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The Bay of Bengal (BoB) Sea Surface Salinity (SSS) is highly contrasted and variable, in response to the large monsoonal wind and freshwater forcing. In addition to this strong seasonal cycle, previous studies have underlined strong SSS non-seasonal variations associated with the Indian Ocean Dipole (IOD) and mesoscale eddies. In this study, we quantify the relative contributions of externally forced (wind, freshwater) and internally generated (mesoscale eddies) SSS non-seasonal variability in the BoB. To that end, we use Ocean General Circulation Model 10-member ensemble experiments from the IMHOTEP (IMpacts of freshwater discHarge interannual variability on Ocean heaT-salt contents and rEgional sea-level change over the altimetry Period) project.The model reproduces the large forced interannual SSS signals in the Northernmost part of the BoB and along the east coast of India, associated with the East Indian Coastal Current (EICC) modulation by the IOD. The internal SSS variability is largest in boreal fall in the North-Western BoB and more tightly controlled by the climatological SSS gradient distribution than by that of eddy kinetic energy. The external atmospheric forcing dominates the total variability in the regions of strongest variability, near the Ganges mouth and along the east coast of India in boreal fall and winter. Internal variability, however, contributes to 50-70% of the variability further offshore in boreal fall and winter. This confirms the strong role of eddies in controlling the freshwater extension up to ~700 km away from the coast, through stirring of the intense gradient between the coastal fresh and offshore saltier water. We finally discuss the consequences of these findings for comparing model and observations, in view of the chaotic nature of internal eddy variability.

International audience; This review paper reports on the state-of-the-art concerning observations of surface winds, waves and currents from space and their use for scientific research and subsequent applications. The development of observations of sea state parameters from space dates back to the 1970s, with a significant increase in the number and diversity of space missions since the 1990s. Sensors used to monitor the sea-state parameters from space are mainly based on microwave techniques. They are either specifically designed to monitor surface parameters or are used for their abilities to provide opportunistic measurements complementary to their primary purpose. The principles on which is based the estimation of the sea surface parameters are first described, including the performance and limitations of each method. Numerous examples and references on the use of these observations for scientific and operational applications are then given. The richness and diversity of these applications are linked to the importance of knowledge of the sea state in many fields. Firstly, surface wind, waves and currents are significant factors influencing exchanges at the air/sea interface, impacting oceanic and atmospheric boundary layers, contributing to sea level rise at the coasts, and interacting with the sea-ice formation or destruction in the polar zones. Secondly, ocean surface currents combined with wind-and wave-induced drift contribute to the transport of heat, salt and pollutants. Waves and surface currents also impact sediment transport and erosion in coastal areas. For operational applications, observations of surface parameters are necessary on the one hand to constrain the numerical solutions of predictive models (numerical wave, oceanic or atmospheric models), and on the other hand to validate their results. In turn, these predictive models are used to guarantee safe, efficient and successful offshore operations, including the commercial shipping and energy sector, as well as tourism and coastal activities. Long time series of global sea-state observations are also becoming increasingly important to analyze the impact of climate change on our environment. All these aspects are recalled in the article, relating to both historical and contemporary activities in these fields. Article Highlights • The different techniques and satellite missions to monitor winds, waves and currents at the ocean surface from space are described • Sea-state observations from space are widely used in operational meteorology, as well as for research on ocean, atmosphere and climate processes • Sea-state observations from space are also actively used by the offshore industry, as well as in coastal zone management

