Sea Surface Salinity Variability Research Articles

Evaluation of CMIP6 models for sea surface temperature and sea surface salinity variability over the Arabian Sea

The capability of Coupled Model Inter-comparison Project Phase-6 (CMIP6) is analysed in simulating the seasonal variability of Sea Surface Temperature (SST) and Sea Surface Salinity (SSS) over the Arabian Sea in this study. The historical simulations of 11 CMIP6 models, which come from various source are validated with the observational data (HadISST for SST and SODA for SSS). This study covers the Arabian Sea, spanning 5°S to 32°N and 33°E to 80°E from 1985 to 2014 (30 years). Seasonal mean climatology and annual cycle of SST and SSS are estimated over the Arabian Sea. The deviation of coupled model-simulated SST and SSS from the observation is quantified by determining the biases during all the seasons. Four distinct model evaluation measures are used to access the performance of CMIP6 models: root mean square error (RMSE), standard deviation (STD), correlation coefficient (CC) and interannual variability skill score (IVS). CMCC-CM2-SR5, NESM3 and IITM-ESM models for SST yield CC/RMSE values of 0.988/0.174, 0.983/0.204 and 0.963/0.299, respectively. CMCC-CM2-SR5, IPSL-CM6A-LR and CanESM5 models for SSS yield CC/RMSE values of 0.956/0.035, 0.937/0.042 and 0.820/0.069, respectively. Thus, these models having higher CC and lower RMSE among all models, give better performance. The present study is important as the future scenarios can be predicted using the best models.

Impact of river-discharged freshwater on surface ocean environments revealed by synergistic use of satellite measurements in the East China Sea

River discharge forms a plume of low-salinity water that spreads offshore, delivering terrestrial substances into the ocean and thus plays a critical role in controlling marine environments as well as the carbon cycle. This study investigated how freshwater discharged from the Changjiang impacts the physical and biological responses and oceanic uptake of carbon dioxide (CO2) in the northern East China Sea (ECS) by combining the recently available sea surface salinity (SSS) product from the Soil Moisture Active Passive (SMAP) mission with other satellite measurements of sea surface temperature (SST) and chlorophyll-a (chl-a) concentration. The partial pressure of CO2 (pCO2) and its interannual variability were estimated using empirical regression with satellite-derived environmental variables. The bias-corrected SMAP SSS revealed that river discharge largely contributed to the distinct interannual SSS variations, with a seasonal cycle reaching a maximum in winter and a minimum in summer. Compared to the SST and chl-a anomalies, we observed an increase in SST and primary production in the region where sea surface freshening was robust. Freshwater from rivers contributes to sea surface warming by trapping heat from the atmosphere at the surface layer. Nutrient-enriched freshwater within the buoyant plume enhances phytoplankton production, which in turn enriches the ocean surface with chl-a. Simultaneously, the pCO2 was relatively low in the region where the SST and primary production were high, highlighting that the heat and riverine nutrients trapped within the buoyant plume contributed to the reduction in pCO2 by promoting the biological uptake of CO2. The estimates conducted here illustrate the synergistic utility of multiple satellite measurements for the evaluation of CO2 uptake capacity, complemented by in situ measurements of river-dominated marginal seas.

Evaluating CMIP6 models in simulating the North Pacific decadal variability in sea surface salinity

Evaluating CMIP6 models in simulating the North Pacific decadal variability in sea surface salinity

Gap-filling techniques applied to the GOCI-derived daily sea surface salinity product for the Changjiang diluted water front in the East China Sea

Abstract. The spatial and temporal resolutions of contemporary microwave-based sea surface salinity (SSS) measurements are insufficient. Thus, we developed a gap-free gridded daily SSS product with higher spatial and temporal resolutions, which can provide information on short-term variability in the East China Sea (ECS), such as the front changes by Changjiang diluted water (CDW). Specifically, we conducted gap-filling for daily SSS products based on the Geostationary Ocean Color Imager (GOCI) with a spatial resolution of 1 km (0.01°), using a machine learning approach during the summer seasons from 2015 to 2019. The comparison of the Soil Moisture Active Passive (SMAP), Copernicus Marine Environment Monitoring Service (CMEMS), and Hybrid Coordinate Ocean Model (HYCOM) SSS products with the GOCI-derived SSS over the entire SSS range showed that the SMAP SSS was highly consistent, whereas the HYCOM SSS was the least consistent. In the < 31 psu range, the SMAP SSS was still the most consistent with the GOCI-derived SSS (R2=0.46; root mean squared error: RMSE = 2.41 psu); in the > 31 psu range, the CMEMS and HYCOM SSS products showed similar levels of agreement with that of the SMAP SSS. We trained and tested three machine learning models – the fine trees, boosted trees, and bagged trees models – using the daily GOCI-derived SSS as output, including the three SSS products, environmental variables, and geographical data. We combined the three SSS products to construct input datasets for machine learning. Using the test dataset, the bagged trees model showed the best results (mean R2=0.98 and RMSE = 1.31 psu), and the models that used the SMAP SSS as input had the highest level. For the dataset in the > 31 psu range, all the models exhibited similarly reasonable performances (RMSE = 1.25–1.35 psu). The comparison with in situ SSS data, time series analysis, and the spatial SSS distribution derived from models showed that all the models had proper CDW distributions with reasonable RMSE levels (0.91–1.56 psu). In addition, the CDW front derived from the model gap-free daily SSS product clearly demonstrated the daily oceanic mechanism during the summer season in the ECS at a detailed spatial scale. Notably, the CDW front in the zonal direction, as captured by the Ieodo Ocean Research Station (I-ORS), moved approximately 3.04 km d−1 in 2016, which is very fast compared with the cases in other years. Our model yielded a gap-free gridded daily SSS product with reasonable accuracy and enabled the successful recognition of daily SSS fronts at the 1 km level, which was previously not possible with ocean color data. Such successful application of machine learning models can further provide useful information on the long-term variation of daily SSS in the ECS. The gridded gap-free SSS dataset at 0.01°×0.01° spatial resolution is freely available at https://doi.org/10.22808/DATA-2023-2 (Shin et al., 2023).

Estimation of sea surface salinity from MODIS-Aqua data in the northern South China Sea

Sea Surface Salinity (SSS) is a key variable in physical oceanography and biogeochemical research. Microwave remote sensing technology has been used to monitor SSS in open ocean areas. However, the limited spatial resolution and suboptimal precision exhibited by microwave remote sensing derived SSS data have led to the omission of crucial oceanic information, consequently constraining its utilization. In this study, an artificial neural network (ANN) model for the regional SSS inversion was constructed utilizing in situ SSS measurements in the northern South China Sea and remote sensing sea surface temperature and reflectance at wavelengths of 412, 443, 488, 555 and 667 nm derived from MODIS-Aqua satellite Level 2 data. The model showed a good performance of root mean square error (RMSE) of 0.32 and 0.34 for the model training and testing datasets, with mean bias (MB) of 0 and 0.01, and correlation coefficients (R2) reaching 0.93. The model validation indicated that the ANN model reproduced the spatial distribution of SSS highly consistent with the measured SSS (RMSE = 2.75, MB = −1.67, R2 = 0.95), demonstrating its strong applicability in the NSCS. In contrast to other local SSS inversion models, our ANN model clearly showed the fine-scale SSS feature under the intrusion of Pearl River plume near to Dongsha Island with the help of anticyclonic-cyclonic eddies. This prediction closely aligned with the observations from the Soil Moisture Active Passive SSS dataset. The ANN model was applied to MODIS-Aqua data to generate the monthly SSS from 2003 to 2022. A decreasing SSS trend in SSS of 0.02 psu y−1 during 2005–2013 and 2017–2020 and a slightly increasing SSS trend of 0.01 psu y−1 from 2013 to 2017 were observed. Further analysis shows the low-frequency variability of SSS is significantly correlated with the Pacific Decadal Oscillation.

