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The wintertime Northern Hemisphere (NH) atmospheric circulation response to current (2007–12) and projected (2080–99) Arctic sea ice decline is examined with the latest version of the Community Atmospheric Model (CAM5). The numerical experiments suggest that the current sea ice conditions force a remote atmospheric response in late winter that favors cold land surface temperatures over midlatitudes, as has been observed in recent years. Anomalous Rossby waves forced by the sea ice anomalies penetrate into the stratosphere in February and weaken the stratospheric polar vortex, resulting in negative anomalies of the northern annular mode (NAM) that propagate downward during the following weeks, especially over the North Pacific. The seasonality of the response is attributed to timing of the phasing between the forced and climatological waves. When sea ice concentration taken from projections of conditions at the end of the twenty-first century is prescribed to the model, negative anomalies of the NAM are visible in the troposphere, both in early and late winter. This response is mainly driven by the large warming of the lower troposphere over the Arctic, as little impact is found in the stratosphere in this experiment. As a result of the thermal expansion of the polar troposphere, the westerly flow is decelerated and a weak but statistically significant increase of the midlatitude meanders is identified. However, the thermodynamical response extends beyond the Arctic and offsets the dynamical effect, such that the stronger sea ice forcing has limited impact on the intensity of cold extremes over midlatitudes.

The North Atlantic sea surface temperature exhibits fluctuations on the multidecadal time scale, a phenomenon known as the Atlantic Multidecadal Oscillation (AMO). This letter demonstrates that the multidecadal fluctuations of the wintertime North Atlantic Oscillation (NAO) are tied to the AMO, with an opposite-signed relationship between the polarities of the AMO and the NAO. Our statistical analyses suggest that the AMO signal precedes the NAO by 10-15 years with an interesting predictability window for decadal forecasting. The AMO footprint is also detected in the multidecadal variability of the intraseasonal weather regimes of the North Atlantic sector. This observational evidence is robust over the entire 20th century and it is supported by numerical experiments with an atmospheric global climate model. The simulations suggest that the AMO-related SST anomalies induce the atmospheric anomalies by shifting the atmospheric baroclinic zone over the North Atlantic basin. As in observations, the positive phase of the AMO results in more frequent negative NAO-and blocking episodes in winter that promote the occurrence of cold extreme temperatures over the eastern United States and Europe. Thus, it is plausible that the AMO plays a role in the recent resurgence of severe winter weather in these regions and that wintertime cold extremes will be promoted as long as the AMO remains positive.

Abstract. Polar amplification – the phenomenon where external radiative forcing produces a larger change in surface temperature at high latitudes than the global average – is a key aspect of anthropogenic climate change, but its causes and consequences are not fully understood. The Polar Amplification Model Intercomparison Project (PAMIP) contribution to the sixth Coupled Model Intercomparison Project (CMIP6; Eyring et al., 2016) seeks to improve our understanding of this phenomenon through a coordinated set of numerical model experiments documented here. In particular, PAMIP will address the following primary questions: (1) what are the relative roles of local sea ice and remote sea surface temperature changes in driving polar amplification? (2) How does the global climate system respond to changes in Arctic and Antarctic sea ice? These issues will be addressed with multi-model simulations that are forced with different combinations of sea ice and/or sea surface temperatures representing present-day, pre-industrial and future conditions. The use of three time periods allows the signals of interest to be diagnosed in multiple ways. Lower-priority tier experiments are proposed to investigate additional aspects and provide further understanding of the physical processes. These experiments will address the following specific questions: what role does ocean–atmosphere coupling play in the response to sea ice? How and why does the atmospheric response to Arctic sea ice depend on the pattern of sea ice forcing? How and why does the atmospheric response to Arctic sea ice depend on the model background state? What have been the roles of local sea ice and remote sea surface temperature in polar amplification, and the response to sea ice, over the recent period since 1979? How does the response to sea ice evolve on decadal and longer timescales? A key goal of PAMIP is to determine the real-world situation using imperfect climate models. Although the experiments proposed here form a coordinated set, we anticipate a large spread across models. However, this spread will be exploited by seeking “emergent constraints” in which model uncertainty may be reduced by using an observable quantity that physically explains the intermodel spread. In summary, PAMIP will improve our understanding of the physical processes that drive polar amplification and its global climate impacts, thereby reducing the uncertainties in future projections and predictions of climate change and variability.

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The Crocus snowpack model within the Interactions between Soil-Biosphere-Atmosphere (ISBA) land surface model was run over northern Eurasia from 1979 to 1993, using forcing data extracted from hydrometeorological datasets and meteorological reanalyses. Simulated snow depth, snow water equivalent, and density over open fields were compared with local observations from over 1000 monitoring sites, available either once a day or three times per month. The best performance is obtained with European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim). Provided blowing snow sublimation is taken into account, the simulations show a small bias and high correlations in terms of snow depth, snow water equivalent, and density. Local snow cover durations as well as the onset and vanishing dates of continuous snow cover are also well reproduced. A major result is that the overall performance of the simulations is very similar to the performance of existing gridded snow products, which, in contrast, assimilate local snow depth observations. Soil temperature at 20-cm depth is reasonably well simulated. The methodology developed in this study is an efficient way to evaluate different meteorological datasets, especially in terms of snow precipitation. It reveals that the temporal disaggregation of monthly precipitation in the hydrometeorological dataset from Princeton University significantly impacts the rain-snow partitioning, deteriorating the simulation of the onset of snow cover as well as snow depth throughout the cold season.

This study explores the early‐winter atmospheric response to Ural blocking anomalies in November, using a nudging technique to constrain the temperature and dynamics in a high‐top atmospheric model. Persistent Ural blocking anomalies in November are associated with a warm Arctic/cold Siberia pattern and increased upward planetary waves entering the stratosphere, leading to a warming of the polar vortex. This stratospheric response then propagates in the troposphere, leading to increased occurrence of the negative North Atlantic Oscillation in December and January. In contrast, simulations with perturbed Barents‐Kara sea ice and Siberian snow in November do not reproduce a significant atmospheric response. In simulations including a slab ocean, the Ural blocking induces Barents‐Kara sea ice and Siberia snow anomalies that resemble composite analyses from observations. These results highlight Ural blocking variability in November as a robust driver of early‐winter stratospheric warming while questioning causality between sea ice/snow and Ural blocking anomalies.

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