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A Kinematic Kinetic Energy Backscatter Parametrization : from Implementation to Global Ocean Simulations


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Juricke, Stephan ; Danilov, Sergey ; Koldunov, Nikolay ; Oliver, Marcel ; Sein, Dmitry ; Sidorenko, Dmitry ; Wang, Qiang:
A Kinematic Kinetic Energy Backscatter Parametrization : from Implementation to Global Ocean Simulations.
In: Journal of advances in modeling earth systems : JAMES. 12 (2020) 12: e2020MS002175. - 20 S.
ISSN 1942-2466


Open Access
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Ocean models at eddy-permitting resolution are generally overdissipative, damping the intensity of the mesoscale eddy field. To reduce overdissipation, we propose a simplified, kinematic energy backscatter parametrization built into the viscosity operator in conjunction with a new flow-dependent coefficient of viscosity based on nearest neighbor velocity differences. The new scheme mitigates excessive dissipation of energy and improves global ocean simulations at eddy-permitting resolution. We find that kinematic backscatter substantially raises simulated eddy kinetic energy, similar to an alternative, previously proposed dynamic backscatter parametrization. While dynamic backscatter is scale-aware and energetically more consistent, its implementation is more complex. Furthermore, it turns out to be computationally more expensive, as it applies, among other things, an additional prognostic subgrid energy equation. The kinematic backscatter proposed here, by contrast, comes at no additional computational cost, following the principle of simplicity. Our primary focus is the discretization on triangular unstructured meshes with cell placement of velocities (an analog of B-grids), as employed by the Finite-volumE Sea ice-Ocean Model (FESOM2). The kinematic backscatter scheme with the new viscosity coefficient is implemented in FESOM2 and tested in the simplified geometry of a zonally reentrant channel as well as in a global ocean simulation on a 1/4° mesh. This first version of the new kinematic backscatter needs to be tuned to the specific resolution regime of the simulation. However, the tuning relies on a single parameter, emphasizing the overall practicality of the approach.

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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:10.1029/2020MS002175
Open Access: Freie Zugänglichkeit des Volltexts?:Ja
Die Zeitschrift ist nachgewiesen in:
Titel an der KU entstanden:Nein
Eingestellt am: 25. Apr 2022 14:18
Letzte Änderung: 07. Jun 2023 10:36
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