Soil Moisture And Ocean Salinity Sea Surface Salinity Research Articles

Salinity Fronts in the South Atlantic

Monthly climatology data for salinity fronts in the South Atlantic have been created from satellite SMOS sea surface salinity (SSS) measurements taken from 2011–2019, processed at the Barcelona Expert Center of Remote Sensing (BEC), and provided as high-resolution (1/20°) daily SSS data. The SSS fronts have been identified with narrow zones of enhanced horizontal gradient magnitude (GM) of SSS, computed using the Belkin–O’Reilly algorithm (BOA). The SSS gradient fields generated by the BOA have been log-transformed to facilitate feature recognition. The log-transformation of SSS gradients markedly improved the visual contrast of gradient maps, which in turn allowed new features to be revealed and previously known features to be documented with a monthly temporal resolution and a mesoscale (~100 km) spatial resolution. Monthly climatologies were generated and analyzed for large-scale open-ocean SSS fronts and for low-salinity regions maintained by the Rio de la Plata discharge, Magellan Strait outflow, Congo River discharge, and Benguela Upwelling. A 2000 km-long triangular area between Africa and Brazil was found to be filled with regular quasi-meridional mesoscale striations that form a giant ripple field with a 100 km wave length. South of the Tropical Front, within the subtropical high-salinity pool, a trans-ocean quasi-zonal narrow linear belt of meridional SSS maximum (Smax) was documented. The meridional Smax belt shifts north–south seasonally while retaining its well-defined linear morphology, which is suggestive of a yet unidentified mechanism that maintains this feature. The Subtropical Frontal Zone (STFZ) consists of two tenuously connected fronts, western and eastern. The Brazil Current Front (BCF) extends SE between 40 and 45°S to join the subantarctic front (SAF). The STFZ trends NW–SE across the South Atlantic, seemingly merging with the SAF/BCF south of Africa to form a single front between 40 and 45°S. In the SW Atlantic, the Rio de la Plata plume migrates seasonally, expanding northward in winter (June–July) from 39°S into the South Brazilian Bight, up to Cabo Frio (23°S) and beyond. The inner Plata front moves in and out seasonally. Farther south, the Magellan Strait outflow expands northward in winter (June–July) from 53°S up to 39–40°S to nearly join the Plata outflow. In the SE Atlantic, the Congo River plume spreads radially from the river mouth, with the spreading direction varying seasonally. The plume is often bordered from the south by a quasi-zonal front along 6°S. The diluted Congo River water spreads southward seasonally down to the Angola–Benguela Front at 16°S. The Benguela Upwelling is delineated by a meridional front, which extends north alongshore up to 20°S, where the low-salinity Benguela Upwelling water forms a salinity front, which is separate from the thermal Angola–Benguela Front at 16°S. The high-salinity tropical water (“Angola water”) forms a wedge between the low-salinity waters of the Congo River outflow and Benguela Upwelling. This high-salinity wedge is bordered by salinity fronts that migrate north–south seasonally.

Assimilation of sea surface salinities from SMOS in an Arctic coupled ocean and sea ice reanalysis

Abstract. In the Arctic, the sea surface salinity (SSS) plays a key role in processes related to water mixing and sea ice. However, the lack of salinity observations causes large uncertainties in Arctic Ocean forecasts and reanalysis. Recently the Soil Moisture and Ocean Salinity (SMOS) satellite mission was used by the Barcelona Expert Centre to develop an Arctic SSS product. In this study, we evaluate the impact of assimilating this data in a coupled ocean–ice data assimilation system. Using the deterministic ensemble Kalman filter from July to December 2016, two assimilation runs respectively assimilated two successive versions of the SMOS SSS product on top of a pre-existing reanalysis run. The runs were validated against independent in situ salinity profiles in the Arctic. The results show that the biases and the root-mean-squared differences (RMSD) of SSS are reduced by 10 % to 50 % depending on the area and highlight the importance of assimilating satellite salinity data. The time series of freshwater content (FWC) further shows that its seasonal cycle can be adjusted by assimilation of the SSS products, which is encouraging of the assimilation of SSS in a long-time reanalysis to better reproduce the Arctic water cycle.

