The Indian Ocean differs from other ocean basins primarily due to its geographical characteristics, which have far-reaching consequences for atmospheric and oceanic circulation. The first unique geographical feature is the Asian landmass. It limits the northern extent of the Indian Ocean in the subtropics at approximately 30° N and enforces the development of the Asian monsoon. The monsoon is characterized by seasonal changes in wind direction, southwest (SW) in summer and northeast (NE) in winter (Gadgil et al., 2007), leading to an overall clockwise surface circulation in the ocean in summer and a counterclockwise surface circulation in winter (Fig. 1) (Wyrtki, 1973; Phillips et al., 2021; Schott et al., 2009). The Southwest Monsoon Current, in turn, carries Arabian Sea Water into the Bay of Bengal in summer, whereas the Northeast Monsoon Current transports Bay of Bengal Water into the Arabian Sea in winter (Fig. 1).

Another unique feature of the Indian Ocean is the Indonesian Archipelago, which allows the Indonesian Throughflow Water (ITFW) to flow from the Pacific into the Indian Ocean. It represents the only connection between major ocean basins at low latitudes (Gordon et al., 2003, 2010) and connects to the outflow of water from the Indian Ocean into the Atlantic Ocean through Agulhas leakage (Durgadoo et al., 2017; Gordon, 1986). Both the Agulhas leakage and the ITF act as bottlenecks within the surface water return flow of the global thermohaline circulation (THC, circulation linking the Atlantic, Indian, and Pacific basins via the ITF and Agulhas leakage) (Durgadoo et al., 2017; Broecker, 1991). The strength of the ITF is sensitive to climate variability, and it plays a key role in regulating heat and water mass exchange between the two ocean basins. The ITF also influences the composition of thermocline waters in both basins, as well as the strength of the Agulhas Current (AC) and South Equatorial Current (SEC) in the Indian Ocean (Gordon et al., 2010; Ditkovsky et al., 2023; Lee et al., 2002; Godfrey, 1996). According to numerical model results, blockage of the ITF weakens the SEC and AC (Lee et al., 2002).

Contrary to the Atlantic and Pacific Oceans, where major eastern boundary upwelling systems exist off California, NW Africa, Peru, and Namibia/South Africa, the only, but much weaker, upwelling system in the eastern Indian Ocean (EIO) develops off South Java and Bali (Susanto et al., 2001; Chavez and Messié, 2009; Rixen et al., 2006). However, driven by strong SW monsoon winds, a coastal upwelling system emerges along the Arabian Peninsula in summer, with an intensity comparable to that of the eastern boundary upwelling systems in other ocean basins (Sastry and D'Souza, 1972; Brock et al., 1991; Rixen et al., 2019). In summer, weaker but upwelling-favorable winds also occur along the Indian coasts in the Arabian Sea and the Bay of Bengal (Abbott and Mahadevan, 2024; Sharma, 1978; Shetye et al., 1990).

