New insights into SMOS Sea Surface Salinity retrievals in the Arctic Ocean.

Since 2010, the Soil Moisture and Ocean Salinity (SMOS) satellite mission monitors the earth emission at L-Band, providing the longest time series of Sea Surface Salinity (SSS) from space over the global ocean. However, retrieving SSS at high latitudes with a reasonable accuracy remains challenging, in particular due to the low sensitivity of L-Band radiometric measurements to SSS in cold waters and to the contamination of SMOS measurements by the vicinity of continents and sea ice as well as the presence of Radio Frequency Interferences.&#160;In this paper, we assess the quality of weekly SSS fields derived from swath-ordered instantaneous SMOS SSS (so called Level 2) distributed by the European Space Agency. These products are filtered according to new criteria. We use&#160;the pseudo-dielectric constant retrieved from SMOS brightness temperatures to filter SSS pixels polluted by sea ice. We identify that the dielectric constant model and the sea surface temperature auxiliary parameter&#160;used as prior information in the SMOS SSS retrieval&#160;are significant sources of uncertainty. We develop a novel correction methodology accordingly.SSS Standard deviation of differences (STDD) between weekly SMOS SSS and in-situ near surface salinity significantly decrease after applying the SSS correction, from 1.46 pss to 1.26 pss. The correlation between new SMOS SSS and in-situ near surface salinity reaches 0.94. SMOS estimates better capture SSS variability in the Arctic Ocean in comparison to TOPAZ reanalysis (STDD = 1.86&#160;pss), particularly in river plumes fresher by about 10&#160;pss than surrounding waters. Furthermore, comparisons with in-situ measurements ranging from 1 to 11 m depths identify huge vertical stratification in fresh regions. This emphasizes the need to consider in-situ salinity as close as possible to the sea surface when validating L-band radiometric SSS which are representative of the first top centimeter.

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Intercomparison of Salinity Products in the Beaufort Gyre and Arctic Ocean

Remote Sensing ◽

10.3390/rs14010071 ◽

2021 ◽

Vol 14 (1) ◽

pp. 71

Author(s):

Sarah B. Hall ◽

Bulusu Subrahmanyam ◽

James H. Morison

Keyword(s):

Soil Moisture ◽

Arctic Ocean ◽

Satellite Observations ◽

Sea Surface ◽

Sea Surface Salinity ◽

The Arctic ◽

Surface Salinity ◽

Beaufort Gyre ◽

The Arctic Ocean

Salinity is the primary determinant of the Arctic Ocean’s density structure. Freshwater accumulation and distribution in the Arctic Ocean have varied significantly in recent decades and certainly in the Beaufort Gyre (BG). In this study, we analyze salinity variations in the BG region between 2012 and 2017. We use in situ salinity observations from the Seasonal Ice Zone Reconnaissance Surveys (SIZRS), CTD casts from the Beaufort Gyre Exploration Project (BGP), and the EN4 data to validate and compare with satellite observations from Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Aquarius Optimally Interpolated Sea Surface Salinity (OISSS), and Arctic Ocean models: ECCO, MIZMAS, HYCOM, ORAS5, and GLORYS12. Overall, satellite observations are restricted to ice-free regions in the BG area, and models tend to overestimate sea surface salinity (SSS). Freshwater Content (FWC), an important component of the BG, is computed for EN4 and most models. ORAS5 provides the strongest positive SSS correlation coefficient (0.612) and lowest bias to in situ observations compared to the other products. ORAS5 subsurface salinity and FWC compare well with the EN4 data. Discrepancies between models and SIZRS data are highest in GLORYS12 and ECCO. These comparisons identify dissimilarities between salinity products and extend challenges to observations applicable to other areas of the Arctic Ocean.

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Evaluation and Intercomparison of SMOS, Aquarius, and SMAP Sea Surface Salinity Products in the Arctic Ocean

Remote Sensing ◽

10.3390/rs11243043 ◽

2019 ◽

Vol 11 (24) ◽

pp. 3043 ◽

Cited By ~ 3

Author(s):

Séverine Fournier ◽

Tong Lee ◽

Wenqing Tang ◽

Michael Steele ◽

Estrella Olmedo

Keyword(s):

Soil Moisture ◽

Arctic Ocean ◽

European Space Agency ◽

In Situ Measurements ◽

Sea Surface ◽

Sea Surface Salinity ◽

The Arctic ◽

Surface Salinity ◽

The Arctic Ocean

Salinity is a critical parameter in the Arctic Ocean, having potential implications for climate and weather. This study presents the first systematic analysis of 6 commonly used sea surface salinity (SSS) products from the National Aeronautics and Space Administration (NASA) Aquarius and Soil Moisture Active Passive (SMAP) satellites and the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, in terms of their consistency among one another and with in-situ data. Overall, the satellite SSS products provide a similar characterization of the time mean SSS large-scale patterns and are relatively consistent in depicting the regions with strong SSS temporal variability. When averaged over the Arctic Ocean, the SSS show an excellent consistency in describing the seasonal and interannual variations. Comparison of satellite SSS with in-situ salinity measurements along ship transects suggest that satellite SSS captures salinity gradients away from regions with significant sea-ice concentration. The root-mean square differences (RMSD) of satellite SSS with respect to in-situ measurements improves with increasing temperature, reflecting the limitation of L-band radiometric sensitivity to SSS in cold water. However, the satellite SSS biases with respect to the in-situ measurements do not show a consistent dependence on temperature. The results have significant implications for the calibration and validation of satellite SSS as well as for the modeling community and the design of future satellite missions.

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Using Saildrones to Validate Arctic Sea-Surface Salinity from the SMAP Satellite and from Ocean Models

Remote Sensing ◽

10.3390/rs13050831 ◽

2021 ◽

Vol 13 (5) ◽

pp. 831

Author(s):

Jorge Vazquez-Cuervo ◽

Chelle Gentemann ◽

Wenqing Tang ◽

Dustin Carroll ◽

Hong Zhang ◽

...

Keyword(s):

Arctic Ocean ◽

Sea Surface ◽

Sea Surface Salinity ◽

The Arctic ◽

Future Research ◽

Surface Salinity ◽

Cold Temperatures ◽

Yukon River ◽

Arctic Sea ◽

The Arctic Ocean

The Arctic Ocean is one of the most important and challenging regions to observe—it experiences the largest changes from climate warming, and at the same time is one of the most difficult to sample because of sea ice and extreme cold temperatures. Two NASA-sponsored deployments of the Saildrone vehicle provided a unique opportunity for validating sea-surface salinity (SSS) derived from three separate products that use data from the Soil Moisture Active Passive (SMAP) satellite. To examine possible issues in resolving mesoscale-to-submesoscale variability, comparisons were also made with two versions of the Estimating the Circulation and Climate of the Ocean (ECCO) model (Carroll, D; Menmenlis, D; Zhang, H.). The results indicate that the three SMAP products resolve the runoff signal associated with the Yukon River, with high correlation between SMAP products and Saildrone SSS. Spectral slopes, overall, replicate the −2.0 slopes associated with mesoscale-submesoscale variability. Statistically significant spatial coherences exist for all products, with peaks close to 100 km. Based on these encouraging results, future research should focus on improving derivations of satellite-derived SSS in the Arctic Ocean and integrating model results to complement remote sensing observations.

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Using Remotely Sensed Sea Surface Salinity and Colored Detrital Matter to Characterize Freshened Surface Layers in the Kara and Laptev Seas during the Ice-Free Season

Remote Sensing ◽

10.3390/rs13193828 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3828

Author(s):

Marta Umbert ◽

Carolina Gabarro ◽

Estrella Olmedo ◽

Rafael Gonçalves-Araujo ◽

Sebastien Guimbard ◽

...

