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Physical Insights from the Multidecadal Prediction of North Atlantic Sea Surface Temperature Variability Using Explainable Neural Networks
  • Glenn Yu-zu Liu,
  • Peidong Wang,
  • Young-Oh Kwon
Glenn Yu-zu Liu
MIT-WHOI Joint Program

Corresponding Author:glennliu@mit.edu

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Peidong Wang
Massachusetts Institute of Technology
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Young-Oh Kwon
Woods Hole Oceanographic Institution
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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.
15 Sep 2023Submitted to ESS Open Archive
18 Sep 2023Published in ESS Open Archive