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Read Full Article (file size: 636099 bytes) Cited by
REVIEWS OF GEOPHYSICS,
VOL. 40, NO. 1,
1003,
doi:10.1029/2000RG000092,
2002
Advanced spectral methods for climatic time series
M. Ghil
Department of Atmospheric Sciences and Institute of Geophysics and Planetary Physics,
University of California, Los Angeles,
Los Angeles,
California,
USA.
M. R. Allen
Space Science and Technology Department,
Rutherford Appleton Laboratory,
Chilton,
Didcot,
England.
M. D. Dettinger
U.S. Geological Survey,
San Diego,
California,
USA.
K. Ide
Department of Atmospheric Sciences and Institute of Geophysics and Planetary Physics,
University of California, Los Angeles,
Los Angeles,
California,
USA.
D. Kondrashov
Department of Atmospheric Sciences and Institute of Geophysics and Planetary Physics,
University of California, Los Angeles,
Los Angeles,
California,
USA.
M. E. Mann
Department of Environmental Sciences,
University of Virginia,
Charlottesville,
Virginia,
USA.
A. W. Robertson
Department of Atmospheric Sciences and Institute of Geophysics and Planetary Physics,
University of California, Los Angeles,
Los Angeles,
California,
USA.
A. Saunders
Department of Atmospheric Sciences and Institute of Geophysics and Planetary Physics,
University of California, Los Angeles,
Los Angeles,
California,
USA.
Y. Tian
Department of Atmospheric Sciences and Institute of Geophysics and Planetary Physics,
University of California, Los Angeles,
Los Angeles,
California,
USA.
F. Varadi
Department of Atmospheric Sciences and Institute of Geophysics and Planetary Physics,
University of California, Los Angeles,
Los Angeles,
California,
USA.
P. Yiou
Laboratoire des Sciences du Climat et de l'Environnement,
UMR CEA-CNRS,
Gif-sur-Yvette,
France.
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.
Published 13
September
2002.
Index Terms: 1620 Global Change: Climate dynamics (3309); 3220 Mathematical Geophysics: Nonlinear dynamics; 4522 Oceanography: Physical: El Nino; 9820 General or Miscellaneous: Techniques applicable in three or more fields.
Read Full Article (file size: 636099 bytes) Cited by
Citation: Ghil, M., et al.
(2002),
Advanced spectral methods for climatic time series,
Rev. Geophys.,
40(1),
1003,
doi:10.1029/2000RG000092.
Copyright 2002 by the American Geophysical Union.
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