Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 11
Articles | Volume 19, issue 11
https://doi.org/10.5194/gmd-19-4763-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-4763-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 11
Development and technical paper
 | 
03 Jun 2026
Development and technical paper |  | 03 Jun 2026

Applying corrective machine learning in the E3SM atmosphere model in C+ +  (EAMxx)

Aaron S. Donahue, Elynn Wu, W. Andre Perkins, Peter M. Caldwell, Christopher S. Bretherton, Finn Rebassoo, and Jean-Christophe Golaz

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Short summary
This study tested using machine learning to speed up detailed simulations in the SCREAM (Simple Cloud-Resolving E3SM Atmosphere Model) model. By training ML (machine learning) models to correct a simpler version of SCREAM, some results improved, but others did not. Technical challenges were addressed, and new tools were developed. The work shows promise for making simulations more efficient, though further improvements are needed.
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