Climate change assessment on blowing snow in Hokkaido using a large ensemble dataset

Abstract

This study evaluated the impact of climate change on the intensity of blowing-snow events across a wide probability spectrum including extreme events by a dynamically-downscaled meteorological dataset with a large number of ensembles called d4PDF. Focusing on four sites in Hokkaido, the hourly snow transport rate (STR) was estimated from wind speed, temperature, and snowfall. The historical experiment of d4PDF can reproduce the observed distribution of STR. The +2K experiment of d4PDF indicated that the severe blowing-snow events became rarer. Moreover, the monthly maximum STR exhibited a decrease, yet it showed significant spatial differences and seasonal variations. The monthly maximum STR and its drifting term in the mid-winter was the most significantly reduced at a site along the Pacific coast. At this site, the mean snow-covered duration (SCD) from December to February was shorter than that of the other sites. Such a decrease in STR would be due to the shortening of the SCD and the substantially related to the critical temperature at the freezing point.