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
Related authors
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-519, https://doi.org/10.5194/essd-2024-519, 2024
Preprint under review for ESSD
Short summary
Short summary
The Global Carbon Budget 2024 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
Short summary
Short summary
In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
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
Short summary
Short summary
Recently, it was found that cold-to-warm changes in Antarctic shelf sea areas are possible and lead to higher ice shelf melt rates. In modelling experiments, we found that if the highest density in front of the ice shelf becomes lower than the density of the warmer water off-shelf at the deepest access to the shelf, the off-shelf water will flow onto the shelf. Our results also indicate that this change will offer some, although not much, resistance to reversal and constitutes a tipping point.
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
Revised manuscript under review for GMD
Short summary
Short summary
Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change the storage of carbon in sediments slows down carbon cycling and influences feedbacks in the atmosphere-ocean-sediment system. Here we coupled a sediment model to an ocean biogeochemistry model and found a shift of carbon storage from the atmosphere to the ocean-sediment system.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, https://doi.org/10.5194/essd-14-1917-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-519, https://doi.org/10.5194/essd-2024-519, 2024
Preprint under review for ESSD
Short summary
Short summary
The Global Carbon Budget 2024 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Anisbel Leon-Marcos, Moritz Zeising, Manuela van Pinxteren, Sebastian Zeppenfeld, Astrid Bracher, Elena Barbaro, Anja Engel, Matteo Feltracco, Ina Tegen, and Bernd Heinold
EGUsphere, https://doi.org/10.5194/egusphere-2024-2917, https://doi.org/10.5194/egusphere-2024-2917, 2024
Short summary
Short summary
This study represents the Primary marine organic aerosols (PMOA) emission, focusing on their sea-atmosphere transfer. Using the FESOM2.1-REcoM3 model, concentrations of key organic biomolecules were estimated and integrated into the ECHAM6.3–HAM2.3 aerosol-climate model. Results highlight the influence of marine biological activity and surface winds on PMOA emissions, with reasonably good agreement with observations improving aerosol representation in the Southern Oceans.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
Short summary
Short summary
In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Ja-Yeon Moon, Jan Streffing, Sun-Seon Lee, Tido Semmler, Miguel Andrés-Martínez, Jiao Chen, Eun-Byeoul Cho, Jung-Eun Chu, Christian Franzke, Jan P. Gärtner, Rohit Ghosh, Jan Hegewald, Songyee Hong, Nikolay Koldunov, June-Yi Lee, Zihao Lin, Chao Liu, Svetlana Loza, Wonsun Park, Woncheol Roh, Dmitry V. Sein, Sahil Sharma, Dmitry Sidorenko, Jun-Hyeok Son, Malte F. Stuecker, Qiang Wang, Gyuseok Yi, Martina Zapponini, Thomas Jung, and Axel Timmermann
EGUsphere, https://doi.org/10.5194/egusphere-2024-2491, https://doi.org/10.5194/egusphere-2024-2491, 2024
Short summary
Short summary
Based on a series of storm-resolving greenhouse warming simulations conducted with the AWI-CM3 model at 9 km global atmosphere, 4–25 km ocean resolution, we present new projections of regional climate change, modes of climate variability and extreme events. The 10-year-long high resolution simulations for the 2000s, 2030s, 2060s, 2090s were initialized from a coarser resolution transient run (31 km atmosphere) which follows the SSP5-8.5 greenhouse gas emission scenario from 1950–2100 CE.
