Interannual variability of Great Lakes ice cover and its relationship to NAO and ENSO

The impacts of North Atlantic Oscillation (NAO) and El Niño–Southern Oscillation (ENSO) on Great Lakes ice cover were investigated using lake ice observations for winters 1963–2010 and National Centers for Environmental Prediction reanalysis data. It is found that both NAO and ENSO have impacts on G...

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Bibliographic Details
Main Authors: Bai, Xuezhi, Wang, Jia, Sellinger, Cynthia, Clites, Anne, Assel, Raymond
Other Authors: College of Oceanic and Atmospheric Sciences
Format: Article in Journal/Newspaper
Language:English
unknown
Published: American Geophysical Union
Subjects:
North Atlantic
North Atlantic oscillation
Online Access:https://ir.library.oregonstate.edu/concern/articles/k35699219
Description
Summary:The impacts of North Atlantic Oscillation (NAO) and El Niño–Southern Oscillation (ENSO) on Great Lakes ice cover were investigated using lake ice observations for winters 1963–2010 and National Centers for Environmental Prediction reanalysis data. It is found that both NAO and ENSO have impacts on Great Lakes ice cover. The Great Lakes tend to have lower (higher) ice cover during the positive (negative) NAO. El Niño events are often associated with lower ice cover. The influence of La Niña on Great Lakes ice cover is intensity-dependent: strong (weak ) La Niña events are often associated with lower (higher) ice cover. The interference of impacts of ENSO and NAO complicates the relationship between ice cover and either of them. The nonlinear effects of ENSO on Great Lakes ice cover are important in addition to NAO effects. The correlation coefficient between the quadratic Nino3.4 index and ice cover (−0.48) becomes significant at the 99% confidence level. The nonlinear response of Great Lakes ice cover to ENSO is mainly due to the phase shift of the teleconnection patterns during the opposite phases of ENSO. Multiple-variable nonlinear regression models were developed for ice coverage. Using the quadratic Nino3.4 index instead of the index itself can significantly improve the prediction of Great Lakes ice cover (the correlation between the modeled and observed increases from 0.35 to 0.51). Including the interactive term NAO·Nino3.4² further improves the prediction skill (the correlation increases from 0.51 to 0.59). The analysis is also applied to individual lakes. The model for Lake Michigan has the highest prediction skill, while Lake Erie has the smallest skill.

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