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Proceedings Paper

Using independent component analysis to separate signals in climate data
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Paper Abstract

Observed and simulated global temperature series include the effects of many different sources, such as volcano eruptions and El Nino Southern Oscillation (ENSO) variations. In order to compare the results of different models to each other, and to the observed data, it is necessary to first remove contributions from sources that are not commonly shared across the models considered. Such a separation of sources is also desired in order to assess the effect of human contributions on the global climate. Atmospheric scientists currently use parametric models and iterative techniques to remove the effects of volcano eruptions and ENSO variations from global temperature trends. Drawbacks of the parametric approach include the non-robustness of the results to the estimated values of the parameters, and the possible lack of fit of the data to the model. In this paper, we investigate ICA as an alternative method for separating independent sources in global temperature series. Instead of fitting parametric models, we let the data guide the estimation, and separate automatically the effects of the underlying sources. We first assess ICA on simple artificial datasets to establish the conditions under which ICA is feasible in our context, then we study its results on climate data from the National Centers for Environmental Predictions.

Paper Details

Date Published: 1 April 2003
PDF: 12 pages
Proc. SPIE 5102, Independent Component Analyses, Wavelets, and Neural Networks, (1 April 2003); doi: 10.1117/12.487277
Show Author Affiliations
Imola K. Fodor, Lawrence Livermore National Lab. (United States)
Chandrika Kamath, Lawrence Livermore National Lab. (United States)


Published in SPIE Proceedings Vol. 5102:
Independent Component Analyses, Wavelets, and Neural Networks
Anthony J. Bell; Mladen V. Wickerhauser; Harold H. Szu, Editor(s)

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