## 5 Summary  
In this study, we first provide an assessment of the sensitivity of the MODIS SIC towards the choice of the ice tie-point, which is an essential parameter for the MODIS SIC retrieval. This is done by varying the starting position of the region which is used for the ice tie-point retrieval. This yields an ensemble of up to 48 possible MODIS ice tie-points, the mean of which is selected as final ice tie-point and the standard deviation of which is used as an estimate of the tie-point uncertainty. We find that the standard deviation averaged over all seasons is 0.33 K, which corresponds to 1.7% of the dynamic range. Converted to SIC, this yields an uncertainty of on average 1.9% and at maximum 6.2%. We mitigate this uncertainty as much as possible by varying the region for the ice tie-point retrieval and taking the average as ice tie-point. Furthermore, our study presents an intercomparison of our merged SIC dataset and its constituents--the MODIS and AMSR2 SIC--with independent reference data. To this end, we produce a reference SIC dataset from 79 Sentinel-2 scenes by classifying the reflectances into water, thin ice and thick ice. The scenes cover the period from February 22 nd to May 27 th and are located in the Arctic marginal seas and in the Fram Strait (Figure 1). They are mostly dominated by a compact sea-ice cover and leads, but also comprise open-water areas. In theory, thin ice is part of the ice cover and should be reproduced as high SIC by a SIC retrieval. Thus, treating thin ice as ice (thin-ice SIC) is the primary reference dataset. However, to evaluate the sensitivity of our SIC retrievals to thin ice, we created a second reference SIC dataset for which we included the thin ice in the open water class and only consider thick ice as ice (thick-ice SIC). Treating only thick ice as ice (thick-ice SIC) yielded a mean Sentinel-2 SIC of 87.3%, and treating thin and thick ice as ice (thin-ice SIC) yielded a mean SIC of 94.4%. In the latter case, the standard deviation decreases from 13.2% (thick-ice SIC) to 9.2%. The mean merged and MODIS SIC are 93.1% and 92.8%, respectively, which means that there is closer agreement with the thin-ice SIC than with the thick-ice SIC. Thus, both retrievals correctly identify thin ice as ice and only show a small underestimation of about 1% due to the presence of thin ice for the 66 Sentinel-2 reference scenes used for the intercomparison. The RMSD between the merged and MODIS SIC is 5%, which means that the algorithms do yield different results despite the small bias. We further investigate this by analysing one scene with good-quality MODIS SIC and one scene with poor-quality MODIS SIC. In the first case, the combination of the fine resolution of the MODIS SIC and the magnitude of the AMSR2 SIC used for the merged SIC retrieval allows better representation of the reference data than with either dataset alone. In this scene, the merged SIC has a smaller difference and a smaller RMSD than the MODIS SIC compared to the thin-ice reference dataset, while, at the same time, the fine resolution causes an open-water extent which is closer to the thin-ice SIC than the MODIS or AMSR2 SIC. In the poor-quality MODIS SIC case, an unscreened cloud and high ISTs deteriorate the quality of the MODIS SIC. This is mitigated by the merging, so that the result has a similar quality as the AMSR2 SIC alone, however likely not improving it. In the third part of our study, we give an uncertainty estimate for the merged, MODIS and AMSR2 SIC. Uncertainties are derived by Gaussian error propagation based on the ice and water tie-point uncertainties and the observational uncertainty for the MODIS SIC and by expressing them as a function of the SIC according to [17] for the AMSR2 SIC. Gaussian error propagation then yields the merged SIC uncertainty. We use those uncertainties to determine a sanity threshold for the MODIS ice tie-point above which retrieval of meaningful MODIS SIC is no longer possible.