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alidation of satellite sea surface salinity (SSS) products is typically based on comparisons with in-situ measurements at a few meters’ depth, which are mostly done at a single location and time. The difference in term of spatio-temporal resolution between the in-situ near-surface salinity and the two-dimensional satellite SSS results in a sampling mismatch uncertainty. The Climate Change Initiative (CCI) project has merged SSS from three satellite missions. Using an optimal interpolation, weekly and monthly SSS and their uncertainties are estimated at a 50 km spatial resolution over the global ocean. Over the 2016–2018 period, the mean uncertainty on weekly CCI SSS is 0.13, whereas the standard deviation of weekly CCI minus in-situ Argo salinities is 0.24. Using SSS from a high-resolution model reanalysis, we estimate the expected uncertainty due to the CCI versus Argo sampling mismatch. Most of the largest spatial variability of the satellite minus Argo salinity is observed in regions with large estimated sampling mismatch. A quantitative validation is performed by considering the statistical distribution of the CCI minus Argo salinity normalized by the sampling and retrieval uncertainties. This quantity should follow a Gaussian distribution with a standard deviation of 1, if all uncertainty contributions are properly taken into account. We find that (1) the observed differences between Argo and CCI data in dynamical regions (river plumes, fronts) are mainly due to the sampling mismatch; (2) overall, the uncertainties are well estimated in CCI version 3, much improved compared to CCI version 2. There are a few dynamical regions where discrepancies remain and where the satellite SSS, their associated uncertainties and the sampling mismatch estimates should be further validated.

Decade‐long satellite Sea Surface Salinity (SSS) observations show that rain dilution prevails in wakes of Tropical Depressions (∼‐0.1 pss) and Tropical Storms (∼‐0.05 pss) on the left (right) side of Northern (Southern) Hemisphere storms. For stronger storms, the rain‐induced dilution is dominated by the saltier water entrainment, leading to surface median salinification of 0.3 pss for the most intense storms, peaking on the right‐hand side at around twice the maximum wind radius. The magnitude of the salty wake increases for stronger slowly‐moving storms. The vertical salinity gradient in the upper ocean is a key factor explaining the geographic distribution of the SSS response. A striking example is the systematic mixing of fresh near‐surface river plume waters with saltier subsurface waters. It is also found that barrier layers lead to saltier and warmer storm wakes compared to wakes produced over barrier layer free areas.

Sea Surface Salinity (SSS) is an increasingly-used Essential Ocean and Climate Variable. The SMOS, Aquarius, and SMAP satellite missions all provide SSS measurements, with very different instrumental features leading to specific measurement characteristics. The Climate Change Initiative Salinity project (CCI+SSS) aims to produce a SSS Climate Data Record (CDR) that addresses well-established user needs based on those satellite measurements. To generate a homogeneous CDR, instrumental differences are carefully adjusted based on in-depth analysis of the measurements themselves, together with some limited use of independent reference data. An optimal interpolation in the time domain without temporal relaxation to reference data or spatial smoothing is applied. This allows preserving the original datasets variability. SSS CCI fields are well-suited for monitoring weekly to interannual signals, at spatial scales ranging from 50 km to the basin scale. They display large year-to-year seasonal variations over the 2010-2019 decade, sometimes by more than +/-0.4 over large regions. The robust standard deviation of the monthly CCI SSS minus in situ Argo salinities is 0.15 globally, while it is at least 0.20 with individual satellite SSS fields. r2 is 0.97, similar or better than with original datasets. The correlation with independent ship thermosalinographs SSS further highlights the CCI dataset excellent performance, especially near land areas. During the SMOS-Aquarius period, when the representativity uncertainties are the largest, r2 is 0.84 with CCI while it is 0.48 with the Aquarius original dataset. SSS CCI data are freely available and will be updated and extended as more satellite data become available. Plain Language Summary Salinity measures the mass of dissolved salts in seawater. Together with temperature and pressure, it determines the seawater density, which is crucial in driving oceanic motions. Low sea surface salinity (SSS) can be the result of freshwater inp

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 sl

Operated since the end of 2009, the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite mission is the first orbiting radiometer that collects regular and global observations from space of two Essential Climate Variables of the Global Climate Observing System: Sea Surface Salinity (SSS) and Soil Moisture. The National Aeronautics and Space Administration (NASA) Aquarius mission, with the primary objective to provide global SSS measurements from space operated from mid-2011 to mid-2015. NASA's Soil Moisture Active-Passive (SMAP) mission, primarily dedicated to soil moisture measurements, but also monitoring SSS, has been operating since early 2015. The primary sensors onboard these three missions are passive microwave radiometers operating at 1.4 GHz (L-band). SSS is retrieved from radiometer measurements of the sea surface brightness temperature (TB). In this paper, we first provide a historical review of SSS remote sensing with passive L-band radiometry beginning with the discussions of measurement principles, technology, sensing characteristics and complementarities of the three aforementioned missions. The assessment of satellite SSS products is then presented in terms of individual mission characteristics, common algorithms, and measurement uncertainties, including the validation versus in situ data, and, the consideration of sampling differences between satellite SSS and in situ salinity measurements. We next review the major scientific achievements of the combined first 10 years of satellite SSS data, including the insights enabled by these measurements regarding the linkages of SSS with the global water cycle, climate variability, and ocean biochemistry. We also highlight the new ability provided by satellites to monitor mesoscale and synoptic-scale SSS features and to advance our understanding of SSS' role in air-sea interactions, constraining ocean models, and improving seasonal predictions. An overview of satellite SSS observation

