Optimizing simulated oxygen variability, circulation, and export in the subpolar North Atlantic Ocean using BGC-Argo & ship-based observations

The subpolar North Atlantic (SPNA) transports surface-ocean properties deep into the interior via deep convection and is one of the most intense regions of air-sea gas exchange globally. Deep convection in the SPNA exports highly-oxygenated water masses to depth, which subsequently ventilate intermediate and deep waters throughout the North Atlantic. The SPNA thus plays a critical role in setting the oxygen inventory of the global ocean. Due to intensifying ocean warming, many climate models predict substantial global-ocean oxygen loss — albeit at magnitudes which vary widely by model. Therefore, there is a need to better understand the impacts of SPNA convective variability on oxygen saturation in intermediate and deep water masses. Here we use a physical-biogeochemical model, ASTE-BGC, which couples the Arctic Subpolar gyre sTate Estimate (ASTE) with the Biogeochemistry with Light, Iron, Nutrients, and Gas (BLING) model to quantify oxygen cycling and deep ventilation in the SPNA. ASTE utilizes the MIT General Circulation Model (MITgcm) and assimilates physical in-situ and satellite data using tools developed by the Estimating the Circulation and Climate of the Ocean (ECCO) consortium. We use a Green’s Functions approach to optimize ASTE-BGC biogeochemistry using BGC-Argo and GLODAPv2 ship-based profiles of O₂ and NO₃. The Green’s Functions approach allows us to adjust the biogeochemical parameters of the BLING ecosystem towards O(10⁶) in-situ data constraints over the 2002–2017 model period. We then evaluate the optimized simulation against independent data and construct an oxygen budget for the central Labrador Sea to assess the interannual variability of SPNA oxygen.