First SMOS Sea Surface Salinity dedicated products over the Baltic Sea

This paper presents the first Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) dedicated products over the Baltic Sea. The SSS retrieval from L-band brightess temperature (TB) measurements over this basin is really challenging due to important technical issues, such as the land-sea and ice-sea contamination, the high contamination by Radio-Frequency Interferences (RFI) sources, the low sensitivity of L-band TB at SSS changes in cold waters and the poor characterization of dielectric constant models for the low SSS and SST ranges in the basin. For these reasons, exploratory research in the algorithms used from the level 0 up to level 4 has been required to develop these dedicated products. This work has been performed in the framework of the European Space Agency regional initiative Baltic+ Salinity Dynamics. Two Baltic+ SSS products have been generated for the period 2011–2019 and are freely distributed: the Level 3 (L3) product (daily generated 9-day maps in a 0.25° grid, https://doi.org/10.20350/digitalCSIC/13859) (González-Gambau et al., 2021a) and the Level 4 (L4) product (daily maps in a 0.05° grid, https://doi.org/10.20350/digitalCSIC/13860) (González-Gambau et al., 2021b)), that are computed by applying multifractal fusion to L3 SSS with Sea Surface Temperature (SST) maps. The accuracy of L3 SSS products is typically around 0.7–0.8 psu. The L4 product has an improved spatio-temporal resolution with respect to the L3 and the accuracy is typically around 0.4 psu. Regions with the highest errors and limited coverage are located in Arkona and Bornholm basins and Gulfs of Finland and Riga. The impact assessment of Baltic+ SSS products has shown that they can help in the understanding of salinity dynamics in the basin. They complement the temporally and spatially very sparse in situ measurements, covering data gaps in the region and they can also be useful for the validation of numerical models, particularly in areas where in situ data are very sparse.

Indian Ocean Subtropical Underwater and the Interannual Variability in its Annual Subduction Rate Associated with the Southern Annular Mode

In this study, the Indian Ocean subtropical underwater (IOSTUW) was investigated as a subsurface salinity maximum using Argo floats (2000–2020) for the first time. It has mean salinity, potential temperature and potential density values of 35.54 ± 0.29 psu, 17.91 ± 1.66 °C, and 25.56 ± 0.35 kg m−3, respectively, and mainly extends between 10°S and 30°S along the isopycnal surface in the subtropical south Indian Ocean. The annual subduction rate of the IOSTUW during the period of 2004-2019 was investigated based on a gridded Argo dataset. The results revealed a mean value of 4.39 Sv (1 Sv=106 m3s−1) with an interannual variability that is closely related to the Southern Annular Mode (SAM). The variation in the annual subduction rate of the IOSTUW is dominated by the lateral induction term, which largely depends on the winter mixed layer depth (MLD) in the sea surface salinity (SSS) maximum region. The anomalies of winter MLD is primarily determined by SAM-related air-sea heat flux and zonal wind anomalies through modulation of the buoyancy. As a result, the annual subduction rate of the IOSTUW generally increased when the SAM index showed negative anomalies and decreased when the SAM index showed positive anomalies. Exceptional cases occurred when the wind anomaly within the SSS maximum region was weak or was dominated by its meridional component.

Intercomparison of Salinity Products in the Beaufort Gyre and Arctic Ocean

Salinity is the primary determinant of the Arctic Ocean’s density structure. Freshwater accumulation and distribution in the Arctic Ocean have varied significantly in recent decades and certainly in the Beaufort Gyre (BG). In this study, we analyze salinity variations in the BG region between 2012 and 2017. We use in situ salinity observations from the Seasonal Ice Zone Reconnaissance Surveys (SIZRS), CTD casts from the Beaufort Gyre Exploration Project (BGP), and the EN4 data to validate and compare with satellite observations from Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Aquarius Optimally Interpolated Sea Surface Salinity (OISSS), and Arctic Ocean models: ECCO, MIZMAS, HYCOM, ORAS5, and GLORYS12. Overall, satellite observations are restricted to ice-free regions in the BG area, and models tend to overestimate sea surface salinity (SSS). Freshwater Content (FWC), an important component of the BG, is computed for EN4 and most models. ORAS5 provides the strongest positive SSS correlation coefficient (0.612) and lowest bias to in situ observations compared to the other products. ORAS5 subsurface salinity and FWC compare well with the EN4 data. Discrepancies between models and SIZRS data are highest in GLORYS12 and ECCO. These comparisons identify dissimilarities between salinity products and extend challenges to observations applicable to other areas of the Arctic Ocean.