Frauke Bunsen, Judith Hauck, Lars Nerger, and Sinhué Torres-Valdés
EGUsphere, https://doi.org/10.5194/egusphere-2024-1750, https://doi.org/10.5194/egusphere-2024-1750, 2024
Short summary
Short summary
Computer models are used to derive estimates of the ocean CO2 uptake. Because such idealized models don't always correspond precisely to the real-world, we combine real-world observations of ocean temperature and salinity with a model, and study the effect on the modeled air-sea CO2 flux (2010–2020). The corrections of temperature and salinity have their largest effect on regional CO2 fluxes in the Southern Ocean during winter, but a comparatively small effect on the global ocean CO2 uptake.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Alexei Koldunov, Tobias Kölling, Josh Kousal, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Domokos Sármány, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
EGUsphere, https://doi.org/10.5194/egusphere-2024-913, https://doi.org/10.5194/egusphere-2024-913, 2024
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale"), and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
Short summary
Short summary
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.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
Short summary
Short summary
In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
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., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
Short summary
Short summary
Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Sebastian Zeppenfeld, Manuela van Pinxteren, Markus Hartmann, Moritz Zeising, Astrid Bracher, and Hartmut Herrmann
Atmos. Chem. Phys., 23, 15561–15587, https://doi.org/10.5194/acp-23-15561-2023, https://doi.org/10.5194/acp-23-15561-2023, 2023
Short summary
Short summary
Marine carbohydrates are produced in the surface of the ocean, enter the atmophere as part of sea spray aerosol particles, and potentially contribute to the formation of fog and clouds. Here, we present the results of a sea–air transfer study of marine carbohydrates conducted in the high Arctic. Besides a chemo-selective transfer, we observed a quick atmospheric aging of carbohydrates, possibly as a result of both biotic and abiotic processes.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
Short summary
Short summary
Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
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
Short summary
Short summary
Recently, it was found that cold-to-warm changes in Antarctic shelf sea areas are possible and lead to higher ice shelf melt rates. In modelling experiments, we found that if the highest density in front of the ice shelf becomes lower than the density of the warmer water off-shelf at the deepest access to the shelf, the off-shelf water will flow onto the shelf. Our results also indicate that this change will offer some, although not much, resistance to reversal and constitutes a tipping point.
Christoph Heinze, Thorsten Blenckner, Peter Brown, Friederike Fröb, Anne Morée, Adrian L. New, Cara Nissen, Stefanie Rynders, Isabel Seguro, Yevgeny Aksenov, Yuri Artioli, Timothée Bourgeois, Friedrich Burger, Jonathan Buzan, B. B. Cael, Veli Çağlar Yumruktepe, Melissa Chierici, Christopher Danek, Ulf Dieckmann, Agneta Fransson, Thomas Frölicher, Giovanni Galli, Marion Gehlen, Aridane G. González, Melchor Gonzalez-Davila, Nicolas Gruber, Örjan Gustafsson, Judith Hauck, Mikko Heino, Stephanie Henson, Jenny Hieronymus, I. Emma Huertas, Fatma Jebri, Aurich Jeltsch-Thömmes, Fortunat Joos, Jaideep Joshi, Stephen Kelly, Nandini Menon, Precious Mongwe, Laurent Oziel, Sólveig Ólafsdottir, Julien Palmieri, Fiz F. Pérez, Rajamohanan Pillai Ranith, Juliano Ramanantsoa, Tilla Roy, Dagmara Rusiecka, J. Magdalena Santana Casiano, Yeray Santana-Falcón, Jörg Schwinger, Roland Séférian, Miriam Seifert, Anna Shchiptsova, Bablu Sinha, Christopher Somes, Reiner Steinfeldt, Dandan Tao, Jerry Tjiputra, Adam Ulfsbo, Christoph Völker, Tsuyoshi Wakamatsu, and Ying Ye
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-182, https://doi.org/10.5194/bg-2023-182, 2023
Preprint under review for BG
Short summary
Short summary
For assessing the consequences of human-induced climate change for the marine realm, it is necessary to not only look at gradual changes but also at abrupt changes of environmental conditions. We summarise abrupt changes in ocean warming, acidification, and oxygen concentration as the key environmental factors for ecosystems. Taking these abrupt changes into account requires greenhouse gas emissions to be reduced to a larger extent than previously thought to limit respective damage.
Claudia Hinrichs, Peter Köhler, Christoph Völker, and Judith Hauck
Biogeosciences, 20, 3717–3735, https://doi.org/10.5194/bg-20-3717-2023, https://doi.org/10.5194/bg-20-3717-2023, 2023
Short summary
Short summary
This study evaluated the alkalinity distribution in 14 climate models and found that most models underestimate alkalinity at the surface and overestimate it in the deeper ocean. It highlights the need for better understanding and quantification of processes driving alkalinity distribution and calcium carbonate dissolution and the importance of accounting for biases in model results when evaluating potential ocean alkalinity enhancement experiments.
