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Abstract Atmospheric rivers (ARs) in winter can induce significant melting of sea ice as they approach the ice cover. However, due to the complex physical properties of sea ice, the specific processes within the ice pack that are responsible for its response to ARs remain poorly understood. This study aims to shed light on this question using a stand‐alone sea ice model forced by observed atmospheric boundary conditions. The findings reveal that the AR induced ice melt and hindered ice growth in the marginal seas are attributed to a combination of thermodynamic and dynamic processes. The AR‐wind transports ice floes from the marginal seas back to the central Arctic dynamically, resulting in a thickening of the ice cover in that region. Among the thermodynamic processes, reduced congelation growth (54%–56%), enhanced basal melting (17%–26%), and inhibited snow‐ice formation (11%–21%) play major roles in the sea ice loss in the marginal seas. 

Abstract Despite global warming, the sea surface temperature (SST) in the subpolar North Atlantic has decreased since the 1900s. This local cooling, known as the North Atlantic cold blob, signifies a unique role of the subpolar North Atlantic in uptaking heat and hence impacts downstream weather and climate. However, a lack of observational records and their constraints on climate models leave the North Atlantic cold blob formation mechanism inconclusive. Using simulations from phase 6 of Coupled Model Intercomparison Project, we assess the primary processes driving the North Atlantic cold blob within individual models and whether the mechanisms are consistent across models. We show that 11 out of 32 models, which we call “Cold Blob” models, simulate the subpolar North Atlantic cooling over 1900–2014. Further analyzing the heat budget of the subpolar North Atlantic SST shows that models have distinct mechanisms of cold blob formation. While 4 of the 11 Cold Blob models indicate decreased oceanic heat transport convergence (OHTC) as the key mechanism, another four models suggest changes in radiative processes making predominant contributions. The contribution of OHTC and radiative processes is comparable in the remaining three models. Such a model disagreement on the mechanism of cold blob formation may be associated with simulated base-state Atlantic meridional overturning circulation (AMOC) strength, which explains 39% of the intermodel spread in the contribution of OHTC to the simulated cold blob. Models with a stronger base-state AMOC suggest a greater role of OHTC, whereas those with a weaker base-state AMOC indicate that radiative processes are more responsible. This model discrepancy suggests that the cold blob formation mechanism diagnosed from single model should be interpreted with caution. Significance StatementThe mechanisms driving sea surface temperatures over the subpolar North Atlantic to cool since the 1900s remain uncertain due to the lack of direct observations. Here, we use a temperature change decomposition framework to dissect the historical trend of surface temperature simulated in multiple global climate models. The models diverge on whether the subpolar North Atlantic cooling is induced by reduced ocean heat transport convergence or altered radiative processes. Notably, the importance of ocean heat transport convergence is influenced by the simulated base-state strength of Atlantic meridional overturning circulation and the Irminger Sea’s mixed layer depth. This finding cautions against concluding the cooling mechanism from a single model and highlights a need for ongoing observations to constrain AMOC-related climate projection in the subpolar North Atlantic. 

A well-known exception to rising sea surface temperatures (SST) across the globe is the subpolar North Atlantic, where SST has been declining at a rate of 0.39 (± 0.23) K century−1 during the 1900–2017 period. This cold blob has been hypothesized to result from a slowdown of the Atlantic Meridional Overturning Circulation (AMOC). Here, observation-based evidence is used to suggest that local atmospheric forcing can also contribute to the century-long cooling trend. Specifically, a 100-year SST trend simulated by an idealized ocean model forced by historical atmospheric forcing over the cold blob region matches 92% (± 77%) of the observed cooling trend. The data-driven simulations suggest that 54% (± 77%) of the observed cooling trend is the direct result of increased heat loss from the ocean induced by the overlying atmosphere, while the remaining 38% is due to strengthened local convection. An analysis of surface wind eddy kinetic energy suggests that the atmosphere-induced cooling may be linked to a northward migration of the jet stream, which exposes the subpolar North Atlantic to intensified storminess. 

Abstract By modulating the moisture flux from ocean to adjacent land, the North Atlantic Subtropical High (NASH) western ridge significantly influences summer-season total precipitation over the Conterminous United States (CONUS). However, its influence on the frequency and intensity of daily rainfall events over the CONUS remains unclear. Here we introduce a Bayesian statistical model to investigate the impacts of the NASH western ridge position on key statistics of daily-scale summer precipitation, including the intensity of rainfall events, the probability of precipitation occurrence, and the probability of extreme values. These statistical quantities play a key role in characterizing both the impact of wet extremes (e.g., the probability of floods) and dry extremes. By applying this model to historical rain gauge records (1948-2019) covering the entire CONUS, we find that the western ridge of the NASH influences the frequency of rainfall as well as the distribution of rainfall intensities over extended areas of the CONUS. In particular, we find that the NASH ridge also modulates the frequency of extreme rainfall, especially that over part of the Southeast and upper Midwest. Our analysis underlines the importance of including the NASH western ridge position as a predictor for key statistical rainfall properties to be used for hydrological applications. This result is especially relevant for projecting future changes in daily rainfall regimes over the CONUS based on the predicted strengthening of the NASH in a warming climate. 

Understanding the extent to which Atlantic sea surface temperatures (SSTs) are predictable is important due to the strong climate impacts of Atlantic SST on Atlantic hurricanes and temperature and precipitation over adjacent landmasses. However, models differ substantially on the degree of predictability of Atlantic SST and upper-ocean heat content (UOHC). In this work, a lower bound on predictability time scales for SST and UOHC in the North Atlantic is estimated purely from gridded ocean observations using a measure of the decorrelation time scale based on the local autocorrelation. Decorrelation time scales for both wintertime SST and UOHC are longest in the subpolar gyre, with maximum time scales of about 4–6 years. Wintertime SST and UOHC generally have similar decorrelation time scales, except in regions with very deep mixed layers, such as the Labrador Sea, where time scales for UOHC are much larger. Spatial variations in the wintertime climatological mixed layer depth explain 51%–73% (range for three datasets analyzed) of the regional variations in decorrelation time scales for UOHC and 26%–40% (range for three datasets analyzed) of the regional variations in decorrelation time scales for wintertime SST in the extratropical North Atlantic. These results suggest that to leading order decorrelation time scales for UOHC are determined by the thermal memory of the ocean.