paper
2023
Climate analysis & attribution
Explainability, uncertainty & physics Application study
Classical ML (trees, GP, linear, kernels)OtherClimate projections / centennialColorado State University (incl. CIRA) Journal
2023-06-01
022
Jun 2023
Antonios Mamalakis, Elizabeth A. Barnes, James Wilson Hurrell
Geophysical Research Letters
Colorado State University (incl. CIRA)
Explainable neural networks trained on Earth system model output quantify climate distinguishability after stratospheric aerosol injection, showing slower change under SAI.
Climate analysis & attribution
Quantifying “climate distinguishability” after stratospheric aerosol injection using explainable artificial intelligence
In one sentence. Explainable neural networks trained on Earth system model output quantify climate distinguishability after stratospheric aerosol injection, showing slower change under SAI.
Labels
- Topic
- Climate analysis & attribution, Explainability, uncertainty & physics
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels), Other
- Time scale
- Climate projections / centennial
- Domain
- Global
- Data
- CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.22541/essoar.168565419.96137434/v1 OpenAlex:W4378978926
- Added
- 2026-10-22
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