DOI 10.1007/s00382-013-1830-9 Atmospheric impacts of Arctic sea-ice loss, 1979–2009: separating forced change from atmospheric internal variability

Abstract The ongoing loss of Arctic sea-ice cover has implications for the wider climate system. The detection and importance of the atmospheric impacts of sea-ice loss depends, in part, on the relative magnitudes of the sea-ice forced change compared to natural atmospheric internal variability (AIV...

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Bibliographic Details
Main Authors: Clim Dyn, James A. Screen, Clara Deser, Ian Simmonds, Robert Tomas, J. A. Screen, I. Simmonds, C. Deser, R. Tomas
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Published: 2013
Subjects:
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.391.7805
http://www.cgd.ucar.edu/staff/cdeser/docs/screen.seaice_atm_impacts.climdyn13.pdf
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Summary:Abstract The ongoing loss of Arctic sea-ice cover has implications for the wider climate system. The detection and importance of the atmospheric impacts of sea-ice loss depends, in part, on the relative magnitudes of the sea-ice forced change compared to natural atmospheric internal variability (AIV). This study analyses large ensembles of two independent atmospheric general circulation models in order to separate the forced response to historical Arctic sea-ice loss (1979–2009) from AIV, and to quantify signalto-noise ratios. We also present results from a simulation with the sea-ice forcing roughly doubled in magnitude. In proximity to regions of sea-ice loss, we identify statistically significant near-surface atmospheric warming and precipitation increases, in autumn and winter in both models. In winter, both models exhibit a significant lowering of sea level pressure and geopotential height over the Arctic. All of these responses are broadly similar, but strengthened and/or more geographically extensive, when the sea-ice forcing is doubled in magnitude. Signal-to-noise ratios differ considerably between variables and locations. The temperature and precipitation responses are significantly easier to detect (higher signal-to-noise ratio) than the sea level pressure or geopotential height responses. Equally