## 4 Discussion  
The quality of the MODIS SIC which we present in Section 3.1 depends crucially on the reliability of the cloud screening. Specifically, the MODIS cloud mask does not always identify thin clouds and fog correctly [10]. We account for this by masking clouds strictly by only tolerating pixels which are flagged \"confident clear\". This means that the confidence in the pixel being cloud-free is more than 99% [21]. Even better cloud screening might be achieved with the Fuzzy Cloud Artifact Filter proposed by the authors of [22], but using the MODIS cloud mask alone; this is the best we can do for cloud screening. Additionally, the merging procedure makes sure that even if there are unscreened clouds, the quality of the merged SIC is not much worse than that of the AMSR2 SIC, as we show in Section 3.2 and Figure 9.  
Figure 14: Density scatter plot of the absolute difference to the thin-ice Sentinel-2 SIC against the merged, MODIS and AMSR2 SIC uncertainty in panels **(a)**, textbf(b) and **(c)**, respectively. All pixels, i.e., each pixel from each scene used for the time-series in Figure 8 are shown. We exclude pixels where one dataset is not available from all other datasets as well. This results in 743,202 pixels. We use 40 bins per dimension and limit the data to the range between 0 and 0.6. All difference/uncertainty pairs which occur less than 150 times are excluded. The colour scale is cut at 50,000. Calculating the MODIS SIC, we find that changing the freezing point from \(-1.87\)\({}^{\circ}\)C to \(-1.09\)\({}^{\circ}\)C (corresponding to salinities of 34 and 20, respectively) only introduces a mean difference of 0.5%. A salinity of 20 is the lower limit of what one would expect for the marginal seas [23]. It is higher in the Central Arctic, so that the small mean difference of 0.5% is already an upper limit of the actual error. The authors of [15] find an even lower sensitivity of 0.2%. We thus consider the error introduced by the assumption of a constant freezing point as negligible. Additionally, not retrieving SIC for ice tie-points higher than 266.5 K in future makes sure that the dynamic range is large enough to be robust against small freezing point variations. Both MODIS SIC and AMSR2 SIC are influenced by sea-ice thickness. The AMSR2 SIC underestimate a SIC of 100% by up to 50% for very thin ice (thinner than 6 cm), but are close to 100% SIC for ice thicker than this under the surface conditions of our study [4]. The authors of [5] confirm this by reporting SIC underestimations of 5% or less for sea-ice thicker than 10 cm. Other passive microwave SIC algorithms are affected more strongly by sea-ice thickness [4; 5]. The MODIS SIC underestimation due to sea-ice thickness is not linked to a certain thickness. Instead, it depends on the distribution of sea-ice thickness. The MODIS SIC algorithm assumes a bimodal ice thickness distribution within the region used for the ice tie-point retrieval. If more than one sea-ice thickness class is present in this region, the ice tie-point will represent the ice-surface temperature above the thicker ice, while the thin ice will appear warmer due to the oceanic heat flux and will thus be retrieved as reduced SIC. This is desired by the authors of [1] as their primary goal is to represent the thermal forcing of the atmosphere rather than the actual fraction of ice. We want to retrieve this fraction and thus tune the MODIS SIC to the mean of the AMSR2 SIC. A situation with inhomogeneously distributed sea-ice thickness as described above can happen throughout the winter season, while sea ice which is thin enough to appear as reduced SIC in the AMSR2 SIC is not expected that often, especially in winter then the large temperature contrast between ocean and air lets the ice grow quickly. We thus consider the AMSR2 SIC to be less dependent on sea-ice thickness than the MODIS SIC, which is again an argument for tuning the MODIS SIC to match the mean of the AMSR2 SIC. The main benefit of including the MODIS data is the higher potential for identifying leads.