A novel global gridded ocean oxygen product derived from a neural network emulator and in-situ observations

Abstract. Ocean deoxygenation, driven by climate change, poses significant challenges to marine ecosystems and can profoundly alter nutrient and carbon cycling. Quantifying the rate and regional patterns of deoxygenation relies on spatio-temporal interpolation tools to fill gaps in observational coverage of dissolved oxygen. However, this task is challenging due to the sparsity of observations, and classical interpolation methods often lead to high uncertainty and biases, typically underestimating long-term deoxygenation trends. In this work, we develop a novel gridded dissolved oxygen product by integrating direct oxygen observations with machine-learning-based emulated oxygen estimates derived from temperature and salinity profiles. The gridded product is then generated through optimal interpolation of both the observed and emulated data. The resulting product shows strong agreement with baseline climatology and captures well-known patterns of seasonal variability and long-term deoxygenation trends. It also outperforms current state-of-the-art products by more accurately capturing dissolved oxygen variability at synoptic and decadal scales, and by reducing uncertainty around long-term changes. This study highlights the potential of combining machine learning with classical interpolation methods to generate improved gridded biogeochemical products, enhancing our ability to study and understand ocean biogeochemical processes and their variability under a changing climate.