Articles | Volume 18, issue 6
https://doi.org/10.5194/gmd-18-1917-2025
https://doi.org/10.5194/gmd-18-1917-2025
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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Cited articles

Bechtold, P., Chaboureau, J.-P., Beljaars, A., Betts, A. K., Köhler, M., Miller, M., and Redelsperger, J.-L.: The simulation of the diurnal cycle of convective precipitation over land in a global model, Q. J. Roy. Meteor. Soc., 130, 3119–3137, https://doi.org/10.1256/qj.03.103, 2004. 
Bogenschutz, P. A., Tang, S., Caldwell, P. M., Xie, S., Lin, W., and Chen, Y.-S.: The E3SM version 1 single-column model, Geosci. Model Dev., 13, 4443–4458, https://doi.org/10.5194/gmd-13-4443-2020, 2020. 
Brenowitz, N. D. and Bretherton, C. S.: Prognostic validation of a neural network unified physics parameterization, Geophys. Res. Lett., 45, 6289–6298, https://doi.org/10.1029/2018gl078510, 2018. 
Brenowitz, N. D. and Bretherton, C. S.: Spatially extended tests of a neural network parametrization trained by coarse-graining, J. Adv. Model. Earth Sy., 11, 2728–2744, https://doi.org/10.1029/2019ms001711, 2019. 
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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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