Sea ice charts, an important tool for navigation in the Arctic, are to this day manually drawn by professional ice analysts. The primary descriptor of the charts -- the Sea Ice Concentration (SIC) - indicates the ratio of ice to open-water in an area. Automating the SIC chart production is desired but the optimal representation of the corresponding machine learning task is ambivalent. Here, we explore it with either regressional or classification objective, each with two different (weighted) loss functions: Mean Square Error and Binary Cross-Entropy, and Categorical Cross-Entropy and the Earth Mover's Distance, respectively. While all perform well, the regression-based models achieve higher numerical similarity to the ground truth, whereas classification results in more visually pleasing and consistent charts. Weighting the loss functions improves the performance for intermediate classes at expense of open-water and fully-covered sea ice areas.