ALBERT

Description: 〈div data-abstract-type="normal"〉〈p〉Scaling arguments are presented to quantify the widely used diapycnal (irreversible) mixing coefficient 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline1.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉 in stratified flows as a function of the turbulent Froude number 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline2.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉. Here, 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline3.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉 is the buoyancy frequency, 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline4.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉 is the turbulent kinetic energy, 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline5.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉 is the rate of dissipation of turbulent kinetic energy and 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline6.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉 is the rate of dissipation of turbulent potential energy. We show that for 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline7.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉, 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline8.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉, for 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline9.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉, 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline10.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉 and for 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline11.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉, 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline12.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉. These scaling results are tested using high-resolution direct numerical simulation (DNS) data from three different studies and are found to hold reasonably well across a wide range of 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline13.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉 that encompasses weakly stratified to strongly stratified flow conditions. Given that the 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline14.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉 cannot be readily computed from direct field measurements, we propose a practical approach that can be used to infer the 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline15.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉 from readily measurable quantities in the field. Scaling analyses show that 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline16.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉 for 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline17.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉, 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline18.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉 for 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline19.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉, and 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline20.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉 for 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline21.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉, where 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline22.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉 is the Thorpe length scale and 〈span〉〈span〉〈img data-mimesubtype="gif" data-type="simple" src="http://static.cambridge.org/resource/id/urn:cambridge.org:id:binary:20190320133914837-0850:S0022112019001423:S0022112019001423_inline23.gif"〉 〈span data-mathjax-type="texmath"〉 〈/span〉 〈/span〉〈/span〉 is the Ozmidov length scale. These formulations are also tested with DNS data to highlight their validity. These novel findings could prove to be a significant breakthrough not only in providing a unifying (and practically useful) parameterization for the mixing efficiency in stably stratified turbulence but also for inferring the dynamic state of turbulence in geophysical flows.〈/p〉〈/div〉

Description: Scaling arguments are presented to quantify the widely used diapycnal (irreversible) mixing coefficient Γ = ∈PE/∈ in stratified flows as a function of the turbulent Froude number Fr = ∈/Nk. Here, N is the buoyancy frequency, k is the turbulent kinetic energy, ∈ is the rate of dissipation of turbulent kinetic energy and ∈PE is the rate of dissipation of turbulent potential energy. We show that for Fr≫1, Γ ∝ Fr-2, for Fr∼ O(1), Γ ∝ Fr-1 and for Fr ≪ 1, Γ ∝ Fr0. These scaling results are tested using highresolution direct numerical simulation (DNS) data from three different studies and are found to hold reasonably well across a wide range of Fr that encompasses weakly stratified to strongly stratified flow conditions. Given that the Fr cannot be readily computed from direct field measurements, we propose a practical approach that can be used to infer the Fr from readily measurable quantities in the field. Scaling analyses show that Fr ∝ (LT/LO)-2 for LT/LO 〉 O(1), Fr ∝ (LT/LO)-1 for LT/LO ∼ O(1), and Fr∝(LT/LO)-2/3 for LT/LO 〈O(1), where LT is the Thorpe length scale and LO is the Ozmidov length scale. These formulations are also tested with DNS data to highlight their validity. These novel findings could prove to be a significant breakthrough not only in providing a unifying (and practically useful) parameterization for the mixing efficiency in stably stratified turbulence but also for inferring the dynamic state of turbulence in geophysical flows. © 2019 Cambridge University Press.

Description: Author Posting. © The Oceanography Society, 2016. This article is posted here by permission of The Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 29, no. 2 (2016): 62–71, doi:10.5670/oceanog.2016.39.

Description: The Bay of Bengal has a complex upper-ocean temperature and salinity structure that is, in places, characterized by strong salinity stratification and multiple inversions in temperature. Here, two short time series from continuously profiling floats, equipped with microstructure sensors to measure subsurface mixing, are used to highlight implications of complex hydrography on upper-ocean heat content and the evolution of sea surface temperature. Weak mixing coupled with the existence of subsurface warm layers suggest the potential for storage of heat below the surface mixed layer over relatively long time scales. On the diurnal time scale, these data demonstrate the competing effects of surface heat flux and subsurface mixing in the presence of thin salinity-stratified mixed layers with temperature inversions. Pre-existing stratification can amplify the sea surface temperature response through control on the vertical extent of heating and cooling by surface fluxes. In contrast, subsurface mixing entrains relatively cool water during the day and relatively warm water during the night, damping the response to daytime heating and nighttime cooling at the surface. These observations hint at the challenges involved in improving monsoon prediction at longer, intraseasonal time scales as models may need to resolve upper-ocean variability over short time and fine vertical scales.

Description: This work was funded by Office of Naval Research grants N00014-14-1-0236 (ELS, JNM), N00014-13-1-0483 (DLR), N00014-13-1- 0453 (JTF), and N00014-12-1-0938 (SKV, AG).