Articles | Volume 20, issue 8
https://doi.org/10.5194/tc-20-4437-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-20-4437-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Aug 2026
Research article |
| 14 Aug 2026
| 14 Aug 2026
Data-driven equation discovery of a sea ice albedo parametrisation
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Katja Weigel
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Arthur Grundner
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Marika M. Holland
National Center for Atmospheric Research, Boulder, CO, USA
Dmitry Sidorenko
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), Bremerhaven, Germany
Veronika Eyring
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
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Geosci. Model Dev., 19, 7415–7455, https://doi.org/10.5194/gmd-19-7415-2026, https://doi.org/10.5194/gmd-19-7415-2026, 2026
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The Rapid Evaluation Framework (REF) is a community-driven platform for benchmarking and performance assessment of Earth system models. Built upon four disparate community evaluation tools, the REF is designed to provide model-data comparisons for the Assessment Fast Track for the Seventh Phase of the Coupled Model Intercomparison Project. The REF will be run at the Earth System Grid Federation to enable model devleopers and scientists to quickly identify model biases and performance issues.
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Earth Syst. Dynam., 17, 955–986, https://doi.org/10.5194/esd-17-955-2026, https://doi.org/10.5194/esd-17-955-2026, 2026
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Heatwaves are among the deadliest natural hazards, yet their evolving atmospheric drivers remain unclear. We apply an unsupervised machine learning model to reanalysis data to identify multivariate heatwave patterns over Europe. The model captures seasonally distinct regimes and reveals significant shifts between historical and recent events across all seasons, highlighting changes in heatwave dynamics over time.
Axel Lauer, Manuel Schlund, Lisa Bock, Birgit Hassler, Gunnar Behrens, Bettina Gier, Lukas Lindenlaub, Stephan Lorenz, Jan-Hendrik Malles, Wolfgang A. Müller, Trang van Pham, Katja Weigel, Guang Zeng, and Veronika Eyring
EGUsphere, https://doi.org/10.5194/egusphere-2026-2288, https://doi.org/10.5194/egusphere-2026-2288, 2026
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ICONEval is a new framework to facilitate researchers to continuously monitor and check their climate models during the development phase. It builds on the ESMValTool software package to run tests verifying that the results are plausible, follow physical laws, and that the model has sufficient skill in reproducing the observed climate. An important aim is to make it easier to spot model errors early, for example when implementing new model components that use machine learning.
Rohit Ghosh, Suvarchal Kumar Cheedela, Sebastian Beyer, Nikolay Koldunov, Stella Berzina, Audrey Delpech, Svetlana Loza, Chathurika Wikramage, Stephy Libera, Matthias Aengenheyster, Amal John, Armelle Remedio, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Fabian Wachsmann, and Thomas Jung
EGUsphere, https://doi.org/10.5194/egusphere-2026-1289, https://doi.org/10.5194/egusphere-2026-1289, 2026
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Understanding climate change requires detailed computer simulations of the Earth system. Here we present one of the first century-long global simulations in which both the atmosphere and ocean are represented at kilometre-scale resolution. Such simulations are extremely computationally demanding, but we demonstrate their feasibility and show that they can capture key climate patterns while representing important small-scale ocean processes.
Katharina Hafner, Sara Shamekh, Guillaume Bertoli, Axel Lauer, Robert Pincus, Julien Savre, and Veronika Eyring
Geosci. Model Dev., 19, 3875–3891, https://doi.org/10.5194/gmd-19-3875-2026, https://doi.org/10.5194/gmd-19-3875-2026, 2026
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Most climate models cannot resolve clouds and cloud-radiation interactions at coarse horizontal resolutions of about 100 km, which introduces uncertainties. High-resolution models resolve clouds better but are expensive to run. We use short high-resolution simulations and artificial intelligence to learn the cloud-radiation interactions without making any assumptions about the small scales. We propose a new method that significantly reduces cloud related errors.
Francisco J. Doblas-Reyes, Jenni Kontkanen, Irina Sandu, Mario Acosta, Mohammed Hussam Al Turjmam, Ivan Alsina-Ferrer, Miguel Andrés-Martínez, Costanza Anerdi, Leo Arriola, Marvin Axness, Marc Batlle Martín, Peter Bauer, Tobias Becker, Daniel Beltrán, Sebastian Beyer, Hendryk Bockelmann, Pierre-Antoine Bretonnière, Sebastien Cabaniols, Silvia Caprioli, Miguel Castrillo, Aparna Chandrasekar, Suvarchal Cheedela, Victor Correal, Emanuele Danovaro, Paolo Davini, Jussi Enkovaara, Claudia Frauen, Barbara Früh, Aina Gaya Àvila, Paolo Ghinassi, Rohit Ghosh, Supriyo Ghosh, Iker González, Katherine Grayson, Matthew Griffith, Ioan Hadade, Christopher Haine, Carl Hartick, Utz-Uwe Haus, Shane Hearne, Heikki Järvinen, Bernat Jiménez, Amal John, Marlin Juchem, Thomas Jung, Jessica Kegel, Matthias Kelbling, Kai Keller, Bruno Kinoshita, Theresa Kiszler, Daniel Klocke, Lukas Kluft, Nikolay Koldunov, Tobias Kölling, Joonas Kolstela, Luis Kornblueh, Sergey Kosukhin, Aleksander Lacima-Nadolnik, Jeisson Javier Leal Rojas, Jonni Lehtiranta, Tuomas Lunttila, Anna Luoma, Pekka Manninen, Alexey Medvedev, Sebastian Milinski, Ali Mohammed, Sebastian Müller, Devaraju Naryanappa, Natalia Nazarova, Sami Niemelä, Bimochan Niraula, Henrik Nortamo, Aleksi Nummelin, Matteo Nurisso, Pablo Ortega, Stella Paronuzzi, Xabier Pedruzo-Bagazgoitia, Charles Pelletier, Carlos Peña, Suraj Polade, Himansu Kesari Pradhan, Rommel Quintanilla, Tiago Quintino, Thomas Rackow, Jouni Räisänen, Maqsood Mubarak Rajput, René Redler, Balthasar Reuter, Nuno Rocha Monteiro, Francesc Roura-Adserias, Silva Ruppert, Susan Sayed, Reiner Schnur, Tanvi Sharma, Dmitry Sidorenko, Outi Sievi-Korte, Albert Soret, Christian Steger, Bjorn Stevens, Jan Streffing, Jaleena Sunny, Luiggi Tenorio, Stephan Thober, Ulf Tigerstedt, Oriol Tinto, Juha Tonttila, Heikki Tuomenvirta, Lauri Tuppi, Ginka Van Thielen, Emanuele Vitali, Jost von Hardenberg, Ingo Wagner, Nils Wedi, Jan Wehner, Sven Willner, Xavier Yepes-Arbós, Florian Ziemen, and Janos Zimmermann
Geosci. Model Dev., 19, 2821–2848, https://doi.org/10.5194/gmd-19-2821-2026, https://doi.org/10.5194/gmd-19-2821-2026, 2026
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The Climate Change Adaptation Digital Twin (Climate DT) pioneers the operationalisation of global climate projections. It produces global simulations with local granularity for adaptation decision-making. Applications are embedded to generate tailored indicators. A unified workflow orchestrates all components in several supercomputers. Data management ensures consistency and streaming enables real-time use. It is a complementary innovation to initiatives like CMIP, CORDEX, and climate services.
