Projection of Great Lakes seasonal ice cover using multi-variable regression models
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Projection of Great Lakes seasonal ice cover using multi-variable regression models

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  • Journal Title:
    Proceedings of the 23rd IAHR International Symposium on Ice.
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  • Description:
    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 Niño3.4. Based on these dynamic relationships, the original multiple variable regression models established using the indices of NAO and Niño3.4 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 Niño3.4 only. The new model was used to project the annual maximum ice coverage using projected indices of Niño3.4, NAO, PDO, and AMO. On November 30, 2015, the AMIC of 2015/16 winter was projected to be 31%.
  • Source:
    Proceedings of the 23rd IAHR International Symposium on Ice.
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    Submitted
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