The global area-weighted mean O₂ profile is first presented in Fig. 3a, showing a well-established vertical structure and a good consistency among all products. O₂ is higher near the surface than in the deeper ocean because of the gas exchange with the atmosphere and photosynthesis. The O₂ reaches the lowest value at ∼ 1000 m because of the respiration and limited O₂ supply from the surface. The O₂ increases from ∼ 1000 m to the deep ocean (∼ 5000 m) because of the weaker respiration and the intermediate, deep, and bottom water formation that supplies higher O₂ water into the deep layers, where cold, dense surface water sinks, and is then distributed globally by deep ocean currents, slowly losing O₂ along its centuries-long journey (Musan et al., 2023).

To better quantify the differences among the datasets, we take the difference between all seven other data products (WOA23, WOA18, GOBAI, RB, GLODAP, SJTU, and Jingwei), and IAP data (Fig. 3e–h). The differences are mostly within ±3 µmol kg⁻¹ among all the datasets with a common land-ocean mask from ocean surface to 5500 m, except that the differences for SJTU and Jingwei range between -6 and 5 µmol kg⁻¹ for the upper 200 m. The differences are comparable to the magnitude of O₂ anomaly, whose semi-decadal median is within ±10 µmol kg⁻¹ within major ocean basins (Schmidtko et al., 2017).

There is a negative offset between GOBAI and IAP (∼-2 µmol kg⁻¹). The differences between WOA18, RB, and IAP are positive. One possible reason for these differences is that different data products construct climatologies using data from different time periods with different resolutions. The SJTU climatology uses CTD, OSD, BGC-Argo, and additional GLD data, then interpolates them at coarser 2° × 2° resolution. The GOBAI climatology is reflective of a more recent period compared to the other datasets (2004–2024), which might at least partly explain the negative difference between GOBAI and IAP (where data from 1960–2022 are used). Because of the general deoxygenation trend, a “newer” climatology is expected to show less global mean O₂ than an “older” climatology. IAP is close to WOA23 at the upper 2000 m within 1 µmol kg⁻¹, which is reasonable because both IAP and WOA23 used bottle (OSD), CTD, and delayed-mode BGC-Argo data using the same 1° × 1° data resolution, and they have similar time coverage of data used to generate climatology (IAP, 1960–2022, and WOA23, 1965–2022).

Below 2000 m, there is an offset between IAP and WOA23: WOA23 O₂ is 0.2–0.6 µmol kg⁻¹ higher than IAP. Possible explanations may be the differences in quality control and mapping methods. Another possibility is the “jump” around 2000 m of IAP minus WOA23. It is common to have a discontinuity around 2000 m because of the big differences in data amount and data distributions at upper and deeper layers (observations are concentrated in the upper ocean and most of the BGC-Argo data are in the upper 2000 m). Differences in infilling the empty grid nodes during the mapping procedure may also play a significant role in deep ocean layers where the number of observation data is severely limited. Results of applying different mapping methods to the same in situ datasets suggest that mapping methods may contribute to a difference of less than ±1 µmol kg⁻¹ for the 0–5500 m area-weighted averaged O₂ (Ito et al., 2025). And using two different quality control processes and the same mapping procedure yields a difference of only ±0.5 µmol kg⁻¹ (Ito et al., 2025).

