| Summary: | As the decline in Arctic sea ice extent makes this region more accessible, the need is increasing for effective seasonal sea ice forecasting to facilitate operational planning. Recently, coupled global climate models (CGCMs) have been used to address the need for effective sea ice forecasting on seasonal time scales. This thesis assesses the operational utility of the Canadian Seasonal to Interannual Prediction System (CanSIPS) for seasonal sea ice forecasting. This assessment consists of two separate studies. The first uses hindcasting to analyze the skill of two versions of CanSIPS, as well as an intermediate version, on the pan-Arctic as well as regional scales. This approach allows for an overall assessment of the system's skill in addition to providing insight with regards to the features in each version which improved that skill. This study finds that the use of a new initialization procedure for sea ice concentration and thickness improved forecast skill on the pan-Arctic scale as well as in the Central Arctic, Barents Sea, Laptev Sea, and Sea of Okhotsk. This study also shows that the substitution of one of the constituent models in the system improved forecast skill on the pan-Arctic scale as well as in the GIN, Barents, Kara, East Siberian, Chukchi, Bering, and Beaufort Seas. Overall, the new version of CanSIPS was found to be generally more skillful than previous versions. The second study conducts a potential predictability experiment on CanCM4, the constituent CGCM common to all versions of CanSIPS considered in this study. This study follows the methodology introduced by \cite{Bushuk2018} which allows for a more complete assessment of the dependency of potential predictability on initialization month than previous studies and for comparisons to be made between potential predictability and operational skill. This analysis is again done on both the pan-Arctic and regional scale. The findings of this experiment show that CanCM4 has relatively low potential predictability relative to other models and ...
|
|---|