Articles | Volume 24, issue 12
https://doi.org/10.5194/acp-24-7041-2024
https://doi.org/10.5194/acp-24-7041-2024
Opinion
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19 Jun 2024
Opinion | Highlight paper |  | 19 Jun 2024

Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence

Tapio Schneider, L. Ruby Leung, and Robert C. J. Wills

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Cited articles

Adler, R. F., Sapiano, M. R. P., Huffman, G. J., Wang, J.-J., Gu, G., Bolvin, D., Chiu, L., Schneider, U., Becker, A., Nelkin, E., Xie, P., Ferraro, R., and Shin, D.-B.: The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation, Atmosphere, 9, 138, https://doi.org/10.3390/atmos9040138, 2018. a, b
Anber, U. M., Giangrande, S. E., Donner, L. J., and Jensen, M. P.: Updraft constraints on entrainment: insights from Amazonian deep convection, J. Atmos. Sci., 76, 2429–2442, https://doi.org/10.1175/JAS-D-18-0234.1, 2019. a
Arakawa, A. and Schubert, W. H.: Interaction of a cumulus cloud ensemble with the large-scale environment. Part I, J. Atmos. Sci., 31, 674–701, 1974. a, b, c
Arakawa, A. and Wu, C.-M.: A unified representation of deep moist convection in numerical modeling of the atmosphere: Part I, J. Atmos. Sci., 70, 1977–1992, https://doi.org/10.1175/JAS-D-12-0330.1, 2013. a
Arakawa, A., Jung, J.-H., and Wu, C.-M.: Toward unification of the multiscale modeling of the atmosphere, Atmos. Chem. Phys., 11, 3731–3742, https://doi.org/10.5194/acp-11-3731-2011, 2011. a
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Executive editor
This article was solicited for the ACP 20th Anniversary collection. It received positive reviews that very nicely contributed to the ideas and to which the authors responded thoroughly. It is a stimulating read, combining 'big-picture' considerations with more detailed technical discussion of important and illuminating examples.
Short summary

Climate models are crucial for predicting climate change in detail. This paper proposes a balanced approach to improving their accuracy by combining traditional process-based methods with modern artificial intelligence (AI) techniques while maximizing the resolution to allow for ensemble simulations. The authors propose using AI to learn from both observational and simulated data while incorporating existing physical knowledge to reduce data demands and improve climate prediction reliability.

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