Monitoring Black Sea environmental changes from space: New products for altimetry, ocean colour and salinity. Potentialities and requirements for a dedicated in-situ observing system

In this paper, satellite products developed during the Earth Observation for Science and Innovation in the Black Sea (EO4SIBS) ESA project are presented. Ocean colour, sea level anomaly and sea surface salinity datasets are produced for the last decade and validated with regional in-situ observations. New data processing is tested to appropriately tackle the Black Sea’s particular configuration and geophysical characteristics. For altimetry, the full rate (20Hz) altimeter measurements from Cryosat-2 and Sentinel-3A are processed to deliver a 5Hz along-track product. This product is combined with existing 1Hz product to produce gridded datasets for the sea level anomaly, mean dynamic topography, geostrophic currents. This new set of altimetry gridded products offers a better definition of the main Black Sea current, a more accurate reconstruction and characterization of eddies structure, in particular, in coastal areas, and improves the observable wavelength by a factor of 1.6. The EO4SIBS sea surface salinity from SMOS is the first satellite product for salinity in the Black Sea. Specific data treatments are applied to remedy the issue of land-sea and radio frequency interference contamination and to adapt the dielectric constant model to the low salinity and cold waters of the Black Sea. The quality of the SMOS products is assessed and shows a significant improvement from Level-2 to Level -3 and Level-4 products. Level-4 products accuracy is 0.4-0.6 psu, a comparable value to that in the Mediterranean Sea. On average SMOS sea surface salinity is lower than salinity measured by Argo floats, with a larger error in the eastern basin. The adequacy of SMOS SSS to reproduce the spatial characteristics of the Black Sea surface salinity and, in particular, plume patterns is analyzed. For ocean colour, chlorophyll-a, turbidity and suspended particulate materials are proposed using regional calibrated algorithms and satellite data provided by OLCI sensor onboard Sentinel-3 mission. The seasonal cycle of ocean colour products is described and a water classification scheme is proposed. The development of these three types of products has suffered from important in-situ data gaps that hinder a sound calibration of the algorithms and a proper assessment of the datasets quality. We propose recommendations for improving the in-situ observing system that will support the development of satellite products.

First SMOS Sea Surface Salinity dedicated products over the Baltic Sea

Abstract. 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 brightness 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 interference (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 range 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 d 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)​​​​​​​, which are computed by applying multifractal fusion to L3 SSS with SST maps. The accuracy of L3 SSS products is typically around 0.7–0.8 psu. The L4 product has an improved spatiotemporal 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.

Improved BEC SMOS Arctic Sea Surface Salinity product v3.1

Abstract. Measuring salinity from space is challenging since the sensitivity of the brightness temperature (TB) to sea surface salinity (SSS) is low (about 0.5 K psu−1), while the SSS range in the open ocean is narrow (about 5 psu, if river discharge areas are not considered). This translates into a high accuracy requirement of the radiometer (about 2–3 K). Moreover, the sensitivity of the TB to SSS at cold waters is even lower (0.3 K psu−1), making the retrieval of the SSS in the cold waters even more challenging. Due to this limitation, the ESA launched a specific initiative in 2019, the Arctic+Salinity project (AO/1-9158/18/I-BG), to produce an enhanced Arctic SSS product with better quality and resolution than the available products. This paper presents the methodologies used to produce the new enhanced Arctic SMOS SSS product (Martínez et al., 2019) . The product consists of 9 d averaged maps in an EASE 2.0 grid of 25 km. The product is freely distributed from the Barcelona Expert Center (BEC, http://bec.icm.csic.es/, last access: 25 January 2022) with the DOI number https://doi.org/10.20350/digitalCSIC/12620 (Martínez et al., 2019). The major change in this new product is its improvement of the effective spatial resolution that permits better monitoring of the mesoscale structures (larger than 50 km), which benefits the river discharge monitoring.

Evaluation of SMOS L4 Sea Surface Salinity Product in the Western Iberian Coast

Salinity is one of the oldest parameters being measured in oceanography and one of the most important to study in the context of climate change. However, its quantification by satellite remote sensing has been a relatively recent achievement. Currently, after over ten years of data gathering, there are still many challenges in quantifying salinity from space, especially when it is intended for coastal environments study. That is mainly due to the spatial resolution of the available products. Recently, a new higher resolution (5 km) L4 SMOS sea surface salinity (SSS) product was developed by the Barcelona Expert Center (BEC). In this study, the quality of this product was tested along the Western Iberian Coast through its comparison with in situ observations and modelled salinity estimates (CMEMS IBI Ocean Reanalysis system). Moreover, several parameters such as the temperature and depth of in situ measurements were tested to identify the variables or processes that induced higher errors in the product or influenced its performance. Lastly, a seasonal and interannual analysis was conducted considering data between 2011 to 2019 to test the product as a potential tool for long-term studies. The results obtained in the present analysis showed a high potential of using the L4 BEC SSS SMOS product in extended temporal and spatial analyses along the Portuguese coast. A good correlation between the satellite and the in situ datasets was observed, and the satellite dataset showed lower errors in retrieving coastal salinities than the oceanic model. Overall, the distance to the coast and the closest rivers were the factors that most influenced the quality of the product. The present analysis showed that great progress has been made in deriving coastal salinity over the years and that the SMOS SSS product is a valuable contribution to worldwide climatological studies. In addition, these results reinforce the need to continue developing satellite remote sensing products as a global and cost-effective methodology for long-term studies.

Using Remotely Sensed Sea Surface Salinity and Colored Detrital Matter to Characterize Freshened Surface Layers in the Kara and Laptev Seas during the Ice-Free Season

The overall volume of freshwater entering the Arctic Ocean has been growing as glaciers melt and river runoff increases. Since 1980, a 20% increase in river runoff has been observed in the Arctic system. As the discharges of the Ob, Yenisei, and Lena rivers are an important source of freshwater in the Kara and Laptev Seas, an increase in river discharge might have a significant impact on the upper ocean circulation. The fresh river water mixes with ocean water and forms a large freshened surface layer (FSL), which carries high loads of dissolved organic matter and suspended matter into the Arctic Ocean. Optically active material (e.g., phytoplankton and detrital matter) are spread out into plumes, which are evident in satellite data. Russian river signatures in the Kara and Laptev Seas are also evident in recent SMOS Sea Surface Salinity (SSS) Arctic products. In this study, we compare the new Arctic+ SSS products, produced at the Barcelona Expert Center, with the Ocean Color absorption coefficient of colored detrital matter (CDM) in the Kara and Laptev Seas for the period 2011–2019. The SSS and CDM are found to be strongly negatively correlated in the regions of freshwater influence, with regression coefficients between −0.72 and −0.91 in the studied period. Exploiting this linear correlation, we estimate the SSS back to 1998 using two techniques: one assuming that the relationship between the CDM and SSS varies regionally in the river-influenced areas, and another assuming that it does not. We use the 22-year time-series of reconstructed SSS to estimate the interannual variability of the extension of the FSL in the Kara and Laptev Seas as well as their freshwater content. For the Kara and Laptev Seas, we use 32 and 28 psu as reference salinities, and 26 and 24 psu isohalines as FSL boundaries, respectively. The average FSL extension in the Kara Sea is 2089–2611 km2, with a typical freshwater content of 11.84–14.02 km3. The Laptev Sea has a slightly higher mean FSL extension of 2320–2686 km2 and a freshwater content of 10.15–12.44 km3. The yearly mean freshwater content and extension of the FSL, computed from SMOS SSS and Optical data, is (as expected) found to co-vary with in situ measurements of river discharge from the Arctic Great Rivers Observatory database, demonstrating the potential of SMOS SSS to better monitor the river discharge changes in Eurasia and to understand the Arctic freshwater system during the ice-free season.

Seasonal Variability of Freshwater Plumes in the Eastern Gulf of Guinea as Inferred From Satellite Measurements

AbstractIn the eastern Gulf of Guinea (GG), freshwater originated from rivers discharges into the ocean and high precipitation rate are key contributors to the upper ocean vertical density stratification, and play a key role in modulating local air‐sea interactions as well as biogeochemical cycle. Nevertheless, the dynamics of the GG freshwater plumes remain poorly documented because of the scarcity of historical, in situ observations and the lack of an ad hoc satellite‐based analysis in this region. Recent advances in remote sensing capabilities from the Soil Moisture and Ocean Salinity (SMOS) satellite mission offer unprecedented coverage and spatiotemporal resolution of Sea Surface Salinity (SSS) in the GG. Using SMOS SSS and available in situ measurements, the seasonal variability of freshwater plumes and associated physical mechanisms controlling their seasonal cycle are presented and analyzed. Freshwater plumes in the GG follow two dynamical regimes. They present maximum offshore extension during boreal winter and exhibit minimum signature during summer. In the northeastern GG, SSS variability is mainly explained by high precipitation rate and Niger River runoff during winter, while during late summer, SSS is mainly driven by horizontal advection. In contrast, southeast of GG, freshwater plumes are mainly supplied by Congo River runoff. From September to March, SSS variability is driven by zonal advection, with a major contribution from Ekman wind‐driven currents. During spring‐summer, the observed SSS increase is likely explained by entrainment and vertical mixing. SSS budget and freshwater advection processes are discussed in the context of the shallow stratification induced by freshwater.

