Neural general circulation models for weather and climate Article Swipe
Dmitrii Kochkov
,
Janni Yuval
,
Ian Langmore
,
Peter Nørgaard
,
Jamie Smith
,
Griffin Mooers
,
Milan Klöwer
,
James Lottes
,
Stephan Rasp
,
Peter Düben
,
Sam Hatfield
,
Peter Battaglia
,
Álvaro Sánchez‐González
,
Matthew Willson
,
Michael P. Brenner
,
Stephan Hoyer
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41586-024-07744-y
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41586-024-07744-y
General circulation models (GCMs) are the foundation of weather and climate prediction 1,2 . GCMs are physics-based simulators that combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine-learning models trained on reanalysis data have achieved comparable or better skill than GCMs for deterministic weather forecasting 3,4 . However, these models have not demonstrated improved ensemble forecasts, or shown sufficient stability for long-term weather and climate simulations. Here we present a GCM that combines a differentiable solver for atmospheric dynamics with machine-learning components and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best machine-learning and physics-based methods. NeuralGCM is competitive with machine-learning models for one- to ten-day forecasts, and with the European Centre for Medium-Range Weather Forecasts ensemble prediction for one- to fifteen-day forecasts. With prescribed sea surface temperature, NeuralGCM can accurately track climate metrics for multiple decades, and climate forecasts with 140-kilometre resolution show emergent phenomena such as realistic frequency and trajectories of tropical cyclones. For both weather and climate, our approach offers orders of magnitude computational savings over conventional GCMs, although our model does not extrapolate to substantially different future climates. Our results show that end-to-end deep learning is compatible with tasks performed by conventional GCMs and can enhance the large-scale physical simulations that are essential for understanding and predicting the Earth system.
Related Topics
Concepts
Numerical weather prediction
Climate model
Meteorology
General Circulation Model
Model output statistics
Climatology
Weather forecasting
North American Mesoscale Model
Scale (ratio)
Environmental science
Computer science
Tropical cyclone forecast model
Climate change
Global Forecast System
Machine learning
Geography
Geology
Cartography
Oceanography
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41586-024-07744-y
- OA Status
- hybrid
- Cited By
- 222
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400880443
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4400880443Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41586-024-07744-yDigital Object Identifier
- Title
-
Neural general circulation models for weather and climateWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-22Full publication date if available
- Authors
-
Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter Nørgaard, Jamie Smith, Griffin Mooers, Milan Klöwer, James Lottes, Stephan Rasp, Peter Düben, Sam Hatfield, Peter Battaglia, Álvaro Sánchez‐González, Matthew Willson, Michael P. Brenner, Stephan HoyerList of authors in order
- Landing page
-
https://doi.org/10.1038/s41586-024-07744-yPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://doi.org/10.1038/s41586-024-07744-yDirect OA link when available
- Concepts
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Numerical weather prediction, Climate model, Meteorology, General Circulation Model, Model output statistics, Climatology, Weather forecasting, North American Mesoscale Model, Scale (ratio), Environmental science, Computer science, Tropical cyclone forecast model, Climate change, Global Forecast System, Machine learning, Geography, Geology, Cartography, OceanographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
222Total citation count in OpenAlex
- Citations by year (recent)
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2025: 177, 2024: 45Per-year citation counts (last 5 years)
- References (count)
-
53Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.climate | 11, 75, 106, 152, 158 |
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| abstract_inverted_index.enhance | 219 |
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| abstract_inverted_index.savings | 187 |
| abstract_inverted_index.surface | 146 |
| abstract_inverted_index.system. | 233 |
| abstract_inverted_index.ten-day | 125 |
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| abstract_inverted_index.weather | 9, 54, 73, 104, 177 |
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| abstract_inverted_index.climate, | 179 |
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| abstract_inverted_index.weather, | 102 |
| abstract_inverted_index.Forecasts | 135 |
| abstract_inverted_index.NeuralGCM | 116, 148 |
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| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| corresponding_author_ids | https://openalex.org/A5041495428 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 16 |
| corresponding_institution_ids | https://openalex.org/I1291425158 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.99995001 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |