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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

Special Issue Remote Sensing of Ocean-Atmosphere Interactions.-- 25 pages, 14 figures, 7 tables, 1 appendix.-- Data Availability Statement: Data supporting reported results can be found at: Meteosat Second Generation rain rates https://msgcpp.knmi.nl/ (accessed on 15 January 2022); wind divergence (as an L3 swath gridded (interpolated) product) at https://doi.org/10.48670/moi-00182 (accessed on 15 January 2022) (EU Copernicus Marine Service). The L3 product is produced by KNMI from L2 swath divergence and curl, which is available from KNMI on request (scat@knmi.nl), Air–sea fluxes are greatly enhanced by the winds and vertical exchanges generated by mesoscale convective systems (MCSs). In contrast to global numerical weather prediction models, space-borne scatterometers are able to resolve the small-scale wind variability in and near MCSs at the ocean surface. Downbursts of heavy rain in MCSs produce strong gusts and large divergence and vorticity in surface winds. In this paper, 12.5 km wind fields from the ASCAT-A and ASCAT-B tandem mission, collocated with short time series of Meteosat Second Generation 3 km rain fields, are used to quantify correlations between wind divergence and rain in the Inter-Tropical Convergence Zone (ITCZ) of the Atlantic Ocean. We show that when there is extreme rain, there is extreme convergence/divergence in the vicinity. Probability distributions for wind divergence and rain rates were found to be heavy-tailed: exponential tails for wind divergence (P∼e−αδ with slopes that flatten with increasing rain rate), and power-law tails for rain rates (P∼(R∗)−β with a slower and approximately equal decay for the extremes of convergence and divergence). Co-occurring points are tabulated in two-by-two contingency tables from which cross-correlations are calculated in terms of the odds and odds ratio for each time lag in the collocation. The odds ratio for extreme convergence and extreme divergence both have a well-defined peak. The divergence time lag is close to zero, while it is 30 min for the convergence peak, implying that extreme rain generally appears after (lags) extreme convergence. The temporal scale of moist convection is thus determined by the slower updraft process, as expected. A structural analysis was carried out that demonstrates consistency with the known structure of MCSs. This work demonstrates that (tandem) ASCAT winds are well suited for air–sea exchange studies in moist convection, This work was supported in part by the European Organization for the Exploitation of Meteorological Satellites Ocean and Sea Ice Satellite Application Facility (OSI-SAF) Visiting Scientist Program under reference OSI-SAF-AVS-15-02 and in part by the Spanish R&D projects L-BAND (ESP2017-89463-C3-1-R), which is funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe”, and INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. We also acknowledge funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S). The publication fee was partially supported by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI), Peer reviewed

This work is a contribution to CSIC PTI Teledetect.-- 23 pages, 11 figures.-- Data Availability Statement: HYCOM reanalysis outputs (GLBu0.08/expt_19.1) analyzed in this study can be found at hycom.org/data/glbu0pt08/expt-19pt1, Satellite altimeters provide quasi-global measurements of sea surface height, and from those the vertically integrated geostrophic velocity can be directly estimated, but not its vertical structure. This study discusses whether the mesoscale (30–400 km) dynamics of three regions in the South Atlantic can be described by the surface quasi-geostrophic (SQG) theory, both at the surface and in depth, using outputs from an ocean general circulation model. At these scales, the model surface eddy kinetic energy (EKE) spectra show slopes close to k−5/3 (k−3) in winter (summer), characterizing the SQG and quasi-geostrophic (QG) turbulence regimes. We use surface density and temperature to (a) reconstruct the stream function under the SQG theory, (b) assess its capability of reproducing mesoscale motions, and (c) identify the main parameters that improve such reconstruction. For mixed layers shallower than 100 m, the changes in the mixed-layer depth contributes nine times more to the surface SQG reconstruction than the EKE, indicating the strong connection between the quality of the reconstruction and the seasonality of the mixed layer. To further explore the reconstruction vertical extension, we add the barotropic and first baroclinic QG modes to the surface solution. The SQG solutions reproduce the model density and geostrophic velocities in winter, whereas in summer, the interior QG modes prevail. Together, these solutions can improve surface correlations (>0.98) and can depict spatial patterns of mesoscale structures in both the horizontal and vertical domains. Improved spatial resolution from upcoming altimeter missions poses a motivating scenario to extend our findings into future observational studies, We would like to acknowledge the financial support of Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq (130215/2018-0) and Fundação de Amparo à Pesquisa do Estado de São Paulo—FAPESP (2019/02968-9, 2019/13830-8, and 2017/09659-6). 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. We also acknowledge the financial support from the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S). [...] This work supports the SWOT-Brésil project, in preparation for the future SWOT wide-swath altimetry mission. SWOT-Brésil project is financed by CNES/INSU via the French TOSCA programme, Peer reviewed

