UCLA

Light absorbing impurities (LAIs), like dust and black carbon (BC), initiate powerful albedo feedbacks when deposited on snow cover, yet due to a scarcity of observations radiative forcing by LAIs is often neglected, or poorly constrained, in climate and hydrological models. This has important consequences for regions like the Colorado River Basin, where dust deposition to mountain snow cover frequently occurs in the upper basin in the springtime, a relatively new phenomenon since western expansion of the US. Previous work showed that dust on snow (DOS) enhances snowmelt by 3-7 weeks, shifts timing and intensity of runoff, and reduces total water yield.

Here, advanced methods are presented to measure, model, and monitor DOS in the hydrologically sensitive Colorado River Basin. A multi-year multi-site spatial variability analysis indicates the heaviest dust loading comes from point sources in the southern Colorado Plateau, but also shows that lower levels of dust loading from diffuse sources still advances melt by 3-4 weeks. A high-resolution snow property dataset, including vertically resolved measurements of snow optical grain size and dust/BC concentrations, confirms that impurity layers remain in the layer in which they are deposited and converge at the surface as snow melts: influencing snow properties, rapidly reducing snow albedo, and increasing snowmelt rates. The optical properties of deposited impurities, which are mainly dust, are determined using an inversion technique from measurements of hemispherical reflectance and particle size distributions. Using updated optical properties in the snow+aerosols radiative transfer model SNICAR improves snow albedo modeling over a more general dust characterization, reducing errors by 50% across the full range of snow reflectance. Radiative forcing by LAIs in the CRB, estimated directly from measurements and updated optical properties, is most strongly controlled by dust concentrations in the uppermost surface layer, as dust comprises 99%+ of the impurity mixture, and therefore, dominates absorption. Coupling the physically based snow model SNOWPACK, modified to track dust layers, to SNICAR, simulates the impacts of DOS radiative forcing on snow properties. This improved understanding, and representation, of DOS processes has important implications for assessing regional impacts of LAIs, for representing LAIs in climate and hydrologic models, for remote sensing of these processes.