Land Surface Albedo (LSA) of forested environments continues to be a source of uncertainty in land surface modeling, especially across seasonally snow covered domains. Assessment and improvement of global scale model performance has been hampered by the contrasting spatial scales of model resolution and in‐situ LSA measurements. In this study, point‐scale simulations of the Community Land Model 5.0 (CLM5) were evaluated across a large range of forest structures and solar angles at two climatically different locations. LSA measurements, using an uncrewed aerial vehicle with up and down‐looking shortwave radiation sensors, showed canopy structural shading of the snow surface exerted a primary control on LSA. Diurnal patterns of measured LSA revealed strong effects of both azimuth and zenith angles, neither of which were adequately represented in simulations. In sparse forest environments, LSA were overestimated by up to 66%. Further analysis revealed a lack of correlation between Plant Area Index (PAI), the primary canopy descriptor in CLM5, and measured LSA. Instead, measured LSA showed considerable correlation with the fraction of snow visible in the sensor's field of view, a correlation which increased further when only considering the sunlit fraction of visible snow. The use of effective PAI values as a simple first‐order correction for the discrepancy between measured and simulated LSA in sparse forest environments substantially improved model results (64%–76% RMSE reduction). However, the large biases suggest the need for a more generic solution, for example, by introducing a canopy metric that represents canopy gap fraction rather than assuming a spatially homogeneous canopy.

中文翻译：

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森林冠层结构对冬季陆地表面反照率的影响：通过现场测量评估CLM5模拟

森林环境的地表反照率（LSA）仍然是地表建模不确定性的来源，尤其是在季节性积雪地区。相对于模型分辨率和原位LSA测量的空间尺度差异，阻碍了对全球尺度模型性能的评估和改进。在这项研究中，在两个气候不同的地点，对大范围的森林结构和太阳角进行了社区土地模型5.0（CLM5）的点规模模拟。LSA测量结果是使用一台带有上下短波辐射传感器的无人驾驶飞机进行的，测量结果表明，雪表面的冠层结构阴影对LSA起到了主要控制作用。测得的LSA的昼夜模式揭示了方位角和天顶角的强烈影响，在仿真中都没有充分体现它们。在稀疏的森林环境中，LSA被高估了66％。进一步的分析表明，植物面积指数（PAI），CLM5中的主要树冠描述符与测得的LSA之间缺乏相关性。取而代之的是，测得的LSA与传感器视场中可见雪的比例显示出显着的相关性，当仅考虑阳光照射下可见雪的比例时，相关性会进一步增加。使用有效的PAI值作为稀疏森林环境中实测LSA与模拟LSA之间差异的简单的一阶校正，可以大大改善模型结果（RMSE降低64％至76％）。但是，偏差很大，这表明需要更通用的解决方案，例如，