A methodology for statistically downscaling seasonal snow cover characteristics over the Northeastern United States

Abstract

ABSTRACTSnow cover amount and duration have a large role in both the natural and anthropogenic systems of the Northeast United States. Any changes in the climatology of the amount, duration, and timing of the snowpack may have long‐lasting environmental and economic consequences. At present, the principal tools used to examine future climatic changes are general circulation models; however, they do not provide information at the scales required for investigating many of the impacts in which snow plays a major role. In order to address this gap, a statistical downscaling methodology for simulating climatological snow cover parameters was developed. The methodology utilizes the Statistical DownScaling Model to construct climate change scenarios at daily time scales that are subsequently evaluated based on their ability to reproduce seasonal snow cover statistics. Two different observational datasets (station and gridded) were used as predictands and empirical relationships were created between the predictands and regional scale predictors. The methodology was tested at 20 locations across the Northeast and was then applied to output from HadCM3 under the A2 and B2 emissions scenarios. The methodology performed well at capturing key properties of station‐based snow cover over a range of climates and was found to perform better than the technique used in previous work in the Northeast. By the end of the century, the projections revealed characteristics that are consistent with declining snow cover, yet there are likely to be regional variations in the next several decades, especially when elevation is considered.