Investigating Atmospheric Responses to and Mechanisms Governing North Atlantic Sea Surface Temperatures over 10-Year Periods

Abstract

Abstract North Atlantic sea surface temperature (SST) variability plays a critical role in modulating the climate system. However, characterizing patterns of North Atlantic SST variability and diagnosing the associated mechanisms is challenging because they involve coupled atmosphere–ocean interactions with complex spatiotemporal relationships. Here we address these challenges by applying a time-evolving self-organizing map approach to a long preindustrial coupled control simulation and identify a variety of 10-yr spatiotemporal evolutions of winter SST anomalies, including but not limited to those associated with the North Atlantic Oscillation–Atlantic multidecadal variability (NAO–AMV)-like interactions. To assess mechanisms and atmospheric responses associated with various SST spatiotemporal evolutions, composites of atmospheric and oceanic variables associated with these evolutions are investigated. Results show that transient-eddy activities and atmospheric circulation responses exist in almost all the evolutions that are closely correlated to the details of the SST pattern. In terms of the mechanisms responsible for generating various SST evolutions, composites of ocean heat budget terms demonstrate that contributions to upper-ocean temperature tendency from resolved ocean advection and surface heat fluxes rarely oppose each other over 10-yr periods in the subpolar North Atlantic. We further explore the potential for predictability for some of these 10-yr SST evolutions that start with similar states but end with different states. However, we find that these are associated with abrupt changes in atmospheric variability and are unlikely to be predictable. In summary, this study broadly investigates the atmospheric responses to and the mechanisms governing the North Atlantic SST evolutions over 10-yr periods. Significance Statement Climate variability in the North Atlantic Ocean has wide-ranging impacts on global and regional climate. However, the processes involved include interactions between the ocean and atmosphere that vary across both space and time, making it challenging to characterize and predict. Using a novel machine learning approach, this study identifies various time evolutions of North Atlantic sea surface temperature patterns over 10-yr periods. This includes evolutions with similar start states but different trajectories, which have important implications for predictability. Furthermore, we investigate the mechanisms responsible for these evolutions and how different sea surface temperature patterns affect atmospheric circulation through small-scale atmospheric disturbances. These new insights into the complex ocean–atmosphere interactions over time are critical for improving decadal prediction skill.