Snow Melt Enhancement by Deposition of Black Carbon and Dust: Insight from a new global-scale land surface model

Abstract: Snow is a fundamental component of the hydrological cycle: The presence of snowpack has wide ranging impacts on human communities and the environment, including feeding streamflow over extended arid regions, impacting groundwater recharge, and modulating surface and sub-surface land temperature. Despite their importance, snow processes are challenging to represent in hydrological and climate models. One of the main challenges for predicting snowpack is quantifying the effects of light-absorbing-particles such as black carbon and dust. Once deposited on snow, these particles lead to a decreased snow albedo, that is, to a darkening of the snow surface. This darkening in turn accelerates snow aging (another albedo-decreasing process) and thus increases snow melt by increasing the energy absorbed by the snowpack. To quantify the effect of light-absorbing particles on snow, we present here a global-scale analysis performed with a recently developed snow model (GLASS, or “Global Land Snow Scheme”) which explicitly models the deposition of black carbon and dust, as well as the evolution of snow grains with aging. GLASS was developed as a component of an Earth System Model, and thus can fully simulate the interactions of snowpack with soil, vegetation, atmosphere, and land biogeochemistry. We test this new model over a set of sites in Europe and North America where multi-year observations are available. We find that at most sites the effect of impurities is quite significant, with an average reduction in number of snow cover days between 5 and 25 days/year. For sites in the western US this enhanced snow melt is primarily due to dust deposition, while we find the role of black carbon to be more relevant for the sites in the Alps. Finally, we present a global-scale quantification of the effects of impurities on snow and their spatial distribution in current climate conditions.