Articles | Volume 19, issue 2
https://doi.org/10.5194/gmd-19-713-2026
https://doi.org/10.5194/gmd-19-713-2026
Model description paper
 | 
22 Jan 2026
Model description paper |  | 22 Jan 2026

Toward exascale climate modelling: a python DSL approach to ICON's (icosahedral non-hydrostatic) dynamical core (icon-exclaim v0.2.0)

Anurag Dipankar, Mauro Bianco, Mona Bukenberger, Till Ehrengruber, Nicoletta Farabullini, Oliver Fuhrer, Abishek Gopal, Daniel Hupp, Andreas Jocksch, Samuel Kellerhals, Clarissa A. Kroll, Xavier Lapillonne, Matthieu Leclair, Magdalena Luz, Christoph Müller, Chia Rui Ong, Carlos Osuna, Praveen Pothapakula, Andreas Prein, Matthias Röthlin, William Sawyer, Christoph Schär, Sebastian Schemm, Giacomo Serafini, Hannes Vogt, Ben Weber, Robert C. Jnglin Wills, Nicolas Gruber, and Thomas C. Schulthess

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Cited articles

Adamidis, P., Pfister, E., Bockelmann, H., Zobel, D., Beismann, J.-O., and Jacob, M.: The real challenges for climate and weather modelling on its way to sustained exascale performance: a case study using ICON (v2.6.6), Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, 2025. 
Afanasyev, A., Bianco, M., Mosimann, L., Osuna, C., Thaler, F., Vogt, H., Fuhrer, O., VandeVondele, J., and Schulthess, T. C.: GridTools: A framework for portable weather and climate applications, SoftwareX, 15, https://doi.org/10.1016/j.softx.2021.100707, 2021.  
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, 2021. 
Ben-Nun, T., de Fine Licht, J., Ziogas, A. N., Schneider, T., and Hoefler, T.: Stateful Dataflow Multigraphs: A DataCentric Model for Performance Portability on Heterogeneous Architectures, in: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC '19, November 2019, Association for Computing Machinery, New York, NY, USA, 81, 1–14, https://doi.org/10.1145/3295500.3356173, 2019. 
Bloch-Johnson, J., Rugenstein, M. A. A., Alessi, M. J., Proistosescu, C., Zhao, M., Zhang, B., Williams, A. I. L., Gregory, J. M., Cole, J., Dong, Y., Dufy M. L., Kang, S. M., and Zhou C.: The green's function model intercomparison project (GFMIP) protocol, J. Adv. Model. Earth Syst., 16, e2023MS003700, https://doi.org/10.1029/2023MS003700, 2024. 
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Short summary
Climate models are becoming more detailed and accurate by simulating weather at scales of just a few kilometers. Simulating at km-scale is computationally demanding requiring powerful supercomputers and efficient code. This work presents a refactored dynamical core of a state-of-the-art climate model using a Python-based approach. The refactored code has passed through a sequence of verification and validation demonstrating its usability in performing km-scale global simulations.
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