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Pushing the frontiers in climate modelling and analysis with machine learning

Journal Article · · Nature Climate Change
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [9];  [10];  [10];  [11];  [12];  [13];  [14];  [15];  [4];  [10];  [16];  [17] more »;  [18];  [19];  [20];  [21];  [9];  [8];  [1];  [22];  [23] « less
  1. German Aerospace Center (DLR), Oberpfaffenhofen (Germany); Univ. of Bremen (Germany). Inst. of Environmental Physics (IUP)
  2. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)
  3. Columbia Univ., New York, NY (United States)
  4. Colorado State Univ., Fort Collins, CO (United States)
  5. Universidad de la República, Montevideo (Uruguay)
  6. University of Lausanne (Switzerland)
  7. École des Ponts, Île-de-France (France); EdF R&D, Île-de-France (France); Centre d'Enseignement et de Recherche en Environnement Atmosphérique (CEREA), Île-de-France (France)
  8. Allen Institute for Artificial Intelligence, Seattle, WA (United States)
  9. Univ. of Oxford (United Kingdom)
  10. National Center for Atmospheric Research (NCAR), Boulder, CO (United States)
  11. NVIDIA Corporation, Santa Clara, CA (United States)
  12. Colorado School of Mines, Golden, CO (United States)
  13. Google Research, Mountain View, CA (United States)
  14. German Aerospace Center (DLR), Oberpfaffenhofen (Germany)
  15. Google Research, Mountain View, CA (United States); California Institute of Technology (CalTech), Pasadena, CA (United States)
  16. National Center for Atmospheric Research (NCAR), Boulder, CO (United States); Univ. of Maryland, College Park, MD (United States)
  17. Univ. of Colorado, Boulder, CO (United States); INRIA Paris (France)
  18. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  19. NVIDIA Corporation, Santa Clara, CA (United States); Univ. of California, Irvine, CA (United States)
  20. McGill Univ., Montreal, QC (Canada); Mila - Quebec AI Institute, Montreal, QC (Canada)
  21. German Aerospace Center (DLR), Jena (Germany); Technische Univ. Berlin (Germany)
  22. Univ. of California, San Diego, CA (United States)
  23. New York Univ. (NYU), NY (United States)
+ Show Author Affiliations
Climate modelling and analysis are facing new demands to enhance projections and climate information. Here, in this study, 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.
Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
National Science Foundation (NSF); Schmidt Futures; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC); USDOE Office of Science (SC), Biological and Environmental Research (BER)
Grant/Contract Number:
AC02-05CH11231; AC36-08GO28308; FE0032311; SC0022070; SC0022255; SC0023368
OSTI ID:
2447145
Report Number(s):
NREL/JA--2C00-88119; MainId:88894; UUID:856bccb4-2e70-4bd3-8888-f7956292ebc9; MainAdminId:73798
Journal Information:
Nature Climate Change, Journal Name: Nature Climate Change Journal Issue: 9 Vol. 14; ISSN 1758-678X
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

54 ENVIRONMENTAL SCIENCES
97 MATHEMATICS AND COMPUTING
causal inference
climate change
climate modeling and analysis
explainable AI
extreme events
generalization
machine learning
uncertainty quantification