Ghil Michael author Allen M. R. author Dettinger M. D. author Ide K. author Kondrashov D. author Mann M. E. author Robertson Andrew W. author Columbia University. International Research Institute for Climate and Society Saunders A. author Tian Y. author Varadi F. author Yiou P. author Columbia University. International Research Institute for Climate and Society originator text Articles 2002 English The analysis of univariate or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical field of study. Considerable progress has also been made in interpreting the information so obtained in terms of dynamical systems theory. In this review we describe the connections between time series analysis and nonlinear dynamics, discuss signal-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 methods, are illustrated by their application to an important climatic time series, the Southern Oscillation Index. This index captures major features of interannual climate variability 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. Atmospheric sciences Physical oceanography 40 1003 1 41 2002-03 http://dx.doi.org/10.1029/2000RG000092 http://hdl.handle.net/10022/AC:P:14382 NNC NNC 2012-08-14 15:11:16 -0400 2012-10-19 11:50:52 -0400 8402 eng