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Eurasian rivers provide a quarter of total fresh water to the Arctic, maintaining a persistent fresh layer that covers the surface Arctic Ocean. This freshwater export controls Arctic Ocean stratification, circulation, and basin-wide sea ice concentration. The Lena River supplies the largest volume of runoff and plays a key role in this system, as runoff outflows into the Laptev Sea as a particularly shallow plume. Previous in situ and modelling studies suggest that local wind forcing is a driver of variability in Laptev sea surface salinity (SSS) but there is no consensus on the roles of Lena River discharge and sea ice cover in contributing to this variability or on the dominant driver of variability. Until recently, satellite SSS retrievals were insufficiently accurate for use in the Arctic. However, retreating sea ice cover and continuous progress in satellite product development have significantly improved SSS retrievals, giving satellite SSS data true potential in the Arctic. In this region, satellite-based SSS is found to agree well with in situ data (r>0.8) and provides notable improvements compared to the reanalysis product used in this study (r>0.7) in capturing patterns and variability observed in in situ data. This study demonstrates a novel method of identifying the dominant drivers of interannual variability in Laptev Sea dynamics within reanalysis products and testing if these relationships appear to hold in satellite-based SSS, sea surface temperature (SST) data, and in situ observations. The satellite SSS data firmly establish what is suggested by reanalysis products and what has previously been subject to debate due to the limited years and locations analysed with in situ data; the zonal wind is the dominant driver of offshore or onshore Lena River plume transport. The eastward wind confines the plume to the southern Laptev Sea and drives alongshore transport into the East Siberian Sea, and westward wind drives offshore plume transport into the northern Laptev Sea. This finding is affirmed by the strong agreement in SSS pattern under eastward and westward wind regimes in all reanalyses and satellite products used in this study, as well as with in situ data. The pattern of SST also varies with the zonal wind component and drives spatial variability in sea ice concentration. [ABSTRACT FROM AUTHOR]

Eurasian Rivers provide a quarter of total fresh water to the Arctic, maintaining a persistent fresh layer that covers the surface Arctic Ocean. The Lena River supplies the largest volume of runoff and plays a key role in this system, as runoff outflows into the Laptev Sea as a particularly shallow plume. This freshwater export controls Arctic Ocean stratification, circulation, and basin-wide sea ice area. Previous in-situ and modelling studies suggest that local wind forcing is a primary driver of variability in Laptev sea surface salinity (SSS) with no consensus over the roles of Lena river discharge and sea ice cover in contributing to this variability. Until recently, satellite SSS retrievals were insufficiently accurate for use in the Arctic, due to the low sensitivity of the L-band signal they utilise in cold water and challenges of retrieval near sea ice. However, retreating sea ice cover and continuous progress in satellite product development have significantly improved SSS retrievals, giving satellite SSS data true potential in the Arctic. This study demonstrates a novel method of using satellite-based SSS, sea surface temperature (SST) data, in-situ observations, and reanalysis products to identify the dominant drivers of interannual variability in Laptev Sea dynamics. Satellite-based SSS is found to agree well with in-situ data in this region (r > 0.8) and provides notable improvements compared to the reanalysis product used in this study (r > 0.7) in capturing patterns and variability observed in in-situ data. The satellite SSS data firmly establishes what has previously been subject to debate due to the limited years and locations analysed with in-situ data: that the zonal wind is the dominant driver of offshore or onshore Lena river plume transport. This finding is affirmed by the strong agreement in SSS pattern in all reanalyses and satellite products used in this study under eastward and westward wind regimes. The pattern of SST also varies with the zonal wind component, and drives spatial variability in sea ice area. The strong correspondence between large scale and local zonal wind dynamics and the key role of SSS and SST variability in driving sea ice and stratification dynamics demonstrates the importance of changes in large-scale atmospheric dynamics for variability in this region as well as for future Arctic sea ice dynamics and freshwater transport. [ABSTRACT FROM AUTHOR]

Validation of satellite sea surface salinity (SSS) products is typically based on comparisons with in-situ measurements at a few meters' depth, which are mostly done at a single location and time. The difference in term of spatio-temporal resolution between the in-situ near-surface salinity and the two-dimensional satellite SSS results in a sampling mismatch uncertainty. The Climate Change Initiative (CCI) project has merged SSS from three satellite missions. Using an optimal interpolation, weekly and monthly SSS and their uncertainties are estimated at a 50 km spatial resolution over the global ocean. Over the 2016–2018 period, the mean uncertainty on weekly CCI SSS is 0.13, whereas the standard deviation of weekly CCI minus in-situ Argo salinities is 0.24. Using SSS from a high-resolution model reanalysis, we estimate the expected uncertainty due to the CCI versus Argo sampling mismatch. Most of the largest spatial variability of the satellite minus Argo salinity is observed in regions with large estimated sampling mismatch. A quantitative validation is performed by considering the statistical distribution of the CCI minus Argo salinity normalized by the sampling and retrieval uncertainties. This quantity should follow a Gaussian distribution with a standard deviation of 1, if all uncertainty contributions are properly taken into account. We find that (1) the observed differences between Argo and CCI data in dynamical regions (river plumes, fronts) are mainly due to the sampling mismatch; (2) overall, the uncertainties are well estimated in CCI version 3, much improved compared to CCI version 2. There are a few dynamical regions where discrepancies remain and where the satellite SSS, their associated uncertainties and the sampling mismatch estimates should be further validated. [ABSTRACT FROM AUTHOR]