|Title||21st century Wisconsin snow projections based on an operational snow model driven by statistically downscaled climate data|
|Publication Type||Journal Article|
|Year of Publication||2010|
|Authors||Notaro, Michael, Lorenz David J., Vimont Daniel J., Vavrus Stephen, Kucharik Christopher, and Franz Kristie|
|Journal||International Journal of Climatology|
|Pagination||n/a - n/a|
|Keywords||climate change, operational snow model, snow, statistical downscaling, WISCONSIN|
Output from the Climate Model Intercomparison Project Phase 3 (CMIP3) global climate models are statistically downscaled across Wisconsin, using a method that restores the observed mean, variance, and extremes of daily temperature and precipitation. The downscaled climate data for the late 20th century, mid-21st century, and late 21st-century is used to drive the National Weather Service operational snow model, SNOW-17, to produce high-resolution (0.1° × 0.1°) projections of daily snowfall, snow depth, and snow cover for Wisconsin. These snow projections will guide wildlife scientists in climate change impact studies and the development of adaptation strategies for the state, in addition to being of value to hydrologists, agricultural scientists, and other experts. SNOW-17 simulations suggest a dramatic shortening of the Wisconsin snow season, with the greatest snowfall and snow depth reductions in spring, particularly over northern Wisconsin. Snowfall is substantially reduced in response to projected warming and only slightly offset by a projected increase in cold-season precipitation. Percent reductions in snow depth are likely to be even more impressive than in snowfall, given not only a reduced frequency that falling precipitation will be in frozen form but also an enhanced snowmelt due to rising temperatures.