Sciences in Cold and Arid Regions ›› 2020, Vol. 12 ›› Issue (2): 83-94.doi: 10.3724/SP.J.1226.2020.00083.

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Quantitative estimation of the influence factors on snow/ice albedo

ZhongMing Guo1,2(),NingLian Wang1,2,BaoShou Shen1,2,ZhuJun Gu3,HongBo Wu4,YuWei Wu1,2,AnAn Chen1,2,Xi Jiang5   

  1. 1.Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Xi'an, Shannxi 710127, China
    2.Institute of Earth Surface System and Hazards, College of Urban and Environmental Sciences, Northwest University, Xi'an, Shannxi 710127, China
    3.School of Environmental Science, Nanjing Xiaozhuang University, Nanjing, Jiangsu 211171, China
    4.School of History and Tourism, Shaanxi Sci-Tech University, Hanzhong, Shannxi 723000, China
    5.Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210000, China
  • Received:2018-10-10 Accepted:2019-03-21 Online:2020-04-30 Published:2020-04-27
  • Contact: ZhongMing Guo


Quantitative estimation of the influence of various factors, such as black carbon, snow grain, dust content, and water content on albedo is essential in obtaining an accurate albedo. In this paper, field measurement data, including snow grain size, density, liquid water content, and snow depth was obtained. Black carbon and dust samples were collected from the snow surface. A simultaneous observation using ASD (Analytical Spectral Devices) spectral data was employed in the Qiyi glacier located on Qilian Mountain. The measurements were compared with results obtained from the Snow, Ice, and Aerosol Radiation (SNICAR) model. Additionally, a HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) air mass backward trajectory model was used to track the source of black carbon. The simulation was found to correlate well with observed data. Liquid water content was the most influential factor of albedo among the several influencing factors, followed by black carbon content and snow grain size. Finally, snow density change had the least toward albedo. HYSPLIT atmospheric trajectories model can only approximately show the source of black carbon and not clearly indicate the source region of black carbon.

Key words: albedo, black carbon, snow grain size, quantitative estimation

Figure 1

Locations of the Qiyi Glacier and the sampling sites"

Figure 2

Mean snow grain size on September 15, 2011 at 30 min intervals. The error bars represent ± one standard deviation from the mean"

Table 1

Comparison of the retrieved albedo with the measure albedo in 2011 and black carbon content"

IDRetrieval albedoCalculated albedoDifferenceErrorBC content (ng/g)

Figure 3

Spectral reflectance of snow for different black carbon content"

Figure 4

The sync photos of the sky condition with ASD spectral observation"

Figure 5

Variation of spectral reflectance of a fixed point during the time of 11:00-17:00 on September 15, 2011 (a); and the calculated albedo at the corresponding time (b)"

Figure 6

Measured and simulated spectral reflectance using the SNICAR model at 11:00-17:00 on September 15, 2011 (Red line represent the simulated reflectance; black line represent the measured reflectance)"

Figure 7

The relationship between the measured reflectance and simulated reflectance"

Figure 8

Simulated the influence of black carbon content on albedo when the other parameters are controlled through the SNICAR model"

Figure 9

Simulated the influence of snow grain size on albedo when the other parameters are controlled through the SNICAR model"

Figure 10

Quantitative estimation the influence of factors on snow albedo using measured data"

Figure 11

The backward air mass trajectories on August 24 and September 15 in 2011"

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