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St Helena Bay (SHB), a retentive zone in the productive southern Benguela Upwelling System off western South Africa, experiences seasonal hypoxia and episodic anoxic events that threaten local fisheries. To understand the drivers of oxygen variability in SHB, we queried 25 years of dissolved oxygen (DO) observations alongside high‐resolution wind and hydrographic data, and dynamical data from a high‐resolution model. At 70 m in SHB (mid‐bay), upwelling‐favorable winds in spring drove replenishment of cold, oxygenated water. Hypoxia developed in summer, becoming most severe in autumn. Bottom waters in autumn were replenished with warmer, less oxygenated water than in spring—suggesting a seasonal change in source waters upwelled into the bay. Downwelling and deep mixing in winter ventilated mid‐bay bottom waters, which reverted to hypoxic conditions during wind relaxations and reversals. In the nearshore (20 m), hypoxia occurred specifically during periods of upwelling‐favorable wind stress and was most severe in autumn. Using a statistical model, we extended basic hydrographic observations to nitrate and DO concentrations and developed metrics to identify the accumulation of excess nutrients on the shelf and nitrogen‐loss to denitrification, both of which were most prominent in autumn. A correspondence of the biogeochemical properties of hypoxic waters at 20 m to those at 70 m implicates the latter as the source waters upwelled inshore in autumn. We conclude that wind‐driven upwelling drives the replenishment of respired bottom waters in SHB with oxygenated waters, noting that less‐oxygenated water is imported later in the upwelling season, which exacerbates hypoxia. 

Mean-field limit has been successfully applied to neural networks, leading to many results in optimizing overparametrized networks. However, existing works often focus on two-layer networks and/or require large number of neurons. We give a new framework for extending the mean-field limit to multilayer network, and show that a polynomial-size three-layer network in our framework can learn the function constructed by Safran et al. (2019) – which is known to be not approximable by any two-layer networks