scholarly journals First SMOS Sea Surface Salinity dedicated products over the Baltic Sea

2022 ◽  
Author(s):  
Verónica González-Gambau ◽  
Estrella Olmedo ◽  
Antonio Turiel ◽  
Cristina González-Haro ◽  
Aina García-Espriu ◽  
...  

Abstract. 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 brightess 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 Interferences (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 and SST ranges 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-day 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)), that are computed by applying multifractal fusion to L3 SSS with Sea Surface Temperature (SST) maps. The accuracy of L3 SSS products is typically around 0.7–0.8 psu. The L4 product has an improved spatio-temporal 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.

2020 ◽  
Author(s):  
Veronica Gonzalez-Gambau ◽  
Estrella Olmedo ◽  
Cristina Gonzalez-Haro ◽  
Antono Turiel ◽  
Justino Martinez ◽  
...  

<p>Accurate satellite-based sea surface salinity (SSS) fields would address some gaps of knowledge and benefit the understanding of Baltic Sea salinity dynamics.  In particular, these fields can contribute to the monitoring of long-term salinity changes and to the detection of periods with anomalous salinity. These products can also be very useful as initial fields and validation data for improving the existing numerical models.</p><p><br>The Baltic Sea is one of the most challenging regions for the retrieval of SSS from L-band satellite measurements. Nowadays, available EO-based SSS products are quite limited over this region both in terms of spatio-temporal coverage and quality. This is mainly due to several technical limitations that strongly affect the SMOS TB particularly over semi-enclosed seas, such as the high contamination by Radio-Frequency Interference (RFI) sources and the contamination close to land and ice edges. Besides, the sensitivity of TB to SSS changes is very low in cold waters and much larger errors are expected compared to temperate oceans. Salinity and temperature values are very low in this basin, which implies that dielectric constant models are not fully tested in such conditions. In the recent years, the Barcelona Expert Center team has been working on the development of innovative algorithms for improving the quality of SMOS TB and SSS retrievals dealing with the main processing issues. </p><p><br>In the context of the ESA Baltic+ Salinity Dynamics project (https://balticsalinity.argans.co.uk/), these methodologies have been adapted and consolidated towards the generation of the first  regional SMOS SSS product (2011-2020) that would suit to the needs of the Baltic research community. Very recently, the first version of the Baltic+ SSS product has been produced (3-year series) and is currently under validation against in-situ measurements. The quality assessment of the SSS product in the Baltic Sea is also an issue and its representativeness must be carefully assessed. The basin is strongly stratified and then, the differences between SMOS measurements (first centimeters) and in-situ observations (few meters depth) can be noticeable. Differences are more probable during ice melting and high runoff events in spring where there might be a freshwater layer at the top shallow surface. Feedback from the users will help identifying the limitations of the product. Additional technical developments will be addressed to meet the requirements of the communities working in the study of Baltic processes. </p><p><br>We will present at the conference the Baltic+ SSS v1 product and its added-value with respect to other existing EO-based datasets. The potential scientific impact of this satellite SSS product in advancing on-going regional research initiatives like the Baltic Earth Working Group on Salinity dynamics will be discussed.</p>


2021 ◽  
Author(s):  
Veronica Gonzalez Gambau ◽  
Estrella Olmedo ◽  
Cristina Gonzalez Haro ◽  
Antonio Turiel ◽  
Aina Garcia ◽  
...  

<p>The Baltic Sea is a strongly stratified semi-enclosed sea with a large freshwater supply from rivers, net precipitation and water exchange and high-saline water from the North Sea through the Kattegat Strait. In the Danish Straits the water exchange is hampered by bathymetric constraints , such as narrow and shallow sills, and by hydrodynamic restrictions, such as fronts and mixing. The shallow depth of the Baltic Sea (i.e. 54 m in average) yields to highly variable ocean dynamics controlled mainly by local atmospheric forcing. The water exchange between the Baltic Sea and the North Atlantic Ocean is restricted by the narrows and sills of the Danish Straits (i.e. via Kattergat Strait at the East of the Baltic Sea) and by different river outflows distributed across the Baltic Sea. The bottom water in the deep sub-basins is ventilated mainly by large perturbations, so-called major Baltic saltwater inflows. The occurrence of these events needs still further investigation. The description of the complex oceanographic conditions within the Baltic Sea in current model simulations could also be developed. Furthermore, model simulations of the Baltic Sea are constrained to the initialization of the model (i.e. parametrization of the initial surface atmospheric and ocean conditions).</p><p>For this, the Earth Observation salinity measurements have a great potential to help in the understanding of the dynamics in the basin and to improve the regional models there. However, the Baltic Sea is one of the most challenging regions for the sea surface salinity (SSS) retrieval from satellite measurements. The available EO-based SSS products are quite limited over this region both in terms of spatio-temporal coverage and quality. This is mainly due to several technical limitations that strongly affect the satellite brightness temperatures (TB) measurements, particularly over semi-enclosed seas, such as the high contamination by Radio-Frequency Interferences (RFI) and the contamination close to land and ice edges. Besides, the sensitivity of TB to SSS changes is very low in cold waters and much larger errors are expected compared to temperate oceans.</p><p>As a main result of the ESA Baltic+ Salinity Dynamics project (<span></span>), a new regional SSS product derived from the measurements provided by the European Soil Moisture and Ocean Salinity (SMOS) mission has been developed. In this work, first, we describe briefly the enhanced algorithms used in the generation of SMOS SSS fields. Second, we show a complete quality assessment by comparing the satellite and the in situ salinity measurements. For this, we use in situ measurements provided by SeaDataNet and Helcom and Ferry box lines. Finally, we compare the satellite salinity measurements with the salinity fields provided by a model. We focus our analysis in two aspects: i) the description of the freswater fluxes coming from continental discharge and sea-ice melting; and ii) the capability of describing the dynamics of the saltier Atlantic water that enters into the basin through the Kattegat strait.</p><p> </p>


Baltica ◽  
2014 ◽  
Vol 27 (2) ◽  
pp. 131-140 ◽  
Author(s):  
Bartosz Kotrys ◽  
Michał Tomczak ◽  
Andrzej Witkowski ◽  
Jan Harff ◽  
Jan Seidler

A new diatom-based sea surface salinity (SSS) estimation has been applied to a collection of 27 taxa in 48 present-day sediment and surface water samples recovered in the Baltic Sea and Kattegat. The sediment core 303610-12 (2005) from the Eastern Gotland was chosen for study of Holocene sequence, ranging the last 8160 yrs BP. The Artificial Neuronal Network (ANN) method allows the estimation of spring SSS (March-April) values ranging between 7.04 ‰ and 8.25 ‰ at an averaged Root Mean Squared Error (RMSE) of 0.49 ‰. The rather low amplitude of salinity change might be caused by mixing of fresh water with upper surface layer of the Baltic Sea due to high precipitation and riverine input. The estimates of spring SSS from core 303610-12 were compared with independent geochemical proxies for salinity (K, Ti and S) derived from XRF Core Scanner record. Conspicuous correlation between salinity and sulphur records and reverse-correlation to K and Ti demonstrates that the ANN method combined with quantitative and qualitative analyses of diatoms provides a useful tool for palaeosalinity reconstructions in the Holocene sediments of the Baltic Sea


2021 ◽  
Vol 13 (2) ◽  
pp. 259
Author(s):  
Shuping Zhang ◽  
Anna Rutgersson ◽  
Petra Philipson ◽  
Marcus B. Wallin

Marginal seas are a dynamic and still to large extent uncertain component of the global carbon cycle. The large temporal and spatial variations of sea-surface partial pressure of carbon dioxide (pCO2) in these areas are driven by multiple complex mechanisms. In this study, we analyzed the variable importance for the sea surface pCO2 estimation in the Baltic Sea and derived monthly pCO2 maps for the marginal sea during the period of July 2002–October 2011. We used variables obtained from remote sensing images and numerical models. The random forest algorithm was employed to construct regression models for pCO2 estimation and produce the importance of different input variables. The study found that photosynthetically available radiation (PAR) was the most important variable for the pCO2 estimation across the entire Baltic Sea, followed by sea surface temperature (SST), absorption of colored dissolved organic matter (aCDOM), and mixed layer depth (MLD). Interestingly, Chlorophyll-a concentration (Chl-a) and the diffuse attenuation coefficient for downwelling irradiance at 490 nm (Kd_490nm) showed relatively low importance for the pCO2 estimation. This was mainly attributed to the high correlation of Chl-a and Kd_490nm to other pCO2-relevant variables (e.g., aCDOM), particularly in the summer months. In addition, the variables’ importance for pCO2 estimation varied between seasons and sub-basins. For example, the importance of aCDOM were large in the Gulf of Finland but marginal in other sub-basins. The model for pCO2 estimate in the entire Baltic Sea explained 63% of the variation and had a root of mean squared error (RMSE) of 47.8 µatm. The pCO2 maps derived with this model displayed realistic seasonal variations and spatial features of sea surface pCO2 in the Baltic Sea. The spatially and seasonally varying variables’ importance for the pCO2 estimation shed light on the heterogeneities in the biogeochemical and physical processes driving the carbon cycling in the Baltic Sea and can serve as an important basis for future pCO2 estimation in marginal seas using remote sensing techniques. The pCO2 maps derived in this study provided a robust benchmark for understanding the spatiotemporal patterns of CO2 air-sea exchange in the Baltic Sea.


Ocean Science ◽  
2018 ◽  
Vol 14 (3) ◽  
pp. 525-541 ◽  
Author(s):  
Ye Liu ◽  
Weiwei Fu

Abstract. We assess the impact of assimilating the satellite sea surface temperature (SST) data on the Baltic forecast, particularly on the forecast of ocean variables related to SST. For this purpose, a multivariable data assimilation (DA) system has been developed based on a Nordic version of the Nucleus for European Modelling of the Ocean (NEMO-Nordic). We use Kalman-type filtering to assimilate the observations in the coastal regions. Further, a low-rank approximation of the stationary background error covariance metrics is used at the analysis steps. High-resolution SST from the Ocean and Sea Ice Satellite Application Facility (OSISAF) is assimilated to verify the performance of the DA system. The assimilation run shows very stable improvements of the model simulation as compared with both independent and dependent observations. The SST prediction of NEMO-Nordic is significantly enhanced by the DA forecast. Temperatures are also closer to observations in the DA forecast than the model results in the water above 100 m in the Baltic Sea. In the deeper layers, salinity is also slightly improved. In addition, we find that sea level anomaly (SLA) is improved with the SST assimilation. Comparisons with independent tide gauge data show that the overall root mean square error (RMSE) is reduced by 1.8 % and the overall correlation coefficient is slightly increased. Moreover, the sea-ice concentration forecast is improved considerably in the Baltic Proper, the Gulf of Finland and the Bothnian Sea during the sea-ice formation period, respectively.


2021 ◽  
Author(s):  
Jacqueline Boutin ◽  
Jean-Luc Vergely ◽  
Emmanuel Dinnat ◽  
Philippe Waldteufel ◽  
Francesco D'Amico ◽  
...  

<p>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°C and within +/-0.05 between 7 and 28°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. </p><p>The rationale for this new parametrisation, results obtained with this new parametrisation in recent SMOS reprocessings and comparisons with other parametrisations will be discussed.</p><p>Reference:</p><p>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.</p>


2021 ◽  
Author(s):  
Xavier Perrot ◽  
Jacqueline Boutin ◽  
Jean Luc Vergely ◽  
Frédéric Rouffi ◽  
Adrien Martin ◽  
...  

<p>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 25x25km<span>2 </span>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.</p><p>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.</p><p>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.</p><p>Bibliography:</p><p>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.</p><p>F. Gaillard et al. (2016), In Situ–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.</p><p>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. <span>104-111, </span>doi: 10.1109/JOE.1977.1145319.</p><p>Data reference:</p><p>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</p>


2020 ◽  
Author(s):  
Tuomas Kärnä ◽  
Jonni Lehtiranta ◽  
Laura Tuomi

<p>We are developing a new operational circulation model for the Baltic Sea using NEMO v4.0. The model configuration is derived from the NEMO v3.6 1 nmi NemoNordic setup (Hordoir et al., Geoscientific Model Development, 2019). A pre-operational version of the model has been implemented to produce daily forecasts of water level, temperature, salinity, and currents, as well as sea ice coverage. In this poster we present model validation for a two-year hindcast simulation. The results indicate that daily and seasonal variability of water levels and sea surface salinity are well captured. Sea ice coverage is well represented, although slightly over-estimated. Comparisons at several mooring locations show realistic vertical salinity structure, and verify that the model can simulate Baltic inflow events. Overall, the model skill has significantly improved compared to previous operational models.</p>


2015 ◽  
Vol 12 (11) ◽  
pp. 3369-3384 ◽  
Author(s):  
G. Parard ◽  
A. A. Charantonis ◽  
A. Rutgerson

Abstract. Studies of coastal seas in Europe have noted the high variability of the CO2 system. This high variability, generated by the complex mechanisms driving the CO2 fluxes, complicates the accurate estimation of these mechanisms. This is particularly pronounced in the Baltic Sea, where the mechanisms driving the fluxes have not been characterized in as much detail as in the open oceans. In addition, the joint availability of in situ measurements of CO2 and of sea-surface satellite data is limited in the area. In this paper, we used the SOMLO (self-organizing multiple linear output; Sasse et al., 2013) methodology, which combines two existing methods (i.e. self-organizing maps and multiple linear regression) to estimate the ocean surface partial pressure of CO2 (pCO2) in the Baltic Sea from the remotely sensed sea surface temperature, chlorophyll, coloured dissolved organic matter, net primary production, and mixed-layer depth. The outputs of this research have a horizontal resolution of 4 km and cover the 1998–2011 period. These outputs give a monthly map of the Baltic Sea at a very fine spatial resolution. The reconstructed pCO2 values over the validation data set have a correlation of 0.93 with the in situ measurements and a root mean square error of 36 μatm. Removing any of the satellite parameters degraded this reconstructed CO2 flux, so we chose to supply any missing data using statistical imputation. The pCO2 maps produced using this method also provide a confidence level of the reconstruction at each grid point. The results obtained are encouraging given the sparsity of available data, and we expect to be able to produce even more accurate reconstructions in coming years, given the predicted acquisition of new data.


2019 ◽  
Vol 11 (7) ◽  
pp. 750 ◽  
Author(s):  
Emmanuel Dinnat ◽  
David Le Vine ◽  
Jacqueline Boutin ◽  
Thomas Meissner ◽  
Gary Lagerloef

Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency’s (ESA’s) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration’s (NASA’s) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from the Argo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS. We also identify the impact of the corrections for atmospheric effects recently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature.


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