8 pages, 3 figures.-- Data Availability Statement: The details of the model configuration, as well as, the simulated Sea Surface Temperatures generated for this study, and the singularity analysis described in Section 3 are available in https://doi.org/10.20350/digitalCSIC/14487 (Isern-Fontanet et al., 2022), The contribution of ocean fronts to the properties and temporal evolution of Sea Surface Temperature (SST) structure functions have been investigated using a numerical model of the California Current system. First, the intensity of fronts have been quantified by using singularity exponents. Then, leaning on the multifractal theory of turbulence, we show that the departure of the scaling of the structure functions from a straight line, known as anomalous scaling, depends on the intensity of the strongest fronts. These fronts, at their turn, are closely related to the seasonal change of intensity of the coastal upwelling characteristics of this area. Our study points to the need to correctly reproduce the intensity of the strongest fronts and, consequently, properly model processes such as coastal upwelling in order to reproduce SST statistics in ocean models, This work was supported by the Ministry of Economy and Competitiveness, Spain, and FEDER EU through the EXPLORA Ciencia National R + D Plan under TURBOMIX project (CGL2015-73100-EXP) and through the projects PROMISES (ESP2015-67549-C3-1-R) and COSMO (CTM2016-79474-R). 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. We also acknowledge support from Fundación General CSIC (Programa ComFuturo). This work acknowledges the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S). This work is a contribution to CSIC PTI Teledetect, Peer reviewed

9 pages, 4 figures, supplementary information https://doi.org/10.1038/s41598-022-10265-1.-- This work is a contribution to CSIC PTI Teledetect, Changes in the Earth’s water cycle can be estimated by analyzing sea surface salinity. This variable reflects 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 intensification of the water cycle (the freshest waters become fresher and vice-versa) which is not observed at the in-situ near-surface salinity measurements. The largest positive differences 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 stratification of the water column due to global warming. These results highlight the crucial importance of using satellites to unveil critical changes on ocean–atmosphere fluxes, 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 European 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), Peer reviewed

26 pages, 24 figures, 4 tables.-- Data availability: Access to the data is provided by the Barcelona Expert Center, through its FTP service. The DOI of the L3 product is https://doi.org/10.20350/digitalCSIC/13859 (González-Gambau et al., 2021a). The DOI of the L4 product is https://doi.org/10.20350/digitalCSIC/13860 (González-Gambau et al., 2021b). Seasonal averaged L4 SSS products are also available in the HELCOM catalogue (https://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/9d979033-1136-4dd1-a09b-7ee9e512ad14, BEC team, 2021b), and they can be visualized in the HELCOM Map and Data service (https://maps.helcom.fi/website/mapservice/?datasetID=9d979033-1136-4dd1-a09b-7ee9e512ad14, last access: 9 November 2021).-- This work is a contribution to the CSIC Thematic Interdisciplinary Platform Teledetect, This paper presents the first Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) dedicated products over the Baltic Sea. The SSS retrieval from L-band brightness temperature (TB) measurements over this basin is really challenging due to important technical issues, such as the land–sea and ice–sea contamination, the high contamination by radio-frequency interference (RFI) sources, the low sensitivity of L-band TB at SSS changes in cold waters, and the poor characterization of dielectric constant models for the low SSS range in the basin. For these reasons, exploratory research in the algorithms used from the level 0 up to level 4 has been required to develop these dedicated products. This work has been performed in the framework of the European Space Agency regional initiative Baltic+ Salinity Dynamics.

