Surface Drift Currents Induced by Waves and Wind in a Large Tank

The empirical features of surface drift currents induced by both mechanical and wind waves are presented. The measurements were made by using surface floats in a large tank with dimensions of 32.5 × 1 × 2 m3. Three cases were studied: (i) regular (narrowband) mechanical waves, (ii) irregular (wideband) mechanical waves, and (iii) wind waves. The measured surface drift currents induced by mechanical waves Ud are compared with the Stokes drift at the surface USt estimated by a well-known formula with an integral over the wave spectrum. In this case, the ratio Ud/USt varies in the range of 0.5–0.93 and slightly increases with decreasing wave steepness. No visible dependence on the breaking intensity is observed. In the case of wind waves, the wind-induced part of the surface drift Udw is compared with the friction velocity u*. In our measurements, the ratio Udw/u* varies systematically in the range of 0.65–1.2. Considering the percentage of wave breaking Br, the wave age A, and the wave steepness Ϭ, the parameterization of Udw was obtained in the form Udw = (Br + ϬA)u*, which corresponds to the observations with a mean error of 10%. For the first time, this ratio provides the dependence of wind-induced drift on the surface wave parameters. The obtained results and problems related to measuring surface drift currents are discussed.

Sensitive dependence of trajectories on tracer seeding positions – coherent structures in German Bight surface drift simulations

Backward drift simulations can aid the interpretation of in situ monitoring data. Some trajectories, however, are sensitive to even small changes of the tracer release position. A corresponding spread of backward simulations implies convergence in the forward passage of time. Such uncertainty about the probed water body's origin complicates the interpretation of measurements. This study examines surface drift simulations in the German Bight (North Sea). Lines across which drift behaviour changes non-smoothly are obtained as ridges in the fields of the finite-time Lyapunov exponent (FTLE), a parameter used in dynamical systems theory to identify Lagrangian coherent structures (LCS). Results are shown to closely resemble those obtained considering a) two-particle relative dispersion and b) the average divergence of Eulerian velocities that tracers experience. Structures observed in simulated sea surface temperature and salinity further corroborate the FTLE results.

Empirical Parameterization of the Wind-induced Drift Currents

<p>Results of measurements of the drift currents induced by waves and wind at the wavy water surface are presented. The measurements were executed by means of surface floats in a large tank with the dimensions of 32.5x1x2 m<sup>3</sup>. Three cases were studied: (i) regular (narrow-band) mechanical waves; (ii) irregular (wide-band) mechanical waves; and (iii) wind waves.</p><p>The measured surface-drift currents induced by mechanical waves, U<sub>d</sub>, are compared with the Stokes drift at the surface, U<sub>St</sub>, estimated by the well-known formula with the integral over a wave spectrum. In this case, it was found that ratio U<sub>d</sub> / U<sub>St</sub> is varying in the range 0.5 – 0.93 and slightly growing with the decrease of wave steepness, having no visible dependence on the breaking intensity. These estimations are used to separate the wind-induced drift current, U<sub>dw</sub>, from the total drift at the presence of wind.</p><p>In the case of wind waves, the wind-induced part of the surface drift, U<sub>dw</sub>, is compared with the friction velocity, u<sub>*</sub>. In our measurements, the ratio U<sub>dw</sub> / u<sub>*</sub> varies systematically in the range 0.65 – 1.2. Taking into account the percentage of wave breaking, Br, the wave age, A, and the wave steepness, Ϭ = ak<sub>p</sub>, it was found the parameterization:  U<sub>dw</sub> = (Br + Ϭ A) u<sub>*</sub>, which corresponds to the observations with the mean error less than 10%. For the first time, this ratio provides the dependence of the surface wind drift on the surface wave parameters.</p>

The effects of wind and waves on in-situ surface drift in the Baltic Sea

<p>Wind and waves often have a strong influence on surface drift, especially in the strongly stratified Baltic Sea. However due to the limitations of wave models and analytical solutions, the quantification of the influence of the waves is a complicated problem. In this study we employ a more observational approach by utilizing one of the longest time series of in-situ surface drifters deployed in the Gulf of Finland, Baltic Sea for the period of 2011−2019. Analysis is performed both qualitatively and quantitatively to understand the effects of the wind and waves on surface drift. The forty-seven in-situ surface drifters utilized were designed to follow the uppermost 2 m layer of currents. In addition, a web-based software (DrifterTrack) was specifically developed for real time data monitoring, data collection, storage and access solution. The wind and wave data were obtained by wave buoys and meteorological stations located in the central part of the gulf.  <br>Several hypothesis tests combined with statistical analysis of drifter trajectories, wind and wave data were utilized for the analysis. Qualitatively the drifter trajectories displayed a variety of shapes and maneuvers, hinting the complexity of the surface drift. Nevertheless, drifter trajectory maps showed for most years a predominance of surface drift towards the east which also coincides with the predominant wind and wave direction. Interestingly the results also suggest that when surface drift towards the west occurred it was generally quicker than the drift to the east. The average current speed was in the range of 0.05−0.15 m/s for approximately 45% of the occurrences. The drifter speed within the range of 0.3−0.5 m/s accounted for approximately 9% of the occurrences. The drifter speed was found to vary between 1.5−2.5 % of the wind speed. Hypothesis tests show that wave heights of >1 m (created by >10 m/s wind speed) have the most significant effect on the drifter speed within the range of 0.15−0.3 m/s. These tests also demonstrated that wind and waves effects are not the only forces influencing strong surface drift in the gulf. Several other processes (e.g. eddies, density gradients, upwellings, downwellings etc.) can substantially contribute to the surface drift.</p>

The Stokes drift in ocean surface drift prediction

<p>Ocean surface drift forecasts are essential for numerous applications. It is a central asset in search and rescue and oil spill response operations, but it is also used for predicting the transport of pelagic eggs, larvae and detritus or other organisms and solutes, for evaluating ecological isolation of marine species, for tracking plastic debris, and for environmental planning and management. The accuracy of surface drift forecasts depends to a large extent on the quality of ocean current, wind and waves forecasts, but also on the drift model used. The standard Eulerian leeway drift model used in most operational systems considers near-surface currents provided by the top grid cell of the ocean circulation model and a correction term proportional to the near-surface wind. Such formulation assumes that the 'wind correction term' accounts for many processes including windage, unresolved ocean current vertical shear, and wave-induced drift. However, the latter two processes are not necessarily linearly related to the local wind velocity. We propose three other drift models that attempt to account for the unresolved near-surface current shear by extrapolating the near-surface currents to the surface assuming Ekman dynamics. Among them two models consider explicitly the Stokes drift, one without and the other with a wind correction term. We assess the performance of the drift models using observations from drifting buoys deployed in the Estuary and Gulf of St. Lawrence, Canada. Drift model inputs are obtained from regional atmospheric, ocean circulation, and spectral wave models. The performance of these drift models is evaluated based on a number of error metrics (e.g. speed, direction, separation distance between the observed and simulated positions) and skill scores determined at different lead times ranging from 3h to 72h. Results show that extrapolating the top-layer ocean model currents to the surface assuming Ekman dynamics for the ageostrophic currents, and adding the Stokes drift predicted by a spectral wave model, leads to the best drift forecast skills without the need to include a wind correction term.</p>