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Title: Chaos, dynamical structure, and climate variability

Deterministic chaos in dynamical systems offers a new paradigm for understanding irregular fluctuations. Techniques for identifying deterministic chaos from observed data, without recourse to mathematical models, are being developed. Powerful methods exist for reconstructing multidimensional phase space from an observed time series of a single scalar variable; these methods are invaluable when only a single scalar record of the dynamics is available. However in some applications multiple concurrent time series may be available for consideration as phase space coordinates. Here we propose some basic analytical tools for such multichannel time series data, and illustrate them by applications to a simple synthetic model of chaos, to a low-order model of atmospheric circulation, and to two high-resolution paleoclimate proxy data series. {copyright} {ital 1996 American Institute of Physics.}
Authors:
 [1]
  1. Department of Applied Science, Brookhaven National Laboratory, Upton, New York 11973 (United States)
Publication Date:
OSTI Identifier:
385617
Report Number(s):
CONF-9504196--
Journal ID: APCPCS; ISSN 0094-243X; TRN: 96:026547
DOE Contract Number:
AC02-76CH00016
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 376; Journal Issue: 1; Conference: Workshop on chaos and the changing nature of science and medicine, Mobile, AL (United States), 29 Apr 1995; Other Information: PBD: Jun 1996
Research Org:
Brookhaven National Laboratory
Country of Publication:
United States
Language:
English
Subject:
66 PHYSICS; CLIMATIC CHANGE; TIME-SERIES ANALYSIS; FLUCTUATIONS; PHASE SPACE; ATTRACTORS; DYNAMICS; ATMOSPHERIC CIRCULATION; PALEOCLIMATOLOGY CHAOTIC SYSTEMS; DYNAMICAL SYSTEMS