The mean O₂ profiles and the differences between datasets for major ocean basins (Pacific, Atlantic, Indian) are calculated separately and presented in Fig. 3b–d and f–h. The differences are mostly within ±4 µmol kg⁻¹ among all the datasets for the Pacific and Indian Oceans, except that the difference for Jingwei ranges between -8 and 2 µmol kg⁻¹ for the upper 200 m in the Pacific Ocean (Fig. 3f). The mean O₂ profiles for the Atlantic Ocean show more notable differences compared to other ocean basins (Fig. 3c), including that the mean O₂ profile of SJTU data shows a difference of -10 to -5 µmol kg⁻¹ for the depth of 1000–2000 m, while the differences of all the other datasets are constrained to -5 to 5 µmol kg⁻¹ from ocean surface to the depth of 5500 m (Fig. 3g). For the Atlantic Ocean, the mean O₂ reached the minimun at the depth of ∼ 500 m (Fig. 3c), which is much shallower than the O₂ minimun layers for the Pacifc and Indian Oceans (∼ 1000 m, Fig. 3b, d), possibly due to a combined effect of the formation, transport, and mixing process of the North Atlantic Deep Water (NADW), the ventilation processes such as Atlantic Meridional Ocean Circulation (AMOC), and so on (Levin, 2018; Koelling et al., 2022; Musan et al., 2023; Ruhl et al., 2025).

The global zonal mean O₂ concentration of the eight datasets for the upper 1000 m shows consistency for the zonal structure (Fig. 4a). There is a minimum of mean O₂ levels around the tropical regions for all the datasets, associated with a shoaling of the tropical and subtropical thermocline depth (Deutsch et al., 2011) and the presence of the tropical upper ocean OMZ. The differences in the global zonal mean O₂ for the upper 1000 m between the seven data products (WOA23, WOA18, GOBAI, RB, GLODAP, SJTU, and Jingwei) and IAP are shown in Fig. 4b. The differences in zonal mean 0–1000 m averaged O₂ are mostly within ∼ 5 µmol kg⁻¹. The zonal mean O₂ difference between IAP and WOA23 is the smallest, which is generally within ∼ 1.5 µmol kg⁻¹. The zonal mean of GOBAI is generally lower than that of IAP, especially in the Northern Hemisphere, which may imply stronger deoxygenation trends in the Northern Hemisphere, as the GOBAI climatology baseline is newer than that of IAP. The zonal mean of the RB dataset is higher than that of IAP by ∼ 0.78 µmol kg⁻¹ on average, consistent with the positive offset shown in Fig. 3. Jingwei dataset has the strongest differences from IAP and other products, showing a notable zonal fluctuation.

The results for the depth layers 0–600 and 0–2000 m (Fig. S2) show similar variation pattern to the depth layer 0–1000 m. The differences in the global zonal mean O₂ between data products are mostly within ∼ 5 µmol kg⁻¹ for 0–600 m and within ∼ 3 µmol kg⁻¹ for 0–2000 m (Fig. S2b, d). For 0–600 m, the zonal mean O₂ difference between IAP and WOA23 is the smallest and Jingwei shows the strongest differences from IAP and other products. For 0–2000 m, the depth average makes the difference between datasets much smaller. Jingwei still shows relatively large discrepancy from the other datasets in the regions around 60° N and the equator. Within the latitude range 20–50° S where all other data products show the best agreement, Jingwei exhibits a significant deviation of ∼ 5 µmol kg⁻¹ from the rest.

However, the zonal mean O₂ concentration for 0–100 m (Fig. 4b) shows a relatively distinct pattern. The zonal mean O₂ decreases linearly with the decrease of latitude for all the datasets, except that for the high latitude range 60–80° S, the zonal mean O₂ profiles maintain a relatively stable value and the estimations between different data products vary to an extent of ∼ 20 µmol kg⁻¹ (Fig. 4d), which illustrates the relatively higher uncertainty in the data reconstruction in this high latitude range.