The large-scale meridional transport of water masses in the Indian Ocean is governed by the IO-MOC. It consists of shallow and deep overturning cells, with the shallow cell encompassing the upper 300–450 m and the deep cell water masses below this depth range (Schott and McCreary, 2001; Stramma et al., 2002). The shallow IO-MOC is controlled by monsoon-driven upwelling in the northern Indian Ocean and the formation and subduction of subsurface water masses, primarily near the Subantarctic Front in the south (Schott and McCreary, 2001). The high-salinity Subtropical Surface Water (STSW) forms preferentially north of the Subantarctic Front between 25 and 35° S and at approximately 90° E (Wyrtki et al., 1971; Beal et al., 2006). In contrast, south of the Subantarctic Front, at around 50–40° S, cool, low-salinity water is subducted below the warmer, lighter subtropical water masses (Fine, 1993; Koch-Larrouy et al., 2010; Talley, 1996). These bodies of water are called Subantarctic Mode Water (SAMW) and Antarctic Intermediate Water (AAIW), the latter of which is the deepest of these mode waters. It enters the Indian Ocean through the western boundary at approximately 47–39° S and 45–80° E (Fine, 1993; You, 1998). The deep IO-MOC comprises an intermediate limb ventilated by AAIW and an abyssal limb supplied by the Antarctic Bottom Water (AABW) (Schott and McCreary, 2001; Talley, 2013; Haine et al., 1998). The AABW forms around the Antarctic continental margins (Menezes et al., 2017) and enters the Indian Ocean through the Crozet-Kerguelen gap at depths below 2000 m. Since the AABW undergoes significant modification due to mixing in the abyssal Indian Ocean, it is referred to as modified Antarctic Bottom Water (mAABW) (Mantyla and Reid, 1995). Preferentially in spring, a non-upwelling season, when the Arabian Sea begins to warm and the winter MLD flattens, the Arabian Sea High Salinity Water (ASHSW) forms. This water mass typically occurs at depths between 0 and 100 m in the Arabian Sea (Kumar and Prasad, 1999). In addition, Red Sea Water (RSW) and Persian Gulf Water (PGW) from marginal seas flow through the Gulf of Aden and Gulf of Oman into the Arabian Sea at intermediate depths of approximately 300–1000 m and 250–300 m, respectively (Beal et al., 2000; Bower et al., 2000; Tchernia, 1980). The Bay of Bengal Water (BoBW) is another SWT that forms in the northern Indian Ocean within the strongly stratified upper surface of the Bay of Bengal, where excessive freshwater input from the Ganga and Brahmaputra rivers together with high precipitation produces a warm, low-salinity surface layer usually around 100 m depth (Shetye et al., 1996). The influence of the fresh BoBW extends up to 300 m, marked by a salinity maximum of about 35 (Shetye et al., 1996). SWTs described above can be referred to as primary source waters and differ from secondary mixtures, such as the Arabian Sea Water (ASW) found in the northern Indian Ocean, which originates from mixing between RSW and PGW in the Arabian Sea (You and Tomczak, 1993).

The Indian Ocean is also affected by climate anomalies such as the El Niño-Southern Oscillation (ENSO) (e.g. Bjerknes, 1966, 1969) and Indian Ocean Dipole (IOD) (Saji et al., 1999; Webster et al., 1999). ENSO has far-reaching consequences for the global and coupled circulations of the atmosphere and ocean, and it is often described in terms of sea surface temperature (SST) anomalies in the equatorial Pacific Ocean (https://psl.noaa.gov/data/timeseries/month, last access: 11 April 2026). The IOD describes anomalies in the difference between SSTs in the EIO and the western Indian Ocean (WIO). Both the IOD and ENSO influence upwelling in the EIO (Rixen et al., 2006; Abram et al., 2003).

The data used in this study were obtained from the GLODAPv2 database (Lauvset et al., 2022; Olsen et al., 2019) and from a cruise with the German research vessel SONNE in the Indian Ocean in 2024 (hereafter referred to as cruise data) (Gaye et al., 2024). The GLODAPv2 data consist of 1995 and 2018 transects in the WIO, 1995 and 2016 transects in the EIO, and 1995 and 2019 transects in the central equatorial Indian Ocean (i.e., central Indian Ocean, CIO) (Fig. 2). We focused on the WIO and EIO transects because they protrude into the northern Indian Ocean OMZs and are therefore important in representing the AS and BoB OMZs. The GLODAPv2 data provide oceanographic data from the surface to a depth of 4000 m and include key parameters such as temperature, salinity, and concentrations of dissolved oxygen, nitrate, phosphate, and silicate. We specifically used the merged data product from GLODAPv2, which has undergone extensive quality control, including systematic bias evaluation and correction for calibration and measurement errors (Olsen et al., 2019). In addition to GLODAPv2, we incorporated cruise data collected near the equator in the CIO.

The entire WIO and EIO transects were analyzed over a depth range of 150–1200 m. This filtering enabled us to exclude upper ocean surface waters with high variability and focus on intermediate depths, which align with the depth range of OMZs. Furthermore, we defined the core of the OMZs within the transects as regions where the dissolved oxygen concentration fell below 20 µmol kg⁻¹. This corresponds to depths of 150–1000 m for the AS OMZ and 150–700 m for the BoB OMZ. For the central equatorial region, we compared the 1995 GLODAPv2 data with a combination of the 2019 GLODAPv2 and 2024 cruise data (Fig. 2). The latter two were combined to increase the dataset size.

