Potential effects of summer Cryosat-2 sea ice thickness observations on sea ice forecast

Skillful Arctic sea ice forecast for the melting season remains a great challenge because there is no reliable pan-Arctic sea ice thickness (SIT) data set for the summertime. A new summer Cryosat-2 SIT observation data set based on an artificial intelligence algorithm may mitigate the situation. We assess the impact of this new data set on the initialization of both short-term and long-term sea ice forecasts in the melting seasons of 2015 and 2016 in a sea-ice couple model with data assimilation. We find that the assimilation of the new summer CryoSat-2 SIT observations can reduce the summer ice edge prediction error. Further, adding SIT observations to an established forecast system with sea ice concentration assimilation leads to a more realistic short-term summer ice edge forecast in the Arctic Pacific sector. The long-term Arctic-wide SIT prediction is also improved especially before the onset of freezing. In spite of remaining uncertainties,  summer CryoSat-2 SIT observations have the potential to enhance Arctic sea ice forecast on multiple time scales.