Reply to the Comments by Referee # 2 for Manuscript gmdd-8-3197-2015 “ Parameterization of the snow-covered surface albedo in the Noah-MP Version 1 . 6 by implementing vegetation effects
Reply to the Comments by Referee # 2 for Manuscript gmdd-8-3197-2015 “ Parameterization of the snow-covered surface albedo in the Noah-MP Version 1 . 6 by implementing vegetation effects
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2015
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积雪覆盖面覆盖率随雪粒径、积雪厚度、雪龄、森林遮光系数等因素而变化，其参数化仍存在很大的不确定性。对于积雪覆盖的地表条件，森林植被的覆盖度通常低于矮植被，因此，积雪覆盖度取决于特征土地覆盖的空间分布以及郁闭度和结构。在Noah陆面模式（Noah-MP）中，东亚地区冬季几乎所有植被类型的叶面积指数（LAI）和茎面积指数（SAI）都存在极小值，且数值过低，没有考虑植被类型。由于叶面积指数和叶面积指数是用光合活性来表示的，所以冬季的树干和茎不能用这些参数来表示。我们发现，植被效应的这种不充分的代表性是主要负责的大的正偏差计算冬季地面降水在Noah-MP。在这项研究中，我们调查了植被对积雪覆盖的地表覆盖的观测结果，并通过实施一个新的参数化方案，提高了模式的性能。我们开发了新的参数，称为叶指数（LI）和茎指数（SI），适当地管理植被结构对积雪覆盖的地表植被的影响。因此，Noah-MP在冬季地表的性能得到了显著改善-均方根误差减少了约69%。
The snow-covered surface albedo varies with many factors, including snow grain size, snow cover thickness, snow age, forest shading factor, etc., and its parameterization is still under great uncertainty. For the snow-covered surface condition, albedo of forest is typically lower than that of short vegetation; thus snow albedo is dependent on the spatial distributions of characteristic land cover and on the canopy density and structure. In the Noah land surface model with multiple 5 physics options (Noah-MP), almost all vegetation types in East Asia during winter have the minimum values of leaf area index (LAI) and stem area index (SAI), which are too low and do not consider the vegetation types. Because LAI and SAI are represented in terms of photosynthetic activeness, stem and trunk in winter are not well represented with only these parameters. We found that such inadequate representation of the vegetation effect is mainly responsible for the large posi10 tive bias in calculating the winter surface albedo in the Noah-MP. In this study, we investigated the vegetation effect on the snow-covered surface albedo from observations and improved the model performance by implementing a new parameterization scheme. We developed new parameters, called leaf index (LI) and stem index (SI), which properly manage the effect of vegetation structure on the snow-covered surface albedo. As a result, the Noah-MP’s performance in the winter surface albedo 15 has significantly improved – the root mean square error is reduced by approximately 69 %.