Detlef P. Van Vuuren, Brian C. O'Neill, Claudia Tebaldi, Benjamin M. Sanderson, Louise P. Chini, Pierre Friedlingstein, Tomoko Hasegawa, Keywan Riahi, Bala Govindasamy, Nico Bauer, Veronika Eyring, Cheikh M. N. Fall, Katja Frieler, Matthew J. Gidden, Laila K. Gohar, Annika Högner, Andrew D. Jones, Jarmo Kikstra, Andrew King, Reto Knutti, Elmar Kriegler, Peter Lawrence, Chris Lennard, Jason Lowe, Camilla Mathison, Shahbaz Mehmood, Zebedee Nicholls, Luciana F. Prado, Qiang Zhang, Steven K. Rose, Alex C. Ruane, Marit Sandstad, Carl-Friedrich Schleussner, Roland Seferian, Jana Sillmann, Chris Smith, Anna A. Sörensson, Swapna Panickal, Kaoru Tachiiri, Naomi Vaughan, Saritha S. Vishwanathan, Tokuta Yokohata, Marco Zecchetto, and Tilo Ziehn
Geosci. Model Dev., 19, 2627–2656, https://doi.org/10.5194/gmd-19-2627-2026, https://doi.org/10.5194/gmd-19-2627-2026, 2026
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We propose a set of seven plausible 21st century emission scenarios, and their multi-century extensions, that will be used by the international community of climate modeling centers to produce the next generation of climate projections. These projections will support climate, impact and mitigation researchers, provide information to practitioners to address future risks from climate change, and contribute to policymakers’ considerations of the trade-offs among various levels of mitigation.
Laura L. Landrum, Alice K. DuVivier, Marika M. Holland, Kristen Krumhardt, and Zephyr Sylvester
The Cryosphere, 20, 1815–1840, https://doi.org/10.5194/tc-20-1815-2026, https://doi.org/10.5194/tc-20-1815-2026, 2026
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Lukas Lindenlaub, Katja Weigel, Birgit Hassler, Colin Jones, and Veronika Eyring
Earth Syst. Dynam., 17, 81–105, https://doi.org/10.5194/esd-17-81-2026, https://doi.org/10.5194/esd-17-81-2026, 2026
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This study explores changes in drought characteristic based on projections by 18 different Earth system models. Their performance is evaluated by comparing historical simulations to observation based reanalysis. The analysis of a standardized drought index under different future scenarios revealed that the harvest area that is projected to experience extreme drought conditions towards the end of this century ranges from 10 % to 40 % depending on the emission scenario.
Fernanda DI Alzira Oliveira Matos, Dmitry Sidorenko, Xiaoxu Shi, Lars Ackermann, Janini Pereira, Gerrit Lohmann, and Christian Stepanek
Ocean Sci., 21, 2895–2914, https://doi.org/10.5194/os-21-2895-2025, https://doi.org/10.5194/os-21-2895-2025, 2025
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The Atlantic Meridional Overturning Circulation (AMOC) is responsible for about 25 % of the poleward ocean heat transport. Currently, the AMOC strength is mostly calculated in depth space (z-AMOC). However, we argue that, in warmer climates, the AMOC should be calculated in density space (ρ-AMOC). We performed simulations with CO2 forcing of 280 ppmv (PI) and 1120 ppmv of (4xCO2) and find that ρ-AMOC provides more physical and meaningful information about the AMOC in warmer climates.
Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
Geosci. Model Dev., 18, 7735–7761, https://doi.org/10.5194/gmd-18-7735-2025, https://doi.org/10.5194/gmd-18-7735-2025, 2025
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The Next Generation of Earth Modeling Systems project (nextGEMS) developed two Earth system models that use horizontal grid spacing of 10 km and finer, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS simulated the Earth System climate over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
Ja-Yeon Moon, Jan Streffing, Sun-Seon Lee, Tido Semmler, Miguel Andrés-Martínez, Jiao Chen, Eun-Byeoul Cho, Jung-Eun Chu, Christian L. E. Franzke, Jan P. Gärtner, Rohit Ghosh, Jan Hegewald, Songyee Hong, Dae-Won Kim, Nikolay Koldunov, June-Yi Lee, Zihao Lin, Chao Liu, Svetlana N. 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
Earth Syst. Dynam., 16, 1103–1134, https://doi.org/10.5194/esd-16-1103-2025, https://doi.org/10.5194/esd-16-1103-2025, 2025
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Based on a series of storm-resolving greenhouse warming simulations conducted with the AWI-CM3 model at 9 km global atmosphere and 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, and 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.
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025, https://doi.org/10.5194/gmd-18-3681-2025, 2025
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Tuning a climate model means adjusting uncertain parameters in the model to best match observations like the global radiation balance and cloud cover. This is usually done by running many simulations of the model with different settings, which can be time-consuming and relies heavily on expert knowledge. To make this process faster and more objective, we developed a machine learning emulator to create a large ensemble and apply a method called history matching to find the best settings.
Kevin Debeire, Lisa Bock, Peer Nowack, Jakob Runge, and Veronika Eyring
Earth Syst. Dynam., 16, 607–630, https://doi.org/10.5194/esd-16-607-2025, https://doi.org/10.5194/esd-16-607-2025, 2025
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Projecting future precipitation is essential for preparing for climate change, but current climate models still have large uncertainties, especially over land. This study presents a new method to improve precipitation projections by identifying which models best capture key climate patterns. By giving more weight to models that better represent these patterns, our approach leads to more reliable future precipitation projections over land.
