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Advances in L-band microwave satellite radiometry in the past decade, pioneered by ESA's SMOS and NASA's Aquarius and SMAP missions, have demonstrated an unprecedented capability to observe global sea surface salinity (SSS) from space. Measurements from these missions are the only means to probe the very-near surface salinity (top cm), providing a unique monitoring capability for the interfacial exchanges of water between the atmosphere and the upper-ocean, and delivering a wealth of information on various salinity processes in the ocean, linkages with the climate and water cycle, including land-sea connections, and providing constraints for ocean prediction models. The satellite SSS data are complimentary to the existing in situ systems such as Argo that provide accurate depiction of large-scale salinity variability in the open ocean but under-sample mesoscale variability, coastal oceans and marginal seas, and energetic regions such as boundary currents and fronts. In particular, salinity remote sensing has proven valuable to systematically monitor the open oceans as well as coastal regions up to approximately 40 km from the coasts. This is critical to addressing societally relevant topics, such as land-sea linkages, coastal-open ocean exchanges, research in the carbon cycle, near-surface mixing, and air-sea exchange of gas and mass. In this paper, we provide a community perspective on the major achievements of satellite SSS for the aforementioned topics, the unique capability of satellite salinity observing system and its complementarity with other platforms, uncertainty characteristics of satellite SSS, and measurement versus sampling errors in relation to in situ salinity measurements. We also discuss the need for technological innovations to improve the accuracy, resolution, and coverage of satellite SSS, and the way forward to both continue and enhance salinity remote sensing as part of the integrated Earth Observing System in order to address societal needs.

Salinity observing satellites have the potential to monitor river freshwater plumes mesoscale spatiotemporal 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. Highlights ► Improved SMOS salinity systematic error correction from Kolodziejczyk et al. (2016) ► Refined variability of sea surface salinity near e.g. major river mouths ► Consistent mesoscale patterns observed by SMOS and SMAP satellite missions

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International audienceA quantitative assessment of Cloudsat reflectivities and basic ice cloud properties (cloud base, top, and thickness) is conducted in the present study from both airborne and ground-based observations. Airborne observations allow direct comparisons on a limited number of ocean backscatter and cloud samples, whereas the ground-based observations allow statistical comparisons on much longer time series but with some additional assumptions. Direct comparisons of the ocean backscatter and ice cloud reflectivities measured by an airborne cloud radar and Cloudsat during two field experiments indicate that, on average, Cloudsat measures ocean backscatter 0.4 dB higher and ice cloud reflectivities 1 dB higher than the airborne cloud radar. Five ground-based sites have also been used for a statistical evaluation of the Cloudsat reflectivities and basic cloud propertie

The tropical Pacific Ocean remained in a La Niña phase from mid-2010 to mid-2012. In this study, the 2010-2011 near-surface salinity signature of ENSO (El Niño-Southern Oscillation) is described and analyzed using a combination of numerical model output, in situ data, and SMOS satellite salinity products.

International audienceThis study investigates causes for the formation and variability of the Sea Surface Salinity maximum (SSS > 36) centered near 18°S-124°W in the South Pacific Ocean over the 1990-2011 period at the seasonal time scale and above. We use two monthly gridded products of SSS based on in situ measurements, high-resolution along-track Voluntary Observing Ships thermo-salinograph data, new SMOS satellite data, and a validated ocean general circulation model with no direct SSS relaxation. All products reveal a seasonal cycle of the location of the 36-isohaline barycenter of about ±400 km in longitude in response to changes in the South Pacific Convergence Zone location and Easterly winds intensity. They also show a low frequency westward shift of the barycenter of 1400 km from the mid 1990s to the early 2010s that could not be linked to the El Nino Southern Oscillation phenomena. In the model, the processes maintaining the 22 year equilibrium of the high salinity in the mixed layer are the surface forcing (˜+0.73 pss/yr), the horizontal salinity advection (˜-0.37 pss/yr), and processes occurring at the mixed layer base (˜-0.35 pss/yr)

Two L‐Band (1.4 GHz) microwave radiometer missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) currently provide sea‐surface salinity (SSS) measurements. At this frequency, salinity is measured in the first centimetre below the sea surface, which makes it very sensitive to the presence of fresh water lenses linked to rain events. A relationship between salinity anomaly (ΔS) and rain rate (RR) is derived in the Pacific intertropical convergence zone from SMOS SSS measurements and Special Sensor Microwave Imager/Sounder (SSMIS) RR. It is then used to develop an algorithm to estimate RR from SMOS SSS measurements. A heuristic function is developed to correct the SMOS‐estimated negative RR due to measurement noise. Correlation between SMOS and SSMIS RR and between SMOS and Integrated MultisatellitE Retrievals for GPM (IMERG) RR are high when SMOS and SSMIS passes are less than 15 min apart (r = 0.7 at 1° × 1° resolution), showing a good quality of SMOS RR retrievals. When the time shift between SMOS and SSMIS passes increases, the correlation between SMOS and IMERG RR diminishes. This suggests that L‐band radiometry can provide information complementary to GPM missions to improve RR products interpolated at high temporal resolution. The retrieval is successfully tested on SMAP SSS. We also check that our algorithm provides reliable estimates of RR when averaged at a monthly time‐scale.