The spatial distribution of the upper 1000 m mean O₂ and the difference between IAP and six other datasets is shown in Fig. 5a–m. The dataset of Jingwei is not included because the spatial maps are not currently available. The difference of the 0–1000 m mean O₂ is mainly within the magnitude of ∼ 15 µmol kg⁻¹, which shows substantial local differences even though their differences are relatively small when averaged globally (∼ 3 µmol kg⁻¹, Fig. 3). The mean O₂ of the GOBAI dataset is generally lower than IAP for 0–1000 m (Fig. 5f), consistent with the conclusions from the previous comparisons. There are bigger differences located in the regions such as the subpolar North Pacific, the Southern Ocean fronts, and the eastern Pacific regions close to OMZ boundaries, where the strong spatial O₂ gradient, makes the reconstruction sensitive to the mapping process and data distribution (Ito et al., 2025). GOBAI, SJTU, and GLODAP show a negative difference (∼-15 µmol kg⁻¹) compared to IAP for the upper 1000 m mean O₂ in most of the north and equatorial Pacific Ocean, and equatorial Atlantic Ocean. WOA23 and GOBAI show a similar pattern of difference in the Indian and Pacific Oceans: more positive offsets in the eastern Pacific/Indian Oceans and negative offsets in the western Pacific/Indian Oceans. For the two generations of WOA products, the difference between WOA23 and IAP is more negative globally than the difference between WOA18 and IAP, likely due to the use of more recent data in WOA23. SJTU exhibits a distinct pattern of differences, with substantial negative differences occurring in the tropics.

The spatial map of the spread of the upper 1000 m mean climatological O₂ among all the datasets except Jingwei is presented in Fig. 5n. The spread here is defined as the maximum absolute value of the differences between all the other products and IAP. The spread is within 12 µmol kg⁻¹ in most of the ocean areas, with the largest spread reaching 25 µmol kg⁻¹ where strong spatial O₂ gradients exist. The spread is generally lower in the Southern Hemisphere than in the Northern Hemisphere, in contrast to the fact that there are more observations in the North. This might be related to three issues: (1) A common error in the mapping method, where the spatial interpolation generates over-smoothed or similar-biased fields in the Southern Hemisphere; (2) the variability is lower in the Southern Hemisphere than in the Northern Hemisphere, which reduces the reconstruction errors in the Southern Hemisphere; (3) the deoxygenation trends are higher in the Northern Hemisphere than in the Southern thus the spread reveals the O₂ level at different periods.

The exact reason can be explored with single-factor reconstruction experiments such as those performed by Ito et al. (2025), which use the same input data but different mapping methods to isolate the impact of mapping on climatology reconstruction. Further analyses are required, but it is useful to know the differences between the products. When comparing the depth-mean O₂ between the eight datasets we adopted here which didn't distinguish different impact factors in the process of generating gridded data products and aimed to illustrate a comprehensive discrepancy, the difference for all the datasets for 0–300 m is within ±25 µmol kg⁻¹ and the spread is up to 35 µmol kg⁻¹ (Fig. S3). The regions of high variability mainly locate in the tropical and East Pacific, subpolar North Pacific and Southern Ocean fronts where there are strong spatial O₂ gradients. However, when using the same quality-controlled observational data as input and constraining the impact on data reconstruction to only the mapping method, the difference of the mapped O₂ concentration between different mapping methods is within ±10 µmol kg⁻¹ for 0–300 m (Ito et al., 2025). The overall difference between the data products is more than two times of the difference constrained to a single factor of the mapping method, indicating that the other factors, such as the quality control technique, bias correction, vertical interpolation, and so on, also contribute to the uncertainty source to a large extent. Further analyses are required to assess and quantify the impact of every individual procedure on the whole process of O₂ data reconstruction, but it is useful to know the differences between the data products as a start.

The annual cycle of the four products that provide a monthly climatology (IAP, WOA23, WOA18, and GOBAI) is presented in Fig. 6 for the 0–100, 100–600, and 600–1500 m, and for Northern and Southern hemispheres, respectively calculated by the monthly anomalies derived by subtracting the annual mean climatology of every data product respectively. The annual cycle of the 0–100 m temperature for northern and southern hemispheres is also calculated using the gridded temperature climatology product of IAP (Cheng et al., 2024). The magnitude of the O₂ seasonal cycle, defined as the maximum amplitude of the annual variation, is greatest near the surface and decreases with depth. Specifically, it is approximately 6 µmol kg⁻¹ for the 0–100 m layer and 2 µmol kg⁻¹ for the deeper layer (100–600 m). The reduced annual cycle with depth is associated with stronger annual variations of ocean temperature, wind-driven ventilation, and biological processes in the upper ocean compared to the deep ocean.

