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Winter surface air temperature (Tas) over the Barents–Kara Seas (BKS) and other Arctic regions has experienced rapid warming since the late 1990s that has been linked to the concurring cooling over Eurasia, and these multidecadal trends are attributed partly to internal variability. However, how such variability is generated is unclear. Through analyses of observations and model simulations, we show that sea ice–air two-way interactions amplify multidecadal variability in sea-ice cover, sea surface temperatures (SST) and Tas from the North Atlantic to BKS, and the Atlantic Meridional Overturning Circulation (AMOC) mainly through variations in surface fluxes. When sea ice is fixed in flux calculations, multidecadal variations are reduced substantially (by 20–50%) not only in Arctic Tas, but also in North Atlantic SST and AMOC. The results suggest that sea ice–air interactions are crucial for multidecadal climate variability in both the Arctic and North Atlantic, similar to air-sea interactions for tropical climate.

 

Abstract Arctic amplification (AA) reduces meridional temperature gradients ( dT / dy ) over the northern mid-high latitudes, which may weaken westerly winds. It is suggested that this may lead to wavier and more extreme weather in the midlatitudes. However, temperature variability is shown to decrease over the northern mid-high latitudes under increasing greenhouse gases due to reduced dT / dy . Here, through analyses of coupled model simulations and ERA5 reanalysis, it is shown that consistent with previous studies, cold-season surface and lower-mid troposphere temperature ( T ) variability decreases over northern mid-high latitudes even in simulations with suppressed AA and sea ice loss under increasing CO 2 ; however, AA and sea ice loss further reduce the T variability greatly, leading to a narrower probability distribution and weaker cold or warm extreme events relative to future mean climate. Increased CO 2 strengthens meridional wind ( υ ) with a wavenumber-4 pattern but weakens meridional thermal advection [− υ ( dT / dy )] over most northern mid-high latitudes, and AA weakens the climatological υ and − υ ( dT / dy ). The weakened thermal advection and its decreased variance are the primary causes of the T variability decrease, which is enlarged by a positive feedback between the variability of T and − υ ( dT / dy ). AA not only reduces dT / dy , but also its variance, which further decreases T variability through − υ ( dT / dy ). While the mean snow and ice cover decreases, its variability increases over many northern latitudes, and these changes do not weaken the T variability. Thus, AA’s influence on midlatitude temperature variability comes mainly from its impact on thermal advection, rather than on winds as previously thought. 

Abstract The Northern Hemisphere (NH) has experienced winter Arctic warming and continental cooling in recent decades, but the dominant patterns in winter surface air temperature (SAT) are not well understood. Here, a self-organizing map (SOM) analysis is performed to identify the leading patterns in winter daily SAT fields from 1979 to 2018, and their associated atmospheric and ocean conditions are also examined. Three distinct winter SAT patterns with two phases of nearly opposite signs and a time scale of 7–12 days are found: one pattern exhibits concurrent SAT anomalies of the same sign over North America (NA) and northern Eurasia, while the other two patterns show SAT anomalies of opposite signs between, respectively, NA and the Bering Sea, and the Kara Sea and East Asia (EA). Winter SAT variations may arise from changes in the SOM frequencies. Specifically, the observed increasing trends of winter cold extremes over NA, central Eurasia, and EA during 1998–2013 can be understood as a result of the increasing occurrences of some specific SAT patterns. These SOMs are closely related to poleward advection of midlatitude warm air and equatorward movements of polar cold airmass. These meridional displacements of cold and warm airmasses cause concurrent anomalies over different regions not only in SAT but also in water vapor and surface downward longwave radiation. Anomalous sea surface temperatures in the tropical Pacific, midlatitude North Pacific, and North Atlantic and anomalous Arctic sea ice concentrations also concur to support and maintain the anomalous atmospheric circulation that causes the SAT anomalies. 

Atmospheric CO2 and anthropogenic aerosols (AA) have increased simultaneously. Because of their opposite radiative effects, these increases may offset each other, which may lead to some nonlinear effects. Here the seasonal and regional characteristics of this nonlinear effect from the CO2 and AA forcings are investigated using the fully coupled Community Earth System Model. Results show that nonlinear effects are small in the global mean of the top-of-the-atmosphere radiative fluxes, surface air temperature, and precipitation. However, significant nonlinear effects exist over the Arctic and other extratropical regions during certain seasons. When both forcings are included, Arctic sea ice in September–November decreases less than the linear combination of the responses to the individual forcings due to a higher sea ice sensitivity to the CO2-induced warming than the sensitivity to the AA-induced cooling. This leads to less Arctic warming in the combined-forcing experiment due to reduced energy release from the Arctic Ocean to the atmosphere. Some nonlinear effects on precipitation in June–August are found over East Asia, with the northward-shifted East Asian summer rain belt to oppose the CO2 effect. In December–February, the aerosol loading over Europe in the combined-forcing experiment is higher than that due to the AA forcing, resulting from CO2-induced circulation changes. The changed aerosol loading results in regional thermal responses due to aerosol direct and indirect effects, weakening the combined changes of temperature and circulation. This study highlights the need to consider nonlinear effects from historical CO2 and AA forcings in seasonal and regional climate attribution analyses.