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Harnessing AI and computing to advance climate modelling and prediction

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There are contrasting views on how to produce the accurate predictions that are needed to guide climate change adaptation. Here, we argue for harnessing artificial intelligence, building on domain-specific knowledge and generating ensembles of moderately high-resolution (10–50 km) climate simulations as anchors for detailed hazard models.

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Fig. 1: Improving climate models and predictions by learning from observational and simulated data.

NASA (top image and satellites); K. Pressel, D. Menemenlis, C. Hill and G. Manucharyan (Clouds, ocean turbulence and sea ice images)

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Acknowledgements

T.S., R.F. and A.S. acknowledge support from E. and W. Schmidt (by recommendation of Schmidt Futures) and the National Science Foundation (grant AGS-1835860). K.E. acknowledges support from the National Science Foundation (grant AGS-1906768). T.M. acknowledges support from VolkswagenStiftung (grant Az:97721). L.R.L. is supported by the Office of Science, US Department of Energy Biological and Environmental Research, as part of the Earth system model development and regional and global model analysis program areas. The Pacific Northwest National Laboratory is operated for the Department of Energy by the Battelle Memorial Institute under contract no. DE-AC05-76RLO1830. N.L. acknowledges support from the National Science Foundation (grant no. 2103754, as part of the Megalopolitan Coastal Transformation Hub). The National Center for Atmospheric Research is sponsored by the National Science Foundation. We thank M. Hell for preparing Fig. 1, and K. Pressel, D. Menemenlis, C. Hill and G. Manucharyan for providing the high-resolution visualizations of clouds, ocean flows and Arctic sea ice.

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Authors and Affiliations

  1. California Institute of Technology, Pasadena, CA, USA

    Tapio Schneider & Andrew Stuart

  2. Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

    Swadhin Behera & Toshio Yamagata

  3. Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce, Italy

    Giulio Boccaletti & Antonio Navarra

  4. National Center for Atmospheric Research, Boulder, CO, USA

    Clara Deser & Joseph Tribbia

  5. Massachusetts Institute of Technology, Cambridge, MA, USA

    Kerry Emanuel & Raffaele Ferrari

  6. Pacific Northwest National Laboratory, Richland, WA, USA

    L. Ruby Leung

  7. Princeton University, Princeton, NJ, USA

    Ning Lin

  8. University of Konstanz, Konstanz, Germany

    Thomas Müller

  9. Dipartimento di Scienze Biologiche, Geologiche e Ambientali, Universita’ di Bologna, Bologna, Italy

    Antonio Navarra

  10. National Agency for Civil Aviation and Meteorology, Dakar, Senegal

    Ousmane Ndiaye

Authors
  1. Tapio Schneider
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  2. Swadhin Behera
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  3. Giulio Boccaletti
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  4. Clara Deser
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  5. Kerry Emanuel
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  6. Raffaele Ferrari
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  7. L. Ruby Leung
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  8. Ning Lin
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  9. Thomas Müller
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  10. Antonio Navarra
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  11. Ousmane Ndiaye
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  12. Andrew Stuart
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  13. Joseph Tribbia
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  14. Toshio Yamagata
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Corresponding author

Correspondence to Tapio Schneider.

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Competing interests

T.S. has an additional affiliation as a visiting researcher at Google LLC. All other authors declare no competing interests.

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Schneider, T., Behera, S., Boccaletti, G. et al. Harnessing AI and computing to advance climate modelling and prediction. Nat. Clim. Chang. 13, 887–889 (2023). https://doi.org/10.1038/s41558-023-01769-3

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