Nine years of SMOS sea surface salinity global maps at the Barcelona Expert Center

Abstract. After more than 10 years in orbit, the Soil Moisture and Ocean Salinity (SMOS) European mission is still a unique, high-quality instrument for providing soil moisture over land and sea surface salinity (SSS) over the oceans. At the Barcelona Expert Center (BEC), a new reprocessing of 9 years (2011–2019) of global SMOS SSS maps has been generated. This work presents the algorithms used in the generation of BEC global SMOS SSS product v2.0, as well as an extensive quality assessment. Three SMOS SSS fields are distributed: a high-resolution level-3 product (with DOI https://doi.org/10.20350/digitalCSIC/12601, Olmedo et al., 2020a) consisting of binned SSS in 9 d maps at 0.25∘×0.25∘; low-resolution level-3 SSS computed from the binned salinity by applying a smoothing spatial window of 50 km radius; and level-4 SSS (with DOI https://doi.org/10.20350/digitalCSIC/12600, Olmedo et al., 2020b) consisting of daily 0.05∘×0.05∘ maps that are computed by multifractal fusion with sea surface temperature maps. For the validation of BEC SSS products, we have applied a battery of tests aimed at the assessment of quality of the products both in value and in structure. First, we have compared BEC SSS products with near-to-surface salinity measurements provided by Argo floats. Secondly, we have assessed the geophysical consistency of the products characterized by singularity analysis, and the effective spatial resolutions are also estimated by means of power density spectra and singularity density spectra. Finally, we have calculated full maps of SSS errors by using correlated triple collocation. We have compared the performance of the BEC SMOS product with other satellite SSS and reanalysis products. The main outcomes of this quality assessment are as follows. (i) The bias between BEC SMOS and Argo salinity is lower than 0.02 psu at a global scale, while the standard deviation of their difference is lower than 0.34 and 0.27 psu for the high- and low-resolution level-3 fields (respectively) and 0.24 psu for the level-4 salinity. (ii) The effective spatial resolution is around 40 km for all SSS products and regions. (iii) The results from triple collocation show the BEC SMOS level-4 product as the product with the lowest estimated salinity error in most of the global ocean and the BEC SMOS high-resolution level-3 as the one with the lowest estimated salinity error in regions strongly affected by rainfall and continental freshwater discharge.

Accuracy and Error Characteristics of SMOS Sea Surface Salinity in the Seas around Korea

Accuracy and Error Characteristics of SMOS Sea Surface Salinity in the Seas around Korea

New insights into SMOS sea surface salinity retrievals in the Arctic Ocean

Since 2010, the Soil Moisture and Ocean Salinity (SMOS) satellite mission monitors the earth emission at L-Band. It provides the longest time series of Sea Surface Salinity (SSS) from space over the global ocean. However, the SSS retrieval at high latitudes is a challenge because of the low sensitivity L-Band radiometric measurements to SSS in cold waters and to the contamination of SMOS measurements by the vicinity of continents, of sea ice and of Radio Frequency Interferences. In this paper, we assess the quality of weekly SSS fields derived from swath-ordered instantaneous SMOS SSS (so called Level 2) distributed by the European Space Agency. These products are filtered according to new criteria. We use the pseudo-dielectric constant retrieved from SMOS brightness temperatures to filter SSS pixels polluted by sea ice. We identify that the dielectric constant model and the sea surface temperature auxiliary parameter used as prior information in the SMOS SSS retrieval induce significant systematic errors at low temperatures. We propose a novel empirical correction to mitigate those sources of errors at high latitudes.Comparisons with in-situ measurements ranging from 1 to 11 m depths spotlight huge vertical stratification in fresh regions. This emphasizes the need to consider in-situ salinity as close as possible to the sea surface when validating L-band radiometric SSS which are representative of the first top centimeter.SSS Standard deviation of differences (STDD) between weekly SMOS SSS and in-situ near surface salinity significantly decrease after applying the SSS correction, from 1.46 pss to 1.28 pss. The correlation between new SMOS SSS and in-situ near surface salinity reaches 0.94. SMOS estimates better capture SSS variability in the Arctic Ocean in comparison to TOPAZ reanalysis (STDD between TOPAZ and in-situ SSS = 1.86 pss), particularly in river plumes with very large SSS spatial gradients.

