Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 6
Articles | Volume 18, issue 6
https://doi.org/10.5194/gmd-18-1917-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-1917-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 6
Development and technical paper
 | 
24 Mar 2025
Development and technical paper |  | 24 Mar 2025

A Fortran–Python interface for integrating machine learning parameterization into earth system models

Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues

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Short summary
Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
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