《advanced spectral methods for climatic time series》.pdfVIP

ADVANCED SPECTRAL METHODS FOR CLIMATIC TIME SERIES M. Ghil,1 M. R. Allen,2 M. D. Dettinger,3 K. Ide,1 D. Kondrashov,1 M. E. Mann,4 A. W. Robertson,1 A. Saunders,1 Y. Tian,1 F. Varadi,1 and P. Yiou5 Received 28 August 2000; revised 3 July 2001; accepted 18 September 2001; published XX Month 2001. [1] The analysis of univariate or multivariate time series well as the advantages and disadvantages of these meth- provides crucial information to describe, understand, ods, are illustrated by their application to an important and predict climatic variability. The discovery and im- climatic time series, the Southern Oscillation Index. This plementation of a number of novel methods for extract- index captures major features of interannual climate vari- ing useful information from time series has recently ability and is used extensively in its prediction. Regional revitalized this classical field of study. Considerable and global sea surface temperature data sets are used to progress has also been made in interpreting the infor- illustrate multivariate spectral methods. Open questions mation so obtained in terms of dynamical systems the- and further prospects conclude the review. INDEX TERMS: ory. In this review we describe the connections between 1620 Climate dynamics (3309); 3220 Nonlinear dynam- time series analysis and nonlinear dynamics, discuss sig- ics; 4522 El Nin ˜o; 9820 Techniques applicable in three nal-to-noise enhancement, and present some of the or more fields; KEYWORDS: climate; dynamical systems; El novel methods for spectral analysis. The various steps, as Nin˜o; pre