<p>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 [<em>Boutin et al.</em>, 2021]. 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.</p><p>In this presentation, we review recent advances and performances of the last (version 3) CCI+SSS product.</p><p>The CCI v3 processing has been updated to improve the long-term stability of the SMOS SSS [<em>Perrot et al.</em>, 2021] and to improve the level 4 SSS uncertainty estimates. A correction for the instantaneous rainfall impact [<em>Supply et al.</em>, 2020] is applied, so that, in rainy regions the CCI v3 fields are close to bulk salinities. In the level 4 optimal interpolation, a full least square propagation of the errors is implemented, instead of a simplified propagation.</p><p>When compared with Argo upper salinities, the robust standard deviation of the pairwise difference is 0.16 pss. However, this number includes a sampling mismatch between the in-situ near-surface salinity done at a single space and time and the two-dimensional satellite SSS. We use a small-scale resolution simulation (1/12° GLORYS) to quantitatively estimate the sampling uncertainty. A quantitative validation of CCI v3 SSS and its associated uncertainties is performed by considering the satellite minus Argo salinity normalized by the sampling and retrieval uncertainties [<em>Merchant et al.</em>, 2017]. We find that, at global scale, the sampling mismatch contributes to ~20% of the observed differences between Argo and satellite data; in highly variable regions (river plumes, fronts), the sampling mismatch is the dominant term explaining satellite minus Argo salinity differences.</p><p>References</p><p>Boutin, J., et al. (2021), Satellite-Based Sea Surface Salinity Designed for Ocean and Climate Studies, <em>JGR-Oceans</em>, <em>126</em>(11), doi:10.1029/2021JC017676.</p><p>Merchant, C. J., et al. (2017), Uncertainty information in climate data records from Earth observation, <em>Earth Syst. Sci. Data</em>, doi:10.5194/essd-9-511-2017.</p><p>Perrot, X., et al. (2021), CCI+SSS: A New SMOS L2 Reprocessing Reduces Errors on Sea Surface Salinity Time Series, IGARSS proceedings, doi: 10.1109/IGARSS47720.2021.9554451.</p><p>Supply, A.et al. (2020), Variability of Satellite Sea Surface Salinity Under Rainfall, in <em>Satellite Precipitation Measurement: Volume 2</em>, doi:10.1007/978-3-030-35798-6_34.</p>

<p>The Bay of Bengal is under the influence of the monsoon and has a highly contrasted and variable Sea Surface Salinity (SSS). In situ salinity data is however too sparse to reconstruct interannual SSS variability of the Bay of Bengal prior to synoptic SSS mapping of SMOS launched in 2009.</p><p>Previous studies have demonstrated the ability of X minus C-band measurements, such as those of AMSR-E (May 2002-Oct 2011), to track SSS changes in high-contrast regions and at high Sea Surface Temperature (SST). Here, we apply this approach to reconstruct the Bay of Bengal SSS before 2010. We remove the effects of other geophysical variables such as SST, surface wind, and atmospheric water content using an empirical approach. SSS is then retrieved based on another empirical fit, trained on the ESA Climate Change Initiative (CCI) SSS dataset, over the AMSR-E and CCI common period (Jan 2010 to Oct 2011). Our first results are encouraging: spatial contrast between the low post-monsoon SSS values close to estuaries and along the west coast of India are reproduced. Our algorithm, however, tends to overestimate low SSS and underestimate high SSS values, possibly due to data contamination near the coast and/or a suboptimal removal of the signals from other geophysical variables. Nevertheless, the first results show a correct representation of the recognizable Indian Ocean Dipole (IOD) phenomena. Furthermore, we are currently creating and studying the use of a neuronal network with the intention to include more parameters in the algorithm.</p><p>The long-term goal of this work is to merge the C-, X-, and L-band data with in-situ measurements thus providing a long-term reconstruction of monthly SSS in the Bay of Bengal with a ~50 km resolution This dataset will be used to explore the physical processes that drive interannual SSS variability in regions where it is strong, such as near major river estuaries or along the west coast of India.</p>

<p><span>An</span> impact of <span>the</span> upper <span>ocean response</span> to tropical cyclones (TC) is usually <span>considered</span> <span>as</span> a negative feedback mechanism between cooling of the mixed layer (ML) and intensity of a TC. <span>Influence of</span> TCs <span>on</span> the upper ocean is manifested as <span>anomalies</span> in sea surface temperature (SST) and sea surface salinity (SSS) <span>in wakes of hurricanes, that can vary significantly along tracks of TCs (Reul et al. 2021). Proper modelling of ML dynamics is still vital to explain surface cooling observed in satellite and in situ data. Although numerous models of the ML evolution have been developed (e.g., Zilitinkevich et al. 1979, Gillian et al. 2020, and works cited therein including many schemes incorporated in numerical models), there is still a controversy as to turbulent closure schemes and simplified approaches that could allow for a quick and high quality assessment of ML parameters.</span></p><p>The purpose of the <span>this work</span> is to apply a simplified model of the upper ocean response to TCs suggested by Kudryavtsev et al. 2019 with barotropic and baroclinic modes resolved. To describe ML dynamics, <span>results of Zilitinkevich and Esau (2003) are applied.</span> The cases studied are those of hurricanes passing over the Amazon-Orinoco river plume: Igor (Reul et al. 2014), Katia (Grodsky et al. 2012) and Irma (Balaguru et al. 2020).</p><p>Best track parameters of the TCs are obtained from the IBTrACKS archive. Multi-source GHRSST data on SST as well as SMOS and SMAP satellite data on SSS are used to compare the observed ocean responses to the simulated ones. ISAS20 in situ archive data are used to provide vertical profiles of temperature and salinity as an input to the model. Precipitation and evaporation data are obtained from TRMM measurements and ERA5 reanalysis, respectively. Subsets of IBTrACKS, GHRSST, ISAS20, TRMM and ERA5 data specific to domain of a TC’s wake were produced by the Centre de Recherche et d'Exploitation Satellitaire (CERSAT), at IFREMER, Plouzane (France) for ESA funded project MAXSS (Marine Atmosphere eXtreme Satellite Synergy). Model simulations are consistent with the observations and provide a deeper insight in the physics of relationship between SST and SSS anomalies in TC wakes. On the basis of analysis of the observations and model results, a semi-empirical expressions to predict SSS and SST anomalies using TC parameters (radius, wind speed and translation velocity) and prestorm stratification are suggested.</p><p>The work was supported by the Russian Science Foundation through the Project No. 21-47-00038, by Ministry of Science and Education of the Russian Federation under State Assignment No. 0555-2021-0004 at MHI RAS, and State Assi<span>gnment No. 0763-2020-0005 at RSHU (P.P. and V.K.). T</span><span>he ESA/MAXSS project support is also gratefully acknowledged (N.R. and B.C.).</span></p>

In the affiliations section at the end of the article, the affiliation of Hayley Evers-King was incorrectly indicated as "Met Office". The correct affiliation is: EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites),