Project

Downscaling is the process of making a coarse-scale global climate model into a finer resolution in order to capture some of the localized detail that the coarse global models cannot resolve. There are two general approaches of downscaling: dynamical and statistical. Within those, many dynamical models have been developed by different institutions, and there are a number of statistical algorithms that have been developed over the years. Many past studies have evaluated the performance of these two broad approaches of downscaling with respect to climate variables (e.g., temperature, precipitation), but few have translated these evaluations to ecological metrics that managers use to make decisions in planning for climate change. This study uses maple syrup production as a case study for evaluating how these two downscaling techniques perform in terms of projecting changes in the tapping season

Project

The goal of this project was to identify how winter severity, snowpack, and lake ice could change through the mid- and late-21st century, and how species such as the white-tailed deer and mallard duck will respond. Because currently available climate data is at too coarse a scale to provide information on future conditions for the Great Lakes, researchers transformed these models from a global-scale to a regional-scale. Using these models, researchers found that the region could experience substantial warming, reduced lake ice cover, and increased precipitation, with more precipitation falling as rain than snow, among other changes. 

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