, Etsushi Kato, Markus Kautz, Ralph F. Keeling, Kees Klein Goldewijk, Arne Körtzinger, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Ivan Lima, Danica Lombardozzi, Nicolas Metzl, Frank Millero, Pedro M. S. Monteiro, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Yukihiro Nojiri, X. Antonio Padin, Anna Peregon, Benjamin Pfeil, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Janet Reimer, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Benjamin D. Stocker, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Steven van Heuven, Nicolas Viovy, Nicolas Vuichard, Anthony P. Walker, Andrew J. Watson, Andrew J. Wiltshire, Sönke Zaehle, and Dan Zhu
Earth Syst. Sci. Data, 10, 405–448, https://doi.org/10.5194/essd-10-405-2018, https://doi.org/10.5194/essd-10-405-2018, 2018
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The Global Carbon Budget 2017 describes data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. It is the 12th annual update and the 6th published in this journal.
Sergey Danilov, Dmitry Sidorenko, Qiang Wang, and Thomas Jung
Geosci. Model Dev., 10, 765–789, https://doi.org/10.5194/gmd-10-765-2017, https://doi.org/10.5194/gmd-10-765-2017, 2017
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Numerical models of global ocean circulation are used to learn about future climate. The ocean circulation is characterized by processes on different spatial scales which are still beyond the reach of present computers. We describe a new model setup that allows one to vary a model's spatial resolution and hence focus the computational power on regional dynamics, reaching a better description of local processes in areas of interest.
Corinne Le Quéré, Robbie M. Andrew, Josep G. Canadell, Stephen Sitch, Jan Ivar Korsbakken, Glen P. Peters, Andrew C. Manning, Thomas A. Boden, Pieter P. Tans, Richard A. Houghton, Ralph F. Keeling, Simone Alin, Oliver D. Andrews, Peter Anthoni, Leticia Barbero, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Kim Currie, Christine Delire, Scott C. Doney, Pierre Friedlingstein, Thanos Gkritzalis, Ian Harris, Judith Hauck, Vanessa Haverd, Mario Hoppema, Kees Klein Goldewijk, Atul K. Jain, Etsushi Kato, Arne Körtzinger, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Danica Lombardozzi, Joe R. Melton, Nicolas Metzl, Frank Millero, Pedro M. S. Monteiro, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Kevin O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Christian Rödenbeck, Joe Salisbury, Ute Schuster, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Benjamin D. Stocker, Adrienne J. Sutton, Taro Takahashi, Hanqin Tian, Bronte Tilbrook, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Nicolas Viovy, Anthony P. Walker, Andrew J. Wiltshire, and Sönke Zaehle
Earth Syst. Sci. Data, 8, 605–649, https://doi.org/10.5194/essd-8-605-2016, https://doi.org/10.5194/essd-8-605-2016, 2016
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The Global Carbon Budget 2016 is the 11th annual update of emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land, and ocean. This data synthesis brings together measurements, statistical information, and analyses of model results in order to provide an assessment of the global carbon budget and their uncertainties for years 1959 to 2015, with a projection for year 2016.
Dorothee C. E. Bakker, Benjamin Pfeil, Camilla S. Landa, Nicolas Metzl, Kevin M. O'Brien, Are Olsen, Karl Smith, Cathy Cosca, Sumiko Harasawa, Stephen D. Jones, Shin-ichiro Nakaoka, Yukihiro Nojiri, Ute Schuster, Tobias Steinhoff, Colm Sweeney, Taro Takahashi, Bronte Tilbrook, Chisato Wada, Rik Wanninkhof, Simone R. Alin, Carlos F. Balestrini, Leticia Barbero, Nicholas R. Bates, Alejandro A. Bianchi, Frédéric Bonou, Jacqueline Boutin, Yann Bozec, Eugene F. Burger, Wei-Jun Cai, Robert D. Castle, Liqi Chen, Melissa Chierici, Kim Currie, Wiley Evans, Charles Featherstone, Richard A. Feely, Agneta Fransson, Catherine Goyet, Naomi Greenwood, Luke Gregor, Steven Hankin, Nick J. Hardman-Mountford, Jérôme Harlay, Judith Hauck, Mario Hoppema, Matthew P. Humphreys, Christopher W. Hunt, Betty Huss, J. Severino P. Ibánhez, Truls Johannessen, Ralph Keeling, Vassilis Kitidis, Arne Körtzinger, Alex Kozyr, Evangelia Krasakopoulou, Akira Kuwata, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Claire Lo Monaco, Ansley Manke, Jeremy T. Mathis, Liliane Merlivat, Frank J. Millero, Pedro M. S. Monteiro, David R. Munro, Akihiko Murata, Timothy Newberger, Abdirahman M. Omar, Tsuneo Ono, Kristina Paterson, David Pearce, Denis Pierrot, Lisa L. Robbins, Shu Saito, Joe Salisbury, Reiner Schlitzer, Bernd Schneider, Roland Schweitzer, Rainer Sieger, Ingunn Skjelvan, Kevin F. Sullivan, Stewart C. Sutherland, Adrienne J. Sutton, Kazuaki Tadokoro, Maciej Telszewski, Matthias Tuma, Steven M. A. C. van Heuven, Doug Vandemark, Brian Ward, Andrew J. Watson, and Suqing Xu
Earth Syst. Sci. Data, 8, 383–413, https://doi.org/10.5194/essd-8-383-2016, https://doi.org/10.5194/essd-8-383-2016, 2016
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Version 3 of the Surface Ocean CO2 Atlas (www.socat.info) has 14.5 million CO2 (carbon dioxide) values for the years 1957 to 2014 covering the global oceans and coastal seas. Version 3 is an update to version 2 with a longer record and 44 % more CO2 values. The CO2 measurements have been made on ships, fixed moorings and drifting buoys. SOCAT enables quantification of the ocean carbon sink and ocean acidification, as well as model evaluation, thus informing climate negotiations.
Charlotte Laufkötter, Meike Vogt, Nicolas Gruber, Olivier Aumont, Laurent Bopp, Scott C. Doney, John P. Dunne, Judith Hauck, Jasmin G. John, Ivan D. Lima, Roland Seferian, and Christoph Völker
Biogeosciences, 13, 4023–4047, https://doi.org/10.5194/bg-13-4023-2016, https://doi.org/10.5194/bg-13-4023-2016, 2016
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We compare future projections in marine export production, generated by four ecosystem models under IPCC's high-emission scenario RCP8.5. While all models project decreases in export, they differ strongly regarding the drivers. The formation of sinking particles of organic matter is the most uncertain process with models not agreeing on either magnitude or the direction of change. Changes in diatom concentration are a strong driver for export in some models but of low significance in others.
M. Werner, B. Haese, X. Xu, X. Zhang, M. Butzin, and G. Lohmann
Geosci. Model Dev., 9, 647–670, https://doi.org/10.5194/gmd-9-647-2016, https://doi.org/10.5194/gmd-9-647-2016, 2016
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This paper presents the first results of a new isotope-enabled GCM set-up, based on the ECHAM5/MPI-OM fully coupled atmosphere-ocean model. Results of two equilibrium simulations under pre-industrial and Last Glacial Maximum conditions reveal a good to very good agreement with many delta O-18 and delta D observational records, and a remarkable improvement for the modelling of the deuterium excess signal in Antarctic ice cores.
C. Laufkötter, M. Vogt, N. Gruber, M. Aita-Noguchi, O. Aumont, L. Bopp, E. Buitenhuis, S. C. Doney, J. Dunne, T. Hashioka, J. Hauck, T. Hirata, J. John, C. Le Quéré, I. D. Lima, H. Nakano, R. Seferian, I. Totterdell, M. Vichi, and C. Völker
Biogeosciences, 12, 6955–6984, https://doi.org/10.5194/bg-12-6955-2015, https://doi.org/10.5194/bg-12-6955-2015, 2015
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We analyze changes in marine net primary production (NPP) and its drivers for the 21st century in 9 marine ecosystem models under the RCP8.5 scenario. NPP decreases in 5 models and increases in 1 model; 3 models show no significant trend. The main drivers include stronger nutrient limitation, but in many models warming-induced increases in phytoplankton growth outbalance the nutrient effect. Temperature-driven increases in grazing and other loss processes cause a net decrease in biomass and NPP.
C. Le Quéré, R. Moriarty, R. M. Andrew, J. G. Canadell, S. Sitch, J. I. Korsbakken, P. Friedlingstein, G. P. Peters, R. J. Andres, T. A. Boden, R. A. Houghton, J. I. House, R. F. Keeling, P. Tans, A. Arneth, D. C. E. Bakker, L. Barbero, L. Bopp, J. Chang, F. Chevallier, L. P. Chini, P. Ciais, M. Fader, R. A. Feely, T. Gkritzalis, I. Harris, J. Hauck, T. Ilyina, A. K. Jain, E. Kato, V. Kitidis, K. Klein Goldewijk, C. Koven, P. Landschützer, S. K. Lauvset, N. Lefèvre, A. Lenton, I. D. Lima, N. Metzl, F. Millero, D. R. Munro, A. Murata, J. E. M. S. Nabel, S. Nakaoka, Y. Nojiri, K. O'Brien, A. Olsen, T. Ono, F. F. Pérez, B. Pfeil, D. Pierrot, B. Poulter, G. Rehder, C. Rödenbeck, S. Saito, U. Schuster, J. Schwinger, R. Séférian, T. Steinhoff, B. D. Stocker, A. J. Sutton, T. Takahashi, B. Tilbrook, I. T. van der Laan-Luijkx, G. R. van der Werf, S. van Heuven, D. Vandemark, N. Viovy, A. Wiltshire, S. Zaehle, and N. Zeng
Earth Syst. Sci. Data, 7, 349–396, https://doi.org/10.5194/essd-7-349-2015, https://doi.org/10.5194/essd-7-349-2015, 2015
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Accurate assessment of anthropogenic carbon dioxide emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to understand the global carbon cycle, support the development of climate policies, and project future climate change. We describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on a range of data and models and their interpretation by a broad scientific community.
P. R. Halloran, B. B. B. Booth, C. D. Jones, F. H. Lambert, D. J. McNeall, I. J. Totterdell, and C. Völker
Biogeosciences, 12, 4497–4508, https://doi.org/10.5194/bg-12-4497-2015, https://doi.org/10.5194/bg-12-4497-2015, 2015
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The oceans currently take up around a quarter of the carbon dioxide (CO2) emitted by human activity. While stored in the ocean, this CO2 is not causing global warming. Here we explore high latitude North Atlantic CO2 uptake across a set of climate model simulations, and find that the models show a peak in ocean CO2 uptake around the middle of the century after which time CO2 uptake begins to decline. We identify the causes of this long-term change and interannual variability in the models.
S. Danilov, Q. Wang, R. Timmermann, N. Iakovlev, D. Sidorenko, M. Kimmritz, T. Jung, and J. Schröter
Geosci. Model Dev., 8, 1747–1761, https://doi.org/10.5194/gmd-8-1747-2015, https://doi.org/10.5194/gmd-8-1747-2015, 2015
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Unstructured meshes allow multi-resolution modeling of ocean dynamics. Sea ice models formulated on unstructured meshes are a necessary component of ocean models intended for climate studies. This work presents a description of a finite-element sea ice model which is used as a component of a finite-element sea ice ocean circulation model. The principles underlying its design can be of interest to other groups pursuing ocean modelling on unstructured meshes.
V. Schourup-Kristensen, D. Sidorenko, D. A. Wolf-Gladrow, and C. Völker
Geosci. Model Dev., 7, 2769–2802, https://doi.org/10.5194/gmd-7-2769-2014, https://doi.org/10.5194/gmd-7-2769-2014, 2014
K. Gribanov, J. Jouzel, V. Bastrikov, J.-L. Bonne, F.-M. Breon, M. Butzin, O. Cattani, V. Masson-Delmotte, N. Rokotyan, M. Werner, and V. Zakharov
Atmos. Chem. Phys., 14, 5943–5957, https://doi.org/10.5194/acp-14-5943-2014, https://doi.org/10.5194/acp-14-5943-2014, 2014
M. Butzin, M. Werner, V. Masson-Delmotte, C. Risi, C. Frankenberg, K. Gribanov, J. Jouzel, and V. I. Zakharov
Atmos. Chem. Phys., 14, 5853–5869, https://doi.org/10.5194/acp-14-5853-2014, https://doi.org/10.5194/acp-14-5853-2014, 2014
Q. Wang, S. Danilov, D. Sidorenko, R. Timmermann, C. Wekerle, X. Wang, T. Jung, and J. Schröter
Geosci. Model Dev., 7, 663–693, https://doi.org/10.5194/gmd-7-663-2014, https://doi.org/10.5194/gmd-7-663-2014, 2014
Related subject area
Biogeosciences
AdaScape 1.0: a coupled modelling tool to investigate the links between tectonics, climate, and biodiversity
An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas
Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions
Quantification of hydraulic trait control on plant hydrodynamics and risk of hydraulic failure within a demographic structured vegetation model in a tropical forest (FATES–HYDRO V1.0)
SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes
A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO, and NH3 emissions from enhanced rock weathering with croplands
Forcing the Global Fire Emissions Database burned-area dataset into the Community Land Model version 5.0: impacts on carbon and water fluxes at high latitudes
The community-centered aquatic biogeochemistry model unified RIVE v1.0: a unified version for water column
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water and nitrogen perturbations
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage
The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0
CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics
Validation of a new spatially explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex forest stands in eastern North America
A Novel Eulerian Reaction-Transport Model to Simulate Age and Reactivity Continua Interacting with Mixing Processes
AgriCarbon-EO: v1.0.1: Large Scale and High Resolution Simulation of Carbon Fluxes by Assimilation of Sentinel-2 and Landsat-8 Reflectances using a Bayesian approach
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model
ForamEcoGEnIE 2.0: incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal model
FABM-NflexPD 2.0: testing an instantaneous acclimation approach for modeling the implications of phytoplankton eco-physiology for the carbon and nutrient cycles
Evaluating the vegetation–atmosphere coupling strength of ORCHIDEE land surface model (v7266)
Non-Redfieldian carbon model for the Baltic Sea (ERGOM version 1.2) – implementation and budget estimates
Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1
Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small-scale microbial processes: the Soil Enzyme Steady Allocation Model (SESAM; v3.0)
Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)
Forest fluxes and mortality response to drought: model description (ORCHIDEE-CAN-NHA r7236) and evaluation at the Caxiuanã drought experiment
Matrix representation of lateral soil movements: scaling and calibrating CE-DYNAM (v2) at a continental level
CANOPS-GRB v1.0: a new Earth system model for simulating the evolution of ocean–atmosphere chemistry over geologic timescales
Low sensitivity of three terrestrial biosphere models to soil texture over the South American tropics
FESDIA (v1.0): exploring temporal variations of sediment biogeochemistry under the influence of flood events using numerical modelling
Impact of changes in climate and CO2 on the carbon storage potential of vegetation under limited water availability using SEIB-DGVM version 3.02
FORCCHN V2.0: an individual-based model for predicting multiscale forest carbon dynamics
Climate and parameter sensitivity and induced uncertainties in carbon stock projections for European forests (using LPJ-GUESS 4.0)
Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon
SurEau-Ecos v2.0: a trait-based plant hydraulics model for simulations of plant water status and drought-induced mortality at the ecosystem level
Improved representation of plant physiology in the JULES-vn5.6 land surface model: photosynthesis, stomatal conductance and thermal acclimation
Representation of the phosphorus cycle in the Joint UK Land Environment Simulator (vn5.5_JULES-CNP)
CLM5-FruitTree: a new sub-model for deciduous fruit trees in the Community Land Model (CLM5)
The impact of hurricane disturbances on a tropical forest: implementing a palm plant functional type and hurricane disturbance module in ED2-HuDi V1.0
A validation standard for area of habitat maps for terrestrial birds and mammals
Soil Cycles of Elements simulator for Predicting TERrestrial regulation of greenhouse gases: SCEPTER v0.9
Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2)
A map of global peatland extent created using machine learning (Peat-ML)
Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)
ECOSMO II(CHL): a marine biogeochemical model for the North Atlantic and the Arctic
Water Ecosystems Tool (WET) 1.0 – a new generation of flexible aquatic ecosystem model
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
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The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
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We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
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Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
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We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Jianghui Du
Geosci. Model Dev., 16, 5865–5894, https://doi.org/10.5194/gmd-16-5865-2023, https://doi.org/10.5194/gmd-16-5865-2023, 2023
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Trace elements and isotopes (TEIs) are important tools to study the changes in the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
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In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
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Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
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Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-135, https://doi.org/10.5194/gmd-2023-135, 2023
Revised manuscript accepted for GMD
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This paper presents unified RIVE v1.0, a unified version of aquatic biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganisms’ activities to describe full biogeochemical cycles in the water column (e.g. carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams, and public services.
