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The spatial spectra of three Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) datasets, produced by the Barcelona Expert Center (BEC), were assessed in this study along zonal and meridional directions. The datasets are the Level 3 (L3) SM gridded at 25 km, the Level 4 (L4) SM at 1 km and an experimental L4 SM at ~300 m. Since the L4 products are obtained by a downscaling algorithm that uses Normalized Difference Vegetation Index (NDVI), NDVI data from MODIS (1 km) and Sentinel-3 (~300 m) were also analyzed.Both L4 SM products provide useful spatial information of small-scale structures, with estimated effective spatial resolutions of ~2.5 km (for the L4 at 1 km) and ~500 m (for the L4 at ~300 m). The NDVI data used for the downscaling have a significant impact not only on the spatial patterns of the resulting SM product, but also on its spectrum., This work was supported by the Spanish Ministry of Science, Innovation and Universities (MCIN/AEI/10.13039/501100011033) through the projects INTERACT (PID2020-114623RB-C32 and PID2020-114623RBC31), the ICM-CSIC Severo Ochoa Excellence Award (CEX2019- 000928-S), the CSIC Interdisciplinary Thematic Platform (PTI) TELEDETECT, and ERDF funds., Peer Reviewed, Postprint (author's final draft)

La eutrofización es una gran amenaza para las aguas costeras, lo que ha llevado a la adopción en todo el mundo de políticas destinadas a protegerlas y restaurarlas. Muchas de estas leyes se basan en el marco de fuerzas motrices-presión-estado-impacto-respuesta y siguen el enfoque ecosistémico. Para aplicarlos, se necesita evaluar la presión de la eutrofización y su impacto, así como proporcionar información útil para los gestores. Por un lado, la presión de eutrofización en tierra se basa en los usos del suelo, en su cobertura y en las influencias fluviales (que pueden estimarse a partir de la salinidad en aguas costeras) y en aguas costeras en la concentración de nutrientes. Y por el otro, el impacto de eutrofización en aguas costeras se basa en la concentración de clorofila-a. A partir de esta información puede establecerse la relación presión impacto y rellenar la tabla Presión-Impacto-Gestión (PIG). Esta última, ayuda en el diseño de planes de gestión para zonas costeras, al identificar dónde se deben implementar acciones de monitoreo y de mitigación. Hasta ahora esta aproximación se ha realizado en base a datos in situ, pero la teledetección permite conocer esta información a partir de imágenes satelitales de microondas, infra rojos (incluyendo las térmicas) y visibles. Para comprobar si el enfoque PIG tiene éxito basándose en datos satelitales, se aplicará a dos casos de estudio: (i) en la costa noreste española (Costa Catalana), donde esta metodología ya ha tenido éxito basándose en datos in situ, y (ii) en la costa Atlántica de Honduras (Departamentos de Atlántida y Colón), donde no existe información sobre el proceso de eutrofización. Si tiene éxito, el abordaje PIG basado en datos satelitales será una piedra angular esencial para la gestión de las aguas costeras en todo el mundo., Postprint (published version)

Sea surface currents probably are the most relevant essential ocean variable affecting diverse societal challenges concerning the marine environmental (as, for example, safe and efficient navigation, marine pollution and ecological connectivity). This work introduces a climatological Atlas (monthly resolution) of currents in the Mediterranean and Canary–Iberian–Biscay basins, based on today's state of the art reanalyses of the ocean circulation. The focus is on surface and subsurface reanalyses (here understood as z∼0.5 and z∼15 m, respectively) provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The climatological values are computed from the median of the empirical probability density functions and the Atlas also includes the variance matrix and a bimodality index to have quantitative information on their variability. For both domains, the subsurface climatological fields are reasonably consistent with circulation schemes proposed in the previous literature but clearly improving the time and space resolution of the emerging patterns. For the Canary–Iberian–Biscay domain, the monthly climatological surface currents capture accurately the characteristic seasonal signal and its transition between a favourable and non-favourable upwelling regime. In the Mediterranean basin, differences between the near-surface and the 15 m velocity fields suggest a non-negligible role of winds over the variability of the uppermost ocean layer, specially in the Eastern Mediterranean basin. This is, up to our knowledge, the first time that such near-surface climatological patterns are computed. It has been found that, in general, the resulting patterns agree with surface drifter trajectories. In several regions, interannual variability foster bimodal and multimodal probability distributions. The Atlas has been conceived with the purpose of providing a first quantitative assessment on the surface circulation, thus being a complementary tool of real-time ocean foreca

Dedicated regional Sea Surface Salinity (SSS) product in the Southern Ocean. Level 3 9-day maps. Data acquisition: Satellite ESA SMOS mission (Soil Moisture and Ocean Salinity). Time coverage 01 February 2011 - 31 December 2022. Time resolution: 9-day. Maps frequency generation: Daily. Spatial coverage: Latitude range: 30ºS-90ºS Longitude range: 180ºW-180ºE. Spatial resolution: 25 km (EASE-SL grid). Sensor Satellite SMOS / MIRAS. Format NetCDF. Climate and Forecast (CF) conventions version: 1.6

Improvement of the current SMOS soil moisture products produced by the Barcelona Expert Centre (BEC) and development of new added-value products and/or applications over land.

Improvement of the current SMOS soil moisture products produced by the Barcelona Expert Centre (BEC) and development of new added-value products and/or applications over land

Improvement of the current SMOS soil moisture products produced by the Barcelona Expert Centre (BEC) and development of new added-value products and/or applications over land

Improvement of the current SMOS soil moisture products produced by the Barcelona Expert Centre (BEC) and development of new added-value products and/or applications over land

A modification of the Barcelona Expert Center (BEC) algorithm to downscale the Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) to 300 m spatial resolution is presented. It maintains the same functional relationship as the currently implemented version but employs the following inputs: SMOS brightness temperature (TB) and SM (25 km), European Center for Medium Weather Forecast (ECMWF) skin temperature (9 km), and Sentinel 3 Normalized Difference Vegetation Index (NDVI, 300 m).The performance of the downscaled SMOS SM at 300 m is analyzed by means of a temporal validation with in-situ observations from the Soil Moisture Measurements Stations Network of the University of Salamanca (REMEDHUS) and the Continuous Soil Moisture and Temperature Ground-based Observation Network (RSMN) during the year 2021. No significant differences in correlation, unbiased root mean square difference (ubRMSD) and bias are obtained over both networks compared to the 25 km and 1 km SM products, suggesting the BEC downscaling algorithm could work at hundreds of meters and result in a similar SM accuracy., This work was supported by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) through the projects INTERACT (PID2020-114623RB-C32 and PID2020-114623RB-C31), ERDF funds, the Institute of Space Studies of Catalonia (IECC), the ICM-CSIC Severo Ochoa Excellence Award (CEX2019-000928-S), and the CSIC Interdisciplinary Thematic Platform (PTI) TELEDETECT., Peer Reviewed, Postprint (author's final draft)

Los sistemas de teledetección, que permiten la monitorización y observación del medio ambiente terrestre y marino, han constatado una mayor frecuencia de eventos extremos, como las olas de calor

For the first time, an accurate and complete picture of Mixed Layer (ML) water mass dynamics can be inferred at high spatio-temporal resolution via the material derivative derived from Sea Surface Salinity/Temperature (SSS/T) and Currents (SSC). The product between this satellite derived material derivative and in-situ derived Mixed Layer Depth (MLD) provides a satellite based kinematic approach to the water mass (trans)formation framework (WMT/F) above ML. We compare this approach to the standard thermodynamic approach based on air-sea fluxes provided by satellites, an ocean state estimate and in-situ observations. Southern Hemisphere surface density flux and water mass (trans)formation framework (WMT/F) were analysed in geographic and potential density space for the year 2014. Surface density flux differences between the satellite derived thermodynamic and kinematic approaches and ECCO (an ocean state estimate) underline: 1) air-sea heat fluxes dominate variability in the thermodynamic approach; and 2) fine scale structures from the satellite derived kinematic approach are most likely geophysical and not artefacts from noise in SSS/T or SSC—as suggested by a series of smoothing experiments. Additionally, ECCO revealed surface density flux integrated over ML are positively biased as compared to similar estimates assuming that surface conditions are homogeneous over ML—in part owing to the e-folding nature of shortwave solar radiation. Major differences between the satellite derived kinematic and thermodynamic approaches are associated to: 1) lateral mixing and mesoscale dynamics in the kinematic framework; 2) vertical excursions of, and vertical velocities through the ML base; and 3) interactions between ML horizontal velocities and ML base spatial gradients