It is important to precisely define the SWTs' properties when performing eOMP analysis, as this method uses SWT characteristics to quantify mixing proportions and biogeochemical processes, representing a water sample as a linear combination of different SWTs (Shrikumar et al., 2022; Tomczak and Large, 1989). SWT is an identifiable body of water with distinct physical and chemical properties (e.g., temperature, salinity, and concentrations of oxygen and nutrients) that originates from a specific formation region and has a specific mixing history (Tomczak, 1999).

Defining SWTs in the Indian Ocean is challenging. Many studies rely on secondary rather than primary SWTs (Acharya and Panigrahi, 2016; Ditkovsky et al., 2023; Narvekar et al., 2025). Following the definition of water mass ages (Jeansson et al., 2021; Olsen et al., 2019), a primary SWT forms when water enters the ocean interior and becomes decoupled from atmospheric influences. Therefore, impacts of deeper water masses on the properties of the SWT, due to vertical and lateral mixing, cannot be ruled out. However, secondary SWTs are mixtures, such as ASW, which is a mixture of RSW and PGW as mentioned earlier. We focused on primary SWTs because they are essentially responsible for OMZs ventilation and carry the influence of global warming on their properties into the depths of the ocean.

However, we did not include the Indian Central Water (ICW) and SAMW as separate SWTs in the eOMP analysis, because, although ICW is formed by subduction in the Subtropical Front and advected northward (You and Tomczak, 1993), it is not considered as an independent principal water mass as it mixes with STSW and is identified by a mixing T/S line connecting AAIW and STSW (Schott and McCreary, 2001). Additionally, it can get diluted with ASW, BoBW, and ITFW (Gordon et al., 1997). SAMW, on the other hand, share the same density class and overlap with AAIW (Koch-Larrouy et al., 2010).

The properties of primary SWTs must be defined at their sites of origin, including a geographic area, characteristic depth range, and, in some cases, a specific time of the year. These variables are not precisely constrained and may shift in response to climate change; therefore, they are typically estimated to the best of our knowledge. Based on a literature review, we summarized the specific latitude, longitude, and depth ranges for all SWTs relevant to the OMZs ventilation in the northern Indian Ocean in Table 3 and marked the region of origin/definition in Fig. 3.

Eight SWTs were selected for our initial analyses: Persian Gulf Water (PGW), Subtropical Surface Water (STSW), modified Antarctic Bottom Water (mAABW), Indonesian Throughflow Water (ITFW), Antarctic Intermediate Water (AAIW), Arabian Sea High Salinity Water (ASHSW), Red Sea Water (RSW), and Bay of Bengal Water (BOBW), as they are known to influence the OMZ in the Indian Ocean (Chinni et al., 2019; Sabu et al., 2015; Singh et al., 2012; Yu et al., 2018; Song et al., 2004; Beal et al., 2000; Bower et al., 2000; Prasad et al., 2001; Shee et al., 2023).

Out of the eight selected SWTs for our initial analyses, the mAABW is an exception. It enters the Indian Ocean not as a primary SWT but as a deep-water mass that, as mentioned before, undergoes significant modification through mixing in the abyssal Indian Ocean (Mantyla and Reid, 1995). However, owing to its relevance for the ventilation of the northern Indian Ocean, and in particular the deep IO-MOC (Song et al., 2004; Johnson, 2008), it cannot be ignored in the eOMP analysis.

OMP analysis is a mathematical technique developed by Tomczak and Large (1989) to study water mass mixing in a given ocean region. In this approach, a water sample is considered a mixture of multiple SWTs. The objective is to determine the fraction of each SWT in the water sample using a non-negative least-squares method to estimate the contribution of each SWT to a given measurement point in the sample. For this study, we performed an eOMP analysis, which, unlike the classic OMP analysis, accounts for the region's biogeochemistry. The eOMP analysis was performed using a Python-based model (PYOMPA) (Shrikumar et al., 2022). PYOMPA is based on the original OMP analysis by Tomczak and Large (1989). The mathematical formula used to solve the objective function is presented in Eq. (1): 1∑iepixji+rBAΔjB=spj+ϵpj Here, spj denotes the value of the property p in measured water sample j, xji stands for the estimated fraction of SWT i in water sample j, epi stands for the value of property p in the SWT, ϵpj denotes the residual in explaining property p for sample j, rBAΔjB represents the biogeochemical terms, where rBA denotes the exchange ratio of two properties A (oxygen) and B (phosphate and nitrate), while ΔjB denotes the amount of property B that is remineralized due to the consumption of property A in sample j.

