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| Titel |
ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation |
| VerfasserIn |
G. Forget, J.-M. Campin, P. Heimbach, C. N. Hill, R. M. Ponte, C. Wunsch |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 8, no. 10 ; Nr. 8, no. 10 (2015-10-06), S.3071-3104 |
| Datensatznummer |
250116595
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| Publikation (Nr.) |
 copernicus.org/gmd-8-3071-2015.pdf |
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| Zusammenfassung |
| This paper presents the ECCO v4 non-linear inverse modeling
framework and its baseline solution for the evolving ocean state
over the period 1992–2011. Both components are publicly available and subjected to
regular, automated regression tests. The modeling framework includes
sets of global conformal grids, a global model setup,
implementations of data constraints and control
parameters, an interface to algorithmic differentiation, as well as
a grid-independent, fully capable Matlab toolbox. The baseline ECCO
v4 solution is a dynamically consistent ocean state estimate
without unidentified sources of heat
and buoyancy, which any interested user will be able to reproduce
accurately. The solution is an acceptable fit to most data and has
been found to be physically plausible in many respects, as documented here
and in related publications. Users are being provided with
capabilities to assess model–data misfits for themselves. The
synergy between modeling and data synthesis is asserted through the
joint presentation of the modeling framework and the state
estimate. In particular, the inverse estimate of parameterized
physics was instrumental in improving the fit to the observed
hydrography, and becomes an integral part of the ocean model setup
available for general use. More generally, a first assessment of the
relative importance of external, parametric and structural model
errors is presented. Parametric and external model uncertainties
appear to be of comparable importance and dominate over structural
model uncertainty. The results generally underline the importance
of including turbulent transport parameters in the inverse problem. |
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