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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 Observations show predictive skill of the minimum sea ice extent (Min SIE) from late winter anomalous offshore ice drift along the Eurasian coastline, leading to local ice thickness anomalies at the onset of the melt season—a signal then amplified by the ice–albedo feedback. We assess whether the observed seasonal predictability of September sea ice extent (Sept SIE) from Fram Strait Ice Area Export (FSIAE; a proxy for Eurasian coastal divergence) is present in global climate model (GCM) large ensembles, namely the CESM2-LE, GISS-E2.1-G, FLOR-LE, CNRM-CM6-1, and CanESM5. All models show distinct periods where winter FSIAE anomalies are negatively correlated with the May sea ice thickness (May SIT) anomalies along the Eurasian coastline, and the following Sept Arctic SIE, as in observations. Counterintuitively, several models show occasional periods where winter FSIAE anomalies are positively correlated with the following Sept SIE anomalies when the mean ice thickness is large, or late in the simulation when the sea ice is thin, and/or when internal variability increases. More important, periods with weak correlation between winter FSIAE and the following Sept SIE dominate, suggesting that summer melt processes generally dominate over late-winter preconditioning and May SIT anomalies. In general, we find that the coupling between the winter FSIAE and ice thickness anomalies along the Eurasian coastline at the onset of the melt season is a ubiquitous feature of GCMs and that the relationship with the following Sept SIE is dependent on the mean Arctic sea ice thickness.