The residual equation is given by Eq. (2), where μp represents the property mean. 2ϵpj=Wpuserσp∑iepi-μpxji+rBAΔjB-spj-μp Weights were assigned to each property to regulate its effect on the equation. The larger the weight, the greater its influence on the eOMP solution. Weights were adopted from Acharya and Panigrahi (2016), with higher weights assigned to temperature and salinity and lower weights assigned to non-conservative biogeochemical properties (Table 2). Remineralization was tracked as the conversion of oxygen to phosphate and nitrate according to the ratio -155:1:15, adopted from Hupe and Karstensen (2000). The low parameter residue in the objective and low mass residual in the OMZs (<0.003) recorded by the majority (>98.7 %) of the water samples suggests that the water samples were well represented by the SWTs included in the eOMP analysis (Poole and Tomczak, 1999; Shrikumar et al., 2022).

The physical oxygen supply analysis determines the amount of oxygen (O₂) in the sample (j) supplied via mixing by a specific SWT (siO2j). Therefore, the fraction of SWT (xji) in the measured sample, j, was multiplied by the defined oxygen concentration of the SWT (eO2i) according to Eq. (3). 3siO2j=xji×eO2i Oxygen consumption in this study represents net biological oxygen utilization and is closely related to Apparent Oxygen Utilization (AOU). Biological oxygen consumption in a given sample (j) due to remineralization (s-O2j), was calculated using the measured oxygen in the sample (sO2j), and the overall oxygen due to mixing of all SWTs ∑snO2j, according to Eq. (4): 4s-O2j=sO2j-∑snO2j The oxygen consumption term is exactly equal to the remineralization term calculated within the eOMP solution when the oxygen residual is added to it. We calculated the physical oxygen supply and biological oxygen consumption for the OMZ core, which corresponded to depth ranges of 150–1000 m for the AS OMZ and 150–700 m for the BoB OMZ. The same was calculated for the entire WIO and EIO transects (150–1200 m) and equatorial transects (15° S–3° N, 150–1200 m).

To determine whether the observed changes in the SWTs mixing fraction between 1995 and recent years were statistically distinguishable from the combined uncertainty in the defined SWTs properties, we implemented a sparsity-weighted Monte Carlo perturbation analysis (n=10000 iterations). This approach propagates two uncertainty components (temporal and spatial) through the eOMP solution.

To evaluate the sensitivity of the eOMP solution to variability in SWT oxygen concentrations, we conducted oxygen sensitivity test, where we used a higher oxygen scenario (i.e., added the calculated oxygen change described earlier to the 1995 oxygen values), a constant oxygen scenario (i.e., used the 1995 oxygen values for eOMP analysis of recent years), and a lower oxygen scenario (where the oxygen change was subtracted). The resulting SWT fractions and changes between 1995 and recent years were not significantly different (Supplement Fig. S6). Therefore, the variability in the SWT oxygen concentration does not alter the conclusions of this study.

We also performed a variability test on the SWT properties to understand how the SWTs fraction derived from the eOMP analysis are affected by variability in their properties. To achieve this, we defined all six selected SWTs using literature values, as shown in Table 1 (see Table S1 in the Supplement for the SWTs properties used in the sensitivity analysis). We performed a sensitivity analysis using the 2018 WIO transect. The results did not differ significantly (Supplement Fig. S7), as the major SWTs contribution remained similar to the output when the SWTs properties were defined using the WOA data, as shown in Table 2.

