Author
Listed:
- Veronika Eyring
(Institut für Physik der Atmosphäre
Institute of Environmental Physics)
- William D. Collins
(Lawrence Berkeley National Laboratory
University of California, Berkeley)
- Pierre Gentine
(Columbia University)
- Elizabeth A. Barnes
(Colorado State University)
- Marcelo Barreiro
(Universidad de la República)
- Tom Beucler
(University of Lausanne)
- Marc Bocquet
(École des Ponts and EdF R&D)
- Christopher S. Bretherton
(Allen Institute for Artificial Intelligence)
- Hannah M. Christensen
(University of Oxford)
- Katherine Dagon
(NSF National Center for Atmospheric Research)
- David John Gagne
(NSF National Center for Atmospheric Research)
- David Hall
(NVIDIA Corporation)
- Dorit Hammerling
(Colorado School of Mines)
- Stephan Hoyer
(Google Research)
- Fernando Iglesias-Suarez
(Institut für Physik der Atmosphäre)
- Ignacio Lopez-Gomez
(Google Research
California Institute of Technology)
- Marie C. McGraw
(Colorado State University)
- Gerald A. Meehl
(NSF National Center for Atmospheric Research)
- Maria J. Molina
(NSF National Center for Atmospheric Research
University of Maryland)
- Claire Monteleoni
(University of Colorado Boulder
INRIA Paris)
- Juliane Mueller
(National Renewable Energy Laboratory)
- Michael S. Pritchard
(NVIDIA Corporation
University of California, Irvine)
- David Rolnick
(McGill University
Mila - Quebec AI Institute)
- Jakob Runge
(Institut für Datenwissenschaften
Technische Universität Berlin)
- Philip Stier
(University of Oxford)
- Oliver Watt-Meyer
(Allen Institute for Artificial Intelligence)
- Katja Weigel
(Institut für Physik der Atmosphäre
Institute of Environmental Physics)
- Rose Yu
(San Diego)
- Laure Zanna
(New York University)
Abstract
Climate modelling and analysis are facing new demands to enhance projections and climate information. Here we argue that now is the time to push the frontiers of machine learning beyond state-of-the-art approaches, not only by developing machine-learning-based Earth system models with greater fidelity, but also by providing new capabilities through emulators for extreme event projections with large ensembles, enhanced detection and attribution methods for extreme events, and advanced climate model analysis and benchmarking. Utilizing this potential requires key machine learning challenges to be addressed, in particular generalization, uncertainty quantification, explainable artificial intelligence and causality. This interdisciplinary effort requires bringing together machine learning and climate scientists, while also leveraging the private sector, to accelerate progress towards actionable climate science.
Suggested Citation
Veronika Eyring & William D. Collins & Pierre Gentine & Elizabeth A. Barnes & Marcelo Barreiro & Tom Beucler & Marc Bocquet & Christopher S. Bretherton & Hannah M. Christensen & Katherine Dagon & Davi, 2024.
"Pushing the frontiers in climate modelling and analysis with machine learning,"
Nature Climate Change, Nature, vol. 14(9), pages 916-928, September.
Handle:
RePEc:nat:natcli:v:14:y:2024:i:9:d:10.1038_s41558-024-02095-y
DOI: 10.1038/s41558-024-02095-y
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcli:v:14:y:2024:i:9:d:10.1038_s41558-024-02095-y. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.