Bay of Bengal Sea surface salinity variability using a decade of improved SMOS re-processing

Monsoon rain and rivers bring large freshwater input to the Northern Bay of Bengal (BoB), yielding low Sea Surface Salinity (SSS) after the monsoon. The resulting sharp upper-ocean salinity stratification is thought to influence tropical cyclones intensity and biological productivity by inhibiting vertical mixing. Despite recent progresses, the density of in situ data is far from sufficient to monitor the BoB SSS variability, even at the seasonal timescale. The advent of satellite remotely-sensed SSS (SMOS, Aquarius, SMAP) offers a unique opportunity to provide synoptic maps of the BoB SSS every ~8 days. Previous SMOS SSS retrievals did not perform well in the BoB. Here, we show that improved systematic error corrections and quality control procedures yield a much better performance of the new “debiased v4” CATDS level-3 SSS from SMOS (~0.8 correlation, 0.04 bias and 0.64 root-mean-square difference to more than 28,000 collocated in situ data points over 2010–2019). The SMOS product now performs equivalently to Aquarius, and is slightly inferior to SMAP over the BoB. In particular, SMAP and SMOS are able to capture salinity variations close to the east coast of India (r > 0.8 within 75–150 km of the coast). They thus capture the seasonal freshening there, associated with equatorward advection of the Northern BoB low-salinity water by the East Indian Coastal Current (EICC) after the summer monsoon. The 10-year long SMOS record further allows to describe the BoB interannual SSS variability, which is strongest in boreal fall in relation with the Indian Ocean Dipole (IOD). Positive IOD events induce a weakening of the southward export of freshwater by the EICC, and hence negative SSS anomalies in the Northern BoB and positive ones along the East Indian coast. This confirms results from earlier studies based on modelling, sparse in situ data, or shorter satellite records, but this time from a 10-year long SSS record. Overall, our study indicates that the new SMOS retrieval can be confidently used to monitor the BoB SSS and to study its mechanisms. We end by a brief description of the BoB SSS anomalies associated with the extreme 2019 IOD event and highlight the very good performance over the BoB of a new multi-satellite product developed by the European Space Agency merging SMOS, Aquarius and SMAP data.

Evaluation and Intercomparison of SMOS, Aquarius, and SMAP Sea Surface Salinity Products in the Arctic Ocean

Salinity is a critical parameter in the Arctic Ocean, having potential implications for climate and weather. This study presents the first systematic analysis of 6 commonly used sea surface salinity (SSS) products from the National Aeronautics and Space Administration (NASA) Aquarius and Soil Moisture Active Passive (SMAP) satellites and the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, in terms of their consistency among one another and with in-situ data. Overall, the satellite SSS products provide a similar characterization of the time mean SSS large-scale patterns and are relatively consistent in depicting the regions with strong SSS temporal variability. When averaged over the Arctic Ocean, the SSS show an excellent consistency in describing the seasonal and interannual variations. Comparison of satellite SSS with in-situ salinity measurements along ship transects suggest that satellite SSS captures salinity gradients away from regions with significant sea-ice concentration. The root-mean square differences (RMSD) of satellite SSS with respect to in-situ measurements improves with increasing temperature, reflecting the limitation of L-band radiometric sensitivity to SSS in cold water. However, the satellite SSS biases with respect to the in-situ measurements do not show a consistent dependence on temperature. The results have significant implications for the calibration and validation of satellite SSS as well as for the modeling community and the design of future satellite missions.

