Articles | Volume 18, issue 6
https://doi.org/10.5194/essd-18-3757-2026
© 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-18-3757-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
03 Jun 2026
Data description article |
| 03 Jun 2026
| 03 Jun 2026
Global monthly ocean dissolved oxygen (1960–2023) reconstructed to 5902 m with BLENDR, a Bayesian-optimized ensemble learning framework
Mingyu Han
State Key Laboratory of Submarine Geoscience and School of Oceanography, Shanghai Jiao Tong University, Shanghai, China
Xiaogang Xing
State Key Laboratory of Satellite Ocean Environment Dynamics (SOED), Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China
State Key Laboratory of Submarine Geoscience and School of Oceanography, Shanghai Jiao Tong University, Shanghai, China
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Mingyu Han and Yuntao Zhou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-273, https://doi.org/10.5194/essd-2025-273, 2025
Manuscript not accepted for further review
Short summary
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In recent decades, ocean oxygen levels have fallen steadily, threatening marine life and ecosystem health. To fill gaps in sparse observations, we used an advanced ensemble of six machine learning models to reconstruct monthly oxygen maps from the surface down to 5902 m, covering 1960–2023. Our dataset reveals where and how fast oxygen loss is occurring, especially in deeper waters, and provides a reliable resource for studying climate impacts on oceans and guiding conservation efforts.
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This study assesses the spread of mean state and annual cycle of ocean dissolved oxygen by multiple observational gridded data products. A good consistency is validated globally, although substantial local differences exist in areas of strong spatial gradient. Quantifying the discrepancies could give an insight into regions relatively more sensitive to data reconstruction processes and further advance the improvement of oxygen data products.
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-273, https://doi.org/10.5194/essd-2025-273, 2025
Manuscript not accepted for further review
Short summary
Short summary
In recent decades, ocean oxygen levels have fallen steadily, threatening marine life and ecosystem health. To fill gaps in sparse observations, we used an advanced ensemble of six machine learning models to reconstruct monthly oxygen maps from the surface down to 5902 m, covering 1960–2023. Our dataset reveals where and how fast oxygen loss is occurring, especially in deeper waters, and provides a reliable resource for studying climate impacts on oceans and guiding conservation efforts.
Viktor Gouretski, Lijing Cheng, Juan Du, Xiaogang Xing, Fei Chai, and Zhetao Tan
Earth Syst. Sci. Data, 16, 5503–5530, https://doi.org/10.5194/essd-16-5503-2024, https://doi.org/10.5194/essd-16-5503-2024, 2024
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High-quality observations are crucial to understanding ocean oxygen changes and their impact on marine biota. We developed a quality control procedure to ensure the high quality of the heterogeneous ocean oxygen data archive and to prove data consistency. Oxygen data obtained by means of oxygen sensors on autonomous Argo floats were compared with reference data based on the chemical analysis, and estimates of the residual offsets were obtained.
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Short summary
We combined ship and float measurements with machine learning to reconstruct monthly dissolved oxygen in the global ocean from 1960 to 2023, from the surface to 5902 m. The results reveal a persistent loss of oxygen, strongest below the surface and in major low-oxygen zones, with recent acceleration in several ocean regions. This open dataset supports climate research and assessments of risks for marine ecosystems.
We combined ship and float measurements with machine learning to reconstruct monthly dissolved...
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