Articles | Volume 19, issue 12
https://doi.org/10.5194/gmd-19-5225-2026
© 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/gmd-19-5225-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Jun 2026
Model description paper |
| 17 Jun 2026
| 17 Jun 2026
Veris: fast & efficient sea-ice modeling in Python with GPU acceleration
Alfred-Wegener-Institut, Helmholtz Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
Martin Losch
Alfred-Wegener-Institut, Helmholtz Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
Suvarchal K. Cheedela
Alfred-Wegener-Institut, Helmholtz Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
Markus Jochum
Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
Roman Nuterman
Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
Related authors
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
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 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.
Ulrike Proske, Nils Brüggemann, Jan P. Gärtner, Oliver Gutjahr, Helmuth Haak, Dian Putrasahan, and Karl-Hermann Wieners
EGUsphere, https://doi.org/10.5194/egusphere-2024-3493, https://doi.org/10.5194/egusphere-2024-3493, 2024
Preprint archived
Short summary
Short summary
Climate models contain coding mistakes, which may look mundane, but can affect the results of interconnected and complex models in unforeseen ways. We describe a sea ice bug in the coupled atmosphere-ocean-sea ice model ICON, giving an example of visual and concise bug communication. This bug represents a novel species of resolution-dependent bugs. The case illustrates the value of open documentation of bugs in climate models and to encourage our community to adopt a similar approach.
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
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
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.
Jean-Francois Lemieux, Damien Ringeisen, Martin Losch, William Lipscomb, and Jinro Ukita
EGUsphere, https://doi.org/10.5194/egusphere-2026-1362, https://doi.org/10.5194/egusphere-2026-1362, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
The sea ice cover in the Arctic and in the Southern Ocean strongly varies spatially due to the formation of leads and pressure ridges. Leads and pressure ridges are formed when sea ice fails because forces inside the ice cover reach critical values. This work describes how the representation of leads and ridges in sea ice models should be modified when different critical forces are used.
Damien Ringeisen, Bruno Tremblay, Jean-Francois Lemieux, and Martin Losch
EGUsphere, https://doi.org/10.5194/egusphere-2026-1845, https://doi.org/10.5194/egusphere-2026-1845, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Sea ice moves and deforms as wind and ocean currents push it around, creating narrow fracture lines on the surface. To accurately reproduce these fracture patterns in computer models used for climate predictions and navigation, we tested a mathematical approach to sea ice dynamics, common in other fields but rare in sea ice modeling, to see if it could better capture these patterns. Our results show this approach could improve fracture representation, though it requires more computing power.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
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 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.
Swantje Bastin, Aleksei Koldunov, Florian Schütte, Oliver Gutjahr, Marta Agnieszka Mrozowska, Tim Fischer, Radomyra Shevchenko, Arjun Kumar, Nikolay Koldunov, Helmuth Haak, Nils Brüggemann, Rebecca Hummels, Mia Sophie Specht, Johann Jungclaus, Sergey Danilov, Marcus Dengler, and Markus Jochum
Geosci. Model Dev., 18, 1189–1220, https://doi.org/10.5194/gmd-18-1189-2025, https://doi.org/10.5194/gmd-18-1189-2025, 2025
Short summary
Short summary
Vertical mixing is an important process, for example, for tropical sea surface temperature, but cannot be resolved by ocean models. Comparisons of mixing schemes and settings have usually been done with a single model, sometimes yielding conflicting results. We systematically compare two widely used schemes with different parameter settings in two different ocean models and show that most effects from mixing scheme parameter changes are model-dependent.
Ulrike Proske, Nils Brüggemann, Jan P. Gärtner, Oliver Gutjahr, Helmuth Haak, Dian Putrasahan, and Karl-Hermann Wieners
EGUsphere, https://doi.org/10.5194/egusphere-2024-3493, https://doi.org/10.5194/egusphere-2024-3493, 2024
Preprint archived
Short summary
Short summary
Climate models contain coding mistakes, which may look mundane, but can affect the results of interconnected and complex models in unforeseen ways. We describe a sea ice bug in the coupled atmosphere-ocean-sea ice model ICON, giving an example of visual and concise bug communication. This bug represents a novel species of resolution-dependent bugs. The case illustrates the value of open documentation of bugs in climate models and to encourage our community to adopt a similar approach.
Roman Nuterman, Alexander Mahura, Alexander Baklanov, Bjarne Amstrup, and Ashraf Zakey
Atmos. Chem. Phys., 21, 11099–11112, https://doi.org/10.5194/acp-21-11099-2021, https://doi.org/10.5194/acp-21-11099-2021, 2021
Short summary
Short summary
The street air pollution is usually higher than the pollution at regional and urban scales. It mostly associated with both local emission sources and urban weather conditions. We present the downscaling system for regional, subregional-urban and street scales and evaluate it for acute air-pollution episode. Its evaluation showed a good prediction score across various spatiotemporal scales as well as feasibility of deterministic modelling approach for the operational street scale forecasting.
