Current predictions for the changing Arctic focus on the evolution of sea ice from perennial to seasonal cover, with sea ice thinning as the Arctic warms. Significant efforts in developing climate forecasts for the Arctic on the decadal time-frame are underway. But, ships in transit inside the Arctic basin require high-resolution, near-term forecasts to provide guidance on scales of interest to their operations: 1-5 km. Climate predictions are insufficient for such short-term forecasts, as they are computationally intensive and at lower resolution than needed for single vessel operations. Deep learning techniques can offer the capability to enable rapid assimilation and analysis of multiple sources of information for these critical sea ice forecasts. In this study, data from NOAA’s Global Forecast System (GFS), Multi-scale Ultra-high Resolution (MUR) Sea Surface Temperature (SST), and Multisensor Analyzed Sea Ice Extent (MASIE) data from the National Snow and Ice Data Center (NSIDC) were used to develop the sea ice extent forecast model. All layers were sampled to the same 1 km Equal-Area Scalable Earth (EASE) grid. Sea ice prediction was achieved for subsequent weeks from seven days prior plus seven days of historical forecast data. The novelty in this approach was using forecast data as data to aid in the prediction of sea ice extent. The models were trained using data from the freeze-up periods of 2016, 2018, 2019, and 2020. A four-fold cross-validation was performed, with each year held out for validation. Our deep learning models are trained using overlapping 256x256 pixel tiles over the entire region of interest, the Beaufort Sea. Sigmoid outputs are averaged together in overlap regions, weighted by the distance from the edge to ensure smooth transitions from one subregion to the next. These are converted to a binary ice mask by thresholding at 0.5, then computing a per-pixel accuracy, which is averaged over the ocean/ice regions. Accuracy for a baseline persistence model was also computed over the seven-day forecast period. While the average accuracy of the persistence model dropped from 97% to 90% for forecast days one to seven, the deep learning model accuracy dropped only to 93%. With these initial positive results, we plan to extend the model outputs from ice extent to ice concentration and thickness in future work.