Articles | Volume 17, issue 14
https://doi.org/10.5194/gmd-17-5459-2024
https://doi.org/10.5194/gmd-17-5459-2024
Model description paper
 | 
22 Jul 2024
Model description paper |  | 22 Jul 2024

TorchClim v1.0: a deep-learning plugin for climate model physics

David Fuchs, Steven C. Sherwood, Abhnil Prasad, Kirill Trapeznikov, and Jim Gimlett

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Cited articles

Beucler, T., Pritchard, M., Rasp, S., Ott, J., Baldi, P., and Gentine, P.: Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems, Phys. Rev. Lett., 126, 098302, https://doi.org/10.1103/PhysRevLett.126.098302, 2021. a, b, c
Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q.: Accurate medium-range global weather forecasting with 3D neural networks, Nature, 619, 533–538, 2023. a
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. a, b, c
Brenowitz, N. D., Beucler, T., Pritchard, M., and Bretherton, C. S.: Interpreting and Stabilizing Machine-Learning Parametrizations of Convection, J. Atmos. Sci., 77, 4357–4375, https://doi.org/10.1175/JAS-D-20-0082.1, 2020. a, b, c
Dunbar, O. R. A., Garbuno-Inigo, A., Schneider, T., and Stuart, A. M.: Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM, J. Adv. Model. Earth Sy., 13, e2020MS002454, https://doi.org/10.1029/2020MS002454, 2021. a
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Short summary
Machine learning (ML) of unresolved processes offers many new possibilities for improving weather and climate models, but integrating ML into the models has been an engineering challenge, and there are performance issues. We present a new software plugin for this integration, TorchClim, that is scalable and flexible and thereby allows a new level of experimentation with the ML approach. We also provide guidance on ML training and demonstrate a skillful hybrid ML atmosphere model.
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