Previous studies have indicated that black carbon (BC) potentially induces snow albedo reductions across northern China. However, the effects of other light-absorbing particles (LAPs, e.g., mineral dust, MD), snow grain shape, or BC-snow mixing state on snow albedo have been largely ignored. Here we evaluate the BC- and MDinduced snow albedo reductions and radiative forcings (RFs) using an updated Snow, Ice, and Aerosol Radiation radiative transfer model, considering all of the potential factors that can be derived from the field observations across northern China. The results highlight that the LAP-induced albedo reductions for nonspherical snow grains are 2%-30% less than those for spherical grains. Furthermore, BC-snow internal mixing can significantly enhance albedo reduction by a factor of 1.42-1.48 relative to external mixing, with snow grain radius ranging from 100 to 1000 mu m. The mean regional BC + MD-induced snow albedo reductions are amplified by the increase of snow grain radius, ranging from 0.012 to 0.123 for fresh snow to 0.016-0.227 for old snow. Finally, we discuss the relative contributions of BC and MD to the albedo reductions and RFs, highlighting the dominant role of BC in reducing snow albedo across northern China.

Depositions of light-absorbing particles (LAPs), such as black carbon (BC) and dust, on the snow surface modulate the snow albedo; therefore, they are considered key factors of snow-atmosphere interaction in the present-day climate system. However, their detailed roles have not yet been fully elucidated, mainly due to the lack of in-situ measurements. Here, we develop a new model chain NHM-Chem-SMAP, which is composed of a detailed regional meteorology-chemistry model and a multilayered physical snowpack model, and evaluate it using LAPs concentrations data measured at Sapporo, Japan during the 2011-2012 winter. NHM-Chem-SMAP successfully reproduces the in-situ measured seasonal variations in the mass concentrations of BC and dust in the surface snowpack. Furthermore, we find that LAPs from domestic and foreign sources played a role in shortening the snow cover duration by 5 and 10 days, respectively, compared to the completely pure snow condition.

As the Earth warms, the spatial and temporal response of seasonal snow remains uncertain. The global snow science community estimates snow cover and mass with information from land surface models, numerical weather prediction, satellite observations, surface measurements, and combinations thereof. Accurate estimation of snow at the spatial and temporal scales over which snow varies has historically been challenged by the complexity of land cover and terrain and the large global extent of snow-covered regions. Like many Earth science disciplines, snow science is in an era of rapid advances as remote sensing products and models continue to gain granularity and physical fidelity. Despite clear progress, the snow science community continues to face challenges related to the accuracy of seasonal snow estimation. Namely, advances in snow modeling remain limited by uncertainties in modeling parameterization schemes and input forcings, and advances in remote sensing techniques remain limited by temporal, spatial, and technical constraints on the variables that can be observed. Accurate monitoring and modeling of snow improves our ability to assess Earth system conditions, trends, and future projections while serving highly valued global interests in water supply and weather forecasts. Thus, there is a fundamental need to understand and improve the errors and uncertainties associated with estimates of snow. A potential method to overcome model and observational shortcomings is data assimilation, which leverages the information content in both observations and models while minimizing their limitations due to uncertainty. This article proposes data assimilation as a way to reduce uncertainties in the characterization of seasonal snow changes and reviews current modeling, remote sensing, and data assimilation techniques applied to the estimation of seasonal snow. Finally, remaining challenges for seasonal snow estimation are discussed.

Black carbon (BC) is the most effective insoluble light-absorbing particulate (ILAP), which can strongly absorb solar radiation at visible wavelengths. Once BC is deposited in snow via dry or wet process, even a small amount of BC could significantly decrease snow albedo, enhance absorption of solar radiation, accelerate snow melting, and cause climate feedback. BC is considered the second most important component next to CO2 in terms of global warming. Similarly, mineral dust (MD) is another type of ILAP. So far, little attention has been paid to quantitative measurements of BC and MD deposition on snow surface in the midlatitudes of East Asia, especially over northern China. In this paper, we focus on reviewing several experiments performed for collecting and measuring scavenging BC and MD in the high Asian glaciers over the mountain range (such as the Himalayas) and in seasonal snow over northern China. Results from the surveyed literature indicate that the absorption of ILAP in seasonal snow is dominated by MD in the Qilian Mount's and by local soil dust in the Inner Mongolian region close to dust sources. The detection of BC in snow and ice cores using modern techniques has a large bias and uncertainty when the snow sample is mixed with MD. Evidence also indicates that the reduction of snow albedo by BC and MD perturbations can significantly increase the net surface solar radiation, cause surface air temperature to rise, reduce snow accumulation, and accelerate snow melting.