## 3 Results
### MODIS Sea-Ice Concentration
These are more challenging to detect and thus more likely to be flagged \"probably clear\" or \"probably cloudy\". These flags are discarded by the stricter cloud mask, but not by the conservative cloud mask. We judge the number of discarded pixels as an acceptable trade-off for the reduced variability and outliers and thus adopt the stricter cloud mask. In Figure 4, we show the variability of the ice tie-point. This is done by analysing the variation of the 48 possible ice tie-point values per pixel. Ice tie-points on the same day but for different overflights typically differ by 10 K but by up to 20 K in single cases. This reflects the daily cycle and the spatial variability of the IST. Moreover, more than one region is covered on some days. Adjacent granules differ by up to 10 K, which we attribute partly to spatial variability, but mainly to the different cloud cover. Even if the IST distribution in two adjacent granules would be identical, the mean IST would differ depending on which part of the respective granule is obscured by clouds. The difference between minimal and maximal ice tie-point within one 48 km-by-48 km region is between 1 K and 2 K in February and March and slightly decreases until the end of May. We interpret this a combination of two effects. The first is that under rising temperatures the temperature variability in our 48 km-by-48 km region is smaller than in winter. The second factor is the increased cloud cover. As more pixels are cloud-covered in April and May, the requirements for the tie-point retrieval to be performed are violated more often (see Section 2.4), thus less of the 48 iterations actually yield a possible tie-point and there is a smaller difference between minimal and maximal tie-point. To assess the influence on our retrieval, we next look into the differences normalised to the dynamic range. This is more meaningful for our purpose as the same difference between minimal and maximal ice tie-point has a larger influence at smaller dynamic ranges and vice versa. Because the ice tie-point increases in May, the normalised difference (Figure 4c) increases from 5% to 20% and more of the dynamic range at the end of May. This is consistent with the assumption that the retrieval yields less reliable results in summer when the ice surface temperature approaches the freezing point. The mean normalised difference of all months is 0.08. This means that choosing an arbitrary starting point for the bilinear regression like it is done in the original retrieval [1] changes the resulting final ice tie-point by up to 8% on average. This is an upper limit of the introduced inaccuracy as it is the difference between the minimum and maximum ice tie-point. The standard deviation of the 48 iterations gives a more representative estimate of the actual inaccuracy introduced by the arbitrary starting point. The mean standard deviation of all 478 granules is 0.33 K. Normalised with respect to the dynamic range, this amounts to 1.7% of the dynamic range. Although the ice tie-point increases in April and May, the standard deviation does not. This means that the accuracy of the approach does not depend on the season until mid May. Next, we investigate how much this affects the variability of the MODIS SIC. For this, we calculate the MODIS SIC with the final ice tie-point plus/minus one standard deviation and show the resulting SIC together with the difference in Figure 5. The difference increases when the SIC decreases, which we expect because a less compact sea-ice cover is naturally associated with larger variability. The maximal difference is 6.2% and the mean difference amounts to 1.9%. This can be interpreted as the inaccuracy introduced by an arbitrarily chosen starting position for the ice tie-point. Our approach of varying the starting position mitigates this inaccuracy as far as possible. Our retrieval assumes a constant freezing point, but in reality the freezing point varies depending on salinity.