Evaluating and validating satellite sea surface salinity (SSS) measurements is fundamental. There are two types of errors in satellite SSS: measurement error due to the instrument’s inaccuracy and problems in retrieval, and sampling error due to unrepresentativeness in the way that the sea surface is sampled in time and space by the instrument. In this study, we focus on sampling errors, which impact both satellite and in situ products. We estimate the sampling errors of Level 3 satellite SSS products from Aquarius, SMOS and SMAP, and in situ gridded products. To do that, we use simulated L2 and L3 Aquarius, SMAP and SMOS SSS data, individual Argo observations and gridded Argo products derived from a 12-month high-resolution 1/48° ocean model. The use of the simulated data allows us to quantify the sampling error and eliminate the measurement error. We found that the sampling errors are high in regions of high SSS variability and are globally about 0.02/0.03 psu at weekly time scales and 0.01/0.02 psu at monthly time scales for satellite products. The in situ-based product sampling error is significantly higher than that of the three satellite products at monthly scales (0.085 psu) indicating the need to be cautious when using in situ-based gridded products to validate satellite products. Similar results are found using a Correlated Triple Collocation method that quantifies the standard deviation of products’ errors acquired with different instruments. By improving our understanding and quantifying the effect of sampling errors on satellite-in situ SSS consistency over various spatial and temporal scales, this study will help to improve the validation of SSS, the robustness of scientific applications and the design of future salinity missions

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 m

Sea surface temperature (SST) is a key oceanic variable controlling energy fluxes, as well as several atmospheric parameters such as wind speed, air temperature, humidity and cloudiness. During the last decades, mesoscale has received much attention and the new frontier for the coming years is the understanding of sub-mesoscale dynamics and its impact on climate. In order to address this challenge, there is a need of developing high-resolution observing systems, remote sensing sensors in conjunction with in-situ observations. Some traditional climate-oriented SST observational datasets generally do not include satellite observations and are typically based on in-situ observations, prominent examples being NOAA Extended Reconstructed SST (ERSST) and Hadley Centre SST version 3 (HadSST3). Other datasets combine both, in-situ and satellites observations, like the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST). The main objective of this work is to characterize sea surface temperature (SST) climatology and variability in the tropical Atlantic region. For that purpose, we thoroughly compare two standard, climate-oriented datasets, HadISST (1° resolution) and ERSSTv5 (2° resolution), with the GHRSST product developed by the European Space Agency (ESA) Climate Change Initiative (CCI) (0.05° resolution). Our results show that, at grid-point level, the three datasets behave similarly on a large scale, but they show consistent differences in all seasons, with CCI distinctly displaying more expansive and larger variability in the equatorial Atlantic and also in the subtropical North Atlantic. The differences in climatology are less apparent. In particular, over the ATL3 region, CCI is systematically colder than ERSST and HadISST, and displays higher variability

A modification of the Barcelona Expert Center (BEC) algorithm to downscale the Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) to 300 m spatial resolution is presented. It maintains the same functional relationship as the currently implemented version but employs the following inputs: SMOS brightness temperature (TB) and SM (25 km), European Center for Medium Weather Forecast (ECMWF) skin temperature (9 km), and Sentinel 3 Normalized Difference Vegetation Index (NDVI, 300 m).The performance of the downscaled SMOS SM at 300 m is analyzed by means of a temporal validation with in-situ observations from the Soil Moisture Measurements Stations Network of the University of Salamanca (REMEDHUS) and the Continuous Soil Moisture and Temperature Ground-based Observation Network (RSMN) during the year 2021. No significant differences in correlation, unbiased root mean square difference (ubRMSD) and bias are obtained over both networks compared to the 25 km and 1 km SM products, suggesting the BEC downscaling algorithm could work at hundreds of meters and result in a similar SM accuracy

To develop a novel Baltic L3 SSS (Sea Surface Salinity) product from the measurements provided by the SMOS (Soil Moisture and Ocean Salinity) mission and to study the potential benefit of incorporating this SSS product into oceanographic and environmental applications within the Baltic Sea. Data acquisition: Satellite ESA SMOS mission (Soil Moisture and Ocean Salinity). Time coverage 05 February 2011 - 27 December 2019. Time resolution: 9 days. Maps frequency generation: Daily. Spatial coverage: Latitude range: 53.5ºN-68ºN Longitude range: 8ºE-30.5ºE. Spatial resolution: 0.25 degrees. Spatial grid: WGS 84 / Regular longitude-latitude. Sensor Satellite SMOS / MIRAS. Format NetCDF. Climate and Forecast (CF) conventions version: 1.6

