Impact of acoustic Doppler current profiler (ADCP) motion on structure function estimates of turbulent kinetic energy dissipation rate

Abstract

Abstract. Turbulent mixing is a key process in the transport of heat, salt, and nutrients in the marine environment, with fluxes commonly derived directly from estimates of the turbulent kinetic energy dissipation rate, ε. Time series of ε estimates are therefore useful in helping to identify and quantify key biogeochemical processes. The velocity structure function method can be used to determine time series of ε estimates using along-beam velocity measurements from suitably configured acoustic Doppler current profilers (ADCPs). Shear in the background current can bias such estimates; therefore, standard practice is to deduct the mean or linear trend from the along-beam velocity over the period of an observation burst. This procedure is effective if the orientation of the ADCP to the current remains constant over the burst period. However, if the orientation of the ADCP varies, a proportion of the velocity difference between bins is retained in the structure function and the resulting ε estimates will be biased. Long-term observations from a mooring with three inline ADCPs show the heading oscillating with an angular range that depends on the flow speed: from large, slow oscillations at low flow speeds to smaller, higher-frequency oscillations at higher flow speeds. The mean tilt was also determined by the flow speed, whilst the tilt oscillation range was primarily determined by surface wave height. Synthesised along-beam velocity data for an ADCP subject to sinusoidal oscillation in a sheared flow indicate that the retained proportion of the potential bias is primarily determined by the angular range of the oscillation, with the impact varying between beams depending on the mean heading relative to the flow. Since the heading is typically unconstrained in a tethered mooring, heading oscillation is likely to be the most significant influence on the retained bias for a given level of shear. Use of an instrument housing designed to reduce oscillation would mitigate the impact, whilst if the shear is linear over the observation depth range, the bias can be corrected using a modified structure function method designed to correct for bias due to surface waves.

Hydrographical implications of ship-induced turbulence in stratified waters, studied through field observations and CFD modelling

“…Moreover, the stationary and bottom mounted S500 produced less noisy data compared to the moored S1000, as the latter were affected by currents and internal waves and do not compensate for the instrument movements. For the slanted beams it is mainly the rotation of the instrument that causes observational bias when using the mooring setup (Scannell et al, 2022). The static bottom mounted setup used for the S500, on the other hand, would not be affected by rotational bias, and therefore only the S500 observations were used when estimating the e decay rate.…”

“…The S1000 recorded higher e values compared to the S500 instrument. This can likely partly be attributed to the rotational bias of the moored instrument (Scannell et al, 2022), but could also indicate that the higher resolution of the S1000 better captured the actual turbulence signal when calculating e using the structure function method (Lucas et al, 2014). A higher resolution of the velocity observations (0.5 m for S1000 compared to 1 m for S500), could potentially capture more of the relevant turbulent eddies, in which case our method would underestimate the dissipation rates when using the S500 instrument.…”

The deep water oxygen deficit in stratified shallow seas is mediated by diapycnal mixing

“…The second-order structure function was calculated using a bin-centred difference scheme, evaluated for separation distances of two bins [44] and a least-squared regression using the modified model applied to extract coefficients a 0 , a 1 and a 3 for all instances where the regression was possible. Instances where a 3 <0; a 0 < − 1 × 10 −4 or a 0 >3 × 10 −4 ; or the number of data points available for the regression was less than eight were all excluded.…”