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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ftcaltechauth:oai:authors.library.caltech.edu:z0c0r-2jt68 2024-09-15T18:02:30+00:00 Harnessing AI and computing to advance climate modelling and prediction 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 2023-09 https://doi.org/10.1038/s41558-023-01769-3 https://rdcu.be/dnb53 eng eng Nature Publishing Group https://www.caltech.edu/about/news/artificial-intelligence-is-key-to-better-climate-models-say-researchers https://doi.org/10.1038/s41558-023-01769-3 oai:authors.library.caltech.edu:z0c0r-2jt68 issn:1758-6798 https://rdcu.be/dnb53 info:eu-repo/semantics/closedAccess No commercial reproduction, distribution, display or performance rights in this work are provided. Nature Climate Change, 13(9), 887-889, (2023-09) Social Sciences (miscellaneous) Environmental Science (miscellaneous) info:eu-repo/semantics/article 2023 ftcaltechauth https://doi.org/10.1038/s41558-023-01769-3 2024-08-06T15:35:04Z 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. Article in Journal/Newspaper Climate change Sea ice Caltech Authors (California Institute of Technology) Nature Climate Change 13 9 887 889 |
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Open Polar |
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Caltech Authors (California Institute of Technology) |
op_collection_id |
ftcaltechauth |
language |
English |
topic |
Social Sciences (miscellaneous) Environmental Science (miscellaneous) |
spellingShingle |
Social Sciences (miscellaneous) Environmental Science (miscellaneous) 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 Harnessing AI and computing to advance climate modelling and prediction |
topic_facet |
Social Sciences (miscellaneous) Environmental Science (miscellaneous) |
description |
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. |
format |
Article in Journal/Newspaper |
author |
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 |
author_facet |
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 |
author_sort |
Schneider, Tapio |
title |
Harnessing AI and computing to advance climate modelling and prediction |
title_short |
Harnessing AI and computing to advance climate modelling and prediction |
title_full |
Harnessing AI and computing to advance climate modelling and prediction |
title_fullStr |
Harnessing AI and computing to advance climate modelling and prediction |
title_full_unstemmed |
Harnessing AI and computing to advance climate modelling and prediction |
title_sort |
harnessing ai and computing to advance climate modelling and prediction |
publisher |
Nature Publishing Group |
publishDate |
2023 |
url |
https://doi.org/10.1038/s41558-023-01769-3 https://rdcu.be/dnb53 |
genre |
Climate change Sea ice |
genre_facet |
Climate change Sea ice |
op_source |
Nature Climate Change, 13(9), 887-889, (2023-09) |
op_relation |
https://www.caltech.edu/about/news/artificial-intelligence-is-key-to-better-climate-models-say-researchers https://doi.org/10.1038/s41558-023-01769-3 oai:authors.library.caltech.edu:z0c0r-2jt68 issn:1758-6798 https://rdcu.be/dnb53 |
op_rights |
info:eu-repo/semantics/closedAccess No commercial reproduction, distribution, display or performance rights in this work are provided. |
op_doi |
https://doi.org/10.1038/s41558-023-01769-3 |
container_title |
Nature Climate Change |
container_volume |
13 |
container_issue |
9 |
container_start_page |
887 |
op_container_end_page |
889 |
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1810439955376242688 |