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Persistence and predictability in a perfect modelA realistic two-level GCM is used to examine the relationship between predictability and persistence. Predictability is measured by the average divergence of ensembles of solutions starting from perturbed initial conditions, and persistence is defined in terms of the autocorrelation function based on a single long-term model integration. The average skill of the dynamical forecasts is compared with the skill of simple persistence-based statistical forecasts. For initial errors comparable in magnitude to present-day analysis errors, the statistical forecast loses all skill after about one week, reflecting the lifetime of the lowest frequency fluctuations in the model. Large ensemble mean dynamical forecasts would be expected to remain skillful for about 3 wk. The disparity between the skill of the statistical and dynamical forecasts is greater for the higher frequency modes, which have little memory beyond 1 d, yet remain predictable for about 2 wk. The results are analyzed in terms of two characteristic time scales.
Document ID
19920043855
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Schubert, Siegfried D.
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Suarez, Max J.
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Schemm, Jae-Kyung
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Date Acquired
August 15, 2013
Publication Date
February 1, 1992
Publication Information
Publication: Journal of the Atmospheric Sciences
Volume: 49
ISSN: 0022-4928
Subject Category
Meteorology And Climatology
Accession Number
92A26479
Distribution Limits
Public
Copyright
Other

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