Keyword(s):

Arctic Ocean ◽

River Runoff ◽

River Discharge ◽

Active Material ◽

Sea Surface ◽

Sea Surface Salinity ◽

The Arctic ◽

Optical Data ◽

Surface Salinity ◽

The Arctic Ocean

The overall volume of freshwater entering the Arctic Ocean has been growing as glaciers melt and river runoff increases. Since 1980, a 20% increase in river runoff has been observed in the Arctic system. As the discharges of the Ob, Yenisei, and Lena rivers are an important source of freshwater in the Kara and Laptev Seas, an increase in river discharge might have a significant impact on the upper ocean circulation. The fresh river water mixes with ocean water and forms a large freshened surface layer (FSL), which carries high loads of dissolved organic matter and suspended matter into the Arctic Ocean. Optically active material (e.g., phytoplankton and detrital matter) are spread out into plumes, which are evident in satellite data. Russian river signatures in the Kara and Laptev Seas are also evident in recent SMOS Sea Surface Salinity (SSS) Arctic products. In this study, we compare the new Arctic+ SSS products, produced at the Barcelona Expert Center, with the Ocean Color absorption coefficient of colored detrital matter (CDM) in the Kara and Laptev Seas for the period 2011–2019. The SSS and CDM are found to be strongly negatively correlated in the regions of freshwater influence, with regression coefficients between −0.72 and −0.91 in the studied period. Exploiting this linear correlation, we estimate the SSS back to 1998 using two techniques: one assuming that the relationship between the CDM and SSS varies regionally in the river-influenced areas, and another assuming that it does not. We use the 22-year time-series of reconstructed SSS to estimate the interannual variability of the extension of the FSL in the Kara and Laptev Seas as well as their freshwater content. For the Kara and Laptev Seas, we use 32 and 28 psu as reference salinities, and 26 and 24 psu isohalines as FSL boundaries, respectively. The average FSL extension in the Kara Sea is 2089–2611 km2, with a typical freshwater content of 11.84–14.02 km3. The Laptev Sea has a slightly higher mean FSL extension of 2320–2686 km2 and a freshwater content of 10.15–12.44 km3. The yearly mean freshwater content and extension of the FSL, computed from SMOS SSS and Optical data, is (as expected) found to co-vary with in situ measurements of river discharge from the Arctic Great Rivers Observatory database, demonstrating the potential of SMOS SSS to better monitor the river discharge changes in Eurasia and to understand the Arctic freshwater system during the ice-free season.

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Correcting sea surface temperature spurious effects in salinity retrieved from spaceborne L-band Radiometer measurements

10.5194/egusphere-egu21-655 ◽

2021 ◽

Author(s):

Jacqueline Boutin ◽

Jean-Luc Vergely ◽

Emmanuel Dinnat ◽

Philippe Waldteufel ◽

Francesco D'Amico ◽

...

Keyword(s):

Dielectric Constant ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Dielectric Constants ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

L Band ◽

Band Radiometer

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&#176;C and within +/-0.05 between 7 and 28&#176;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.&#160;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.

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New insights into SMOS sea surface salinity retrievals in the Arctic Ocean

Remote Sensing of Environment ◽

10.1016/j.rse.2020.112027 ◽

2020 ◽

Vol 249 ◽

pp. 112027

Author(s):

Alexandre Supply ◽

Jacqueline Boutin ◽

Jean-Luc Vergely ◽

Nicolas Kolodziejczyk ◽

Gilles Reverdin ◽

...

Keyword(s):

Arctic Ocean ◽

Sea Surface ◽

Sea Surface Salinity ◽

The Arctic ◽

Surface Salinity ◽

The Arctic Ocean

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Towards an improved temporal stability of CCI+SSS time series

10.5194/egusphere-egu21-1231 ◽

2021 ◽

Author(s):

Xavier Perrot ◽

Jacqueline Boutin ◽

Jean Luc Vergely ◽

Frédéric Rouffi ◽

Adrien Martin ◽

...

Keyword(s):

Climate Change ◽

Time Series ◽

Dielectric Constant ◽

Soil Moisture ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

L Band ◽

Band Radiometer

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&#8211;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

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Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-20-0093.1 ◽

2020 ◽

Author(s):

Nicolas Kolodziejczyk ◽

Mathieu Hamon ◽

Jacqueline Boutin ◽

Jean-Luc Vergely ◽

Gilles Reverdin ◽

...

