Much of climate science is viewed as a signal-to-noise problem and the field has many statistical methods for extracting the signal of interest. Here, we argue that artificial neural networks (ANNs) are an additional useful tool for the "climate toolbox". As an example, we demonstrate their utility for extracting forced climate patterns from model simulations and observations whereby the ANN identifies patterns that are complex, non-linear combinations of signal and noise. While neural networks are often viewed as black boxes, we further demonstrate how to visualize what the network has learned using recent advances in visualization tools within the computer science community. This approach suggests that viewing climate patterns through an AI lens has the power to uncover new insights into climate variability and change
Dr. Elizabeth (Libby) Barnes is an associate professor of Atmospheric Science at Colorado State University. Her research is focused on large scale atmospheric variability and the data analysis tools used to understand its dynamics. Topics of interest include jet-stream dynamics, Arctic-midlatitude connections, subseasonal-to-seasonal (S2S) prediction of extreme weather events, health-related climate impacts, and data science methods for climate research (e.g. machine learning, causal discovery).
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