Salinity plays an important role in the oceanic circulation, because of its impact on pressure gradients and the upper ocean stability. This is particularly the case in the North Indian Ocean where freshwater inputs from monsoonal rain and rivers into the Bay of Bengal and strong evaporation in the Arabian Sea leads to high salinity contrasts, and a strong variability tied to the large monsoonal currents seasonal cycle.In situ salinity data is however too sparse to allow a detailed study of the contrasted and variable Northern Indian Ocean Sea Surface Salinity (SSS). This situation has changed since the launch of SMOS in 2009, and the advent of L-band-based SSS remote sensing with a much higher spatio-temporal sampling. Here, we explore the capacity of C and X-band measurements, such as those of AMSR-E (May 2002-October 2011) to reconstruct Northern Indian Ocean SSS prior 2009. Previous studies have indeed demonstrated the ability of C- and X-band products to reconstruct SSS in high-contrast regions like river estuaries, especially at high Sea Surface Temperature (SST), like in the Northern Indian Ocean. We are currently focusing on the development of the algorithm to reconstruct salinity from the C- and X-band data of AMSR-E. The ESA Climate Change Initiative (CCI) SSS dataset build from a merge of SMOS, Aquarius and SMAP data, provides a reference SSS that is both used for training our algorithm and for validation, over the common AMSR-E and CCI period (January 2010 to October 2011).Our first results are encouraging: spatial contrast between the low-SSS values close to estuaries and along the coast and higher SSS in the middle of the Bay of Bengal as well as some aspects of the seasonal cycle are reproduced. However, spurious signals linked to either radio frequency interferences still need to be filtered out and signals associated with other residual geophysical contributions (e.g. wind, atmospheric vapor content) need to be better estimated. The long-term goal of this work is to merge the C-, X-, and L-band data with in-situ measurements and thus provide a long-term reconstruction of monthly SSS in the north Indian Ocean with a ~50km resolution.

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 [Boutin et al., 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.In this presentation, we review recent advances and performances of the last (version 3) CCI+SSS product.The CCI v3 processing has been updated to improve the long-term stability of the SMOS SSS [Perrot et al., 2021] and to improve the level 4 SSS uncertainty estimates. A correction for the instantaneous rainfall impact [Supply et al., 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.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 [Merchant et al., 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.ReferencesBoutin, J., et al. (2021), Satellite-Based Sea Surface Salinity Designed for Ocean and Climate Studies, JGR-Oceans, 126(11), doi:10.1029/2021JC017676.Merchant, C. J., et al. (2017), Uncertainty information in climate data records from Earth observation, Earth Syst. Sci. Data, doi:10.5194/essd-9-511-2017.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.Supply, A.et al. (2020), Variability of Satellite Sea Surface Salinity Under Rainfall, in Satellite Precipitation Measurement: Volume 2, doi:10.1007/978-3-030-35798-6_34.

An impact of the upper ocean response to tropical cyclones (TC) is usually considered as a negative feedback mechanism between cooling of the mixed layer (ML) and intensity of a TC. Influence of TCs on the upper ocean is manifested as anomalies in sea surface temperature (SST) and sea surface salinity (SSS) 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.The purpose of the this work 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, results of Zilitinkevich and Esau (2003) are applied. 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).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.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 Assignment No. 0763-2020-0005 at RSHU (P.P. and V.K.). The ESA/MAXSS project support is also gratefully acknowledged (N.R. and B.C.).

28 pages, 9 figures, 6 tables, supporting information https://doi.org/10.1029/2021JC017676.-- Data Availability Statement: SSS CCI datasets are freely available at https://catalogue.ceda.ac.uk/uuid/4ce685bff631459fb2a30faa699f3fc5. The PI-MEP MDB are freely available as NetCDF files at https://www.salinity-pimep.org/data/mdb.html; corresponding validation reports are available on https://www.salinity-pimep.org/reports/mdb.html. SMAP salinity data are produced by Remote Sensing Systems and sponsored by the NASA Ocean Salinity Science Team. Data are available at www.remss.com. Aquarius data are available at https://podaac.jpl.nasa.gov/dataset/AQUARIUS_L3_SSS_SMID_ANNUAL_V5. CATDS SSS are available at the CATDS Production Data Center (CPDC), www.catds.fr. ISAS and Glorys fields are taken from (https://catalogue.marine.copernicus.eu/; INSITU_GLO_TS_OA_REP_OBSERVATIONS_013_002 and GLOBAL-REANALYSIS-PHY-001-030 products, respectively). The GOSUD RV data set is available at https://doi.org/10.17882/39475, the ships of opportunity data set is available at http://www.legos.obs-mip.fr/observations/sss/datadelivery/dmdata. Argo data are taken from http://www.coriolis.eu.org/, Sea Surface Salinity (SSS) is an increasingly used Essential Ocean and Climate Variable. The Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active Passive (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 data set 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 data set. SSS CCI data are freely available and will be updated and extended as more satellite data become available, This study was funded by ESA CCI contract 4000123663/18/I-NB, With the institutional support of the ‘Severo OchoaCentre of Excellence’ accreditation (CEX2019-000928-S)