We performed a sensitivity analysis to assess the sensitivity of the eOMP solution to O2:PO4:NO3 remineralization ratios and the parameter weights. For the remineralization ratios, we performed a sensitivity analysis based on three O2:PO4:NO3 ratio scenarios: -138:1:16 (the standard Redfield ratios), -155:1:15 (Hupe and Karstensen, 2000), and -170:1:16 (Anderson and Sarmiento, 1994). For each scenario, we computed the model's root mean square (RMS) error from the total weighted sum of squared residuals and the mean fraction of SWTs. The results revealed that the scenario with the lowest oxygen ratio (i.e., O2:-138) led to the highest RMS errors and a more varied distribution of SWTs compared to the other two scenarios, while ratios with O2:-170 and O2:-155 both had similar RMS and SWTs distribution (Supplement Fig. S8), we selected -155:15:1 because it recorded the lowest RMS and aligned more closely with the standard Redfield stoichiometry. Additionally, we conducted a sensitivity analysis to evaluate how variations in individual parameter weights from Acharya and Panigrahi (2016) influence the model solution. Each weight (e.g., for temperature, salinity, oxygen, phosphate, nitrate, and silicate) was independently varied by ±3 units from its baseline value, while all other weights remained fixed. All runs used constant remineralization ratios (O2:PO4:NO3=-155:1:15). For each scenario, we computed the model's RMS error and mean SWT fractions. The results showed that the model was more sensitive to changes in nutrients and relatively stable to changes in temperature and salinity (Supplement Fig. S9). Finally, we adopted the parameter weights from Acharya and Panigrahi (2016) because, despite higher sensitivity to variations in nutrient weights, the overall SWTs contributions remained relatively stable, indicating a robust solution.

The primary objective of this study was to investigate the distribution of SWTs along the GLODAP transects in the WIO, CIO, and EIO in 1995 and in recent years (2016–2024), and to quantify the associated physical oxygen supply and resulting biological oxygen consumption. Thereby, recent year refer to 2018 in the WIO (15° N–30° S, 150–1200 m), 2016 in the EIO (15° N–30° S, 150–1200 m), and 2019/2024 in the CIO (3° N–15° S, 150–1200 m). Based on the overdetermined eOMP analyses, we identified six SWTs as the major contributors in the entire WIO. From a northern Indian Ocean/OMZ perspective, these six SWTs can be considered as remote sources (STSW, AAIW, and mAABW) with a southern Indian Ocean pathway, an equatorial source (ITFW), and local sources (PGW and RSW), i.e., formed close to marginal seas in the Arabian Sea.

The physical oxygen supply reported below is likely insensitive to uncertainties in the defined SWTs oxygen values, as an oxygen variability sensitivity test in the AS OMZ, with scenarios including decreasing SWT oxygen, constant SWTs oxygen, and increasing SWTs oxygen values, showed no significant changes in the SWTs fraction. In all scenarios, the SWTs fraction were similar, i.e., an increase in STSW and a decline in PGW, ITFW and mAABW (Supplement Fig. S6). This was also supported by Monte Carlo analysis of physical oxygen supply perturbations (see below).

Results from physical oxygen supply show that in the WIO and AS OMZ, the physical oxygen supply increased from 193.2 to 196.5 µmol kg⁻¹ and 135.8 to 143.8 µmol kg⁻¹, respectively, between 1995 and 2018 (Fig. 8a, c). In the EIO and BoB OMZ transects, oxygen supply rose from 191.3 to 198.4 µmol kg⁻¹ and 175.7 to 184.3 µmol kg⁻¹, respectively, between 1995 and 2016 (Fig. 8b, c).

Physical oxygen contributions by individual SWTs were also analyzed. The STSW showed increased physical oxygen supply across all transects, whereas PGW showed decreased oxygen contribution across all transects (Table 5). Furthermore, mAABW showed decreased oxygen contribution in the BoB OMZ (-12.9 µmol kg⁻¹), WIO (-5.7 µmol kg⁻¹), and CIO (-4.1 µmol kg⁻¹), while AAIW showed an increase in the WIO (Table 5). We assessed the robustness of the physical oxygen change associated with individual SWTs from the Monte Carlo perturbation analysis (n=10000 iterations), as described in the methods. The results show that the increase in physical oxygen supply associated with STSW was directionally robust (SNR ≥ 1, Pdir ≥ 0.85) across all transects; additionally, the increase in oxygen due to AAIW was directionally robust only in the WIO (SNR = 1.2, Pdir = 0.9). Furthermore, decrease in physical oxygen supply associated with PGW was directionally robust (SNR ≥ 1, Pdir ≥ 0.85) across all transects and OMZs, while decrease in oxygen supply due mAABW was formally robust in the BoB OMZ (SNR = 3.0, Pdir = 1), and directionally robust in the WIO (SNR = 1.5, Pdir = 0.9) and CIO (SNR = 1.4, Pdir = 0.9) (Table 5).

