The optical properties of snow can be strongly modified by the presence of a variety of impurities including mineral dust and snow algae. We made use of measured concentration of snow algae and mineral dust to parameterize the BioSNICAR radiative transfer model. Surficial snow samples were gathered during a field campaign on 7th July 2020 at the Presena glacier (Rhaetian Alps). We collected 18 samples of surface snow containing different amount of snow algae and mineral dust. Through radiative transfer simulations we estimated an average broadband albedo reduction of 7.4 +/- 6.1 % and 35.3 +/- 7.4 % compared to clean snow, caused by snow algae and mineral dust presence, respectively. When we considered the combined effect of snow algae and dust, we estimated a broadband albedo reduction equal to 40.8 +/- 8.4 %. We estimated an average instantaneous radiative forcing induced by snow algae, mineral dust and both impurities equals to 42.3 (+/- 36.1) W/m(2), 203.7 (+/- 45.5) W/m(2), and 211.8 (+/- 45.9) W/m(2), respectively. Using BioSNICAR simulations, we also tested a series of narrowband spectral indices to determine the concentration of mineral dust and snow algae from multi- and hyper -spectral data. Results showed that most spectral indices used for snow algae mapping are correlated also with mineral dust concentration. We found that only an index correlates uniquely with snow algae: the scaled band integral at 680 nm. A new spectral index, namely the Green Blue Normalized Index, is therefore proposed to discriminate mineral dust from snow algae when both impurities are present. The high spectral resolution of current (e.g. PRISMA, EnMAP) and future (e.g. CHIME, SBG) hyperspectral satellite missions will be fundamental to decouple the effect of mineral dust and snow algae on the optical properties of snow. In fact, from those data it is possible to calculate all narrowband indices presented in this study.

Emissions from road traffic are one of the most important sources of soot aerosols and they can affect the surfaces in the area. In the case of snow surfaces, this effect may lead to changes in the radiative forcing and snow melt, also influenced by particle transport such as particle diffusion and advection. An experimental campaign in The Andes, Chile, was carried out measuring the radiance and irradiance of the snow perpendicularly to a road with high traffic load, together with the meteorological conditions (to identify the diffusion and advection phenomena), the aerosol size distribution, the concentration of particles (PM1, PM2.5 and PM10), and the concentration of black carbon (BC) in both, the atmosphere and the snowpack. The aim of the study was to quantify the contribution of four factors affecting the albedo (black carbon -BC- concentration in snow, grain size, cloudiness, and roughness) by comparing the measured albedo of the contaminated snow surface with that of the same surface prior to contamination, considered as a reference. Results showed a trade-off between diffusion and advection. Close to the road, diffusion was predominant, leading to an increase in BC concentration and a reduction in snow albedo. On the contrary, far from the road, where winds are channeled along the centre of the valley, advection of particles became dominant, leading to another increase in particle concentration and another reduction in snow albedo. Among the factors contributing to reduce the snow albedo, BC concentration dominates at all distances from the road, although the effect of the grain size becomes as significant as that of BC in the centre of the valley, with the effects of surface roughness and cloudiness remaining minor. This information can be used in snow models to get a better knowledge of the effect of particle deposition on albedo reductions.

Dust and black carbon (BC) can darken snow and ice surface and play pivotal roles in glacier mass loss. Thus, a quan-titative assessment of their contributions to glacier summer melting is critical. During the summer of 2018, surface snow and ice were sampled, and the albedo and mass balance were continuously measured in the ablation zone of Laohugou Glacier No. 12 in the western Qilian Mountains. The physical properties of dust and BC were measured in the laboratory, and their impacts on glacier surface albedo reduction and melting were simulated. The results indicate that the ice surface in the ablation zone was enriched with substantial amounts of particles, and the average particle concentrations of these samples were hundreds of times higher than those of fresh snow. The BC mass absorption cross-sections (MACs) ranged from 3.1 m2 g-1 at 550 nm for dirty ice to 4.6 m2 g-1 for fresh snow, largely owing to meltwater percolation and particle collapse. The spectral variations in dust MACs were significantly different in the visible light bands and near-infrared bands from those in the other areas. Moreover, the two-layer surface energy and mass balance model with the new albedo parameterization formula was validated and agreed well with the exper-imental measurements of spectral albedo, broadband albedo, and mass balance. BC and dust combined resulted in 26.7 % and 54.4 % of the total mass loss on the cleaner and dirtier (particle enriched) surfaces in the ablation zone, respectively, compared to particle-free surfaces, and although both impurities played vital roles, dust was the more prominent factor in accelerating glacier melting on the northeastern Tibetan Plateau. This study emphasizes the impor-tance of dust in cryosphere changes where Tibetan glaciers are strongly affected by Asian dust deposition.

Aerosol particles of Black carbon in the snow cause a significant decrease in the albedo spectrum of the snow, which results in climatic radiation changes seriously, and will delay or advance the snow melting time, badly affecting the characteristics of surface runoff and processes of water cycle in the arid region. This problem is receiving increasing attention in ecological hydrology issues in the arid region. The data of field measurement were obtained by ASD spectrometer, Snow Folk and HR-1024 external field spectrum radiometer. SNICAR model was used to simulate the snow spectrum spectral characteristics under different parameters. Discussed the sensitivity of BC and snow particle size in different spectral ranges. The results showed that : In the snow spectral curve, the zenith angle changes from 0 degrees to 80 degrees, the albedo at 600 nm in the visible spectrum increases by 0. 045, and the albedo at 1 000, 1 200 and 1 300 nm in the near-infrared band increases by 0. 16, 0. 225 and 0. 249, respectively. The zenith angle is at 60 degrees, when snow particle size increases from 100 to 800 mu m, the albedo reduction can reach 0. 15, and snow particle size in the range of 100 similar to 300 mu m is significantly higher than the albedo in the range of 400 similar to 800 mu m. And the increase of the snow particle size can enhance the absorption effect of the light spectrum absorbing particles; Different BC concentrations have little effect on the spectral albedo in the near-infrared region, but are mainly concentrated in the visible light band. At 800 and 1 100 nm, the BC concentration of 5 mu g . g(-1) reduces the spectral albedo by 0. 13. The BC of 5 mu g . g(-1) can reduce the spectral albedo at 350 and 550 nm by 0. 25 and 0. 23. Compared with the different snow sizes, the decrease of BC concentration on the broad-band albedo of snow spectrum can be found in BC. In the case of the increase in the particle size of the snow, the light absorption effect of BC is increased, and at the higher concentration, the more the absorption increases; from the spectral index, the BC is sensitive in the visible light range of 350-740 nm, and the correlation coefficient is higher; The snow size is sensitive in the near-infrared band 1 100 similar to 1 500 nm, especially around 1 000 and 1 300 nm. The correlation between BC and snow particle size in the sensitive band of the snow spectral curve is high. Finally, the snow albedo simulated by the model is compared with the measured data. The R-2 is 0. 738, and the simulation effect is good. It can lay a data foundation for the study of the snow albedo in the arid region.