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
Revised manuscript under review for GMD
Short summary
Short summary
Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change the storage of carbon in sediments slows down carbon cycling and influences feedbacks in the atmosphere-ocean-sediment system. Here we coupled a sediment model to an ocean biogeochemistry model and found a shift of carbon storage from the atmosphere to the ocean-sediment system.
Nicolas Stoll, Matthias Wietz, Stephan Juricke, Franziska Pausch, Corina Peter, Miriam Seifert, Jana C. Massing, Moritz Zeising, Rebecca A. McPherson, Melissa Käß, and Björn Suckow
Polarforschung, 91, 31–43, https://doi.org/10.5194/polf-91-31-2023, https://doi.org/10.5194/polf-91-31-2023, 2023
Short summary
Short summary
Global crises, such as climate change and the COVID-19 pandemic, show the importance of communicating science to the public. We introduce the YouTube channel "Wissenschaft fürs Wohnzimmer", which livestreams presentations on climate-related topics weekly and is accessible to all. The project encourages interaction between scientists and the public and has been running successfully for over 2 years. We present the concept, what we have learnt, and the challenges after 100 streamed episodes.
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
Short summary
Short summary
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
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
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
Short summary
Short summary
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
Short summary
Short summary
The ocean is an important part of the global carbon cycle, taking up about a quarter of the anthropogenic CO2 emitted by burning of fossil fuels and thus slowing down climate change. However, the CO2 uptake by the ocean is, in turn, affected by variability and trends in climate. Here we use carbon measurements in the surface ocean to quantify the response of the oceanic CO2 exchange to environmental conditions and discuss possible mechanisms underlying this response.
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
Short summary
Short summary
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
Short summary
Short summary
The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Philippe Massicotte, Rémi Amiraux, Marie-Pier Amyot, Philippe Archambault, Mathieu Ardyna, Laurent Arnaud, Lise Artigue, Cyril Aubry, Pierre Ayotte, Guislain Bécu, Simon Bélanger, Ronald Benner, Henry C. Bittig, Annick Bricaud, Éric Brossier, Flavienne Bruyant, Laurent Chauvaud, Debra Christiansen-Stowe, Hervé Claustre, Véronique Cornet-Barthaux, Pierre Coupel, Christine Cox, Aurelie Delaforge, Thibaud Dezutter, Céline Dimier, Florent Domine, Francis Dufour, Christiane Dufresne, Dany Dumont, Jens Ehn, Brent Else, Joannie Ferland, Marie-Hélène Forget, Louis Fortier, Martí Galí, Virginie Galindo, Morgane Gallinari, Nicole Garcia, Catherine Gérikas Ribeiro, Margaux Gourdal, Priscilla Gourvil, Clemence Goyens, Pierre-Luc Grondin, Pascal Guillot, Caroline Guilmette, Marie-Noëlle Houssais, Fabien Joux, Léo Lacour, Thomas Lacour, Augustin Lafond, José Lagunas, Catherine Lalande, Julien Laliberté, Simon Lambert-Girard, Jade Larivière, Johann Lavaud, Anita LeBaron, Karine Leblanc, Florence Le Gall, Justine Legras, Mélanie Lemire, Maurice Levasseur, Edouard Leymarie, Aude Leynaert, Adriana Lopes dos Santos, Antonio Lourenço, David Mah, Claudie Marec, Dominique Marie, Nicolas Martin, Constance Marty, Sabine Marty, Guillaume Massé, Atsushi Matsuoka, Lisa Matthes, Brivaela Moriceau, Pierre-Emmanuel Muller, Christopher-John Mundy, Griet Neukermans, Laurent Oziel, Christos Panagiotopoulos, Jean-Jacques Pangrazi, Ghislain Picard, Marc Picheral, France Pinczon du Sel, Nicole Pogorzelec, Ian Probert, Bernard Quéguiner, Patrick Raimbault, Joséphine Ras, Eric Rehm, Erin Reimer, Jean-François Rontani, Søren Rysgaard, Blanche Saint-Béat, Makoto Sampei, Julie Sansoulet, Catherine Schmechtig, Sabine Schmidt, Richard Sempéré, Caroline Sévigny, Yuan Shen, Margot Tragin, Jean-Éric Tremblay, Daniel Vaulot, Gauthier Verin, Frédéric Vivier, Anda Vladoiu, Jeremy Whitehead, and Marcel Babin
Earth Syst. Sci. Data, 12, 151–176, https://doi.org/10.5194/essd-12-151-2020, https://doi.org/10.5194/essd-12-151-2020, 2020
Short summary
Short summary
The Green Edge initiative was developed to understand the processes controlling the primary productivity and the fate of organic matter produced during the Arctic spring bloom (PSB). In this article, we present an overview of an extensive and comprehensive dataset acquired during two expeditions conducted in 2015 and 2016 on landfast ice southeast of Qikiqtarjuaq Island in Baffin Bay.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCO v4-Hg: the role of surfactants and waves
BOATSv2: New ecological and economic features improve simulations of High Seas catch and effort
Lambda-PFLOTRAN 1.0: Workflow for Incorporating Organic Matter Chemistry Informed by Ultra High Resolution Mass Spectrometry into Biogeochemical Modeling
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
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
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations
A novel Eulerian model based on central moments to simulate age and reactivity continua interacting with mixing processes