Analysis of the CIO transect also revealed an increase in the physical oxygen supply around the equator, primarily due to increased STSW. Overall, the average physical oxygen supply increased from 195.9 to 201.1 µmol kg⁻¹ in the CIO between 1995 and 2019/2024 (Fig. 8c). The core of both the BoB OMZ (from 744 to 733 m) and AS OMZ (from 1092 to 1025 m) became shallower in 2016/2018 than in 1995. Overall, the increase in physical oxygen supply was more pronounced from approximately 15° S towards the northern Indian Ocean (Fig. 9).

Analysis of data across all transects revealed an increase in biological oxygen consumption. In the WIO, oxygen consumption rose from -81.3 to -83.6 µmol kg⁻¹ between 1995 and 2018, whereas in the EIO, it rose from -110.2 to -119.6 µmol kg⁻¹ between 1995 and 2016 (Fig. 8c). Similarly, biological oxygen consumption increased from -111.4 to -125.1 µmol kg⁻¹ in the central equatorial region between 1995 and 2019/2024 (Fig. 8c). Furthermore, the analysis indicated a significant increase in biological oxygen consumption in the core of both the AS and BoB OMZ. In the AS OMZ, oxygen consumption increased from an average of -127.4 µmol kg⁻¹ in 1995 to -135.4 µmol kg⁻¹ in 2018; similarly, in the BoB OMZ, oxygen consumption rose from approximately -165.7 µmol kg⁻¹ in 1995 to -172.5 µmol kg⁻¹ in 2016 (Fig. 8a, b).

The eOMP analysis identified six main SWTs entering the central and north Indian Ocean via the southern Indian Ocean pathway (STSW, AAIW, and mAABW), the Indo-Pacific throughflow (ITFW), and locally (PGW and RSW). Earlier studies identified the ICW as the most prominent SWT in the AS OMZ, associated with the RSW and PGW (Schmidt et al., 2020; Rixen and Ittekkot, 2005; Acharya and Panigrahi, 2016; Hupe and Karstensen, 2000). However, the ICW in the northern Indian Ocean is not considered as an independent principal water mass due to its dilution with ASW (a mixture of RSW and PGW), BoBW and ITFW (Gordon et al., 1997). Therefore, the result derived from our eOMP analysis is largely consistent with previous studies but enables a direct assessment of the role of primary SWTs, rather than mixtures, in ventilating the OMZ.

In line with Monte Carlo analysis, the comparison of the eOMP results between 1995 and recent years showed a significant increase in the STSW at the expense of local and equatorial SWTs at intermediate depths (150–1200 m).

These findings also correspond with studies by Ditkovsky et al. (2023) which highlight that the southwest Indian Ocean experienced increased influx of high-oxygenated waters supplied from the southern Indian Ocean gyre due to a decline in the contribution of the ITF.

Hence, such a shift is consistent with, and likely linked to a broader reorganization of large-scale ocean circulation, including the shallow IO-MOC and the warm water return flow of the THC. A decline in the THC due to global warming has been reported (Caesar et al., 2021; de la Vara et al., 2022; Gou et al., 2024), which has weakened the ITF inflow into the Indian Ocean and Agulhas leakage into the Atlantic Ocean (Großelindemann et al., 2025; Shen et al., 2023; Peng et al., 2023). Such a weakening of the cross-equatorial flow seems to have favored the northward (meridional) flow of remote SWTs along the shallow IO-MOC into the northern Indian Ocean. However, in contrast to previous studies, the detected decline in mAABW implies a reduced influence of the abyssal limb of the deep IO-MOC.

In the Indian Ocean, the increasing equatorial oxygen concentrations and the associated reversal from expansion to contraction in the AS OMZ since 2013 have been attributed to an enhanced inflow of oxygen-enriched water masses from the southern Indian Ocean gyre and decreasing oxygen consumption (Liu et al., 2024), mechanisms that have also been identified in century-scale projections (Vallivattathillam et al., 2023; Ditkovsky et al., 2023; Kwiatkowski et al., 2020).