Damien Ringeisen, L. Bruno Tremblay, and Martin Losch
The Cryosphere, 15, 2873–2888, https://doi.org/10.5194/tc-15-2873-2021, https://doi.org/10.5194/tc-15-2873-2021, 2021
Short summary
Short summary
Deformations in the Arctic sea ice cover take the shape of narrow lines. High-resolution sea ice models recreate these deformation lines. Recent studies have shown that the most widely used sea ice model creates fracture lines with intersection angles larger than those observed and cannot create smaller angles. In our work, we change the way sea ice deforms post-fracture. This change allows us to understand the link between the sea ice model and intersection angles and create more acute angles.
Cited articles
Arakawa, A. and Lamb, V. R.: Computational Design of the Basic Dynamical Processes of the UCLA General Circulation Model, in: General Circulation Models of the Atmosphere, vol. 17, Methods in Computational Physics: Advances in Research and Applications, Elsevier, 173–265, https://doi.org/10.1016/B978-0-12-460817-7.50009-4, 1977. a
Bouillon, S., Fichefet, T., Legat, V., and Madec, G.: The Elastic–Viscous–Plastic Method Revisited, Ocean Model., 71, 2–12, https://doi.org/10.1016/j.ocemod.2013.05.013, 2013. a, b, c
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q.: JAX: composable transformations of Python+NumPy programs, GitHub [code], http://github.com/jax-ml/jax (last access: 1 June 2026), 2018. a
Campin, J.-M., Heimbach, P., Losch, M., Forget, G., edhill3, Adcroft, A., amolod, Menemenlis, D., dfer22, Jahn, O., Hill, C., Scott, J., dngoldberg, stephdut, Mazloff, M., Fox-Kemper, B., antnguyen13, Doddridge, E., Fenty, I., Bates, M., Smith, T., Wang, O., AndrewEichmann-NOAA, mitllheisey, Lauderdale, J., Martin, T., Abernathey, R., samarkhatiwala, Escobar, I., and averdy: MITgcm/MITgcm: checkpoint69k, Zenodo [code], https://doi.org/10.5281/zenodo.18371317, 2026. a
Eden, C.: Closing the energy cycle in an ocean model, Ocean Model., 101, 30–42, https://doi.org/10.1016/j.ocemod.2016.02.005, 2016. a
Eden, C. and Olbers, D.: An energy compartment model for propagation, nonlinear interaction, and dissipation of internal gravity waves, J. Phys. Oceanogr., 44, 2093–2106, https://doi.org/10.1175/JPO-D-13-0224.1, 2014. a
Gärtner, J. P.: Sea Ice Modeling in Python, Zenodo [code], https://doi.org/10.5281/zenodo.11474409, 2023. a, b
Gärtner, J. P.: Veris Benchmarks Data and Scripts, Zenodo [code and data set], https://doi.org/10.5281/zenodo.18620150, 2026a. a
Gärtner, J. P.: Veris, Zenodo [code], https://doi.org/10.5281/zenodo.20643719, 2026b. a
Gärtner, J. P.: Veris (sharded arrays), Zenodo [code], https://doi.org/10.5281/zenodo.20643778, 2026c. a
Häfner, D., Jacobsen, R. L., Eden, C., Kristensen, M. R. B., Jochum, M., Nuterman, R., and Vinter, B.: Veros v0.1 – a fast and versatile ocean simulator in pure Python, Geosci. Model Dev., 11, 3299–3312, https://doi.org/10.5194/gmd-11-3299-2018, 2018. a
Häfner, D., Nuterman, R., and Jochum, M.: Fast, Cheap, and Turbulent – Global Ocean Modeling with GPU Acceleration in Python, J. Adv. Model. Earth Sy., 13, e2021MS002717, https://doi.org/10.1029/2021MS002717, 2021. a
Häfner, D., Jacobsen, R. L., Nuterman, R., and Jochum, M.: Veros, Zenodo [code], https://doi.org/10.5281/zenodo.8430140, 2023. a
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E.: Array Programming with NumPy, Nature, 585, 357–362, https://doi.org/10.1038/s41586-020-2649-2, 2020. a
Hibler, W. D.: A Dynamic Thermodynamic Sea Ice Model, J. Phys. Oceanogr., 9, 815–846, https://doi.org/10.1175/1520-0485(1979)009%3C0815:ADTSIM%3E2.0.CO;2, 1979. a, b, c, d
Hunke, E., Allard, R., Bailey, D. A., Blain, P., Craig, A., Dupont, F., DuVivier, A., Grumbine, R., Hebert, D., Holland, M., Jeffery, N., Lemieux, J.-F., Osinski, R., Poulsen, J., Steketee, A., Rasmussen, T., Ribergaard, M., Roach, L., Roberts, A., Turner, M., Winton, M., and Worthen, D.: CICE-Consortium/CICE: CICE Version 6.5.1, Zenodo [code], https://doi.org/10.5281/zenodo.11223920, 2024. a
Hunke, E. C.: Viscous–plastic sea ice dynamics with the EVP model: Linearization issues, J. Comput. Phys., 170, 18–38, https://doi.org/10.1006/jcph.2001.6710, 2001. a
