paper
2018
Climate emulation
Benchmarks, datasets & evaluation New model or method
Classical ML (trees, GP, linear, kernels)Climate projections / centennial Journal
2018-04-19
016
Apr 2018
Gemma J. Anderson, Donald D. Lucas
Geophysical Research Letters
Random forests trained on a multiresolution perturbed-parameter GCM ensemble predict high-resolution TOA flux and precipitation using few high-resolution runs, cutting simulation cost.
Machine Learning Predictions of a Multiresolution Climate Model Ensemble
In one sentence. Random forests trained on a multiresolution perturbed-parameter GCM ensemble predict high-resolution TOA flux and precipitation using few high-resolution runs, cutting simulation cost.
Labels
- Topic
- Climate emulation, Benchmarks, datasets & evaluation
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1029/2018gl077049 OpenAlex:W2801390916
- Added
- 2026-10-22
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