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A Fortran-Python Interface for Integrating Machine Learning Parameterization into Earth System Models
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  • Tao Zhang,
  • Cyril Julien Morcrette,
  • Meng Zhang,
  • Wuyin Lin,
  • Shaocheng Xie,
  • Ye Liu,
  • Kwinten Van Weverberg,
  • Joana Rodrigues
Tao Zhang
Brookhaven National Laboratory

Corresponding Author:[email protected]

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Cyril Julien Morcrette
Met Office
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Meng Zhang
Lawrence Livermore National Laboratory
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Wuyin Lin
Brookhaven National Laboratory (DOE)
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Shaocheng Xie
Lawrence Livermore National Laboratory
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Ye Liu
Pacific Northwest National Laboratory (DOE)
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Kwinten Van Weverberg
Department of Geography
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Joana Rodrigues
Met Office
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Abstract

Parameterizations in Earth System Models (ESMs) are subject to biases and uncertainties arising from subjective empirical assumptions and incomplete understanding of the underlying physical processes. Recently, the growing representational capability of machine learning (ML) in solving complex problems has spawned immense interests in climate science applications. Specifically, ML-based parameterizations have been developed to represent convection, radiation and microphysics processes in ESMs by learning from observations or high-resolution simulations, which have the potential to improve the accuracies and alleviate the uncertainties. Previous works have developed some surrogate models for these processes using ML. These surrogate models need to be coupled with the dynamical core of ESMs to investigate the effectiveness and their performance in a coupled system. In this study, we present a novel Fortran-Python interface designed to seamlessly integrate ML parameterizations into ESMs. This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and Scikit-learn. We demonstrate the interface’s modularity and reusability through two cases: a ML trigger function for convection parameterization and a ML wildfire model. We conduct a comprehensive evaluation of memory usage and computational overhead resulting from the integration of Python codes into the Fortran ESMs. By leveraging this flexible interface, ML parameterizations can be effectively developed, tested, and integrated into ESMs.
15 Apr 2024Submitted to ESS Open Archive
16 Apr 2024Published in ESS Open Archive