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Drivers and potential predictability of summer time North Atlantic polar front jet variability
Hall, Richard J.; Jones, Julie M.; Hanna, Edward; Scaife, Adam A.; Erdélyi, Róbert
AA(Department of Geography, University of Sheffield), AB(Department of Geography, University of Sheffield), AC(Department of Geography, University of Sheffield), AD(Met Office Hadley Centre), AE(Solar Physics and Space Plasma Research Centre, School of Mathematics and Statistics, University of Sheffield; Debrecen Heliophysical Observatory (DHO), Research Centre for Astronomy and Earth Sciences, Hungarian Academy of Sciences (HAS))
Climate Dynamics, Volume 48, Issue 11-12, pp. 3869-3887 (ClDy Homepage)
Publication Date:
Polar front jet, Predictors, North Atlantic, Predictability
Abstract Copyright:
(c) 2017: The Author(s)
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The variability of the North Atlantic polar front jet stream is crucial in determining summer weather around the North Atlantic basin. Recent extreme summers in western Europe and North America have highlighted the need for greater understanding of this variability, in order to aid seasonal forecasting and mitigate societal, environmental and economic impacts. Here we find that simple linear regression and composite models based on a few predictable factors are able to explain up to 35 % of summertime jet stream speed and latitude variability from 1955 onwards. Sea surface temperature forcings impact predominantly on jet speed, whereas solar and cryospheric forcings appear to influence jet latitude. The cryospheric associations come from the previous autumn, suggesting the survival of an ice-induced signal through the winter season, whereas solar influences lead jet variability by a few years. Regression models covering the earlier part of the twentieth century are much less effective, presumably due to decreased availability of data, and increased uncertainty in observational reanalyses. Wavelet coherence analysis identifies that associations fluctuate over the study period but it is not clear whether this is just internal variability or genuine non-stationarity. Finally we identify areas for future research.
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