Numerical models have proven integral to the study of climate dynamics. Sea ice models are critical to the improvement of general circulation models used to study the global climate. The object of this study is to evaluate a high resolution ice-ocean coupled model by comparing it to derived measurements from SMMR and SSM/I satellite observations. Utilized for this study was the NASA Goddard Space Flight (GSFC) Sea Ice Concentration Data Set from the National Snow and Ice Data Center. Using animations of side-by-side presentations, variability comparisons and anomaly values the of similarities and differences between the model and the satellite were noted. The model shows a true representation of the seasonal cycle of ice concentration variation, with natural growth, advection, decay. Model performance is weakest in the East Siberian and Laptev Seas where excessive ice is developed. A 30 day lag in the freezing and melting of ice in Hudson Bay was noted in the model. The use of monthly mean Levitus temperatures adversely affects model performance evidenced by a tendency to grow and retain excess ice in the marginal seas of the Arctic Ocean.