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You are here : eLibrary : Ice Research and Engineering : 23rd Symposium USA (2016) : Ice in Lakes : Projection of Great Lakes Seasonal Ice Cover using Multi-Variable Regression Models
Projection of Great Lakes Seasonal Ice Cover using Multi-Variable Regression Models
Author : Wang, Jia, Xuezhi Bai, Zifan Yang, Anne Clites, Haoguo Hu, and Philip Chu
In this study, temporal variability of ice cover in the Great Lakes is investigated using historical satellite measurements undated from 1973 to 2015. With high ice cover in the last two winters (2013/14 and 2014/15), the trend was significantly reduced, compared to the period 1973-2013. The decadal variability in lake ice attributed to the decreased trend. It was found that 1) Great Lakes ice cover has a linear relationship with Atlantic Multidecadal Oscillation (AMO), similar to the relationship of lake ice cover with the North Atlantic Oscillation (NAO), and 2) a weak quadratic relation with the Pacific Decadal Oscillation (PDO), similar to the relationship of lake ice cover with the Niņo3.4. Based on these dynamic relationships, the original multiple variable regression models established using the indices of NAO and Niņo3.4 is updated by adding both AMO and PDO, as well their competing mechanism. With the AMO and PDO added, the correlation between the model and observation increases to 0.68, compared to 0.44 using NAO and Niņo3.4 only. The new model was used to project the annual maximum ice coverage using projected indices of Niņo3.4, NAO, PDO, and AMO. On November 30, 2015, the AMIC of 2015/16 winter was projected to be 31%.
File Size : 2,220,231 bytes
File Type : Adobe Acrobat Document
Chapter : Ice Research and Engineering
Category : 23rd Symposium USA (2016)
Article : Ice in Lakes
Date Published : 21/10/2016
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