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The rapid loss of Arctic sea ice is a striking consequence of anthropogenic global warming. Its remote impacts on mid‐latitude weather and climate have attracted scientific and media attention. In this study, we use a hybrid (dynamical plus machine‐learning) atmospheric model—Google's NeuralGCM—to investigate the mid‐latitude atmospheric circulation responses to Arctic sea‐ice loss for the first time. We conduct experiments in which NeuralGCM is forced with pre‐industrial and future sea‐ice concentrations following the protocol of the Polar Amplification Model Intercomparisom Project. To assess the performance of NeuralGCM, we compare the results with those simulated by two physics‐based climate models. NeuralGCM produces a comparable response of near‐surface warming to sea‐ice loss and the subsequent weakened zonal wind in mid‐latitudes. However, there is a substantial discrepancy between the two models' stratospheric responses, where different temperature responses in these models are associated with different zonal wind and geopotential height responses. Further investigation of North Atlantic blocking shows that NeuralGCM produces stronger, more frequent, and more realistic blocking events. Our results demonstrate the capability of NeuralGCM in simulating the tropospheric responses to Arctic sea‐ice loss, but improvements may be needed for the stratospheric representation. 

Abstract Human-induced warming is amplified in the Arctic, but its causes and consequences are not precisely known. Here, we review scientific advances facilitated by the Polar Amplification Model Intercomparison Project. Surface heat flux changes and feedbacks triggered by sea-ice loss are critical to explain the magnitude and seasonality of Arctic amplification. Tropospheric responses to Arctic sea-ice loss that are robust across models and separable from internal variability have been revealed, including local warming and moistening, equatorward shifts of the jet stream and storm track in the North Atlantic, and fewer and milder cold extremes over North America. Whilst generally small compared to simulated internal variability, the response to Arctic sea-ice loss comprises a non-negligible contribution to projected climate change. For example, Arctic sea-ice loss is essential to explain projected North Atlantic jet trends and their uncertainty. Model diversity in the simulated responses has provided pathways to observationally constrain the real-world response. 

Abstract In contrast to surface greenhouse warming, surface greenhouse cooling has been less explored, especially on multi-century timescales. Here, we assess the processes controlling the pacing and magnitude of the multi-century surface temperature response to instantaneously doubling and halving atmospheric carbon dioxide concentrations in a modern global coupled climate model. Over the first decades, surface greenhouse warming is larger and faster than surface greenhouse cooling both globally and at high northern latitudes (45–90° N). Yet, this initial multi-decadal response difference does not persist. After year 150, additional surface warming is negligible, but surface cooling and sea ice expansion continues. Notably, the equilibration timescale for high northern latitude surface cooling (∼437 years) is more than double the equivalent timescale for warming. The high northern latitude responses differ most at the sea ice edge. Under greenhouse cooling, the sea ice edge slowly creeps southward into the mid-latitude oceans amplified by positive lapse rate and surface albedo feedbacks. While greenhouse warming and sea ice loss at high northern latitudes occurs on multi-decadal timescales, greenhouse cooling and sea ice expansion occurs on multi-century timescales. Overall, this work shows the importance of multi-century timescales and sea ice processes for understanding high northern latitude climate responses. 

Abstract Arctic amplification (AA), the greater Arctic surface warming compared to the global average, has been widely attributed to increasing concentrations of greenhouse gases (GHG). However, less is known about the impacts of other forcings - notably, anthropogenic aerosols (AER) - and how they may compare to the impacts of GHG. Here we analyze sets of climate model simulations, specifically designed to isolate the AER and GHG effects on global climate. Surprisingly, we find stronger AA produced by AER than by GHG during the 1955–1984 period, when the strongest global AER increase. This stronger AER-induced AA is due to a greater sensitivity of Arctic sea ice, and associated changes in ocean-to-atmosphere heat exchange, to AER forcing. Our findings highlight the asymmetric Arctic climate response to GHG and AER forcings, and show that clean air policies which have reduced aerosol emissions may have exacerbated the Arctic warming over the past few decades. 

Abstract Arctic amplification (AA), referring to the phenomenon of amplified warming in the Arctic compared to the warming in the rest of the globe, is generally attributed to the increasing concentrations of carbon dioxide (CO₂) in the atmosphere. However, little attention has been paid to the mechanisms and quantitative variations of AA under decreasing levels of CO₂, when cooling where the Arctic region is considerably larger than over the rest of the planet. Analyzing climate model experiments forced with a wide range of CO₂concentrations (from 1/8× to 8×CO₂, with respect to preindustrial levels), we show that AA indeed occurs under decreasing CO₂concentrations, and it is stronger than AA under increasing CO₂concentrations. Feedback analysis reveals that the Planck, lapse-rate, and albedo feedbacks are the main contributors to producing AAs forced by CO₂increase and decrease, but the stronger lapse-rate feedback associated with decreasing CO₂level gives rise to stronger AA. We further find that the increasing CO₂concentrations delay the peak month of AA from November to December or January, depending on the forcing strength. In contrast, decreasing CO₂levels cannot shift the peak of AA earlier than October, as a consequence of the maximum sea-ice increase in September which is independent of forcing strength. Such seasonality changes are also presented in the lapse-rate feedback, but do not appear in other feedbacks nor in the atmospheric and oceanic heat transport processeses. Our results highlight the strongly asymmetric responses of AA, as evidenced by the different changes in its intensity and seasonality, to the increasing and decreasing CO₂concentrations. These findings have significant implications for understanding how carbon removal could impact the Arctic climate, ecosystems, and socio-economic activities. 

