Ghil M., M. R. Allen, M. D. Dettinger, K. Ide, D. Kondrashov, M. E. Mann, A. W. Robertson, A. Saunders, Y. Tian, F. Varadi, P. Yiou, (2001), Advanced Spectral Methods for Climatic Time Series, Rev. Geophys., 4

Abstract

The analysis of uni- 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 eld of study. Considerable progress has also been made in interpreting the information so obtained in terms of dynamical systems theory.

Authors (sorted by name)

Ghil Kondrashov Mann Robertson Tian

Journal / Conference

Rev. Geophys.

Acknowledgments

work was supported by an NSF Special Creativity Award. M.R.A. was supported by a NOAA Postdoctoral Fellowship in Climate and Global and Change (1994–1995), K.I. was supported by NASA grant NAG5-9294, D.K., F.V., and the further development and maintenance of the SSA-MTM Toolkit were supported by NSF grant ATM0082131, A.W.R. was supported by DOE grant DE-FG03- 98ER62615, and P.Y. was supported by the French Commissariat a` l’Energie Atomique. M.G. is also indebted to the French Acade´mie des Sciences for the Elf-Aquitaine/CNRS Chair (1996) that provided the necessary leisure for the writing of this review’s first draft and to his hosts over the years at the Ecole Normale Supe´rieure (ENS) in Paris, the De´partement Terre-Atmosphe`re-Oce´an of the ENS, and the Laboratoire de Me´te´orologie Dynamique du CNRS.

Grants

ATM-0082131 DE-FG03- 98ER62615 NAG5-9294

Bibtex

@article{Ghil01advancedspectral,
author = {M. Ghil and M. R. Allen and M. D. Dettinger and K. Ide and D. Kondrashov and M. E. Mann and A. W. Robertson and A. Saunders and Y. Tian and F. Varadi and P. Yiou},
title = {Advanced Spectral Methods for Climatic Time Series},
year = {2001},
journal = {Rev. Geophys.},
volume = {4},
abstract = {The analysis of uni- 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 eld of study. Considerable progress has also been made in interpreting the information so obtained in terms of dynamical systems theory.}
}