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A zonal dipole pattern of the interannual variability of Eurasian snow cover during late autumn has attracted growing interest due to its extensive influences on large‐scale atmospheric circulations and climate anomalies, but it remains unknown what influences the formation of this pattern. This study revealed the potential drivers and the related physical processes of the dipole mode of the snow cover variability during 1979–2018. Results showed that the atmospheric circulation anomalies play a crucial role in forming the dipole mode, which could be further attributed to the influences of the atmospheric teleconnection patterns and the variations of sea surface temperature (SST) and sea ice concentration. Specifically, the anomalous circulations related to the teleconnections of the Eastern Atlantic pattern, East Atlantic–Western Russia pattern, and Scandinavian pattern could lead to the anomalies of air temperature and snowfall that directly shape the variability of snow cover. In addition, a dipole structure of the SST anomalies over the North Atlantic exerts an important effect on the snow cover variability through exciting an eastward propagating Rossby wave train prevailing over Eurasia. Moreover, the snow cover variability is closely linked to the sea ice variations over the Barents Sea that can regulate the anomalous circulations over Eurasia by altering the atmospheric dynamic and thermodynamic conditions. These influencing factors jointly account for about 66% of variances in the dipole mode of snow cover variability, suggesting that our findings can substantially improve the understanding of the Eurasian snow cover variations. Plain Language Summary: This study investigated the possible influencing factors responsible for the formation of the west‐east dipole structure of Eurasian snow cover variability during late autumn that has notable impacts on the atmospheric circulations and climate anomalies in the following season. Results suggested that the anomalous circulations associated with the atmospheric teleconnections of the Eastern Atlantic pattern, East Atlantic‐Western Russia pattern, and Scandinavian pattern can effectively regulate the snow cover variability related to the dipole pattern through inducing the anomalies of surface air temperature and snowfall. In addition, the North Atlantic sea surface temperature anomalies play a considerable role in forming the dipole mode by exciting a wave train type anomalous circulation over Eurasia. Furthermore, the variations of sea ice concentration in the Barents Sea could lead to the snow cover variability through modulating the temperature gradient over mid‐high latitudes and the resultant circulation anomalies over Eurasia. The synergetic effect of these influencing factors explains about two‐thirds of the snow cover variability related to the dipole mode, indicating the important contribution of our results to understanding the Eurasian autumn snow cover anomaly. Key Points: The west‐east dipole pattern is the intrinsic mode of the Eurasian snow cover variability during late autumnThe dipole mode is closely associated with the Eastern Atlantic pattern, East Atlantic‐Western Russia pattern, and Scandinavian patternAnomalies of North Atlantic sea surface temperature and Barents sea ice can exert considerable effects on the dipole mode [ABSTRACT FROM AUTHOR]

In sharp contrast to rapid global warming, the surface air temperature (SAT) in Northeast Asia (NEA) has exhibited strong cooling in June since 2010. Our results show that this cooling is a possible response to declining Bering Sea sea ice concentrations (BSICs) in March. A physical process analysis shows that decreasing BSICs provoke a south‐eastward‐propagating Rossby wave train and induce a strong anomalous anticyclone over the midlatitude Northwest Atlantic, resulting in tripolar anomalous sea surface temperature (SST) pattern with warm anomalies in the midlatitude Northwest Atlantic. These persistent tripolar SST anomalies generate a new Rossby wave across Eurasia in June, which enhances the cyclonic atmosphere circulation anomaly over NEA and leads to the cold SAT anomalies observed in this area. Correspondingly, in response to the sharp decline in BSIC in March, the NEA has exhibited an unprecedented cooling trend in June over the most recent decade. Plain Language Summary: In contrast to rapid global warming, the surface air temperature (SAT) over Northeast Asia (NEA) in June has exhibited a strong cooling trend since 2010, which has had an important impact on local agriculture. Here we show that reduced Bering Sea sea ice concentrations (BSICs) in March promote cold SATs in the following June in NEA. Declining BSICs in March excite a Rossby wave train that propagates eastward, causing a strong anomalous anticyclone (cyclone) over the midlatitude North Atlantic (subpolar Atlantic) and a tripolar anomalous sea surface temperature (SST) pattern by modifying the surface heat flux. The tripolar SST pattern in the North Atlantic develops in April and May due to the downstream development of the wave train; it persists in June mainly due to the ocean memory effect and subsequently generates a new Rossby wave across Eurasia via eddy‐mediated processes, provoking a cyclonic atmospheric circulation anomaly over NEA and leading to the observed cold SAT anomalies. Accompanying the significant BSIC decline in March since 2010, the atmospheric regime and SST variations over the North Atlantic in association with the cold SAT anomalies observed in NEA in June have strengthened and thus induced strong cooling over NEA. Key Points: Unusual cooling has been observed in June over Northeast Asia (NEA) under the most recent global warmingThis June cooling in NEA is consistent with declining Bering Sea sea ice concentrations (BSICs) in MarchThe Rossby wave train and the sea surface temperature anomalies in Northwest Atlantic bridge the declining BSIC in March and the cooling over NEA in June [ABSTRACT FROM AUTHOR]

