Academic Commons

Articles

Advanced spectral methods for climatic time series

Allen, M. R.; Ghil, Michael; Dettinger, M. D.; Ide, K.; Kondrashov, D.; Mann, M. E.; Robertson, Andrew W.; Saunders, A.; Tian, Y.; Varadi, F.; Yiou, P.

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.

Files

Also Published In

Title
Reviews of Geophysics
DOI
https://doi.org/10.1029/2000RG000092

More About This Work

Academic Units
International Research Institute for Climate and Society
Published Here
August 14, 2012
Academic Commons provides global access to research and scholarship produced at Columbia University, Barnard College, Teachers College, Union Theological Seminary and Jewish Theological Seminary. Academic Commons is managed by the Columbia University Libraries.