Spatio-temporal variability of the equatorial front in the eastern tropical Pacific from remote sensing salinity data (2010–2015)

In the eastern Pacific, the Equatorial Front (EF) separates the relatively cold and salty Equatorial Surface Water to the South from the relatively warm and fresh Surface Tropical Water to the North. Although previous studies have succinctly described the main EF characteristics, little is known about its spatial pattern and temporal variability beyond seasonal scale. In this study, we used 5 years of remote-sensing sea surface salinity (SSS) data, acquired by SMOS in a synoptic and continuous manner, to describe the spatio-temporal variability of the haline EF (HEF). After validating the SSS satellite dataset with concomitant in situ data, we developed an algorithm based on maximum SSS gradients to automatically identify the HEF position on every SMOS SSS map. This algorithm applied to the 4-day resolution SMOS SSS product, allows us to investigate the spatio-temporal variability of the HEF between 2010 and 2015. Seasonally, west of the Galapagos Islands, the HEF is centered at ~2°N and shows relatively weak meridional displacements. In contrast, east of the Galapagos Archipelago, the HEF varies on average between ~4°S in spring and ~2°N in autumn. At intraseasonal scales, the HEF shows meridional fluctuations of ±1.25° around its average position. These intraseasonal fluctuations are partly related to Kelvin Waves, which was more noticeable since 2014. Over the 5-year studied period, the HEF position showed a northward trend of 0.3–0.4 latitudinal degrees per year between 2010 and 2015. However, the relatively short time series does not permit to investigate whether this low-frequency trend is due to long-term variability or interdecadal fluctuations.

Empirical Characterization of the SMOS Brightness Temperature Bias and Uncertainty for Improving Sea Surface Salinity Retrieval

After more than eight years of the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) acquisitions, an exhaustive, empirical characterization of the biases and uncertainties affecting SMOS brightness temperatures over the ocean is possible. We show that both parameters strongly depend not only on the position in the field of view, but also on the geographical location of the acquisition. Metrics based on the differences between expected and theoretical values of the bias and the uncertainty are developed and used for quantitatively assessing the locations where SMOS errors are currently not accurately characterized. This characterization can be used for the definition of a new empirical SMOS sea surface salinity (SSS) bias correction, a better cost function retrieval, and more accurate filtering criteria, which are expected to lead to a better SMOS SSS Level 2 product. We present a new L2 SMOS SSS product based on the described investigation. The performance of this preliminary product is similar to that of the version v662 of the official L2 SMOS SSS product at medium and low latitudes. However, it provides a better coverage at high latitudes and coastal regions affected by radio frequency interference (RFI), which correspond to those regions where the SMOS errors are currently poorly estimated.

Data assimilation of Soil Moisture and Ocean Salinity (SMOS) observations into the Mercator Ocean operational system: focus on the El Niño 2015 event

Abstract. Monitoring sea surface salinity (SSS) is important for understanding and forecasting the ocean circulation. It is even crucial in the context of the intensification of the water cycle. Until recently, SSS was one of the less observed essential ocean variables. Only sparse in situ observations, mostly closer to 5 m depth than the surface, were available to estimate the SSS. The recent satellite ESA Soil Moisture and Ocean Salinity (SMOS), NASA Aquarius SAC-D and Soil Moisture Active Passive (SMAP) missions have made it possible for the first time to measure SSS from space and can bring a valuable additional constraint to control the model salinity. Nevertheless, satellite SSS still contains some residual biases that must be removed prior to bias correction and data assimilation. One of the major challenges of this study is to estimate the SSS bias and a suitable observation error for the data assimilation system. It was made possible by modifying a 3D-Var bias correction scheme and by using the analysis of the residuals and errors with an adapted statistical technique. This article presents the design and the analysis of an observing system experiment (OSE) conducted with the 0.25∘ resolution Mercator Ocean global analysis and forecasting system during the El Niño 2015/16 event. The SSS data assimilation constrains the model to be closer to the near-surface salinity observations in a coherent way with the other data sets already routinely assimilated in an operational context. This also shows that the overestimation of E–P is corrected by data assimilation through salting in regions where precipitations are higher. Globally, the SMOS SSS assimilation has a positive impact in salinity over the top 30 m. Comparisons to independent salinity data sets show a small but positive impact and corroborate the fact that the impact of SMOS SSS assimilation is larger in the Intertropical Convergence Zone (ITCZ) and South Pacific Convergence Zone (SPCZ) regions. There is little impact on the sea surface temperature (SST) and sea surface height (SSH) error statistics. Nevertheless, the SSH seems to be impacted by the tropical instability wave (TIW) propagation, itself linked to changes in barrier layer thickness (BLT). Finally, this study helped us to progress in the understanding of the biases and errors that can degrade the SMOS SSS data assimilation performance.

