Early warning of complex climate risk with integrated artificial intelligence

Markus Reichstein (), Vitus Benson, Jan Blunk, Gustau Camps-Valls, Felix Creutzig, Carina J. Fearnley, Boran Han, Kai Kornhuber, Nasim Rahaman, Bernhard Schölkopf, José María Tárraga, Ricardo Vinuesa, Karen Dall, Joachim Denzler, Dorothea Frank, Giulia Martini, Naomi Nganga, Danielle C. Maddix and Kommy Weldemariam
Additional contact information
Markus Reichstein: Amazon Web Services
Vitus Benson: ELLIS Unit Jena
Jan Blunk: University of Jena
Gustau Camps-Valls: University of Valencia
Felix Creutzig: Potsdam Institute for Climate Impact Research
Carina J. Fearnley: University College London
Boran Han: Amazon Web Services
Kai Kornhuber: Columbia University
Nasim Rahaman: Max-Planck-Institute for Intelligent Systems
Bernhard Schölkopf: Max-Planck-Institute for Intelligent Systems
José María Tárraga: University of Valencia
Ricardo Vinuesa: KTH Royal Institute of Technology
Karen Dall: German Red Cross
Joachim Denzler: ELLIS Unit Jena
Dorothea Frank: Max-Planck-Institute for Biogeochemistry
Giulia Martini: World Food Program
Naomi Nganga: Kenya Red Cross
Danielle C. Maddix: Amazon Web Services
Kommy Weldemariam: Amazon Web Services

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract 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.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-57640-w Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57640-w

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-57640-w

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().