Publications

Hao, DL; Wen, JG; Xiao, Q; Lin, XW; You, DQ; Tang, Y; Liu, Q; Zhang, SS (2019). Sensitivity of Coarse-Scale Snow-Free Land Surface Shortwave Albedo to Topography. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 124(16), 9028-9045.

Abstract
As a widespread landscape, rugged terrain significantly distorts the land surface albedo. Simply neglecting the topographic effects in the land surface albedo modeling and retrievals can lead to large biases and uncertainties over rugged terrain. In spite of gradually increasing research about the topographic effects on the albedo, the albedo sensitivities over rugged terrain to different variables remain unclear. In this paper, the sensitivities of coarse-scale snow-free albedo to topography were quantitatively investigated using the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface albedo data and the variance-based global sensitivity analysis based on a well-established mechanistically-based land surface albedo parameterized model over rugged terrain. The results based on the MODIS data revealed that MODIS land surface albedo over the Tibetan Plateau was highly sensitive to the topographic distribution, and the differences of the spatially averaged snow-free black-sky albedos over grasslands induced by different terrain could reach up to 0.10 in winter. The topographic effects on MODIS albedo are tightly relevant to the land cover type, solar illumination geometries, and vegetation characteristics. The global sensitivity analysis results underscored that topography was an important driving factor of the snow-free albedo, and it accounted for more than 30% of the total variance, respectively. These results highlight the necessities for the topographic consideration in the land surface albedo modeling and retrievals even though the terrain is gentle (10-20 degrees) and advance our understanding of the albedo sensitivities to different variables over rugged terrain, which will facilitate the improvement of land surface albedo parameterization in the land surface models.

DOI:
10.1029/2019JD030660

ISSN:
2169-897X