Viewing Forced Climate Patterns Through an AI Lens

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. Plain Language Summary Many problems in climate science require the identification of signals amidst a sea of climate “noise” and across a variety of models which can disagree with one another. Here, we demonstrate that machine learning techniques, specifically artificial neural networks, can help identify forced patterns of temperature and precipitation within climate model simulations as well as the observations. In fact, the neural network is able to identify patterns of forced change of surface temperature as early as the 1960s in climate model simulations. The results shown here are strongly suggestive of the potential power of machine learning for climate research. Key Points Neural networks can identify forced patterns of surface temperature and precipitation amidst climate noise and model disagreement These “indicator patterns” of forced change are present in the observations Viewing climate patterns through an AI lens has the power to uncover new insights into climate variability and change