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Ocean kinetic energy backscatter parametrizations on unstructured grids : impact on mesoscale turbulence in a channel


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Juricke, Stephan ; Danilov, Sergey ; Kutsenko, Anton ; Oliver, Marcel:
Ocean kinetic energy backscatter parametrizations on unstructured grids : impact on mesoscale turbulence in a channel.
In: Ocean modelling. 138 (2019). - S. 51-67.
ISSN 1463-5003 ; 1463-5011


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We present a new energy backscatter parametrization for primitive equation ocean models at eddy-permitting resolution, specifically for unstructured grids. Traditional eddy parametrizations in terms of viscosity closures lead to excessive dissipation of kinetic energy when used with eddy-permitting meshes. Implemented into the FESOM2 ocean model, the backscatter parametrization leads to a more realistic total dissipation of kinetic energy. It maintains a reservoir of dissipated energy and reinjects this subgrid energy at larger scales at a controlled rate. The separation between dissipation and backscatter scales is achieved by using different-order differential operators and/or spatial smoothing. This ensures numerical model stability.

We perform sensitivity studies with different choices of parameter settings and viscosity schemes in a configuration with a baroclinically unstable flow in a zonally reentrant channel with a horizontally uniform mesh. The best backscatter setup substantially improves eddy-permitting simulations at 1/4° and 1/6° resolution, bringing them close to a 1/12° eddy-resolving reference. Improvements are largest for levels of kinetic energy and variability in temperature and vertical velocity. A selected optimal default scheme is then tested in a mixed resolution setup – a channel with narrow transitions between an eddy-permitting and an eddy-resolving subdomain. The backscatter scheme is able to adapt dynamically to the different resolutions and moves the diagnostics closer to the high resolution reference throughout the domain.

Our study is a first step toward using backscatter in global variable-mesh ocean models and suggests potential for substantial improvements of ocean mean state and variability at reduced computational cost.

Weitere Angaben

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:10.1016/j.ocemod.2019.03.009
Open Access: Freie Zugänglichkeit des Volltexts?:Nein
Die Zeitschrift ist nachgewiesen in:
Titel an der KU entstanden:Nein
Eingestellt am: 26. Apr 2022 09:28
Letzte Änderung: 06. Jun 2023 15:29
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