Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Pushing the frontiers in climate modelling and analysis with machine learning

Nature Climate Change volume 14, pages 916–928 (2024)Cite this article

Subjects

Abstract

Climate modelling and analysis are facing new demands to enhance projections and climate information. Here we argue that now is the time to push the frontiers of machine learning beyond state-of-the-art approaches, not only by developing machine-learning-based Earth system models with greater fidelity, but also by providing new capabilities through emulators for extreme event projections with large ensembles, enhanced detection and attribution methods for extreme events, and advanced climate model analysis and benchmarking. Utilizing this potential requires key machine learning challenges to be addressed, in particular generalization, uncertainty quantification, explainable artificial intelligence and causality. This interdisciplinary effort requires bringing together machine learning and climate scientists, while also leveraging the private sector, to accelerate progress towards actionable climate science.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: How ML can advance climate modelling and analysis.
Fig. 2: Schematic diagram for integrating ML with process modelling.
Fig. 3: Potential for reducing systematic errors in hybrid Earth system models.

Similar content being viewed by others

References

  1. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  Google Scholar 

  2. Lee, J.-Y. et al. in Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) 553–672 (Cambridge Univ. Press, 2021).

  3. Lam, R. et al. Learning skillful medium-range global weather forecasting. Science 382, 1416–1421 (2023).

    Article  CAS  Google Scholar 

  4. Price, I. et al. GenCast: diffusion-based ensemble forecasting for medium-range weather. Preprint at https://doi.org/10.48550/arXiv.2312.15796 (2023).

  5. Monteleoni, C. et al. in Computational Intelligent Data Analysis for Sustainable Development (eds Yu, T. et al.) 81–126 (CRC Press, 2013).

  6. Rolnick, D. et al. Tackling climate change with machine learning. ACM Comput. Surv. 55, 42 (2022).

    Google Scholar 

  7. Gentine, P., Eyring, V. & Beucler, T. in Deep Learning for the Parametrization of Subgrid Processes in Climate Models (Camps-Valls, G. et al.) Ch. 21, 307–314. (John Wiley & Sons, 2021).

  8. Reichstein, M. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204 (2019).

    Article  CAS  Google Scholar 

  9. Eyring, V., Gentine, P., Camps-Valls, G., Lawrence, D. M. & Reichstein, M. AI-empowered next-generation multiscale climate modelling for mitigation and adaptation. Nat. Geosci. https://doi.org/10.1038/s41561-024-01527-w (2024).

  10. Watson-Parris, D. et al. ClimateBench v1. 0: A benchmark for data-driven climate projections. J. Adv. Model. Earth Syst. 14, e2021MS002954 (2022).

  11. Yu, S. et al. ClimSim: an open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. Preprint at https://doi.org/10.48550/arXiv.2306.08754 (2023).

  12. Beucler, T. et al. Climate-invariant machine learning. Sci. Adv. 10, eadj7250 (2024).

    Article  Google Scholar 

  13. Wu, D. et al. Quantifying uncertainty in deep spatiotemporal forecasting. In Proc. 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 1841–1851 (Association for Computing Machinery, 2021).

  14. McGovern, A. et al. Making the black box more transparent: understanding the physical implications of machine learning. Bull. Am. Meteorol. Soc. 100, 2175–2199 (2019).

    Article  Google Scholar 

  15. Runge, J., Gerhardus, A., Varando, G., Eyring, V. & Camps-Valls, G. Causal inference for time series. Nat. Rev. Earth Environ. 10, 2553 (2023).

    Google Scholar 

  16. Iglesias-Suarez, F. et al. Causally-informed deep learning to improve climate models and projections. J. Geophys. Res. Atmos. 129, 2023–039202 (2024).

    Article  Google Scholar 

  17. Eyring, V. et al. Reflections and projections on a decade of climate science. Nat. Clim. Change 11, 279–285 (2021).

    Article  Google Scholar 

  18. Eyring, V. et al. in Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) 423–552 (Cambridge Univ. Press, 2021).

  19. Stevens, B. et al. DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains. Prog. Earth Planet. Sci. 6, 61 (2019).

