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Hacking Kilometer-Scale Models: A Participative Model for Climate Information

  • Andrew Gettelman
  • , Pier Luigi Vidale
  • , Bjorn Stevens
  • , Florian Ziemen
  • , Zhe Feng
  • , Heike Konow
  • , Tobias Kölling
  • , Lukas Kluft
  • , William Jones
  • , Sara Pasqualetto
  • , Yuting Wu
  • , Saskia Brose
  • , John Clyne
  • , Lluís Fita
  • , Samuel Green
  • , Lucas Harris
  • , Melissa Anne Hart
  • , Julia Kukulies
  • , Brian Medeiros
  • , Timothy M. Merlis
  • Mark Muetzelfeldt, Robert Pincus, Rosmeri P. Da Rocha, Masaki Satoh, Hang Su, Daisuke Takasuka, Chris Terai, Paul Ullrich, Tianjun Zhou
  • Pacific Northwest National Laboratory
  • University of Reading
  • Max Planck Institute for Meteorology
  • Deutsches Klima Research Zentrum
  • University of Oxford
  • Deutsches Klimarechenzentrum
  • European Space Agency - ESA
  • Universidad de Buenos Aires
  • Consejo Nacional de Investigaciones Científicas y Técnicas
  • ARC Centre of Excellence for the 21st Century Weather
  • University of New South Wales
  • National Oceanic and Atmospheric Administration
  • University of Tasmania
  • National Center for Atmospheric Research
  • Princeton University
  • Columbia University
  • Universidade de São Paulo
  • The University of Tokyo
  • Yokohama National University
  • CAS - Institute of Atmospheric Physics
  • Tohoku University
  • Lawrence Livermore National Laboratory

Research output: Contribution to journalArticlepeer-review

Abstract

In May 2025, nearly 700 participants from all around the world coalesced at 10 regional nodes and a few satellite nodes to take part in a global hackathon of kilometer-scale (horizontal grid spacing < 10 km) regional and global Earth system models. Exciting science is emerging from these efforts, ranging across novel model analysis, new ways of integrating with satellite data, and emulation with machine learning. New technologies were trialed that enable the community to work in new and complementary ways to democratize access to global information at a local scale from a set of the world’s highest-resolution climate models. The hackathon demonstrated how exascale data can be organized to be accessible to anyone. Fundamentally, the community could apply these techniques and technologies to move toward more participa-tive models for coproduction and delivery of diverse sources of climate information for climate scientists and citizens alike.

Original languageEnglish
Pages (from-to)E1586-E1598
JournalBulletin of the American Meteorological Society
Volume107
Issue number7
DOIs
StatePublished - Jul 2026

Keywords

  • Climate models
  • Climate services
  • General circulation models
  • Model comparison
  • Neural networks
  • Regional models

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