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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 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:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
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
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

Citation Formats

Stewart, H.B. Chaos, dynamical structure, and climate variability. United States: N. p., 1996. Web. doi:10.1063/1.51063.
Stewart, H.B. Chaos, dynamical structure, and climate variability. United States. doi:10.1063/1.51063.
Stewart, H.B. 1996. "Chaos, dynamical structure, and climate variability". United States. doi:10.1063/1.51063.
@article{osti_385617,
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 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.}},
doi = {10.1063/1.51063},
journal = {AIP Conference Proceedings},
number = 1,
volume = 376,
place = {United States},
year = 1996,
month = 6
}
  • 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 amore » 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.« less
  • This study examines multi-year climate variability associated with sea salt aerosols and their contribution to the variability of shortwave cloud forcing (SWCF) using a 150-year simulation for pre-industrial conditions of the Community Earth System Model version 1.0 (CESM1). The results suggest that changes in sea salt and related cloud and radiative properties on interannual timescales are dominated by the ENSO cycle. Sea salt variability on longer (interdecadal) timescales is associated with low-frequency Pacific ocean variability similar to the interdecadal Pacific Oscillation (IPO), but does not show a statistically significant spectral peak. A multivariate regression suggests that sea salt aerosol variabilitymore » may contribute to SWCF variability in the tropical Pacific, explaining up to 25-35% of the variance in that region. Elsewhere, there is only a small aerosol influence on SWCF through modifying cloud droplet number and liquid water path that contributes to the change of cloud effective radius and cloud optical depth (and hence cloud albedo), producing a multi-year aerosol-cloud-wind interaction.« less
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