Dedicated regional Sea Surface Salinity (SSS) product in the Black Sea. Level 2 ascending satellite overpasses. Data acquisition: Satellite ESA SMOS mission (Soil Moisture and Ocean Salinity). Time coverage 01 February 2011 - 31 December 2020. Time resolution: daily. Maps frequency generation: Daily. Spatial coverage: Latitude range: 39ºN-48ºN Longitude range: 26ºE-42ºE. Spatial resolution: 0.25 degrees. Spatial grid: WGS 84 / Regular longitude-latitude. Sensor Satellite SMOS / MIRAS. Format NetCDF. Climate and Forecast (CF) conventions version: 1.6

Dedicated regional Sea Surface Salinity (SSS) product in the Black Sea. Level 3 9-day maps. Data acquisition: Satellite ESA SMOS mission (Soil Moisture and Ocean Salinity). Time coverage 01 February 2011 - 31 December 2020. Time resolution: 9-day. Maps frequency generation: Daily. Spatial coverage: Latitude range: 39ºN-48ºN Longitude range: 26ºE-42ºE. Spatial resolution: 0.25 degrees. Spatial grid: WGS 84 / Regular longitude-latitude. Sensor Satellite SMOS / MIRAS. Format NetCDF. Climate and Forecast (CF) conventions version: 1.6

An Earth Observation Data Exploitation Platform for Science and Innovation in the Black Sea (EO4SIBS)'. To develop a new generation of algorithms that can ingest the wealth of spatial, temporal and spectral information provided by recent sensors providing high quality reference products for the blue and green ocean. Content: EO4SIBS experimental SMOS Colored Detrital Matter (CDM) L4 for the Danube mouth. Time coverage: 01 January, 2019 – 31 May, 2019; 01 December, 2019 – 31 December, 2019. Time resolution: Daily. Maps frequency generation: Daily. Spatial coverage: Latitude range: 44ºN-45.5ºN Longitude range: 28ºE-30.5ºE. Spatial resolution: 0.05 degrees. Spatial grid: WGS 84 / Regular longitude-latitude. YYYYMMDD: Day of the map. Sensor: Satellite SMOS / MIRAS. Format: NetCDF. Climate and Forecast (CF) conventions version: 1.6.

Dedicated regional Sea Surface Salinity (SSS) product in the Black Sea. Level 4 daily maps. Data acquisition: Satellite ESA SMOS mission (Soil Moisture and Ocean Salinity). Time coverage 01 February 2011 - 31 December 2019. Time resolution: daily. Maps frequency generation: Daily. Spatial coverage: Latitude range: 39ºN-48ºN Longitude range: 26ºE-42ºE. Spatial resolution: 0.05 degrees. Spatial grid: WGS 84 / Regular longitude-latitude. Sensor Satellite SMOS / MIRAS. Format NetCDF. Climate and Forecast (CF) conventions version: 1.6

Dedicated regional Sea Surface Salinity (SSS) product in the Black Sea. Level 2 descending satellite overpasses. Data acquisition: Satellite ESA SMOS mission (Soil Moisture and Ocean Salinity). Time coverage 01 February 2011 - 31 December 2020. Time resolution: daily. Maps frequency generation: Daily. Spatial coverage: Latitude range: 39ºN-48ºN Longitude range: 26ºE-42ºE. Spatial resolution: 0.25 degrees. Spatial grid: WGS 84 / Regular longitude-latitude. Sensor Satellite SMOS / MIRAS. Format NetCDF. Climate and Forecast (CF) conventions version: 1.6

Changes in the Earth’s water cycle can be estimated by analyzing sea surface salinity. This variable refects the balance between precipitation and evaporation over the ocean, since the upper layers of the ocean are the most sensitive to atmosphere–ocean interactions. In situ measurements lack spatial and temporal synopticity and are typically acquired at few meters below the surface. Satellite measurements, on the contrary, are synoptic, repetitive and acquired at the surface. Here we show that the satellite-derived sea surface salinity measurements evidence an intensifcation of the water cycle (the freshest waters become fresher and vice-versa) which is not observed at the in-situ nearsurface salinity measurements. The largest positive diferences between surface and near-surface salinity trends are located over regions characterized by a decrease in the mixed layer depth and the sea surface wind speed, and an increase in sea surface temperature, which is consistent with an increased stratifcation of the water column due to global warming. These results highlight the crucial importance of using satellites to unveil critical changes on ocean–atmosphere fuxes., This work was supported in part by the Spanish R&D project L-BAND (ESP2017-89463-C3-1-R), which is funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe”, and project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. , and in part by the Euro-pean Space Agency by means of the Contract SMOS ESL L2OS. We also acknowledge funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S). This work is a contribution to CSIC PTI Teledetect., Peer Reviewed, Postprint (published version)

