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Early warning of complex climate risk with integrated artificial intelligence

Author

Listed:
  • Markus Reichstein

    (Amazon Web Services
    ELLIS Unit Jena
    Max-Planck-Institute for Biogeochemistry)

  • Vitus Benson

    (ELLIS Unit Jena
    Max-Planck-Institute for Biogeochemistry
    ETH Zurich)

  • Jan Blunk

    (University of Jena)

  • Gustau Camps-Valls

    (University of Valencia)

  • Felix Creutzig

    (Potsdam Institute for Climate Impact Research
    University of Sussex)

  • Carina J. Fearnley

    (University College London)

  • Boran Han

    (Amazon Web Services)

  • Kai Kornhuber

    (Columbia University
    International Institute for Applied Systems Analysis (IIASA))

  • Nasim Rahaman

    (Max-Planck-Institute for Intelligent Systems)

  • Bernhard Schölkopf

    (Max-Planck-Institute for Intelligent Systems
    ELLIS Institute Tübingen)

  • José María Tárraga

    (University of Valencia)

  • Ricardo Vinuesa

    (KTH Royal Institute of Technology)

  • Karen Dall

    (German Red Cross)

  • Joachim Denzler

    (ELLIS Unit Jena
    University of 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)

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.

Suggested Citation

  • 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 , 2025. "Early warning of complex climate risk with integrated artificial intelligence," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57640-w
    DOI: 10.1038/s41467-025-57640-w
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