Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 9
Articles | Volume 19, issue 9
https://doi.org/10.5194/gmd-19-3875-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-3875-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 9
Development and technical paper
 | 
13 May 2026
Development and technical paper |  | 13 May 2026

Representing subgrid-scale cloud effects in a radiation parameterization using machine learning: MLe-radiation v1.0

Katharina Hafner, Sara Shamekh, Guillaume Bertoli, Axel Lauer, Robert Pincus, Julien Savre, and Veronika Eyring

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Short summary
Most climate models cannot resolve clouds and cloud-radiation interactions at coarse horizontal resolutions of about 100 km, which introduces uncertainties. High-resolution models resolve clouds better but are expensive to run. We use short high-resolution simulations and artificial intelligence to learn the cloud-radiation interactions without making any assumptions about the small scales. We propose a new method that significantly reduces cloud related errors.
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