In the context of the ESA Baltic+ Salinity Dynamics project, the first SMOS (Soil Moisture and Ocean Salinity) sea surface salinity (SSS) dedicated products over the Baltic Sea have been developed. A first prototype of these SSS products evidenced the sea surface temperature (SST) as one important driver of the SSS errors. Considering the low sensitivity of brightness temperatures to SSS changes in cold waters (with a mean value below 3 degrees C in winter in the Baltic Sea), it is crucial to use the most accurate SST product for the salinity retrieval. Besides, SST is used as a template when using multifractal fusion techniques to increase the spatial resolution of these SSS products. In order to identify the most suitable SST product over the Baltic Sea, we have analysed the state-ofthe- art EO-based SST products (OSTIA, CMC, REMSS, CCI) [Woo and Park, 2020], by comparing them with the collocated in-situ temperature measurements provided by SeaDataNet. This analysis has revealed that the best quality salinity product over the Baltic Sea is the CCI SST, with a standard deviation of the difference with in-situ of 0.62 degrees C, , which is still large compared to open ocean regions. Therefore, EO SSS products would benefit from improvements in SST products over the Baltic Sea

Satellite infrared radiometers have provided high resolution (~1 km) measurements of the ocean surface during the last forty years. The existence of such a time series enables us to investigate different dynamical regimes of the upper ocean and monitor potential changes related to global warming. The main problem, however, is to extract the dynamical characteristics of the upper ocean. This is motivated by two types of difficulties: data related limitations (cloud coverage, noise level, failure of cloud mask algorithms, etc.) and the availability of tools to extract information (statistical tools, dynamical frameworks, etc.) In this session, we proposed to review some of these tools based on the multifractal theory of turbulence and other approaches, and to discuss how they are impacted by data limitations

A keynote lecture was given by one of the GHRSST meeting hosts, Jordi Isern-Fontanet (CSIC-ICM) on the role of thermal fronts and how to characterise high resolution SST. While using spectral slopes is a common tool for assessing the effective spatial resolution of SST products, which typically exhibit a drop in power following a k-2 slope, there are limitations. A common approach in turbulence-related studies is to use structure functions which also exhibit power laws, with e.g. the 2nd order structure function being the spectral slope. A characteristic of turbulent flows is the anomalous scaling, i.e. the departure from a straight line, consistent for various order functions (2nd, 4th, 6th, 8th). Thus, singularity exponents are proposed as a proxy measure for front intensity. The spatial distribution of fronts characterises the scaling of the structure functions. Anomalous scaling is proportional to the intensity of the strongest front; this is robust result that can be theoretically predicted and it implies that if front intensity changes with season, anomalous scaling will also change, thus SST products need to be coherent with this feature. In summary, ocean fronts determine the scaling properties of structure functions, with most intense fronts controlling the anomalous scaling of structure functions. This has a direct practical use in the validation of SST products

Satellite infrared radiometers have provided high resolution (~1 km) measurements of the ocean surface during the last forty years. The existence of such a time series enables us to investigate different dynamical regimes of the upper ocean and monitor potential changes related to global warming. The main problem, however, is to extract the dynamical characteristics of the upper ocean. This is motivated by two types of difficulties: data related limitations (cloud coverage, noise level, failure of cloud mask algorithms, etc.) and the availability of tools to extract information (statistical tools, dynamical frameworks, etc.) In this session, we proposed to review some of these tools based on the multifractal theory of turbulence and other approaches, and to discuss how they are impacted by data limitations

Sea Surface Temperature (SST) plays an important role in the production of satellite based Sea Surface Salinity (SSS) observations. On the one hand, it is used as an auxiliary parameter for SSS retrieval from brightness temperature to produce L2 and L3 datasets. On the other hand, it is used as a template to increase spatial resolution using multifractal fusion techniques (L4 product). Traditionally, Soil Moisture and Ocean Salinity (SMOS) SSS datasets produced at the Barcelona Expert Center were based on OSTIA SST product. In this work, we revisit this election and assess different sources of satellite-derived SST products. The assessment will consist of: - comparison with in-situ data (ARGO floats); - performing a correlated triple collocation analysis [González- Gambau et al., 2020] between the different products to decide which one presents the lower uncertainty; - spectral and singularity analysis to assess the spatial resolution of each SST product [Hoareau et al. 2018].