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
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Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-107, https://doi.org/10.5194/gmd-2023-107, 2023
Revised manuscript accepted for GMD
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Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
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Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-74, https://doi.org/10.5194/gmd-2023-74, 2023
Revised manuscript accepted for GMD
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We develop a machine learning based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a lightweight way.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
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We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-66, https://doi.org/10.5194/gmd-2023-66, 2023
Revised manuscript accepted for GMD
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Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Yimian Ma, Xu Yue, Stephen Sitch, Nadine Unger, Johan Uddling, Lina M. Mercado, Cheng Gong, Zhaozhong Feng, Huiyi Yang, Hao Zhou, Chenguang Tian, Yang Cao, Yadong Lei, Alexander W. Cheesman, Yansen Xu, and Maria Carolina Duran Rojas
Geosci. Model Dev., 16, 2261–2276, https://doi.org/10.5194/gmd-16-2261-2023, https://doi.org/10.5194/gmd-16-2261-2023, 2023
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Plants have been found to respond differently to O3, but the variations in the sensitivities have rarely been explained nor fully implemented in large-scale assessment. This study proposes a new O3 damage scheme with leaf mass per area to unify varied sensitivities for all plant species. Our assessment reveals an O3-induced reduction of 4.8 % in global GPP, with the highest reduction of >10 % for cropland, suggesting an emerging risk of crop yield loss under the threat of O3 pollution.
Winslow D. Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, and Werner Rammer
Geosci. Model Dev., 16, 2011–2036, https://doi.org/10.5194/gmd-16-2011-2023, https://doi.org/10.5194/gmd-16-2011-2023, 2023
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Permafrost and the thick soil-surface organic layers that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and soil organic layer module that operates at fine spatial (1 ha) and temporal (daily) resolutions.
Heewon Jung, Hyun-Seob Song, and Christof Meile
Geosci. Model Dev., 16, 1683–1696, https://doi.org/10.5194/gmd-16-1683-2023, https://doi.org/10.5194/gmd-16-1683-2023, 2023
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Microbial activity responsible for many chemical transformations depends on environmental conditions. These can vary locally, e.g., between poorly connected pores in porous media. We present a modeling framework that resolves such small spatial scales explicitly, accounts for feedback between transport and biogeochemical conditions, and can integrate state-of-the-art representations of microbes in a computationally efficient way, making it broadly applicable in science and engineering use cases.
Arthur Guignabert, Quentin Ponette, Frédéric André, Christian Messier, Philippe Nolet, and Mathieu Jonard
Geosci. Model Dev., 16, 1661–1682, https://doi.org/10.5194/gmd-16-1661-2023, https://doi.org/10.5194/gmd-16-1661-2023, 2023
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Spatially explicit and process-based models are useful to test innovative forestry practices under changing and uncertain conditions. However, their larger use is often limited by the restricted range of species and stand structures they can reliably account for. We therefore calibrated and evaluated such a model, HETEROFOR, for 23 species across southern Québec. Our results showed that the model is robust and can predict accurately both individual tree growth and stand dynamics in this region.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
EGUsphere, https://doi.org/10.5194/egusphere-2023-46, https://doi.org/10.5194/egusphere-2023-46, 2023
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Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, bottom trawling, etc. We derive equations for simulating the effect of mixing on central moments that describe the distributions. Then, we demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Rémy Fieuzal, and Eric Ceschia
EGUsphere, https://doi.org/10.5194/egusphere-2023-48, https://doi.org/10.5194/egusphere-2023-48, 2023
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Quantification of Carbon fluxes of crops is an essential brick for the construction of a Monitoring, Reporting and Verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates through an efficient Bayesian approach, high resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in-situ flux towers, yield maps, and analysed at regional scale.
Maureen Beaudor, Nicolas Vuichard, Juliette Lathière, Nikolaos Evangeliou, Martin Van Damme, Lieven Clarisse, and Didier Hauglustaine
Geosci. Model Dev., 16, 1053–1081, https://doi.org/10.5194/gmd-16-1053-2023, https://doi.org/10.5194/gmd-16-1053-2023, 2023
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Ammonia mainly comes from the agricultural sector, and its volatilization relies on environmental variables. Our approach aims at benefiting from an Earth system model framework to estimate it. By doing so, we represent a consistent spatial distribution of the emissions' response to environmental changes.
We greatly improved the seasonal cycle of emissions compared with previous work. In addition, our model includes natural soil emissions (that are rarely represented in modeling approaches).
Rui Ying, Fanny M. Monteiro, Jamie D. Wilson, and Daniela N. Schmidt
Geosci. Model Dev., 16, 813–832, https://doi.org/10.5194/gmd-16-813-2023, https://doi.org/10.5194/gmd-16-813-2023, 2023
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Planktic foraminifera are marine-calcifying zooplankton; their shells are widely used to measure past temperature and productivity. We developed ForamEcoGEnIE 2.0 to simulate the four subgroups of this organism. We found that the relative abundance distribution agrees with marine sediment core-top data and that carbon export and biomass are close to sediment trap and plankton net observations respectively. This model provides the opportunity to study foraminiferal ecology in any geological era.
Onur Kerimoglu, Markus Pahlow, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 16, 95–108, https://doi.org/10.5194/gmd-16-95-2023, https://doi.org/10.5194/gmd-16-95-2023, 2023
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In classical models that track the changes in the elemental composition of phytoplankton, additional state variables are required for each element resolved. In this study, we show how the behavior of such an explicit model can be approximated using an
instantaneous acclimationapproach, in which the elemental composition of the phytoplankton is assumed to adjust to an optimal value instantaneously. Through rigorous tests, we evaluate the consistency of this scheme.
Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan
Geosci. Model Dev., 15, 9111–9125, https://doi.org/10.5194/gmd-15-9111-2022, https://doi.org/10.5194/gmd-15-9111-2022, 2022
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There are a few studies to examine if current models correctly represented the complex processes of transpiration. Here, we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate the ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes, we also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.
Thomas Neumann, Hagen Radtke, Bronwyn Cahill, Martin Schmidt, and Gregor Rehder
Geosci. Model Dev., 15, 8473–8540, https://doi.org/10.5194/gmd-15-8473-2022, https://doi.org/10.5194/gmd-15-8473-2022, 2022
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Marine ecosystem models are usually constrained by the elements nitrogen and phosphorus and consider carbon in organic matter in a fixed ratio. Recent observations show a substantial deviation from the simulated carbon cycle variables. In this study, we present a marine ecosystem model for the Baltic Sea which allows for a flexible uptake ratio for carbon, nitrogen, and phosphorus. With this extension, the model reflects much more reasonable variables of the marine carbon cycle.
Arsène Druel, Simon Munier, Anthony Mucia, Clément Albergel, and Jean-Christophe Calvet
Geosci. Model Dev., 15, 8453–8471, https://doi.org/10.5194/gmd-15-8453-2022, https://doi.org/10.5194/gmd-15-8453-2022, 2022
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Crop phenology and irrigation is implemented into a land surface model able to work at a global scale. A case study is presented over Nebraska (USA). Simulations with and without the new scheme are compared to different satellite-based observations. The model is able to produce a realistic yearly irrigation water amount. The irrigation scheme improves the simulated leaf area index, gross primary productivity, evapotransipiration, and land surface temperature.
Thomas Wutzler, Lin Yu, Marion Schrumpf, and Sönke Zaehle
Geosci. Model Dev., 15, 8377–8393, https://doi.org/10.5194/gmd-15-8377-2022, https://doi.org/10.5194/gmd-15-8377-2022, 2022
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Soil microbes process soil organic matter and affect carbon storage and plant nutrition at the ecosystem scale. We hypothesized that decadal dynamics is constrained by the ratios of elements in litter inputs, microbes, and matter and that microbial community optimizes growth. This allowed the SESAM model to descibe decadal-term carbon sequestration in soils and other biogeochemical processes explicitly accounting for microbial processes but without its problematic fine-scale parameterization.
Ensheng Weng, Igor Aleinov, Ram Singh, Michael J. Puma, Sonali S. McDermid, Nancy Y. Kiang, Maxwell Kelley, Kevin Wilcox, Ray Dybzinski, Caroline E. Farrior, Stephen W. Pacala, and Benjamin I. Cook
Geosci. Model Dev., 15, 8153–8180, https://doi.org/10.5194/gmd-15-8153-2022, https://doi.org/10.5194/gmd-15-8153-2022, 2022
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We develop a demographic vegetation model to improve the representation of terrestrial vegetation dynamics and ecosystem biogeochemical cycles in the Goddard Institute for Space Studies ModelE. The individual-based competition for light and soil resources makes the modeling of eco-evolutionary optimality possible. This model will enable ModelE to simulate long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems at decadal to centurial temporal scales.
Yitong Yao, Emilie Joetzjer, Philippe Ciais, Nicolas Viovy, Fabio Cresto Aleina, Jerome Chave, Lawren Sack, Megan Bartlett, Patrick Meir, Rosie Fisher, and Sebastiaan Luyssaert
Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, https://doi.org/10.5194/gmd-15-7809-2022, 2022
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To facilitate more mechanistic modeling of drought effects on forest dynamics, our study implements a hydraulic module to simulate the vertical water flow, change in water storage and percentage loss of stem conductance (PLC). With the relationship between PLC and tree mortality, our model can successfully reproduce the large biomass drop observed under throughfall exclusion. Our hydraulic module provides promising avenues benefiting the prediction for mortality under future drought events.
Arthur Nicolaus Fendrich, Philippe Ciais, Emanuele Lugato, Marco Carozzi, Bertrand Guenet, Pasquale Borrelli, Victoria Naipal, Matthew McGrath, Philippe Martin, and Panos Panagos
Geosci. Model Dev., 15, 7835–7857, https://doi.org/10.5194/gmd-15-7835-2022, https://doi.org/10.5194/gmd-15-7835-2022, 2022
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Currently, spatially explicit models for soil carbon stock can simulate the impacts of several changes. However, they do not incorporate the erosion, lateral transport, and deposition (ETD) of soil material. The present work developed ETD formulation, illustrated model calibration and validation for Europe, and presented the results for a depositional site. We expect that our work advances ETD models' description and facilitates their reproduction and incorporation in land surface models.
Kazumi Ozaki, Devon B. Cole, Christopher T. Reinhard, and Eiichi Tajika
Geosci. Model Dev., 15, 7593–7639, https://doi.org/10.5194/gmd-15-7593-2022, https://doi.org/10.5194/gmd-15-7593-2022, 2022
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A new biogeochemical model (CANOPS-GRB v1.0) for assessing the redox stability and dynamics of the ocean–atmosphere system on geologic timescales has been developed. In this paper, we present a full description of the model and its performance. CANOPS-GRB is a useful tool for understanding the factors regulating atmospheric O2 level and has the potential to greatly refine our current understanding of Earth's oxygenation history.
Félicien Meunier, Wim Verbruggen, Hans Verbeeck, and Marc Peaucelle
Geosci. Model Dev., 15, 7573–7591, https://doi.org/10.5194/gmd-15-7573-2022, https://doi.org/10.5194/gmd-15-7573-2022, 2022
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Drought stress occurs in plants when water supply (i.e. root water uptake) is lower than the water demand (i.e. atmospheric demand). It is strongly related to soil properties and expected to increase in intensity and frequency in the tropics due to climate change. In this study, we show that contrary to the expectations, state-of-the-art terrestrial biosphere models are mostly insensitive to soil texture and hence probably inadequate to reproduce in silico the plant water status in drying soils.
