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| Titel |
An extension of conditional nonlinear optimal perturbation approach and its applications |
| VerfasserIn |
M. Mu, W. Duan, Q. Wang, R. Zhang |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 17, no. 2 ; Nr. 17, no. 2 (2010-04-13), S.211-220 |
| Datensatznummer |
250013665
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| Publikation (Nr.) |
 copernicus.org/npg-17-211-2010.pdf |
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| Zusammenfassung |
| The approach of conditional nonlinear optimal perturbation (CNOP)
was previously proposed to find the optimal initial perturbation
(CNOP-I) in a given constraint. In this paper, we extend the CNOP
approach to search for the optimal combined mode of initial
perturbations and model parameter perturbations. This optimal
combined mode, also named CNOP, has two special cases: one is CNOP-I
that only links with initial perturbations and has the largest
nonlinear evolution at a prediction time; while the other is merely
related to the parameter perturbations and is called CNOP-P, which
causes the largest departure from a given reference state at a
prediction time. The CNOP approach allows us to explore not only the
first kind of predictability related to initial errors, but also the
second kind of predictability associated with model parameter
errors, moreover, the predictability problems of the coexistence of
initial errors and parameter errors. With the CNOP approach, we
study the ENSO predictability by a theoretical ENSO model. The
results demonstrate that the prediction errors caused by the CNOP
errors are only slightly larger than those yielded by the CNOP-I
errors and then the model parameter errors may play a minor role in
producing significant uncertainties for ENSO predictions. Thus, it
is clear that the CNOP errors and their resultant prediction errors
illustrate the combined effect on predictability of initial errors
and model parameter errors and can be used to explore the relative
importance of initial errors and parameter errors in yielding
considerable prediction errors, which helps identify the dominant
source of the errors that cause prediction uncertainties. It is
finally expected that more realistic models will be adopted to
investigate this use of CNOP. |
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