Retracing Hypoxia in Eckernförde Bight (Baltic Sea)

Abstract. In recent years, upwelling events of low-oxygenated deep water have been repeatedly observed in Eckernförde Bight (EB) situated in the Baltic Sea, Germany. Many of these events were related to massive fish-kill incidents – with negative consequences for commercial fisheries and tourism. The aim of this study is to dissect underlying mechanisms and to explore the potential of existing monitoring programs to predict these events. Our main tool is an ultra-high spatially resolved general ocean circulation model which drives an elementary representation of the biogeochemical dynamics of dissolved oxygen (dubbed MOMBE and EckO2, respectively). In addition, we integrate artificial clocks that measure the residence time of the water in EB along with timescales of (surface) ventilation. We present an ensemble of hind cast model simulations, covering the period from 2000 up to 2018, designed to cover a range of poorly known model parameters for vertical background mixing (diffusivity) and local oxygen consumption within EB. Our results indicate that the dynamics of low (hypoxic) oxygen concentrations in bottom waters deep inside EB is, to first order, determined by the following antagonistic processes: (1) the inflow of low-oxygenated water from the Kiel Bight (KB) – especially from July to October and (2) the local ventilation of bottom waters by local (within EB) subduction and vertical mixing. Biogeochemical processes that consume oxygen locally, are apparently of minor importance for the development of hypoxic events. Reverse reasoning suggests that subduction and mixing processes in EB contribute, under certain environmental conditions, to the ventilation of the KB by exporting recently-ventilated waters enriched in oxygen. A detailed analysis of the 2017 fish-kill incident highlights the interplay between westerly winds importing hypoxia from KB and ventilating easterly winds which subduct oxygenated water. Finally, we explore the capabilities of – comparably computationally cheap – feed-forward artificial neuronal networks to forecast hypoxia deep in EB based on data at a monitoring site at the entrance of EB.