The size of snow grains is an important parameter in cryosphere studies. It is the main parameter affecting snow albedo and can have a feedback effect on regional climate change, the water cycle and ecological security. Larger snow grains increase the likelihood of light absorption and are important for passive microwave remote sensing, snow physics and hydrological modelling. Snow models would benefit from more observations of surface grain size. This paper uses an asymptotic radiative transfer model (ART model) based on MOD09GA ground reflectance data. A simulation of snow grain size (SGS) in northeast China from 2001 to 2019 was carried out using a two-channel algorithm. We verified the accuracy of the inversion results by using ground-based observations to obtain stratified snow grain sizes at 48 collection sites in northeastern China. Furthermore, we analysed the spatial and temporal trends of snow grain size in Northeastern China. The results show that the ART model has good accuracy in inverting snow grain size, with an RMSD of 65 mu m, which showed a non-significant increasing trend from 2001 to 2019 in northeast China. The annual average SGS distribution ranged from 430.83 to 452.38 mu m in northeast China, 2001-2019. The mean value was 441.78 mu m, with an annual increase of 0.26 mu m/a, showing a non-significant increasing trend and a coefficient of variation of 0.014. The simulations show that there is also intermonth variation in SGS, with December having the largest snow grain size with a mean value of 453.92 mu m, followed by January and February with 450.77 mu m and 417.78 mu m, respectively. The overall spatial distribution of SGS in the northeastern region shows the characteristics of being high in the north and low in the south, with values ranging from 380.248 mu m to 497.141 mu m. Overall, we clarified the size and distribution of snow grains over a long time series in the northeast. The results are key to an accurate evaluation of their effect on snow-ice albedo and their radiative forcing effect.

The melting behavior of glaciers on and around the Tibetan Plateau is strongly influenced by their albedo. In this paper, we report continuous observations made on the Qiangtang (QT) No. 1 Glacier, located in the central Tibetan Plateau, during its 2013-2015 melting seasons. Surface snow on the QT No. 1 Glacier mainly had a dust content less than 600 ppm and a black carbon (BC) content less than 10 ppb. A strong negative correlation was observed between albedo and dust content up to a threshold concentration of 1000 ppm, although albedo remained constant when dust concentrations increased above this value. The radii of snow particles showed a log-normal distribution that had a mean value of similar to 500 mu m, but maximum and minimum values of 2539 mu m and 40 mu m, respectively. Snow density showed a normal distribution with a total range of 193-555 kg/m(3), although most snow had a density of 400 kg/m(3). Snow, ice, and aerosol radiative (SNICAR) simulations showed that dust and BC in the surface snow of the QT No. 1 Glacier reduced the snow and ice albedo by 5.9% and 0.06%, respectively, during the ablation season in 2015; however, the simulated particle impact was greater than the albedo reduction measured from field data. We interpret that dust has played a significantly more important role in melting of the QT No. 1 Glacier than BC over the study period, which is mainly due to the scarcity of human activities in the region and the low concentration of BC being produced.

Purpose of Review Black carbon (BC) deposition in snow can trigger a significant reduction in snow albedo and accelerate snowmelt. As a result, numerous snow surveys have performed to measure BC concentrations in snow across the polar regions, the Tibetan Plateau, and other high-mountain regions. This review is aimed to synthesize the current progresses of the potential feedbacks of snow albedo and its sensitivity by BC in snow across the Northern Hemisphere. Recent Findings Generally, BC concentrations in snow are highest in the mid-latitudes of Northern China and North America, and reduce toward higher latitudes (e.g., Greenland and the rest of the Arctic). We found that the snow albedo reduction attributed to low BC contamination (< 20 ng g(-1)) in older snow (200 mu m snow grains) is 1.2%, compared with 0.6% in fresh snow (50 mu m snow grains). Non-spherical snow grains exhibit a significantly lower snow albedo reduction (2-6%) due to BC contamination compared with spherical snow grains with 100-500 ng g(-1)of BC in the snowpack. Snow-BC-internal mixing reduces the snow albedo (< 10%) more substantially than does external mixing in the case of 50-200 mu m snow grains and a given BC concentration (< 2000 ng g(-1)). Besides the BC and other light-absorbing particles (LAPs), the mixing state of LAPs in snow, snow grain properties, and the scavenging\washing effects are also major challenges in determining snow albedo, which need to be further investigated on a global scale.

The Global Change Observation Mission-Climate (GCOM-C) is a project of Japan Aerospace Exploration Agency (JAXA) for the global and long-term observation of the Earth environment. The GCOM-C is expected to play an important role in monitoring and understanding global climate change. It will be a kind of health checkup of the Earth from space. The GCOM-C also aims to construct, use, and verify systems that enable continuous global-scale observations of various geophysical parameters. The GCOM-C is a part of the JAXA's GCOM mission which consists of two satellite series, GCOM-C and GCOM-W (Water), spanning three generations in order to perform uniform and stable global observations for 13 years. Whereas GCOM-W carries a multi-frequency, dual-polarized, passive microwave radiometer named Advanced Microwave Scanning Radiometer 2 (AMSR2) to observe water-related targets such as precipitation, water vapor, sea surface wind speed, sea surface temperature, soil moisture, and snow depth, GCOM-C carries a multi-spectral optical radiometer named Second Generation Global Imager (SGLI), which will have special features of wide spectral coverage from 380nm to 12 mu m, a high spatial resolution of 250m, a field of view exceeding 1000km, two-direction simultaneous observation, and polarization observation. The GCOM-C mission aims to contribute to improving our knowledge and prediction of the global carbon cycle and radiation budget through high-accuracy observation of global vegetation, ocean color, temperature, cloud, aerosol, and snow and ice through the SGLI observations. The GCOM will take over the Advanced Earth Observing Satellite-II (ADEOS-II) mission and transition into long-term monitoring of the Earth. One of the important targets to be observed by GCOM-C is snow and sea ice in the cryosphere. SGLI on GCOM-C1 will retrieve not only snow cover extent but also snow physical parameters such as snow grain size, temperature, and mass fraction of impurity mixed in snow layer. The snow physical parameters are important factors that determine spectral albedo of the snow surface. Thus it is essential to monitor those parameters from space in order to better understand snow metamorphosis and melting process and also to study the response of snow and sea-ice cover extent in the Polar Regions to a climate forcing such as global warming. A final goal of these observations is to improve land-surface processes in numerical climate models by accumulating knowledge on the evolution of snow and sea ice in the cryosphere.