  20. Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G. & Yacalis, G. Could machine learning break the convection parameterization deadlock? Geophys. Res. Lett. 45, 5742–5751 (2018).

    Article  Google Scholar 

  21. Grundner, A. et al. Deep learning based cloud cover parameterization for ICON. J. Adv. Model. Earth Syst. 14, 2021–002959 (2022).

    Article  Google Scholar 

  22. Behrens, G. et al. Improving atmospheric processes in earth system models with deep learning ensembles and stochastic parameterizations. Preprint at https://doi.org/10.48550/arXiv.2402.03079 (2024).

  23. Bretherton, C. S. et al. Correcting coarse-grid weather and climate models by machine learning from global storm-resolving simulations. J. Adv. Model. Earth Syst. 14, e2021MS002794 (2022).

  24. Rasp, S., Pritchard, M. S. & Gentine, P. Deep learning to represent sub-grid processes in climate models. Proc. Natl Acad. Sci. USA 115, 9684–9689 (2018).

    Article  CAS  Google Scholar 

  25. Brenowitz, N. D. et al. Machine learning climate model dynamics: offline versus online performance. In NeurIPS 2020 Workshop on Tackling Climate Change with Machine Learning (2020).

  26. Beucler, T. et al. Enforcing analytic constraints in neural networks emulating physical systems. Phys. Rev. Lett. 126, 98302 (2021).

    Article  CAS  Google Scholar 

  27. Wang, R., Walters, R. & Yu, R. Incorporating symmetry into deep dynamics models for improved generalization. Preprint at https://doi.org/10.48550/arXiv.2002.03061 (2020).

  28. 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 (2020).

    Article  Google Scholar 

  29. Grundner, A., Beucler, T., Gentine, P. & Eyring, V. Data-driven equation discovery of a cloud cover parameterization. J. Adv. Model. Earth Syst. 16, 2023–003763 (2024).

    Article  Google Scholar 

  30. Camps-Valls, G. et al. Discovering causal relations and equations from data. Phys. Rep. 1044, 1–68 (2023).

    Article  Google Scholar 

  31. Couldrey, M. P. et al. What causes the spread of model projections of ocean dynamic sea-level change in response to greenhouse gas forcing? Clim. Dyn. 56, 155–187 (2021).

    Article  Google Scholar 

  32. Zanna, L. & Bolton, T. Data-driven equation discovery of ocean mesoscale closures. Geophys. Res. Lett. 47, e2020GL088376 (2020).

  33. Zhang, C. et al. Implementation and evaluation of a machine learned mesoscale eddy parameterization into a numerical ocean circulation model. J. Adv. Model. Earth Syst. 15, e2023MS003697 (2023).

    Article  Google Scholar 

  34. Guillaumin, A. P. & Zanna, L. Stochastic-deep learning parameterization of ocean momentum forcing. J. Adv. Model. Earth Syst. 13, e2021MS002534 (2021).

  35. Bolton, T. & Zanna, L. Applications of deep learning to ocean data inference and subgrid parameterization. J. Adv. Model. Earth Syst. 11, 376–399 (2019).

    Article  Google Scholar 

  36. Ross, A., Li, Z., Perezhogin, P., Fernandez-Granda, C. & Zanna, L. Benchmarking of machine learning ocean subgrid parameterizations in an idealized model. J. Adv. Model. Earth Syst. 15, e2022MS003258 (2023).

    Article  Google Scholar 

  37. Friedlingstein, P. et al. Global carbon budget 2023. Earth Syst. Sci. Data 15, 5301–5369 (2023).

    Article  Google Scholar 

  38. Dagon, K., Sanderson, B. M., Fisher, R. A. & Lawrence, D. M. A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5. Adv. Stat. Climatol. Meteorol. Oceanogr. 6, 223–244 (2020).

