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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. 

Abstract The strong sea‐surface temperature (SST) gradient associated with the Gulf Stream (GS) is widely acknowledged to play an important role in shaping mid‐latitude weather and climate. Despite this, an index for the GS SST gradient has not yet been standardized in the literature. This paper introduces a monthly index for the large‐scale SST gradient across the separated GS based on the time‐varying GS position detected from sea‐surface height. Analysis suggests that the variations in the monthly average SST gradient throughout the year result primarily from SST variability to the north of the GS, with little contribution from SST to the south. The index exhibits a weak periodicity at ∼2 years. Sea level pressure and turbulent heat flux patterns suggest that variability in the large‐scale SST gradient is related to atmospheric (rather than oceanic) forcing. Ocean‐to‐atmosphere feedback does not persist throughout the year, but there is some evidence of wintertime feedback.