Small-Scale Dispersion in the Presence of Langmuir Circulation

Abstract

We present an analysis of ocean surface dispersion characteristics, on 1–100-m scales, obtained by optically tracking a release of [Formula: see text] bamboo plates for 2 h in the northern Gulf of Mexico. Under sustained 5–6 m s−1 winds, energetic Langmuir cells are clearly delineated in the spatially dense plate observations. Within 10 min of release, the plates collect in windrows with 15-m spacing aligned with the wind. Windrow spacing grows, through windrow merger, to 40 m after 20 min and then expands at a slower rate to 50 m. The presence of Langmuir cells produces strong horizontal anisotropy and scale dependence in all surface dispersion statistics computed from the plate observations. Relative dispersion in the crosswind direction initially dominates but eventually saturates, while downwind dispersion exhibits continual growth consistent with contributions from both turbulent fluctuations and organized mean shear. Longitudinal velocity differences in the crosswind direction indicate mean convergence at scales below the Langmuir cell diameter and mean divergence at larger scales. Although the second-order structure function measured by contemporaneous GPS-tracked surface drifters drogued at ~0.5 m shows persistent r2/3 power law scaling down to 100–200-m separation scales, the second-order structure function for the very near surface plates observations has considerably higher energy and significantly shallower slope at scales below 100 m. This is consistent with contemporaneous data from undrogued surface drifters and previously published model results indicating shallowing spectra in the presence of direct wind-wave forcing mechanisms.

The physical oceanography of the transport of floating marine debris

“…Convergence and divergence zones in Langmuir circulation are difficult to spot over the water surface (Faller 1964), but become visible when there is foam or flotsam on the surface accumulated by the convergence. After only 15-20 min of wind action, floating material can already be accumulated in stripes, and is kept there as long as the particular row persists (Chang et al 2019). Over time, the cells become larger and merge together, leading to the formation of Y-junctions at the surface (Thorpe 2004, Marmorino et al 2005, pointing generally down-wind in deep waters or equally up-and down-wind in shallow areas (Chubarenko et al 2010).…”

Advective structure functions in anisotropic two-dimensional turbulence

“…The dissipation operators in (3.1) were chosen to allow the development of inertial cascades where the direct effect of forcing and dissipative terms on structure function relations are assumed to be negligible. Recently this inertial cascade assumption has been relaxed to find structure function relations that include dissipative and forcing effects in isotropic 2-D turbulence (Xie & Bühler 2018, 2019, which can be used to diagnose properties of the dissipative operators from structure functions (Xie 2020).…”

“…For example, the structure functions at a given separation r within an inertial range could be affected by non-local dynamics at other r, such as forcing and the other inertial cascade (in a dual cascading system). Third-order structure function laws have been proposed for isotropic 2-D turbulence that apply outside idealised inertial cascades (Xie & Bühler 2018, 2019. It is possible that the derivations presented here could also be modified to extend outside of inertial ranges, such as near the forcing scale or in regions of overlapping energy and enstrophy cascades (Lindborg 1999;Thompson & Young 2006;Cerbus & Chakraborty 2017).…”

“…Potential for practical applications of advective structure functions Third-order structure functions have previously been used to study the dynamics of the atmosphere and ocean Deusebio et al 2014;Balwada, LaCasce & Speer 2016;Poje et al 2017;Pearson et al 2020) as well as other planets (Young & Read 2017). However, making appropriate measurements of flow properties can be challenging owing to the nature and uncertainty of both in situ and remote data collection (LaCasce 2008;Chang et al 2019;Pearson et al 2019). The predictions of dissipation rates from advective structure functions have less variability between snapshots, and are relatively insensitive to sampling direction, when compared with predictions from third-order structure functions.…”

“…However, making appropriate measurements of flow properties can be challenging owing to the nature and uncertainty of both in situ and remote data collection (LaCasce 2008; Chang et al. 2019; Pearson et al. 2019).…”