Articles | Volume 17, issue 1
https://doi.org/10.5194/gmd-17-261-2024
https://doi.org/10.5194/gmd-17-261-2024
Development and technical paper
 | 
12 Jan 2024
Development and technical paper |  | 12 Jan 2024

Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc

Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez

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Cited articles

Bauer, P., Dueben, P. D., Hoefler, T., Quintino, T., Schulthess, T. C., and Wedi, N. P.: The digital revolution of Earth-system science, Nat. Comput. Sci., 1, 104–113, https://doi.org/10.1038/s43588-021-00023-0, 2021a. a
Bauer, P., Stevens, B., and Hazeleger, W.: A digital twin of Earth for the green transition, Nat. Clim. Change, 11, 80–83, https://doi.org/10.1038/s41558-021-00986-y, 2021b. a
Baumeister, P. F. and Hoffmann, L.: Fast infrared radiative transfer calculations using graphics processing units: JURASSIC-GPU v2.0, Geosci. Model Dev., 15, 1855–1874, https://doi.org/10.5194/gmd-15-1855-2022, 2022. a
Betancourt, C., Stomberg, T. T., Edrich, A.-K., Patnala, A., Schultz, M. G., Roscher, R., Kowalski, J., and Stadtler, S.: Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties, Geosci. Model Dev., 15, 4331–4354, https://doi.org/10.5194/gmd-15-4331-2022, 2022. a
Bishnoi, A., Stein, O., Meyer, C. I., Redler, R., Eicker, N., Haak, H., Hoffmann, L., Klocke, D., Kornblueh, L., and Suarez, E.: Earth system modeling on Modular Supercomputing Architectures: coupled atmosphere-ocean simulations with ICON 2.6.6-rc, Edmond [code and data set], https://doi.org/10.17617/3.4NHKPH, 2023. a
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Short summary
We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
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