In this study, temporal and spatial variability of ice cover in the Great Lakes are investigated using historical satellite measurements from 1973 to 2010. The seasonal cycle of ice cover was constructed for all the lakes, including Lake St. Clair. A unique feature found in the seasonal cycle is that the standard deviations (i.e., variability) of ice cover are larger than the climatological means for each lake. This indicates that Great Lakes ice cover experiences large variability in response to predominant natural climate forcing and has poor predictability. Spectral analysis shows that lake ice has both quasi-decadal and interannual periodicities of ~8 and ~4 yr. There was a significant downward trend in ice coverage from 1973 to the present for all of the lakes, with Lake Ontario having the largest, and Lakes Erie and St. Clair having the smallest. The translated total loss in lake ice over the entire 38-yr record varies from 37% in Lake St. Clair (least) to 88% in Lake Ontario (most). The total loss for overall Great Lakes ice coverage is 71%, while Lake Superior places second with a 79% loss. An empirical orthogonal function analysis indicates that a major response of ice cover to atmospheric forcing is in phase in all six lakes, accounting for 80.8% of the total variance. The second mode shows an out-of-phase spatial variability between the upper and lower lakes, accounting for 10.7% of the total variance. The regression of the first EOF-mode time series to sea level pressure, surface air temperature, and surface wind shows that lake ice mainly responds to the combined Arctic Oscillation and El Niño–Southern Oscillation patterns.

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The effects of climate change on north temperate freshwater ecosystems include increasing water temperatures and decreasing ice cover. Here we compare those trends in the Laurentian Great Lakes at three spatial scales to evaluate how warming varies across the surface of these massive inland water bodies. We compiled seasonal ice cover duration and lake summer surface water temperatures (LSSWT; 1994, and analyzed spatial patterns and trends at lake-wide, lake sub-basin, and fine spatial scales and compared those to reported lake-and basin-wide trends. At the lake-wide scale we found declining ice duration and warming LSSWT patterns consistent with previous studies. At the lake sub-basin scale, our statistical models identified distinct warming trends within each lake that included significant breakpoints in ice duration for 13 sub-basins, consistent linear declines in 11 subbasins, and no trends in 4 sub-basins. At the finest scale, we found that the northern-and eastern-most portions of each Great Lake, especially in nearshore areas, have experienced faster rates of LSSWT warming and shortening ice duration than those previously reported from trends at the lake scale. We conclude that lake-level analyses mask significant spatial and

[1] 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 (À0.48) 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 NAOÁNino3.4 2 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.Citation: Bai, X., J. Wang, C. Sellinger, A. Clites, and R. Assel (2012), Interannual variability of Great Lakes ice cover and its relationship to NAO and ENSO,