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| Titel |
Assimilation of sea-ice concentration in a global climate model – physical and statistical aspects |
| VerfasserIn |
S. Tietsche, D. Notz, J. H. Jungclaus, J. Marotzke |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1812-0784
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| Digitales Dokument |
URL |
| Erschienen |
In: Ocean Science ; 9, no. 1 ; Nr. 9, no. 1 (2013-01-15), S.19-36 |
| Datensatznummer |
250017449
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| Publikation (Nr.) |
 copernicus.org/os-9-19-2013.pdf |
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| Zusammenfassung |
| We investigate the initialisation of Northern Hemisphere sea ice in the
global climate model ECHAM5/MPI-OM by assimilating sea-ice
concentration data. The analysis updates for concentration are given by
Newtonian relaxation, and we discuss different ways of specifying the
analysis updates for mean thickness. Because the conservation of mean ice
thickness or actual ice thickness in the analysis updates leads to poor
assimilation performance, we introduce a proportional dependence between
concentration and mean thickness analysis updates. Assimilation with these
proportional mean-thickness analysis updates leads to good assimilation
performance for sea-ice concentration and thickness, both in identical-twin
experiments and when assimilating sea-ice observations. The simulation of
other Arctic surface fields in the coupled model is, however, not
significantly improved by the assimilation. To understand the physical
aspects of assimilation errors, we construct a simple prognostic model of the
sea-ice thermodynamics, and analyse its response to the assimilation. We find
that an adjustment of mean ice thickness in the analysis update is essential
to arrive at plausible state estimates. To understand the statistical aspects
of assimilation errors, we study the model background error covariance
between ice concentration and ice thickness. We find that the spatial
structure of covariances is best represented by the proportional
mean-thickness analysis updates. Both physical and statistical evidence
supports the experimental finding that assimilation with proportional
mean-thickness updates outperforms the other two methods considered. The
method described here is very simple to implement, and gives results that are
sufficiently good to be used for initialising sea ice in a global climate
model for seasonal to decadal predictions. |
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