Stanley I. Nmor, Eric Viollier, Lucie Pastor, Bruno Lansard, Christophe Rabouille, and Karline Soetaert
Geosci. Model Dev., 15, 7325–7351, https://doi.org/10.5194/gmd-15-7325-2022, https://doi.org/10.5194/gmd-15-7325-2022, 2022
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The coastal marine environment serves as a transition zone in the land–ocean continuum and is susceptible to episodic phenomena such as flash floods, which cause massive organic matter deposition. Here, we present a model of sediment early diagenesis that explicitly describes this type of deposition while also incorporating unique flood deposit characteristics. This model can be used to investigate the temporal evolution of marine sediments following abrupt changes in environmental conditions.
Shanlin Tong, Weiguang Wang, Jie Chen, Chong-Yu Xu, Hisashi Sato, and Guoqing Wang
Geosci. Model Dev., 15, 7075–7098, https://doi.org/10.5194/gmd-15-7075-2022, https://doi.org/10.5194/gmd-15-7075-2022, 2022
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Plant carbon storage potential is central to moderate atmospheric CO2 concentration buildup and mitigation of climate change. There is an ongoing debate about the main driver of carbon storage. To reconcile this discrepancy, we use SEIB-DGVM to investigate the trend and response mechanism of carbon stock fractions among water limitation regions. Results show that the impact of CO2 and temperature on carbon stock depends on water limitation, offering a new perspective on carbon–water coupling.
Jing Fang, Herman H. Shugart, Feng Liu, Xiaodong Yan, Yunkun Song, and Fucheng Lv
Geosci. Model Dev., 15, 6863–6872, https://doi.org/10.5194/gmd-15-6863-2022, https://doi.org/10.5194/gmd-15-6863-2022, 2022
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Our study provided a detailed description and a package of an individual tree-based carbon model, FORCCHN2. This model used non-structural carbohydrate (NSC) pools to couple tree growth and phenology. The model could reproduce daily carbon fluxes across Northern Hemisphere forests. Given the potential importance of the application of this model, there is substantial scope for using FORCCHN2 in fields as diverse as forest ecology, climate change, and carbon estimation.
Johannes Oberpriller, Christine Herschlein, Peter Anthoni, Almut Arneth, Andreas Krause, Anja Rammig, Mats Lindeskog, Stefan Olin, and Florian Hartig
Geosci. Model Dev., 15, 6495–6519, https://doi.org/10.5194/gmd-15-6495-2022, https://doi.org/10.5194/gmd-15-6495-2022, 2022
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Understanding uncertainties of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyzed these across European forests. We find that uncertainties are dominantly induced by parameters related to water, mortality, and climate, with an increasing importance of climate from north to south. These results highlight that climate not only contributes uncertainty but also modifies uncertainties in other ecosystem processes.
Marcus Falls, Raffaele Bernardello, Miguel Castrillo, Mario Acosta, Joan Llort, and Martí Galí
Geosci. Model Dev., 15, 5713–5737, https://doi.org/10.5194/gmd-15-5713-2022, https://doi.org/10.5194/gmd-15-5713-2022, 2022
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This paper describes and tests a method which uses a genetic algorithm (GA), a type of optimisation algorithm, on an ocean biogeochemical model. The aim is to produce a set of numerical parameters that best reflect the observed data of particulate organic carbon in a specific region of the ocean. We show that the GA can provide optimised model parameters in a robust and efficient manner and can also help detect model limitations, ultimately leading to a reduction in the model uncertainties.
Julien Ruffault, François Pimont, Hervé Cochard, Jean-Luc Dupuy, and Nicolas Martin-StPaul
Geosci. Model Dev., 15, 5593–5626, https://doi.org/10.5194/gmd-15-5593-2022, https://doi.org/10.5194/gmd-15-5593-2022, 2022
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A widespread increase in tree mortality has been observed around the globe, and this trend is likely to continue because of ongoing climate change. Here we present SurEau-Ecos, a trait-based plant hydraulic model to predict tree desiccation and mortality. SurEau-Ecos can help determine the areas and ecosystems that are most vulnerable to drying conditions.
Rebecca J. Oliver, Lina M. Mercado, Doug B. Clark, Chris Huntingford, Christopher M. Taylor, Pier Luigi Vidale, Patrick C. McGuire, Markus Todt, Sonja Folwell, Valiyaveetil Shamsudheen Semeena, and Belinda E. Medlyn
Geosci. Model Dev., 15, 5567–5592, https://doi.org/10.5194/gmd-15-5567-2022, https://doi.org/10.5194/gmd-15-5567-2022, 2022
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We introduce new representations of plant physiological processes into a land surface model. Including new biological understanding improves modelled carbon and water fluxes for the present in tropical and northern-latitude forests. Future climate simulations demonstrate the sensitivity of photosynthesis to temperature is important for modelling carbon cycle dynamics in a warming world. Accurate representation of these processes in models is necessary for robust predictions of climate change.
Mahdi André Nakhavali, Lina M. Mercado, Iain P. Hartley, Stephen Sitch, Fernanda V. Cunha, Raffaello di Ponzio, Laynara F. Lugli, Carlos A. Quesada, Kelly M. Andersen, Sarah E. Chadburn, Andy J. Wiltshire, Douglas B. Clark, Gyovanni Ribeiro, Lara Siebert, Anna C. M. Moraes, Jéssica Schmeisk Rosa, Rafael Assis, and José L. Camargo
Geosci. Model Dev., 15, 5241–5269, https://doi.org/10.5194/gmd-15-5241-2022, https://doi.org/10.5194/gmd-15-5241-2022, 2022
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In tropical ecosystems, the availability of rock-derived elements such as P can be very low. Thus, without a representation of P cycling, tropical forest responses to rising atmospheric CO2 conditions in areas such as Amazonia remain highly uncertain. We introduced P dynamics and its interactions with the N and P cycles into the JULES model. Our results highlight the potential for high P limitation and therefore lower CO2 fertilization capacity in the Amazon forest with low-fertility soils.
Olga Dombrowski, Cosimo Brogi, Harrie-Jan Hendricks Franssen, Damiano Zanotelli, and Heye Bogena
Geosci. Model Dev., 15, 5167–5193, https://doi.org/10.5194/gmd-15-5167-2022, https://doi.org/10.5194/gmd-15-5167-2022, 2022
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Soil carbon storage and food production of fruit orchards will be influenced by climate change. However, they lack representation in models that study such processes. We developed and tested a new sub-model, CLM5-FruitTree, that describes growth, biomass distribution, and management practices in orchards. The model satisfactorily predicted yield and exchange of carbon, energy, and water in an apple orchard and can be used to study land surface processes in fruit orchards at different scales.
Jiaying Zhang, Rafael L. Bras, Marcos Longo, and Tamara Heartsill Scalley
Geosci. Model Dev., 15, 5107–5126, https://doi.org/10.5194/gmd-15-5107-2022, https://doi.org/10.5194/gmd-15-5107-2022, 2022
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We implemented hurricane disturbance in a vegetation dynamics model and calibrated the model with observations of a tropical forest. We used the model to study forest recovery from hurricane disturbance and found that a single hurricane disturbance enhances AGB and BA in the long term compared with a no-hurricane situation. The model developed and results presented in this study can be utilized to understand the impact of hurricane disturbances on forest recovery under the changing climate.
Prabhat Raj Dahal, Maria Lumbierres, Stuart H. M. Butchart, Paul F. Donald, and Carlo Rondinini
Geosci. Model Dev., 15, 5093–5105, https://doi.org/10.5194/gmd-15-5093-2022, https://doi.org/10.5194/gmd-15-5093-2022, 2022
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This paper describes the validation of area of habitat (AOH) maps produced for terrestrial birds and mammals. The main objective was to assess the accuracy of the maps based on independent data. We used open access data from repositories, such as ebird and gbif to check if our maps were a better reflection of species' distribution than random. When points were not available we used logistic models to validate the AOH maps. The majority of AOH maps were found to have a high accuracy.
Yoshiki Kanzaki, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 15, 4959–4990, https://doi.org/10.5194/gmd-15-4959-2022, https://doi.org/10.5194/gmd-15-4959-2022, 2022
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Increasing carbon dioxide in the atmosphere is an urgent issue in the coming century. Enhanced rock weathering in soils can be one of the most efficient C capture strategies. On the basis as a weathering simulator, the newly developed SCEPTER model implements bio-mixing by fauna/humans and enables organic matter and crushed rocks/minerals at the soil surface with an option to track their particle size distributions. Those features can be useful for evaluating the carbon capture efficiency.
Félicien Meunier, Sruthi M. Krishna Moorthy, Marc Peaucelle, Kim Calders, Louise Terryn, Wim Verbruggen, Chang Liu, Ninni Saarinen, Niall Origo, Joanne Nightingale, Mathias Disney, Yadvinder Malhi, and Hans Verbeeck
Geosci. Model Dev., 15, 4783–4803, https://doi.org/10.5194/gmd-15-4783-2022, https://doi.org/10.5194/gmd-15-4783-2022, 2022
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We integrated state-of-the-art observations of the structure of the vegetation in a temperate forest to constrain a vegetation model that aims to reproduce such an ecosystem in silico. We showed that the use of this information helps to constrain the model structure, its critical parameters, as well as its initial state. This research confirms the critical importance of the representation of the vegetation structure in vegetation models and proposes a method to overcome this challenge.
Joe R. Melton, Ed Chan, Koreen Millard, Matthew Fortier, R. Scott Winton, Javier M. Martín-López, Hinsby Cadillo-Quiroz, Darren Kidd, and Louis V. Verchot
Geosci. Model Dev., 15, 4709–4738, https://doi.org/10.5194/gmd-15-4709-2022, https://doi.org/10.5194/gmd-15-4709-2022, 2022
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Peat-ML is a high-resolution global peatland extent map generated using machine learning techniques. Peatlands are important in the global carbon and water cycles, but their extent is poorly known. We generated Peat-ML using drivers of peatland formation including climate, soil, geomorphology, and vegetation data, and we train the model with regional peatland maps. Our accuracy estimation approaches suggest Peat-ML is of similar or higher quality than other available peatland mapping products.
Qianyu Li, Shawn P. Serbin, Julien Lamour, Kenneth J. Davidson, Kim S. Ely, and Alistair Rogers
Geosci. Model Dev., 15, 4313–4329, https://doi.org/10.5194/gmd-15-4313-2022, https://doi.org/10.5194/gmd-15-4313-2022, 2022
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Stomatal conductance is the rate of water release from leaves’ pores. We implemented an optimal stomatal conductance model in a vegetation model. We then tested and compared it with the existing empirical model in terms of model responses to key environmental variables. We also evaluated the model with measurements at a tropical forest site. Our study suggests that the parameterization of conductance models and current model response to drought are the critical areas for improving models.
Veli Çağlar Yumruktepe, Annette Samuelsen, and Ute Daewel
Geosci. Model Dev., 15, 3901–3921, https://doi.org/10.5194/gmd-15-3901-2022, https://doi.org/10.5194/gmd-15-3901-2022, 2022
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We describe the coupled bio-physical model ECOSMO II(CHL), which is used for regional configurations for the North Atlantic and the Arctic hind-casting and operational purposes. The model is consistent with the large-scale climatological nutrient settings and is capable of representing regional and seasonal changes, and model primary production agrees with previous measurements. For the users of this model, this paper provides the underlying science, model evaluation and its development.
Nicolas Azaña Schnedler-Meyer, Tobias Kuhlmann Andersen, Fenjuan Rose Schmidt Hu, Karsten Bolding, Anders Nielsen, and Dennis Trolle
Geosci. Model Dev., 15, 3861–3878, https://doi.org/10.5194/gmd-15-3861-2022, https://doi.org/10.5194/gmd-15-3861-2022, 2022
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We present the Water Ecosystems Tool (WET) – a new modular aquatic ecosystem model configurable to a wide array of physical setups, ecosystems and research questions based on the popular FABM–PCLake model. We aim for the model to become a community staple, thus helping to consolidate the state of the art under a few flexible models, with the aim of improving comparability across studies and preventing the
re-inventions of the wheelthat are common to our scientific modeling community.
Cited articles
Albani, S., Mahowald, N. M., Perry, A. T., Scanza, R. A., Zender, C. S.,
Heavens, N. G., Maggi, V., Kok, J. F., and Otto-Bliesner, B. L.: Improved
dust representation in the Community Atmosphere Model, J. Adv.
Model. Earth Sy., 6, 541–570, https://doi.org/10.1002/2013MS000279, 2014. a, b, c
Álvarez, E., Thoms, S., and Völker, C.: Chlorophyll to Carbon Ratio Derived
From a Global Ecosystem Model With Photodamage, Global Biogeochem. Cy.,
32, 799–816, https://doi.org/10.1029/2017GB005850, 2018. a, b
Anderson, L. G., Jutterström, S., Hjalmarsson, S., Wåhlström, I., and
Semiletov, I. P.: Out-gassing of CO2 from Siberian Shelf seas by terrestrial
organic matter decomposition, Geophys. Res. Lett., 36, L20601,
https://doi.org/10.1029/2009GL040046, 2009. a
Anderson, T. R., Gentleman, W. C., and Sinha, B.: Influence of grazing
formulations on the emergent properties of a complex ecosystem model in a
global ocean general circulation model, Prog. Oceanogr., 87,
201–213, https://doi.org/10.1016/j.pocean.2010.06.003, 2010. a
Aumont, O., Orr, J. C., Monfray, P., Ludwig, W., Amiotte-Suchet, P., and
Probst, J.-L.: Riverine-driven interhemispheric transport of carbon, Global
Biogeochem. Cy., 15, 393–405,
https://doi.org/10.1029/1999GB001238, 2001. a, b
Aumont, O., Maier-Reimer, E., Blain, S., and Monfray, P.: An ecosystem model of
the global ocean including Fe, Si, P colimitations, Global Biogeochem.
Cy., 17, 1060, https://doi.org/10.1029/2001GB001745, 2003. a
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L., and Gehlen, M.: PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies, Geosci. Model Dev., 8, 2465–2513, https://doi.org/10.5194/gmd-8-2465-2015, 2015. a, b, c
Bakker, D., De Baar, H., and Bathmann, U.: Changes of carbon dioxide in
surface waters during spring in the Southern Ocean, Deep-Sea Res. Pt.
