Oceanic Harbingers of Pacific Decadal Oscillation Predictability in CESM2 Detected by Neural Networks
Emily M Gordon, Elizabeth A. Barnes, James W. Hurrell
Abstract
Abstract Predicting Pacific Decadal Oscillation (PDO) transitions and understanding the associated mechanisms has proven a critical but challenging task in climate science. As a form of decadal variability, the PDO is associated with both large‐scale climate shifts and regional climate predictability. We show that artificial neural networks (ANNs) predict PDO persistence and transitions with lead times of 12 months onward. Using layer‐wise relevance propagation to investigate the ANN predictions, we demonstrate that the ANNs utilize oceanic patterns that have been previously linked to predictable PDO behavior. For PDO transitions, ANNs recognize a build‐up of ocean heat content in the off‐equatorial western Pacific 12–27 months before a transition occurs. The results support the continued use of ANNs in climate studies where explainability tools can assist in mechanistic understanding of the climate system.