The Development of Statistical Models for Extended Range Sea Ice Forecasting in Canada

				      Adrienne Tivy
				       PhD Student
				    University of Calgary


The Canadian Ice Service founded the Canadian long-range ice forecasting (CLIF)
research program in response to recent observed changes in Arctic sea ice conditions and
an increasing need by the general public for more accurate and detailed ice information at
longer lead times.  To date, the focus of the research has been on the development of
statistical forecasting models for predicting sea ice conditions 3-12 months in advance.   
Working towards a seasonal sea ice forecasting model for Canada's ice infested waters,
canonical correlation analysis (CCA) is used to explore the relative predictive importance
of winter sea level pressure, 500mb heights, surface air temperature and sea surface
temperature in forecasting summer sea ice conditions.  Results from the application of
multiple linear regression techniques to forecast shipping specific sea ice parameters will
also be presented.