Paul J. Durack, Karl E. Taylor, Peter J. Gleckler, Gerald A. Meehl, Bryan N. Lawrence, Curt Covey, Ronald J. Stouffer, Guillaume Levavasseur, Atef Ben-Nasser, Sebastien Denvil, Martina Stockhause, Jonathan M. Gregory, Martin Juckes, Sasha K. Ames, Fabrizio Antonio, David C. Bader, John P. Dunne, Daniel Ellis, Veronika Eyring, Sandro L. Fiore, Sylvie Joussaume, Philip Kershaw, Jean-Francois Lamarque, Michael Lautenschlager, Jiwoo Lee, Chris F. Mauzey, Matthew Mizielinski, Paola Nassisi, Alessandra Nuzzo, Eleanor O’Rourke, Jeffrey Painter, Gerald L. Potter, Sven Rodriguez, and Dean N. Williams
EGUsphere, https://doi.org/10.5194/egusphere-2024-3729, https://doi.org/10.5194/egusphere-2024-3729, 2025
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CMIP6 was the most expansive and ambitious Model Intercomparison Project (MIP), the latest in a history, extending four decades. CMIP engaged a growing community focused on improving climate understanding, and quantifying and attributing observed climate change being experienced today. The project's profound impact is due to the combining the latest climate science and technology, enabling the latest-generation climate simulations and increasing community attention in every successive phase.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
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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.
Bettina K. Gier, Manuel Schlund, Pierre Friedlingstein, Chris D. Jones, Colin Jones, Sönke Zaehle, and Veronika Eyring
Biogeosciences, 21, 5321–5360, https://doi.org/10.5194/bg-21-5321-2024, https://doi.org/10.5194/bg-21-5321-2024, 2024
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This study investigates present-day carbon cycle variables in CMIP5 and CMIP6 simulations. Overall, CMIP6 models perform better but also show many remaining biases. A significant improvement in the simulation of photosynthesis in models with a nitrogen cycle is found, with only small differences between emission- and concentration-based simulations. Thus, we recommend using emission-driven simulations in CMIP7 by default and including the nitrogen cycle in all future carbon cycle models.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
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We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Colin G. Jones, Fanny Adloff, Ben B. B. Booth, Peter M. Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene T. Hewitt, Hazel A. Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm J. Roberts, Benjamin M. Sanderson, Roland Séférian, Samuel Somot, Pier Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco Doblas-Reyes, Markus Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Frölicher, Neven S. Fučkar, Matthew J. Gidden, Helge F. Goessling, Rune Grand Graversen, Silvio Gualdi, José M. Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris D. Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Mélia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard A. Wood, Shuting Yang, and Sönke Zaehle
Earth Syst. Dynam., 15, 1319–1351, https://doi.org/10.5194/esd-15-1319-2024, https://doi.org/10.5194/esd-15-1319-2024, 2024
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We propose a number of priority areas for the international climate research community to address over the coming decade. Advances in these areas will both increase our understanding of past and future Earth system change, including the societal and environmental impacts of this change, and deliver significantly improved scientific support to international climate policy, such as future IPCC assessments and the UNFCCC Global Stocktake.
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
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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.
Arndt Kaps, Axel Lauer, Rémi Kazeroni, Martin Stengel, and Veronika Eyring
Earth Syst. Sci. Data, 16, 3001–3016, https://doi.org/10.5194/essd-16-3001-2024, https://doi.org/10.5194/essd-16-3001-2024, 2024
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CCClim displays observations of clouds in terms of cloud classes that have been in use for a long time. CCClim is a machine-learning-powered product based on multiple existing observational products from different satellites. We show that the cloud classes in CCClim are physically meaningful and can be used to study cloud characteristics in more detail. The goal of this is to make real-world clouds more easily understandable to eventually improve the simulation of clouds in climate models.
Soufiane Karmouche, Evgenia Galytska, Gerald A. Meehl, Jakob Runge, Katja Weigel, and Veronika Eyring
Earth Syst. Dynam., 15, 689–715, https://doi.org/10.5194/esd-15-689-2024, https://doi.org/10.5194/esd-15-689-2024, 2024
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This study explores Atlantic–Pacific interactions and their response to external factors. Causal analysis of 1950–2014 data reveals a shift from a Pacific- to an Atlantic-driven regime. Contrasting impacts between El Niño and tropical Atlantic temperatures are highlighted, along with different pathways connecting the two oceans. The findings also suggest increasing remote contributions of forced Atlantic responses in modulating local Pacific responses during the most recent analyzed decades.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
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To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
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
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Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
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Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
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
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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.
Michael Kiefer, Dale F. Hurst, Gabriele P. Stiller, Stefan Lossow, Holger Vömel, John Anderson, Faiza Azam, Jean-Loup Bertaux, Laurent Blanot, Klaus Bramstedt, John P. Burrows, Robert Damadeo, Bianca Maria Dinelli, Patrick Eriksson, Maya García-Comas, John C. Gille, Mark Hervig, Yasuko Kasai, Farahnaz Khosrawi, Donal Murtagh, Gerald E. Nedoluha, Stefan Noël, Piera Raspollini, William G. Read, Karen H. Rosenlof, Alexei Rozanov, Christopher E. Sioris, Takafumi Sugita, Thomas von Clarmann, Kaley A. Walker, and Katja Weigel
Atmos. Meas. Tech., 16, 4589–4642, https://doi.org/10.5194/amt-16-4589-2023, https://doi.org/10.5194/amt-16-4589-2023, 2023
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We quantify biases and drifts (and their uncertainties) between the stratospheric water vapor measurement records of 15 satellite-based instruments (SATs, with 31 different retrievals) and balloon-borne frost point hygrometers (FPs) launched at 27 globally distributed stations. These comparisons of measurements during the period 2000–2016 are made using robust, consistent statistical methods. With some exceptions, the biases and drifts determined for most SAT–FP pairs are < 10 % and < 1 % yr−1.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
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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.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
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The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
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.
Soufiane Karmouche, Evgenia Galytska, Jakob Runge, Gerald A. Meehl, Adam S. Phillips, Katja Weigel, and Veronika Eyring
Earth Syst. Dynam., 14, 309–344, https://doi.org/10.5194/esd-14-309-2023, https://doi.org/10.5194/esd-14-309-2023, 2023
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This study uses a causal discovery method to evaluate the ability of climate models to represent the interactions between the Atlantic multidecadal variability (AMV) and the Pacific decadal variability (PDV). The approach and findings in this study present a powerful methodology that can be applied to a number of environment-related topics, offering tremendous insights to improve the understanding of the complex Earth system and the state of the art of climate modeling.
Pengyang Song, Dmitry Sidorenko, Patrick Scholz, Maik Thomas, and Gerrit Lohmann
Geosci. Model Dev., 16, 383–405, https://doi.org/10.5194/gmd-16-383-2023, https://doi.org/10.5194/gmd-16-383-2023, 2023
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Tides have essential effects on the ocean and climate. Most previous research applies parameterised tidal mixing to discuss their effects in models. By comparing the effect of a tidal mixing parameterisation and tidal forcing on the ocean state, we assess the advantages and disadvantages of the two methods. Our results show that tidal mixing in the North Pacific Ocean strongly affects the global thermohaline circulation. We also list some effects that are not considered in the parameterisation.
Manuel Schlund, Birgit Hassler, Axel Lauer, Bouwe Andela, Patrick Jöckel, Rémi Kazeroni, Saskia Loosveldt Tomas, Brian Medeiros, Valeriu Predoi, Stéphane Sénési, Jérôme Servonnat, Tobias Stacke, Javier Vegas-Regidor, Klaus Zimmermann, and Veronika Eyring
Geosci. Model Dev., 16, 315–333, https://doi.org/10.5194/gmd-16-315-2023, https://doi.org/10.5194/gmd-16-315-2023, 2023
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for routine evaluation of Earth system models. Originally, ESMValTool was designed to process reformatted output provided by large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP). Here, we describe a new extension of ESMValTool that allows for reading and processing native climate model output, i.e., data that have not been reformatted before.
Jan Streffing, Dmitry Sidorenko, Tido Semmler, Lorenzo Zampieri, Patrick Scholz, Miguel Andrés-Martínez, Nikolay Koldunov, Thomas Rackow, Joakim Kjellsson, Helge Goessling, Marylou Athanase, Qiang Wang, Jan Hegewald, Dmitry V. Sein, Longjiang Mu, Uwe Fladrich, Dirk Barbi, Paul Gierz, Sergey Danilov, Stephan Juricke, Gerrit Lohmann, and Thomas Jung
Geosci. Model Dev., 15, 6399–6427, https://doi.org/10.5194/gmd-15-6399-2022, https://doi.org/10.5194/gmd-15-6399-2022, 2022
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We developed a new atmosphere–ocean coupled climate model, AWI-CM3. Our model is significantly more computationally efficient than its predecessors AWI-CM1 and AWI-CM2. We show that the model, although cheaper to run, provides results of similar quality when modeling the historic period from 1850 to 2014. We identify the remaining weaknesses to outline future work. Finally we preview an improved simulation where the reduction in computational cost has to be invested in higher model resolution.
Stephen G. Yeager, Nan Rosenbloom, Anne A. Glanville, Xian Wu, Isla Simpson, Hui Li, Maria J. Molina, Kristen Krumhardt, Samuel Mogen, Keith Lindsay, Danica Lombardozzi, Will Wieder, Who M. Kim, Jadwiga H. Richter, Matthew Long, Gokhan Danabasoglu, David Bailey, Marika Holland, Nicole Lovenduski, Warren G. Strand, and Teagan King
Geosci. Model Dev., 15, 6451–6493, https://doi.org/10.5194/gmd-15-6451-2022, https://doi.org/10.5194/gmd-15-6451-2022, 2022
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The Earth system changes over a range of time and space scales, and some of these changes are predictable in advance. Short-term weather forecasts are most familiar, but recent work has shown that it is possible to generate useful predictions several seasons or even a decade in advance. This study focuses on predictions over intermediate timescales (up to 24 months in advance) and shows that there is promising potential to forecast a variety of changes in the natural environment.
William G. Read, Gabriele Stiller, Stefan Lossow, Michael Kiefer, Farahnaz Khosrawi, Dale Hurst, Holger Vömel, Karen Rosenlof, Bianca M. Dinelli, Piera Raspollini, Gerald E. Nedoluha, John C. Gille, Yasuko Kasai, Patrick Eriksson, Christopher E. Sioris, Kaley A. Walker, Katja Weigel, John P. Burrows, and Alexei Rozanov
Atmos. Meas. Tech., 15, 3377–3400, https://doi.org/10.5194/amt-15-3377-2022, https://doi.org/10.5194/amt-15-3377-2022, 2022
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This paper attempts to provide an assessment of the accuracy of 21 satellite-based instruments that remotely measure atmospheric humidity in the upper troposphere of the Earth's atmosphere. The instruments made their measurements from 1984 to the present time; however, most of these instruments began operations after 2000, and only a few are still operational. The objective of this study is to quantify the accuracy of each satellite humidity data set.
Xiaoxu Shi, Martin Werner, Carolin Krug, Chris M. Brierley, Anni Zhao, Endurance Igbinosa, Pascale Braconnot, Esther Brady, Jian Cao, Roberta D'Agostino, Johann Jungclaus, Xingxing Liu, Bette Otto-Bliesner, Dmitry Sidorenko, Robert Tomas, Evgeny M. Volodin, Hu Yang, Qiong Zhang, Weipeng Zheng, and Gerrit Lohmann
Clim. Past, 18, 1047–1070, https://doi.org/10.5194/cp-18-1047-2022, https://doi.org/10.5194/cp-18-1047-2022, 2022
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Since the orbital parameters of the past are different from today, applying the modern calendar to the past climate can lead to an artificial bias in seasonal cycles. With the use of multiple model outputs, we found that such a bias is non-ignorable and should be corrected to ensure an accurate comparison between modeled results and observational records, as well as between simulated past and modern climates, especially for the Last Interglacial.
Laura L. Landrum and Marika M. Holland
The Cryosphere, 16, 1483–1495, https://doi.org/10.5194/tc-16-1483-2022, https://doi.org/10.5194/tc-16-1483-2022, 2022
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High-latitude Arctic wintertime sea ice and snow insulate the relatively warmer ocean from the colder atmosphere. As the climate warms, wintertime Arctic conductive heat fluxes increase even when the sea ice concentrations remain high. Simulations from the Community Earth System Model Large Ensemble (CESM1-LE) show how sea ice and snow thicknesses, as well as the distribution of these thicknesses, significantly impact large-scale calculations of wintertime surface heat budgets in the Arctic.
