University at Albany, State University of New York
题目：Arctic sea ice predictability and prediction
活动号：189 478 687，密码：888888
There is a rising demand for Arctic sea ice prediction driven in particular by an increasing accessibility of the Arctic in the context of climate change. In this talk, I will first show spatial and temporal variability of melt ponds and sea ice leads using recently developed satellite-based data sets, and discuss potential roles of melt ponds and sea ice leads in seasonal Arctic sea ice prediction. The results suggest that 1) met pond fraction from spring to mid summer and 2) the area of sea ice leads from mid-winter to late spring promise to improve predictability of Arctic sea ice. Hence representations of melt ponds and sea ice leads in Arctic prediction systems might improve predictive skill for the Arctic region. Second, I will introduce an Arctic prediction modeling system that has been developed recently based on the COAWST modeling system. It is a fully-coupled atmosphere, land, ocean and sea ice model, including WRF with Noah land surface, ROMS and CICE. Given that the evolution of Arctic sea ice during the melting season strongly depends on initial sea ice conditions , a localized error subspace transform ensemble Kalman filter is used to assimilate satellite-based sea ice concentration and thickness to improve the initialization in this prediction system. Prediction experiments for summer and early fall of 2017 will be presented and discussed.