Sea surface salinity is retrieved for more than 11 years from the Soil Moisture and Ocean Salinity (SMOS) satellite mission. This data set provides a unique monitoring of the Sea Surface Salinity (SSS) spatio-temporal variability at global scale. It is particularly useful to follow the surface ocean pathway of fresh river plumes water as illustrated here in the Bay of Bengal. A revised adjustment of the whole SMOS SSS time series (CATDS Expertise Center version 5, 2010–2020) leads to clear reduction of local biases in very variable regions and in very noisy regions. The robust std difference between SMOS CEC v5 (18-day, ~70km SSS) and Argo in situ SSS is 0.17 in regions warmer than 5°C. We will discuss how future CATDS products will be improved in view of two ongoing reprocessings, the CATDS L1/L2 v7 reprocessing and the ESA CCI+SSS L2 SMOS reprocessing.

Sea Surface Salinity (SSS) is an Essential Ocean and Climate Variable, which is increasingly used as part of climate studies. SSS measurements are available from three satellite missions, SMOS, Aqu...

Surface roughness and foam are two main components of ocean surface microwave thermal emission. Surface roughness provides scattering element and modifies local incidence angle. Air in foam alters the dielectric property of the surface layer. Bubbles in foam also alter the curvature of foam-water interface and modify emission and scattering properties of the water surface itself. Whitecap coverage $W_{\mathrm {c}}$ is the most accessible oceanographic information to represent surface foam. For emission analysis, it is necessary to establish a function relating to $W_{\mathrm {c}}$ and the effective air fraction $F_{\mathrm {a}}$ interacting with electromagnetic (EM) waves. An empirical relation is established through analyzing several microwave radiometer data sets in high winds covering a wide range of frequency, incidence angle, and vertical and horizontal polarizations. A physical interpretation of the proposed $F_{\mathrm {a}}$ ( $W_{\mathrm {c}}$ ) relationship is discussed. The relationship is used to quantify several important characteristics of surface foam relevant to microwave emission, including effective air fraction and skin depth as functions of wind speed, microwave frequency, and incidence angle.

Whitecaps manifest surface wave breaking that impacts many ocean processes, of which surface wind stress is the driving force. For close to a half century of quantitative whitecap reporting, only a small number of observations are obtained under conditions with wind speed exceeding 25 m s−1. Whitecap contribution is a critical component of ocean surface microwave thermal emission. In the forward solution of microwave thermal emission, the input forcing parameter is wind speed, which is used to generate the modeled surface wind stress, surface wave spectrum, and whitecap coverage necessary for the subsequent electromagnetic (EM) computation. In this respect, microwave radiometer data can be used to evaluate various formulations of the drag coefficient, whitecap coverage, and surface wave spectrum. In reverse, whitecap coverage and surface wind stress can be retrieved from microwave radiometer data by employing precalculated solutions of an analytical microwave thermal emission model that yields good agreement with field measurements. There are many published microwave radiometer datasets covering a wide range of frequency, incidence angle, and both vertical and horizontal polarizations, with maximum wind speed exceeding 90 m s−1. These datasets provide information of whitecap coverage and surface wind stress from global oceans and in extreme wind conditions. Breaking wave energy dissipation rate per unit surface area can be estimated also by making use of its linear relationship with whitecap coverage derived from earlier studies.