Recent observational and modelling studies have documented changes in the hydrography of the upper Arctic Ocean, in particular an increase of its liquid freshwater content (e.g., Haine et al. 2015, Proshutinsky et al. 2019, Solomon et al. 2021). The main factors contributing to this freshening are the melting of the Greenland ice sheet and glaciers, enhanced sea-ice melt, an increase of river discharge, increase in liquid precipitation and an increase of Pacific Ocean water influx to the Arctic Ocean through the Bering Strait. A retreating-thinning sea ice cover, and a concomitant warming-freshening upper ocean, have a widespread impact across the whole Arctic system through a large number of feedback mechanisms and interactions also with the atmospheric circulation of the northern hemisphere, having the potential to destabilize the thermo-haline circulation in the Northern Atlantic. An increase of liquid freshwater content has been found over both the Canadian Basin and the Beaufort Sea that can have a large impact on the Arctic marine ecosystem. The importance of monitoring changes in the Arctic freshwater system and its exchange with subarctic oceans has been widely recognized by the scientific communities. Among the key observable variables, ocean salinity is a proxy for freshwater content and allows to monitor increased freshwater from rivers or ice melt, and it sets the upper ocean stratification, which has important implications in water mass formation and heat storage. Changes in the salinity distribution may affect the water column stability and impact the freshwater pathways over the Arctic Ocean. Sea Surface Salinity (SSS) is observed from space with the L-band (1.4GHz) radiometers such as SMOS (ESA, since 2010) and SMAP (NASA, since 2015). However, retrieving SSS in cold waters is challenging, for different factors. Thanks to the ESA funded the ARCTIC+SSS ITT project, we have now a new enhanced Arctic SMOS Sea Surface Salinity product BEC v.31, which has

The Automated Polynya Identification Tool (APIT) is a machine learning based tool that aims to identify and define polynya formations in both space and time. These often small and short-lived phenomena are frequently undetected and are important for climate scientists to understand polar systems change. The APIT tool is a rapid, computationally efficient, low-cost and more time-efficient method for locating polynya formations relative to current in-situ surveying methods. APIT is currently in early-development and at a prototype stage, where MODIS imagery is the only sensor to have been applied using the widely recognised Weddell Sea polynya from 2017 as a way of training the tool. The use of an optical sensor in the polar regions has been found to be limited due to the quantity of cloud cover present and polar seasonal day-light hours; therefore, going forward, this tool will look to integrate alternative Earth Observation data, including but not limited to Sentinel-1, Soil Moisture and Ocean Salinity (SMOS) and CryoSat-2. Next APIT development stages contemplate the use of other auxiliary datasets, including those from the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) and the European Centre for Medium-Range Weather Forecasts (ECMWF), before implementing a machine learning detection process. Furthermore, for the provision of early-warning predictions, APIT will provide patterns and other oceanographic conditions taking place at different polynya evolutionary formation stages (i.e. before, during and after each event). The deployment of APIT will not only contribute to climate science as a way of providing near-real time locality information of polynya openings, but will also act as an early warning system, using machine-learning algorithms alongside open-source near-real time data enabling the re-routing of research vessels to take in-situ measurements. Providing opportunities for field research to take place during the life cycle of a polynya will co

