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How we compute N matters to estimates of mixing in stratified flows

Published online by Cambridge University Press:  13 October 2017

Robert S. Arthur*
Affiliation:
Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
Subhas K. Venayagamoorthy
Affiliation:
Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO 80523, USA The Bob and Norma Street Environmental Fluid Mechanics Laboratory, Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA
Jeffrey R. Koseff
Affiliation:
The Bob and Norma Street Environmental Fluid Mechanics Laboratory, Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA
Oliver B. Fringer
Affiliation:
The Bob and Norma Street Environmental Fluid Mechanics Laboratory, Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA
*
Email address for correspondence: arthur7@llnl.gov

Abstract

Most commonly used models for turbulent mixing in the ocean rely on a background stratification against which turbulence must work to stir the fluid. While this background stratification is typically well defined in idealized numerical models, it is more difficult to capture in observations. Here, a potential discrepancy in ocean mixing estimates due to the chosen calculation of the background stratification is explored using direct numerical simulation data of breaking internal waves on slopes. Two different methods for computing the buoyancy frequency $N$, one based on a three-dimensionally sorted density field (often used in numerical models) and the other based on locally sorted vertical density profiles (often used in the field), are used to quantify the effect of $N$ on turbulence quantities. It is shown that how $N$ is calculated changes not only the flux Richardson number $R_{f}$, which is often used to parameterize turbulent mixing, but also the turbulence activity number or the Gibson number $Gi$, leading to potential errors in estimates of the mixing efficiency using $Gi$-based parameterizations.

Type
Rapids
Copyright
© 2017 Cambridge University Press 

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