scholarly journals Correlation between Arctic river discharge and sea ice formation in Laptev Sea using sea surface salinity from SMOS satellite

2020 ◽  
Author(s):  
Carolina Gabarro ◽  
Justino Martinez ◽  
Veronica Gonzalez-Gambau ◽  
Cristina González-Haro ◽  
Estrella Olmedo ◽  
...  
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Laptev Sea ◽  
Ice Melting ◽  
Surface Salinity ◽  
The Laptev Sea

<p>During the last 3<span> dec</span><span>ades, the Arctic rivers have increased their discharge around 10%, mainly due to the increase of the</span> <span>g</span><span>lobal atmospheric </span>temperature. The increase of the river discharge carries higher loads of dissolved organic matter (DOM) and suspended matter (SM) entering to the Arctic Ocean. This results in increased absorption of solar energy in the mixed layer, which can potentially contribute to the general sea ice retreat. Observation based studies (e.g. Bauch et al., 2013) showed correlation between river water discharge and local sea ice melting on the Laptev sea shelf due to the change on the ocean heat. Previous studies are based with a limited number of observations, both in space and in time.</p><p>Thanks to the ESA SMOS (Soil Moisture and Ocean Salinity) and NASA SMAP (Soil Moisture Active Passive) missions we have daily the sea surface salinity (SSS) maps from the Arctic, which permit to observe the salinity variations due to the river discharges. The Arctic sea surface salinity products obtained from SMOS measurements have been improved considerable by the Barcelona Expert Center (BEC) team thanks to the project Arctic+Salinity, funded by ESA. The new version of the product (v3) covers the years from 2011 up to 2018, have a spatial resolution of 25km and are daily maps with 9 day averages. The Arctic+ SSS maps provide a better description of the salinity gradients and a better effective spatial resolution than the previous versions of the Arctic product, so the salinity fronts are better resolved. The quality assessment of the Arctic+SSS product is challenging because, in this region, there are scarce number of in-situ measurements.</p><p>The high effective spatial resolution of the Arctic+ SSS maps will permit to study for the first time scientific physical processes that occurs in the Arctic. We will explore if a correlation between the Lena and Ob rivers discharge with the sea ice melting and freeze up is observed with satellite data, as already stated with in-situ measurements by Bauch et al. 2013. Salinity and sea ice thickness maps from SMOS and sea ice concentration from OSISAF will be used in this study.</p><p> </p><p>Bauch, D.,Hölemann, J. , Nikulina, A. , Wegner, C., Janout, M., Timokhov, L. and Kassens, H. (2013): Correlation of river water and local sea-ice melting on the Laptev Sea shelf (Siberian Arctic) , Journal of Geophysical Research C: Oceans, 118 (1), pp. 550-561 . doi: 10.1002/jgrc.20076</p>

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

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

<p>In the Arctic, the sea surface salinity (SSS) has a key role in processes related to mixing, sea ice melt and freeze. However, due to insufficient salinity observations, uncertainties in present Arctic ocean forecasts and reanalysis are still large. Thanks to the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, two successive versions of regional gridded SSS products for the Arctic Ocean have been developed by the Barcelona Expert Centre (BEC). These two SSS products (V2 and V3) are available from the BEC (http://bec.icm.csic.es/) and the Arctic+Salinity project funded by the ESA (https://arcticsalinity.argans.co.uk).<br>In this study, we show the impacts of assimilating the SMOS SSS  in a coupled ocean and sea ice forecasting system.</p><p>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 over the whole Arctic. Via the CMEMS portal, TOPAZ4 provides the products of both reanalysis and operational forecasts. Two parallel runs of TOPAZ4 are integrated from July to December in 2016, during which either the V2 or V3 SSS data product is assimilated in addition to other available data sources (altimeter data, SST, sea ice concentration, sea ice drift, T/S profiles, sea ice thickness). Independent in situ salinity profiles are used for validation of the model runs in three regions: 1) in the Beaufort Sea; 2) around Greenland; 3) in the Nordic Seas. Compared to the runs without SSS assimilation, the results show the reduction of a severe saline bias in the Beaufort Sea: 15.9% (V2) and 28.6% (V3), also the Root Mean Squared differences (RMSD) decreased by 10.8% (V2) and 16.2% (V3). Around Greenland, the SSS bias is decreased by 17.3% and the RMSD by 8.2% (V3 only). There are neither degradations or improvements for V2 both around Greenland and in the Nordic Seas. These basic statistics 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.</p><p><strong>Keywords</strong>: Arctic Ocean; Sea Surface Salinity; TOPAZ4; In situ; RMSD;</p>

scholarly journals Evaluation of Arctic Ocean surface salinities from SMOS and two CMEMS reanalyses against in-situ data sets

