ADVANCED SPECTRAL METHODS FOR CLIMATIC TIME SERIES

TLDR

Time‑series analysis of climatic data, including the Southern Oscillation Index, is essential for describing, understanding, and predicting interannual climate variability, and recent advances in novel spectral methods and dynamical systems interpretations have revitalized this field. This review aims to describe the links between time‑series analysis and nonlinear dynamics, discuss signal‑to‑noise enhancement, and present novel spectral methods. The authors illustrate the various steps, advantages, and disadvantages of these methods by applying them to the Southern Oscillation Index and to regional and global sea‑surface temperature datasets. The review concludes with open questions and future prospects for the field.

Abstract

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.