    Article  Google Scholar 

  39. Humphrey, V. et al. Soil moisture–atmosphere feedback dominates land carbon uptake variability. Nature 592, 65–69 (2021).

    Article  CAS  Google Scholar 

  40. Shen, C. et al. Differentiable modelling to unify machine learning and physical models for geosciences. Nat. Rev. Earth Environ. 4, 552–567 (2023).

    Article  Google Scholar 

  41. Zhao, W. L. et al. Physics-constrained machine learning of evapotranspiration. Geophys. Res. Lett. 46, 14496–14507 (2019).

    Article  Google Scholar 

  42. Yang, T. et al. Evaluation and machine learning improvement of global hydrological model-based flood simulations. Environ. Res. Lett. 14, 114027 (2019).

    Article  Google Scholar 

  43. Wang, N., Zhang, D., Chang, H. & Li, H. Deep learning of subsurface flow via theory-guided neural network. J. Hydrol. 584, 124700 (2020).

    Article  Google Scholar 

  44. Kraft, B., Jung, M., Körner, M., Koirala, S. & Reichstein, M. Towards hybrid modeling of the global hydrological cycle. Hydrol. Earth Syst. Sci. 26, 1579–1614 (2022).

    Article  Google Scholar 

  45. Xie, K. et al. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships. J. Hydrol. 603, 127043 (2021).

    Article  Google Scholar 

  46. Nathaniel, J., Liu, J. & Gentine, P. MetaFlux: meta-learning global carbon fluxes from sparse spatiotemporal observations. Sci. Data 10, 440 (2023).

  47. Peherstorfer, B., Willcox, K. & Gunzburger, M. Survey of multifidelity methods in uncertainty propagation, inference, and optimization. SIAM Rev. 60, 550–591 (2018).

    Article  Google Scholar 

  48. Cutajar, K., Pullin, M., Damianou, A., Lawrence, N. & González, J. Deep Gaussian processes for multi-fidelity modeling. Preprint at https://doi.org/10.48550/arXiv.1903.07320 (2019).

  49. Delaunay, A. & Christensen, H. M. Interpretable deep learning for probabilistic MJO prediction. Geophys. Res. Lett. 49, 2022–098566 (2022).

    Article  Google Scholar 

  50. Kurth, T. et al. FourCastNet: accelerating global high-resolution weather forecasting using adaptive Fourier neural operators. Preprint at https://doi.org/10.48550/arxiv.2208.05419 (2022).

  51. Beusch, L., Gudmundsson, L. & Seneviratne, S. I. Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land. Earth Syst. Dyn. 11, 139–159 (2020).

    Article  Google Scholar 

  52. Doury, A., Somot, S., Gadat, S., Ribes, A. & Corre, L. Regional climate model emulator based on deep learning: concept and first evaluation of a novel hybrid downscaling approach. Clim. Dyn. 60, 1751–1779 (2023).

    Article  Google Scholar 

  53. Quilcaille, Y., Gudmundsson, L., Beusch, L., Hauser, M. & Seneviratne, S. I. Showcasing MESMER-X: spatially resolved emulation of annual maximum temperatures of earth system models. Geophys. Res. Lett. 49, 2022–099012 (2022).

    Article  Google Scholar 

  54. Ham, Y.-G., Kim, J.-H. & Luo, J.-J. Deep learning for multi-year ENSO forecasts. Nature 573, 568–572 (2019).

    Article  CAS  Google Scholar 

  55. Immorlano, F. et al. Transferring climate change knowledge. Preprint at https://doi.org/10.48550/arXiv.2309.14780 (2023).

  56. IPCC. Summary for Policymakers. In Climate Change 2021: The Physical Science Basis (eds Masson Delmotte, V. et al.) 3–32 (Cambridge Univ. Press, 2021).

  57. O’Brien, T. A. et al. Increases in future AR count and size: overview of the ARTMIP tier 2 CMIP5/6 experiment. J. Geophys. Res. Atmos. 127, e2021JD036013 (2022).

