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Title: A Parameterization with a Constrained Potential Energy Conversion Rate of Vertical Mixing Due to Langmuir Turbulence

In this study we develop a new parameterization for turbulent mixing in the ocean surface boundary layer (OSBL), including the effect of Langmuir turbulence. This new parameterization builds on a recent study ( Reichl and Hallberg 2018 , hereafter RH18 ), which predicts the available energy for turbulent mixing against stable stratification driven by shear and convective turbulence. To investigate the role of Langmuir turbulence in the framework of RH18 , we utilize data from a suite of previously published large-eddy simulation (LES) experiments ( Li and Fox-Kemper 2017 , hereafter LF17 ) with and without Langmuir turbulence under different idealized forcing conditions. We find that the parameterization of RH18 is able to reproduce the mixing simulated by the LES in the non-Langmuir cases, but not the Langmuir cases. We therefore investigate the enhancement of the integrated vertical buoyancy flux within the entrainment layer in the presence of Langmuir turbulence using the LES data. An additional factor is introduced in the RH18 framework to capture the enhanced mixing due to Langmuir turbulence. This additional factor depends on the surface-layer averaged Langmuir number with a reduction in the presence of destabilizing surface buoyancy fluxes. It is demonstrated that including this factor more » within the RH18 OSBL turbulent mixing parameterization framework captures the simulated effect of Langmuir turbulence in the LES, which can be used for simulating the effect of Langmuir turbulence in climate simulations. This new parameterization is compared to the KPP-based Langmuir entrainment parameterization introduced by LF17 , and differences are explored in detail.

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Authors:
 ;  
Publication Date:
NSF-PAR ID:
10123231
Journal Name:
Journal of Physical Oceanography
Volume:
49
Issue:
11
Page Range or eLocation-ID:
p. 2935-2959
ISSN:
0022-3670
Publisher:
American Meteorological Society
Sponsoring Org:
National Science Foundation
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