Two Baltic+ SSS products have been generated for the period 2011–2019 and are freely distributed: the Level 3 (L3) product (daily generated 9 d maps in a 0.25∘ grid; https://doi.org/10.20350/digitalCSIC/13859, González-Gambau et al., 2021a) and the Level 4 (L4) product (daily maps in a 0.05∘ grid; https://doi.org/10.20350/digitalCSIC/13860, González-Gambau et al., 2021b)​​​​​​​, which are computed by applying multifractal fusion to L3 SSS with SST maps. The accuracy of L3 SSS products is typically around 0.7–0.8 psu. The L4 product has an improved spatiotemporal resolution with respect to the L3 and the accuracy is typically around 0.4 psu. Regions with the highest errors and limited coverage are located in Arkona and Bornholm basins and Gulfs of Finland and Riga.
The impact assessment of Baltic+ SSS products has shown that they can help in the understanding of salinity dynamics in the basin. They complement the temporally and spatially very sparse in situ measurements, covering data gaps in the region, and they can also be useful for the validation of numerical models, particularly in areas where in situ data are very sparse, This work has been carried out as part of the Baltic+ Salinity Dynamics project (4000126102/18/I-BG), funded by the European Space Agency. It has been also supported in part by the Spanish R&D project INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. We also received funding from the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S), Peer reviewed

Data acquisition: Satellite: ESA SMOS mission (Soil Moisture and Ocean Salinity).
Filenames: BEC_SM____SMOS__GLO_L3__X_YYYYMMDDTHHMMSS_025km_TT_____v4.0.nc, being:
- X the half-orbit type (A for ascending and D for descending),
- YYYYMMDDTHHMMSS the central date (year, month, day, hour, minute and second) in Coordinated Universal Time (UTC) of the period covered by the file,
- TT: indicates the temporal coverage of the data file (1d, 3d, 9d, 1m and 1y for daily, 3 days, 9 days, 1 month and 1 year, respectively).
Sensor: Satellite SMOS / MIRAS.
Spatial resolution: 25 km x 25 km.
Spatial grid: WGS_84 / EASE2_M25km, 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, INTERACT. Enfoques sinergéticos para una nueva generación de productos y aplicaciones de observación de la Tierra (PID2020-114623RB-C31). Ministerio de Ciencia e Innovación a través de PN2020 - PROY I+D+I – Programa Estatal de I+D+I Orientada a los Retos de la Sociedad – Plan Estatal de Investigación Científica Técnica y de Innovación 2017-2020, · Surface soil moisture (SM) · Data quality index of surface soil moisture (SM_DQX) · Variance of surface soil moisture (SM_VARIANCE) · Number of L2 soil moisture measures (N_SM) · Vegetation optical depth at nadir (VOD) · Data quality index of vegetation optical depth at nadir (VOD_DQX) · Variance of vegetation optical depth at nadir (VOD_VARIANCE) · Number of L2 vegetation optical depth measures (N_VOD) · Time (time) · Latitude (lat) · Longitude (lon) · Coordinate reference system (crs), Peer reviewed

Data acquisition: Satellite: ESA SMOS mission (Soil Moisture and Ocean Salinity), ECMWF skin temperature at 12 UTC and 16-dayTerra MODIS NDVI collection 6. Filenames: BEC_SM____SMOS__EUM_L4__X_YYYYMMDDTHHMMSS_001km_TT_REP_v6.0.nc, being:
- X the half-orbit type (A for ascending and D for descending),
- YYYYMMDDTHHMMSS the central date (year, month, day, hour, minute and second) in Coordinated Universal Time (UTC) of the period covered by the file,
- TT: indicates the temporal coverage of the data file (1d for daily and 3d for 3 days).
Sensor: Satellite SMOS / MIRAS.
Spatial resolution: 1 km x 1 km.
Spatial grid: WGS_84 / EASE2_M01km, 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, INTERACT. Enfoques sinergéticos para una nueva generación de productos y aplicaciones de observación de la Tierra (PID2020-114623RB-C31). Ministerio de Ciencia e Innovación a través de PN2020 - PROY I+D+I – Programa Estatal de I+D+I Orientada a los Retos de la Sociedad – Plan Estatal de Investigación Científica Técnica y de Innovación 2017-2020, · Surface soil moisture (SM) · Quality flag of surface soil moisture (quality_flag) · Number of L4 measures (N) · Time (time) · Latitude (lat) · Longitude (lon) · Coordinate reference system (crs), Peer reviewed