International audience; A simplified analytical model is developed to describe the baroclinic and barotropic ocean response to moving tropical cyclones (TCs) and their associated pycnocline erosions. The model builds on classical mixed-layer (ML) models and linear models of ocean response to transient events. As suggested, disturbances of the upper ocean stratification caused by the ML development shall not strongly impact the dynamics of baroclinic modes. Accordingly, the baroclinic response can be estimated using the prestorm ocean stratification condition. To the contrary, the ML is strongly coupled with these interior motions, through the TC-induced upwelling response that affects the entrainment velocity. The ML temperature is then strongly dependent on the local temperature gradient in the upper layer. The model is represented by a set of analytical relationships providing rapid calculations for the ocean response to TC, given a prescribed wind velocity field traveling over an ocean with arbitrary stratification. Compared to satellite observations, simulations demonstrate the model ability to quantitatively reproduce the observed shape and magnitudes of the sea surface height and the sea surface temperature (SST) anomalies. Remarkably, the model is robust and efficient for a wide range of variability of TC characteristics (max wind speed, radius, shape of wind profile, and translation velocity), parameters of the ocean stratification, and Coriolis parameter. Simulations provide solid evidences about the key role of TC-induced upwelling in the ML cooling and formation of SST wake. Cross-track advection by wind-driven currents, though small compared with TC translation velocity, can significantly contribute to broaden the shape and offset of the SST wake. Given its effectiveness and low computational burden, the proposed model can be introduced as a computational module into atmospheric numerical models of TC-coupled evolution with the ocean, through the resulting local changes of surface enthalpy fluxes.

In 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. Plain Language Summary The Gulf of Guinea receives large amount of freshwater from Congo and Niger rivers runoff and high precipitation. Since the freshwater is lighter than the seawater, the surface freshwater input is spread within large plumes that impact the upper layer vertical structure. The vertical stratification induced by the freshwater plume strongly impacts the air‐sea heat flux exchanges but also the vertical exchanges of nutrient and organic matter. The eastern Gulf of Guinea freshwater plumes remain poorly documented because of the scarcity of historical data in this region. This study, based on recent advance in remote sensing of the Sea Surface Salinity from ESA (European Space Agency) Soil Moisture and Ocean Salinity satellite mission, provides a robust characterization of seasonal variability of freshwater plumes in the eastern Gulf of Guinea by describing and quantifying their development, extent and dispersal patterns. The freshwater plumes are mainly supplied by rain (respectively river) input in the northern (respectively southern) part of this region. Their offshore seasonal evolution is mostly driven by surface currents and upward salty water fluxes. Surface wind‐driven currents are also found to play a major role for the freshwater plumes offshore extension.

The Pilot Mission Exploitation Platform (Pi-MEP) for Salinity (www.salinity-pimep.org) has been released operationally in 2019 to the broad oceanographic community, in order to foster satellite sea surface salinity validation and exploitation activities.Specifically, the Platform aims at enhancing salinityvalidation, by allowing systematic inter-comparison of various EO datasets with a broad suite of in-situ data, and also at enabling oceanographic process studies by capitalizing on salinity data in synergy with additional spaceborne estimates. Despite Pi-MEP was originally conceived as an ESA initiative to widen the uptake of the Soil Moisture and Ocean Salinity (SMOS) mission data over ocean, a project partnership with NASA was devised soon after the operational deployment, and an official collaboration endorsed within the ESA-NASA Joint Program Planning Group (JPPG). The Salinity Pi-MEP has therefore become a reference hub for SMOS, SMAP and Aquarius satellite salinity missions, which are assessed in synergy with additional thematic datasets (e.g., precipitation, evaporation, currents, sea level anomalies, ocean color, sea surface temperature). Match-up databases of satellite/in situ (such as Argo, TSG, moorings, drifters) data and corresponding validation reports at different spatiotemporal scales are systematically generated; furthermore, recently-developed dedicated tools allow data visualization, metrics computation and user-driven features extractions. The Platform is also meant to monitor salinity in selected oceanographic “case studies”, ranging from river plumes monitoring to SSS characterization in challenging regions, such as high latitudes or semi-enclosed basins. The two Agencies are currently collaborating to widen the Platform features on several technical aspects - ranging from a triple-collocation software implementation to a sustained exploitation of data from the SPURS-1/2 campaigns. In this context, an upgrade of the satellite/in-situ match-up methodology has been recently agreed, resulting into a redefinition of the validation criteria that will be subsequently implemented in the Platform. A further synthesis of the three satellites salinity algorithms, models and auxiliary data handling is at the core of the ESA Climate Change Initiative (CCI) on Salinity and of ESA-NASA further collaboration.

