Titelangaben
Juricke, Stephan ; Danilov, Sergey ; Koldunov, Nikolay ; Oliver, Marcel ; Sidorenko, Dmitry:
Ocean Kinetic Energy Backscatter Parametrization on Unstructured Grids : Impact on Global Eddy-Permitting Simulations.
In: Journal of advances in modeling earth systems : JAMES. 12 (2020) 1: e2019MS001855.
- 21 S.
ISSN 1942-2466
Volltext
Link zum Volltext (externe URL): https://doi.org/10.1029/2019MS001855 |
Kurzfassung/Abstract
In this study we demonstrate the potential of a kinetic energy backscatter scheme for use in global ocean simulations. Ocean models commonly employ (bi)harmonic eddy viscosities causing excessive dissipation of kinetic energy in eddy-permitting simulations. Overdissipation not only affects the smallest resolved scales but also the generation of eddies through baroclinic instabilities, impacting the entire wave number spectrum. The backscatter scheme returns part of this overdissipated energy back into the resolved flow. We employ backscatter in the FESOM2 multiresolution ocean model with a quasi-uniform 1/4° urn:x-wiley:jame:media:jame21051:jame21051-math-0001 mesh. In multidecadal ocean simulations, backscatter increases eddy activity by a factor 2 or more, moving the simulation closer to observational estimates of sea surface height variability. Moreover, mean sea surface height, temperature, and salinity biases are reduced. This amounts to a globally averaged bias reduction of around 10% for each field, which is even larger in the Antarctic Circumpolar Current. However, in some regions such as the coastal Kuroshio, backscatter leads to a slight overenergizing of the flow and, in the Antarctic, to an unrealistic reduction of sea ice. Some of the bias increases can be reduced by a retuning of the model, and we suggest related adjustments to the backscatter scheme. The backscatter simulation is about 2.5 times as expensive as a simulation without backscatter. Most of the increased cost is due to a halving of the time step to accommodate higher simulated velocities.
Weitere Angaben
Publikationsform: | Artikel |
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Sprache des Eintrags: | Englisch |
Institutionen der Universität: | Mathematisch-Geographische Fakultät > Mathematik > Lehrstuhl für Mathematik - Angewandte Mathematik
Mathematisch-Geographische Fakultät > Mathematik > Mathematisches Institut für Maschinelles Lernen und Data Science (MIDS) |
DOI / URN / ID: | 10.1029/2019MS001855 |
Open Access: Freie Zugänglichkeit des Volltexts?: | Ja |
Peer-Review-Journal: | Ja |
Verlag: | Wiley-Blackwell |
Die Zeitschrift ist nachgewiesen in: | |
Titel an der KU entstanden: | Nein |
KU.edoc-ID: | 30012 |
Letzte Änderung: 07. Jun 2023 10:39
URL zu dieser Anzeige: https://edoc.ku.de/id/eprint/30012/