The appearance time of maximum (April) and minimum (October) O₂ levels in the northern hemisphere are consistent among the four datasets for 0–100 m global mean O₂. Moreover, the O₂ maximum in the depth layer in the northern and southern hemispheres for 0–100 m lags about one month behind the temperature change, reflecting that besides the dominant thermal induced increase in O₂ solubility, some physical/biological processes also impact the concentration of ocean O₂ (Garcia et al., 2005b; Wang et al., 2022). For the northern hemisphere, the annual variation of the subsurface 100–600 m layer also shows a pattern of annual cycle with the maximum (April) and minimum (December) similar to the surface 0–100 m layer, indicating that the O₂ annual cycle could penetrate to the deeper layer of ∼ 600 m. It is primarily due to the mechanisms including climatological winter mixed layer deepening, which ventilates the subsurface, and seasonal thermocline dynamics coupled with organic matter remineralization (Stramma et al., 2010). For the southern hemisphere, similar patterns also exist.

Although the two hemispheric O₂ annual cycles can be well-defined, the global mean is more subtle, as different products show large differences in patterns of the global mean annual cycle (Fig. 7). It appears that global 100–600 m O₂ annual cycles are more consistent across data products than 0–100 m, probably associated with noiser data and more natural variability near the sea surface. For global 100–600 m O₂, all datasets suggest an O₂ reduction from March to September and an increase from October to February, consistent with the Southern Hemisphere changes (Fig. 7b versus Fig. 6b). The magnitude of the global 0–100 and 100–600 m annual cycle ranges from -1 to 0.8 µmol kg⁻¹ for the four datasets, but the standard deviations among the four datasets are ∼ 0.3 µmol kg⁻¹ (0–100 m) and ∼ 0.2 µmol kg⁻¹ (100–600 m), indicating a similar level of signal and noise (Fig. 7).

The annual cycle of O₂ anomalies shows a distinct spatial pattern (0–100 m mean O₂, April versus October, Fig. 8). The anomalies are calculated by subtracting the annual mean from the monthly climatologies for the four datasets (IAP, WOA23, WOA18, and GOBAI) respectively. In April, the O₂ anomalies of 0–100 m in most of the northern hemisphere are positive, and they turn negative in October. The tropical ocean annual cycle is not easily defined, as many regions have semi-annual variability. Different products exhibit a consistent pattern of change, whereas the detailed structures are quite distinct. In general, WOA23 and WOA18 have more patchy characteristics than IAP and GOBAI, likely related to the mapping methods employed by each: WOA uses an anti-distance weighted function to interpolate the field; on the other hand, IAP uses model simulations to provide covariance and GOBAI takes advantage of correlations between O₂ and temperature and salinity, both building physical ocean properties into the reconstruction.

The zonal mean structure of the annual cycle is shown in Fig. 9, and all the datasets show similar annual cycle patterns with some difference in smoothness and magnitude for both 0–100 and 100–600 m mean O₂. For 0–100 m, the largest seasonal changes occur in the extra-tropics in the 30 to 60° N belts of the Northern hemisphere. In the Southern hemisphere, the largest seasonal changes occur in the latitudinal band centered near 40° S. For both 0–100 and 100–600 m, the phase transitions occur around July and January for all datasets, which are consistent with the annual variation analysis in Fig. 6. The phase change of O₂ lags about one month behind the temperature change (June and December as shown in Fig. S4), reflecting that some physical/biological processes also impact the concentration of O₂ besides the dominant thermally induced increase in O₂ solubility. Semi-annual cycles near the equator are visible in the upper ocean for almost all products. There is a distinction of the annual cycle at about 10° N, corresponding to the location of the intertropical convergence zone (ITCZ) (Garcia et al., 2005b).