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
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
Short summary
Short summary
A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
Short summary
Short summary
This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
Short summary
Short summary
We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
Short summary
Short summary
Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
Short summary
Short summary
We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
Short summary
Short summary
Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
Short summary
Short summary
The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
Short summary
Short summary
Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
Short summary
Short summary
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
Short summary
Short summary
A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
Short summary
Short summary
A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
Short summary
Short summary
We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
Short summary
Short summary
This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
Short summary
Short summary
Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
Short summary
Short summary
We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
Short summary
Short summary
Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie Fisher, and Harrie-Jan Hendricks Franssen
EGUsphere, https://doi.org/10.5194/egusphere-2024-978, https://doi.org/10.5194/egusphere-2024-978, 2024
Short summary
Short summary
Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land Surface Models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes and variability of carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research on these processes.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-81, https://doi.org/10.5194/gmd-2024-81, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
The estimation of Hg0 fluxes is of great uncertainty due to neglecting wave breaking and sea surfactant. Integrating these factors into MITgcm significantly rise Hg0 transfer velocity. The updated model shows increased fluxes in high wind and wave regions and vice versa, enhancing the spatial heterogeneity. It shows a stronger correlation between Hg0 transfer velocity and wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-26, https://doi.org/10.5194/gmd-2024-26, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Numerical models that capture key features of the global dynamics of fish communities play a crucial role in addressing the impacts of climate change and industrial fishing on ecosystems and societies. Here, we detail an update of the BiOeconomic marine Trophic Size-spectrum model that corrects the model representation of the dynamic of fisheries in the High Seas. This update also allows a better representation of biodiversity to improve future global and regional fisheries studies.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-34, https://doi.org/10.5194/gmd-2024-34, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
The newly developed Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate respiration and the resulting biogeochemistry. Lambda-PFLOTRAN is a python-based workflow via a Jupyter Notebook interface, that digests raw organic matter chemistry data via FTICR-MS, develops the representative reaction network, and completes a biogeochemical simulation with the open source, parallel reactive flow and transport code PFLOTRAN.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
Short summary
Short summary
By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
Short summary
Short summary
Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
Short summary
Short summary
Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
Short summary
Short summary
Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
Short summary
Short summary
By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
Short summary
Short summary
Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
Short summary
Short summary
Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a 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 and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
Short summary
Short summary
To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
Short summary
Short summary
Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
Short summary
Short summary
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.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
Short summary
Short summary
This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism 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.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
Short summary
Short summary
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.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
Short summary
Short summary
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 simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
Short summary
Short summary
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, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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....
