Sea Surface Salinity (SSS) is a key indicator of the freshwater fluxes and an important variable to understand the changes the Arctic is facing. However, salinity in-situ measurements are very sparse in the Arctic region. For this reason, remote sensing salinity measurements (currently provided by L-band radiometry satellites, SMOS and SMAP) are of special relevance for this region. The retrieval of SSS in the Arctic represents a challenge, because brightness temperatures measured by L-band satellites are less sensitive to salinity in cold waters. An additional drawback consists in the presence of sea ice, that contaminates the brightness temperature and must be adequately processed. The ESA Arctic+ Salinity project (Dec 2018 – June 2020) will contribute to reduce the knowledge gap in the characterization of the freshwater flux changes in the Arctic region.

Gridded product containing a spatial interpolation of the point product onto a uniform grid of elevation and uncertainty. The gridded product is published on a monthly basis with one product per region on a 2km grid in polar stereographic coordinates. The monthly product contains 3 months of data on a rolling basis each month and uses the Thematic point product as its input. For example, the January 2020 gridded product will contain point data for a window starting on 1st December 2019 and ending on 29th February 2020.

1 km Greenland, daily, with cloud mask, geotiff at https://github.com/AdrienWehrle/SICE/tree/master/masks

Gridded product containing a spatial interpolation of the point product onto a uniform grid of elevation and uncertainty. The gridded product is published on a monthly basis with one product per region on a 2km grid in polar stereographic coordinates. The monthly product contains 3 months of data on a rolling basis each month and uses the Thematic point product as its input. For example, the January 2020 gridded product will contain point data for a window starting on 1st December 2019 and ending on 29th February 2020.

1 km Arctic regional land ice areas, daily, with cloud mask, geotiff at https://github.com/AdrienWehrle/SICE/tree/master/masks

1 km Arctic regional land ice areas, daily, with cloud mask, geotiff at https://github.com/AdrienWehrle/SICE/tree/master/masks

Automated open source processing chain using Sentinel-3 OLCI and SLSTR sensors to determine a dry/wet snow and clean/polluted bare ice spectral and broadband optical albedo 1 km daily product for land ice (glaciers, ice caps, ice sheet)

Generated by inverting DTU15 Gravity field (generated from Cryosat Data) and combining with existing bathymetries

Using new techniques to measure pan-Arctic sea ice thickness from the satellite radar altimeter Cryosat-2 during summer months

Prior and posterior uncertainties of sea ice volume (SIV, columns 4-6) and snow volume (SNV, columns 7-9) respectively for three regions in km3. Column 1 indicates observation, column 2 indicates uncertainty range ("product" refers to uncertainty specification provided with product), column 3 indicates uncertainty range of additional hypothetical snow product ("-" means no snow product is used). In each of columns 4-9 the lowest uncertainty range is highlighted in bold face font. The two bottom rows give estimates for the uncertainty due to model error, i.e. the residual uncertainty with optimal control vector.