II, 44, 91–127,
https://doi.org/10.1016/S0967-0645(96)00075-6, 1997. a
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 (data available at: https://socat.info/index.php/previous-versions/, last access: 26 July 2023). a, b, c, d
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
Battaglia, G., Steinacher, M., and Joos, F.: A probabilistic assessment of calcium carbonate export and dissolution in the modern ocean, Biogeosciences, 13, 2823–2848, https://doi.org/10.5194/bg-13-2823-2016, 2016. a
Berelson, W. M., Balch, W. M., Najjar, R., Feely, R. A., Sabine, C., and Lee, K.: Relating estimates of CaCO3 production, export, and dissolution in the water column to measurements of CaCO3 rain into sediment traps and dissolution on the sea floor: A revised global carbonate budget, Global Biogeochem. Cy., 21, GB1024, https://doi.org/10.1029/2006gb002803, 2007. a
Berthet, S., Séférian, R., Bricaud, C., Chevallier, M., Voldoire, A., and
Ethé, C.: Evaluation of an Online Grid-Coarsening Algorithm in a Global
Eddy-Admitting Ocean Biogeochemical Model, J. Adv. Model.
Earth Sy., 11, 1759–1783, https://doi.org/10.1029/2019MS001644,
2019. a, b
Blondeau-Patissier, D., Gower, J. F., Dekker, A. G., Phinn, S. R., and Brando,
V. E.: A review of ocean color remote sensing methods and statistical
techniques for the detection, mapping and analysis of phytoplankton blooms in
coastal and open oceans, Prog. Oceanogr., 123, 123–144,
https://doi.org/10.1016/j.pocean.2013.12.008, 2014. a
Bopp, L., Resplandy, L., Orr, J. C., Doney, S. C., Dunne, J. P., Gehlen, M., Halloran, P., Heinze, C., Ilyina, T., Séférian, R., Tjiputra, J., and Vichi, M.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225–6245, https://doi.org/10.5194/bg-10-6225-2013, 2013. a
Bourgeois, T., Goris, N., Schwinger, J., and Tjiputra, J. F.: Stratification
constrains future heat and carbon uptake in the Southern Ocean between 30∘ S
and 55∘ S, Nat. Commun., 13, 340,
https://doi.org/10.1038/s41467-022-27979-5, 2022. a
Boyd, P., Claustre, H., Levy, M., Siegel, D. A., and Weber, T.: Multi-faceted
particle pumps drive carbon sequestration in the ocean, Nature, 568,
327–335, https://doi.org/10.1038/s41586-019-1098-2, 2019. a
Boye, M., van den Berg, C. M., de Jong, J. T., Leach, H., Croot, P., and
de Baar, H. J.: Organic complexation of iron in the Southern Ocean, Deep-Sea Res. Pt. I, 48, 1477–1497,
https://doi.org/10.1016/S0967-0637(00)00099-6, 2001. a
Buitenhuis, E., Rivkin, R. B., Séailley, S., and Le Quéré,
C.: Biogeochemical fluxes through microzooplankton, Global Biogeochem.
Cy., 24, GB4015, https://doi.org/10.1029/2009GB003601, 2010. a, b, c
Buitenhuis, E. T., Rivkin, R. B., Sailley, S., and Le Quéré, C.: Global distributions of microzooplankton abundance and biomass – Gridded data product (NetCDF) – Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.779970, 2012. a
Bunsen, F.: Impact of recent climate variability on oceanic CO2 uptake in a
global ocean biogeochemistry model, Master's thesis, Kiel University
Christian-Albrechts-Universität, Faculty of Mathematics and Natural
Sciences, https://epic.awi.de/id/eprint/54788/ (last access: 14 August 2023), 2022. 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
Butzin, M. and Pörtner, H. O.: Thermal growth potential of Atlantic cod
by the end of the 21st century, Glob. Change Biol., 22, 4162–4168,
https://doi.org/10.1111/gcb.13375, 2016. a
Campin, J.-M., Adcroft, A. J., Hill, C., and Marshall, J.: Conservation of
properties in a free-surface model, Ocean Model., 6, 221–244,
https://doi.org/10.1016/S1463-5003(03)00009-X, 2004. a
Canadell, J., Monteiro, P., Costa, M., Cotrim da Cunha, L., Cox, P., Eliseev,
A., Henson, S., Ishii, M., Jaccard, S., Koven, C., Lohila, A., Patra, P.,
Piao, S., Rogelj, J., Syampungani, S., Zaehle, S., and Zickfeld, K.: Global
Carbon and other Biogeochemical Cycles and Feedbacks, Cambridge
University Press, Cambridge, United Kingdom and New York, NY, USA, 673–816,
https://doi.org/10.1017/9781009157896.007, 2021. a
Carr, M.-E., Friedrichs, M. A., Schmeltz, M., Noguchi Aita, M., Antoine, D.,
Arrigo, K. R., Asanuma, I., Aumont, O., Barber, R., Behrenfeld, M., Bidigare,
R., Buitenhuis, E. T., Campbell, J., Ciotti, A., Dierssen, H., Dowell, M.,
Dunne, J., Esaias, W., Gentili, B., Gregg, W., Groom, S., Hoepffner, N.,
Ishizaka, J., Kameda, T., Le Quéré, C., Lohrenz, S., Marra, J., Mélin,
F., Moore, K., Morel, A., Reddy, T. E., Ryan, J., Scardi, M., Smyth, T.,
Turpie, K., Tilstone, G., Waters, K., and Yamanaka, Y.: A comparison of
global estimates of marine primary production from ocean color, Deep-Sea
Res. Pt. II, 53, 741–770,
https://doi.org/10.1016/j.dsr2.2006.01.028, 2006. a
Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air–sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087–1109, https://doi.org/10.5194/bg-19-1087-2022, 2022 (data available at: https://data.marine.copernicus.eu/product/MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008/description, last access: 26 July 2023). a, b, c, d, e, f, g, h, i, j
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J., Chhabra,
A., DeFries, R., Galloway, J., Heimann, M., Le Quéré, C., Myneni, R., Piao,
S., and Thornton, P.: Carbon and Other Biogeochemical Cycles, in: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M.,
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 465–570, 2014. a
Claquin, P., Martin-Jézéquel, V., Kromkamp, J. C., Veldhuis, M. J. W., and
Kraay, G. W.: Uncoupling of Silicon Compared With Carbon and
Nitrogen Metabolisms and the Role of the Cell Cycle in Continuous
Cultures of Thalassiosira Pseudonana (Bacillariophyceae) Under
Light, Nitrogen, and Phosphorus Control, J. Phycol., 38,
922–930, https://doi.org/10.1046/j.1529-8817.2002.t01-1-01220.x, 2002. a, b
Cocco, V., Joos, F., Steinacher, M., Frölicher, T. L., Bopp, L., Dunne, J., Gehlen, M., Heinze, C., Orr, J., Oschlies, A., Schneider, B., Segschneider, J., and Tjiputra, J.: Oxygen and indicators of stress for marine life in multi-model global warming projections, Biogeosciences, 10, 1849–1868, https://doi.org/10.5194/bg-10-1849-2013, 2013. a
Cram, J. A., Weber, T., Leung, S. W., McDonnell, A. M. P., Liang, J.-H., and
Deutsch, C.: The Role of Particle Size, Ballast, Temperature, and Oxygen in
the Sinking Flux to the Deep Sea, Global Biogeochem. Cy., 32, 858–876,
https://doi.org/10.1029/2017GB005710, 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
Danabasoglu, G., Yeager, S. G., Bailey, D., Behrens, E., Bentsen, M., Bi, D.,
Biastoch, A., Böning, C., Bozec, A., Canuto, V. M., Cassou, C., Chassignet,
E., Coward, A. C., Danilov, S., Diansky, N., Drange, H., Farneti, R.,
Fernandez, E., Fogli, P. G., Forget, G., Fujii, Y., Griffies, S. M., Gusev,
A., Heimbach, P., Howard, A., Jung, T., Kelley, M., Large, W. G.,
Leboissetier, A., Lu, J., Madec, G., Marsland, S. J., Masina, S., Navarra,
A., George Nurser, A., Pirani, A., y Mélia, D. S., Samuels, B. L.,
Scheinert, M., Sidorenko, D., Treguier, A.-M., Tsujino, H., Uotila, P.,
Valcke, S., Voldoire, A., and Wang, Q.: North Atlantic simulations in
Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Part I: Mean
states, Ocean Model., 73, 76–107,
https://doi.org/10.1016/j.ocemod.2013.10.005, 2014. a
Danilov, S.: Ocean modeling on unstructured meshes, Ocean Model., 69,
195–210, https://doi.org/10.1016/j.ocemod.2013.05.005, 2013. a
Danilov, S., Wang, Q., Timmermann, R., Iakovlev, N., Sidorenko, D., Kimmritz, M., Jung, T., and Schröter, J.: Finite-Element Sea Ice Model (FESIM), version 2, Geosci. Model Dev., 8, 1747–1761, https://doi.org/10.5194/gmd-8-1747-2015, 2015. a, b
de Baar, H. J., de Jong, J. T., Nolting, R. F., Timmermans, K. R., van
Leeuwe, M. A., Bathmann, U., Rutgers van der Loeff, M., and Sildam, J.:
Low dissolved Fe and the absence of diatom blooms in remote Pacific waters of
the Southern Ocean, Mar. Chem., 66, 1–34,
https://doi.org/10.1016/S0304-4203(99)00022-5, 1999. a
Denman, K. L., Brasseur, G., Chidthaisong, A., Ciais, P., Cox, P. M.,
Dickinson, R. E., Hauglustaine, D., Heinze, C., Holland, E., Jacob, D.,
Lohmann, U., Ramachandran, S., Leite da Silva Dias, P., Wofsy, S. C., and
Zhang, X.: Couplings Between Changes in the Climate System and
Biogeochemistry, in: Climate Change 2007: The Physical Science Basis.
Contribution of Working Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D.,
Manning, M., Marquis, M., Averyt, K., Tignor, M. M. B., Miller, H. L., and
Chen, Z. L., Cambridge University Press, Cambridge, UK and
New York, USA, 499–587, 2007. a
DeVries, T., Le Quéré, C., Andrews, O., Berthet, S., Hauck, J., Ilyina, T.,
Landschützer, P., Lenton, A., Lima, I. D., Nowicki, M., Schwinger, J., and
Séférian, R.: Decadal trends in the ocean carbon sink, P. Natl. Acad. Sci. USA, 116,
11646–11651, https://doi.org/10.1073/pnas.1900371116, 2019. a
Doney, S. C., Lindsay, K., Moore, J. K., Dutkiewicz, S., Friedrichs, M. A. M.,
and Matear, R. J.: Marine Biogeochemical Modeling: Recent Advances and Future
Challenges, Oceanography, 14, 93–107,
https://doi.org/10.5670/oceanog.2001.10, 2001. 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, b
Du, J., Ye, Y., Zhang, X., Völker, C., and Tian, J.: Southern Control of
Interhemispheric Synergy on Glacial Marine Carbon Sequestration, Geophys.
Res. Lett., 49, e2022GL099048,
https://doi.org/10.1029/2022GL099048, 2022. a
Elrod, V. A., Berelson, W. M., Coale, K. H., and Johnson, K. S.: The flux of
iron from continental shelf sediments: a missing source for global budgets,
Geophys. Res. Lett., 31, L12307, https://doi.org/10.1029/2004GL020216,
2004. a, b
Fay, A. R. and McKinley, G. A.: Observed Regional Fluxes to Constrain
Modeled Estimates of the Ocean Carbon Sink, Geophys. Res.
