2.1 Glider specifications and deployment

The AlterEco project was a 17-month-long continuous glider sampling campaign in the seasonally stratified North Sea. One of the aims of AlterEco was to demonstrate the feasibility of monitoring the health of the marine ecosystem using only gliders deployed from small boats within 50 km of the coast. Once deployed, gliders were piloted to the sampling site in the central North Sea (Fig. 1). A single Slocum glider (Teledyne Webb Research, Falmouth, USA) from the Marine Autonomous and Robotic Systems group at the National Oceanography Centre (unit 444, called “Kelvin”) was deployed during late autumn (September to November 2018) to measure and quantify the vertical oxygen flux just before the breakdown of stratification. Glider Kelvin was deployed on 28 September 2018 from RV Princess Royal at 55.38° N, 0.51° W and took approximately 9 d to reach the AlterEco study site at 56.2° N, 2° E (Fig. 1). Here, glider Kelvin was instructed to conduct virtual mooring dives from 18 October until 9 November (22 d) and then piloted back nearshore for recovery on 2 December. A “virtual mooring” is a glider flight pattern where the glider does not head towards a new waypoint but attempts to hold a geographic position by turning back to its origin after each dive.

Figure 1Bathymetric map showing the AlterEco study region in the North Sea (inset) and glider tracks (black lines) to the AlterEco sampling site at 56.2° N, 2° E.

Glider Kelvin was equipped with a MicroRider microstructure package (Rockland Scientific International) to measure turbulent shear stress (see Palmer et al., 2015 for full details); a Sea-Bird SBE42 CTD sensor to measure temperature, salinity, and pressure; and an Aanderaa 4831 Oxygen Optode to measure C(O₂). Measurements were taken within 15 m of the bed and 5 m of the surface on most dives, with each dive taking about 20 min. The reason for cautious piloting away from the seabed was to protect the delicate probes on the MicroRider in an area known to host many banks and changing bathymetry.

Glider salinity data were corrected for thermal inertia following Palmer et al. (2015). The glider AA4831 optode displays relatively long lag times (of the order of tens of seconds), and the positioning of the oxygen optode in a flow “shadow zone” at the back of the Slocum glider is not ideal either for resolving sharp oxyclines (Nicholson et al., 2008; Moat et al., 2016). Oxygen concentrations were corrected for optode membrane lag following Bittig et al. (2014). However, where the oxycline was too steep and full C(O₂) variations could not be resolved, the data were omitted. Occasionally, the glider turned close to the deep pycnocline (see Fig. 2b). This meant that the glider was not sampling for long enough to resolve the C(O₂) minimum in the narrow bottom mixed layer (BML) before it turned to climb. This resulted in unresolved BML C(O₂). Glider optode oxygen concentrations were calibrated against Winkler-analysed discrete samples (surface and bottom triplicate, Fig. A1). The AA4831 optode has been shown to have low drift (< 0.5 % a⁻¹) and good precision (better than 0.2 mmol m⁻³) (Körtzinger et al., 2004; Nicholson et al., 2008; Johnson et al., 2010; Champenois and Borges, 2012). Optode drift was estimated to have been 0.004 % d⁻¹ in this study by comparing all optode drift rates throughout the AlterEco project.

Figure 2Time series of (a) glider position latitude and longitude, (b) temperature T, and (c) turbulent kinetic energy dissipation rate ε. The position of the pycnocline (thin black line) is shown in panel (b). The seabed position derived from the glider altimeter is shown in panels (b) and (c).

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2.2 Oxygen flux calculations

Following Sharples et al. (2007), the oxygen flux, J(O₂), was estimated as

where g = 9.81 m s⁻², ρ is water density (in kg m⁻³), ε is the turbulent kinetic energy dissipation rate (in W kg⁻¹), and m = dC(O₂) $/$ dρ (in mmol kg⁻¹) is the oxygen concentration gradient with respect to density. The unit of J is mmol m⁻² s⁻¹. The dimensionless mixing efficiency Γ is defined as the ratio of potential energy gained relative to energy used to activate mixing and assumed to be constant at 0.2 for the stratified water column (Osborn, 1980; Tweddle et al., 2013; Williams et al., 2013a, 2022). While there is ongoing discussion on the assumption of a constant mixing efficiency in stratified fluids, no study has arrived yet at a conclusive improvement, so this simple solution has been employed here as current best practice (Gregg et al., 2018).

As previously discussed, the optode membrane lag combined with the positioning of the optode meant that we could not resolve the oxycline accurately. This issue is particularly acute for shelf seas where there are large C(O₂) and temperature gradients. To overcome this problem, we have substituted m with the product of the CTD temperature–density gradient, dT_CTD $/$ dρ (approximately linear over a sufficiently narrow temperature range), and the optode oxygen concentration–optode temperature gradient, dC(O₂) $/$ dT_opt. This gives the following revised version of Eq. (1):

Testing this method on an independent dataset in a nearby region, which had both accurate measurements of the oxygen–density gradient and a slow-response AA4831 optode to compare against, indicated that our method calculates the oxygen–density gradient to within 13 % of the true oxygen–density gradient value (Appendix B). The base of the pycnocline defines the boundary between the SML and BML and represents the interface for diapycnal mixing and O₂ fluxes. In the BML, tidal mixing vertically homogenizes density and C(O₂). The base of the pycnocline was defined as the depth that marked the top of the BML (Fig. 2b). Confidence limits (95 %) for J(O₂) were calculated using Efron–Gong bootstrap resampling method (Efron and Gong, 1983).