  58. Kurth, T. et al. Exascale deep learning for climate analytics. Preprint at https://doi.org/10.48550/arxiv.1810.01993 (2018).

  59. Salcedo-Sanz, S. et al. Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review. Theor. Appl. Climatol. 155, 1–44 (2024).

    Article  Google Scholar 

  60. O’Brien, T. A. et al. Detection of atmospheric rivers with inline uncertainty quantification: TECA-BARD v1.0.1. Geosci. Model Dev. 13, 6131–6148 (2020).

    Article  Google Scholar 

  61. Prabhat, K. et al. ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather. Geosci. Model Dev. 14, 107–124 (2021).

    Article  Google Scholar 

  62. Liu, Y. et al. Application of deep convolutional neural networks for detecting extreme weather in climate datasets. Preprint at https://doi.org/10.48550/arXiv.1605.01156 (2016).

  63. Muszynski, G., Kashinath, K., Kurlin, V. & Wehner, M. Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets. Geosci. Model Dev. 12, 613–628 (2019).

    Article  Google Scholar 

  64. Kim, S. et al. Deep-Hurricane-Tracker: tracking and forecasting extreme climate events. In 2019 IEEE Winter Conference on Applications of Computer Vision 1761–1769 (IEEE, 2019).

  65. Molina, M. J. et al. A review of recent and emerging machine learning applications for climate variability and weather phenomena. Artif. Intell. Earth Syst. https://doi.org/10.1175/AIES-D-22-0086.1 (2023).

    Article  Google Scholar 

  66. Molina, M. J., Gagne, D. J. & Prein, A. F. A benchmark to test generalization capabilities of deep learning methods to classify severe convective storms in a changing climate. Earth Space Sci. 8, e2020EA001490 (2021).

  67. Vandal, T., Kodra, E. & Ganguly, A. R. Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation. Theor. Appl. Climatol. 137, 557–570 (2018).

    Article  Google Scholar 

  68. Miloshevich, G., Cozian, B., Abry, P., Borgnat, P. & Bouchet, F. Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data. Physical Review Fluids 8, 040501 (2023).

  69. Prodhan, F. A. et al. Projection of future drought and its impact on simulated crop yield over South Asia using ensemble machine learning approach. Sci. Total Environ. 807, 151029 (2022).

    Article  CAS  Google Scholar 

  70. Eyring, V. et al. Earth System Model Evaluation Tool (ESMValTool) v2.0 – an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP. Geosci. Model Dev. 13, 3383–3438 (2020).

    Article  CAS  Google Scholar 

  71. Dijkstra, H., Hernandez-Garcia, E., Masoller, C. & Barreiro, M. Networks in Climate (Cambridge Univ. Press, 2019).

    Book  Google Scholar 

  72. Karmouche, S. et al. Regime-oriented causal model evaluation of Atlantic–Pacific teleconnections in CMIP6. Earth Syst. Dyn. 14, 309–344 (2023).

    Article  Google Scholar 

  73. Nowack, P., Runge, J., Eyring, V. & Haigh, J. D. Causal networks for climate model evaluation and constrained projections. Nature Commun. 11, 1415 (2020).

  74. Barnes, E. A., Barnes, R. J., Martin, Z. K. & Rader, J. K. This looks like that there: interpretable neural networks for image tasks when location matters. Artif. Intell. Earth Syst. 1, e220001 (2022).

  75. Schlund, M. et al. Constraining uncertainty in projected gross primary production with machine learning. J. Geophys. Res. Biogeosci. 125, e2019JG005619 (2020).

  76. Mooers, G. et al. Comparing storm resolving models and climates via unsupervised machine learning. Sci. Rep. 13, 22365 (2023).

    Article  CAS  Google Scholar 

  77. Lopez-Gomez, I., McGovern, A., Agrawal, S. & Hickey, J. Global extreme heat forecasting using neural weather models. Artif. Intell. Earth Syst. 2, 220035 (2023).

