paper
2022
Hybrid physics-ML & parameterization
New model or method
Classical ML (trees, GP, linear, kernels)Climate projections / centennialNASA Journal
2022-08-15
047
Aug 2022
Fangqun Yu, Gan Luo, Arshad Arjunan Nair et al.
Geophysical Research Letters
NASA
Random forest surrogate for particle number concentration in GISS-ModelE2.1 reduces aerosol-cloud radiative forcing from -1.46 to -1.11 W m-2.
Hybrid physics-ML & parameterization
Use of Machine Learning to Reduce Uncertainties in Particle Number Concentration and Aerosol Indirect Radiative Forcing Predicted by Climate Models
In one sentence. Random forest surrogate for particle number concentration in GISS-ModelE2.1 reduces aerosol-cloud radiative forcing from -1.46 to -1.11 W m-2.
Labels
- Topic
- Hybrid physics-ML & parameterization
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Global
- Data
- CMIP / climate model output, Other
- Status
- Research
- Identifiers
- DOI:10.1029/2022gl098551 OpenAlex:W4291383531
- Added
- 2026-10-22
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