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

doi:10.1175/BAMS-D-25-0183.1

Hacking Km-Scale Models: A Participative Model for Climate Information: 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
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 journal › Article › peer-review

1 Scopus citations

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 language	English
Article number	BAMS-D-25-0183.1
Pages (from-to)	E1586-E1598
Number of pages	13
Journal	Bulletin of the American Meteorological Society
Volume	107
Issue number	7
DOIs	https://doi.org/10.1175/BAMS-D-25-0183.1 https://doi.org/10.1175/BAMS-D-25-0183.1
State	Published - Jul 2026

Funding

The Pacific Northwest National Laboratory is operated for the U.S. Department of Energy by the Battelle Memorial Institute under Contract DE-AC05-76RL01830. Lawrence Livermore National Laboratory operates under the auspices of the U.S. Department of Energy under Contract DE-AC52-07NA27344. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy User Facility (Projects m4581 and m1867). Diagnostics in Buenos Aires node were performed using CIMA-FAECI's Papa-Deimos, Argentina, partially funded by INSU Project LEFE ref. 12962. This work was supported by the University of Reading Advancing the Frontiers of Earth System Prediction (AFESP) Programme (Award A3720200). This work was also supported by the National Center for Atmospheric Research (NCAR), which is a major facility sponsored by the NSF under Cooperative Agreement 1852977. The South America participants were supported by Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP: 2025/01342-0) and Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq: 440363/2025-0). Participant travel support to Princeton and NCAR was provided by NSF (Award 2524887). This work was supported by the nextGEMS project under the European Union's Horizon 2020 research program (Grant 101003470) and used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under Project IDs bb1153 and bm1235. The Australian node was supported by the Australian Research Council Centre of Excellence for the Weather of the 21st Century (CE230100012) and the Australian Government's National Collaborative Research Infrastructure Strategy (NCRIS), with access to computational resources and support provided by the National Computational Infrastructure (NCI) and ACCESS-NRI. The Tokyo node was supported by JSPS Core-to-Core Program, "International Core-to-Core Project on Global Storm Resolving Analysis" (Grant JPJSCCA20220001).

Funders	Funder number
U.S. Department of Energy by the Battelle Memorial Institute	DE-AC05-76RL01830
U.S. Department of Energy	DE-AC52-07NA27344
INSU Project LEFE	12962
University of Reading Advancing the Frontiers of Earth System Prediction (AFESP) Programme	A3720200
National Center for Atmospheric Research (NCAR)
NSF	1852977, 2524887
Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)	2025/01342-0
Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)	440363/2025-0
The nextGEMS project under the European Union's Horizon 2020 research program	101003470
Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA)	bb1153, bm1235
Australian Research Council Centre of Excellence	CE230100012
JSPS Core-to-Core Program, "International Core-to-Core Project on Global Storm Resolving Analysis"	JPJSCCA20220001
Australian Government's National Collaborative Research Infrastructure Strategy (NCRIS)

Keywords

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

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Gettelman, A., Vidale, P. L., Stevens, B., Ziemen, F., Feng, Z., Konow, H., Kölling, T., Kluft, L., Jones, W., Pasqualetto, S., Wu, Y., Brose, S., Clyne, J., Fita, L., Green, S., Harris, L., Hart, M. A., Kukulies, J., Medeiros, B., ... Zhou, T. (2026). Hacking Km-Scale Models: A Participative Model for Climate Information: A Participative Model for Climate Information. Bulletin of the American Meteorological Society, 107(7), E1586-E1598. Article BAMS-D-25-0183.1. https://doi.org/10.1175/BAMS-D-25-0183.1, https://doi.org/10.1175/BAMS-D-25-0183.1

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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{\textquoteright}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.",

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doi = "10.1175/BAMS-D-25-0183.1",

language = "English",

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Gettelman, A, Vidale, PL, Stevens, B, Ziemen, F, Feng, Z, Konow, H, Kölling, T, Kluft, L, Jones, W, Pasqualetto, S, Wu, Y, Brose, S, Clyne, J, Fita, L, Green, S, Harris, L, Hart, MA, Kukulies, J, Medeiros, B, Merlis, TM, Muetzelfeldt, M, Pincus, R, Da Rocha, RP, Satoh, M, Su, H, Takasuka, D, Terai, C, Ullrich, P & Zhou, T 2026, 'Hacking Km-Scale Models: A Participative Model for Climate Information: A Participative Model for Climate Information', Bulletin of the American Meteorological Society, vol. 107, no. 7, BAMS-D-25-0183.1, pp. E1586-E1598. https://doi.org/10.1175/BAMS-D-25-0183.1, https://doi.org/10.1175/BAMS-D-25-0183.1

TY - JOUR

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T2 - A Participative Model for Climate Information

AU - Gettelman, Andrew

AU - Vidale, Pier Luigi

AU - Stevens, Bjorn

AU - Ziemen, Florian

AU - Feng, Zhe

AU - Konow, Heike

AU - Kölling, Tobias

AU - Kluft, Lukas

AU - Jones, William

AU - Pasqualetto, Sara

AU - Wu, Yuting

AU - Brose, Saskia

AU - Clyne, John

AU - Fita, Lluís

AU - Green, Samuel

AU - Harris, Lucas

AU - Hart, Melissa Anne

AU - Kukulies, Julia

AU - Medeiros, Brian

AU - Merlis, Timothy M.

AU - Muetzelfeldt, Mark

AU - Pincus, Robert

AU - Da Rocha, Rosmeri P.

AU - Satoh, Masaki

AU - Su, Hang

AU - Takasuka, Daisuke

AU - Terai, Chris

AU - Ullrich, Paul

AU - Zhou, Tianjun

PY - 2026/7

Y1 - 2026/7

N2 - 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.

AB - 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.

KW - Climate models

KW - Climate services

KW - General circulation models

KW - Model comparison

KW - Neural networks

KW - Regional models

UR - https://www.scopus.com/pages/publications/105045463744

U2 - 10.1175/BAMS-D-25-0183.1

DO - 10.1175/BAMS-D-25-0183.1

M3 - Article

AN - SCOPUS:105045463744

SN - 0003-0007

VL - 107

SP - E1586-E1598

JO - Bulletin of the American Meteorological Society

JF - Bulletin of the American Meteorological Society

IS - 7

M1 - BAMS-D-25-0183.1

ER -

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

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