Sunday, 7 January 2018
Exhibit Hall 5 (ACC) (Austin, Texas)
Being the largest group of freshwater lakes on Earth, the Great Lakes supply clean drinking water to millions of people and support many different industries. In recent years, water quality in the western Lake Erie basin has been threatened by increasing occurrences of harmful algal blooms (HABs). The reason for this increase in HAB events is unclear, but recent work suggests that cold season processes may impact nutrient loading in the region, thus driving the variability in HAB formation. This project investigates the simulation of solid-phase precipitation in 18 global climate models (GCMs) from the IPCC Climate Model Intercomparison Project (CMIP5). We examine the seasonal and interannual simulation of solid precipitation in the Great Lakes region over a historical period (1980-1999), identify the range of variability amongst the models, and evaluate the simulations versus available observations. Four snow metrics (snowfall flux, snow area fraction, snow depth, and snow melt) are analyzed temporally and spatially. A monthly average reveals the largest variability in the snow depth variable, with a standard deviation of 0.0766m. Spatially averaged between December and February (DJF), most models underestimate snow area fraction in the northeast region of the Great Lakes by up to 70%. The DJF multi-model average overestimates total precipitation and percent snowfall in the region by 24.15% and 11.26% respectively. Overall, these results suggest substantial variability amongst the models in representing snow processes, and curating model selection for those that reproduce cold-season observations is likely more important than using the multi-model ensemble mean.
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