The state of the Arctic climate system is rapidly changing. These changes are impacting ecosystems, coastal communities, and economic activities. High-quality predictions of the sea-ice conditions are of outstanding interest. In the Sea-Ice Outlook, for example, various international research groups are applying different approaches to predict the Arctic summer minimum sea ice extent in September from the beginning of the melting season in May/June on.
While the strongest greenhouse gas induced changes are currently observed in the Arctic, it is also the area of the largest natural variability leading to a very low theoretically predictability. However, nobody knows at the moment exactly how large or low the predictability is. The methods applied for sea ice predictions depend on the time-scales of the forecasts. For the very long time-scales (multi-decadal to century long) Earth System Models (coupled atmosphere-sea ice-ocean-land models) forced by greenhouse gas emissions are used. For shorter time-scales the quality of predictions depends much stronger on the initial state as for instance in weather prediction where a large network of atmospheric and land surface observations of different kinds is combined with an atmospheric model. With respect to the sea ice many of the data streams necessary for high-quality predictions are still in an infant state.
AWI is participating in the SIO since 2008. We are using a sea ice-ocean model (NAOSIM) forced by atmospheric surface forcing of the past to build up an ensemble of possible developments. The ensemble mean is used as the most probable evolution. However, this approach neglects all feedbacks of the sea ice-ocean system on the atmosphere. Since 2015 the initial state of the sea ice-ocean model is constrained by sea ice observations (data assimilation). The data streams used consist of AWI’s CryoSat-2 ice thickness, ice concentration, snow depth and sea-surface temperature from other sources. In 2019 the method has been refined using a version of the sea ice-ocean model with optimized model parameters (Sumata et al., 2019a and 2019b). This increased the quality of the forecasts considerably. The system has been recently applied as well to forecast the sea ice conditions at the start of the MOSAiC expedition.