Our analysis is consistent with these findings, raising the question of whether signals of projected changes have already emerged in observations, as the Indian Ocean OMZs experienced increased physical oxygen supply due to a greater contribution from remote SWTs (mainly STSW) across all transects in recent years compared to 1995. This was accompanied by a reduced influence from relatively oxygen-poor local and equatorial SWTs (e.g., PGW and ITFW). Furthermore, recent studies have suggested a weakening of the summer monsoon (Zhou et al., 2008; Roxy et al., 2015), in combination with the decline in primary productivity (PP) in the Arabian Sea/equatorial Indian Ocean since 2005 (Supplement Fig. S1), has increased the equatorial oxygen concentrations additionally by lowering the biological oxygen consumption (Kwiatkowski et al., 2020). In contrast, our analysis points towards increased oxygen consumption, suggesting that alternative mechanisms may be involved.

One possible explanation for this increased biological oxygen consumption is that water masses remain longer in the Indian Ocean upon arrival. This means that water masses are trapped within the OMZs for a longer period, prolonging their exposure to remineralization processes (Cavan et al., 2017; Rixen et al., 2020). Hence, it seems that the weakening of the monsoon-driven upwelling, in addition to the slowdown in the THC (i.e., the associated reduced inflow via the ITF and lower outflow via Agulhas leakage), has not only rearranged the STWs' contributions but also slowed the shallow IO-MOC. While the former seems to have increased the physical oxygen supply, the latter appears to have increased the oxygen consumption by increasing the residence time of water in the OMZs. Due to these counteracting processes, the measured increase in dissolved oxygen in the GLODAPv2 data is relatively small, approximately 0.1 µmol kg⁻¹ in the AS OMZ and, on average, 1.8 µmol kg⁻¹ in the BoB OMZ (Fig. 8a, b). This indicates that, in the EIO, the influence of SWTs' reorganization in the shallow IO-MOC on the BoB OMZ dominates and overcompensates for the possible slowdown between 1995 and 2016.

Historical results obtained from the global atmosphere-ocean model and eddy-resolving regional models indicate that the reduced inflow of oxygen-rich waters from the south has contributed to the intensification of the AS OMZ over the past 6000 years, as reported in a review by Rixen et al. (2020). Our results, in line with model results (Vallivattathillam et al., 2023; Ditkovsky et al., 2023), suggest the opposite mechanism: an enhanced supply of oxygen-rich waters from the south has caused the shrinkage of the AS OMZ since 2013 (Liu et al., 2024). This trend reversal has been attributed to the reorganization of the shallow IO-MOC in response to the weakening of the warm-water return flow of the THC in the Indian Ocean, and the monsoon-driven upwelling in the Arabian Sea. However, whether this trend will persist seems to depend on the competing influences of the enhanced physical oxygen supply and the biological oxygen consumption due to the reorganization and the associated slowdown of the IO-MOC, respectively.

These results also support paleoceanographic studies indicating a strong link between climate changes in the North Atlantic and the AS OMZ on glacial and interglacial time scales (Altabet et al., 1999; Schulz et al., 1998; Suthhof et al., 2001). In contrast to our results, global warming was associated with intensified OMZ, and cooling with reduced intensification. However, on these paleo-time scales, climate changes are linked to changes in sea level (Rosenthal et al., 2003; DiNezio and Tierney, 2013). During the Last Glacial Maximum (∼20000 years ago), the sea level fell by approximately 120 m, exposing large areas of the Sunda and Sahul shelves and closing the shallow Java Sea ITF pathway (DiNezio and Tierney, 2013). The deeper main ITF straits (e.g., Makassar and Lombok) remained submerged and active; nevertheless, the ITF was reduced during this cooler glacial phase. This reduction in ITF transport would, in turn, have favored an enhanced meridional inflow into the northern Indian Ocean. In contrast, in the 21st century, the reduced influx of the ITF is not caused by a drop in sea level but is linked to the slowdown of the THC in response to global warming (Großelindemann et al., 2025; Shen et al., 2023; Peng et al., 2023). However, the mechanism appears to be similar: a reduction in cross-equatorial (ITF) transport increases the relative contribution of meridional, southern-sourced inflow to the Indian Ocean OMZs, even as the absolute volume transport associated with the shallow IO-MOC itself is likely also slowing. Hence, how the OMZs develop in the future depends on both the reorganization of the SWTs contributions and the transport rate of the shallow IO-MOC.