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Reanalysing the impacts of atmospheric teleconnections on cold‐season weather using multivariate surface weather types and self‐organizing maps
Author(s) -
Lee Cameron C.
Publication year - 2017
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.4950
Subject(s) - teleconnection , climatology , environmental science , multivariate statistics , north atlantic oscillation , geography , meteorology , el niño southern oscillation , geology , mathematics , statistics
While regional‐ to hemispheric‐scale oscillations in oceanographic and atmospheric variables have long been known to have teleconnective impacts on the surface weather at distant locations, the impacts of these teleconnections and their interactions on multivariate weather types ( WTs ) are relatively under‐researched. Using a recently developed gridded weather typing classification ( GWTC ) and a self‐organizing maps‐based clustering of five different teleconnection indices, this research aims to explore the impacts of teleconnections on surface weather in the United States and Canada. Individual teleconnections have a predictable impact on GWTC WTs , with the Pacific/North American pattern, the Western Pacific ( WP ) pattern and the North Atlantic Oscillation showing the most widespread significant correlations with cool and warm WTs , in agreement with previous research. While many teleconnection clusters are dominated by one teleconnection's WT correlations, certain clusters reveal surprising regional‐ to continental‐scale impacts considering many teleconnections are in a relatively neutral phase. Furthermore, some expected impacts of the Southern Oscillation Index and WP are offset when considered in tandem with neutral phases of the other teleconnections examined. Overall, the clustering results highlight the importance of examining multiple teleconnections simultaneously when researching teleconnection impacts on surface weather and making statistically based monthly‐ to seasonal‐range climate projections.