Southern Ocean Freshwater (SO FRESH) is a recent ESA funded project (2021-2023) included in the Polar Cluster Initiative. Polar Cluster aims at establishing collaboration with the existing projects in polar areas to maximize unique, added-value capabilities from ESA missions and remote sensing missions in general. In that sense, Polar Cluster intends to fill knowledge gaps in specific hot topics of polar research. SO FRESH main goal is to improve our understanding of the different processes governed or affected by freshwater fluxes in the Southern Ocean. SO FRESH scientific objectives are based on four specific case studies: i) to improve our understanding on the changes in Antarctic Sea Ice; ii) to characterize the drivers of the formation of the Weddell Polynya in 2016-2017; iii) to assess salinity changes in the Antarctic coastal region and elucidate their causes/consequences; and iv) to analyze the formation of deep water via remote sensing variables. A key aspect to SO FRESH is the availability of continuous series of accurate geophysical variables with a space-time resolution adequate for the case studies. The key ocean variable for the four case studies is Sea Surface Salinity (SSS). SSS can be used in combination with other ocean variables (i.e. Sea Surface Temperature, Sea Surface Height Anomalies) to enhance the state of the art of SO freshwater fluxes, Sea Surface Density variability and Water Mass Transformation Rates. In that regards, special effort will be put in applying all recent advancements in SSS processing in the context of ESA SMOS Mission, in order to enhance its quality: nodal sampling, PSF correction, TB fusion, enhanced Debiased non-Bayesian retrieval, etc. Specific quality metrics, developed for SO FRESH, will be used for the data quality control and validation. SO FRESH started in May 2021, and the first set of data is expected to be available for distribution by the beginning of 2022. In this talk, we will present the results obtained du

The understanding of the marine and coastal bio-optical processes has important benefits for society especially in the areas with high human marine activities, as well as for environmental protection. The Black Sea receives drainage from almost one-third of continental Europe (five times its own surface) that includes significant portions of 17 countries, 13 capital cities, and some 160 million people. However, biogeochemical variables are generally derived from data acquired in the visible range of the spectrum, and thus they are hampered by the presence of clouds, a significant issue in a basin that has a 40-50% of average cloud coverage. At some especially strong river discharges (such as the Danube plume), there is a strong correlation between Sea Surface Salinity (SSS) and the Colored Detrital Organic Matter (CDM) to the point that one can be considered as a proxy for the other, at least over the area most influenced by the river discharge. This connection is caused by the capability of SSS to track the proportion of freshwater contributed by the river. As far as the proportion of sedimentary material contributed by the river is approximately constant, some biogeochemical variables associated with primary productivity will strongly correlate with the amount of river water being mixed in the sea basin and thus with SSS. In the framework of the ESA EO4SIBS (Earth Observation data for Science and Innovation in the Black Sea) project, we used this connection to derive a new CDM product in the Black Sea generated from SMOS SSS measurements. Despite its limited resolution (we use L4 SMOS SSS with spatial resolution grid of 0.05x0.05º), this new SMOS-derived product provides CDM information over all-weather conditions. Here we present the methods and the assessment of the performance of this new experimental product in comparison with the available CDM products in the Black Sea

The Black Sea is a small enclosed basin where coastal regions have a large influence and mesoscale signals dominate the dynamics (the Rossby radius of deformation is about 20km). Large riverine inputs, mainly on the northwestern shelf, induce well-marked horizontal gradients in the distribution of the Black Sea salinity and optical characteristics: coastal and shelf waters have very low salinity and contain large amounts of optically active materials (e.g. coloured dissolved organic matter) and its oligotrophic deep sea has a salinity around 18.2. The presence of these contrasting water characteristics in a relatively small enclosed environment, combined with land contamination and the specificities of its atmospheric composition(e.g. high cloud coverage, aerosols) make the Black Sea a challenging area for the development of high quality satellite products. We present results from the ESA EO4SIBS (Earth Observation for Science and Innovation in the Black Sea) project dedicated to the development, and subsequent scientific analysis, of new algorithms for the development of satellite products in the Black Sea. In particular, ocean colour products (chlorophyll-a, total suspended matter concentrations, turbidity) are produced from Sentinel 3 (S3) OLCI data combining different algorithms and then, a classification of water masses is proposed. A revised gridded altimetry product based on 5-Hz along track data (combining Cryosat and S3 SAR over 2011-2019) is produced and validated in the coastal zone with tide gauge data. L2, L3 and L4 Sea Surface Salinity is derived over 2011-2020 from the L-Band measured by SMOS and compared with in-situ surface salinity data from field sampling and Argo. In the presentation, we will describe the technical development that are needed to obtain high quality products in the Black Sea. An experimental CDOM product is also proposed. All these products are integrated to further understand the Black Sea physical and biogeochemical functioning.

