ADVANCED SPECTRAL METHODS FOR CLIMATIC TIME SERIES

[1] The analysis of univariate or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and im-plementation of a number of novel methods for extract-ing useful information from time series has recently revitalized this classical field of study. Considerable progress has also been made in interpreting the infor-mation so obtained in terms of dynamical systems the-ory. In this review we describe the connections between time series analysis and nonlinear dynamics, discuss sig-nal-to-noise enhancement, and present some of the novel methods for spectral analysis. The various steps, as well as the advantages and disadvantages of these meth-ods, are illustrated by their application to an important climatic time series, the Southern Oscillation Index. This index captures major features of interannual climate vari-ability and is used extensively in its prediction. Regional and global sea surface temperature data sets are used to illustrate multivariate spectral methods. Open questions and further prospects conclude the review. INDEX TERMS