ALBERT

Description: Ocean observations carried out in the framework of the Collaborative Research Center 754 (SFB 754) "Climate-Biogeochemistry Interactions in the Tropical Ocean" are used to study (1) the structure of tropical oxygen minimum zones (OMZs), (2) the processes that contribute to the oxygen budget, and (3) long-term changes in the oxygen distribution. The OMZ of the eastern tropical North Atlantic (ETNA), located between the well-ventilated subtropical gyre and the equatorial oxygen maximum, is composed of a deep OMZ at about 400 m depth with its core region centred at about 20° W, 10° N and a shallow OMZ at about 100 m depth with lowest oxygen concentrations in proximity to the coastal upwelling region off Mauritania and Senegal. The oxygen budget of the deep OMZ is given by oxygen consumption mainly balanced by the oxygen supply due to meridional eddy fluxes (about 60%) and vertical mixing (about 20%, locally up to 30%). Advection by zonal jets is crucial for the establishment of the equatorial oxygen maximum. In the latitude range of the deep OMZ, it dominates the oxygen supply in the upper 300 to 400 m and generates the intermediate oxygen maximum between deep and shallow OMZs. Water mass ages from transient tracers indicate substantially older water masses in the core of the deep OMZ (about 120-180 years) compared to regions north and south of it. The deoxygenation of the ETNA OMZ during recent decades suggests a substantial imbalance in the oxygen budget: about 10% of the oxygen consumption during that period was not balanced by ventilation. Long-term oxygen observations show variability on interannual, decadal and multidecadal time scales that can partly be attributed to circulation changes. In comparison to the ETNA OMZ the eastern tropical South Pacific OMZ shows a similar structure including an equatorial oxygen maximum driven by zonal advection, but overall much lower oxygen concentrations approaching zero in extended regions. As the shape of the OMZs is set by ocean circulation, the widespread misrepresentation of the intermediate circulation in ocean circulation models substantially contributes to their oxygen bias, which might have significant impacts on predictions of future oxygen levels.

Description: GEOMAR moorings are typically equipped with instruments recording pressure, temperature, conductivity, dissolved oxygen and current velocity. Instruments with pressure, temperature, conductivity and oxygen sensors were calibrated in situ immediately prior to and after a mooring deployment period by attaching them to the CTD frame during CTDO casts. Correction terms were then developed from the difference between the sensor readings and the calibrated CTDO data during several minute long calibration stops. These correction terms were then applied to the full deployment periods. This ensured best data quality with recognition of potential sensor drifts and also allowed for the estimation of calibration and measurement errors (Hahn et al. 2014, Bittig et al. 2018, Berx et al. 2019).

Description: GEOMAR moorings are typically equipped with instruments recording pressure, temperature, conductivity, dissolved oxygen and current velocity. Instruments with pressure, temperature, conductivity and oxygen sensors were calibrated in situ immediately prior to and after a mooring deployment period by attaching them to the CTD frame during CTDO casts. Correction terms were then developed from the difference between the sensor readings and the calibrated CTDO data during several minute long calibration stops. These correction terms were then applied to the full deployment periods. This ensured best data quality with recognition of potential sensor drifts and also allowed for the estimation of calibration and measurement errors (Hahn et al. 2014, Bittig et al. 2018, Berx et al. 2019). Moored Acoustic Doppler Current Profiler bin depths were corrected using the sound speed at instrument depth following the approach by Shcherbina et al. (2005). Velocities were not corrected, but respective measurement errors were assumed as described in Hahn et al. (2014). For all instruments within a mooring that did not record pressure, full deployment pressure time series were estimated by linearly interpolating between the instruments having a pressure sensor.