Correction: Olmedo, E., et al. Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in Arctic and Sub-Arctic Regions. Remote Sensing 2018, 10, 1772

The authors wish to make the following corrections to this paper [...]

Assimilation of SMOS Sea Surface Salinity in the Regional Ocean Model for South China Sea

Ocean salinity has an important impact on marine environment simulations. The Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite in the world to provide large-scale global salinity observations of the oceans. Salinity remote sensing observations in the open ocean have been successfully applied in data assimilations, while SMOS salinity observations contain large errors in the coastal ocean (including the South China Sea (SCS)) and high latitudes and cannot be effectively applied in ocean data assimilations. In this paper, the SMOS salinity observation data are corrected with the Generalized Regression Neural Network (GRNN) in data assimilation preprocessing, which shows that after correction, the bias and root mean square error (RMSE) of the SMOS sea surface salinity (SSS) compared with the Argo observations can be reduced from 0.155 PSU and 0.415 PSU to −0.003 PSU and 0.112 PSU, respectively, in the South China Sea. The effect is equally significant in the northwestern Pacific region. The preprocessed salinity data were applied to an assimilation in a coastal region for the first time. The six groups of assimilation experiments set in the South China Sea showed that the assimilation of corrected SMOS SSS can effectively improve the upper ocean salinity simulation.

Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in Arctic and Sub-Arctic Regions

This paper aims to present and assess the quality of seven years (2011–2017) of 25 km nine-day Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) objectively analyzed maps in the Arctic and sub-Arctic oceans ( 50 ∘ N– 90 ∘ N). The SMOS SSS maps presented in this work are an improved version of the preliminary three-year dataset generated and freely distributed by the Barcelona Expert Center. In this new version, a time-dependent bias correction has been applied to mitigate the seasonal bias that affected the previous SSS maps. An extensive database of in situ data (Argo floats and thermosalinograph measurements) has been used for assessing the accuracy of this product. The standard deviation of the difference between the new SMOS SSS maps and Argo SSS ranges from 0.25 and 0.35. The major features of the inter-annual SSS variations observed by the thermosalinographs are also captured by the SMOS SSS maps. However, the validation in some regions of the Arctic Ocean has not been feasible because of the lack of in situ data. In those regions, qualitative comparisons with SSS provided by models and the remotely sensed SSS provided by Aquarius and SMAP have been performed. Despite the differences between SMOS and SMAP, both datasets show consistent SSS variations with respect to the model and the river discharge in situ data, but present a larger dynamic range than that of the model. This result suggests that, in those regions, the use of the remotely sensed SSS may help to improve the models.

New SMOS Sea Surface Salinity with reduced systematic errors and improved variability

Salinity observing satellites have the potential to monitor river fresh-water plumes mesoscale spatio-temporal variations better than any other observing system. In the case of the Soil Moisture and Ocean Salinity (SMOS) satellite mission, this capacity was hampered due to the contamination of SMOS data processing by strong land-sea emissivity contrasts. Kolodziejczyk et al. (2016) (hereafter K2016) developed a methodology to mitigate SMOS systematic errors in the vicinity of continents, that greatly improved the quality of the SMOS Sea Surface Salinity (SSS). Here, we find that SSS variability, however, often remained underestimated, such as near major river mouths. We revise the K2016 methodology with: a) a less stringent filtering of measurements in regions with high SSS natural variability (inferred from SMOS measurements) and b) a correction for seasonally-varying latitudinal systematic errors. With this new mitigation, SMOS SSS becomes more consistent with the independent SMAP SSS close to land, for instance capturing consistent spatio-temporal variations of low salinity waters in the Bay of Bengal and Gulf of Mexico. The standard deviation of the differences between SMOS and SMAP weekly SSS is <0.3 pss in most of the open ocean. The standard deviation of the differences between 18-day SMOS SSS and 100-km averaged ship SSS is 0.20 pss (0.24 pss before correction) in the open ocean. Even if this standard deviation of the differences increases closer to land, the larger SSS variability yields a more favorable signal-to-noise ratio, with r2 between SMOS and SMAP SSS larger than 0.8. The correction also reduces systematic biases associated with man-made Radio Frequency Interferences (RFI), although SMOS SSS remains more impacted by RFI than SMAP SSS. This newly-processed dataset will allow the analysis of SSS variability over a larger than 8 years period in regions previously heavily influenced by land-sea contamination, such as the Bay of Bengal or the Gulf of Mexico.