Abstract The observed winter Barents-Kara Sea (BKS) sea ice concentration (SIC) has shown a close association with the second empirical orthogonal function (EOF) mode of Eurasian winter surface air temperature (SAT) variability, known as Warm Arctic Cold Eurasia (WACE) pattern. However, the potential role of BKS SIC on this WACE pattern of variability and on its long-term trend remains elusive. Here, we show that from 1979 to 2022, the winter BKS SIC and WACE association is most prominent and statistically significant for the variability at the sub-decadal time scale for 5–6 years. We also show the critical role of the multi-decadal trend in the principal component of the WACE mode of variability for explaining the overall Eurasian winter temperature trend over the same period. Furthermore, a large multi-model ensemble of atmosphere-only experiments from 1979 to 2014, with and without the observed Arctic SIC forcing, suggests that the BKS SIC variations induce this observed sub-decadal variability and the multi-decadal trend in the WACE. Additionally, we analyse the model simulated first or the leading EOF mode of Eurasian winter SAT variability, which in observations, closely relates to the Arctic Oscillation (AO). We find a weaker association of this mode to AO and a statistically significant positive trend in our ensemble simulation, opposite to that found in observation. This contrasting nature reflects excessive hemispheric warming in the models, partly contributed by the modelled Arctic Sea ice loss. 

Abstract Emission of anthropogenic greenhouse gases has resulted in greater Arctic warming compared to global warming, known as Arctic amplification (AA). From an energy‐balance perspective, the current Arctic climate is in radiative‐advective equilibrium (RAE) regime, in which radiative cooling is balanced by advective heat flux convergence. Exploiting a suite of climate model simulations with varying carbon dioxide () concentrations, we link the northern high‐latitude regime variation and transition to AA. The dominance of RAE regime in northern high‐latitudes under reduction relates to stronger AA, whereas the RAE regime transition to non‐RAE regime under increase corresponds to a weaker AA. Examinations on the spatial and seasonal structures reveal that lapse‐rate and sea‐ice processes are crucial mechanisms. Our findings suggest that if concentration continues to rise, the Arctic could transition into a non‐RAE regime accompanied with a weaker AA. 

Key Points The external radiative forcing is the primary driver of the 1979–2013 warming for April–September, with varying decadal warming rates The interdecadal Pacific and Atlantic multidecadal variability intensify/dampen the warming when transitioning to positive/negative phase The combined effects of these factors reproduce the observed varied pace of decadal Arctic troposphere warming during 1979–2013 

Abstract. The main drivers of the continental Northern Hemisphere snow cover are investigated in the 1979–2014 period. Four observational datasets are usedas are two large multi-model ensembles of atmosphere-only simulations with prescribed sea surface temperature (SST) and sea ice concentration (SIC). Afirst ensemble uses observed interannually varying SST and SIC conditions for 1979–2014, while a second ensemble is identical except for SIC witha repeated climatological cycle used. SST and external forcing typically explain 10 % to 25 % of the snow cover variance in modelsimulations, with a dominant forcing from the tropical and North Pacific SST during this period. In terms of the climate influence of the snow coveranomalies, both observations and models show no robust links between the November and April snow cover variability and the atmospheric circulation1 month later. On the other hand, the first mode of Eurasian snow cover variability in January, with more extended snow over western Eurasia, isfound to precede an atmospheric circulation pattern by 1 month, similar to a negative Arctic oscillation (AO). A decomposition of the variabilityin the model simulations shows that this relationship is mainly due to internal climate variability. Detailed outputs from one of the modelsindicate that the western Eurasia snow cover anomalies are preceded by a negative AO phase accompanied by a Ural blocking pattern and astratospheric polar vortex weakening. The link between the AO and the snow cover variability is strongly related to the concomitant role of thestratospheric polar vortex, with the Eurasian snow cover acting as a positive feedback for the AO variability in winter. No robust influence of theSIC variability is found, as the sea ice loss in these simulations only drives an insignificant fraction of the snow cover anomalies, with fewagreements among models. 

Abstract Large ensemble simulations with six atmospheric general circulation models involved are utilized to verify the interdecadal Pacific oscillation (IPO) impacts on the trend of Eurasian winter surface air temperatures (SAT) during 1998–2013, a period characterized by the prominent Eurasia cooling (EC). In our simulations, IPO brings a cooling trend over west-central Eurasia in 1998–2013, about a quarter of the observed EC in that area. The cooling is associated with the phase transition of the IPO to a strong negative. However, the standard deviation of the area-averaged SAT trends in the west EC region among ensembles, driven by internal variability intrinsic due to the atmosphere and land, is more than three times the isolated IPO impacts, which can shadow the modulation of the IPO on the west Eurasia winter climate.