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Abstract The role of ocean dynamics in Atlantic climate variability and predictability is often studied through the lens of sea surface temperature (SST). Unlike SST, sea surface salinity (SSS) is not directly damped by surface fluxes, and its low-frequency variability responds primarily to oceanic processes. This study investigates the drivers of SSS variability using a stochastic model hierarchy to disentangle oceanic and atmospheric contributions to Atlantic climate variability, in particular, the role of local vertical processes. Representation of SST and SSS persistence and variance is especially improved by the introduction of damping of anomalies below the mixed layer, though SSS anomalies remain too persistent. The effect of SST–evaporation feedback on SSS is comparatively smaller except in regions with strong SST–SSS correlation. Despite the lack of representation of geostrophic advection, the stochastic model successfully reproduces spatial patterns of recurring SST/SSS anomalies in the Community Earth System Model 1 (CESM1) Large Ensemble at monthly to interannual time scales. At multidecadal time scales, the stochastic model is unable to simulate the amplitude of SST/SSS variability, but their spatial patterns are broadly reproduced, suggesting that direct atmospheric forcing and local vertical processes are important for capturing these features. Further analysis of the processes missing from the stochastic model suggests that the lack of geostrophic advection is largely responsible for too persistent SSS in the stochastic model, while the lack of interannual mixed-layer depth variability explains the underestimated persistence and variance in some regions for both SST and SSS. 

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.