The zonal mean annual cycles of solubility and Apparent Oxygen Utilization (AOU) of different data products for 0–100 and 100–600 m are shown in Fig. 10. The solubility is calculated using the gridded temperature and salinity products of IAP (Cheng et al., 2024) following the Garcia and Gordon (1992) method. Generally, the zonal 0–100 m O₂ seasonal cycle is mainly dominated by the solubility, whereas the AOU dominates the 100–600 m O₂ pattern.

For 0–100 m, the AOU seasonal cycle in the mid-latitude is about two to three months lagging that of the temperature/solubility seasonal variation (Figs. 6, 10, S4). Within 20–60° N, the zonal mean AOU anomaly of 0–100 m is mostly negative from March to August. The phase transition of AOU occurs in March, which corresponds to the temperature minimum/solubility maximum. And AOU reaches a minimum in about May/June, corresponding to the phase transition of the zonal mean temperature/solubility anomaly (Figs. S4, 10). A possible explanation is from the combined impacts of biological and physical processes (Garcia et al., 2005a). The spring phytoplankton bloom lags the onset of temperature increase (starting in March) because phytoplankton proliferation requires sufficient light from increasing spring insolation and nutrient entrainment due to a shallower mixed layer (Martin, 2012). And the net community production (NCP, the difference between gross community photosynthesis and community respiration) reaches a maximum in about May (Wang et al., 2022). The solubility reaches a minimum in September, which corresponds with the phase change of AOU. And the maximum of AOU lags about two months behind the temperature maximum/solubility minimum, which is also a combined effect of the prolonged response time of biological processes and the physical processes possibly induced by the deepening autumn mixed layer. The situation in 20–60° S is similar to 20–60° N for 0–100 m, mainly with the signs reversed. For 100–600 m, the annual variation in temperature/solubility is relatively small, and the pattern of AOU variation dominates, with the underlying processes requiring further investigation.

To assess the patterns of annual cycle regionally, the zonal mean structures of the annual cycle for different ocean basins are shown in Figs. S5–S7. Distinct seasonal changes exist in the Pacific, Atlantic and Indian Oceans. The annual cycle patterns for the Pacific and Atlantic Ocean coincide with the global O₂ annual cycle both for the 0–100 and 100–600 m layers closely. For the Indian Ocean, there are larger seasonal changes in the latitude range of ∼ 10–20° N for 0–100 m than the Pacific and Atlantic Oceans. The annual variations of the mean O₂ for three major ocean basins (Pacific, Atlantic, Indian) for 0–100 and 100–600 m are shown in Figs. S8–S10, and also for the North and South Pacific/Atlantic, respectively, in Figs. S11–S12. The magnitude of the O₂ seasonal cycle is greatest in the Atlantic Ocean (∼ 3 µmol kg⁻¹) for the 0–100 m layer, which corresponds to a magnitude of ∼ 6 µmol kg⁻¹ in the North Atlantic and ∼ 4 µmol kg⁻¹ in the South Atlantic. The minimum and maximum O₂ levels (-4 to 6 µmol kg⁻¹) and also occurrance time (July/October) of WOA18 for 0–100 m differ the most from those of the other three datasets in the South Atlantic (Fig. S12b).

Oxygen minimum zones (OMZ) are important regions that impact marine organism distributions and biogeochemical cycling. OMZ occur in various regions such as the tropical Pacific and Atlantic Oceans, the North Pacific Ocean, and the North Indian Ocean, posing challenges for marine organisms adapted to higher O₂ concentrations (Stramma et al., 2008, 2021). In various studies, OMZ are bounded by different thresholds of O₂ levels. Here, we select two typical thresholds of 60 and 90 µmol kg⁻¹ to define OMZ (Ito et al., 2025), referred to as OMZ60 and OMZ90, respectively.

