Wednesday, 10 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Given the potential ecological and socio-economical impacts, prediction of seasonal Arctic sea ice has become a topic of high interest for both research and forecasting applications. Previous studies have extensively highlighted two aspects of sea ice prediction: The majority of skill comes from accurately capturing the long term trends in sea ice extent (SIE) and using a multi-model ensemble provides skillful improvements to prediction. With the North American Multimodel Ensemble (NMME) already providing operational guidance to forecasts for other variables, it is logical to explore the potential usefulness for future Arctic sea ice prediction. Here five NMME models (CanCM3, CanCM4, CFSv2, FLORB-01, and CCSM4) are utilized to provide analysis on the NMME representation of SIE in terms of the climatology, long term trend, interannual variability, and prediction skill.
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