Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 24, issue 12
Articles | Volume 24, issue 12
https://doi.org/10.5194/acp-24-7041-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-24-7041-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 24, issue 12
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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Latest update: 29 Jun 2024
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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.
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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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