Great Lakes Basin Heat Waves: An Analysis of Their Increasing Probability of Occurrence Under Global Warming

Extreme heat events in the Great Lakes Basin (GLB) region of eastern North America are expected to increase in concert with greenhouse gas (GHG) induced global warming. The extent of this regional increase is also influenced by the direct effects of the Great Lakes themselves. This paper describes results from an ensemble of dynamically downscaled global warming projection using the Weather Research and Forecast (WRF) regional climate model coupled to the Freshwater Lake (FLake) model over the Great Lakes region. In our downscaling pipeline, we explore two sets of WRF physics configurations, with the initial and boundary conditions provided by four different fully coupled Global Climate Models (GCMs). Three time periods are investigated, namely an instrumental period (1979–1989) that is employed for validation, and a mid-century (2050–2060) and an end-century (2085–2100) periods that are used to understand the future impacts of global warming. Results from the instrumental period are characterized by large variations in climate states between the ensemble members, which is attributed to differences in both GCM forcing and WRF physics configuration. Results for the future periods, however, are such that the regional model results have good agreement with GCM results insofar as the rise of average temperature with GHG is concerned. Analysis of extreme heat events suggests that the occurrence rate of such events increase steadily with rising temperature, and that the Great Lakes exert strong lake effect influence on extreme heat events in this region.

The Seasonal Snowfall Contributions of Different Snowstorm Types in Central New York State

Located at the eastern extent of the Great Lakes snowbelt, Central New York averages some of the highest annual snowfall totals east of the Rocky Mountains. This is in large part due to the variety of snowstorms that affect the region including lake-effect storms, coastal storms, and overrunning storms. Previous estimates suggest that lake-effect snowstorms account for approximately half of the seasonal snow in the Great Lakes basin, but ignore the spatial variability that exists within the region. Therefore, this study examines the seasonal snowfall contributions of the different snowstorm types to affect Central New York. Results suggest that although lake-effect snowstorms are the dominant snowstorm type in the region, their seasonal snowfall contributions vary between 13 and 48%. Although lake-effect snowstorms produce more snow during the peak and mid-seasons, their relative contribution is greatest during the early and mid-winter seasons. Generally, higher contributions occur near the Tug Hill Plateau, with lower contributions in southern Central New York. Instead, snowfall in southern Central New York is mostly dominated by Nor'easters (16–35%), with lesser contributions from Rocky lows (14–29%). Overrunning storms that originate in Canada (e.g., Alberta clippers) and non-cyclonic storms contribute the least to seasonal snowfall totals across Central New York; however, they are often the catalyst for lake-effect snowstorms in the region, as they advect continental polar air masses that destabilize across the lake. Understanding the actual snowfall contribution from different snowstorm types is needed for future climate predictions. Since the potential trajectory of future snowfall varies according to the type of storm, climate models must accurately predict the type of storm that is producing the snow.

Characterization of polycyclic aromatic hydrocarbons in the Great Lakes Basin using dreissenid mussels

The National Oceanic and Atmospheric Administration (NOAA), National Centers for Coastal Ocean Science (NCCOS) Mussel Watch Program (MWP), conducts basin-wide monitoring and place-based assessments using dreissenid mussels as bioindicators of chemical contamination in the Laurentian Great Lakes. Polycyclic aromatic hydrocarbons (PAHs) body burden results for the period 2009–2018 were combined into one dataset from multiple MWP studies allowing for a robust characterization of PAH contamination. Patterns in PAH data were identified using descriptive statistics and machine learning techniques. Relationships between total PAH concentration in dreissenid mussel tissue, impervious surface percentages, and PAH relative concentration were identified and used to build a predictive model for the Great Lakes Basin. Significant positive correlation was identified by the Spearman’s rank correlation test between total PAH concentration and percent impervious surface. The findings support the paradigm that PAHs are primarily derived from land-based sources. Offshore and riverine locations had the lowest and highest median total PAH concentrations, respectively. PAH assemblages and ratios indicated that pyrogenic sources were more predominant than petrogenic sources and that PAHs at offshore sites exhibited relatively more weathering compared to inshore sites.

Structure and Evolution of Non-Lake-Effect Snow Producing Alberta Clippers

Alberta Clippers (clippers) have long been associated with lake-effect snow (LES) events due to their frequent passage over the Great Lakes basin. However, not all clippers produce LES, and no research has inquired into which synoptic fields most influence LES formation. This study analyzes clippers during non-LES situations to further knowledge on which atmospheric variables most regulate LES development on the synoptic scale. As no such database currently exists, a clipper repository is developed using National Centers for Environmental Prediction Reanalysis data. The repository is then cross referenced with a previously developed LES repository to identify clippers responsible for LES. Composite synoptic-scale patterns were then constructed on the remaining non-LES clippers to identify synoptic conditions that ultimately inhibited LES formation. This analysis is supplemented by an assessment of lake surface conditions in each composite to evaluate how influential the lake characteristics were in the suppression of LES activity. In total, 51 non-LES clippers were identified, tracked, and separated into three composite map types that exhibited unique storm track and spatial characteristics. Permutation testing revealed that lake surface conditions were not significantly (p ≤ 0.05) different between LES and non-LES associated clippers implying the main LES inhibition factors were meteorological.