scholarly journals 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

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.

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

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.

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

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

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

<p>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. 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 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 used as prior information in the SMOS SSS retrieval are significant sources of uncertainty. We develop a novel correction methodology accordingly.</p><p>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 pss), particularly in river plumes fresher by about 10 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.</p>

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.

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 Arctic Ocean CO<sub>2</sub> uptake: an improved multi-year estimate of the air–sea CO<sub>2</sub> flux incorporating chlorophyll-a concentrations

2017 ◽  
Author(s):  
Sayaka Yasunaka ◽  
Eko Siswanto ◽  
Are Olsen ◽  
Mario Hoppema ◽  
Eiji Watanabe ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Chlorophyll A ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Co2 Uptake ◽  
Self Organizing Map ◽  
Surface Salinity ◽  
Sea Ice Concentration ◽  
Chl A

Abstract. We estimated monthly air–sea CO2 fluxes in the Arctic Ocean and its adjacent seas north of 60° N from 1997 to 2014, after mapping partial pressure of CO2 in the surface water (pCO2w) using a self-organizing map (SOM) technique incorporating chlorophyll-a concentration (Chl-a), sea surface temperature, sea surface salinity, sea ice concentration, atmospheric CO2 mixing ratio, and geographical position. The overall relationship between pCO2w and Chl-a is negative in most regions when Chl-a ≤ 1 mg m−3, whereas there is no significant relationship when Chl-a > 1 mg m−3. In the Kara Sea and the East Siberian Sea and the Bering Strait, however, the relationship is typically positive in summer. The addition of Chl-a as a parameter in the SOM process enabled us to improve the estimate of pCO2w via better representation of its decline in spring, which resulted from biologically mediated pCO2w reduction. Mainly as a result of the inclusion of Chl-a, the uncertainty in the CO2 flux estimate was reduced, and a net annual Arctic Ocean CO2 uptake of 180 ± 130 TgC y−1 was determined to be significant.

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

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

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;

Sign in / Sign up

Export Citation Format

Share Document