A regime view of northern hemisphere atmospheric variability and change under global warming, Geophys

Abstract. The leading mode of wintertime variability in Northern Hemisphere sea level pressure (SLP) is the Arctic Oscillation (AO). It is usually obtained using linear principal component analysis, which produces the optimal, although somewhat restrictive, linear approximation to the SLP data. Here...

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Bibliographic Details
Main Author: A. H. Monahan
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Published: 2000
Subjects:
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.475.7287
http://web.uvic.ca/~monahana/cccma.nlpca.pdf
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Summary:Abstract. The leading mode of wintertime variability in Northern Hemisphere sea level pressure (SLP) is the Arctic Oscillation (AO). It is usually obtained using linear principal component analysis, which produces the optimal, although somewhat restrictive, linear approximation to the SLP data. Here we use a recently introduced nonlinear principal com-ponent analysis to nd the optimal nonlinear approxima-tion to SLP data produced by a 1001 year integration of the CCCma coupled general circulation model (CGCM1). This approximation’s associated time series is strongly bimodal and partitions the data into two distinct regimes. The rst and more persistent regime describes a standing oscillation whose signature in the mid-troposphere is alternating am-pli cation and attenuation of the climatological ridge over Northern Europe, with associated decreasing and increasing daily variance over Northern Eurasia. The second and more episodic regime describes a split-flow south of Greenland with much enhanced daily variance in the Arctic. In a 500 year integration with atmospheric CO2 stabilized at concen-trations projected for year 2100, the occupation statistics of these preferred modes of variability change, such that the episodic split-flow regime occurs less frequently while the standing oscillation regime occurs more frequently. 1.