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Independent component analysis of climate data: a new look at EOF rotation

Hannachi, A.; Unkel, S.; Trendafilov, N. T. and Joliffe, I. T. (2009). Independent component analysis of climate data: a new look at EOF rotation. Journal of Climate, 22(11) pp. 2797–2812.

DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1175/2008JCLI2571.1
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Abstract

The complexity inherent in climate data makes it necessary to introduce more than one statistical tool to the researcher to gain insight into the climate system. Empirical orthogonal function (EOF) analysis is one of the most widely used methods to analyze weather/climate modes of variability and to reduce the dimensionality of the system. Simple structure rotation of EOFs can enhance interpretability of the obtained patterns but cannot provide anything more than temporal uncorrelatedness. In this paper, an alternative rotation method based on independent component analysis (ICA) is considered. The ICA is viewed here as a method of EOF rotation. Starting from an initial EOF solution rather than rotating the loadings toward simplicity, ICA seeks a rotation matrix that maximizes the independence between the components in the time domain. If the underlying climate signals have an independent forcing, one can expect to find loadings with interpretable patterns whose time coefficients have properties that go beyond simple noncorrelation observed in EOFs. The methodology is presented and an application to monthly means sea level pressure (SLP) field is discussed. Among the rotated (to independence) EOFs, the North Atlantic Oscillation (NAO) pattern, an Arctic Oscillation–like pattern, and a Scandinavian-like pattern have been identified. There is the suggestion that the NAO is an intrinsic mode of variability independent of the Pacific.

Item Type: Journal Article
Copyright Holders: 2010 American Meteorological Society
ISSN: 0894-8755
Keywords: empirical orthogonal functions, climate records
Academic Unit/Department: Mathematics, Computing and Technology
Mathematics, Computing and Technology > Mathematics and Statistics
Item ID: 22506
Depositing User: Sarah Frain
Date Deposited: 10 Aug 2010 11:21
Last Modified: 03 Dec 2012 12:05
URI: http://oro.open.ac.uk/id/eprint/22506
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