paper
2018
Climate analysis & attribution
Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)Climate projections / centennialUniversity of California, Berkeley Journal
2018-11-21
018
Nov 2018
Michael V. Saha, Todd M. Scanlon, Paolo D’Odorico
Global Ecology and Biogeography
University of California, Berkeley
Random forests show that climate seasonality metrics of precipitation and temperature predict 66-87% of global multi-year fire activity variability.
Climate analysis & attribution
Climate seasonality as an essential predictor of global fire activity
In one sentence. Random forests show that climate seasonality metrics of precipitation and temperature predict 66-87% of global multi-year fire activity variability.
Labels
- Topic
- Climate analysis & attribution
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1111/geb.12836 OpenAlex:W2901742406
- Added
- 2026-10-22
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