The zonal mean O₂ of the IAP climatology dataset for global and three major basins are shown in Fig. 11. Globally, the zonal mean OMZ90 regions are mainly located within 200–1200 m and 5° S–30° N, associated with upwelling and high O₂ consumption. The Pacific Ocean contains the largest volume of OMZ60 and OMZ90 among the three ocean basins, with the OMZ90 extending from ∼ 15° S to ∼ 60° N and from a depth of ∼ 200 to ∼ 3000 m. There is a gradual increase in the max depth of OMZ90 and OMZ60 in the Pacific Ocean from ∼ 15° S to ∼ 60° N; thus, the OMZ in the North Pacific Ocean has greater vertical extent. The OMZ in the North Pacific Ocean is also more severe because there is a notable region with zonal O₂ levels less than 30 µmol kg⁻¹ in the ∼ 700 to ∼ 1500 m layer. The OMZ90 in the Indian Ocean extends from ∼ 10° S to the northernmost end and is located from ∼ 20 down to a maximum depth of 1800 m. In the Atlantic Ocean, the OMZ90 is located within a 300–500 m layer, with an area much smaller than that of the Pacific and Indian Oceans (Table 2).

We also calculated the volume of OMZ60 and OMZ90 globally and for each basin in Table 2 using six datasets (excluding GOBAI and SJTU because their maximum depth only reaches about 2000 m below the ocean surface). The global volume of OMZ60 and OMZ90 is generally consistent among the datasets, with a standard deviation of 1.95 % and 1.72 % across the products for OMZ60 and OMZ90, respectively. The Pacific Ocean contains most of the OMZ (83.05 % of the global OMZ60 and 81.29 % of the global OMZ90), with a standard deviation of 3.93 % and 2.37 % across the products. The Atlantic Ocean contains about 1.04 % and 2.38 % of the global OMZ60 and OMZ90, respectively, which is the lowest among the three ocean basins. The standard deviation is 20.24 % and 10.77 % for the Atlantic Ocean OMZ60 and OMZ90, respectively. The estimated OMZ for the Atlantic Ocean may be more sensitive to the horizontal resolution, mapping method, and so on.

The OMZ areas as a function of depth and density are shown in Fig. 12. The density layers are calculated with the Gibbs-SeaWater Oceanographic Toolbox (IOC et al., 2010; McDougall and Barker, 2011; Kwiecinski and Babbin, 2021), using the gridded temperature and salinity climatology products of IAP (Cheng et al., 2024). The maximum OMZ60 area occurs at a depth of ∼ 800 m and a density of 27.75 kg m⁻³ for all the datasets. Most OMZ60s exist in the upper 2000 m. So that all the climatologies including GOBAI and SJTU with the maximum depth of 2000 m could be involved in the analysis of OMZ areas at depth levels. GOBAI and SJTU exhibit a larger OMZ60 volume within the upper 2000 m compared to other products, and the maximum depth of SJTU OMZ60 is ∼ 1700 m, which is shallower than that of the other products. At density levels, the products are very consistent, and SJTU shows a larger area of OMZ60 near the 27.4 kg m⁻³ level and Jingwei shows a smaller area of OMZ60 near the 27 kg m⁻³ level.

Figure 11a–c shows the horizontal OMZ60 area at depth levels for the Pacific, Atlantic, and Indian Oceans, respectively. Consistent with the volume assessment in Table 2, the Pacific Ocean contains the largest OMZ60 area, followed by the Indian and Atlantic Oceans. The maximum OMZ60 area occurs at a depth of about 800 m in the Pacific and Indian Oceans, but at a much shallower depth of ∼ 400 m in the Atlantic Ocean. Jingwei shows the largest difference from the other products in the Pacific and Indian Oceans at the upper 500 m depth. SJTU shows a larger area of OMZ60 in the Pacific and Indian Oceans but a smaller area in the Atlantic Ocean than the other products (Fig. 13). Collectively, different products show a spread of ±4 × 10⁶ km² for the Pacific OMZ60 and of ±1 × 10⁶ km² for the Indian and Atlantic OMZ60 (Fig. 13d–f), except that Jingwei shows a very large difference of up to -9×106 km² at the depth around 500 m for the Pacific OMZ60 area compared to the ensemble mean (Fig. 13d).