Data acquisition: Satellite: ESA SMOS mission (Soil Moisture and Ocean Salinity), ECMWF skin temperature at 12 UTC and NRT 8-rolling day Terra MODIS NDVI collection 6.
Filenames: BEC_SM____SMOS__EUM_L4__X_YYYYMMDDTHHMMSS_001km_TT_NRT_v6.0.nc, being:
- X the half-orbit type (A for ascending and D for descending),
- YYYYMMDDTHHMMSS the central date (year, month, day, hour, minute and second) in Coordinated Universal Time (UTC) of the period covered by the file,
- TT: indicates the temporal coverage of the data file (1d for daily and 3d for 3 days).
Sensor: Satellite SMOS / MIRAS. Spatial resolution: 1 km x 1 km. Spatial grid: WGS_84 / EASE2_M01km, 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, INTERACT. Enfoques sinergéticos para una nueva generación de productos y aplicaciones de observación de la Tierra (PID2020-114623RB-C31). Ministerio de Ciencia e Innovación a través de PN2020 - PROY I+D+I – Programa Estatal de I+D+I Orientada a los Retos de la Sociedad – Plan Estatal de Investigación Científica Técnica y de Innovación 2017-2020, · Surface soil moisture (SM) · Quality flag of surface soil moisture (quality_flag) · Number of L4 measures (N) · Time (time) · Latitude (lat) · Longitude (lon) · Coordinate reference system (crs), Peer reviewed

8 pages, 7 figures, The airborne stepped frequency microwave radiometer (SFMR) provides the measurements of 10-m ocean surface wind speed in high and extreme wind conditions. These winds are calibrated using the surface-adjusted wind estimates from the so-called dropsondes. The surface-adjusted winds are obtained from layer-averaged winds scaled to 10-m altitude to eliminate the local surface variability not associated with the storm strength. The SFMR measurements and, consequently, the surface-adjusted dropsonde winds represent a possible reference for satellite instrument and model calibration/validation at high and extreme wind conditions. To this end, representativeness errors that those measurements may introduce need to be taken into account to ensure that the storm variability is correctly resolved in satellite retrievals and modeling. In this work, we compare the SFMR winds with the dropsonde surface-adjusted winds derived from the so-called WL150 algorithm, which uses the lowest 150-m layer between 10 and 350 m. We use nine years of data from 2009 to 2017. We focus on the effects of the layer altitude and thickness. Our analysis shows that the layer altitude has a significant impact on dropsonde/SFMR wind comparisons. Moreover, the averaged winds obtained from layers thinner than the nominal 150 m and closer to the surface are more representative of the SFMR surface wind speed than the WL150 speeds. We also find that the surface-adjusted winds are more representative of 10-km horizontally averaged SFMR winds. We conclude that for calibration/validation purposes, the WL150 algorithm can introduce noise, and the use of actual 10-m dropsonde measurements should be further investigated, This work was supported in part by the MCIN/AEI/10.13039/501100011033 and ERDF A way of making Europe through the Spanish Research and Development Project L-BAND under Grant ESP2017-89463-C3-1-R, in part by the MCIN/AEI/10.13039/501100011033 through the Project INTERACT under Grant PID2020-114623RB-C31, in part by the Spanish Government through the Severo Ochoa Center of Excellence Accreditation under Grant CEX2019-000928-S, in part by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) through the Tender 16_166-STC under C-band High and Extreme- Force Speeds (CHEFS) Project EUM/CO/16/4600001953, and in part by the Jet Propulsion Laboratory, California Institute of Technology, through the National Aeronautics and Space Administration (NASA) Postdoctoral Program (NPP), initially administered by Universities Space Research Association and now administered by Oak Ridge Associated Universities, under a contract with NASA, Peer reviewed