Articles | Volume 17, issue 14
https://doi.org/10.5194/gmd-17-5459-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-5459-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
TorchClim v1.0: a deep-learning plugin for climate model physics
David Fuchs
CORRESPONDING AUTHOR
Climate Change Research Centre, Biological, Earth and Environmental Sciences, University of New South Wales, 2052 Sydney, NSW, Australia
Climate and Atmospheric Science Branch, Department of Planning and Environment, Sydney, NSW, Australia
Steven C. Sherwood
Climate Change Research Centre, Biological, Earth and Environmental Sciences, University of New South Wales, 2052 Sydney, NSW, Australia
ARC Centre of Excellence for Climate Extremes, University of New South Wales, 2052 Sydney, NSW, Australia
Abhnil Prasad
Climate Change Research Centre, Biological, Earth and Environmental Sciences, University of New South Wales, 2052 Sydney, NSW, Australia
ARC Centre of Excellence for Climate Extremes, University of New South Wales, 2052 Sydney, NSW, Australia
School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, 2052 Sydney, NSW, Australia
Kirill Trapeznikov
STR, 01801 Woburn, MA, USA
Jim Gimlett
STR, 01801 Woburn, MA, USA
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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
Eaton, B.: User's guide to the Community Atmosphere Model CAM-5.1, NCAR, http://www.cesm.ucar.edu/models/cesm1.0/cam (last access: 1 January 2020), 2011. a
Fuchs, D., Sherwood, S. C., Prasad, A., Trapeznikov, K., and Gimlett, J.: TorchClim v1.0: A deep-learning framework for climate model physics, Zenodo [code and data set], https://doi.org/10.5281/zenodo.8390519, 2023a. a, b
Fuchs, D., Sherwood, S. C., Waugh, D., Dixit, V., England, M. H., Hwong, Y.-L., and Geoffroy, O.: Midlatitude Jet Position Spread Linked to Atmospheric Convective Types, J. Climate, 36, 1247–1265, https://doi.org/10.1175/JCLI-D-21-0992.1, 2023b. a
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., and Yacalis, G.: Could Machine Learning Break the Convection Parameterization Deadlock?, Geophys. Res. Lett., 45, 5742–5751, https://doi.org/10.1029/2018GL078202, 2018. a
Geoffroy, O., Sherwood, S. C., and Fuchs, D.: On the role of the stratiform cloud scheme in the inter-model spread of cloud feedback, J. Adv. Model. Earth Sy., 9, 423–437, https://doi.org/10.1002/2016MS000846, 2017. a
Grise, K. M. and Polvani, L. M.: Southern Hemisphere Cloud–Dynamics Biases in CMIP5 Models and Their Implications for Climate Projections, J. Climate, 27, 6074–6092, https://doi.org/10.1175/JCLI-D-14-00113.1, 2014. a
Howland, M. F., Dunbar, O. R. A., and Schneider, T.: Parameter Uncertainty Quantification in an Idealized GCM With a Seasonal Cycle, J. Adv. Model. Earth Sy., 14, e2021MS002735, https://doi.org/10.1029/2021MS002735, 2022. a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2391 pp., https://doi.org/10.1017/9781009157896, 2021. a
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., and Yang, L.: Physics-informed machine learning, Nature Reviews Physics, 3, 422–440, https://doi.org/10.1038/s42254-021-00314-5, 2021. a, b, c
Kelly, P., Mapes, B., Hu, I.-K., Song, S., and Kuang, Z.: Tangent linear superparameterization of convection in a 10 layer global atmosphere with calibrated climatology, J. Adv. Model. Earth Sy., 9, 932–948, https://doi.org/10.1002/2016MS000871, 2017. a, b
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, arXiv [preprint], https://doi.org/10.48550/ARXIV.1412.6980, 2014. a
Majda, A. and Khouider, B.: Stochastic and mesoscopic models for tropical convection, P. Natl. Acad. Sci. USA, 99, 1123–1128, https://doi.org/10.1073/pnas.032663199, 2002. a
