Assimilating Summer Sea-Ice Thickness Observations Improves Arctic Sea-Ice Forecast

Accurate Arctic sea-ice forecasting for the melt season is still a major challenge because of the lack of reliable pan-Arctic summer sea-ice thickness (SIT) data. A new summer CryoSat-2 SIT observation data set based on an artificial intelligence algorithm may alleviate this situation. We assess the impact of this new data set on the initialization of sea-ice forecasts in the melt seasons of 2015 and 2016 in a coupled sea ice-ocean model with data assimilation. We find that the assimilation of the summer CryoSat-2 SIT observations can reduce the summer ice-edge forecast error. Further, adding SIT observations to an established forecast system with sea-ice concentration assimilation leads to more realistic short-term summer ice-edge forecasts in the Arctic Pacific sector. The long-term Arctic-wide SIT prediction is also improved. In spite of remaining uncertainties, summer CryoSat-2 SIT observations have the potential to improve Arctic sea-ice forecast on multiple time scales. Arctic sea ice is rapidly declining due to global warming, especially in summer. Accurate sea-ice forecasting is important to understand the potential influence of these changes and devise effective responses. The performance of sea-ice forecasts highly depends on the accuracy of the initial sea-ice states. So refining the initial conditions of sea-ice forecasts with satellite observations is a common way to reduce forecast errors. However, obtaining reliable summer pan-Arctic satellite sea-ice thickness (SIT) data is challenging due to complex ice-surface conditions in summer. A new artificial-intelligence-based summer SIT satellite data product may improve initial SIT states. We integrate this data set into a sea-ice forecast system to evaluate its impact on forecast skill. We find that the new summer satellite SIT data can reduce short-term ice-edge location forecast errors and benefit long-term SIT forecasts. Assimilating summer CryoSat-2 sea-ice thickness (SIT) observations makes more skillful Arctic ice-edge forecasts on multiple time scales The long-term SIT forecasts improve with the assimilation of summer CryoSat-2 SIT observations Further refinement is needed for summer CryoSat-2 SIT observations