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.

中文翻译：

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EckernfördeBight（波罗的海）的缺氧恢复

摘要。近年来，在德国波罗的海的EckernfördeBight（EB）反复观察到低氧深水的上升流事件。这些事件中有许多与大规模的杀鱼事件有关，这给商业渔业和旅游业带来了负面影响。这项研究的目的是剖析潜在的机制，并探索现有监测程序预测这些事件的潜力。我们的主要工具是超高空间分辨率的一般海洋环流模型，该模型可以驱动溶解氧的生物地球化学动力学（分别称为MOMBE和EckO ₂）的基本表示。此外，我们还集成了人造钟用于测量水在EB中的停留时间以及（地面）通风的时间尺度。我们提供了从2000年到2018年的后铸模型模拟合奏，旨在涵盖一系列鲜为人知的模型参数，这些参数用于垂直背景混合（扩散性）和EB中的局部耗氧量。我们的结果表明，EB内部深水中低（低氧）氧气浓度的动力学首先取决于以下拮抗作用：（1）从基尔湾（KB）流入低氧水–尤其是从7月到10月，以及（2）通过局部（在EB内）俯冲和垂直混合对底水进行局部通风。局部消耗氧气的生物地球化学过程显然对于发生缺氧事件的重要性较小。反向推理表明，在某些环境条件下，EB的俯冲和混合过程通过出口最近通风的富含氧气的水来促进KB的通风。对2017年鱼类致死事件的详细分析突显了从KB引入缺氧的西风和通风的东风之间的相互作用，后者消减了含氧水。最后，我们探索了前馈人工神经元网络（相对于计算而言便宜）的能力，该功能基于EB入口处监视站点的数据来预测EB深处的缺氧。对2017年鱼类致死事件的详细分析突显了从KB引入缺氧的西风和通风的东风之间的相互作用，后者消减了含氧水。最后，我们探索了前馈人工神经元网络（相对于计算而言便宜）的能力，该功能基于EB入口处监视站点的数据来预测EB深处的缺氧。对2017年鱼类致死事件的详细分析突显了从KB引入缺氧的西风和通风的东风之间的相互作用，后者消减了含氧水。最后，我们探索了前馈人工神经元网络（相对于计算而言便宜）的能力，该功能基于EB入口处监视站点的数据来预测EB深处的缺氧。