paper
2021
Climate analysis & attribution
Explainability, uncertainty & physics Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)Climate projections / centennial Journal
2021-03-29
008
Mar 2021
J. Jake Nichol, Matthew Gregor Peterson, Kara Peterson et al.
Journal of Computational and Applied Mathematics
Applies machine learning feature analysis to E3SM climate model output to explain its disparity with observed climate change; abstract unavailable.
Climate analysis & attribution
Machine learning feature analysis illuminates disparity between E3SM climate models and observed climate change
In one sentence. Applies machine learning feature analysis to E3SM climate model output to explain its disparity with observed climate change; abstract unavailable.
Labels
- Topic
- Climate analysis & attribution, Explainability, uncertainty & physics
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Global
- Data
- CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.1016/j.cam.2021.113451 OpenAlex:W3147885337
- Added
- 2026-10-22
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