Madison M. Smith, Marika Holland, and Bonnie Light
The Cryosphere, 16, 419–434, https://doi.org/10.5194/tc-16-419-2022, https://doi.org/10.5194/tc-16-419-2022, 2022
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Climate models represent the atmosphere, ocean, sea ice, and land with equations of varying complexity and are important tools for understanding changes in global climate. Here, we explore how realistic variations in the equations describing how sea ice melt occurs at the edges (called lateral melting) impact ice and climate. We find that these changes impact the progression of the sea-ice–albedo feedback in the Arctic and so make significant changes to the predicted Arctic sea ice.
Patrick Scholz, Dmitry Sidorenko, Sergey Danilov, Qiang Wang, Nikolay Koldunov, Dmitry Sein, and Thomas Jung
Geosci. Model Dev., 15, 335–363, https://doi.org/10.5194/gmd-15-335-2022, https://doi.org/10.5194/gmd-15-335-2022, 2022
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Structured-mesh ocean models are still the most mature in terms of functionality due to their long development history. However, unstructured-mesh ocean models have acquired new features and caught up in their functionality. This paper continues the work by Scholz et al. (2019) of documenting the features available in FESOM2.0. It focuses on the following two aspects: (i) partial bottom cells and embedded sea ice and (ii) dealing with mixing parameterisations enabled by using the CVMix package.
Vera Fofonova, Tuomas Kärnä, Knut Klingbeil, Alexey Androsov, Ivan Kuznetsov, Dmitry Sidorenko, Sergey Danilov, Hans Burchard, and Karen Helen Wiltshire
Geosci. Model Dev., 14, 6945–6975, https://doi.org/10.5194/gmd-14-6945-2021, https://doi.org/10.5194/gmd-14-6945-2021, 2021
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We present a test case of river plume spreading to evaluate coastal ocean models. Our test case reveals the level of numerical mixing (due to parameterizations used and numerical treatment of processes in the model) and the ability of models to reproduce complex dynamics. The major result of our comparative study is that accuracy in reproducing the analytical solution depends less on the type of applied model architecture or numerical grid than it does on the type of advection scheme.
Marika M. Holland, David Clemens-Sewall, Laura Landrum, Bonnie Light, Donald Perovich, Chris Polashenski, Madison Smith, and Melinda Webster
The Cryosphere, 15, 4981–4998, https://doi.org/10.5194/tc-15-4981-2021, https://doi.org/10.5194/tc-15-4981-2021, 2021
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As the most reflective and most insulative natural material, snow has important climate effects. For snow on sea ice, its high reflectivity reduces ice melt. However, its high insulating capacity limits ice growth. These counteracting effects make its net influence on sea ice uncertain. We find that with increasing snow, sea ice in both hemispheres is thicker and more extensive. However, the drivers of this response are different in the two hemispheres due to different climate conditions.
Qiang Wang, Sergey Danilov, Longjiang Mu, Dmitry Sidorenko, and Claudia Wekerle
The Cryosphere, 15, 4703–4725, https://doi.org/10.5194/tc-15-4703-2021, https://doi.org/10.5194/tc-15-4703-2021, 2021
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Using simulations, we found that changes in ocean freshwater content induced by wind perturbations can significantly affect the Arctic sea ice drift, thickness, concentration and deformation rates years after the wind perturbations. The impact is through changes in sea surface height and surface geostrophic currents and the most pronounced in warm seasons. Such a lasting impact might become stronger in a warming climate and implies the importance of ocean initialization in sea ice prediction.
Cited articles
Atmojo, D. W.: EyringMLClimateGroup/atmojo26tc_EquationDiscovery_SeaIceAlbedo: Data-driven equation discovery of a sea ice albedo parametrisation, Zenodo [code], https://doi.org/10.5281/zenodo.21873084, 2026. a
Batrak, Y. and Müller, M.: On the warm bias in atmospheric reanalyses induced by the missing snow over Arctic sea-ice, Nat. Commun., 10, 4170, https://doi.org/10.1038/s41467-019-11975-3, 2019. a, b
Bertino, L. and Xie, J.: Synthesis Quality Overview for Arctic Ocean Physical Multi Year Product, https://documentation.marine.copernicus.eu/SQO/CMEMS-ARC-SQO-002-003.pdf (last access: 30 March 2026), 2023. a
Beucler, T., Grundner, A., Shamekh, S., Ukkonen, P., Chantry, M., and Lagerquist, R.: Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications, Artificial Intelligence for the Earth Systems, 4, e240078, https://doi.org/10.1175/AIES-D-24-0078.1, 2025. a, b, c
Bleck, R.: An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates, Ocean Model., 4, 55–88, https://doi.org/10.1016/S1463-5003(01)00012-9, 2002. a
Bracco, A., Brajard, J., Dijkstra, H. A., Hassanzadeh, P., Lessig, C., and Monteleoni, C.: Machine learning for the physics of climate, Nature Reviews Physics, 7, 6–20, https://doi.org/10.1038/s42254-024-00776-3, 2024. a
Brandt, R. E., Warren, S. G., Worby, A. P., and Grenfell, T. C.: Surface Albedo of the Antarctic Sea Ice Zone, J. Climate, 18, 3606–3622, https://doi.org/10.1175/JCLI3489.1, 2005. a, b
Briegleb, B. and Light, B.: A Delta-Eddington Mutiple Scattering Parameterization for Solar Radiation in the Sea Ice Component of the Community Climate System Model, Tech. rep., UCAR/NCAR, https://doi.org/10.5065/D6B27S71, 2007. a