2019 ◽  
Author(s):  
Jiping Xie ◽  
Roshin P. Raj ◽  
Laurent Bertino ◽  
Annette Samuelsen ◽  
Tsuyoshi Wakamatsu
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Large Set ◽  
Surface Salinity ◽  
Ocean Salinity ◽  
In Situ Observations

Abstract. Although the stratification of the upper Arctic Ocean is mostly salinity-driven, the sea surface salinity (SSS) is still poorly known in the Arctic, due to its strong variability and the sparseness of in-situ observations. Recently, two gridded SSS products have been derived from the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, independently developed by the Barcelona Expert Centre (BEC) in Spain and the Ocean Salinity Expertise Center (CECOS) of the Centre Aval de Traitemenent des Donnees SMOS (CATDS) in France, respectively. In parallel, there are two reanalysis products providing the Arctic SSS in the framework of the Copernicus Marine Environment Monitoring Services (CMEMS), one global, and another regional product. While the regional Arctic TOPAZ4 system assimilates a large set of sea-ice and ocean observations with an Ensemble Kalman Filter, the global reanalysis combines in-situ and satellite data using a multivariate ensemble optimal interpolation method. In this study, focused on the Arctic Ocean, these four salinity products, together with the climatology both World Ocean Atlas (WOA) of 2013 and Polar science center Hydrographic Climatology (PHC), are evaluated against in-situ datasets during 2011–2013. For the validation the in-situ observations are divided in two; those that have been assimilated and those that have not. The deviations of SSS between the different products and against the in-situ observations show largest disagreements below the sea-ice and in the marginal ice zone (MIZ), especially during the summer months. In the Beaufort Sea, the summer SSS from the BEC product has the smallest – saline – bias (~0.6 psu) with the smallest root mean squared difference (RSMD) of 2.6 psu. This suggests a potential value of assimilating of this product into the forthcoming Arctic reanalyses. Keywords: Arctic Ocean; sea surface salinity; SMOS; reanalysis; absolute deviation;

scholarly journals Intercomparison of Salinity Products in the Beaufort Gyre and Arctic Ocean

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.

scholarly journals Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in Arctic and Sub-Arctic Regions

2018 ◽  
Vol 10 (11) ◽  
pp. 1772 ◽  
Author(s):  
Estrella Olmedo ◽  
Carolina Gabarró ◽  
Verónica González-Gambau ◽  
Justino Martínez ◽  
Joaquim Ballabrera-Poy ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Remotely Sensed ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Surface Salinity ◽  
In Situ Data ◽  
Arctic Regions ◽  
The Difference

This paper aims to present and assess the quality of seven years (2011–2017) of 25 km nine-day Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) objectively analyzed maps in the Arctic and sub-Arctic oceans ( 50 ∘ N– 90 ∘ N). The SMOS SSS maps presented in this work are an improved version of the preliminary three-year dataset generated and freely distributed by the Barcelona Expert Center. In this new version, a time-dependent bias correction has been applied to mitigate the seasonal bias that affected the previous SSS maps. An extensive database of in situ data (Argo floats and thermosalinograph measurements) has been used for assessing the accuracy of this product. The standard deviation of the difference between the new SMOS SSS maps and Argo SSS ranges from 0.25 and 0.35. The major features of the inter-annual SSS variations observed by the thermosalinographs are also captured by the SMOS SSS maps. However, the validation in some regions of the Arctic Ocean has not been feasible because of the lack of in situ data. In those regions, qualitative comparisons with SSS provided by models and the remotely sensed SSS provided by Aquarius and SMAP have been performed. Despite the differences between SMOS and SMAP, both datasets show consistent SSS variations with respect to the model and the river discharge in situ data, but present a larger dynamic range than that of the model. This result suggests that, in those regions, the use of the remotely sensed SSS may help to improve the models.

scholarly journals The Potential of Space-Based Sea Surface Salinity on Monitoring the Hudson Bay Freshwater Cycle

2020 ◽  
Vol 12 (5) ◽  
pp. 873 ◽  
Author(s):  
Wenqing Tang ◽  
Simon H. Yueh ◽  
Daqing Yang ◽  
Ellie Mcleod ◽  
Alexander Fore ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Sea Ice ◽  
River Discharge ◽  
Open Water ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Hudson Bay ◽  
Surface Salinity ◽  
Ice Melt

Hudson Bay (HB) is the largest semi-inland sea in the Northern Hemisphere, connecting with the Arctic Ocean through the Foxe Basin and the northern Atlantic Ocean through the Hudson Strait. HB is covered by ice and snow in winter, which completely melts in summer. For about six months each year, satellite remote sensing of sea surface salinity (SSS) is possible over open water. SSS links freshwater contributions from river discharge, sea ice melt/freeze, and surface precipitation/evaporation. Given the strategic importance of HB, SSS has great potential in monitoring the HB freshwater cycle and studying its relationship with climate change. However, SSS retrieved in polar regions (poleward of 50°) from currently operational space-based L-band microwave instruments has large uncertainty (~ 1 psu) mainly due to sensitivity degradation in cold water (<5°C) and sea ice contamination. This study analyzes SSS from NASA Soil Moisture Active and Passive (SMAP) and European Space Agency (ESA) Soil Moisture and Ocean Salinity(SMOS) missions in the context of HB freshwater contents. We found that the main source of the year-to-year SSS variability is sea ice melting, in particular, the onset time and places of ice melt in the first couple of months of open water season. The freshwater contribution from surface forcing P-E is smaller in magnitude comparing with sea ice contribution but lasts on longer time scale through the whole open water season. River discharge is comparable with P-E in magnitude but peaks before ice melt. The spatial and temporal variations of freshwater contents largely exceed the remote sensed SSS uncertainty. This fact justifies the use of remote sensed SSS for monitoring the HB freshwater cycle.

scholarly journals Surface Freshwater Fluxes in the Arctic and Subarctic Seas during Contrasting Years of High and Low Summer Sea Ice Extent

2021 ◽  
Vol 13 (8) ◽  
pp. 1570
Author(s):  
Sarah B. Hall ◽  
Bulusu Subrahmanyam ◽  
Ebenezer S. Nyadjro ◽  
Annette Samuelsen
Keyword(s):  
Sea Ice ◽  
Global Climate ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Spatial And Temporal Variability ◽  
Polar Regions ◽  
Surface Salinity ◽  
Sea Ice Concentration ◽  
Sea Ice Extent

Freshwater (FW) flux between the Arctic Ocean and adjacent waterways, predominantly driven by wind and oceanic currents, influences halocline stability and annual sea ice variability which further impacts global circulation and climate. The Arctic recently experienced anomalous years of high and low sea ice extent in the summers of 2013/2014 and 2012/2016, respectively. Here we investigate the interannual variability of oceanic surface FW flux in relation to spatial and temporal variability in sea ice concentration (SIC), sea surface salinity (SSS), and sea surface temperature (SST), focusing on years with summer sea–ice extremes. Our analysis between 2010–2018 illustrate high parameter variability, especially within the Laptev, Kara, and Barents seas, as well as an overall decreasing trend of FW flux through the Fram Strait. We find that in 2012, a maximum average FW flux of 0.32 × 103 ms−1 in October passed over a large portion of the Northeast Atlantic Ocean at 53°N. This study highlights recent changes in the Arctic and Subarctic Seas and the importance of continued monitoring of key variables through remote sensing to understand the dynamics behind these ongoing changes. Observations of FW fluxes through major Arctic routes will be increasingly important as the polar regions become more susceptible to warming, with major impacts on global climate.

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

2020 ◽  
Author(s):  
Alexandre Supply ◽  
Jacqueline Boutin ◽  
Jean-Luc Vergely ◽  
Nicolas Kolodziejczyk ◽  
Gilles Reverdin ◽  
...  
Keyword(s):  
Dielectric Constant ◽  
Arctic Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Surface Salinity ◽  
Near Surface ◽  
The Arctic Ocean ◽  
L Band

&lt;p&gt;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.&amp;#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&amp;#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&amp;#160;used as prior information in the SMOS SSS retrieval&amp;#160;are significant sources of uncertainty. We develop a novel correction methodology accordingly.&lt;/p&gt;&lt;p&gt;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&amp;#160;pss), particularly in river plumes fresher by about 10&amp;#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.&lt;/p&gt;

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

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.

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

2019 ◽  
Vol 11 (24) ◽  
pp. 3043 ◽  
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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