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Title: Chaos, dynamical structure and climate variability

Abstract

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 the authors 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. The atmospheric circulation model, originally proposed by Lorenz, has 27 principal unknowns; they establish that the chaotic attractor can be embedded in a subspace of eight dimensions by exhibiting a specific subset of eight unknowns which pass multichannel tests for false nearest neighbors. They also show that one of the principal unknowns in the 27-variable model--the global mean sea surface temperature--is of no discernible usefulness in making short-term forecasts.

Authors:
 [1]
  1. Brookhaven National Lab., Upton, NY (United States). Dept. of Applied Science
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE, Washington, DC (United States)
OSTI Identifier:
102163
Report Number(s):
BNL-62120; CONF-9504196-1
ON: DE95017160; TRN: AHC29524%%43
DOE Contract Number:  
AC02-76CH00016
Resource Type:
Technical Report
Resource Relation:
Conference: Workshop on chaos and the changing nature of science and medicine, Mobile, AL (United States), 29 Apr 1995; Other Information: PBD: [1995]
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; CLIMATIC CHANGE; FORECASTING; EXPERIMENTAL DATA; TIME-SERIES ANALYSIS; CLIMATE MODELS

Citation Formats

Stewart, H B. Chaos, dynamical structure and climate variability. United States: N. p., 1995. Web. doi:10.2172/102163.
Stewart, H B. Chaos, dynamical structure and climate variability. United States. https://doi.org/10.2172/102163
Stewart, H B. 1995. "Chaos, dynamical structure and climate variability". United States. https://doi.org/10.2172/102163. https://www.osti.gov/servlets/purl/102163.
@article{osti_102163,
title = {Chaos, dynamical structure and climate variability},
author = {Stewart, H B},
abstractNote = {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 the authors 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. The atmospheric circulation model, originally proposed by Lorenz, has 27 principal unknowns; they establish that the chaotic attractor can be embedded in a subspace of eight dimensions by exhibiting a specific subset of eight unknowns which pass multichannel tests for false nearest neighbors. They also show that one of the principal unknowns in the 27-variable model--the global mean sea surface temperature--is of no discernible usefulness in making short-term forecasts.},
doi = {10.2172/102163},
url = {https://www.osti.gov/biblio/102163}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Sep 01 00:00:00 EDT 1995},
month = {Fri Sep 01 00:00:00 EDT 1995}
}