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| Titel |
Mitigation of coupled model biases induced by dynamical core misfitting through parameter optimization: simulation with a simple pycnocline prediction model |
| VerfasserIn |
G.-J. Han, X.-F. Zhang, S. Zhang, X.-R. Wu, Z. Liu |
| 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 ; 21, no. 2 ; Nr. 21, no. 2 (2014-03-07), S.357-366 |
| Datensatznummer |
250120897
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| Publikation (Nr.) |
 copernicus.org/npg-21-357-2014.pdf |
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| Zusammenfassung |
| Imperfect dynamical core is an important source of model biases that
adversely impact on the model simulation and predictability of a coupled
system. With a simple pycnocline prediction model, in this study, we show the
mitigation of model biases through parameter optimization when the
assimilation model consists of a "biased" time-differencing. Here, the
"biased" time-differencing is defined by a different time-differencing
scheme from the "truth" model that is used to produce "observations",
which generates different mean values, climatology and variability of the
assimilation model from the "truth" model. A series of assimilation
experiments is performed to explore the impact of parameter optimization on
model bias mitigation and climate estimation, as well as the role of
different media parameter estimations. While the stochastic "physics"
implemented by perturbing parameters can enhance the ensemble spread
significantly and improve the representation of the model ensemble,
signal-enhanced parameter estimation is able to mitigate the model biases on
mean values and climatology, thus further improving the accuracy of estimated
climate states, especially for the low-frequency signals. In addition, in a
multiple timescale coupled system, parameters pertinent to low-frequency
components have more impact on climate signals. Results also suggest that
deep ocean observations may be indispensable for improving the accuracy of
climate estimation, especially for low-frequency signals. |
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