Global distribution and interannual variation in the winter halocline

The distribution and interannual variation in the winter halocline in the upper layers of the world ocean were investigated via analyses of hydrographic data from the World Ocean Database 2013 using a simple definition of the halocline. A halocline was generally observed in the tropics, equatorward portions of subtropical regions, subarctic North Pacific and Southern Ocean. A strong halocline tended to occur in areas where the sea surface salinity (SSS) was low. The interannual variation in halocline strength was correlated with variation in SSS. The correlation coefficients were usually negative: the halocline was strong when the SSS was low. However, in the Gulf of Alaska in the northeastern North Pacific, the correlation coefficient was positive. There, halocline strength was influenced by interannual variation in Ekman pumping.

SMAP Salinity Retrievals near the Sea-Ice Edge Using Multi-Channel AMSR2 Brightness Temperatures

Sea-ice contamination in the antenna field of view constitutes a large error source in retrieving sea-surface salinity (SSS) with the spaceborne Soil Moisture Active Passive (SMAP) L-band radiometer. This is a major obstacle in the current NASA/Remote Sensing Systems (RSS) SMAP SSS retrieval algorithm in regards to obtaining accurate SSS measurements in the polar oceans. Our analysis finds a strong correlation between 8-day averaged SMAP L-band brightness temperature (TB) bias and TB measurements from the Advanced Microwave Scanning Radiometer (AMSR2) in the C-through Ka-band frequency range for sea-ice contaminated ocean scenes. We show how this correlation can be employed to develop: (1) a discriminant analysis that is able to reliably flag the SMAP observations for sea-ice contamination and (2) subsequently remove the sea-ice contamination from the SMAP observations, which results in significantly more accurate SMAP SSS retrievals near the sea-ice edge. We provide a case study that evaluates the performance of the proposed sea-ice flagging and correction algorithm. Our method is also able to detect drifting icebergs, which go often undetected in many available standard sea-ice products and thus result in spurious SMAP SSS retrievals.

Influence of river discharge on circulation and tidal process in the Java Sea, Indonesia

This study investigates the effect of river discharge in transport and tidal processes in the Java Sea using the Coastal and Regional Ocean Community (CROCO) hydrodynamic model. The model has 20 vertical layers and a horizontal resolution of 1/18 degrees. The oceanic and atmospheric forcing of this model is taken from the global Copernicus Marine Environment Monitoring Service (CMEMS) model and the fifth generation ECMWF atmospheric reanalysis (ERA5) hourly data. Daily Global Flood Awareness System (GloFAS) data has been successfully implemented as river flow data for this study. Two scenarios have been applied, namely, with and without river discharge. This study shows that the two scenarios and the satellite observational data agree in terms of water level with Root Mean Square Difference RMSD) about 4 cm, Sea Surface Temperature with RMSD about 0.29 °C, and Sea Surface Salinity with RMSD about 0.39 psu. The model was also validated using seven tide gauges and produced a good agreement. River discharge increase eastward transport in the eastern part of the Java Sea up to 0.1 Sv (1 Sv= 106 m3s−1). Both scenarios produce similar tidal amplitude and phase and agree well with previous studies and other tidal data sources.