This study investigates the variability of summer surface air temperature (SAT) over northern Eurasia and its precursory signals in the tropical Atlantic and northern Asian land. The leading mode of summer SAT variations features a northern Eurasian SAT tripole (NEST) pattern, with two same‐sign SAT anomaly regions over eastern Europe–western Siberia and the Far East region and an opposite‐sign SAT anomaly region around the Baikal Lake. It is found that sea surface temperature (SST) or SAT anomalies in the tropical Atlantic and rainfall‐soil moisture anomalies around the Baikal Lake during May can modulate the NEST pattern. The SST anomalies in the tropical Atlantic persist from May to summer and induce a downstream zonal wave train across northern Eurasia, consequently causing the variation in the summer NEST pattern. May rainfall anomalies around the Baikal Lake affect the overlying atmospheric circulation during summer through the 'memory' effect of soil moisture and the soil moisture‐rainfall interaction, correspondingly modulating the downstream wave train and the associated NEST pattern. Based on the above results, a statistical prediction model is established using the two precursory signals, that is, SAT in the tropical Atlantic and rainfall around the Baikal Lake during May. The leave‐three‐out cross‐validation shows that the model has a high skill in predicting the summer NEST pattern, with a correlation coefficient of 0.51 (significant at the 99.8% confidence level) between observation and prediction during the period 1980–2016. [ABSTRACT FROM AUTHOR]

A coordinated set of large ensemble atmosphere‐only simulations is used to investigate the impacts of observed Arctic sea ice‐driven variability (SIDV) on the atmospheric circulation during 1979–2014. The experimental protocol permits separating Arctic SIDV from internal variability and variability driven by other forcings including sea surface temperature and greenhouse gases. The geographic pattern of SIDV is consistent across seven participating models, but its magnitude strongly depends on ensemble size. Based on 130 members, winter SIDV is ~0.18 hPa2 for Arctic‐averaged sea level pressure (~1.5% of the total variance), and ~0.35 K2 for surface air temperature (~21%) at interannual and longer timescales. The results suggest that more than 100 (40) members are needed to separate Arctic SIDV from other components for dynamical (thermodynamical) variables, and insufficient ensemble size always leads to overestimation of SIDV. Nevertheless, SIDV is 0.75–1.5 times as large as the variability driven by other forcings over northern Eurasia and Arctic. Plain Language Summary: Changing Arctic sea ice conditions since the late 1970s have exerted profound impacts on environment and ecosystem at the high latitudes and have been suggested to affect midlatitude weather and climate, although this topic has been controversial. In order to improve our understanding on how Arctic sea ice changes influence local and remote weather and climate, a coordinated set of experiments has been performed using various state‐of‐the‐art atmosphere‐only models to study the linkages between the Arctic climate change and lower latitudes. This study uses seven models following a common experimental protocol to investigate the atmospheric circulation changes forced by Arctic sea ice variability. The protocol allows the Arctic sea ice‐driven variability (SIDV) to be singled out. In boreal winter, the Arctic SIDV is ~0.18 hPa2 and accounts for only ~1.5% of the total variance for sea level pressure, while it is ~0.35 K2 and accounts for ~21% for surface air temperature. The results also suggest that using insufficient ensembles always leads to an overestimation of SIDV, and more than 100 and 40 ensemble members are needed for sea level pressure and surface air temperature within the Arctic, respectively, to separate the SIDV from the variability due to other factors, primarily the atmospheric internal variability. Key Points: Arctic sea ice variations drive ~1.5% of boreal winter Arctic SLP variance during 1979–2014 in coordinated 130‐member AGCM experimentsAn accurate quantification of the impacts of Arctic sea ice‐driven SLP variability requires an ensemble size greater than 100 membersInsufficient ensemble size always leads to an overestimation of the Arctic sea ice‐driven variance within and outside Arctic [ABSTRACT FROM AUTHOR]