Link between fall sea ice variability and atmospheric patterns in the following winter

Type: Presentation

Venue: 25th Conference on Climate Variability and Change


Judah Cohen, and S. Hopsch (2013) Link between fall sea ice variability and atmospheric patterns in the following winter. 25th Conference on Climate Variability and Change, Austin, TX.

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The strong decline in autumn sea ice over the last decades has intensified interest in the relationship and interactions between sea-ice conditions and the atmosphere. Recent studies have pointed to possible impacts on atmospheric flow following anomalous sea ice concentrations (SICs). These studies were based on limited number of years and restricted to only using events with large anomalies (>1 std dev) – a restriction that left only very few sample members for comparisons or further statistical analysis. Here, we revisit the issue by analyzing a longer time series and also comparing results for two different time periods: the most recent, satellite-era period (1979-2010) and a longer time series that also includes the pre-satellite period (1950-2010).

Based on September SICs for each time–period, an index was constructed which was used to identify anomalous high/low SIC years for both the original, as well as for the linearly detrended sea ice index. Identified years were then used to derive composites for the following winter's monthly atmospheric variables. Mid-troposphere geopotential height composites for winter months are in general reminiscent of the NAO pattern with high latitude maximum shifted towards the Barents Sea. Also, lower troposphere temperatures indicate presence of cooler conditions over the continents during low SIC years. However, differences in the composite patterns are significant only for areas with limited spatial extent. While suggested pathways in previously published studies seem reasonable, our results show that these findings are not yet robust enough from a statistical significance perspective. More data (e.g. provided by longer, climate-quality reanalysis datasets) are needed before conclusions of impacts and feedbacks can be drawn with certainty.