Mineral dust is a major light-absorbing aerosol, which can significantly reduce snow albedo and accelerate snow/glacier melting via wet and dry deposition on snow. In this study, three scenarios of internal mixing of dust in ice grains were analyzed theoretically by combining asymptotic radiative transfer theory and (core–shell) Mie theory to evaluate the effects on absorption coefficient and albedo of the semi-infinite snowpack consisting of spherical snow grains. In general, snow albedo was substantially reduced at wavelengths of <1.0 µm by internal dust–snow mixing, with stronger reductions at higher dust concentrations and larger snow grain sizes. Moreover, calculations showed that a nonuniform distribution of dust in snow grains can lead to significant differences in the values of the absorption coefficient and albedo of dust-contaminated snowpack at visible wavelengths relative to a uniform dust distribution in snow grains. Finally, using comprehensive in situ measurements across the Northern Hemisphere, we found that broadband snow albedo was further reduced by 5.2 % and 9.1 % due to the effects of internal dust–snow mixing on the Tibetan Plateau and North American mountains. This was higher than the reduction in snow albedo caused by black carbon in snow over most North American and Arctic regions. Our results suggest that significant dust–snow internal mixing is important for the melting and retreat of Tibetan glaciers and North American mountain snowpack.

Water-soluble organic carbon (WSOC) in the cryosphere has an important impact on the biogeochemistry cycling and snow–ice surface energy balance through changes in the surface albedo. This work reports on the chemical characterization of WSOC in 28 representative snowpack samples collected across a regional area of northern Xinjiang, northwestern China. We employed multimodal analytical chemistry techniques to investigate both bulk and molecular-level composition of WSOC and its optical properties, informing the follow-up radiative forcing (RF) modeling estimates. Based on the geographic differences and proximity of emission sources, the snowpack collection sites were grouped as urban/industrial (U), rural/remote (R), and soil-influenced (S) sites, for which average WSOC total mass loadings were measured as 1968 ± 953 ng g⁻¹ (U), 885 ± 328 ng g⁻¹ (R), and 2082 ± 1438 ng g⁻¹ (S), respectively. The S sites showed the higher mass absorption coefficients at 365 nm (MAC₃₆₅) of 0.94 ± 0.31 m² g⁻¹ compared to those of U and R sites (0.39 ± 0.11 m² g⁻¹ and 0.38 ± 0.12 m² g⁻¹, respectively). Bulk composition of WSOC in the snowpack samples and its basic source apportionment was inferred from the excitation–emission matrices and the parallel factor analysis featuring relative contributions of one protein-like (PRLIS) and two humic-like (HULIS-1 and HULIS-2) components with ratios specific to each of the S, U, and R sites. Additionally, a sample from site 120 showed unique pollutant concentrations and spectroscopic features remarkably different from all other U, R, and S samples. Molecular-level characterization of WSOC using high-resolution mass spectrometry (HRMS) provided further insights into chemical differences among four types of samples (U, R, S, and 120). Specifically, many reduced-sulfur-containing species with high degrees of unsaturation and aromaticity were uniquely identified in U samples, suggesting an anthropogenic source. Aliphatic/protein-like species showed the highest contribution in R samples, indicating their biogenic origin. The WSOC components from S samples showed high oxygenation and saturation levels. A few unique CHON and CHONS compounds with high unsaturation degree and molecular weight were detected in the 120 sample, which might be anthraquinone derivatives from plant debris. Modeling of the WSOC-induced RF values showed warming effects of 0.04 to 0.59 W m⁻² among different groups of sites, which contribute up to 16 % of that caused by black carbon (BC), demonstrating the important influences of WSOC on the snow energy budget.

