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Quantifying predictability variations in a low-order ocean-atmosphere model - A dynamical systems approachThe predictability of the weather and climatic states of a low-order moist general circulation model is quantified using a dynamic systems approach, and the effect of incorporating a simple oceanic circulation on predictability is evaluated. The predictability and the structure of the model attractors are compared using Liapunov exponents, local divergence rates, and the correlation and Liapunov dimensions. It was found that the activation of oceanic circulation increases the average error doubling time of the atmosphere and the coupled ocean-atmosphere system by 10 percent and decreases the variance of the largest local divergence rate by 20 percent. When an oceanic circulation develops, the average predictability of annually averaged states is improved by 25 percent and the variance of the largest local divergence rate decreases by 25 percent.
Document ID
19930046864
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Nese, Jon M.
(Pennsylvania State Univ. Monaca, United States)
Dutton, John A.
(Pennsylvania State Univ. University Park, United States)
Date Acquired
August 16, 2013
Publication Date
February 1, 1993
Publication Information
Publication: Journal of Climate
Volume: 6
Issue: 2
ISSN: 0894-8755
Subject Category
Meteorology And Climatology
Accession Number
93A30861
Funding Number(s)
CONTRACT_GRANT: NAGW-1434
Distribution Limits
Public
Copyright
Other

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