Forecast of Sea Ice Conditions
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 (SIO), 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. AWI is participating in the SIO since 2008 by using a coupled sea-ice ocean model (NAOSIM) forced by atmospheric surface boundary conditions of the past (ensemble approach).
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 (CryoSat -2).
Short-term predictions (IRO2)
In the German project 'Ice Routing Optimization IRO2' funded by the German Ministry of Economy and Transport the data assimilation system ICEDAS (developed by the scientific companies O.A.Sys and FastOpt) has been used to combine sea-ice observations (ice concentration, ice thickness from SMOS, snow depth) as well as ocean observations (sea-surface temperature) to optimize the initial state of the sea ice-ocean system for short-term (up to 6 days) forecasts. ICEDAS was build around the sea ice-ocean model NAOSIM. Atmospheric forecast from the European Center of Medium Range weather forecast was used to force the system. The sea-ice forecasts of ICEDAS have been further processed by a high-resolution regional coupled atmosphere-sea ice-ocean of the area east of Svalbard by the University of Hamburg and feed finally into a navigation module of the Hamburg Ship Basin Facility which gives recommendations for save travel through the sea ice. The ice routing system has been tested in March 2014 on board of the Norwegian research vessel Lance against in-situ observation.
Seasonal predictions (SIO)
As mentioned above AWI is participating since several years in the Sea Ice Outlook. On seasonal time-scales initializing the system with sea-ice observation is as important as for short-term time-scales. The method that AWI applied in the SIO is 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 probably evolution. However, this approach neglects all feedback of the sea ice-ocean system onto the atmosphere. This year (2015) the initial state of the sea ice-ocean model has been constrained by sea-ice observations in a second out-of-competition forecast for the September sea-ice extent. The data streams used comprise CryoSat-2 ice thickness generated by AWI, ice concentration, snow depth and sea-surface temperature. A final survey of the benefit of the sea-ice observations for the quality of AWI's outlook will be possible in autumn 2015. Shown is the probability in September 2015 to have at least 15% ice concentration based on March and April 2015 sea-ice observations.
Decadal predictions (MODINI)
The climate system generates internal fluctuations on decadal and multidecadal timescales. Decadal forecast skill can only be achieved when the phases of the decadal fluctuations are known at the starting point of the forecast. The thus required initialization of a climate system model is technically difficult. AWI climate scientists have, however, proposed a relatively simple method for initialization of a climate model by incorporating observed winds over a prolonged period before the starting point of the forecast. Thoma et al. (2015) describe the method and first results.
Thoma, M. , Greatbatch, R. J. , Kadow, C. and Gerdes, R. (2015): Decadal hindcasts initialized using observed surface wind stress: Evaluation and prediction out to 2024 , Geophysical Research Letters, 42 (15), pp. 6454-6461 . doi: 10.1002/2015GL064833