Independent component analysis of multivariate time series:: Application to the tropical SST variability

With the aim of identifying the physical causes of variability of a given dynamical system, the geophysical community has made an extensive use of classical component extraction techniques such as principal component analysis (PCA) or rotational techniques (RT). We introduce a recently developed algorithm based on information theory: independent component analysis (ICA). This new technique presents two major advantages over classical methods. First, it aims at extracting statistically independent components where classical techniques search for decorrelated components (i.e., a weaker constraint). Second, the linear hypothesis for the mixture of components is not required. In this paper, after having briefly summarized the essentials of classical techniques, we present the new method in the context of geophysical time series analysis. We then illustrate the ICA algorithm by applying it to the study of the variability of the tropical sea surface temperature (SST), with a particular emphasis on the analysis of the links between El Nino Southern Oscillation (ENSO) and Atlantic SST variability. The new algorithm appears to be particularly efficient in describing the complexity of the phenomena and their various sources of variability in space and time.