Lett., 48, e2021GL095325, https://doi.org/10.1029/2021GL095325, 2021. a, b
Fay, A. R., Gregor, L., Landschützer, P., McKinley, G. A., Gruber, N., Gehlen, M., Iida, Y., Laruelle, G. G., Rödenbeck, C., Roobaert, A., and Zeng, J.: SeaFlux: harmonization of air–sea CO2 fluxes from surface pCO2 data products using a standardized approach, Earth Syst. Sci. Data, 13, 4693–4710, https://doi.org/10.5194/essd-13-4693-2021, 2021. 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,
76, https://doi.org/10.1038/s43586-022-00154-2, 2022. a
Friedlingstein, P., Dufresne, J.-L., Cox, P. M., and Rayner, P. J.: How
positive is the feedback between climate change and the carbon cycle?,
Tellus B, 55, 692–700, https://doi.org/10.1034/j.1600-0889.2003.01461.x, 2003. a
Friedlingstein, P., Jones, M. W., O'Sullivan, M., Andrew, R. M., Bakker, D. C. E., Hauck, J., Le Quéré, C., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Bates, N. R., Becker, M., Bellouin, N., Bopp, L., Chau, T. T. T., Chevallier, F., Chini, L. P., Cronin, M., Currie, K. I., Decharme, B., Djeutchouang, L. M., Dou, X., Evans, W., Feely, R. A., Feng, L., Gasser, T., Gilfillan, D., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., Gürses, Ö., Harris, I., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Luijkx, I. T., Jain, A., Jones, S. D., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., Körtzinger, A., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lienert, S., Liu, J., Marland, G., McGuire, P. C., Melton, J. R., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I., Niwa, Y., Ono, T., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., Rödenbeck, C., Rosan, T. M., Schwinger, J., Schwingshackl, C., Séférian, R., Sutton, A. J., Sweeney, C., Tanhua, T., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F., van der Werf, G. R., Vuichard, N., Wada, C., Wanninkhof, R., Watson, A. J., Willis, D., Wiltshire, A. J., Yuan, W., Yue, C., Yue, X., Zaehle, S., and Zeng, J.: Global Carbon Budget 2021, Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, 2022a (data available at: https://globalcarbonbudgetdata.org, last access: 26 July 2023). a, b, c, d, e, f, g, h, i
Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Gregor, L., Hauck, J., Le Quéré, C., Luijkx, I. T., Olsen, A., Peters, G. P., Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Alkama, R., Arneth, A., Arora, V. K., Bates, N. R., Becker, M., Bellouin, N., Bittig, H. C., Bopp, L., Chevallier, F., Chini, L. P., Cronin, M., Evans, W., Falk, S., Feely, R. A., Gasser, T., Gehlen, M., Gkritzalis, T., Gloege, L., Grassi, G., Gruber, N., Gürses, Ö., Harris, I., Hefner, M., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Jain, A. K., Jersild, A., Kadono, K., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., Landschützer, P., Lefèvre, N., Lindsay, K., Liu, J., Liu, Z., Marland, G., Mayot, N., McGrath, M. J., Metzl, N., Monacci, N. M., Munro, D. R., Nakaoka, S.-I., Niwa, Y., O'Brien, K., Ono, T., Palmer, P. I., Pan, N., Pierrot, D., Pocock, K., Poulter, B., Resplandy, L., Robertson, E., Rödenbeck, C., Rodriguez, C., Rosan, T. M., Schwinger, J., Séférian, R., Shutler, J. D., Skjelvan, I., Steinhoff, T., Sun, Q., Sutton, A. J., Sweeney, C., Takao, S., Tanhua, T., Tans, P. P., Tian, X., Tian, H., Tilbrook, B., Tsujino, H., Tubiello, F., van der Werf, G. R., Walker, A. P., Wanninkhof, R., Whitehead, C., Willstrand Wranne, A., Wright, R., Yuan, W., Yue, C., Yue, X., Zaehle, S., Zeng, J., and Zheng, B.: Global Carbon Budget 2022, Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, 2022b. a, b, c, d, e, f, g
Frölicher, T. L., Rodgers, K. B., Stock, C. A., and Cheung, W. W. L.: Sources
of uncertainties in 21st century projections of potential ocean ecosystem
stressors, Global
Biogeochem. Cy., 30, 1224–1243, https://doi.org/10.1002/2015GB005338, 2016. a
Galbraith, E. D. and Skinner, L. C.: The Biological Pump During the Last
Glacial Maximum, Annu. Rev. Mar. Sci., 12, 559–586,
https://doi.org/10.1146/annurev-marine-010419-010906, 2020. a
Gangstø, R., Gehlen, M., Schneider, B., Bopp, L., Aumont, O., and Joos, F.: Modeling the marine aragonite cycle: changes under rising carbon dioxide and its role in shallow water CaCO3 dissolution, Biogeosciences, 5, 1057–1072, https://doi.org/10.5194/bg-5-1057-2008, 2008. a
Garcia, H. E., Locarnini, R. A., Boyer, T. P., Antonov, J. I., Baranova, O. K., Zweng, M. M., Reagan, J. R., and Johnson, D. R.: World ocean atlas 2013. Volume 4, Dissolved inorganic nutrients (phosphate, nitrate, silicate), U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data and Information Service [data set], https://doi.org/10.7289/V5J67DWD, 2013. a
Garcia, H. E., Locarnini, R. A., Boyer, T. P., Antonov, J. I., Baranova, O. K.,
Zweng, M. M., Reagan, J. R., and Johnson, D. R.: World Ocean Atlas 2013,
Volume 4: Dissolved Inorganic Nutrients (phosphate, nitrate, silicate), edited by:
Levitus, S., Technical Ed.: Mishonov, A., Tech. Rep., NOAA Atlas NESDIS 76, https://doi.org/10.7289/V5J67DWD,
2014. a, b, c
Garcia, H. E., Weathers, K. W., Paver, C. R., Smolyar, I., Boyer, T. P.,
Locarnini, R. A., Zweng, M. M., Mishonov, A. V., Baranova, O. K., Seidov, D.,
and Reagan, J. R.: World Ocean Atlas 2018, Volume 3: Dissolved Oxygen,
Apparent Oxygen Utilization, and Oxygen Saturation, Technical
Editor: Mishonov, A., Tech. Rep., NOAA Atlas NESDIS 83, https://www.ncei.noaa.gov/access/world-ocean-atlas-2018/bin/woa18oxnu.pl?parameter=o (last access 26 July 2023), 2019a. a, b
Garcia, H. E., Weathers, K. W., Paver, C. R., Smolyar, I., Boyer, T. P.,
Locarnini, R. A., Zweng, M. M., Mishonov, A. V., Baranova, O. K., Seidov, D.,
and Reagan, J. R.: World Ocean Atlas 2018, Volume 4: Dissolved Inorganic
Nutrients (phosphate, nitrate, silicate), Technical Editor: Mishonov, A., Tech.
Rep., NOAA Atlas NESDIS 84, 2019b. a, b, c, d
Gehlen, M., Bopp, L., Emprin, N., Aumont, O., Heinze, C., and Ragueneau, O.: Reconciling surface ocean productivity, export fluxes and sediment composition in a global biogeochemical ocean model, Biogeosciences, 3, 521–537, https://doi.org/10.5194/bg-3-521-2006, 2006. a
Geider, R. J. and La Roche, J.: The role of iron in phytoplankton
photosynthesis, and the potential for iron-limitation of primary productivity
in the sea, Photosynth. Res., 39, 275–301, https://doi.org/10.1007/BF00014588,
1994. a
Gent, P. and McWilliams, J.: Isopycnal Mixing in Ocean Circulation Models, J.
Phys. Oceanogr., 20, 150–155, 1990. 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
Goris, N., Tjiputra, J. F., Olsen, A., Schwinger, J., Lauvset, S. K., and
Jeansson, E.: Constraining Projection-Based Estimates of the Future North
Atlantic Carbon Uptake, J. Climate, 31, 3959–3978,
https://doi.org/10.1175/JCLI-D-17-0564.1, 2018. a
Gray, A., 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
Gregor, L. and Gruber, N.: OceanSODA-ETHZ: a global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification, Earth Syst. Sci. Data, 13, 777–808, https://doi.org/10.5194/essd-13-777-2021, 2021. a, b
Gregor, L., Lebehot, A. D., Kok, S., and Scheel Monteiro, P. M.: A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall?, Geosci. Model Dev., 12, 5113–5136, https://doi.org/10.5194/gmd-12-5113-2019, 2019. a
Griffies, S.: The Gent-McWilliams Skew Flux, J. Phys. Oceanogr., 28,
831–841, https://doi.org/10.1175/1520-0485(1998)028<0831:TGMSF>2.0.CO;2, 1998. a
Griffies, S. M., Biastoch, A., Böning, C., Bryan, F., Danabasoglu, G.,
Chassignet, E. P., England, M. H., Gerdes, R., Haak, H., Hallberg, R. W.,
Hazeleger, W., Jungclaus, J., Large, W. G., Madec, G., Pirani, A., Samuels,
B. L., Scheinert, M., Gupta, A. S., Severijns, C. A., Simmons, H. L.,
Treguier, A. M., Winton, M., Yeager, S., and Yin, J.: Coordinated Ocean-ice
Reference Experiments (COREs), Ocean Model., 26, 1–46,
https://doi.org/10.1016/j.ocemod.2008.08.007, 2009. a, b
Gruber, N., Gloor, M., Mikaloff Fletcher, S. E., 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
Gruber, N., Clement, D., Carter, B. R., Feely, R. A., van Heuven, S., Hoppema,
M., Ishii, M., Key, R. M., Kozyr, A., Lauvset, S. K., Monaco, C. L., Mathis,
J. T., Murata, A., Olsen, A., Perez, F. F., Sabine, C. L., Tanhua, T., and
Wanninkhof, R.: The oceanic sink for anthropogenic CO2 from 1994
to 2007, Science, 363, 1193–1199, https://doi.org/10.1126/science.aau5153, 2019. a, b, c, d, e, f, g, h, i, j, k
Gürses, Ö.: Model code used in FESOM2.1-REcoM3 description paper, Zenodo [code], https://doi.org/10.5281/zenodo.7502419, 2023. a
Gürses, Ö., Oziel, L., Karakus, 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 (0.1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.8276875, 2023. a
Hatta, M., Measures, C. I., Wu, J., Roshan, S., Fitzsimmons, J. N., Sedwick,
P., and Morton, P.: An overview of dissolved Fe and Mn distributions
during the 2010–2011 U.S. GEOTRACES north Atlantic cruises:
GEOTRACES GA03, Deep-Sea Res. Pt. II, 116, 117–129, https://doi.org/10.1016/j.dsr2.2014.07.005, 2015. a
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, c, d
Hauck, J., Völker, C., Wolf‐Gladrow, D. A., Laufkötter, C., Vogt, M.,
Aumont, O., Bopp, L., Buitenhuis, E. T., Doney, S. C., Dunne, J., Gruber, N.,
Hashioka, T., John, J., Quéré, C. L., Lima, I. D., Nakano, H., Séférian,
R., and Totterdell, I.: On the Southern Ocean CO2 uptake
and the role of the biological carbon pump in the 21st century, Global
Biogeochem. Cy., 29, 1451–1470, https://doi.org/10.1002/2015GB005140, 2015. a
Hauck, J., Köhler, P., Wolf-Gladrow, D., and Völker, C.: Iron fertilisation
and century-scale effects of open ocean dissolution of olivine in a simulated
CO2 removal experiment, Environ. Res. Lett., 11, 024007,
https://doi.org/10.1088/1748-9326/11/2/024007, 2016. a
Hauck, J., Lenton, A., Langlais, C., and Matear, R.: The Fate of Carbon and
Nutrients Exported Out of the Southern Ocean, Global Biogeochem.
Cy., 32, 1556–1573, https://doi.org/10.1029/2018GB005977, 2018. a
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, Frontiers in Marine Science, 7,
https://doi.org/10.3389/fmars.2020.571720, 2020. a, b, c, d, e, f, g, h, i, j
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, 2023. a, b
Henson, S. A., Sanders, R., Madsen, E., Morris, P. J., Le Moigne, F., and
Quartly, G. D.: A reduced estimate of the strength of the ocean’s
biological carbon pump, Geophys. Res. Lett., 38, L04606,
https://doi.org/10.1029/2011GL046735, 2011. a
Henson, S. A., Laufkötter, C., Leung, S., Giering, S. L. C., Palevsky, H. I.,
and Cavan, E. L.: Uncertain response of ocean biological carbon export in a
changing world, Nat. Geosci., 15, 248–254,
https://doi.org/10.1038/s41561-022-00927-0, 2022. a
Ho, D. T., Law, C. S., Smith, M. J., Schlosser, P., Harvey, M., and Hill, P.:
Measurements of air-sea gas exchange at high wind speeds in the Southern
Ocean: Implications for global parameterizations, Geophys. Res.
Lett., 33, L16611, https://doi.org/10.1029/2006GL026817, 2006. a
Iida, Y., Kojima, A., Takatani, Y., Nakano, T., Sugimoto, H., Midorikawa, T.,
and Ishii, M.: Trends in pCO2 and sea–air CO2 flux over the global open
oceans for the last two decades, J. Oceanogr., 71,
637–661, https://doi.org/10.1007/s10872-015-0306-4, 2015. a, b
Japan Meteorological Agency:
JRA-55 based surface dataset for driving ocean-sea ice models (JRA55-do), Research Data Server [data set], https://climate.mri-jma.go.jp/pub/ocean/JRA55-do/, last access: 23 August 2023. a
Jin, X., Gruber, N., Dunne, J. P., Sarmiento, J. L., and Armstrong, R. A.: Diagnosing the contribution of phytoplankton functional groups to the production and export of particulate organic carbon, CaCO3, and opal from global nutrient and alkalinity distributions, Global Biogeochem. Cy., 20, GB2015, https://doi.org/10.1029/2005gb002532, 2006. a
Johnson, R., Strutton, P. G., Wright, S. W., McMinn, A., and Meiners, K. M.:
Three improved Satellite Chlorophyll algorithms for the Southern Ocean,
J. Geophys. Res.-Oceans, 118, 3694–3703,
https://doi.org/10.1002/jgrc.20270, 2013 (data available at: https://imos.org.au/facilities/srs/oceancolour, last access: 26 July 2023). a, b, c, d, e
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
Jungclaus, J. H., Fischer, N., Haak, H., Lohmann, K., Marotzke, J., Matei, D.,
Mikolajewicz, U., Notz, D., and von Storch, J. S.: Characteristics of the
ocean simulations in the Max Planck Institute Ocean Model (MPIOM) the ocean
component of the MPI-Earth system model, J. Adv. Model.
Earth Sy., 5, 422–446, https://doi.org/10.1002/jame.20023, 2013. a
Juricke, S., Danilov, S., Koldunov, N., Oliver, M., and Sidorenko, D.: Ocean
Kinetic Energy Backscatter Parametrization on Unstructured Grids: Impact on
Global Eddy-Permitting Simulations, J. Adv. Model. Earth
Sy., 12, e2019MS001855, https://doi.org/10.1029/2019MS001855, 2020. a, b
Karakuş, O., Völker, C., Iversen, M., Hagen, W., and Hauck, J.: The Role of
Zooplankton Grazing and Nutrient Recycling for Global Ocean Biogeochemistry
and Phytoplankton Phenology, J. Geophys. Res.-Biogeo.,
127, e2022JG006798, https://doi.org/10.1029/2022JG006798, 2022. a, b, c
Keerthi, M. G., Prend, C. J., Aumont, O., and Lévy, M.: Annual variations in
phytoplankton biomass driven by small-scale physical processes, Nat.
Geosci., 15, 1027–1033, https://doi.org/10.1038/s41561-022-01057-3, 2022. a
Keppler, L., Landschützer, P., Gruber, N., Lauvset, S. K., and Stemmler, I.:
Seasonal Carbon Dynamics in the Near-Global Ocean, Global Biogeochem.
