Climate analysis & attribution
Viewing Forced Climate Patterns Through an AI Lens
In one sentence. A neural network trained to predict year from temperature maps detects forced climate patterns without explicitly separating signal from noise.
Abstract
Abstract Many problems in climate science require extracting forced signals from a background of internal climate variability. We demonstrate that artificial neural networks (ANNs) are a useful addition to the climate science “toolbox” for this purpose. Specifically, forced patterns are detected by an ANN trained on climate model simulations under historical and future climate scenarios. By identifying spatial patterns that serve as indicators of change in surface temperature and precipitation, the ANN can determine the approximate year from which the simulations came without first explicitly separating the forced signal from the noise of both internal climate variability and model uncertainty. Thus, the ANN indicator patterns are complex, nonlinear combinations of signal and noise and are identified from the 1960s onward in simulated and observed surface temperature maps. This approach suggests that viewing climate patterns through an artificial intelligence (AI) lens has the power to uncover new insights into climate variability and change.
Labels
- Topic
- Climate analysis & attribution, Explainability, uncertainty & physics
- Type
- New model or method
- Method
- Other
- Time scale
- Climate projections / centennial
- Domain
- Global
- Data
- CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.1029/2019gl084944 OpenAlex:W2991419155
- Added
- 2026-10-22
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