One of the saltiest seas, the Mediterranean, experiences significant salinity variations in near surface layers. Satellite sea surface salinity (SSS) data obtained using Soil Moisture Active Passive (SMAP) mission indicate steady salinification of the eastern Mediterranean Levantine Basin at a rate of ~0.14 psu/year during 2015–2018. Satellite-observed salinity changes are confirmed by Argo float data and suggest possible changes in properties of the Levantine Intermediate Water. Eastern Mediterranean salinification often coincides with a freshening of the western Ionian Sea. Based on satellite altimetry geostrophic currents, these salinity changes are concurrent with a weakening cyclonic circulation in the Levantine Basin and strengthening anticyclonic circulation in the Ionian Sea. The latter is indicative of the Adriatic-Ionian Bimodal Oscillation System. It is known that such circulation changes reduce (increase) the transport of fresh Modified Atlantic Water into the eastern Mediterranean (western Ionian Sea), and this is consistent with observed SSS changes. The quality and availability of satellite L-band (1.41 GHz) SSS estimates near the coast can be limited by land contamination. It is shown that absolute SSS retrievals exhibit up to 1 psu biases in the Mediterranean. This study's use of SMAP SSS anomaly mapping instead of absolute SSS illustrates that observed spatial/temporal SSS patterns allow investigation of time variable change in this basin and augment the existing regional observing system.

The Eastern Pacific Fresh Pool (EPFP) is a large region of low Sea Surface Salinity (SSS) defined by values lower than 34 practical salinity scale within [5°S-30°N, 75°W-180°W]. The fresh pool dynamically responds to strong regional and seasonally varying ocean-atmosphere-land interactions (including monsoon rain, trade and gap winds and strong currents). Using more than five years of Soil Moisture and Ocean Salinity (SMOS) satellite sea surface salinity (SSS) and complementary satellite wind, rain, currents, and sea surface temperature data together with a historical ensemble of in situ products, the present study explores the seasonal and interannual dynamics of the fresh pool over the period 2004-2015. An important interannual variability of the maximal surface extension of the EPFP over the past decade is revealed with two extreme events (2012, 2015) occurring during the SMOS satellite period. These extremes are found to be related to the El Niño-Southern Oscillation (ENSO) phases and associated anomalies of precipitation, surface currents and trade wind in the central Pacific. In 2012 (La Niña), stronger trade winds coupled with a deficit of precipitation induced a minimum extension of the pool during the rainy season. Whereas, during the strong El Niño 2014-2015, the EPFP extension reached an unprecedented maximum value. A modification of the atmospheric freshwater fluxes and ocean surface currents during winter 2014 are found to have favored the onset of this abnormal fresh event.

Radar observations of the sea surface at C-Band and small incidence angles are used to investigate some properties of the surface slope probability density function (pdf). The method is based on the analysis of the variation of the radar cross-section with incidence angle, assuming a backscattering process following the Geometrical Optics theory. First, we assess the limit of this model in our experimental configuration by using simulations of radar cross-sections with a more accurate backscattering model, namely the Physical Optics model. We show that roughness properties with scales larger than 12 cm can be analyzed in our configuration (C-Band, incidence 7 to 16 degrees). The radar data are then analyzed in terms of filtered mean square slope under the assumption of a Gaussian slope pdf. Dependence of the radar-derived mean square slopes (mss) with wind speed is analyzed, thanks to wind estimates obtained by using coincident observations of the same radar at larger incidence (around 32 degrees). Furthermore an analysis of the anisotropy of the mean square slope is proposed. The results are discussed in comparison with those of Cox and Munk (1954a, 1954b), and with the mean square slopes derived from two surface models (Elfouhaily et al., 1997 and Kudryavtsev et al., 2003). We find that the radar-derived values are in good agreement with Cox and Munk results, taking into account the filtering effect on radar-derived values. We also show that the surface model of Elfouhaily et al. yields good agreement for the omni directional mss, but a too large anisotropy of the mss. The model of Kudryavtsev provides a reasonable anisotropy of the mss, but overestimates the mss values in all directions. Finally, we propose an analysis of the radar data under a non-Gaussian assumption for the slope pdf, by applying the compound model suggested by Chapron et al. (2000) to our observations. To our knowledge, it is the first time that peakedness values are explicitly derived from ra