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Harnessing AI and computing to advance climate modelling and prediction

Author

Listed:
  • Tapio Schneider

    (California Institute of Technology)

  • Swadhin Behera

    (Japan Agency for Marine-Earth Science and Technology)

  • Giulio Boccaletti

    (Centro Euro-Mediterraneo sui Cambiamenti Climatici)

  • Clara Deser

    (National Center for Atmospheric Research)

  • Kerry Emanuel

    (Massachusetts Institute of Technology)

  • Raffaele Ferrari

    (Massachusetts Institute of Technology)

  • L. Ruby Leung

    (Pacific Northwest National Laboratory)

  • Ning Lin

    (Princeton University)

  • Thomas Müller

    (University of Konstanz)

  • Antonio Navarra

    (Centro Euro-Mediterraneo sui Cambiamenti Climatici
    Universita’ di Bologna)

  • Ousmane Ndiaye

    (National Agency for Civil Aviation and Meteorology)

  • Andrew Stuart

    (California Institute of Technology)

  • Joseph Tribbia

    (National Center for Atmospheric Research)

  • Toshio Yamagata

    (Japan Agency for Marine-Earth Science and Technology)

Abstract

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.

Suggested Citation

  • Tapio Schneider & Swadhin Behera & Giulio Boccaletti & Clara Deser & Kerry Emanuel & Raffaele Ferrari & L. Ruby Leung & Ning Lin & Thomas Müller & Antonio Navarra & Ousmane Ndiaye & Andrew Stuart & Jo, 2023. "Harnessing AI and computing to advance climate modelling and prediction," Nature Climate Change, Nature, vol. 13(9), pages 887-889, September.
    • Handle: RePEc:nat:natcli:v:13:y:2023:i:9:d:10.1038_s41558-023-01769-3
    DOI: 10.1038/s41558-023-01769-3
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