    Google Scholar 

  78. Boulaguiem, Y., Zscheischler, J., Vignotto, E., van der Wiel, K. & Engelke, S. Modeling and simulating spatial extremes by combining extreme value theory with generative adversarial networks. Environ. Data Sci. 1, 5 (2022).

    Article  Google Scholar 

  79. Jiang, C. et al. MeshfreeFlowNet: a physics-constrained deep continuous space-time super-resolution framework. In Proc. International Conference for High Performance Computing, Networking, Storage and Analysis (IEEE, 2020).

  80. Kendall, A. & Gal, Y. What uncertainties do we need in Bayesian deep learning for computer vision? Preprint at https://doi.org/10.48550/arXiv.1703.04977 (2017).

  81. Gal, Y. & Ghahramani, Z. Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In Proc. 33rd International Conference on Machine Learning (eds Balcan, M. F. & Weinberger, K. Q.) 1050–1059 (Proceedings of Machine Learning Research, 2016).

  82. Fort, S., Hu, H. & Lakshminarayanan, B. Deep ensembles: a loss landscape perspective. Preprint at https://doi.org/10.48550/arXiv.1912.02757 (2019).

  83. Osband, I., Blundell, C., Pritzel, A. & Van Roy, B. Deep exploration via bootstrapped DQN. In Advances in Neural Information Processing Systems (eds Lee, D. et al.) Vol. 29 (Curran Associates, 2016).

  84. Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. Preprint at https://doi.org/10.48550/arXiv.1312.6114 (2013).

  85. Cachay, S. R., Zhao, B., Joren, H. & Yu, R. DYffusion: a dynamics-informed diffusion model for spatiotemporal forecasting. Preprint at https://doi.org/10.48550/arXiv.2306.01984 (2023).

  86. Hall, D. et al. NVIDIA’s Earth-2: an interactive digital twin of the Earth and its subsystems. In XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (2023); https://doi.org/10.57757/IUGG23-3980

  87. van Straaten, C., Whan, K., Coumou, D., van den Hurk, B. & Schmeits, M. Using explainable machine learning forecasts to discover subseasonal drivers of high summer temperatures in Western and Central Europe. Mon. Weather Rev. 150, 1115–1134 (2022).

    Article  Google Scholar 

  88. Mamalakis, A., Ebert-Uphoff, I. & Barnes, E. A. in xxAI - Beyond Explainable AI (eds Holzinger, A. et al.) 315–339 (Springer, 2022).

  89. Rader, J. K., Barnes, E. A., Ebert-Uphoff, I. & Anderson, C. Detection of forced change within combined climate fields using explainable neural networks. J. Adv. Model. Earth Syst. 14, e2021MS002941 (2022).

  90. Toms, B. A., Barnes, E. A. & Hurrell, J. W. Assessing decadal predictability in an earth-system model using explainable neural networks. Geophys. Res. Lett. 48, e2021GL093842 (2021).

  91. Mamalakis, A., Barnes, E. A. & Ebert-Uphoff, I. Carefully choose the baseline: lessons learned from applying XAI attribution methods for regression tasks in geoscience. Artif. Intell. Earth Syst. 2, 220058 (2023).

    Google Scholar 

  92. Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206–215 (2019).

    Article  Google Scholar 

  93. McGraw, M. C. & Barnes, E. A. Memory matters: a case for granger causality in climate variability studies. J. Clim. 31, 3289–3300 (2018).

    Article  Google Scholar 

  94. Bauer, P., Stevens, B. & Hazeleger, W. A digital twin of Earth for the green transition. Nat. Clim. Change 11, 80–83 (2021).

    Article  Google Scholar 

  95. Foundational Research Gaps and Future Directions for Digital Twins (National Academies Press, 2024).

  96. Nguyen, T., Brandstetter, E. J., Kapoor, A., Gupta, J. K. & Grover, A. ClimaX: a foundation model for weather and climate. Preprint at https://doi.org/10.48550/arxiv.2301.10343 (2023).

