Physical Insights from the Multidecadal Prediction of North Atlantic Sea
Surface Temperature Variability Using Explainable Neural Networks
North Atlantic sea surface temperatures (NASST), particularly in the
subpolar region, are among the most predictable locations in the world’s
oceans. However, the relative importance of atmospheric and oceanic
controls on their variability at multidecadal timescales remain
uncertain. Neural networks (NNs) are trained to examine the relative
importance of oceanic and atmospheric predictors in predicting the NASST
state in the Community Earth System Model 1 (CESM1). In the presence of
external forcings, oceanic predictors outperform atmospheric predictors,
persistence, and random chance baselines out to 25-year leadtimes.
Layer-wise relevance propagation is used to unveil the sources of
predictability, and reveal that NNs consistently rely upon the Gulf
Stream-North Atlantic Current region for accurate predictions.
Additionally, CESM1-trained NNs do not need additional transfer learning
to successfully predict the phasing of multidecadal variability in an
observational dataset, suggesting consistency in physical processes
driving NASST variability between CESM1 and observations.