Mooers, G., Pritchard, M., Beucler, T., Ott, J., Yacalis, G., Baldi, P., and Gentine, P.: Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models With Real-Geography Boundary Conditions, J. Adv. Model. Earth Sy., 13, e2020MS002385, https://doi.org/10.1029/2020MS002385, 2021. a, b, c
Neale, R. B., Chen, C.-C., Gettelman, A., Lauritzen, P. H., Park, S., Williamson, D. L., Conley, A. J., Garcia, R., Kinnison, D., Lamarque, J.-F., Mills, M. J., Tilmes, S., Morrison, H., Cameron-Smith, P., Collins, W. D., Iacono, M. J., Easter, R. C., Liu, X., Ghan, S. J., Rasch, P. J., and Taylor, M. A.: Description of the NCAR community atmosphere model (CAM 5.0), NCAR Tech. Note NCAR/TN-486+ STR, 1–12, https://doi.org/10.5065/wgtk-4g06, 2010. a
Nuijens, L., Medeiros, B., Sandu, I., and Ahlgrimm, M.: Observed and modeled patterns of covariability between low-level cloudiness and the structure of the trade-wind layer, J. Adv. Model. Earth Sy., 7, 1741–1764, https://doi.org/10.1002/2015MS000483, 2015. a
O'Gorman, P. A. and Dwyer, J. G.: Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events, J. Adv. Model. Earth Sy., 10, 2548–2563, https://doi.org/10.1029/2018MS001351, 2018. a
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S.: PyTorch: An Imperative Style, High-Performance Deep Learning Library, in: Advances in Neural Information Processing Systems 32, 8024–8035, Curran Associates, Inc., http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (last access: 8 December 2019), 2019. a
Rampal, N., Gibson, P. B., Sood, A., Stuart, S., Fauchereau, N. C., Brandolino, C., Noll, B., and Meyers, T.: High-resolution downscaling with interpretable deep learning: Rainfall extremes over New Zealand, Weather and Climate Extremes, 38, 100525, https://doi.org/10.1016/j.wace.2022.100525, 2022. a
Rasp, S.: Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz 96 case study (v1.0), Geosci. Model Dev., 13, 2185–2196, https://doi.org/10.5194/gmd-13-2185-2020, 2020. a, b
Satoh, M., Stevens, B., Judt, F., Khairoutdinov, M., Lin, S.-J., Putman, W. M., and Duben, P.: Global Cloud-Resolving Models, Current Climate Change Reports, 5, 172–184, https://doi.org/10.1007/s40641-019-00131-0, 2019. a
Schneider, T., Lan, S., Stuart, A., and Teixeira, J.: Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations, Geophys. Res. Lett., 44, 12396–12417, https://doi.org/10.1002/2017gl076101, 2017. a, b
Simpson, I. R., McKinnon, K. A., Kennedy, D., Lawrence, D. M., Lehner, F., and Seager, R.: Observed humidity trends in dry regions contradict climate models, P. Natl. Acad. Sci. USA, 121, e2302480120, https://doi.org/10.1073/pnas.2302480120, 2024. a
Wang, X., Han, Y., Xue, W., Yang, G., and Zhang, G. J.: Stable climate simulations using a realistic general circulation model with neural network parameterizations for atmospheric moist physics and radiation processes, Geosci. Model Dev., 15, 3923–3940, https://doi.org/10.5194/gmd-15-3923-2022, 2022. a, b, c, d, e, f, g, h
Watt-Meyer, O., Brenowitz, N. D., Clark, S. K., Henn, B., Kwa, A., McGibbon, J., Perkins, W. A., and Bretherton, C. S.: Correcting Weather and Climate Models by Machine Learning Nudged Historical Simulations, Geophys. Res. Lett., 48, e2021GL092555, https://doi.org/10.1029/2021GL092555, e2021GL092555 2021GL092555, 2021. a
Wikipedia: Moore's Law, Wikipedia, https://en.wikipedia.org/wiki/Moore's_law (last access: 5 July 2024), 2022. a
Yuval, J. and O'Gorman, P. A.: Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions, Nat. Commun., 11, 3295, https://doi.org/10.1038/s41467-020-17142-3, 2020. a
Zelinka, M. D., Myers, T. A., Mccoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of Higher Climate Sensitivity in CMIP6 Models, Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020. a
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.
Machine learning (ML) of unresolved processes offers many new possibilities for improving...