Proper Arctic sea-ice forecasting for the melt season is still a major challenge because of the recent 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.

Global, regional and local sea-level projections rely on complex process-based models of the climate-ocean-cryosphere system. While extrapolation of observational data has been examined on global and regional scales, this approach has not yet been used for the additional complexities of local coastal sea-level projections. Here, we evaluate the sea level trend and acceleration for a global network of tide-gauge observations over 1970-2023, which are then extrapolated to provide local projections up to 2050 and compared with the process-based projections from the IPCC Sixth Assessment Report (AR6). For 2050 relative to 2020, the observation-based projections agree with AR6 process-based projections within the 90% uncertainty range at the majority (99%) of 237 tide gauges. Thus, the observation-based projections provide complementary perspectives of near-term local sea-level changes, and this agreement provides increased confidence in the current understanding and projections of sea-level changes over coming decades.