###### Abstract
The quantity which is inferred from thermal infrared measurements is called \"potential open water\". Potential open water is the open-water fraction of a pixel which would be required for the pixel to have the ice-surface temperature (IST) measured by the satellite, assuming that the pixel comprises water and ice which is thick enough to prevent oceanic heat flux [1; 2]. The inverse of the potential open water is used as thermal infrared SIC in this study. The temperature contrast can also be used for lead retrieval, as is done in [3]. Under the assumption that leads (linear openings in the ice) represent the high end of the surface temperature anomaly histogram, they evaluate different segmentation approaches to get binarised lead/no lead images from which daily lead composite maps are derived. While thermal infrared measurements (MODIS measurements hereafter) have a finer spatial resolution than passive microwave measurements (AMSR2 measurements hereafter), they are limited to cloud-free scenes. Furthermore, they are more sensitive to sea-ice thickness than AMSR2 measurements, which hinders the development of a spatially continuous SIC dataset with the correct magnitude from MODIS measurements alone. Generally speaking, MODIS SIC capture the spatial variability of SIC well, but do often not reflect the correct magnitude. The correct magnitude, together with a spatially continuous coverage, can be obtained from AMSR2 radiometers. This comes at the cost of a coarser spatial resolution. We use the best of the two measurements by merging them and obtaining a spatially continuous dataset at 1 km by 1 km resolution which combines the small-scale features resolved by the MODIS data with the correct magnitude retrieved from the AMSR2 measurements. If no MODIS data are available, the actual resolution decreases to 5 km by 5 km, which corresponds to one grid cell of AMSR2 measurements at the 89 GHz channels. One advantage of our merged SIC dataset over AMSR2 SIC is the enhanced potential for lead retrieval, where leads show up as reduced SIC. The enhanced potential owes to the fact that AMSR2 SIC show 100% SIC for ice thicker than 10 cm [4; 5], while MODIS data show leads also if they are covered by thicker ice. Leads are of large importance for heat flux [3; 6] and ocean-air gas exchange [7]. Furthermore, the they are important for navigation. For use as model input, spatial continuity is required, which our dataset has due to the inclusion of AMSR2 SIC. We thus provide a dataset which is better suited for calculating ocean-air fluxes than AMSR2 SIC alone and better suited for model input than MODIS SIC alone. We have been producing the merged SIC dataset operationally between October 2019 and May 2020. During this time, more than 60% of the pixels over sea ice were cloud-free at least once per day on 84% of the days. More than 80% of the pixels were cloud-free on 61% of the days. The actual coverage may have been even better, but sometimes not all MODIS data were available yet at the time of processing. This shows that the operational provision of an Arctic-wide 1 km SIC dataset is possible, which motivates the intercomparison and uncertainty assessment in this study. First results of the merged dataset are presented in [8], together with a case study of a polynya which opened north of Greenland in February 2018. In this study, the authors of [8] show that, especially during the opening of the polynya, the merged SIC dataset is better suited for monitoring the situation than either of the input datasets alone. While the work in [8] focuses on the application of the merged dataset, this study presents an extensive analysis of the sensitivity of the MODIS SIC dataset to the choice of the MODIS ice tie-point, an intercomparison with an independently derived SIC dataset from Sentinel-2 reflectances and an uncertainty estimate. Specifically, the following questions shall be answered. 1. How sensitive is the merged sea-ice concentration towards the choice of the MODIS ice tie-point? 2.