The European Space Agency (ESA) Climate Change Initiative (CCI+) for Sea Surface Salinity (CCI+SSS) project aims at generating long-term, improved, calibrated global SSS fields from space. The project started in mid-2018 and in its second year (version 2) has produced a 10-year dataset (2010-2019) from the three available L-band radiometer satellites (SMOS: Soil Moisture and Ocean Salinity; Aquarius; SMAP: Soil Moisture Active Passive) and validated it against in situ references (Argo and ISAS: In Situ Analysis System). The comparisons with in situ ground truth indicate much better performances than the ones obtained with a single satellite data product, with global precision against in situ references of 0.15 pss. CCI SSS version 2 products show similar performance than version 1 but is one year longer. There is a very good agreement between the CCI dataset and references, including long-term stability, with differences within +-0.05 pss for global ocean within [40°S-20°N]. At higher latitude, we observe seasonal oscillation of the CCI SSS difference against references. The uncertainty provided in the CCI SSS product are in good agreement with observations (within +-25%).

This study is performed in the frame of the European Space Agency (ESA) Climate Change Initiative (CCI+) for Sea Surface Salinity (SSS), which aims at generating global SSS fields from all available satellite L-band radiometer measurements over the longest possible period with a great stability. By combining SSS from the Soil Moisture and Ocean Salinity, SMOS, Aquarius and the Soil Moisture Active Passive, SMAP missions, CCI+SSS fields (Boutin et al. 2020) are the only one to provide a 10 year time series of satellite salinity with such quality: global rms difference of weekly 25x25km2 CCI+SSS with respect to in situ Argo SSS of 0.17 pss, correlation coefficient of 0.97 (see https://pimep.ifremer.fr/diffusion/analyses/mdb-database/GO/cci-l4-esa-merged-oi-v2.31-7dr/argo/report/pimep-mdb-report_GO_cci-l4-esa-merged-oi-v2.31-7dr_argo_20201215.pdf). Nevertheless, we found that some systematic biases remained. In this presentation, we will show how they will be reduced in the next CCI+SSS version.The key satellite mission ensuring the longest time period, since 2010, at global scale, is SMOS. We implemented a re-processing of the whole SMOS dataset by changing some key points. Firstly we replace the Klein and Swift (1977) dielectric constant parametrization by the new Boutin et al. (2020) one. Secondly we change the reference dataset used to perform a vicarious calibration over the south east Pacific Ocean (the so-called Ocean Target Transformation), by using Argo interpolated fields (ISAS, Gaillard et al. 2016) contemporaneous to the satellite measurements instead of the World Ocean Atlas climatology. And thirdly the auxiliary data (wind, SST, atmospheric parameters) used as priors in the retrieval scheme, which come in the original SMOS processing from the ECMWF forecast model were replaced by ERA5 reanalysis.Our results are showing a quantitative improvement in the stability of the SMOS CCI+SSS with respect to in situ measurements for all the period as well as a decrease of the spread of the difference between SMOS and in situ salinity measurements.Bibliography:J. Boutin et al. (2020), Correcting Sea Surface Temperature Spurious Effects in Salinity Retrieved From Spaceborne L-Band Radiometer Measurements, IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3030488.F. Gaillard et al. (2016), In Situ–Based Reanalysis of the Global Ocean Temperature and Salinity with ISAS: Variability of the Heat Content and Steric Height, Journal of Climate, vol. 29, no. 4, pp. 1305-1323, doi: 10.1175/JCLI-D-15-0028.1.L. Klein and C. Swift (1977), An improved model for the dielectric constant of sea water at microwave frequencies, IEEE Transactions on Antennas and Propagation, vol. 25, no. 1, pp. 104-111, doi: 10.1109/JOE.1977.1145319.Data reference:J. Boutin et al. (2020): ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product, v2.31, for 2010 to 2019. Centre for Environmental Data Analysis. https://catalogue.ceda.ac.uk/uuid/eacb7580e1b54afeaabb0fd2b0a53828