Cy., 34, e2020GB006571, https://doi.org/10.1029/2020GB006571, 2020. a
Koeve, W., Duteil, O., Oschlies, A., Kähler, P., and Segschneider, J.: Methods to evaluate CaCO3 cycle modules in coupled global biogeochemical ocean models, Geosci. Model Dev., 7, 2393–2408, https://doi.org/10.5194/gmd-7-2393-2014, 2014. a
Köhler, P., Abrams, J. F., Völker, C., Hauck, J., and Wolf-Gladrow, D. A.:
Geoengineering impact of open ocean dissolution of olivine on atmospheric
CO2, surface ocean pH and marine biology, Environ. Res. Lett., 8,
014009, https://doi.org/10.1088/1748-9326/8/1/014009, 2013. a
Koldunov, N. V., Aizinger, V., Rakowsky, N., Scholz, P., Sidorenko, D., Danilov, S., and Jung, T.: Scalability and some optimization of the Finite-volumE Sea ice–Ocean Model, Version 2.0 (FESOM2), Geosci. Model Dev., 12, 3991–4012, https://doi.org/10.5194/gmd-12-3991-2019, 2019. a
Kriest, I. and Oschlies, A.: On the treatment of particulate organic matter sinking in large-scale models of marine biogeochemical cycles, Biogeosciences, 5, 55–72, https://doi.org/10.5194/bg-5-55-2008, 2008. a, b, c
Kulk, G., Platt, T., Dingle, J., Jackson, T., Jönsson, B. F., Bouman, H. A.,
Babin, M., Brewin, R. J. W., Doblin, M., Estrada, M., Figueiras, F. G.,
Furuya, K., González-Benítez, N., Gudfinnsson, H. G., Gudmundsson, K.,
Huang, B., Isada, T., Kovac, Z., Lutz, V. A., Marañón, E., Raman, M.,
Richardson, K., Rozema, P. D., Poll, W. H. v. d., Segura, V., Tilstone,
G. H., Uitz, J., Dongen-Vogels, V. v., Yoshikawa, T., and Sathyendranath, S.:
Primary Production, an Index of Climate Change in the Ocean: Satellite-Based
Estimates over Two Decades, Remote Sensing, 12, 826, https://doi.org/10.3390/rs12050826,
2020. a, b
Kwiatkowski, L., Torres, O., Bopp, L., Aumont, O., Chamberlain, M., Christian, J. R., Dunne, J. P., Gehlen, M., Ilyina, T., John, J. G., Lenton, A., Li, H., Lovenduski, N. S., Orr, J. C., Palmieri, J., Santana-Falcón, Y., Schwinger, J., Séférian, R., Stock, C. A., Tagliabue, A., Takano, Y., Tjiputra, J., Toyama, K., Tsujino, H., Watanabe, M., Yamamoto, A., Yool, A., and Ziehn, T.: Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections, Biogeosciences, 17, 3439–3470, https://doi.org/10.5194/bg-17-3439-2020, 2020. a
Kwon, E. Y., Primeau, F., and Sarmiento, J. L.: The impact of remineralization
depth on the air–sea carbon balance, Nat. Geosci., 2, 630–635,
https://doi.org/10.1038/ngeo612, 2009. a
Lacroix, F., Ilyina, T., and Hartmann, J.: Oceanic CO2 outgassing and biological production hotspots induced by pre-industrial river loads of nutrients and carbon in a global modeling approach, Biogeosciences, 17, 55–88, https://doi.org/10.5194/bg-17-55-2020, 2020. a
Lacroix, F., Ilyina, T., Mathis, M., Laruelle, G. G., and Regnier, P.:
Historical increases in land-derived nutrient inputs may alleviate effects of
a changing physical climate on the oceanic carbon cycle, Glob. Change
Biol., 27, 5491–5513, https://doi.org/10.1111/gcb.15822, 2021. a, b
Landschützer, P., Gruber, N., and Bakker, D. C. E.: Decadal variations and
trends of the global ocean carbon sink, Global Biogeochem. Cy., 30,
1396–1417, https://doi.org/10.1002/2015GB005359, 2016. a, b
Large, W. G. and Yeager, S. G.: Diurnal to decadal global forcing for ocean and
sea-ice models: the datasets and flux climatologies, Tech. Rep., CGD
division of the National Center for Atmospheric Research, NCAR technical note, NCAR/TN-460+STR, https://doi.org/10.5065/D6KK98Q6, 2004. a, b
Large, W. G. and Yeager, S. G.: The global climatology of an interannually
varying air-sea flux data set., Clim. Dynam., 33, 341–364,
https://doi.org/10.1007/s00382-008-0441-3, 2009. a
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
Lauderdale, J. M. and Cael, B. B.: Impact of remineralization profile shape on
the air-sea carbon balance, Geophys. Res. Lett., 48,
e2020GL091746, https://doi.org/10.1029/2020GL091746, 2021. a, b
Laufkötter, C., Vogt, M., Gruber, N., Aita-Noguchi, M., Aumont, O., Bopp, L., Buitenhuis, E., Doney, S. C., Dunne, J., Hashioka, T., Hauck, J., Hirata, T., John, J., Le Quéré, C., Lima, I. D., Nakano, H., Seferian, R., Totterdell, I., Vichi, M., and Völker, C.: Drivers and uncertainties of future global marine primary production in marine ecosystem models, Biogeosciences, 12, 6955–6984, https://doi.org/10.5194/bg-12-6955-2015, 2015. a, b
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 (data available at: https://glodap.info/index.php/mapped-data-product/, last access: 26 July 2023). a, b, c, d, e
Lee, K.: Global net community production estimated from the annual cycle of surface water total dissolved inorganic carbon, Limnol. Oceanogr., 46, 1287–1297, Wiley, https://doi.org/10.4319/lo.2001.46.6.1287, 2001. a
Lee, Z. and Marra, J. F.: The Use of VGPM to Estimate Oceanic Primary
Production: A “Tango” Difficult to Dance, Journal of Remote Sensing,
2022, 9851013, https://doi.org/10.34133/2022/9851013, 2022. a
Lenton, A., Tilbrook, B., Law, R. M., Bakker, D., Doney, S. C., Gruber, N., Ishii, M., Hoppema, M., Lovenduski, N. S., Matear, R. J., McNeil, B. I., Metzl, N., Mikaloff Fletcher, S. E., Monteiro, P. M. S., Rödenbeck, C., Sweeney, C., and Takahashi, T.: Sea–air CO2 fluxes in the Southern Ocean for the period 1990–2009, Biogeosciences, 10, 4037–4054, https://doi.org/10.5194/bg-10-4037-2013, 2013. a
Le Quéré, C., Takahashi, T., Buitenhuis, E. T., Rödenbeck, C., and
Sutherland, S. C.: Impact of climate change and variability on the global
oceanic sink of CO2, Global Biogeochem. Cy., 24,
GB4007, https://doi.org/10.1029/2009GB003599, 2010. a
Lévy, M., Franks, P. J. S., and Smith, K. S.: The role of submesoscale
currents in structuring marine ecosystems, Nat. Commun., 9, 47–58,
https://doi.org/10.1038/s41467-018-07059-3, 2018. a
Lewis, K. M. and Arrigo, K. R.: Ocean Color Algorithms for Estimating
Chlorophyll a, CDOM Absorption, and Particle Backscattering in the Arctic
Ocean, J. Geophys. Res.-Oceans, 125, e2019JC015706,
https://doi.org/10.1029/2019JC015706, 2020. a
Lewis, K. M., van Dijken, G. L., and Arrigo, K. R.: Changes in phytoplankton
concentration now drive increased Arctic Ocean primary production, Science,
369, 198–202, https://doi.org/10.1126/science.aay8380, 2020. 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, b
Long, M. C., Moore, J. K., Lindsay, K., Levy, M., Luo, J. Y., Krumhardt, K. M.,
Letscher, R. T., and Sylvester, Z. T.: Simulations with the Marine
Biogeochemistry Library (MARBL), J. Adv. Model. Earth
Sy., 13, e2021MS002647, https://doi.org/10.1029/2021MS002647, 2021a. a, b, c, d
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, 2021b. a
Losch, M., Strass, V., B. Cisewski, C. K., and Bellerby, R. G.: Ocean state
estimation from hydrography and velocity observations during EIFEX with a re-
gional biogeochemical ocean circulation model, J. Marine Syst., 129, 437–451,
https://doi.org/10.1016/j.jmarsys.2013.09.003, 2014. a
Maerz, J., Six, K. D., Stemmler, I., Ahmerkamp, S., and Ilyina, T.: Microstructure and composition of marine aggregates as co-determinants for vertical particulate organic carbon transfer in the global ocean, Biogeosciences, 17, 1765–1803, https://doi.org/10.5194/bg-17-1765-2020, 2020. a
Mahowald, N., Luo, C., del Corral, J., and Zender, C. S.: Interannual
variability in atmospheric mineral aerosols from a 22-year model simulation
and observational data, J. Geophys. Res.-Atmos., 108, 4352,
https://doi.org/10.1029/2002JD002821, 2003. a
Maier-Reimer, E., Mikolajewicz, U., and Winguth, A.: Future ocean uptake of
CO2: interaction between ocean circulation and biology, Clim. Dynam.,
12, 711–722, https://doi.org/10.1007/s003820050138, 1996. a
Maritorena, S., Siegel, D. A., and Peterson, A. R.: Optimization of a
semianalytical ocean color model for global-scale applications, Appl. Opt.,
41, 2705–2714, https://doi.org/10.1364/AO.41.002705, 2002. a
Marshall, J., Adcroft, A., Hill, C., Perelman, L., and Heisey, C.: A
finite-volume, incompressible Navier Stokes model for studies of the ocean on
parallel computers, J. Geophys. Res., 102, 5753–5766, 1997. a
Matsuoka, A., Bricaud, A., Benner, R., Para, J., Sempéré, R., Prieur, L., Bélanger, S., and Babin, M.: Tracing the transport of colored dissolved organic matter in water masses of the Southern Beaufort Sea: relationship with hydrographic characteristics, Biogeosciences, 9, 925–940, https://doi.org/10.5194/bg-9-925-2012, 2012. a
McWilliams, J. C.: Submesoscale currents in the ocean, P. Roy.
Soc. A-Math. Phy., 472, 20160117,
https://doi.org/10.1098/rspa.2016.0117, 2016. a
Metzl, N., Brunet, C., Jabaud-Jan, A., Poisson, A., and Schauer, B.: Summer and
winter air–sea CO2 fluxes in the Southern Ocean, Deep-Sea Res. Pt. I, 53, 1548–1563,
https://doi.org/10.1016/j.dsr.2006.07.006, 2006. a
Misumi, K., Tsumune, D., Yoshida, Y., Uchimoto, K., Nakamura, T., Nishioka, J.,
Mitsudera, H., Bryan, F. O., Lindsay, K., Moore, J. K., and Doney, S. C.:
Mechanisms controlling dissolved iron distribution in the North Pacific:
A model study, J. Geophys. Res., 116, G03005,
https://doi.org/10.1029/2010JG001541, 2011. a
Mitchell, B.: Predictive bio-optical relationships for polar oceans and
marginal ice zones, J. Marine Syst., 3, 91–105,
https://doi.org/10.1016/0924-7963(92)90032-4, 1992. a
Mongwe, N. P., Vichi, M., and Monteiro, P. M. S.: The seasonal cycle of pCO2 and CO2 fluxes in the Southern Ocean: diagnosing anomalies in CMIP5 Earth system models, Biogeosciences, 15, 2851–2872, https://doi.org/10.5194/bg-15-2851-2018, 2018. a
Moore, C. M., Mills, M. M., Arrigo, K. R., Berman-Frank, I., Bopp, L., Boyd,
P. W., Galbraith, E. D., Geider, R. J., Guieu, C., Jaccard, S. L., Jickells,
T. D., La Roche, J., Lenton, T. M., Mahowald, N. M., Marañón, E., Marinov,
I., Moore, J. K., Nakatsuka, T., Oschlies, A., Saito, M. A., Thingstad,
T. F., Tsuda, A., and Ulloa, O.: Processes and patterns of oceanic nutrient
limitation, Nat. Geosci., 6, 701–710, https://doi.org/10.1038/ngeo1765, 2013. a, b
Moriarty, R.: Global distributions of epipelagic macrozooplankton abundance and biomass – Gridded data product (NetCDF) – Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.777398, 2012. a
Moriarty, R. and O'Brien, T. D.: Distribution of mesozooplankton biomass in the global ocean, Earth Syst. Sci. Data, 5, 45–55, https://doi.org/10.5194/essd-5-45-2013, 2013. a, b, c
Moriarty, R., Buitenhuis, E. T., Le Quéré, C., and Gosselin, M.-P.: Distribution of known macrozooplankton abundance and biomass in the global ocean, Earth Syst. Sci. Data, 5, 241–257, https://doi.org/10.5194/essd-5-241-2013, 2013. a, b, c, d
Munhoven, G.: Mathematics of the total alkalinity–pH equation – pathway to robust and universal solution algorithms: the SolveSAPHE package v1.0.1, Geosci. Model Dev., 6, 1367–1388, https://doi.org/10.5194/gmd-6-1367-2013, 2013. a
Mustapha, S. B., Bélanger, S., and Larouche, P.: Evaluation of ocean color
algorithms in the southeastern Beaufort Sea, Canadian Arctic: New
parameterization using SeaWiFS, MODIS, and MERIS spectral bands, Can.
J. Remote Sens., 38, 535–556, https://doi.org/10.5589/m12-045, 2012. a
Myriokefalitakis, S., Ito, A., Kanakidou, M., Nenes, A., Krol, M. C., Mahowald, N. M., Scanza, R. A., Hamilton, D. S., Johnson, M. S., Meskhidze, N., Kok, J. F., Guieu, C., Baker, A. R., Jickells, T. D., Sarin, M. M., Bikkina, S., Shelley, R., Bowie, A., Perron, M. M. G., and Duce, R. A.: Reviews and syntheses: the GESAMP atmospheric iron deposition model intercomparison study, Biogeosciences, 15, 6659–6684, https://doi.org/10.5194/bg-15-6659-2018, 2018. a
Nakano, H., Tsujino, H., Hirabara, M., Yasuda, T., Motoi, T., Ishii, M., and
Yamanaka, G.: Uptake mechanism of anthropogenic CO2 in the Kuroshio Extension
region in an ocean general circulation model, J. Oceanogr., 67,
765–783, https://doi.org/10.1007/s10872-011-0075-7, 2011. a, b
Nevison, C. D., Keeling, R. F., Kahru, M., Manizza, M., Mitchell, B. G., and
Cassar, N.: Estimating net community production in the Southern Ocean based
on atmospheric potential oxygen and satellite ocean color data, Global
Biogeochem. Cycles, 26, GB1020, https://doi.org/10.1029/2011GB004040, 2012. a
Nielsdóttir, M. C., Moore, C. M., Sanders, R., Hinz, D. J., and Achterberg,
E. P.: Iron limitation of the postbloom phytoplankton communities in the
Iceland Basin, Global Biogeochem. Cy., 23, GB3001,
https://doi.org/10.1029/2008GB003410, 2009. a
Nissen, C., Timmermann, R., Hoppema, M., Gürses, Ö., and Hauck, J.:
Abruptly attenuated carbon sequestration with Weddell Sea dense waters by
2100, Nat. Commun., 13, 3402, https://doi.org/10.1038/s41467-022-30671-3,
2022. a, b
O'Brien, T. and Moriarty, R.: Global distributions of mesozooplankton abundance and biomass – Gridded data product (NetCDF) – Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.785501, 2012. a
Orr, J. C.: On ocean carbon-cycle model comparison, Tellus B, 51, 509–510, https://doi.org/10.3402/tellusb.v51i2.16334, 1999. 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, b, c, d
Oziel, L., Schourup-Kristensen, V., Wekerle, C., and Hauck, J.: The pan-Arctic
continental slope as an intensifying conveyer belt for nutrients in the
central Arctic Ocean (1985–2015), Global Biogeochem. Cy., 36,
e2021GB007268, https://doi.org/10.1029/2021GB007268, 2022. a
Pagnone, A., Völker, C., and Ye, Y.: Processes affecting dissolved iron
across the Subtropical North Atlantic: a model study, Ocean Dynam.,
69, 989–1007, https://doi.org/10.1007/s10236-019-01288-w, 2019. a, b, c
Parekh, P., Follows, M. J., and E., B.: Modeling the global ocean iron cycle,
Global Biogeochem. Cy., 18, GB1002, https://doi.org/10.1029/2003GB002061, 2004. a
Pradhan, H. K., Völker, C., Losa, S. N., Bracher, A., and Nerger, L.:
Assimilation of Global Total Chlorophyll OC‐CCI Data and Its Impact on
Individual Phytoplankton Fields, J. Geophys. Res.-Oceans,
124, 470–490, https://doi.org/10.1029/2018JC014329, 2019. a
Prowe, A. F., Pahlow, M., Dutkiewicz, S., Follows, M., and Oschlies, A.:
Top-down control of marine phytoplankton diversity in a global ecosystem
model, Prog. Oceanogr., 101, 1–13,
https://doi.org/10.1016/j.pocean.2011.11.016, 2012. a
Raven, J. A.: The iron and molybdenum use efficiencies of plant growth with
different energy, carbon and nitrogen sources, New Phytol., 109,
279–287, 1988. a
Redfield, A., Ketchum, B., and Richards, F.: The influence of organisms on the
composition of sea water, in: The Sea, edited by: Hill, M., vol. 2,
Interscience, 26–77, 1963. 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, b, c
Resplandy, L., Keeling, R. F., Roedenbeck, C., Stephens, B. B., Khatiwala, S.,
Rodgers, K. B., Long, M. C., Bopp, L., and Tans, P. P.: Revision of global
carbon fluxes based on a reassessment of oceanic and riverine carbon
transport, Nat. Geosci., 11, 504–509,
https://doi.org/10.1038/s41561-018-0151-3, 2018. a
Resplandy, L., Hogikyan, A., Bange, H. W., Bianchi, D., Weber, T. S., Cai,
W.-J., Doney, S. C., Fennel, K., Gehlen, M., Hauck, J., Lacroix, F.,
Landschützer, P., Quéré, C. L., Müller, J. D., Najjar, R. G.,
Roobaert, A., Berthet, S., Bopp, L., Chau, T. T.-T., Dai, M., Gruber, N.,
Ilyina, T., Kock, A., Manizza, M., Lachkar, Z., Laruelle, G. G., Liao, E.,
Lima, I. D., Nissen, C., Rödenbeck, C., Séférian, R., Schwinger,
J., Toyama, K., Tsujino, H., and Regnier, P.: A Synthesis of Global Coastal
Ocean Greenhouse Gas Fluxes, ESS Open Archive, https://doi.org/10.22541/essoar.168182303.39621839/v1,
2023. a
Rödenbeck, C., DeVries, T., Hauck, J., Le Quéré, C., and Keeling, R. F.: Data-based estimates of interannual sea–air CO2 flux variations 1957–2020 and their relation to environmental drivers, Biogeosciences, 19, 2627–2652, https://doi.org/10.5194/bg-19-2627-2022, 2022. a, b
Rohr, T., Richardson, A. J., Lenton, A., and Shadwick, E.: Recommendations for
the formulation of grazing in marine biogeochemical and ecosystem models,
Prog. Oceanogr., 208, 102878, https://doi.org/10.1016/j.pocean.2022.102878,
2022. a
Sabine, C. L., Feely, R. A., Gruber, N., Key, R. M., Lee, K., Bullister, J. L.,
Wanninkhof, R., Wong, C. S., Wallace, D. W. R., Tilbrook, B., Millero, F. J.,
Peng, T.-H., Kozyr, A., Ono, T., and Rios, A. F.: The Oceanic Sink for
Anthropogenic CO2, Science, 305, 367–371,
https://doi.org/10.1126/science.1097403, 2004. a, b
Sallée, J.-B., Pellichero, V., Akhoudas, C., Pauthenet, E., Vignes, L.,
Schmidtko, S., Garabato, A. N., Sutherland, P., and Kuusela, M.: Summertime
increases in upper-ocean stratification and mixed-layer depth, Nature, 591,
592–598, https://doi.org/10.1038/s41586-021-03303-x, 2021a. a, b, c, d
Sallée, J.-B., Pellichero, V., Akhoudas, C., Pauthenet, E., Vignes, L., Schmidtko, S., Naveira Garabato, A., Sutherland, P., and Kuusela, M.: Fifty-year changes of the world ocean's surface layer in response to climate change (Version v2), Zenodo [data set], https://doi.org/10.5281/zenodo.5776180, 2021. a
Sarmiento, J. L. and Gruber, N.: Ocean Biogeochemical Dynamics, Princeton
University Press, Princeton, NJ, ISBN 9780691017075, 2006. a
Sathyendranath, S., Brewin, R. J., Brockmann, C., Brotas, V., Calton, B.,
Chuprin, A., Cipollini, P., Couto, A. B., Dingle, J., Doerffer, R., Donlon,
C., Dowell, M., Farman, A., Grant, M., Groom, S., Horseman, A., Jackson, T.,
Krasemann, H., Lavender, S., Martinez-Vicente, V., Mazeran, C., Mélin, F.,
Moore, T. S., Müller, D., Regner, P., Roy, S., Steele, C. J., Steinmetz, F.,
Swinton, J., Taberner, M., Thompson, A., Valente, A., Zühlke, M., Brando,
V. E., Feng, H., Feldman, G., Franz, B. A., Frouin, R., Gould, R. W., Hooker,
S. B., Kahru, M., Kratzer, S., Mitchell, B. G., Muller-Karger, F. E., Sosik,
H. M., Voss, K. J., Werdell, J., and Platt, T.: An Ocean-Colour Time Series
for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change
Initiative (OC-CCI), Sensors, 19, 4285, https://doi.org/10.3390/s19194285, 2019 (data available at: https://www.oceancolour.org/thredds/catalog-cci.html, last access 26 July 2023). a, b, c, d, e
Scaife, A. A., Copsey, D., Gordon, C., Harris, C., Hinton, T., Keeley, S.,
O'Neill, A., Roberts, M., and Williams, K.: Improved Atlantic winter blocking
in a climate model, Geophys. Res. Lett., 38, L23703,
https://doi.org/10.1029/2011GL049573, 2011. a
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
Schlitzer, R.: Carbon export fluxes in the Southern Ocean: results from inverse
modeling and comparison with satellite-based estimates, Deep-Sea Res.
Pt. II, 49, 1623–1644,
https://doi.org/10.1016/S0967-0645(02)00004-8, 2002. a
Schlitzer, R.: Export Production in the Equatorial and North Pacific Derived
from Dissolved Oxygen, Nutrient and Carbon Data, J. Oceanogr., 60,
53–62, https://doi.org/10.1023/B:JOCE.0000038318.38916.e6, 2004. a
Schlitzer, R., Anderson, R. F., Dodas, E. M., Lohan, M. C., Geibert, W.,
Tagliabue, A., Bowie, A. R., Jeandel, C., Maldonado, M. T., Landing, W. M.,
Cockwell, D., Abadie, C., Abouchami, W., Achterberg, E. P., Agather, A. M.,
Aguilar-Islas, A. M., van Aken, H. M., Andersen, M., Archer, C., Auro, M.,
de Baar, H. J. W., Baars, O., Baker, A. R., Bakker, K., Basak, C., Baskaran,
M., Bates, N. R., Bauch, D., van Beek, P., Behrens, M. K., Black, E., Bluhm,
K., Bopp, L., Bouman, H. A., Bowman, K., Bown, J., Boyd, P. W., Boye, M.,
Boyle, E. A., Branellec, P., Bridgestock, L., Brissebrat, G., Browning,
T. J., Bruland, K. W., Brumsack, H. J., Brzezinski, M. A., Buck, C. S., Buck,
K. N., Buesseler, K. O., Bull, A., Butler, E., Cai, P., Mor, P. C., Cardinal,
D., Carlson, C., Carrasco, G., Casacuberta, N., Casciotti, K. L.,
Castrillejo, M., Chamizo, E., Chance, R., Charette, M. A., Chaves, J. E.,
Cheng, H., Chever, F., Christl, M., Church, T. M., Closset, I., Colman, A.,
Conway, T. M., Cossa, D., Croot, P. L., Cullen, J. T., Cutter, G. A.,
Daniels, C., Dehairs, F., Deng, F., Dieu, H. T., Duggan, B., Dulaquais, G.,
Dumousseaud, C., Echegoyen-Sanz, Y., Edwards, R. L., Ellwood, M., Fahrbach,
E., Fitzsimmons, J. N., Russell Flegal, A., Fleisher, M. Q., van de Flierdt,
T., Frank, M., Friedrich, J., Fripiat, F., Fröllje, H., Galer, S. J., Gamo,
T., Ganeshram, R. S., Garcia-Orellana, J., Garcia-Solsona, E., Gault-Ringold,
M., George, E., Gerringa, L. J., Gilbert, M., Godoy, J. M., Goldstein, S. L.,
Gonzalez, S. R., Grissom, K., Hammerschmidt, C., Hartman, A., Hassler, C. S.,
Hathorne, E. C., Hatta, M., Hawco, N., Hayes, C. T., Heimbürger, L. E.,
Helgoe, J., Heller, M., Henderson, G. M., Henderson, P. B., van Heuven, S. M.
