Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 13
Articles | Volume 19, issue 13
https://doi.org/10.5194/gmd-19-5907-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-5907-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 13
Development and technical paper
 | 
07 Jul 2026
Development and technical paper |  | 07 Jul 2026

Contribution of physical latent knowledge to the emulation of an atmospheric physics model: a study based on the LMDZ Atmospheric General Circulation Model

Ségolène Crossouard, Soulivanh Thao, Thomas Dubos, Masa Kageyama, Mathieu Vrac, and Yann Meurdesoif

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Short summary
Current atmospheric models are limited by the computational time required for physical processes, known as physical parameterizations. To address this, we developed neural network-based emulators to replace these parameterizations in the IPSL climate model, using a simplified aquaplanet setup and a realistic configuration. We found that incorporating some physical knowledge, such as latent variables, into the learning process can improve predictions.
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