Snow is the most reflective natural surface on Earth and consequently plays an important role in Earth's climate. Light-absorbing particles (LAPs) deposited on the snow surface can effectively decrease snow albedo, resulting in positive radiative forcing. In this study, we used remote-sensing data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and the Snow, Ice, and Aerosol Radiative (SNICAR) model to quantify the reduction in snow albedo due to LAPs before validating and correcting the data against in situ observations. We then incorporated these corrected albedo-reduction data in the Santa Barbara DISORT (Discrete Ordinate Radiative Transfer) Atmospheric Radiative Transfer (SBDART) model to estimate Northern Hemisphere radiative forcing except for midlatitude mountains in December–May for the period 2003–2018. Our analysis reveals an average corrected reduction in snow albedo ($\mathrm{\Delta }{\mathit{\alpha }}_{\text{MODIS,corrected}}^{\mathrm{LAPs}}$ ) of ∼ 0.021 under all-sky conditions, with daily radiative forcing (${\mathrm{RF}}_{\text{MODIS,daily}}^{\mathrm{LAPs}}$ ) values of ∼ 2.9 W m⁻², over land areas with complete or near-complete snow cover and with little or no vegetation above the snow in the Northern Hemisphere. We also observed significant spatial variations in $\mathrm{\Delta }{\mathit{\alpha }}_{\text{MODIS,corrected}}^{\mathrm{LAPs}}$ and ${\mathrm{RF}}_{\text{MODIS,daily}}^{\mathrm{LAPs}}$ , with the lowest respective values (∼ 0.016 and ∼ 2.6 W m⁻²) occurring in the Arctic and the highest (∼ 0.11 and ∼ 12 W m⁻²) in northeastern China. From MODIS retrievals, we determined that the LAP content of snow accounts for 84 % and 70 % of the spatial variability in albedo reduction and radiative forcing, respectively. We also compared retrieved radiative forcing values with those of earlier studies, including local-scale observations, remote-sensing retrievals, and model-based estimates. Ultimately, estimates of radiative forcing based on satellite-retrieved data are shown to represent true conditions on both regional and global scales.

Light-absorbing particles (LAPs) deposited on snow can decrease snow albedo and affect climate through snow-albedo radiative forcing. In this study, we use MODIS observations combined with a snow-albedo model (SNICAR – Snow, Ice, and Aerosol Radiative) and a radiative transfer model (SBDART – Santa Barbara DISORT Atmospheric Radiative Transfer) to retrieve the instantaneous spectrally integrated radiative forcing at the surface by LAPs in snow (RFMODISLAPs) under clear-sky conditions at the time of MODIS Aqua overpass across northeastern China (NEC) in January–February from 2003 to 2017. RFMODISLAPs presents distinct spatial variability, with the minimum (22.3 W m−2) in western NEC and the maximum (64.6 W m−2) near industrial areas in central NEC. The regional mean RFMODISLAPs is ∼45.1±6.8 W m−2 in NEC. The positive (negative) uncertainties of retrieved RFMODISLAPs due to atmospheric correction range from 14 % to 57 % (−14 % to −47 %), and the uncertainty value basically decreases with the increased RFMODISLAPs. We attribute the variations of radiative forcing based on remote sensing and find that the spatial variance of RFMODISLAPs in NEC is 74.6 % due to LAPs and 21.2 % and 4.2 % due to snow grain size and solar zenith angle. Furthermore, based on multiple linear regression, the BC dry and wet deposition and snowfall could explain 84 % of the spatial variance of LAP contents, which confirms the reasonability of the spatial patterns of retrieved RFMODISLAPs in NEC. We validate RFMODISLAPs using in situ radiative forcing estimates. We find that the biases in RFMODISLAPs are negatively correlated with LAP concentrations and range from ∼5 % to ∼350 % in NEC.

The Taklamakan Desert (TD) is a major source of mineral dust emissions into the atmosphere. These dust particles have the ability to darken the surface of snow on the surrounding high mountains after deposition, significantly impacting the regional radiation balance. However, previous field measurements have been unable to capture the effects of severe dust storms accurately, and their representation on regional scales has been inadequate. In this study, we propose a modified remote-sensing approach that combines data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and simulations from the Snow, Ice, and Aerosol Radiative (SNICAR) model. This approach allows us to detect and analyze the substantial snow darkening resulting from dust storm deposition. We focus on three typical dust events originating from the Taklamakan Desert and observe significant snow darkening over an area of ∼ 2160, ∼ 610, and ∼ 640 km² in the Tien Shan, Kunlun, and Qilian mountains, respectively. Our findings reveal that the impact of dust storms extends beyond the local high mountains, reaching mountains located approximately 1000 km away from the source. Furthermore, we observe that dust storms not only darken the snowpack during the spring but also in the summer and autumn seasons, leading to increased absorption of solar radiation. Specifically, the snow albedo reduction (radiative forcing) triggered by severe dust deposition is up to 0.028–0.079 (11–31.5 W m⁻²), 0.088–0.136 (31–49 W m⁻²), and 0.092–0.153 (22–38 W m⁻²) across the Tien Shan, Kunlun, and Qilian mountains, respectively. This further contributes to the aging of the snow, as evidenced by the growth of snow grain size. Comparatively, the impact of persistent but relatively slow dust deposition over several months during non-event periods is significantly lower than that of individual dust events. This highlights the necessity of giving more attention to the influence of extreme events on the regional radiation balance. This study provides a deeper understanding of how a single dust event can affect the extensive snowpack and demonstrates the potential of employing satellite remote sensing to monitor large-scale snow darkening.