the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Climatology and annual cycle of global ocean dissolved oxygen represented by multiple observational gridded products
Abstract. Ocean dissolved oxygen (O2) is an essential climate variable crucial for sustaining the marine life; thus, changes of O2 at various spatiotemporal scales should be quantified and understood. Here, we study the climatology and annual cycle of O2 at regional to global scales using eight available gridded observational products. These datasets are generated by different groups using different primary data selection, quality control, bias correction and interpolation methods including statistical and machine-learning based mapping methods. A common set of metrics was collaboratively developed by the community of Gridded Observational Dataset Intercomparison Project-Dissolved Oxygen (GODIP-DO) to facilitate the inter-comparison, which allows assessing the robustness of the uncertainties through the spread of the products. Global mean O2 profiles are consistent among all products (±3 µmol kg-1), with the well-established decrease from high surface values to a minimum ~1000 meters, and subsequent increase to higher O2 at depth, although local differences could reach ±25 µmol kg-1 (0–1000 m). The hemispheric O2 annual cycle correlates strongly with ocean temperature changes, suggesting the key driver of temperature for the O2 annual cycle. However, there is substantial variation in the global mean 0–100 m O2 annual cycle, the magnitude ranges from -1 to 0.8 μmol kg-1, with a standard deviation of the datasets of ~0.3 μmol kg-1. Average oxygen minimum zones (OMZ) volume among the products is 80.92 × 106 km3 (±1.95 %) for a 60 µmol kg-1 threshold and 152.00 × 106 km3 (±1.72 %) for a 90 µmol kg-1 threshold. Our results serve as a starting point for resolving the uncertainty budget of the ocean O2 changes.
- Preprint
(3640 KB) - Metadata XML
-
Supplement
(2863 KB) - BibTeX
- EndNote
Status: open (until 14 Apr 2026)
- RC1: 'Comment on egusphere-2026-641', Anonymous Referee #1, 26 Mar 2026 reply
Data sets
Global Dissolved Oxygen Gridded Climatological Datasets GODIP-DO Group https://doi.org/10.5281/zenodo.16664650
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 156 | 96 | 14 | 266 | 33 | 25 | 33 |
- HTML: 156
- PDF: 96
- XML: 14
- Total: 266
- Supplement: 33
- BibTeX: 25
- EndNote: 33
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Du et al. presents a worthwhile comparison of nine different oceanic dissolved oxygen datasets from differing methods. As a key biogeochemical property to understand current climate, including deoxygenation, patterns, this analysis is extremely worthwhile and valuable to the community. Despite some significant regionality, I am thoroughly impressed with how well these datasets align and only have minor questions for the authors.
Major comments:
Minor comments:
Line 65: Please define IPCC.
Lines 66-68: Please keep numerical range formatting consistent.
Lines 75-77: Should be commas not semi colons.
Line 77: I would add “Since the late 19th century, oceanographers have measured ocean O2 using many instruments with varying sampling resolutions.”
Lines 78-85: Similar to my comment from above, I would expand on this by noting that Winklers are labour intensive, leading to lower sampling resolution, whereas sensor-based measurements have better spatiotemporal resolution, and the proliferation of the BGC-Argo program has dramatically increased observations.
Lines 118-122: Personally, I don’t think this type of paper outline is necessary, but that is up to you.
Lines 188-190: Have you trimmed both GOBAI and IAP so that the exact years match up (i.e., 2004-2022)? That should correct for any bias specifically due to the dataset age.
Line 307: Gradients, plural.
Lines 338-349: I appreciate the discussion of biological and physical controls on the annual cycle, but this feels like the first time underlying mechanistic drivers are being discussed. Can you similarly discuss biological and physical controls on spatial patterns or zonal structures?