Decadal Variability of Great Lakes Ice Cover in Response to AMO and PDO, 1963–2017

• Published in: Journal of climate 2018-09, Vol.31 (18), p.7249-7268
• Main Authors: Wang, Jia, Kessler, James, Bai, Xuezhi, Clites, Anne, Lofgren, Brent, Assuncao, Alexandre, Bratton, John, Chu, Philip, Leshkevich, George
• Format: Article
• Language: English
• Subjects: Air temperature; Airborne sensing; Atmospheric forcing; Climate; Curvature; Decades; Heat; Ice; Ice cover; Lake ice; Lakes; Land surface temperature; Mathematical models; North Atlantic Oscillation; Ocean-atmosphere system; Pacific Decadal Oscillation; Regression analysis; Regression models; Satellites; Statistical analysis; Surface temperature; Surface-air temperature relationships; Temperature effects; Trends; Variability; Water temperature; Winter; Winter ice
• ISSN: 0894-8755; 1520-0442
• DOI: 10.1175/JCLI-D-17-0283.1

In this study, decadal variability of ice cover in the Great Lakes is investigated using historical airborne and satellite measurements from 1963 to 2017. It was found that Great Lakes ice cover has 1) a linear relationship with the Atlantic multidecadal oscillation (AMO), similar to the relationship of lake ice cover with the North Atlantic Oscillation (NAO), but with stronger impact than NAO; 2) a quadratic relationship with the Pacific decadal oscillation (PDO), which is similar to the relationship of lake ice cover to Niño-3.4, but with opposite curvature; and 3) decadal variability with a positive (warming) trend in AMO contributes to the decreasing trend in lake ice cover. Composite analyses show that during the positive (negative) phase of AMO, the Great Lakes experience a warm (cold) anomaly in surface air temperature (SAT) and lake surface temperature (LST), leading to less (more) ice cover. During the positive (negative) phase of PDO, the Great Lakes experience a cold (warm) anomaly in SAT and LST, leading to more (less) ice cover. Based on these statistical relationships, the original multiple variable regression model established using the indices of NAO and Niño-3.4 only was improved by adding both AMO and PDO, as well as their interference (interacting or competing) mechanism. With the AMO and PDO added, the correlation between the model and observation increases to 0.69, compared to 0.48 using NAO and Niño-3.4 only. When November lake surface temperature was further added to the regression model, the prediction skill of the coming winter ice cover increased even more.