Abstract

Wave–current interaction (WCI) dynamics energizes and mixes the ocean thermocline by producing a combination of Langmuir circulation, internal waves and turbulent shear flows, which interact over a wide range of time scales. Two complementary approaches exist for approximating different aspects of WCI dynamics. These are the Generalized Lagrangian Mean (GLM) approach and the Gent–McWilliams (GM) approach. Their complementarity is evident in their Kelvin circulation theorems. GLM introduces a wave pseudomomentum per unit mass into its Kelvin circulation integrand, while GM introduces an additional ‘bolus velocity’ to transport its Kelvin circulation loop. The GLM approach models Eulerian momentum, while the GM approach models Lagrangian transport. In principle, both GLM and GM are based on the Euler–Boussinesq (EB) equations for an incompressible, stratified, rotating flow. The differences in their Kelvin theorems arise from differences in how they model the flow map in the Lagrangian for the Hamilton variational principle underlying the EB equations. A recently developed approach for uncertainty quantification in fluid dynamics constrains fluid variational principles to require that Lagrangian trajectories undergo Stochastic Advection by Lie Transport (SALT). Here, we introduce stochastic closure strategies for quantifying uncertainty in WCI by adapting the SALT approach to both the GLM and GM approximations of the EB variational principle. In the GLM framework, we introduce a stochastic group velocity for transport of wave properties, relative to the frame of motion of the Lagrangian mean flow velocity and a stochastic pressure contribution from the fluctuating kinetic energy. In the GM framework, we introduce a stochastic bolus velocity in addition to the mean drift velocity by imposing the SALT constraint in the GM variational principle.

Highlights

  • The wind drives gravity waves on the ocean surface

  • Aims of the Paper This paper aims to lay down a mathematical foundation which has the potential for both quantifying and reducing the uncertainties in the numerical simulation of ocean–atmosphere mixing layer dynamics, by developing new methods of enhanced modeling of subgrid-scale (SGS) circulation effects in the ocean surface boundary layers (OSBLs) produced by wave–current interactions (WCI)

  • Motivated by the challenge to create consistent theories of mesoscale and submesoscale wave–current interaction (WCI) discussed in the Introduction, the investigation here began by reviewing Generalized Lagrangian Mean (GLM), as guided by its WKB formulation in Gjaja and Holm

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Summary

Introduction

The wind drives gravity waves on the ocean surface. Over time, the collective action of these wind-driven gravity waves on the ocean surface generates Langmuir circulations (LC) which transport heat and mix material properties deeper into the ocean. The elusiveness of data for the two GLM wave closures suggests the formulation of an alternative closure which directly separates the time scales of the fluid transport velocity into its slow fluid and fast wave parts This approach is reminiscent of the introduction of the bolus velocity in the celebrated Gent–McWilliams (GM) parameterization of subgrid-scale transport (Gent 2011; Gent and McWilliams 1990, 1996), which is generally used in computational simulations of ocean circulation. The SALT approach separates the slow and fast time scales of the fluid transport velocity into drift and stochastic parts Implementation of this closure has already been tested in Cotter et al (2018a, b) and found to be quite accessible for calibration by observational data from both high-resolution computational simulations.

Brief Review of GLM Theory for Euler–Boussinesq Fluids
Relevant Information from the GLM Theory
The Pull-Back Representation of Fluctuations in Fluid Motion
How Pull-Back Dynamics Leads to Lie Derivatives
GLM Advective Transport Relations for Euler–Boussinesq
GLM Circulation Transport
Stochastic Transport via the Kunita–Itô–Wentzell Formula
Stochastic Closures for GLM Approximation of the Euler–Boussinesq Equations
Brief Review of the Deterministic GM Approach
Example
Kelvin Circulation Theorem
PV Conservation
Energetics in the Hamiltonian Formulation
Stochastic VGM Equations
Conclusion
A Survey of Results for the GLM Euler–Boussinesq Stratified Fluid
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