A Novel Global Gridded Ocean Oxygen Product Derived from Neural Network Emulators (1965–2022)

Over the past decades, global ocean oxygen inventories have declined by 0.5–3.3% relative to historical averages, with significant uncertainties in data-sparse regions such as the South Pacific and Indian Oceans. These gaps hinder accurate estimates of deoxygenation rates, potentially leading to underestimation of its magnitude. In this context, gridded oxygen products are essential for assessing global and regional trends and projecting the impacts of deoxygenation on marine ecosystems. However, traditional Optimal Interpolation (OI) methods are known to underestimate ocean oxygen loss, particularly in poorly observed areas.

To address these limitations, we propose a novel approach to build a gridded oxygen concentration product. Specifically, we develop a neural network emulator of oxygen concentration based on temperature and salinity measurements. This neural network is then used to generate emulated oxygen concentration data, which are combined with dissolved oxygen measurements to produce a new global gridded oxygen concentration product spanning 1965 to 2022. We evaluate our product against climatological estimates from the World Ocean Atlas and other gridded oxygen products. Future work will leverage this gridded product to study the regional evolution of ocean deoxygenation, particularly in Oxygen Minimum Zone (OMZ) regions.