## 3 Results  
### MODIS Sea-Ice Concentration  
Analysing the MODIS ice tie-point time-series (Figure 3), we notice that a small number of clear-sky pixels often coincides with a high ice tie-point. We explain this by enhanced evaporation, and thus more fog and convective clouds under higher temperatures. High ice tie-points are problematic for the retrieval because they decrease the dynamic range and thus increase the susceptibility to small IST variations. These cloud and fog features often also are patchy, and thus the high ice tie-point is not representative for a larger area. We therefore omit granules with less than 20% clear-sky pixels for this part of our study. This leaves us with 478 granules, which amounts to 47% of all granules. As the discarded granules are mostly cloud-covered anyway, we only discard 20% of the cloud-free pixels. Using the conservative cloud masking (see Section 2.1) would result in 519 available  
Figure 2: Uncertainty of the AMSR2 SIC as derived in [17]. granules (52%) and also 20% of discarded pixels. The effect of the stricter cloud mask is presented in Figure 3:  
The time-series of the strictly masked data shows less outliers than that of the conservatively masked data. This happens because the high spikes in the conservatively masked time-series occur when most of the granule is cloud-covered, as described above. Then, a small number of additionally discarded pixels is enough to violate the criteria which must be met for the tie-point retrieval to be performed (see Section 2.4). For example, for the high tie-point on 18 April (red cross in Figures 3 and 4) the retrieval is performed in only one iteration. This also explains why the standard deviation is 0 for this granule (see again Figure 4). Figure 3: Time-series of the ice tie-point when applying the conservative cloud mask (panel (**a**)) and the strict cloud mask (panel (**b**)). The x-axis shows the date; the vertical lines mark the days with Sentinel-2 scenes. The dashed horizontal line shows the water tie-point. The dash-dotted line gives the threshold above which no SIC is retrieved in the current version of our retrieval. Red crosses mark the granules above the threshold. The number of pixels which are only discarded by the strict cloud mask decreases with time (not shown here). For example, in February, on average 66.4% of a MODIS granule is discarded when applying the conservative cloud mask, while 74.0% of a granule is discarded if the strict cloud masking is applied. This corresponds to a relative increase of 11.5% when applying the stricter cloud mask. In May, on average 81.9% of the MODIS granule is covered by clouds when applying the conservative cloud mask, but not by the conservative cloud mask. Figure 4: Effect of shifting the starting position of the box used for the ice tie-point calculation. The starting position is shifted by one pixel before redoing the calculation, so that each pixel is covered 48 times. The x-axis shows the date; the vertical lines mark the days with Sentinel-2 scenes. Panel **(a)** shows the mean of the 48 iterations, same as in Figure 3b. The dashed horizontal line shows the water tie-point. The dash-dotted line gives a discarding threshold. If the ice tie-point is above this threshold, no SIC is derived in the current version of our retrieval. As in Figure 3, red crosses mark the granules above this threshold. Panel **(b)** shows the difference between the maximal and minimal ice tie-point. Panel **(c)** also shows the maximal difference, but normalised by the dynamic range (difference between ice and water tie-point). Panel **(d)** shows the standard deviation of the 48 iterations, also normalised by the dynamic range. cloud mask and 83.7% when applying the strict cloud mask, which means a relative increase of 1.8% by using the stricter cloud mask. We explain this by the limited presence of low, thick clouds in the early months of the year [18; 19]. Therefore, the cloud cover is dominated by the presence of thin high clouds whose seasonal cycle is less pronounced [20].