Keyword(s):

Soil Moisture ◽

High Latitude ◽

Large Scale ◽

Sea Surface ◽

Sea Surface Salinity ◽

The Arctic ◽

Surface Salinity ◽

Radiometric Measurements ◽

L Band

AbstractTen 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 latitude, L-Band measurements still suffer from large scale and time systematic errors. Here, a simple method is proposed to mitigate the large scale and seasonal varying biases. First, an Optimal Interpolation (OI) using a large correlation scale (~500 km) is used to map independently Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) Level 3 data. The mapping is compared to 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 RMSD of in situ-satellite products by about 26-32% from mid to high latitude, respectively, in comparison to the existing SMOS and SMAP L3 products. However, in the Arctic Ocean, some issues on satellite retrieved SSS related to e.g. radio frequency interferences, land-sea contamination, ice-sea contamination remain challenging to reduce given the low sensitivity of L-Band radiometric measurements to SSS in cold water. Using the thermodynamic equation of state (TEOS-10), the resulting L4 SSS satellite product is combined with satellite-microwave SST products to estimate sea surface density, spiciness, haline contraction and thermal expansion coefficients. For the first time, we illustrate how useful are these satellite derived parameters to fully characterize the surface ocean water masses at large mesoscale.

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Impacts of assimilating Arctic surface sea salinities from SMOS in a coupled ocean and sea ice reanalysis

10.5194/egusphere-egu21-2150 ◽

2021 ◽

Author(s):

Jiping Xie ◽

Roshin P. Raj ◽

Laurent Bertino ◽

Justino Martínez ◽

Carolina Gabarró ◽

...

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

European Space Agency ◽

Sea Surface ◽

Sea Surface Salinity ◽

The Arctic ◽

Altimeter Data ◽

Surface Salinity ◽

Nordic Seas

In the Arctic,&#160;the sea surface salinity (SSS) has a key role in processes related to&#160;mixing, sea ice melt and freeze. However, due to insufficient salinity observations, uncertainties in present Arctic ocean forecasts and reanalysis are still large.&#160;Thanks to the&#160;European Space Agency&#8217;s (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, two successive versions of regional gridded SSS products for the Arctic Ocean have&#160;been developed by the Barcelona Expert Centre (BEC). These two SSS products (V2 and V3) are available from&#160;the BEC (http://bec.icm.csic.es/)&#160;and&#160;the Arctic+Salinity project funded by&#160;the&#160;ESA&#160;(https://arcticsalinity.argans.co.uk). In this study, we show the impacts of assimilating the SMOS SSS&#160; in a coupled ocean and sea ice forecasting system.TOPAZ4, the Arctic component of the Copernicus Marine Environment Monitoring Services (CMEMS), is a coupled ice-ocean data assimilative system, using the Ensemble Kalman Filter (EnKF) to assimilate jointly all available ocean and sea ice observations&#160;over the whole Arctic.&#160;Via the CMEMS portal, TOPAZ4 provides the products of both reanalysis and operational forecasts.&#160;Two parallel runs&#160;of&#160;TOPAZ4&#160;are&#160;integrated from July to&#160;December&#160;in&#160;2016, during which&#160;either&#160;the&#160;V2 or V3&#160;SSS&#160;data product&#160;is assimilated&#160;in addition to other available data sources (altimeter data, SST, sea ice concentration, sea ice&#160;drift, T/S profiles, sea ice thickness).&#160;Independent in situ salinity profiles are used for validation of the model runs&#160;in three&#160;regions:&#160;1)&#160;in the&#160;Beaufort&#160;Sea;&#160;2)&#160;around&#160;Greenland;&#160;3)&#160;in the&#160;Nordic Seas.&#160;Compared to the runs without SSS assimilation, the results show&#160;the reduction of&#160;a&#160;severe saline bias in the Beaufort&#160;Sea: 15.9%&#160;(V2) and 28.6% (V3), also the&#160;Root Mean&#160;Squared&#160;differences&#160;(RMSD)&#160;decreased&#160;by&#160;10.8%&#160;(V2) and 16.2%&#160;(V3).&#160;Around Greenland, the SSS bias is decreased by 17.3% and the RMSD by 8.2% (V3 only).&#160;There are neither degradations or improvements for V2 both around Greenland and in the Nordic Seas.&#160;These basic statistics&#160;suggest the benefits of assimilating SMOS data on the TOPAZ4 outputs and the advantages from the V3 SSS product especially compared to the V2 product.Keywords: Arctic Ocean; Sea Surface Salinity; TOPAZ4; In situ; RMSD;

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