Harnessing AI and computing to advance climate modelling and prediction

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 simulati...

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Bibliographic Details
Published in:Nature Climate Change
Main Authors: Schneider, Tapio, Behera, Swadhin, Boccaletti, Giulio, Deser, Clara, Emanuel, Kerry, Ferrari, Raffaele, Leung, L. Ruby, Lin, Ning, Müller, Thomas, Navarra, Antonio, Ndiaye, Ousmane, Stuart, Andrew, Tribbia, Joseph, Yamagata, Toshio
Format: Article in Journal/Newspaper
Language:English
Published: Nature Publishing Group 2023
Subjects:
Social Sciences (miscellaneous)
Environmental Science (miscellaneous)
Arctic
Pacific
Climate change
Sea ice
Online Access:https://doi.org/10.1038/s41558-023-01769-3
https://rdcu.be/dnb53
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Summary: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. 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. T.S. has an additional affiliation as a visiting researcher at Google LLC. All other authors declare no competing interests.

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