Climate-invariant machine learning Article Swipe
Tom Beucler
,
Pierre Gentine
,
Janni Yuval
,
Ankitesh Gupta
,
Liran Peng
,
Jerry Lin
,
Sungduk Yu
,
Stephan Rasp
,
Fiaz Ahmed
,
Paul A. O’Gorman
,
J. David Neelin
,
Nicholas J. Lutsko
,
Michael S. Pritchard
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1126/sciadv.adj7250
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1126/sciadv.adj7250
Projecting climate change is a generalization problem: We extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations but tend to extrapolate poorly to climate regimes that they were not trained on. To get the best of the physical and statistical worlds, we propose a framework, termed “climate-invariant” ML, incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes.
Related Topics
Concepts
Generalizability theory
Computer science
Climate model
Climate change
Generalization
Range (aeronautics)
Consistency (knowledge bases)
Invariant (physics)
Grid
Climate science
Machine learning
Artificial intelligence
Mathematics
Ecology
Statistics
Biology
Geometry
Composite material
Mathematical analysis
Materials science
Mathematical physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1126/sciadv.adj7250
- https://www.science.org/doi/pdf/10.1126/sciadv.adj7250?download=true
- OA Status
- gold
- Cited By
- 60
- References
- 113
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391629221
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391629221Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1126/sciadv.adj7250Digital Object Identifier
- Title
-
Climate-invariant machine learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-07Full publication date if available
- Authors
-
Tom Beucler, Pierre Gentine, Janni Yuval, Ankitesh Gupta, Liran Peng, Jerry Lin, Sungduk Yu, Stephan Rasp, Fiaz Ahmed, Paul A. O’Gorman, J. David Neelin, Nicholas J. Lutsko, Michael S. PritchardList of authors in order
- Landing page
-
https://doi.org/10.1126/sciadv.adj7250Publisher landing page
- PDF URL
-
https://www.science.org/doi/pdf/10.1126/sciadv.adj7250?download=trueDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.science.org/doi/pdf/10.1126/sciadv.adj7250?download=trueDirect OA link when available
- Concepts
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Generalizability theory, Computer science, Climate model, Climate change, Generalization, Range (aeronautics), Consistency (knowledge bases), Invariant (physics), Grid, Climate science, Machine learning, Artificial intelligence, Mathematics, Ecology, Statistics, Biology, Geometry, Composite material, Mathematical analysis, Materials science, Mathematical physicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
60Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 36, 2024: 22, 2022: 2Per-year citation counts (last 5 years)
- References (count)
-
113Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| corresponding_author_ids | https://openalex.org/A5045746109 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 13 |
| corresponding_institution_ids | https://openalex.org/I204250578, https://openalex.org/I97565354 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.8600000143051147 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.99795898 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |