Physical Insights From the Multidecadal Prediction of North Atlantic Sea Surface Temperature Variability Using Explainable Neural Networks

Abstract North Atlantic sea surface temperatures (NASST), particularly in the subpolar region, are among the most predictable in the world's oceans. However, the relative importance of atmospheric and oceanic controls on their variability at multidecadal timescales remain uncertain. Neural netw...

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Bibliographic Details
Published in:Geophysical Research Letters
Main Authors: Glenn Liu, Peidong Wang, Young‐Oh Kwon
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2023
Subjects:
climate variability
explainable machine learning
North Atlantic
ocean dynamics
artificial intelligence
climate dynamics
Geophysics. Cosmic physics
QC801-809
north atlantic current
Online Access:https://doi.org/10.1029/2023GL106278
https://doaj.org/article/ab3e0f5cbc4d4627ae8da56a5821e999
Description
Summary:Abstract North Atlantic sea surface temperatures (NASST), particularly in the subpolar region, are among the most predictable 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 successfully predict the phasing of multidecadal variability in an observational data set, suggesting consistency in physical processes driving NASST variability between CESM1 and observations.

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