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Abstract Over the coming century, both Arctic and Antarctic sea ice cover are projected to substantially decline. While many studies have documented the potential impacts of projected Arctic sea ice loss on the climate of the mid-latitudes and the tropics, little attention has been paid to the impacts of Antarctic sea ice loss. Here, using comprehensive climate model simulations, we show that the effects of end-of-the-century projected Antarctic sea ice loss extend much further than the tropics, and are able to produce considerable impacts on Arctic climate. Specifically, our model indicates that the Arctic surface will warm by 1 °C and Arctic sea ice extent will decline by 0.5 × 10⁶km²in response to future Antarctic sea ice loss. Furthermore, with the aid of additional atmosphere-only simulations, we show that this pole-to-pole effect is mediated by the response of the tropical SSTs to Antarctic sea ice loss: these simulations reveal that Rossby waves originating in the tropical Pacific cause the Aleutian Low to deepen in the boreal winter, bringing warm air into the Arctic, and leading to sea ice loss in the Bering Sea. This pole-to-pole signal highlights the importance of understanding the climate impacts of the projected sea ice loss in the Antarctic, which could be as important as those associated with projected sea ice loss in the Arctic. 

Previous work identified an anthropogenic fingerprint pattern in 𝑇AC (𝑥, 𝑡), the amplitude of the seasonal cycle of mid- to upper tropospheric temperature (TMT), but did not explicitly consider whether fingerprint identification in satellite 𝑇AC(𝑥,𝑡) data could have been influenced by real-world multidecadal internal variability (MIV). We address this question here using large ensembles (LEs) performed with five climate models. LEs provide many different sequences of internal variability noise superimposed on an underlying forced signal. Despite differences in historical external forcings, climate sensitivity, and MIV properties of the five models, their 𝑇AC (𝑥, 𝑡) fingerprints are similar and statistically identifiable in 239 of the 240 LE realizations of historical climate change. Comparing simulated and observed variability spectra reveals that consistent fingerprint identification is unlikely to be biased by model underestimates of observed MIV. Even in the presence of large (factor of 3-4) inter-model and inter-realization differences in the amplitude of MIV, the anthropogenic fingerprints of seasonal cycle changes are robustly identifiable in models and satellite data. This is primarily due to the fact that the distinctive, global-scale fingerprint patterns are spatially dissimilar to the smaller-scale patterns of internal 𝑇AC(𝑥,𝑡) variability associated with the Atlantic Multidecadal Oscillation and the El Niño~Southern Oscillation. The robustness of the seasonal cycle D&A results shown here, taken together with the evidence fromidealized aquaplanet simulations, suggest that basic physical processes are dictating a common pattern of forced𝑇AC(𝑥,𝑡) changes in observations and in the five LEs. The key processes involved include GHG-induced expansion of the tropics, lapse-rate changes, land surface drying, and sea ice decrease.