3.1 Water column characteristics

Glider Kelvin measured well-defined late-autumn stratification in the water column with a depth of 85 m and a relatively deep (50–60 m) thermocline both on transit to and once on station (Fig. 2b). Stratification was thermally driven, with the 50–60 m thick SML at 11.5 °C being 4.5 °C warmer than the comparably thinner 20–30 m bottom mixed later at 7.0 °C (Fig. 2a). The SML temperature decreased by 2.3 °C to 9.2 °C over the 22 d sampling period. There were further layers of vertical stratification within the SML and above the BML at the beginning of the deployment until 23 October (Fig. 2b). The relatively deep pycnocline was observed until early November, later than predicted by the regional 7 km resolution Atlantic Margin Model (AMM7; Fig. C1). The glider started travelling west on 9 November to the recovery site, but it is unclear whether the glider moved into colder water or was capturing additional heat loss from the SML (Fig. 2).

The turbulent kinetic energy (TKE) dissipation rate ε within the water column ranged between 1 × 10⁻¹⁰ and 1 × 10⁻⁶ W kg⁻¹, with the strongest mixing observed at the surface and the lowest ε values observed within the mid-water below the boundary of surface mixing. Prolonged periods of intense wind-driven boundary mixing (ε=1 × 10⁻⁶ W kg⁻¹) at the surface were evident from the measurements of TKE dissipation, with this turbulence occasionally penetrating and eroding the top of the pycnocline, as observed between 23 and 27 October (Fig. 2c). In comparison to the surrounding midwater ε levels (1 × 10⁻⁹ W kg⁻¹), enhanced ε was observed within the pycnocline when the glider dived deep enough to sample across it (Fig. 3c). Furthermore, episodic intense bursts of ε (1 × 10⁻⁶ W kg⁻¹) were also occasionally observed within the pycnocline (Figs. 2c and 3c). However, the glider did not measure clear bottom-boundary turbulence driven by the barotropic tide due to it having to turn within 15 m of the bed and thus not always being in the narrow BML layer as discussed.

The deep pycnocline separated the oxygen-replete SML (280 µmol kg⁻¹, 100 % saturation) from the oxygen-depleted BML (240–250 µmol kg⁻¹, 75 % saturation; Fig. 3). Evidently the glider only sampled across the pycnocline and into the BML on two separate occasions (Fig. 2). Therefore, for this study, the two periods are treated as two separate time series (A, from 18 to 30 October 2018; B, from 8 to 13 November 2018 – travelling SW back to shore for recovery from 9 November) for flux calculations.

Figure 3Time series of (a) oxygen content μ (b) oxygen saturation, (c) turbulent kinetic energy dissipation rate ε_pycno within the pycnocline (thin black line in panels a and b), and (d) the instantaneous oxygen flux J(O₂) across the pycnocline. The thick black line indicates the seabed position derived from the glider altimeter. Shaded grey boxes highlight where spikes in dissipation appear to coincide with enhanced oxygen fluxes.

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3.2 Diapycnal mixing of O₂

The instantaneous diapycnal turbulent kinetic dissipation rate ε over both time series A and B ranged from 1 × 10⁻⁹ to 1 × 10⁻⁷ W kg⁻¹, with the average for time series A calculated as ε_pycno= (8.3 ± 2.0) × 10⁻⁸ W kg⁻¹ (95 % confidence interval). For time series B, ε_pycno was slightly lower at (2.0 ± 0.8) × 10⁻⁸ W kg⁻¹ (Table 1). The instantaneous J(O₂) flux into the BML ranged between 10⁻⁶ and 10⁻⁴ mmol m⁻² s⁻¹ (Fig. 3d) with the highest values corresponding to the highest ε_pycno values (Fig. 3c). Mean J(O₂) was (5.4 ± 1.0) mmol m⁻² d⁻¹ and (3.5 ± 1.0) mmol m⁻² d⁻¹ for time series A and B respectively (Table 1). The mean J(O₂) was higher in time series A due to a significantly larger mean ε_pycno (Table 1), despite experiencing a weaker oxygen–density gradient compared to time series B (−34.4 and −92.5 respectively). The stronger oxygen gradient in time series B was due to the SML decreasing in temperature between time series A and time series B (Fig. 2b), resulting in a lower density gradient between the SML and BML, which in turn increased the observed oxygen–density gradient.

Table 1Estimates of pycnocline turbulent energy dissipation rate ε_pycno and oxygen fluxes J(O₂) during time series A and B, with and without episodic mixing events, defined as spikes where ε>10^−7.5 W kg⁻¹. Uncertainties correspond to 95 % confidence intervals.

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There was a total of 504 measurements of ε_pycno over both time series A and B, and, of these measurements, there were 204 episodic mixing events or “spikes” (defined as periods when ε_pycno > 10^−7.5 W kg⁻¹, which was 25 standard deviations from the mean) which made up 40 % of the observations of ε_pycno. Despite episodic events making up such a small proportion of the ε_pycno observations, when these were omitted, the recalculated mean ε_pycno decreased from (8.3 ± 2.0) to (1.5 ± 0.1) × 10⁻⁸ W kg⁻¹ for time series A and from (2.0 ± 0.8) to (0.7 ± 0.2) × 10⁻⁸ W kg⁻¹ for time series B. We used these mean ε_pycno values (with spikes omitted) to recalculate J(O₂) and found it decreased to 1.0 mmol m⁻² s⁻¹ for time series A and time series B. This equated to episodic mixing events making up 74 % to 89 % of the observed J(O₂) into the BML.