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ADVANCED SPECTRAL METHODS FOR CLIMATIC TIME SERIES
Author(s) -
Ghil M.,
Allen M. R.,
Dettinger M. D.,
Ide K.,
Kondrashov D.,
Mann M. E.,
Robertson A. W.,
Saunders A.,
Tian Y.,
Varadi F.,
Yiou P.
Publication year - 2002
Publication title -
reviews of geophysics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.087
H-Index - 156
eISSN - 1944-9208
pISSN - 8755-1209
DOI - 10.1029/2000rg000092
Subject(s) - univariate , series (stratigraphy) , time series , multivariate statistics , computer science , spectral analysis , field (mathematics) , singular spectrum analysis , climatology , data mining , climate change , geology , artificial intelligence , mathematics , machine learning , oceanography , physics , paleontology , quantum mechanics , singular value decomposition , spectroscopy , pure mathematics
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.