The effects of anthropogenic climate change are most drastic in the Arctic. This amplification of climate change signals is strongly connected to the sea ice in the Arctic Ocean. This thesis presents an analysis of the sea ice cover in numerical ocean – sea ice models with a focus on two different parameter- izations: an active ice thickness distribution and an ice strength parameter- ization that is based on this additional thickness information. The research questions are: (1) can the parameterizations improve the reproduction of Arctic-wide sea ice observations? (2) Do the parameterizations actually re- produce physically observed behavior? (3) How can the parameterizations and their use in basin-scale models be improved further? In a first step, model quality is assessed by a quantitative measure of the reproduction of satellite observations of sea ice concentration, thickness and drift. Including a full ice thickness distribution in each grid cell instead of only two ice categories clearly improves the model results. At the same time, a strength parameterization based on a two-category approach produces better model results than a multi-category strength parameterization. In a next step, the two parameterizations are evaluated in more detail. The ice thickness distribution parameterization reproduces local observations in the Arctic to a large degree and simulates faithfully regional and seasonal differences found in observed distributions. The poor performance of the multi-category ice strength parameterization is explained by the physical assumptions that were made in its original derivation and that do not agree with the current understanding of the ice cover. In conclusion, using an ice thickness distribution improves model performance, but a multi-category parameterization of the ice strength should be avoided. In future work, a new ice strength parameterization could be derived from the physical properties of the ice pack that are demonstrated in this work.