Articles | Volume 18, issue 1
https://doi.org/10.5194/essd-18-287-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-287-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
12 Jan 2026
Data description paper |
| 12 Jan 2026
| 12 Jan 2026
A novel global gridded ocean oxygen product derived from a neural network emulator and in-situ observations
Said Ouala
CORRESPONDING AUTHOR
IMT Atlantique, CNRS UMR Lab-STICC, INRIA Team Odyssey, Brest, France
Oussama Hidaoui
African Institute for Mathematical Sciences, Cape Town, South Africa
Zouhair Lachkar
Mubadala Arabian Center for Climate and Environmental Sciences, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
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In this study, we develop a novel gridded ocean oxygen concentration product by combining observed oxygen data with emulated measurements derived from temperature and salinity profiles. This approach increases the density of observations, particularly in data-sparse regions, allowing for more accurate oxygen concentration estimates than current state-of-the-art products.
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An analysis of model and reanalysis data reveals that extreme summer sea surface temperatures in the Arabian/Persian Gulf are driven by weakened local Shamal winds and intensified monsoon winds over the Arabian Sea. These conditions – typically associated with La Niña and a negative North Atlantic Oscillation phase – increase air moisture over the Gulf and enhance surface heat trapping. The findings offer promising prospects for forecasting summer marine heatwaves in the Gulf.
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Preprint withdrawn
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In this study, we develop a novel gridded ocean oxygen concentration product by combining observed oxygen data with emulated measurements derived from temperature and salinity profiles. This approach increases the density of observations, particularly in data-sparse regions, allowing for more accurate oxygen concentration estimates than current state-of-the-art products.
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Biogeosciences, 18, 5831–5849, https://doi.org/10.5194/bg-18-5831-2021, https://doi.org/10.5194/bg-18-5831-2021, 2021
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This study documents and quantifies a significant recent oxygen decline in the upper layers of the Arabian Sea and explores its drivers. Using a modeling approach we show that the fast local warming of sea surface is the main factor causing this oxygen drop. Concomitant summer monsoon intensification contributes to this trend, although to a lesser extent. These changes exacerbate oxygen depletion in the subsurface, threatening marine habitats and altering the local biogeochemistry.
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Using a Lagrangian modeling approach, this study provides a quantitative analysis of water and nitrogen offshore transport in the Canary Current System. We investigate the timescales, reach and structure of offshore transport and demonstrate that the Canary upwelling is a key source of nutrients to the open North Atlantic Ocean. Our findings stress the need for improving the representation of the Canary system and other eastern boundary upwelling systems in global coarse-resolution models.
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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.
Ocean deoxygenation poses major challenges to marine life and can alter carbon cycling. Direct...
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