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

Viewed

Total article views: 1,857 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,284 318 255 1,857 36 37
  • HTML: 1,284
  • PDF: 318
  • XML: 255
  • Total: 1,857
  • BibTeX: 36
  • EndNote: 37
Views and downloads (calculated since 03 Jun 2024)
Cumulative views and downloads (calculated since 03 Jun 2024)

Viewed (geographical distribution)

Total article views: 1,857 (including HTML, PDF, and XML) Thereof 1,815 with geography defined and 42 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 05 May 2025
Download
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.
Read more
Share