Reconstruction of the marine carbonate system at the Western Tropical Atlantic: trends and variabilities from 20 years of the PIRATA program

The Western Tropical Atlantic Ocean (WTAO) is crucial for understanding CO2 dynamics due to inputs from major rivers (Amazon and Orinoco), substantial rainfall from the Intertropical Convergence Zone (ITCZ), and CO2-rich waters from equatorial upwelling. This study, spanning 1998 to 2018, utilized sea surface temperature (SST) and sea surface salinity (SSS) data from the PIRATA buoy at 8°N 38°W to reconstruct the surface marine carbonate system. Empirical models derived total alkalinity (TA) and dissolved inorganic carbon (DIC) from SSS, with subsequent estimation of pH and fCO2 from TA, DIC, SSS, and SST data. Linear trend analysis showed statistically significant temporal trends: DIC and fCO2 increased at a rate of 0.7 µmol kg-1 year-1 and 1.539 µatm year-1, respectively, and pH decreased at a rate of -0.001 pH units year-1, although DIC did not show any trend after data was de-seasoned. Rainfall analysis revealed distinct dry (July to December) and wet (January to June) seasons, aligning with lower and higher freshwater influence on the ocean surface, respectively. TA, DIC, and pH correlated positively with SSS, exhibiting higher values during the dry season and lower values during the wet season. Conversely, fCO2 correlated positively with SST, showcasing higher values during the wet season and lower values during the dry season. This emphasizes the influential roles of SSS and SST variability in CO2 solubility within the region. Finally, we have analysed the difference between TA and DIC (TA-DIC) as an indicator for ocean acidification and found a decreasing trend of -0.93 ± 0.02 μmol kg-1 year-1, reinforcing the reduction in the surface ocean buffering capacity in this area. All trends found for the region agree with data from other stations in the tropical and subtropical Atlantic Ocean. In conclusion, the use of empirical models proposed in this study has proven to help filling the gaps in marine carbonate system data in the Western Tropical Atlantic.

Interannual Variation of Summer Sea Surface Salinity in the Dotson–Getz Trough, West Antarctica

In this study, we explore the interannual variability of Sea Surface Salinity (SSS) in the Dotson–Getz Trough located in West Antarctica, focusing on the month of February. Utilizing the oceanic analysis product EN4, we first validate the EN4 SSS with data from a singular ship-based survey, and then delve into potential factors that may influence SSS, with a particular emphasis on surface freshwater flux, sea ice concentration (SIC), and also the surface stress curl, which will induce upwelling via Ekman transport to affect the SSS. Our findings primarily indicate a link between SSS and sea ice concentration, showcasing a negative correlation where the peak (average) coefficient is around −0.6 (−0.4), further affirming the substantial interannual variability of SSS in this region.

Generating a monthly variability of sea surface salinity based on source tracing of salt concentration and the estimated SEBAL-evaporation

Abstract The variation and spatial distribution of sea surface salinity (SSS) depend on the geographic condition of the water surfaces and the temporal variation of atmospheric conditions. The SSS might differ in a local coastal area compared to similar situations in global and regional oceans. The SSS values have been estimated based on spatial regression of extracted water-salt concentration as a source tracing of salt against corrected Landsat 8 satellite data during the drought season of April 2023. Here, the electrical conductivity (EC) from the Cimanuk River can be used as primary data. This result, paired with the evaporation-derived surface energy balance algorithm for land (SEBAL) algorithm, explains a monthly SSS variability after the validation using pre-defined resampled regional SSS and evaporation data. The result shows variations in estimated SSS values along with fluctuated SEBAL evaporation ranging from 1.64 to 1.62 dS/m and 1.04 to 0.41 W/m2, respectively. It describes monthly variability and their relationship in a local coastal area limited to the condition of a drought season. However, the validation shows that the root means square error (RMSE) of 1.00 from the SSS map, produced by the regression model involving band 7 of Landsat 8 and 9, has satisfied the reasonable SSS value ranges besides the best accuracy.

Global Wavenumber Spectra of Sea Surface Salinity in the Mesoscale Range Using Satellite Observations

Sea surface salinity (SSS) variability at mesoscales has become an important research topic in recent decades, thanks to satellite missions enabling observations of SSS with global capacity and mesoscale resolution. Here, we analyze the near-global data of the Aquarius/SAC-D along-track SSS, focusing on the slopes of SSS variance spectra in the mesoscale range from 180 to 430 km. In the vast extratropics, the spectral slope is close to −2, indicating a dynamical regime for the inverse cascade of depth-integrated energy identified by the surface quasi-geostrophic theory. However, the spectral slopes in regions near the mouths of the largest rivers are steeper than −2, reaching −3, possibly due to the large river freshwater flux. In addition, data from high-resolution thermosalinograph are used to validate satellite measurements and show good consistency in terms of SSS variance spectral slopes.

Satellite-Derived Variability of Sea Surface Salinity and Geostrophic Currents off Western Patagonia

The coastal ocean off western Patagonia is one of the main coastal regions with high freshwater inputs from rivers, rain, and glaciers in the Southern Hemisphere. This study conducts an analysis of the seasonal and interannual variations in sea surface salinity and meridional geostrophic transports, specifically focusing on the Cape Horn Current, using improved satellite-derived data of sea surface salinity (SSS) and geostrophic velocities spanning an ∼11-year period (September 2011–August 2022). Our results reveal a clear salinity minimum in a coastal band between 42–54°S associated with the highest freshwater content. The average geostrophic currents are stronger south of 49°S, in line with the location of the Cape Horn Current. The average salinity minimum tends to disappear south of 54°S, with salinity values increasing slightly southward. The seasonal cycle of salinity shows the most pronounced minimum in summer (∼33.2–33.4). The greatest variability in salinity (standard deviation of salinity fields) occurs in the southern region of the Cape Horn Current. Hovmöller plots reveal two cores of minimum salinity observed in spring and summer (∼33.3–33.4). The freshwater off the Gulf of Penas contributes to the northern core. The meridional geostrophic transport differs between the northern and southern sections, with transports predominantly towards the Equator (Pole) north (south) of about 47–48°S during spring–summer. There is a marked seasonal variability in the magnitude and northern limit of the southward-flowing Cape Horn Current, being extended further north during winter and with a maximum average magnitude during summer–fall (about −2×104 m2 s−1). On the interannual scale, a major drop in surface salinity occurred off northern and central Patagonia during 2018–2019. Finally, a potential long-term freshening trend is observed in the coastal area off southern Patagonia (south of 52°S), although prolonged data records are essential to confirm this pattern.

Late summer northwestward Amazon plume pathway under the action of the North Brazil Current rings

The North Brazil Current (NBC) flows offshore of the mouth of the Amazon River and seasonally sheds anticyclonic rings (NBC rings) that propagate northwestward and interact with the Amazon River plume (ARP). Mesoscale features have a high temporal variability that is hard to monitor from current weekly and monthly sea surface salinity (SSS) satellite fields. Novel SSS fields with a higher temporal resolution analyzed together with satellite geostrophic currents, chlorophyll-a, and wind speed and in-situ data from the “Microbiomes cruise” on the SV Tara in August–September 2021 revealed a late summer freshwater pathway, which was not well documented in earlier studies. By combining these datasets, we improved the characterization of summer ARP pathways. In 2021, the ARP was a succession of freshwater patches cut off from the main plume by the NBC rings. A patch of about 200.000 km2 with salinity <33.5 pss was observed in September 2021, bringing 0.5 Sv of Amazon water northwestward in a period where the mean ocean currents lead to eastward transport. This patch was shallow, very stratified, and it created a surface steric-height anomaly that was identified as an anticyclonic feature in altimetric sea level products. Once separated from the NBC retroflection, it was mainly driven by Ekman currents. Other similar patches were observed during the 2021 summer, leading to a strong intermittency of the ARP transport. They strongly contributed to make 2021 the year with the largest northwestward freshwater transport in late summer within the 2010–2021 time-period investigated. This freshwater transport pathway is important for all plume-related phenomena, and show the ability of combined SMOS and SMAP data to accurately represent the day-to-day SSS variability.

Ocean Variability in the Costa Rica Thermal Dome Region from 2012 to 2021

Satellite ocean color and sea surface temperature (SST) observations from 2012 to 2021 and sea surface salinity (SSS) measurements from the Soil Moisture Active Passive (SMAP) mission from 2015 to 2021 are used to characterize and quantify the seasonal and interannual variability in the physical, optical, and biological sea surface features in the Costa Rica Thermal Dome (CRTD) region. High-resolution climatology and the seasonal variability in SST, SSS, and ocean color properties are produced. Chlorophyll-a (Chl-a) concentration, SST, and SSS show these three properties are linked with similar spatial patterns and seasonal variations, i.e., elevated Chl-a concentrations, match the depressed SST and increased SSS and vice versa. This reflects that the physical driving force is the same for these three ocean properties and implies that nutrient supply associated with the physical processes is the major driver for the seasonal biological variability. The interannual changes in Chl-a, SST, and SSS also show that these three ocean properties are consistent among themselves. The positive (negative) Chl-a anomaly generally occurs with negative (positive) SST anomaly and enhanced (reduced) SSS. The in situ measurements evidently show that the subsurface ocean dynamics in the upper 100 m controls the sea surface variability for Chl-a, SST, and SSS. We report that no significant enhancement of Chl-a is observed in the CRTD region during the central Pacific (CP)-type 2020–2021 La Niña event, while Chl-a changes are significant in the other three of four ENSO events between 2012 and 2021. Furthermore, the difference in Chl-a variability driven by the CP-type ENSO and eastern Pacific (EP)-type ENSO is further discussed.

Atlantic Niño induced sea surface salinity variability as observed from the satellite

The Atlantic Niño exerts great impact on surrounding weather and climate anomalies, leading to anomalous wind, temperature, precipitation, etc. However, the impact of Atlantic Niño on ocean salinity in the tropical Atlantic has not been well disclosed. The present study examines the Atlantic Niño induced sea surface salinity (SSS) distribution in both boreal summer and winter seasons by using the satellite data as well as various reanalysis and objective analysis data sets. It concludes that the summer Atlantic Niño leads to large fresh SSS anomalies in the eastern equatorial basin, while the winter Atlantic Niño leads to a meridional dipole structure of SSS anomalies. The former is mostly controlled by the dynamical processes of ocean, while the latter is largely controlled by the atmospheric processes. Accordingly, two SSS indices are developed to describe the relationships in the two seasons. The present study advances our understanding of the Atlantic Niño and its associated SSS variability and reveals the possible deficiencies of current reanalysis and objective analysis data sets in the tropical Atlantic Ocean.

Observed Extreme Freshening in the Central Andaman Sea Induced by Strong Positive Indian Ocean Dipole

AbstractThe Indian Ocean Dipole (IOD) has been extensively studied for its significant impact on salinity distribution in tropical regions. However, its off‐equatorial influence in the northeastern Indian Ocean (IO) has received limited documentation thus far. In October 2019, a buoy in the central Andaman Sea (AS) observed an extreme freshening event that resulted in intensified upper‐ocean stratification and increased internal wave activities. The salt budget evaluation revealed the dominant role of horizontal advection. Interannual variability in autumn and winter sea surface salinity (SSS) showed a significant correlation with the IOD. Extreme freshening was observed exclusively during strong positive IOD (pIOD) years. This freshening primarily resulted from the outflow of low‐salinity water from the northeastern coast of the AS, driven by an anomalous anti‐cyclonic coastal‐trapped circulation exclusive to strong pIODs in the northeastern IO. This circulation tends to hinder southward freshwater transport in the western region while enhancing it in the eastern region. This circulation pattern is primarily influenced by Kelvin wave forcing, which is triggered by robust equatorial easterly anomalies that are typically more pronounced during strong pIOD events and weaker during weak pIOD events. The anticipated increase in the frequency of extreme freshening events due to the greenhouse warming has the potential to significantly modify the salinity distribution and freshwater transport in the future.

Sensitivity of the thermohaline circulation during the Messinian: Toward constraining the dynamics of Mediterranean deoxygenation

During the Messinian, the sensitivity of the Mediterranean Basin to ecosystem perturbation was enhanced in response to the progressive restriction of water exchange with the Atlantic Ocean. The widespread deposition of organic-rich layers (i.e. sapropel) during the Messinian testifies the perturbation of the carbon and oxygen cycles; indeed, these sediments were deposited under conditions of oxygen starvation, presumably in response to a periodic deterioration of the thermohaline circulation strength. Disentangling the causes, the effect and magnitude of the thermohaline circulation weakening during the geological past is crucial for better constraining present and near-future deoxygenation dynamics in the Mediterranean region under the current climate warming. For this purpose, we investigate a Messinian sapropel-bearing succession cropping out at Monte dei Corvi (Ancona, central Italy) with mineralogical, petrographic, micropaleontological and stable Carbon and Oxygen isotopic analyses. We show that sapropel layers were deposited in response to an increase of the sea surface buoyancy, which hampered the thermohaline circulation and thus the oxygenation of bottom water, in turn affecting the bioturbating organisms. Within the lithological cycle, the recovery of an efficient thermohaline circulation is recorded by thin packstone layers underlying the marly limestone/marlstone, which record intense bottom currents. The marly limestone/marlstone accumulated during periods of intense primary productivity and organic carbon export to the sea bottom, which promoted bottom hypoxia but not organic matter preservation. We infer that these lithological changes resulted from variations in the Adriatic Deep Water formation system, controlled by precession-driven climatic and oceanographic changes.By integrating previously published Sea Surface Temperature (SST) with new isotopic and mineralogical data, we show that variations in Sea Surface Salinity (SSS) were the leading factor controlling sapropel deposition, minimizing the role of primary productivity. The SSTs characterizing the sapropel deposits are close to the range of those projected in the Eastern Mediterranean at the end of this century under climate warming. In this scenario, future warming will be coupled with SSS increase, which will counteract the density loss provided by temperature, making the bottom deoxygenation in the Eastern Mediterranean abysses unlikely. However, additional forcing, such as winter heat waves and eutrophication, could contribute to negatively affecting the Mediterranean oxygen balance and should be considered in model-based projections.

Automated retrieval of sea surface salinity from satellite data and investigation of the spatio-temporal variation of ocean salinity across the West and Central African coasts

Sea Surface Salinity (SSS) is an important variable that affects the physical properties of the oceanic environment. Satellite sensors have been developed to routinely acquire SSS concentrations. However, many in the broader scientific community do not engage with this data, because it requires relevant remote sensing skills and tools to extract and process into formats that can be readily utilized by non-remote sensing experts. To address this issue, we used Python, PostgreSQL database, and Power BI visualization tool, to develop an automated tool, that facilitates the retrieval, processing, analysis, and visualisation of data from the Optimally Interpolated Sea Surface Salinity (OISSS) dataset, comprising data from the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP), and Soil Moisture and Ocean Salinity (SMOS) satellite missions. Using this tool, we extracted long term series of SSS from 2011 to March 2022. In addition, an interactive reporting dashboard was developed in this research to enable users to visualize and interrogate the underlying dataset. Analysis of this data revealed detailed spatiotemporal patterns of SSS variations in the study area across varying spatial and temporal scales. We were able to identify the cold and hot spots of SSS, as well as longitudinal (interand intra-annual) variations in SSS concentrations. We anticipate that the outcome of this research will boost further research in this area, contribute to future climatic change studies and stimulate the democratization of remote sensing data. Climatic changes have been found to have profound effects on ocean salinity. The data obtained in this research could also feed into machine learning models for predicting future impacts of climate change on salinity concentrations across the ocean. In addition, the ability to monitor the salinity on a regular basis can be deployed alongside other relevant variables to forecast future climatic changes. Due to the fact that salinity is an excellent indicator for water cycle changes, the ability to routinely monitor salinity at relatively higher spatial and temporal resolutions is of great significance to the local community, as it could be used to understand current and future water cycle regimes in the area. The water cycle has profound effect on various socio-economic activities including agriculture and food security, ecology, hydrology (water quality), and drought.

Mechanism and impact of zonally contrasting seasonal variations in sea-surface salinity in the North Pacific and North Atlantic oceans

We investigated the characteristics of seasonal variations in sea surface salinity (SSS) in the subtropical North Pacific and North Atlantic oceans by analyzing observational datasets. As noted in previous studies, the seasonal variation in SSS markedly differs between the western and eastern parts of the basins. These contrasting seasonal variations in SSS have nonnegligible impacts on surface density for each basin and translate into changes in temperature at the outcropping isopycnals of about 1.0 °C. Further analysis of the mixed-layer salinity budget reveals that the zonally contrasting seasonality is attributed to a combination of seasonal variations in the freshwater flux and mixed layer (ML) depth. In the western basins, the wintertime increase in evaporation and reduction in precipitation, as well as the vertical entrainment of salty water below the ML contribute to salinification, whereas the seasonal freshening in summer is mostly attributed to increase in precipitation, reduction in evaporation, and strengthening of geostrophic currents. In contrast, evaporation exceeds precipitation throughout a year in the eastern basins, and seasonal variations in freshwater flux cannot fully explain the seasonal cycle of SSS in the region. Rather, the wintertime freshening there is aided by the deepening of the ML, which acts to mitigate salinification by the evaporation and sustain vertical entrainment of fresher water below the ML. Summertime salinification there can be understood as a mirror image of the wintertime condition, but seasonal strengthening of Ekman currents and associated salinity advection also play a role. (241 words)

Analysis of chlorophyll-a, sea surface temperature, and sea surface salinity in the Bay of Bengal

The waters of the Bay of Bengal (BoB) are influenced by the northeast monsoon (November-February) and the southwest monsoon (June-September). This study aims to determine the relationship between sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll-a (Chl-a) during January 2021 in the BoB. The method used in this study is the method of correlation analysis, hypothesis testing, and analysis of variance using monthly average data for January 2021. The data comes from the Copernicus Marine Environment Monitoring Service (CMEMS) portal. Our analysis showed that high Chl-a was inversely related to low SST and SSS conditions and vice versa. This finding confirms the results of other previous studies. Medium and negative correlations occurred for the Chl-a - SST and Chl-a - SSS pairs. Meanwhile, the SST- SSS pair shows a strong and positive correlation. The hypothesis test shows that the conclusions drawn from these correlation relationships are acceptable. Furthermore, from the analysis of variance obtained, it was found that Chl-a had a significant effect on variations in SST and SSS, while SST had a significant effect on variations in SSS.

Dynamics of sewage outfall plumes based on Landsat-8-derived sea surface salinity and tidal characteristics.

In the face of growing marine pollution, assessment of the sewage outfall discharges is essential as it affects the seawater quality. The study demonstrates sea surface salinity (SSS) variations caused by sewage discharges and links it with tidal characteristics to hypothesize the dynamics of sewage outfall plumes. SSS is estimated using a multilinear regression model based on Landsat-8 (L8) OLI reflectance and in situ SSS data of 2013-2014. Using the validated model, the SSS of the 2018 image is predicted and evidenced by its relationship with colored dissolved organic matter (CDOM). The preliminary results of the hypothesis are encouraging and found that the dispersion patterns of outfall plumes exhibit distinct characteristics depending on the intra-tidal range and hour. The findings indicate a lower SSS in the outfall plume zone than in ambient seawater due to dilution caused by partially treated sewage discharges from diffusers. The plumes observed during the macro tidal range are long and narrowly spread alongshore. In contrast, during the meso and microtidal ranges, the plumes are shorter and are primarily dispersed offshore rather than alongshore. During slack times, low salinity levels are visibly concentrated around outfalls as there is no water movement to disperse the accumulated sewage discharges from diffusers. These observations suggest that slack periods and low-tidal conditions could be significant factors contributing to the accumulation of pollutants in coastal waters. The study further suggests more datasets such as wind speed, wind direction, and density variations are needed to understand the processes influencing the outfall plume dynamics and variation in SSS. The study recommends increasing the treatment capabilities of existing treatment facilities from primary to tertiary treatment levels. Furthermore, it is important to warn and educate the public about the health risks associated with exposure to partially treated sewage that is discharged from outfalls.

Characteristics and mechanisms of the intraseasonal variability of sea surface salinity in the southeastern Arabian Sea during 2015–2020

Characteristics and mechanisms of the intraseasonal variability of sea surface salinity in the southeastern Arabian Sea during 2015–2020