A. C., Ho, P., Horner, T. J., Hsieh, Y. T., Huang, K. F., Humphreys, M. P.,
Isshiki, K., Jacquot, J. E., Janssen, D. J., Jenkins, W. J., John, S., Jones,
E. M., Jones, J. L., Kadko, D. C., Kayser, R., Kenna, T. C., Khondoker, R.,
Kim, T., Kipp, L., Klar, J. K., Klunder, M. B., Kretschmer, S., Kumamoto, Y.,
Laan, P., Labatut, M., Lacan, F., Lam, P. J., Lambelet, M., Lamborg, C. H.,
Le Moigne, F. A., Le Roy, E., Lechtenfeld, O. J., Lee, J. M., Lherminier, P.,
Little, S., López-Lora, M., Lu, Y., Masque, P., Mawji, E., Mcclain, C. R.,
Measures, C., Mehic, S., Barraqueta, J. L. M., van der Merwe, P., Middag, R.,
Mieruch, S., Milne, A., Minami, T., Moffett, J. W., Moncoiffe, G., Moore,
W. S., Morris, P. J., Morton, P. L., Nakaguchi, Y., Nakayama, N.,
Niedermiller, J., Nishioka, J., Nishiuchi, A., Noble, A. E., Obata, H., Ober,
S., Ohnemus, D. C., van Ooijen, J., O'Sullivan, J., Owens, S., Pahnke, K.,
Paul, M., Pavia, F., Pena, L. D., Peters, B. D., Planchon, F., Planquette,
H. F., Pradoux, C., Puigcorbé, V., Quay, P., Queroue, F., Radic, A.,
Rauschenberg, S., Rehkämper, M., Rember, R., Remenyi, T. A., Resing, J. A.,
Rickli, J., Rigaud, S., Rijkenberg, M. J. A., Rintoul, S., Robinson, L. F.,
Roca-Martí, M., Rodellas, V., Roeske, T., Rolison, J. M., Rosenberg, M.,
Roshan, S., Rutgers van der Loeff, M. M., Ryabenko, E., Saito, M. A., Salt,
L. A., Sanial, V., Sarthou, G., Schallenberg, C., Schauer, U., Scher, H.,
Schlosser, C., Schnetger, B., Scott, P., Sedwick, P. N., Semiletov, I.,
Shelley, R., Sherrell, R. M., Shiller, A. M., Sigman, D. M., Singh, S. K.,
Slagter, H. A., Slater, E., Smethie, W. M., Snaith, H., Sohrin, Y., Sohst,
B., Sonke, J. E., Speich, S., Steinfeldt, R., Stewart, G., Stichel, T.,
Stirling, C. H., Stutsman, J., Swarr, G. J., Swift, J. H., Thomas, A.,
Thorne, K., Till, C. P., Till, R., Townsend, A. T., Townsend, E., Tuerena,
R., Twining, B. S., Vance, D., Velazquez, S., Venchiarutti, C.,
Villa-Alfageme, M., Vivancos, S. M., Voelker, A. H., Wake, B. D., Warner,
M. J., Watson, R., van Weerlee, E., Alexandra Weigand, M., Weinstein, Y.,
Weiss, D. J., Wisotzki, A., Woodward, E. M. S., Wu, J., Wu, Y., Wuttig, K.,
Wyatt, N. J., Xiang, Y., Xie, R. C., Xue, Z., Yoshikawa, H., Zhang, J.,
Zhang, P., Zhao, Y., Zheng, L., Zheng, X. Y., Zieringer, M., Zimmer, L. A.,
Ziveri, P., Zunino, P., and Zurbrick, C.: The GEOTRACES Intermediate
Data Product 2017, Chemical Geology, 493, 210–223,
https://doi.org/10.1016/j.chemgeo.2018.05.040, 2018. a
Schmidtko, S., Stramma, L., and Visbeck, M.: Decline in global oceanic oxygen
content during the past five decades, Nature, 542, 335–339,
https://doi.org/10.1038/nature21399, 2017. a
Schneider, B., Bopp, L., Gehlen, M., Segschneider, J., Frölicher, T. L., Cadule, P., Friedlingstein, P., Doney, S. C., Behrenfeld, M. J., and Joos, F.: Climate-induced interannual variability of marine primary and export production in three global coupled climate carbon cycle models, Biogeosciences, 5, 597–614, https://doi.org/10.5194/bg-5-597-2008, 2008. a, b, c
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, c, d, e, f
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, b
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, c, d, e, f, g, h, i, j, k, l, m, n, o, p
Schourup-Kristensen, V., Wekerle, C., Wolf-Gladrow, D. A., and Völker, C.:
Arctic Ocean biogeochemistry in the high resolution FESOM 1.4-REcoM2 model,
Prog. Oceanogr., 168, 65–81, https://doi.org/10.1016/j.pocean.2018.09.006,
2018. a
Schwinger, J., Goris, N., Tjiputra, J. F., Kriest, I., Bentsen, M., Bethke, I., Ilicak, M., Assmann, K. M., and Heinze, C.: Evaluation of NorESM-OC (versions 1 and 1.2), the ocean carbon-cycle stand-alone configuration of the Norwegian Earth System Model (NorESM1), Geosci. Model Dev., 9, 2589–2622, https://doi.org/10.5194/gmd-9-2589-2016, 2016. a, b
Séférian, R., Gehlen, M., Bopp, L., Resplandy, L., Orr, J. C., Marti, O., Dunne, J. P., Christian, J. R., Doney, S. C., Ilyina, T., Lindsay, K., Halloran, P. R., Heinze, C., Segschneider, J., Tjiputra, J., Aumont, O., and Romanou, A.: Inconsistent strategies to spin up models in CMIP5: implications for ocean biogeochemical model performance assessment, Geosci. Model Dev., 9, 1827–1851, https://doi.org/10.5194/gmd-9-1827-2016, 2016. a
Séférian, R., Berthet, S., Yool, A., Palmiéri, J., Bopp, L., Tagliabue, A.,
Kwiatkowski, L., Aumont, O., Christian, J., Dunne, J., Gehlen, M., Ilyina,
T., John, J. G., Li, H., Long, M. C., Luo, J. Y., Nakano, H., Romanou, A.,
Schwinger, J., Stock, C., Santana-Falcón, Y., Takano, Y., Tjiputra, J.,
Tsujino, H., Watanabe, M., Wu, T., Wu, F., and Yamamoto, A.: Tracking
Improvement in Simulated Marine Biogeochemistry Between CMIP5 and
CMIP6, Current Climate Change Reports, 6, 95–119,
https://doi.org/10.1007/s40641-020-00160-0, 2020. a, b, c, d
Seifert, M., Nissen, C., Rost, B., and Hauck, J.: Cascading effects augment
the direct impact of CO2 on phytoplankton growth in a biogeochemical model,
Elementa: Science of the Anthropocene, 10, 00104, https://doi.org/10.1525/elementa.2021.00104,
2022. a
Sidorenko, D., Wang, Q., Danilov, S., and Schröter, J.: FESOM under
coordinated ocean-ice reference experiment forcing, Ocean Dynam., 61,
881–890, https://doi.org/10.1007/s10236-011-0406-7, 2011. a
Sidorenko, D., Rackow, T., Jung, T., Semmler, T., Barbi, D., Danilov, S.,
Dethloff, K., Dorn, W., Fieg, K., Gößling, H. F., Handorf, D., Harig, S., Hiller, W., Juricke, S., Losch, M., Schröter, J., Sein, D. V., and Wang, Q.: Towards
multi-resolution global climate modeling with ECHAM6–FESOM. Part I: model
formulation and mean climate, Clim. Dynam., 44, 757–780,
https://doi.org/10.1007/s00382-014-2290-6, 2015. a
Siegel, D. A., Buesseler, K. O., Doney, S. C., Sailley, S. F., Behrenfeld,
M. J., and Boyd, P. W.: Global assessment of ocean carbon export by combining
satellite observations and food-web models, Global Biogeochem. Cy., 28,
181–196, https://doi.org/10.1002/2013GB004743, 2014. a
Steele, M., Morley, R., and Ermold, W.: PHC: a global ocean hydrography with a
high-quality Arctic Ocean, J. Climate, 14, 2079–2087,
https://doi.org/10.1175/1520-0442(2001)014<2079:PAGOHW>2.0.CO;2, 2001. a, b, c, d
Stewart, K., Kim, W., Urakawa, S., Hogg, A., Yeager, S., Tsujino, H., Nakano,
H., Kiss, A., and Danabasoglu, G.: JRA55-do-based repeat year forcing
datasets for driving ocean–sea-ice models, Ocean Model., 147, 101557,
https://doi.org/10.1016/j.ocemod.2019.101557, 2020. a
Storkey, D., Blaker, A. T., Mathiot, P., Megann, A., Aksenov, Y., Blockley, E. W., Calvert, D., Graham, T., Hewitt, H. T., Hyder, P., Kuhlbrodt, T., Rae, J. G. L., and Sinha, B.: UK Global Ocean GO6 and GO7: a traceable hierarchy of model resolutions, Geosci. Model Dev., 11, 3187–3213, https://doi.org/10.5194/gmd-11-3187-2018, 2018. a
Sundquist, E. T.: Geological Perspectives on Carbon Dioxide and the Carbon
Cycle, American Geophysical Union (AGU), 55–59,
https://doi.org/10.1029/GM032p0005, 1985. 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
Tagliabue, A. and Völker, C.: Towards accounting for dissolved iron speciation in global ocean models, Biogeosciences, 8, 3025–3039, https://doi.org/10.5194/bg-8-3025-2011, 2011. a
Tagliabue, A., Mtshali, T., Aumont, O., Bowie, A. R., Klunder, M. B., Roychoudhury, A. N., and Swart, S.: A global compilation of dissolved iron measurements: focus on distributions and processes in the Southern Ocean, Biogeosciences, 9, 2333–2349, https://doi.org/10.5194/bg-9-2333-2012, 2012. a
Tagliabue, A., Aumont, O., DeAth, R., Dunne, J. P., Dutkiewicz, S., Galbraith,
E., Misumi, K., Moore, J. K., Ridgwell, A. J., Sherman, E., Stock, C. A.,
Vichi, M., Völker, C., and Yool, A.: How well do global ocean
biogeochemistry models simulate dissolved iron distributions?, Global
Biogeochem. Cy., 30, 149–174, https://doi.org/10.1002/2015GB005289, 2016. a, b, c
Tagliabue, A., Kwiatkowski, L., Bopp, L., Butenschön, M., Cheung, W.,
Lengaigne, M., and Vialard, J.: Persistent Uncertainties in Ocean Net
Primary Production Climate Change Projections at Regional
Scales Raise Challenges for Assessing Impacts on Ecosystem
Services, Frontiers in Climate, 3, 738224,
https://doi.org/10.3389/fclim.2021.738224, 2021. a
Takahashi, T., Sutherland, S. C., Wanninkhof, R., Sweeney, C., Feely, R. A.,
Chipman, D. W., Hales, B., Friederich, G., Chavez, F., Sabine, C., Watson,
A., Bakker, D. C., Schuster, U., Metzl, N., Yoshikawa-Inoue, H., Ishii, M.,
Midorikawa, T., Nojiri, Y., Körtzinger, A., Steinhoff, T., Hoppema, M.,
Olafsson, J., Arnarson, T. S., Tilbrook, B., Johannessen, T., Olsen, A.,
Bellerby, R., Wong, C., Delille, B., Bates, N., and de Baar, H. J.:
Climatological mean and decadal change in surface ocean pCO2, and net
sea–air CO2 flux over the global oceans, Deep-Sea Res. Pt. II, 56, 554–577,
https://doi.org/10.1016/j.dsr2.2008.12.009, 2009. a
Taylor, M. H., Losch, M., and Bracher, A.: On the drivers of phytoplankton
blooms in the Antarctic marginal ice zone: A modeling approach, J.
Geophys. Res., 118, 63–75, https://doi.org/10.1029/2012JC008418, 2013. a
Terhaar, J., Frölicher, T. L., and Joos, F.: Southern Ocean anthropogenic
carbon sink constrained by sea surface salinity, Science Advances, 7,
eabd5964, https://doi.org/10.1126/sciadv.abd5964, 2021. a
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
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
Timmermann, R., Danilov, S., Schröter, J., Böning, C., Sidorenko, D., and
Rollenhagen, K.: Ocean circulation and sea ice distribution in a finite
element global sea ice–ocean model, Ocean Model., 27, 114–129,
https://doi.org/10.1016/j.ocemod.2008.10.009, 2009. a
Timmermann, R., Wang, Q., and Hellmer, H.: Ice-shelf basal melting in a global
finite-element sea-ice/ice-shelf/ocean model, Ann. Glaciol., 53,
303–314, https://doi.org/10.3189/2012AoG60A156, 2012. a
Tjiputra, J. F., Schwinger, J., Bentsen, M., Morée, A. L., Gao, S., Bethke, I., Heinze, C., Goris, N., Gupta, A., He, Y.-C., Olivié, D., Seland, Ø., and Schulz, M.: Ocean biogeochemistry in the Norwegian Earth System Model version 2 (NorESM2), Geosci. Model Dev., 13, 2393–2431, https://doi.org/10.5194/gmd-13-2393-2020, 2020. a
Tohjima, Y., Mukai, H., Machida, T., Hoshina, Y., and Nakaoka, S.-I.: Global carbon budgets estimated from atmospheric
Tréguer, P. J., Sutton, J. N., Brzezinski, M., Charette, M. A., Devries, T., Dutkiewicz, S., Ehlert, C., Hawkings, J., Leynaert, A., Liu, S. M., Llopis Monferrer, N., López-Acosta, M., Maldonado, M., Rahman, S., Ran, L., and Rouxel, O.: Reviews and syntheses: The biogeochemical cycle of silicon in the modern ocean, Biogeosciences, 18, 1269–1289, https://doi.org/10.5194/bg-18-1269-2021, 2021. a, b, c
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, 2018. a, b, c
Urakawa, L. S., Tsujino, H., Nakano, H., Sakamoto, K., Yamanaka, G., and
Toyoda, T.: The sensitivity of a depth-coordinate model to diapycnal mixing
induced by practical implementations of the isopycnal tracer diffusion
scheme, Ocean Model., 154, 101693,
https://doi.org/10.1016/j.ocemod.2020.101693, 2020. a, b
Vaittinada Ayar, P., Bopp, L., Christian, J. R., Ilyina, T., Krasting, J. P., Séférian, R., Tsujino, H., Watanabe, M., Yool, A., and Tjiputra, J.: Contrasting projections of the ENSO-driven CO2 flux variability in the equatorial Pacific under high-warming scenario, Earth Syst. Dynam., 13, 1097–1118, https://doi.org/10.5194/esd-13-1097-2022, 2022. a
Völker, C. and Köhler, P.: Responses of ocean circulation and carbon cycle to
changes in the position of the Southern Hemisphere westerlies at Last Glacial
Maximum, Paleoceanography, 28, 726–739,
https://doi.org/10.1002/2013PA002556, 2013. a
Völker, C. and Tagliabue, A.: Modeling organic iron-binding ligands in a
three-dimensional biogeochemical ocean model, Mar. Chem., 173, 67–77,
https://doi.org/10.1016/j.marchem.2014.11.008, 2015. a, b, c
Waite, A. M., Thompson, P. A., and Harrison, P. J.: Does energy control the
sinking rates of marine diatoms?, Limnol. Oceanogr., 37, 468–477,
https://doi.org/10.4319/lo.1992.37.3.0468, 1992. a
Wang, Q., Danilov, S., Sidorenko, D., Timmermann, R., Wekerle, C., Wang, X., Jung, T., and Schröter, J.: The Finite Element Sea Ice-Ocean Model (FESOM) v.1.4: formulation of an ocean general circulation model, Geosci. Model Dev., 7, 663–693, https://doi.org/10.5194/gmd-7-663-2014, 2014. a
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
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
Wekerle, C., Wang, Q., Danilov, S., Schourup-Kristensen, V., von Appen, W.-J.,
and Jung, T.: Atlantic Water in the Nordic Seas: locally eddy-permitting
ocean simulation in a global setup, J. Geophys. Res.-Oceans, 122,
914–940,
https://doi.org/10.1002/2016JC012121, 2017. a
Wolf-Gladrow, D. A., Zeebe, R. E., Klaas, C., Körtzinger, A., and Dickson,
A. G.: Total alkalinity: The explicit conservative expression and its
application to biogeochemical processes, Mar. Chem., 106, 287–300,
https://doi.org/10.1016/j.marchem.2007.01.006, 2007.
a
Wright, R. M., Le Quéré, C., Buitenhuis, E., Pitois, S., and Gibbons, M. J.: Role of jellyfish in the plankton ecosystem revealed using a global ocean biogeochemical model, Biogeosciences, 18, 1291–1320, https://doi.org/10.5194/bg-18-1291-2021, 2021. a, b
Yamanaka, Y. and Tajika, E.: The role of the vertical fluxes of particulate
organic matter and calcite in the oceanic carbon cycle : Studies using an
ocean biogeochemical general circulation model, Global Biogeochem. Cy.,
10, 361–382, https://doi.org/10.1029/96GB00634, 1996. a
Ye, Y. and Völker, C.: On the Role of Dust-Deposited Lithogenic
Particles for Iron Cycling in the Tropical and Subtropical Atlantic, Global
Biogeochem. Cy., 31, 1543–1558, https://doi.org/10.1002/2017GB005663, 2017. a, b
Ye, Y., Völker, C., and Gledhill, M.: Exploring the Iron-Binding Potential of
the Ocean Using a Combined pH and DOC Parameterization, Global Biogeochem.
Cy., 34, e2019GB006425, https://doi.org/10.1029/2019GB006425, 2020. a, b, c
Zeng, J., Nojiri, Y., Landschützer, P., Telszewski, M., and Nakaoka, S.: A
Global Surface Ocean fCO2 Climatology Based on a Feed-Forward Neural Network,
J. Atmos. Ocean. Tech., 31, 1838–1849,
https://doi.org/10.1175/JTECH-D-13-00137.1, 2014. a, b
Zhang, R. and Vallis, G. K.: The Role of Bottom Vortex Stretching on the Path
of the North Atlantic Western Boundary Current and on the Northern
Recirculation Gyre, J. Phys. Oceanogr., 37, 2053–2080,
https://doi.org/10.1175/JPO3102.1, 2007. a
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.
This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1....