  97. Bonev, B. et al. Spherical Fourier neural operators: learning stable dynamics on the sphere. Preprint at https://doi.org/10.48550/arXiv.2306.03838 (2023).

  98. Mardani, M. et al. Residual diffusion modeling for km-scale atmospheric downscaling. Preprint at https://doi.org/10.48550/arXiv.2309.15214 (2023).

  99. Brenowitz, N. D. et al. A practical probabilistic benchmark for AI weather models. Preprint at https://doi.org/10.48550/arXiv.2401.15305 (2023).

  100. Nevo, S. et al. Flood forecasting with machine learning models in an operational framework. Hydrol. Earth Syst. Sci. 26, 4013–4032 (2022).

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge the Aspen Global Change Institute for hosting a workshop on Exploring the Frontiers in Earth System Modeling with Machine Learning and Big Data in June 2022 as part of its traditionally landmark summer interdisciplinary sessions (https://www.agci.org/event/22s3). The workshop was funded by NASA’s (National Aeronautics and Space Administration) Earth Science Division, the National Oceanic and Atmospheric Administration’s Climate Program Office, the National Science Foundation’s (NSF) Directorate for Geosciences (GEO), and travel support from the World Climate Research Programme. V.E., P.G. and F.I.-S. were funded by the European Research Council (ERC) Synergy Grant ‘Understanding and Modelling the Earth System with Machine Learning (USMILE)’ under the EU Horizon 2020 research and innovation programme (grant agreement 855187). V.E. and K.W. were also supported by the Deutsche Forschungsgemeinschaft (German Research Foundation) through the Gottfried Wilhelm Leibniz Prize awarded to V.E. (EY 22/2-1). W.D.C. was supported by the Director, Office of Science, Office of Biological and Environmental Research of the US Department of Energy (contract DE-AC02-05CH11231) and used resources of the National Energy Research Scientific Computing Center, also supported by the Office of Science of the US Department of Energy (DOE; contract DE-AC02-05CH11231). P.G. acknowledges additional funding from the NSF Science and Technology Center Learning the Earth with Artificial Intelligence and Physics (LEAP; award 2019625-STC) and DOE Advanced Scientific Computing Research programme. Additional support was received for E.A.B.: NSF (AGS-1749261) and US Department of Energy supported by the Regional and Global Model Analysis programme; C.S.B.: Allen Institute for AI; H.M.C.: Natural Environment Research Council (NE/P018238/1); K.D.: US DOE (DE-SC0022070) and NSF (IA 1947282) and NSF (1852977); D.J.G.: NSF (1852977) and NSF (ICER-2019758); D. Hall: NVIDIA; D. Hammerling: US DOE (DE-FE0032311); M.C.M.: NSF (ICER-2019758); G.A.M.: US DOE (DE-SC0022070) and NSF (IA 1947282); M.J.M.: US DOE (DE-SC0022070) and NSF (IA 1947282) and University of Maryland Grand Challenges Grants Program (GC17-2957817); C.M.: NSF (2153040); J.M.: US DOE (DE-AC36-08GO28308); M.S.P.: NSF LEAP (2019625-STC) and DOE (DE-SC0023368 and DE-SC0022255); D.R.: Canada CIFAR AI Chairs programme; J.R.: EU Horizon 2020 research and innovation programme XAIDA (101003469) and ERC Starting Grant CausalEarth (948112); P.S.: ERC project RECAP under the EU Horizon 2020 research and innovation programme (724602) and FORCeS project under the EU Horizon 2020 research programme (821205); O.W.-M.: Allen Institute for AI; R.Y.: US DOE (DE-SC0022255) and NSF (IIS-2146343); L.Z.: Schmidt Futures (G-23-65185) and NSF (GG017158-01). We thank J. Snyder (Lawrence Berkeley National Laboratory) for his work to create Fig. 1.