Estimation of Partial Pressure of CO2 (pCO2) Around Mount Krakatau waters, Sunda Straits, Indonesia

The study aimed to develop the formula and validation the value of oceanic carbon dioxide partial pressure (pCO2sea) around Krakatau Waters in the Sunda Strait using parameters such as sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll-a (Chl). Using observation data from different seasons (September 2017 and April 2018) The formulation of the empirical equation by using a multivariate polynomial regression method. The results of the study show that the empirical equation for estimating the pCO2 sea is as follows: : pCO2= -472.069+1044.043x log(SST) -435.897xlog(SSS) -5.03xlog (Chl).This formula can be applied for both seasons. The results of the analysis of the T-student test and p-value showed a strong relationship between the SST parameters and pCO2 with a correlation of 0.91 then followed by salinity with a correlation of -0.82. Whereas chlorophyll-a holds a weak proportion with a correlation of 0.32. The increase in SST accelerates the solubility of CO2 from atmosphere to the sea thereby increasing CO2 sea concentration and increasing pCO2 sea. While the increase in salinity and chlorophyll-a only gives a weak effect

Inter Annual and Seasonal Cycle of Satellite Derived Sea Surface Salinity in the Bay of Bengal

A spatial and temporal variation of sea surface salinity (SSS) is vital to understand the dynamics of the seasonal and inter-annual changes in the marine environment. In the present study, Soil Moisture Active-Passive (SMAP) derived daily SSS product and Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) remote sensing reflectance (Rrs) based SSS images (Algorithm by Qing et al, 2013), applied in the coastal and offshore region of the Bay of Bengal (BoB). SMAP data validation with in situ data (offshore and coastal water, 10 and 15 points) showed good correlation at offshore water and less correlation at coastal water (R2 = 0.707/0.499, SEE = ± 0.291/±0.546, MNB = -0.0029/-0.0089 and RMSE = ± 0.092/±0.139) respectively. Similarly, MODIS-Aqua Rrs derived salinity data validated with in-situ SSS and observed the correlation as follows with R2 = 0.908/0.891, SEE = ± 2.395/±1.512, MNB = 0.0718/0.0361, RMSE = ± 0.760/±0.316 in offshore and coastal water respectively during April and August 2019. The salinity data observed in the range of 32 to 34.5psu. High SSS mean (35.6-35.8psu) observed during the spring inter-monsoon and low salinity (34.6-34.9psu) observed during winter monsoon phase as depicted from decadal scale interpretation. The present study inferred that MODIS aqua derived SSS is better than SMAP based SSS at coastal and offshore region of the western BoB, irrespective of their resolution and spectral differences. More data points based validation would provide the scope for further improvements and seasonal studies on salinity variability using ocean color sensors reflectance based datasets.

The Salinity Pilot-Mission Exploitation Platform (Pi-MEP): A Hub for Validation and Exploitation of Satellite Sea Surface Salinity Data

The Pilot-Mission Exploitation Platform (Pi-MEP) for salinity is an ESA initiative originally meant to support and widen the uptake of Soil Moisture and Ocean Salinity (SMOS) mission data over the ocean. Starting in 2017, the project aims at setting up a computational web-based platform focusing on satellite sea surface salinity data, supporting studies on enhanced validation and scientific process over the ocean. It has been designed in close collaboration with a dedicated science advisory group in order to achieve three main objectives: gathering all the data required to exploit satellite sea surface salinity data, systematically producing a wide range of metrics for comparing and monitoring sea surface salinity products’ quality, and providing user-friendly tools to explore, visualize and exploit both the collected products and the results of the automated analyses. The Salinity Pi-MEP is becoming a reference hub for the validation of satellite sea surface salinity missions by providing valuable information on satellite products (SMOS, Aquarius, SMAP), an extensive in situ database (e.g., Argo, thermosalinographs, moorings, drifters) and additional thematic datasets (precipitation, evaporation, currents, sea level anomalies, sea surface temperature, etc.). Co-localized databases between satellite products and in situ datasets are systematically generated together with validation analysis reports for 30 predefined regions. The data and reports are made fully accessible through the web interface of the platform. The datasets, validation metrics and tools (automatic, user-driven) of the platform are described in detail in this paper. Several dedicated scienctific case studies involving satellite SSS data are also systematically monitored by the platform, including major river plumes, mesoscale signatures in boundary currents, high latitudes, semi-enclosed seas, and the high-precipitation region of the eastern tropical Pacific. Since 2019, a partnership in the Salinity Pi-MEP project has been agreed between ESA and NASA to enlarge focus to encompass the entire set of satellite salinity sensors. The two agencies are now working together to widen the platform features on several technical aspects, such as triple-collocation software implementation, additional match-up collocation criteria and sustained exploitation of data from the SPURS campaigns.