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High-accuracy ship-based observations were conducted at 80°E in the Indian Ocean. Salinity below the mixed layer in 2019 was observed to be lower than that in 1995. This decrease in salinity was mainly attributed to anomalous advection associated with one of the strongest positive Indian Ocean dipole (pIOD) events in 2019 through analysis of the gridded time series of the salinity distributions based on the Argo float array. Increases and decreases in nitrate and dissolved inorganic carbon (DIC) and dissolved oxygen (DO), respectively, were also detected on the isopycnal surfaces where decreases in salinity were observed, suggesting that the anomalous upwelling and westward advection associated with the pIOD in the eastern part of the equatorial region resulted in low-salinity, low-oxygen, and nutrient-rich waters in the central off-equatorial region of the Indian Ocean. However, downward isopycnal heaving, which was also associated with the pIOD, was too strong to have increased nitrate below the mixed layers, and thus might have suppressed biological activity. The heaving also affected the DIC and DO distributions, and the effect of interannual changes such as those associated with the Indian Ocean dipole is essential to estimating changes in anthropogenic carbon storage. This research represents a case study, based on only two occupations; therefore, an assessment utilizing more intensive observations and more realistic numerical simulations is necessary in the future.

The southeastern Indian Ocean (SEIO) exhibits prominent decadal variability in sea surface salinity (SSS), showing salinity decreases during 1995-2000 and 2005-2011 and increases during 2000-2005 and after 2011. These salinity changes are linked to the Indo-Pacific climate and have impacts on the regional marine environment. Yet, the underlying mechanism has not been firmly established. In this study, decadal SSS variability of the SEIO is successfully simulated by a high-resolution regional ocean model, and the mechanism is explored through a series of sensitivity experiments. The results suggest that freshwater transport of the Indonesian throughflow (ITF) and local precipitation are two major drivers for the SSS decadal variability. They mutually cause most of the variability, with a generally larger contribution of precipitation. Other processes, such as evaporation and advection driven by local winds, play a minor role. Further analysis shows that the decadal precipitation in the SEIO is mainly associated with the decadal variability of Ningaloo Niño. Ocean dynamic processes significantly modify the relationship between SSS and precipitation, greatly shortening their lag time. The changes in both volume transport and salinity of the ITF water can cause large salinity changes in the SEIO region. Although local wind forcing gives rise to considerable changes in evaporation rate and ocean current advection, its overall contribution to decadal SSS variability is small compared to local precipitation and the ITF.

The seasonality of eddy kinetic energy (EKE) is analyzed in the north Indian Ocean by adopting high-resolution ocean reanalysis data. Significant eddy energy can be mainly spotted in six regions, including the Somali Current (SC) region, the Gulf of Aden, the Laccadive Sea, the east of Sri Lanka, the East Indian Coastal Current (EICC) region, and the northwest of Sumatra. As the most energetic region, the EKE averaged above 200 m could exceed 0.15 m2·s-2 in the SC region, whereas the mean EKE above 200 m is less than 0.04 m2·s-2 in the other regions. The barotropic and baroclinic instabilities are vital to eddy energy, and the contribution of each term in the barotropic/baroclinic equations varies with season and region. In the SC region and EICC region, EKE is primarily generated by barotropic conversion due to the sharp velocity shear caused by the strong SC during the summer monsoon and the EICC from March to June. For the other regions, the leading source of EKE is the eddy potential energy (EPE), which is extracted from available potential energy of mean flow via baroclinic conversion, and then the EPE is converted into EKE through vertical density flux. Once generated, EKE will be redistributed by pressure work and advection via eddy energy flux, which varies in sync with the monthly variation of total EKE, transporting EKE to the adjacent region or deeper layer. From the vertical aspect, eddy energy conversions are more prominent above 200 m. The maximal EKE and barotropic conversion mostly occur at the surface, whereas the EPE and baroclinic conversion may have two peaks, which lie at the surface and in the thermocline. Using the satellite altimeter data and wind data, we further investigate the impact of geostrophic eddy wind work, which reveals a slightly dampening effect to EKE in the north Indian Ocean.

Coastal waters are inherently dynamic due to river discharge, industrial effluents, shipping, dredging, waste dumping, and sewage disposal. Population growth in urban cities, climate change and variability, and changes in land-use practices all contribute to pressure on coastal water quality (SKOVSKI et al., 2012; MILLER and HUTCHINS, 2017; KUMAR et al., 2020; Vijay PRAKASH et al., 2021). Anthropogenic activity is evident around these estuaries and coastal and open ocean environments. Hence, it is important to assess the water quality on a regular basis and provide mitigation measures for coastal pollution (YUVARAJ et al., 2018). Improving water quality and variability in coastal waters is necessary and should be prioritized. Observational programs, which are more expensive and time-consuming, aid in understanding the status of water quality and its trends. Many countries have coastal programs that use predictive systems to inform the public and stakeholders about coastal health. Hydrodynamic processes are an integral part of complex surface water systems. The main factor that determines the concentration of pollutants is hydrodynamic transport, which includes advection, dispersion, vertical mixing, and convection (James, 2002). The flow and circulation patterns have a great influence not only on the distribution of temperature, nutrients, and dissolved oxygen (DO) but also on the aggregation and distribution of sediments and pollutants. When a load of pollutants is discharged into coastal waters, it is affected by the fate and transportation processes that change its concentration. Several studies have been conducted to evaluate the coastal water quality spatiotemporally along the east coast of Indian coastal waters using site-specific data and model configuration (PANDA et al., 2006; BHARAHTI et al., 2017; NAIK et al., 2020; MOHANTY et al., 2021). Through numerical modeling and remote sensing, estimation is user-friendly and low-cost in evaluating any water quali

The interannual-decadal variability in the upper-ocean salinity of the southeast Indian Ocean (SEIO) was found to be highly correlated with the El Niño-Southern Oscillation (ENSO). Based on multisource data, this study revealed that this ENSO-like salinity variability mainly resides in the domain between 13°S-30°S and 100°E-120°E, and at depths above 150 m. This variability is principally driven by meridional geostrophic velocity (MGV), which changes with the zonal pattern of the sea surface height (SSH). Previous studies have reported that the variability in the SSH in the south Indian Ocean is principally driven by local-wind forcing and eastern-boundary forcing. Here the eastern-boundary forcing denotes the influence of SSH anomaly radiated from the western coast of Australia. A recent study emphasized the contribution of local-wind forcing in salinity variability in the SEIO, for its significant role in generation of the zonal dipole pattern of SSH anomaly in the south Indian Ocean, which was considered to be responsible for the anomalous MGV in the SEIO. While our results revealed a latitudinal difference between the domain where the SSH dipole pattern exists (north of 20°S) and the region in which the ENSO-like salinity variability is strongest (20°S-30°S), suggesting that this salinity variability cannot be attributed entirely to the SSH dipole pattern. Our further investigation shows that, the MGV in the SEIO changes with local zonal SSH gradient that principally driven by eastern-boundary forcing. In combination with the strong meridional salinity gradient, the boundary-driven MGV anomalies cause significant meridional salinity advection and eventually give rise to the observed ENSO-like salinity variability. This study revealed the leading role of eastern-boundary forcing in interannual variability of the upper-ocean salinity in the SEIO.

Gaining insight into the interannual variability of the Indian Ocean Subtropical Mode Water (IOSTMW) is essential for understanding ocean dynamics in the Southwest Indian Ocean, since it carries the signal of winter mixing and transports it into the ocean interior. As the number of Argo profiles in the Southwest Indian Ocean increases, it has become possible to study temporal variations in IOSTMW using observation data. We used Argo products to examine the interannual variability of the IOSTMW from 2005 to 2020. We examined various definitions to determine the most suitable definition for IOSTMW in this study, choosing to define the IOSTMW as a layer with a vertical temperature gradient of less than 1°C per 100 meters (dT/dz< 1°C/100 m) and a temperature range of 16°C-18°C because this correlates strongly with winter heat loss in the same year. This method is particularly useful for investigating how mode water captures anomalous winter mixing signals and advects them to the ocean interior via subduction. Furthermore, we found that summer stratification can play a role in either facilitating or hindering the formation of thick IOSTMW layers. Our study indicates that thin IOSTMW layers are primarily caused by extremely weak winter heat loss associated with anomalously weak latent heat, whereas thick IOSTMW formation is aided by weak summer stratification.

Aluminum and manganese are both key parameters in the GEOTRACES program. Data on dissolved aluminum (dAl) and dissolved manganese (dMn) relative to their geochemical behavior remain limited in the northeastern Indian Ocean (IO; including the Bay of Bengal (BoB) and equatorial Indian Ocean (Eq. IO)). Seawater samples collected in the BoB and Eq. IO during the spring inter-monsoon period (7 March to 9 April) of 2017 were analyzed to investigate the behavior and main processes controlling the distributions of dAl and dMn in the northeastern IO. The average concentrations of dAl and dMn in the mixed layer of the BoB were 16.6 and 6.7 nM, respectively. A modified 1-D box-model equation was utilized to estimate the contributions of different sources to dAl and dMn in the mixed layer. Al released from the desorption of and/or dissolution of the lithogenic sediments discharged by the Ganga-Brahmaputra (G-B) river system predominantly controlled the dAl distributions in the mixed layer of the BoB, while the desorption from the lithogenic sediments only contributed approximately 13%-21% dMn. Additional dMn input from the advection of Andaman Sea water and photo-reduction-dissolution of particulate Mn(IV) contributed more than 60% dMn in the mixed layer of the BoB. dAl and dMn in the surface mixed layer of the Eq. IO were mainly affected by the mixing of dAl- and dMn-enriched BoB surface water and low-dAl, low-dMn southern Arabian Sea surface water. Considering water mass properties and dAl concentrations, the distributions of dAl in the intermediate water (750-1,500 m) of northeastern IO were controlled by the mixing of Red Sea Intermediate Water, Indonesian Intermediate Water, and intermediate water of the BoB. Different from dAl, the apparent oxygen utilization relationship with dMn concentrations indicated that the regeneration of lithogenic particles under hypoxic conditions played a more important role than the remineralization of settling organic particles in co