Articles | Volume 18, issue 1
https://doi.org/10.5194/essd-18-287-2026
https://doi.org/10.5194/essd-18-287-2026
Data description article
 | 
12 Jan 2026
Data description article |  | 12 Jan 2026

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

Said Ouala, Oussama Hidaoui, and Zouhair Lachkar

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Short summary
Ocean deoxygenation poses major challenges to marine life and can alter carbon cycling. Direct measurements of dissolved oxygen are sparse, and interpolation methods are needed to study the variability and changes in oxygen content. In this work, we used machine learning to improve estimates of oxygen levels across the global ocean. Our approach produces a new gridded product that captures detailed changes in oxygen over time and space.
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