How we compute N matters to estimates of mixing in stratified flows
Abstract
We know that 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. There are two different methods for computing the buoyancy frequency$$N$$, one based on a threedimensionally 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.
 Authors:

 Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
 Colorado State Univ., Fort Collins, CO (United States). Dept. of Civil and Environmental Engineering; Stanford Univ., CA (United States). Bob and Normal Street Environmental Fluid Mechanics Lab., Dept. of Civil and Environmental Engineering
 Stanford Univ., CA (United States). Bob and Normal Street Environmental Fluid Mechanics Lab., Dept. of Civil and Environmental Engineering
 Publication Date:
 Research Org.:
 Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1414351
 Report Number(s):
 LLNLJRNL733364
Journal ID: ISSN 00221120; applab; TRN: US1800685
 Grant/Contract Number:
 AC5207NA27344
 Resource Type:
 Accepted Manuscript
 Journal Name:
 Journal of Fluid Mechanics
 Additional Journal Information:
 Journal Volume: 831; Journal ID: ISSN 00221120
 Publisher:
 Cambridge University Press
 Country of Publication:
 United States
 Language:
 English
 Subject:
 42 ENGINEERING; 58 GEOSCIENCES; 97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; internal waves; stratified turbulence; turbulent mixing
Citation Formats
Arthur, Robert S., Venayagamoorthy, Subhas K., Koseff, Jeffrey R., and Fringer, Oliver B. How we compute N matters to estimates of mixing in stratified flows. United States: N. p., 2017.
Web. doi:10.1017/jfm.2017.679.
Arthur, Robert S., Venayagamoorthy, Subhas K., Koseff, Jeffrey R., & Fringer, Oliver B. How we compute N matters to estimates of mixing in stratified flows. United States. https://doi.org/10.1017/jfm.2017.679
Arthur, Robert S., Venayagamoorthy, Subhas K., Koseff, Jeffrey R., and Fringer, Oliver B. Fri .
"How we compute N matters to estimates of mixing in stratified flows". United States. https://doi.org/10.1017/jfm.2017.679. https://www.osti.gov/servlets/purl/1414351.
@article{osti_1414351,
title = {How we compute N matters to estimates of mixing in stratified flows},
author = {Arthur, Robert S. and Venayagamoorthy, Subhas K. and Koseff, Jeffrey R. and Fringer, Oliver B.},
abstractNote = {We know that 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. There are two different methods for computing the buoyancy frequency$N$, one based on a threedimensionally 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.},
doi = {10.1017/jfm.2017.679},
journal = {Journal of Fluid Mechanics},
number = ,
volume = 831,
place = {United States},
year = {2017},
month = {10}
}
Web of Science
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Works referencing / citing this record:
Deep learning of mixing by two ‘atoms’ of stratified turbulence
journal, January 2019
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 Journal of Fluid Mechanics, Vol. 861
On the inference of the state of turbulence and mixing efficiency in stably stratified flows
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 Garanaik, Amrapalli; Venayagamoorthy, Subhas K.
 Journal of Fluid Mechanics, Vol. 867
On Energy and Turbulent Mixing in the Thermocline
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