Oceanic harbingers of Pacific Decadal Oscillation predictability in
CESM2 detected by neural networks
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 on the interannual timescale.
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