Author information

Author notes

  1. These authors contributed equally: Veronika Eyring, William D. Collins.

Authors and Affiliations

  1. Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany

    Veronika Eyring, Fernando Iglesias-Suarez & Katja Weigel

  2. University of Bremen, Institute of Environmental Physics, Bremen, Germany

    Veronika Eyring & Katja Weigel

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    William D. Collins

  4. University of California, Berkeley, Berkeley, CA, USA

    William D. Collins

  5. Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA

    Pierre Gentine

  6. Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA

    Elizabeth A. Barnes

  7. Departamento de Ciencias de la Atmósfera y Física de los Océanos, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay

    Marcelo Barreiro

  8. University of Lausanne, Lausanne, Switzerland

    Tom Beucler

  9. CEREA, École des Ponts and EdF R&D, Île-de-France, France

    Marc Bocquet

  10. Allen Institute for Artificial Intelligence, Seattle, WA, USA

    Christopher S. Bretherton & Oliver Watt-Meyer

  11. Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford, UK

    Hannah M. Christensen & Philip Stier

  12. NSF National Center for Atmospheric Research, Boulder, CO, USA

    Katherine Dagon, David John Gagne, Gerald A. Meehl & Maria J. Molina

  13. NVIDIA Corporation, Santa Clara, CA, USA

    David Hall & Michael S. Pritchard

  14. Colorado School of Mines, Golden, CO, USA

    Dorit Hammerling

  15. Google Research, Mountain View, CA, USA

    Stephan Hoyer & Ignacio Lopez-Gomez

  16. Climate Modeling Alliance, California Institute of Technology, Pasadena, CA, USA

    Ignacio Lopez-Gomez

  17. Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA

    Marie C. McGraw

  18. University of Maryland, College Park, MD, USA

    Maria J. Molina

  19. Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA

    Claire Monteleoni

  20. INRIA Paris, Paris, France

    Claire Monteleoni

  21. Computational Science Center, National Renewable Energy Laboratory, Golden, CO, USA

    Juliane Mueller

  22. University of California, Irvine, Irvine, CA, USA

    Michael S. Pritchard

  23. McGill University, Montreal, Quebec, Canada

    David Rolnick

  24. Mila - Quebec AI Institute, Montreal, Quebec, Canada

    David Rolnick

  25. Deutsches Zentrum für Luft- und Raumfahrt, Institut für Datenwissenschaften, Jena, Germany

    Jakob Runge

  26. Technische Universität Berlin, Berlin, Germany

    Jakob Runge

  27. University of California, San Diego, La Jolla, CA, USA

    Rose Yu

  28. Courant Institute, New York University, New York, NY, USA

    Laure Zanna

Authors
  1. Veronika Eyring
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. William D. Collins
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pierre Gentine
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elizabeth A. Barnes
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marcelo Barreiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tom Beucler
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Marc Bocquet
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Christopher S. Bretherton
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hannah M. Christensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Katherine Dagon
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. David John Gagne
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. David Hall
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Dorit Hammerling
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Stephan Hoyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Fernando Iglesias-Suarez
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Ignacio Lopez-Gomez
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Marie C. McGraw
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Gerald A. Meehl
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Maria J. Molina
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Claire Monteleoni
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Juliane Mueller
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Michael S. Pritchard
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. David Rolnick
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Jakob Runge
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Philip Stier
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Oliver Watt-Meyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Katja Weigel
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Rose Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Laure Zanna
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

V.E. and W.D.C. jointly led the writing of the Perspective, and coordinated and oversaw the execution of the study. They conceived the concept of pushing the frontiers of climate modelling and analysis with ML for this study with support from P.G. and contributed to all sections of the Perspective. All authors contributed to the concept, drafting and writing of the manuscript and to the discussions.

Corresponding authors

Correspondence to Veronika Eyring or William D. Collins.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks Peter Bauer, Peter Gibson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Eyring, V., Collins, W.D., Gentine, P. et al. Pushing the frontiers in climate modelling and analysis with machine learning. Nat. Clim. Chang. 14, 916–928 (2024). https://doi.org/10.1038/s41558-024-02095-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-024-02095-y

Search

Advanced search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene