Evaluation of Arctic Ocean surface salinities from the Soil Moisture and Ocean Salinity (SMOS) mission against a regional reanalysis and in situ data

Abstract

Abstract. Recently two gridded sea surface salinity (SSS) products that cover the Arctic Ocean have been derived from the European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) mission: one developed by the Barcelona Expert Centre (BEC) and the other developed by the Ocean Salinity Expertise Center of the Centre Aval de Traitement des Données SMOS at IFREMER (The French Research Institute for Exploitation of the Sea) (CEC). The uncertainties of these two SSS products are quantified during the period of 2011–2013 against other SSS products: one data assimilative regional reanalysis; one data-driven reprocessing in the framework of the Copernicus Marine Environment Monitoring Services (CMEMS); two climatologies – the 2013 World Ocean Atlas (WOA) and the Polar science center Hydrographic Climatology (PHC); and in situ datasets, both assimilated and independent. The CMEMS reanalysis comes from the TOPAZ4 system, which assimilates a large set of ocean and sea-ice observations using an ensemble Kalman filter (EnKF). Another CMEMS product is the Multi-OBservations reprocessing (MOB), a multivariate objective analysis combining in situ data with satellite SSS. The monthly root mean squared deviations (RMSD) of both SMOS products, compared to the TOPAZ4 reanalysis, reach 1.5 psu in the Arctic summer, while in the winter months the BEC SSS is closer to TOPAZ4 with a deviation of 0.5 psu. The comparison of CEC satellite SSS against in situ data shows Atlantic Water that is too fresh in the Barents Sea, the Nordic Seas, and in the northern North Atlantic Ocean, consistent with the abnormally fresh deviations from TOPAZ4. When compared to independent in situ data in the Beaufort Sea, the BEC product shows the smallest bias (< 0.1 psu) in summer and the smallest RMSD (1.8 psu). The results also show that all six SSS products share a common challenge: representing freshwater masses (< 24 psu) in the central Arctic. Along the Norwegian coast and at the southwestern coast of Greenland, the BEC SSS shows smaller errors than TOPAZ4 and indicates the potential value of assimilating the satellite-derived salinity in this system.

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

“…Similarly, we found RMSD values of 1.5 and 3.3 for SMAP JPL and SMOS BEC matchups, while References [21] and [22] found values of 1.2 and 0.35. However, biases found in this study are between −0.6 and 0.5, which is comparable to 0.6 for SMOS in the Beaufort Sea found in Reference [23] and lower than 1, found in Reference [20] in subpolar seas.…”

“…They also found a standard deviation of the difference between the new SMOS SSS maps and Argo SSS that ranges from 0.25 to 0.35. Finally, a recent study from Reference [23] assessed the quality of only two SMOS SSS products in the Arctic Ocean from 2011 to 2013 by comparing these products to in-situ data. They found that in the Beaufort Sea, the SMOS product dedicated to the Arctic has the smallest bias (about 0.6) and the smallest RMSD (2.6).…”

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

“…These observations suggest complementarity between SMOS SSS and TOPAZ reanalysis products. This was already demonstrated by Xie et al (2019) for Arctic SSS produced at the Barcelona Expert center, but this is even more evident with this new product in very variable Arctic Seas (Appendix-H, Laptev Sea and Beaufort Sea).…”

Objective Analysis of SMOS and SMAP Sea Surface Salinity to Reduce Large-Scale and Time-Dependent Biases from Low to High Latitudes

“…After this date, they have been turned off in the northern high latitudes by emitting countries. Second, the sensitivity of L-band radiometric data to SSS gets lower with decreasing SST, hence the signal-to-noise ratio for individual retrievals decreases (Tang et al 2018;Olmedo et al 2018;Xie et al 2019). Furthermore, at high latitudes, SSS retrievals are more sensitive to errors in radiative transfer model components, e.g., uncertainties in sea surface roughness corrections, dielectric constant parameterization, high and variable wind speed more common at high latitude.…”