A numerical model for short-term sea ice forecasting in the Arctic

jlieser [ at ] awi-bremerhaven.de


The passage through the Arctic Ocean is the shortest sea route from European harbours to south-east Asian harbours (and vice versa). Also, bulk cargo and natural resources from high Arctic production locations can be transported with cargo ships through the coastal seas. However, less solar irradiation north of 70 N and even no illumination during polar night in winter results in cold air temperatures and thus freezing of sea water forming sea ice. The Arctic sea ice extent varies between ~ 9 x 10^6 km^2 in summer up to ~ 13 x 10^6 km^2 in winter. A major part of the sea ice is so called second and/or multi year ice and has a typical thickness of 1.5 to 2.5 m. This is not only a very importantvariable in the Earth's climate system but also a very effective barrier for ships in this region. Nowadays, modern and powerfulicebreakers and specially designed and reinforced cargo ships are ableto cope with these circumstances under favourable conditions.Currently, decisions regarding route planning for ships sailingthrough ice-covered Arctic waters are based on remote sensing data,available daily, and on the knowledge and experience of icepilots. This study presents a numerical model for predicting sea iceconditions in the Arctic for 5-10 days, providing an additional toolfor the planning process. It is a dynamic-thermodynamic sea ice modelapplying a viscous-plastic rheology with a horizontal resolution of1/4 degree and a time step of 6 hours. For this thesis themodel has been embedded in a forecast setting. A fast-iceparametrization is implemented to simulate offshore polynyas, whichare important for coastal shipping traffic. New oceanic forcingconditions showing a seasonal variability are used. Atmosphericforcing is obtained from the European Centre for Medium-Range WeatherForecasts (ECMWF) analyses/forecasts.The best possible analysis of the current state of the sea iceconcentration in the Arctic is achieved by assimilation of remotesensing data into the numerical model. This analysis is used asstarting condition for a short term integration of the numerical modelfor the prediction of sea ice conditions (e.g. concentration andthickness). The results shown for two sample cases demonstrate, that thenumerical model is not only able to reproduce past sea ice conditionsfor climatological studies, but also to forecast sea ice on shorttime scales.

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Thesis (PhD)
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Lieser, J. L. (2004): A numerical model for short-term sea ice forecasting in the Arctic , PhD thesis, Universitaet Bremen.

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