Early warning of complex climate risk with integrated artificial intelligence

20458 7 109209 2 331171 document 109209 s41467-025-57640-w.pdf application/pdf 719b9b0ae3824ee29c5ceeb92a7805b3 MD5 1694866 2025-03-17 13:22:30 https://pure.iiasa.ac.at/id/eprint/20458/1/s41467-025-57640-w.pdf 20458 1 1 application/pdf text en public cc_by_4

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/109209 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/109209 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/109209 archive 353 disk0/00/02/04/58 2025-03-17 13:24:27 2025-08-27 05:00:23 2025-03-17 13:24:27 article show Reichstein M. Benson V. Blunk J. Camps-Valls G. Creutzig F. Fearnley C.J. Han B. Kornhuber K. 2521 0000-0001-5466-2059 Rahaman N. Schölkopf B. Tárraga J.M. Vinuesa R. Dall K. Denzler J. Frank D. Martini G. Nganga N. Maddix D.C. Weldemariam K. Early warning of complex climate risk with integrated artificial intelligence pub ECE ICI As climate change accelerates, human societies face growing exposure to disasters and stress, highlighting the urgent need for effective early warning systems (EWS). These systems monitor, assess, and communicate risks to support resilience and sustainable development, but challenges remain in hazard forecasting, risk communication, and decision-making. This perspective explores the transformative potential of integrated Artificial Intelligence (AI) modeling. We highlight the role of AI in developing multi-hazard EWSs that integrate Meteorological and Geospatial foundation models (FMs) for impact prediction. A user-centric approach with intuitive interfaces and community feedback is emphasized to improve crisis management. To address climate risk complexity, we advocate for causal AI models to avoid spurious predictions and stress the need for responsible AI practices. We highlight the FATES (Fairness, Accountability, Transparency, Ethics, and Sustainability) principles as essential for equitable and trustworthy AI-based Early Warning Systems for all. We further advocate for decadal EWSs, leveraging climate ensembles and generative methods to enable long-term, spatially resolved forecasts for proactive climate adaptation. 2025-03-15 published NPG doi:10.1038/s41467-025-57640-w 3792 public Nature Communications 16 1 e2564 TRUE 2041-1723 no info:eu-repo/semantics/article