Camps-Valls, G., Gerhardus, A., Ninad, U., Varando, G., Martius, G., Balaguer-Ballester, E., Vinuesa, R., Diaz, E., Zanna, L., and Runge, J.: Discovering causal relations and equations from data, Phys. Rep., 1044, 1–68, https://doi.org/10.1016/j.physrep.2023.10.005, 2023. a, b
Copernicus Climate Change Service: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/CDS.ADBB2D47, 2018a. a
Copernicus Climate Change Service: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set] https://doi.org/10.24381/CDS.BD0915C6, 2018b. a
Crameri, F.: Scientific colour maps, Zenodo [code], https://doi.org/10.5281/zenodo.1243862, 2023. a
Crameri, F., Shephard, G. E., and Heron, P. J.: The misuse of colour in science communication, Nat. Commun., 11, 5444, https://doi.org/10.1038/s41467-020-19160-7, 2020. a
Cranmer, M., Sanchez-Gonzalez, A., Battaglia, P., Xu, R., Cranmer, K., Spergel, D., and Ho, S.: Discovering Symbolic Models from Deep Learning with Inductive Biases, version Number: 2, arXiv [preprint], https://doi.org/10.48550/arXiv.2006.11287, 2020. a, b
Curry, J. A., Schramm, J. L., and Ebert, E. E.: Sea Ice-Albedo Climate Feedback Mechanism, J. Climate, 8, 240–247, https://doi.org/10.1175/1520-0442(1995)008<0240:siacfm>2.0.co;2, 1995. a
Curry, J. A., Schramm, J. L., Perovich, D. K., and Pinto, J. O.: Applications of SHEBA/FIRE data to evaluation of snow/ice albedo parameterizations, J. Geophys. Res.-Atmos., 106, 15345–15355, https://doi.org/10.1029/2000JD900311, 2001. 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 Franca, F. O., Virgolin, M., Kommenda, M., Majumder, M. S., Cranmer, M., Espada, G., Ingelse, L., Fonseca, A., Landajuela, M., Petersen, B., Glatt, R., Mundhenk, N., Lee, C. S., Hochhalter, J. D., Randall, D. L., Kamienny, P., Zhang, H., Dick, G., Simon, A., Burlacu, B., Kasak, J., Machado, M., Wilstrup, C., and Cavaz, W. G. L.: SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation, IEEE T. Evolut. Comput., 1–1, https://doi.org/10.1109/TEVC.2024.3423681, 2024. a
European Union-Copernicus Marine Service: Global Ocean Sea Ice Concentration Time Series REPROCESSED (EUMETSAT OSI-SAF), European Union-Copernicus Marine Service, https://doi.org/10.48670/MOI-00136, 2015. a
European Union-Copernicus Marine Service: Arctic Ocean Physics Reanalysis, European Union-Copernicus Marine Service, https://doi.org/10.48670/MOI-00007, 2020. a, b, c
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Eyring, V., Collins, W. D., Gentine, P., Barnes, E. A., Barreiro, M., Beucler, T., Bocquet, M., Bretherton, C. S., Christensen, H. M., Dagon, K., Gagne, D. J., Hall, D., Hammerling, D., Hoyer, S., Iglesias-Suarez, F., Lopez-Gomez, I., McGraw, M. C., Meehl, G. A., Molina, M. J., Monteleoni, C., Mueller, J., Pritchard, M. S., Rolnick, D., Runge, J., Stier, P., Watt-Meyer, O., Weigel, K., Yu, R., and Zanna, L.: Pushing the frontiers in climate modelling and analysis with machine learning, Nat. Clim. Change, 14, 916–928, https://doi.org/10.1038/s41558-024-02095-y, 2024. a, b
Flocco, D., Feltham, D. L., and Turner, A. K.: Incorporation of a physically based melt pond scheme into the sea ice component of a climate model, J. Geophys. Res.-Oceans, 115, 2009JC005568, https://doi.org/10.1029/2009JC005568, 2010. a, b
Grenfell, T. C.: The Effects of Ice Thickness on the Exchange of Solar Radiation Over the Polar Oceans, J. Glaciol., 22, 305–320, https://doi.org/10.3189/S0022143000014295, 1979. a
Grenfell, T. C. and Perovich, D. K.: Seasonal and spatial evolution of albedo in a snow-ice-land-ocean environment, J. Geophys. Res.-Oceans, 109, 2003JC001866, https://doi.org/10.1029/2003JC001866, 2004. a
Hansen, J. and Nazarenko, L.: Soot climate forcing via snow and ice albedos, P. Natl. Acad. Sci. USA, 101, 423–428, https://doi.org/10.1073/pnas.2237157100, 2004. a, b
Holland, M. M., Bailey, D. A., Briegleb, B. P., Light, B., and Hunke, E.: Improved Sea Ice Shortwave Radiation Physics in CCSM4: The Impact of Melt Ponds and Aerosols on Arctic Sea Ice, J. Climate, 25, 1413–1430, https://doi.org/10.1175/JCLI-D-11-00078.1, 2012. a, b
Hunke, E. C. and Dukowicz, J. K.: An Elastic–Viscous–Plastic Model for Sea Ice Dynamics, J. Phys. Oceanogr., 27, 1849–1867, https://doi.org/10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2, 1997. a
Hunke, E. C., Lipscomb, W. H., and Turner, A. K.: Sea-ice models for climate study: retrospective and new directions, J. Glaciol., 56, 1162–1172, https://doi.org/10.3189/002214311796406095, 2010. a
Hunke, E. C., Hebert, D. A., and Lecomte, O.: Level-ice melt ponds in the Los Alamos sea ice model, CICE, Ocean Model., 71, 26–42, https://doi.org/10.1016/j.ocemod.2012.11.008, 2013. a, b
Huntingford, C., Nicoll, A. J., Klein, C., and Ahmad, J. A.: Potential for equation discovery with AI in the climate sciences, Earth Syst. Dynam., 16, 475–495, https://doi.org/10.5194/esd-16-475-2025, 2025. a
Jäkel, E., Becker, S., Sperzel, T. R., Niehaus, H., Spreen, G., Tao, R., Nicolaus, M., Dorn, W., Rinke, A., Brauchle, J., and Wendisch, M.: Observations and modeling of areal surface albedo and surface types in the Arctic, The Cryosphere, 18, 1185–1205, https://doi.org/10.5194/tc-18-1185-2024, 2024. a
Kwok, R.: Arctic sea ice thickness, volume, and multiyear ice coverage: losses and coupled variability (1958–2018), Environ. Res. Lett., 13, 105005, https://doi.org/10.1088/1748-9326/aae3ec, 2018. a
Light, B., Perovich, D. K., Webster, M. A., Polashenski, C., and Dadic, R.: Optical properties of melting first-year Arctic sea ice, J. Geophys. Res.-Oceans, 120, 7657–7675, https://doi.org/10.1002/2015JC011163, 2015. a
