Mean and Turbulent Characteristics of a Bottom Mixing‐Layer Forced by a Strong Surface Tide and Large Amplitude Internal Waves

Abstract

We present 15 days of both mean and turbulent field observations bottom mixing‐layer at a gently sloping 250 m deep continental shelf site, energized by tides and nonlinear internal waves (NLIWs). The tidal frequency forcing was due to the combined effects of the barotropic tide and a mode‐1 internal tide (IT), while the NLIWs were predominantly mode‐1 waves of depression. The bottom mixing‐layer thickness varied at both semidiurnal and sub‐tidal ∼O(10)d frequencies, with an average thickness of around 10 m. Compression and expansion of the mixing‐layer by both the IT and NLIWs affected the mean velocity profiles in the mixing‐layer, while the phasing between the barotropic and baroclinic flows led to an asymmetry in mean velocity profiles between periods of rising and falling isotherms. With the exception of periods of flow reversal, the turbulent kinetic energy balance and turbulent stress observations were consistent with the existence of an inertial‐sublayer with thickness of approximately 10%–15% of the mixing‐layer thickness ( ∼1 m), even beneath NLIWs. In the outer portion of the mixing‐layer—that is, above the inertial‐sublayer—NLIWs modulated the local turbulence spectra. We discuss the observations in the context of a predictive model for mixing‐layer thickness. The analysis suggests that the high‐frequency variability in mixing‐layer thickness was dominated by internal wave pumping, though strength of the ambient stratification and the frequency of the forcing were important controls on the time‐averaged (sub‐tidal) variation.

In-situ estimation of erosion model parameters using an advection-diffusion model and Bayesian inversion

“…In addition, the erosion model itself could be re-parameterised to reduce parameter covariance. In this case study we attempt to estimate unobserved parameters using 2017 field observations from the Northwest Shelf of Australia (Zulberti et al, 2022;Edge et al, 2021).…”

“…Equation 8 with the time-varying bottom boundary layer height (BBL) (typically 10 m thick) estimated using backscatter from an acoustic Doppler current profiler (ADCP), as perZulberti et al (2022) [Figure11]. Direct estimates of eddy diffusivity at 1.4 m ASB were typically three times greater than modelled estimates using Equation8(the model is highly idealised) Zulberti et al (2022). analysed backscatter, current profiles and temperature profiles, and determined that backscatter could be used as a suitable proxy to estimate the BBL height.…”

In Situ Estimation of Erosion Model Parameters Using an Advection‐Diffusion Model and Bayesian Inversion

“…In this case study we attempt to estimate unobserved parameters using 2017 field observations from the Northwest Shelf of Australia (Edge et al., 2021; Zulberti et al., 2022). As it was too computationally expensive to include the entire observation period (2–16 April 2017) in the numerical model using this method, we selected a period of 3 consecutive tidal cycles (about 37.6 hr) (Figure 10).…”

“…In these cases, sediment remains trapped within the bottom mixing layer, an unstratified portion of the water column extending from the sea bed to a height, B h (which in shallow water may be all the way to the surface, or in deep water may be capped by a thin pycnocline). For the deep water cases (Case Studies 2 and 3) B h was estimated (Zulberti et al., 2022) and never reached the top of the model domain, that is, B h < H .…”

Spatial-temporal characteristics of the oceanic bottom mixed layer in the South China Sea

“…Since the signature of the BML is typical of a mixing process, it is accepted that the H BML forms as a result of mixing the uniformly stratified fluid, which is closely linked with active dynamic processes and topographic features (Polzin and McDougall, 2022). For example, previous observational studies have shown that the variability of the H BML was strongly associated with bottom currents (Wunsch and Hendry, 1972;Armi and Millard, 1976;Zulberti et al, 2022). The BML often exhibit much more spatially variable and temporally intermittent structures over steeply sloping and/or rough topography (Wunsch and Hendry, 1972;Armi and Millard, 1976;Thorpe, 1987).…”