Journal
GEOPHYSICAL RESEARCH LETTERS
Volume 46, Issue 22, Pages 13389-13398Publisher
AMER GEOPHYSICAL UNION
DOI: 10.1029/2019GL084944
Keywords
climate change; neural network; machine learning; climate patterns
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Funding
- NSF CAREER under the Climate and Large-scale Dynamics program [AGS-1749261]
- NSF under the Climate and Large-scale Dynamics program [AGS-1445978]
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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.
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