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Statistics of near-inertial waves over a background flow via quantum and statistical mechanics

Published online by Cambridge University Press:  05 March 2026

Alexandre Tlili*
Affiliation:
Université Paris-Saclay, CNRS, CEA, Service de Physique de l’Etat Condensé , Gif-sur-Yvette 91191, France
Basile Gallet*
Affiliation:
Université Paris-Saclay, CNRS, CEA, Service de Physique de l’Etat Condensé , Gif-sur-Yvette 91191, France
*
Corresponding authors: Alexandre Tlili, alexandre.tlili@cea.fr; Basile Olivier Gallet, basile.gallet@cea.fr
Corresponding authors: Alexandre Tlili, alexandre.tlili@cea.fr; Basile Olivier Gallet, basile.gallet@cea.fr

Abstract

We revisit the interaction of an initially uniform near-inertial wave (NIW) field with a steady background flow, with the goal of predicting the subsequent organisation of the wave field. To wit, we introduce an exact analogy between the Young–Ben Jelloul (YBJ) equation and the quantum dynamics of a charged particle in a steady electromagnetic field, whose potentials are expressed in terms of the background flow. We derive the time-averaged spatial distributions of wave kinetic energy, potential energy and Stokes drift in two asymptotic limits. In the ‘strongly quantum’ limit, where the background flow is weak compared with wave dispersion, we compute the wave statistics by extending a strong-dispersion expansion initially introduced by YBJ. In the ‘quasi-classical’ limit, where the background flow is strong compared with wave dispersion, we compute the wave statistics by leveraging the equilibrium statistical mechanics of classical systems. We compare our predictions with numerical simulations of the YBJ equation, using an instantaneous snapshot from a two-dimensional turbulent flow as the steady background flow. The agreement is very good in both limits. In particular, we quantitatively describe the preferential concentration of NIW energy in anticyclones. We predict weak NIW concentration in both asymptotic limits of weak and strong background flow, and maximal anticyclonic concentration for background flows of intermediate strength, providing theoretical underpinning to observations reported by Danioux, Vanneste and Bühler (2015 J. Fluid Mech., vol. 773, R1).

Information

Type
JFM Papers
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NC
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial licence (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original article is properly cited. The written permission of Cambridge University Press or the rights holder(s) must be obtained prior to any commercial use.
Copyright
© The Author(s), 2026. Published by Cambridge University Press
Figure 0

Figure 1. A two-layer model with an infinitely deep lower layer. The base state consists of a vertically invariant steady horizontal flow $\boldsymbol {U}_{\!g}(x,y)$ spanning both layers, together with a flat interface between the two layers. We consider perturbations $\boldsymbol {u}(x,y,t)$ to the horizontal velocity in the upper layer only, whose depth is then denoted as $h(x,y,t)$. In line with the rigid-lid approximation, we neglect the fluctuations of the free surface as compared with $h$.

Figure 1

Table 1. Summary of the analogy between the Schrödinger equation for a charged particle (left column) and the YBJ equation (right column).

Figure 2

Figure 2. Steady background flow used in the numerical simulations of the YBJ equation: (a) background streamfunction $\chi (x,y)$; (b) kinetic energy $|\boldsymbol{\nabla } \chi |^2$ and (c) vorticity field $\Delta \chi$. The normalisation is such that $\langle \chi ^2 \rangle =1$ (see text). In all panels, the black contours correspond to streamlines of the background flow.

Figure 3

Figure 3. Time-averaged spatial distributions of (a) NIW kinetic energy, (b) potential energy and (c) Stokes drift. The top row corresponds to the predictions (5.7)–(5.9) from the low-$\gamma$ asymptotic expansion. The bottom row corresponds to a numerical simulation in the strong-dispersion regime ($\gamma =0.05$). Isovalues are indicated with black contours, using the same levels and colourbars for predictions and observations.

Figure 4

Figure 4. A narrow wave packet with mean position ${\boldsymbol{x}}(t)$ and wavevector $\boldsymbol {p}(t)$ behaves like a charged classical particle in a steady 2-D electromagnetic field.

Figure 5

Figure 5. Ergodic predictions for the (a) time-averaged NIW potential energy $\overline {|\boldsymbol{\nabla } M|^2}({\boldsymbol{x}})$ and (b) Stokes’ drift $\overline {\boldsymbol{u}_s}({\boldsymbol{x}})$. (c,d) Same fields as panels (a) and (b) extracted from a numerical run with $\gamma =30$. In panels (a) and (c), black contours indicate isovalues 1, 4, 9 and 16.

Figure 6

Figure 6. (a) Time-averaged NIW kinetic energy $\overline {|M|^2}$ from a numerical simulation with $\gamma =10$. (b) Idem for $\gamma = 30$. (c) Refined ergodic prediction (6.17) for $\gamma = 30$, taking into account non-uniform initial energy and trapping in the two main anticyclones. The colourscale lightness varies linearly on $[0,1]$ and on $[1,5]$ to keep $\overline {|M|^2}=1$ in white. Black contours indicate the isovalue $1/3$ of the r.m.s. value of $|\boldsymbol{U}|$. These contours show that deviations from the uniform distribution preferentially occur in regions of slow background flow.

Figure 7

Figure 7. Preferential concentration of NIW kinetic energy in anticyclonic regions as measured by the quantity $\sigma \in [-1,1]$ defined in (7.1). $\sigma$ is always positive in the simulations (blue diamonds), indicating preferential concentration in anticyclones. Such concentration is maximal for moderate values of $\gamma$, while achieving small values for both small and large $\gamma$. The grey dash-dotted curves show the first-order prediction for slow flows, with $\overline {|M|^2}({\boldsymbol{x}})$ given by (5.7), and the full prediction for fast flows, with $\overline {|M|^2}({\boldsymbol{x}})$ given by (6.17). The solid black curve is the Padé approximant (E13). $\sigma _{\infty }$ is the $\gamma \to \infty$ limiting value of $\sigma$ inferred from trapping in anticyclonic vortex cores. Error bars, estimated from variability across different time-averaging subwindows, are smaller than the symbol size for all simulations.

Figure 8

Table 2. Parameter values employed in the numerical simulations. The time-averaging window is denoted by $[t_{\textit{min}}, \,t_{\textit{max}}]$.