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%3C1849:AEVPMF%3E2.0.CO;2, 1997. a
Kimmritz, M., Danilov, S., and Losch, M.: The Adaptive EVP Method for Solving the Sea Ice Momentum Equation, Ocean Model., 101, 59–67, https://doi.org/10.1016/j.ocemod.2016.03.004, 2016. a, b, c
Lemieux, J.-F., Tremblay, L. B., Dupont, F., Plante, M., Smith, G. C., and Dumont, D.: A Basal Stress Parameterization for Modeling Landfast Ice, J. Geophys. Res.-Oceans, 120, 3157–3173, https://doi.org/10.1002/2014JC010678, 2015. a
Leppäranta, M.: A growth model for black ice, snow ice and snow thickness in subarctic basins, Hydrol. Res., 14, 59–70, https://doi.org/10.2166/nh.1983.0006, 1983. a
Li, B., Basu Roy, R., Wang, D., Samsi, S., Gadepally, V., and Tiwari, D.: Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systems, in: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 1–15, https://doi.org/10.1145/3581784.3607035, 2023. a
Liu, Y., Losch, M., Hutter, N., and Mu, L.: A New Parameterization of Coastal Drag to Simulate Landfast Ice in Deep Marginal Seas in the Arctic, J. Geophys. Res.-Oceans, 127, e2022JC018413, https://doi.org/10.1029/2022JC018413, 2022. a
Losch, M.: MITgcm sea ice dynamics benchmark, Github [code], https://github.com/mjlosch/seaice_benchmark.git (last access: 1 June 2026), 2021. a
Losch, M.: MITgcm sea ice thermodynamics benchmark, Github [code], https://github.com/mjlosch/MITgcm/tree/seaice_test_1d (last access: 1 June 2026), 2023. a
Losch, M., Menemenlis, D., Campin, J.-M., Heimbach, P., and Hill, C.: On the Formulation of Sea-Ice Models. Part 1: Effects of Different Solver Implementations and Prameterizations, Ocean Model., 33, 129–144, https://doi.org/10.1016/j.ocemod.2009.12.008, 2010. a
Marquetti, I., Rodrigues, J., and Desai, S. S.: Ecological Impact of Green Computing using Graphical Processing Units in Molecular Dynamics Simulations, Int. J. Green Comput. (IJGC), 9, 35–48, https://doi.org/10.4018/IJGC.2018010103, 2018. 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.-Oceans, 102, 5753–5766, https://doi.org/10.1029/96JC02775, 1997. a
Mehlmann, C., Danilov, S., Losch, M., Lemieux, J.-F., Hutter, N., Richter, T., Blain, P., Hunke, E., and Korn, P.: Simulating Linear Kinematic Features in Viscous-Plastic Sea Ice Models on Quadrilateral and Triangular Grids with Different Variable Staggering, J. Adv. Model. Earth Sy., 13, e2021MS002523, https://doi.org/10.1029/2021MS002523, 2021. a, b, c
Méndez, M., Tinetti, F. G., and Overbey, J. L.: Climate models: challenges for Fortran development tools, in: 2014 Second International Workshop on Software Engineering for High Performance Computing in Computational Science and Engineering, IEEE, 6–12, https://doi.org/10.1109/SE-HPCCSE.2014.7, 2014. a
Portegies Zwart, S.: The Ecological Impact of High-Performance Computing in Astrophysics, Nat. Astronomy, 4, 819–822, https://doi.org/10.1038/s41550-020-1208-y, 2020. a
Rasmussen, T. A. S., Poulsen, J., Ribergaard, M. H., Sasanka, R., Craig, A. P., Hunke, E. C., and Rethmeier, S.: Refactoring the elastic–viscous–plastic solver from the sea ice model CICE v6.5.1 for improved performance, Geosci. Model Dev., 17, 6529–6544, https://doi.org/10.5194/gmd-17-6529-2024, 2024. a, b, c, d, e
Semtner, A. J.: A Model for the Thermodynamic Growth of Sea Ice in Numerical Investigations of Climate, J. Phys. Oceanogr., 6, 379–389, https://doi.org/10.1175/1520-0485(1976)006%3C0379:AMFTTG%3E2.0.CO;2, 1976. a
Short summary
Climate simulations help us understand the Earth system and its evolution. The models used to perform these simulations are highly complex, require significant programming expertise to build and consume a lot of energy. A key component of climate models is their sea ice components. In this work, we present a sea ice model that offers an easier development process while maintaining strong performance. The model is able to run on a computer's graphics card, which greatly reduces its energy usage.
Climate simulations help us understand the Earth system and its evolution. The models used to...