Light, B., Smith, M. M., Perovich, D. K., Webster, M. A., Holland, M. M., Linhardt, F., Raphael, I. A., Clemens-Sewall, D., Macfarlane, A. R., Anhaus, P., and Bailey, D. A.: Arctic sea ice albedo: Spectral composition, spatial heterogeneity, and temporal evolution observed during the MOSAiC drift, Elementa: Science of the Anthropocene, 10, 000103, https://doi.org/10.1525/elementa.2021.000103, 2022. a, b, c, d, e
Liu, W., Yang, S., Chen, D., Zha, J., Zhang, G., Zhang, Z., Zhang, T., Xu, L., Hu, X., and Deng, K.: Rapid Acceleration of Arctic Near-Surface Wind Speed in a Warming Climate, Geophys. Res. Lett., 51, e2024GL109385, https://doi.org/10.1029/2024GL109385, 2024. a
Meier, W. and Stewart, J.: Arctic and Antarctic Regional Masks for Sea Ice and Related Data Products, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center, https://doi.org/10.5067/CYW3O8ZUNIWC, 2023. a, b
Nath, P., Schemm, S., Moss, H., Haynes, P., Shuckburgh, E., and Webb, M. J.: Making Tunable Parameters State-Dependent in Weather and Climate Models with Reinforcement Learning, arXiv [preprint], https://doi.org/10.48550/arXiv.2601.04268, 2026. a
Nelder, J. A. and Mead, R.: A Simplex Method for Function Minimization, Comput. J., 7, 308–313, https://doi.org/10.1093/comjnl/7.4.308, 1965. a, b
Niehaus, H., Spreen, G., Istomina, L., and Nicolaus, M.: Regional and seasonal evolution of melt ponds on Arctic sea ice, The Cryosphere, 19, 3915–3938, https://doi.org/10.5194/tc-19-3915-2025, 2025. a, b, c
Nocedal, J. and Wright, S. J.: Numerical optimization, Springer series in operations research and financial engineering, 2nd edn., Springer, New York, NY, ISBN 978-0-387-30303-1, 2006. a
Notz, D. and Community, S.: Arctic Sea Ice in CMIP6, Geophys. Res. Lett., 47, e2019GL086749, https://doi.org/10.1029/2019GL086749, 2020. a
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S.: PyTorch: An Imperative Style, High-Performance Deep Learning Library, in: Advances in Neural Information Processing Systems 32, 8024–8035, Curran Associates, Inc., http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (last access: 26 February 2025), 2019. a, b
Peng, J., Yu, Y., Yu, P., and Liang, S.: The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation, Remote Sens.-Basel, 10, 1826, https://doi.org/10.3390/rs10111826, 2018. a
Perovich, D. K. and Grenfell, T. C.: Laboratory Studies of the Optical Properties of Young Sea Ice, J. Glaciol., 27, 331–346, https://doi.org/10.3189/S0022143000015410, 1981. a
Perovich, D. K., Grenfell, T. C., Light, B., and Hobbs, P. V.: Seasonal evolution of the albedo of multiyear Arctic sea ice, J. Geophys. Res.-Oceans, 107, https://doi.org/10.1029/2000JC000438, 2002. a, b, c, d
Pirazzini, R.: Challenges in Snow and Ice Albedo Parameterizations, Geophysica, 45, 41–62, 2009. a
Pistone, K., Eisenman, I., and Ramanathan, V.: Observational determination of albedo decrease caused by vanishing Arctic sea ice, P. Natl. Acad. Sci. USA, 111, 3322–3326, https://doi.org/10.1073/pnas.1318201111, 2014. a
Pithan, F. and Mauritsen, T.: Arctic amplification dominated by temperature feedbacks in contemporary climate models, Nat. Geosci., 7, 181–184, https://doi.org/10.1038/ngeo2071, 2014. a
Popović, P., Silber, M. C., and Abbot, D. S.: Critical Percolation Threshold Restricts Late-Summer Arctic Sea Ice Melt Pond Coverage, J. Geophys. Res.-Oceans, 125, e2019JC016029, https://doi.org/10.1029/2019JC016029, 2020. a
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent subgrid processes in climate models, P. Natl. Acad. Sci. USA, 115, 9684–9689, https://doi.org/10.1073/pnas.1810286115, 2018. a
Rostosky, P., Spreen, G., Farrell, S. L., Frost, T., Heygster, G., and Melsheimer, C.: Snow Depth Retrieval on Arctic Sea Ice From Passive Microwave Radiometers – Improvements and Extensions to Multiyear Ice Using Lower Frequencies, J. Geophys. Res.-Oceans, 123, 7120–7138, https://doi.org/10.1029/2018JC014028, 2018. a, b, c, d, e
Screen, J. A. and Simmonds, I.: Increasing fall-winter energy loss from the Arctic Ocean and its role in Arctic temperature amplification, Geophys. Res. Lett., 37, 2010GL044136, https://doi.org/10.1029/2010GL044136, 2010. a
Selivanova, J., Iovino, D., and Cocetta, F.: Past and future of the Arctic sea ice in High-Resolution Model Intercomparison Project (HighResMIP) climate models, The Cryosphere, 18, 2739–2763, https://doi.org/10.5194/tc-18-2739-2024, 2024. a
Smedsrud, L. H., Esau, I., Ingvaldsen, R. B., Eldevik, T., Haugan, P. M., Li, C., Lien, V. S., Olsen, A., Omar, A. M., Otterå, O. H., Risebrobakken, B., Sandø, A. B., Semenov, V. A., and Sorokina, S. A.: The role of the Barents Sea in the Arctic climate system, Rev. Geophys., 51, 415–449, https://doi.org/10.1002/rog.20017, 2013. a, b
Smith, M. M., Fuchs, N., Salganik, E., Perovich, D. K., Raphael, I., Granskog, M. A., Schulz, K., Shupe, M. D., and Webster, M.: Formation and fate of freshwater on an ice floe in the Central Arctic, The Cryosphere, 19, 619–644, https://doi.org/10.5194/tc-19-619-2025, 2025. a, b
Song, W., Jiang, S., Camps-Valls, G., Williams, M., Zhang, L., Reichstein, M., Vereecken, H., He, L., Hu, X., and Shi, L.: Towards data-driven discovery of governing equations in geosciences, Communications Earth and Environment, 5, 589, https://doi.org/10.1038/s43247-024-01760-6, 2024. a
Spreen, G., Kaleschke, L., and Heygster, G.: Sea ice remote sensing using AMSR-E 89-GHz channels, J. Geophys. Res.-Oceans, 113, 2005JC003384, https://doi.org/10.1029/2005JC003384, 2008. a
Streffing, J., Sidorenko, D., Semmler, T., Zampieri, L., Scholz, P., Andrés-Martínez, M., Koldunov, N., Rackow, T., Kjellsson, J., Goessling, H., Athanase, M., Wang, Q., Hegewald, J., Sein, D. V., Mu, L., Fladrich, U., Barbi, D., Gierz, P., Danilov, S., Juricke, S., Lohmann, G., and Jung, T.: AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model, Geosci. Model Dev., 15, 6399–6427, https://doi.org/10.5194/gmd-15-6399-2022, 2022. a
Stroeve, J. and Notz, D.: Changing state of Arctic sea ice across all seasons, Environ. Res. Lett., 13, 103001, https://doi.org/10.1088/1748-9326/aade56, 2018. a, b, c
Tersigni, I., Nelli, F., Cimoli, E., Heil, P., Bennetts, L., Passerotti, G., and Toffoli, A.: Seasonal Variability of Snow Cover and Impact on Albedo and Thermal Properties in the Antarctic Marginal Ice Zone, version Number: 1, arXiv [preprint], https://doi.org/10.48550/arXiv.2507.10916, 2025. a
Tschudi, M., Meier, W. N., Stewart, J. S., Fowler, C., and Maslanik, J.: Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, NASA National Snow and Ice Data Center Distributed Active Archive Center, https://doi.org/10.5067/INAWUWO7QH7B, 2019a. a
Tschudi, M., Meier, W. N., Stewart, J. S., Fowler, C., and Maslanik, J.: EASE-Grid Sea Ice Age, NASA National Snow and Ice Data Center Distributed Active Archive Center, https://doi.org/10.5067/UTAV7490FEPB, 2019b. a
Vance, T. C., Huang, T., and Butler, K. A.: Big data in Earth science: Emerging practice and promise, Science, 383, eadh9607, https://doi.org/10.1126/science.adh9607, 2024. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., Van Der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., Van Mulbregt, P., Vijaykumar, A., Bardelli, A. P., Rothberg, A., Hilboll, A., Kloeckner, A., Scopatz, A., Lee, A., Rokem, A., Woods, C. N., Fulton, C., Masson, C., Häggström, C., Fitzgerald, C., Nicholson, D. A., Hagen, D. R., Pasechnik, D. V., Olivetti, E., Martin, E., Wieser, E., Silva, F., Lenders, F., Wilhelm, F., Young, G., Price, G. A., Ingold, G.-L., Allen, G. E., Lee, G. R., Audren, H., Probst, I., Dietrich, J. P., Silterra, J., Webber, J. T., Slavič, J., Nothman, J., Buchner, J., Kulick, J., Schönberger, J. L., De Miranda Cardoso, J. V., Reimer, J., Harrington, J., Rodríguez, J. L. C., Nunez-Iglesias, J., Kuczynski, J., Tritz, K., Thoma, M., Newville, M., Kümmerer, M., Bolingbroke, M., Tartre, M., Pak, M., Smith, N. J., Nowaczyk, N., Shebanov, N., Pavlyk, O., Brodtkorb, P. A., Lee, P., McGibbon, R. T., Feldbauer, R., Lewis, S., Tygier, S., Sievert, S., Vigna, S., Peterson, S., More, S., Pudlik, T., Oshima, T., Pingel, T. J., Robitaille, T. P., Spura, T., Jones, T. R., Cera, T., Leslie, T., Zito, T., Krauss, T., Upadhyay, U., Halchenko, Y. O., and Vázquez-Baeza, Y.: SciPy 1.0: fundamental algorithms for scientific computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Warren, S. G. and Wiscombe, W. J.: A Model for the Spectral Albedo of Snow. II: Snow Containing Atmospheric Aerosols, J. Atmos. Sci., 37, 2734–2745, https://doi.org/10.1175/1520-0469(1980)037<2734:AMFTSA>2.0.CO;2, 1980. a
Webster, M. A., Holland, M., Wright, N. C., Hendricks, S., Hutter, N., Itkin, P., Light, B., Linhardt, F., Perovich, D. K., Raphael, I. A., Smith, M. M., Von Albedyll, L., and Zhang, J.: Spatiotemporal evolution of melt ponds on Arctic sea ice, Elementa: Science of the Anthropocene, 10, 000072, https://doi.org/10.1525/elementa.2021.000072, 2022. a
Xie, J. and Bertino, L.: Quality Information Document for Arctic Ocean Physical Multi Year Product, https://documentation.marine.copernicus.eu/QUID/CMEMS-ARC-QUID-002-003.pdf (last access: 30 March 2026), 2023. a
Yamagami, Y., Watanabe, M., Mori, M., and Ono, J.: Barents-Kara sea-ice decline attributed to surface warming in the Gulf Stream, Nat. Commun., 13, 3767, https://doi.org/10.1038/s41467-022-31117-6, 2022. a
Zampieri, L., Arduini, G., Holland, M., Keeley, S. P. E., Mogensen, K., Shupe, M. D., and Tietsche, S.: A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses, Mon. Weather Rev., 151, 1443–1458, https://doi.org/10.1175/MWR-D-22-0130.1, 2023. a, b, c
Zanna, L. and Bolton, T.: Data-Driven Equation Discovery of Ocean Mesoscale Closures, Geophys. Res. Lett., 47, e2020GL088376, https://doi.org/10.1029/2020GL088376, 2020. a
Zhuang, J., Dussin, R., Huard, D., Bourgault, P., Banihirwe, A., Raynaud, S., Malevich, B., Schupfner, M., Correia, F., Gauthier, C., Levang, S., Jüling, A., Almansi, M., Scott, R., Rondeau-Genesse, G., Rasp, S., Smith, T. J., Mares, B., Stachelek, J., Plough, M., Pierre, Bell, R., Caneill, R., and Li, X.: pangeo-data/xESMF: v0.8.8, Zenodo [code], https://doi.org/10.5281/zenodo.4294774, 2024. a
Årthun, M., Eldevik, T., Smedsrud, L. H., Skagseth, Ø., and Ingvaldsen, R. B.: Quantifying the Influence of Atlantic Heat on Barents Sea Ice Variability and Retreat, J. Climate, 25, 4736–4743, https://doi.org/10.1175/JCLI-D-11-00466.1, 2012. a, b
Short summary
This study presents an observation-driven sea ice albedo parametrisation by discovering an equation using symbolic regression, an interpretable machine learning method. Leveraging satellite and reanalyses data, our discovered equation identifies high sensitivity to thin snow and the weighted temperature difference between sea ice surface and 2 m air as critical to determine sea ice albedo. Our findings contribute to improving Arctic climate projections and understanding.
This study presents an observation-driven sea ice albedo parametrisation by discovering an...