he monitoring of the Sea Surface Salinity (SSS) in semi-enclosed seas has a significant impact in the study of climate change. In those basins the oceanographic processes occur at shorter temporal scales than in the open ocean, and therefore, trends and anomalies can be detected before. In the Black Sea, river run-off and precipitation exceed evaporation making the typical salinity values much lower than in the global ocean (17-18 psu versus 32-38 psu). Moreover, unlike other large estuarine basins, the Black Sea is a deep basin (maximum depth of ~2200 m) with a large North-Western shelf. A distinct vertical layering is created between the surface waters in the upper 100m and the deep graphic conditions maintained by strong stratification resulting from river runoffs flowing at the surface and the entrance of saline Bosporus waters at depths. The geophysical characteristics of the basin hinder the satellite SSS acquisition: 1) the dielectric models have some limitations in this range of low salinity values; 2) the strong stratification limits the use of in situ data to calibrate or validate the satellite acquisitions; 3) the sensitivity to potential trends requires a robust stable time series of measurements. Besides there are some already known acquisition issues in the region: 1) Strong land sea contamination; 2) Strong contamination from very close Radio Frequency Interference sources. In the framework of the ESA regional initiative project An Earth Observation Data for Science and Innovation in the Black Sea (EO4SIBS), we have developed new algorithms to deal with these issues. Here we present the enhanced methods used in the generation of the first regional SSS products in the Black Sea and the quality assessment of their performance. The products consists of: a) Daily level 2 maps at 0.25ºx0.25º with an accuracy of 1.5 psu; b) 9-day level 3 maps at 0.25ºx0.25º with an accuracy of 0.5 psu, and; c) daily level 4 at 0.05ºx0.0505º with an accuracy of 0.4 psu

The amount of data that must be processed in satellite missions is increasing over time, directly affecting the hardware resources and time required to carry out this processing. With more than 11 years in orbit, the SMOS mission has a lot of over-sampled data, which implies a more intensive use of the CPU and greater use of disk space if the processing is done without any type of data management. For this reason, it is increasingly necessary to optimize the resources involved in the processing of large volumes of data. Such optimizations include minimizing the processing time, achieving maximum efficiency of computational resources, and doing a good management of the generated data, both to make it more accessible and to optimize the disk space it demands. This work presents different techniques that can be applied when designing software architectures for the particular case of the SMOS Sea Surface Salinity data processing. A study is made on how the data can be aggregated and ordered in the first stages of processing to reduce the processing time of the following stages and the disk usage of intermediate products. The SMOS measurements can be easily divided into smaller independent processing units (such as a half-orbit or a snapshot, which is even smaller and still independent of the other snapshots). The granularity of the data allows the processing to be divided into very small pieces that can be executed in parallel, making an optimal use of CPU resources and reducing the total processing time. Disk operations, such as reading and writing files are also a big part of the processing time. Data has been arranged in a way in which disk operations are minimized (avoiding multiple reads of the same file) . Preliminary results show an improvement of the 20% of computational time and a reduction of the 40% of the required disk space with respect to the current implementation of the Barcelona Expert Center internal data processing chain

Important changes in the Earth’s water cycle can be assessed by analysing sea surface salinity, as this variable on average reflects the balance between precipitation and evaporation over ocean, being the upper layers the most sensitive to atmosphere-ocean interactions. In situ measurements of salinity are relatively scarce, reduced to a limited number of field campaigns, buoys, and drifters, and typically acquired some meters below the sea surface. Thus, they may not necessarily well represent ocean-atmosphere exchanges. Satellite measurements, on the contrary, are synoptic, repetitive and represent the uppermost surface of the ocean. In this work, we show that the dynamics captured by satellite-derived sea surface salinity (SSS) measurements differ from the dynamics shown by in situ near SSS (NSS) measurements. We compare a temporal series of 8 years of the Soil Moisture and Ocean Salinity (SMOS) SSS maps with the output of an ocean model that assimilates in situ salinity measurements, which includes salinity in the first meters of the surface (NSS), as well as the mixed layer depth (MLD) and the sea surface temperature (SST). On the one hand, the satellite SSS measurements present a clear intensification of the water cycle which is somewhat less evident in the NSS. The water cycle is expected to intensify in the context of global warming, according to the Clausius-Clapeyron (CC) relation, which states that the saturation of the water vapor pressure increases at a rate of 7% per degree Celsius of warming. During the analysed 8 years, we observe a positive SST trend ranging between 0.2ºC/year and 0.1ºC/year (depending on the region) and a SSS trend ranging between 0.008 psu/year and 0.015 psu/year, which is consistent with the CC law. On the other hand, we observe that the largest positive differences between the satellite SSS and the NSS trends are in regions that simultaneously present a large positive SST trend, and a negative MLD trend. This suggests that globa