Ten years of L-band radiometric measurements have proven the capability of satellite sea surface salinity (SSS) to resolve large-scale-to-mesoscale SSS features in tropical to subtropical ocean. In mid-to-high latitudes, L-band measurements still suffer from large-scale and time-varying errors. Here, a simple method is proposed to mitigate the large-scale and time-varying errors. First, an optimal interpolation using a large correlation scale (~500 km) is used to map independently Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) level-3 (L3) data. The mapping is compared with the equivalent mapping of in situ observations to estimate the large-scale and seasonal biases. A second mapping is performed on adjusted SSS at the scale of SMOS/SMAP spatial resolution (~45 km). This procedure merges both products and increases the signal-to-noise ratio of the absolute SSS estimates, reducing the root-mean-square difference of in situ satellite products by about 26%–32% from mid- to high latitudes, respectively, in comparison with the existing SMOS and SMAP L3 products. However, in the Arctic Ocean, some issues on satellite retrieved SSS related to, for example, radio frequency interferences, land–sea contamination, and ice–sea contamination remain challenging to reduce given the low sensitivity of L-band radiometric measurements to SSS in cold water. Using the International Thermodynamic Equation Of Seawater—2010 (TEOS-10), the resulting level-4 SSS satellite product is combined with satellite-microwave SST products to estimate sea surface density, spiciness, and haline contraction and thermal expansion coefficients. For the first time, we illustrate how useful these satellite-derived parameters are to fully characterize the surface ocean water masses at large mesoscale.

International audience; Decade-long satellite sea surface slinity (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.

We derived a new parametrisation for the dielectric constant of the ocean (Boutin et al. 2020). Earlier studies have pointed out systematic differences between Sea Surface Salinity retrieved from L-band radiometric measurements and measured in situ, that depend on Sea Surface Temperature (SST). We investigate how to cope with these differences given existing physically based radiative transfer models. In order to study differences coming from seawater dielectric constant parametrization, we consider the model of Somaraju and Trumpf (2006) (ST) which is built on sound physical bases and close to a single relaxation term Debye equation. While ST model uses fewer empirically adjusted parameters than other dielectric constant models currently used in salinity retrievals, ST dielectric constants are found close to those obtained using the Meissner and Wentz (2012) (MW) model. The ST parametrization is then slightly modified in order to achieve a better fit with seawater dielectric constant inferred from SMOS data. Upgraded dielectric constant model is intermediate between KS and MW models. Systematic differences between SMOS and in situ salinity are reduced to less than +/-0.2 above 0°C and within +/-0.05 between 7 and 28°C. Aquarius salinity becomes closer to in situ salinity, and within +/-0.1. The order of magnitude of remaining differences is very similar to the one achieved with the Aquarius version 5 empirical adjustment of wind model SST dependency. The upgraded parametrization is recommended for use in processing the SMOS data. The rationale for this new parametrisation, results obtained with this new parametrisation in recent SMOS reprocessings and comparisons with other parametrisations will be discussed.Reference:Boutin, J.,et al. (2020), Correcting Sea Surface Temperature Spurious Effects in Salinity Retrieved From Spaceborne L-Band Radiometer Measurements, IEEE TGRSS, doi:10.1109/tgrs.2020.3030488.

We show the importance of salinity for rapidly intensifying Atlantic tropical cyclones and demonstrate the potential for improved prediction of rapid intensification through the inclusion of salinity. Tropical Cyclone (TC) rapid intensification (RI) is difficult to predict and poses a formidable threat to coastal populations. A warm upper ocean is well-known to favor RI, but the role of ocean salinity is less clear. This study shows a strong inverse relationship between salinity and TC RI in the eastern Caribbean and western tropical Atlantic due to near-surface freshening from the Amazon-Orinoco River system. In this region, rapidly intensifying TCs induce a much stronger surface enthalpy flux compared to more weakly intensifying storms, in part due to a reduction in SST cooling caused by salinity stratification. This reduction has a noticeable positive impact on TCs undergoing RI, but the impact of salinity on more weakly intensifying storms is insignificant. These statistical results are confirmed through experiments with an ocean mixed layer model, which show that the salinity-induced reduction in SST cold wakes increases significantly as the storm’s intensification rate increases. Currently, operational statistical-dynamical RI models do not use salinity as a predictor. Through experiments with a statistical RI prediction scheme, it is found that the inclusion of surface salinity significantly improves the RI detection skill, offering promise for improved operational RI prediction. Satellite surface salinity may be valuable for this purpose, given its global coverage and availability in near real-time.