基于卫星遥感构建东亚植被特征参数及其在区域气候模拟中的应用

As one of the fundamental processes of climate system, land-atmosphere interaction plays a key role in weather and climate changes at various spatial and temporal scales. The uncertainties of model parameters restrict the performance of land surface model in much degree, especially under the background of better representation of model processes, more accurate forcing field and boundary conditions. Abundant studies focus on determining parameters, which are based on site observation data, with high accuracy. However, the gap between site-determined parameters and regional application is huge due to the inhomogeneous land surface..In this proposal, we are going to take full use of satellite remote sensing data for its spatially and temporally continuous measurement of surface parameters at high-resolution. Among the various parameters involved in the surface physics, such as leaf area index (LAI), roughness length, zero-displacement height, the coefficient of aerodynamic resistance, affect surface radiation balance, evapotranspiration and biochemical processes of vegetation, etc., and thus are key parameters that affect surface thermal and hydrology condition..Based on remote sensing LAI, we are going to derive relevant surface parameters to ensure the agreement among parameters and the reality. Firstly, the total leaf area index, TLAI, will be determined. Then GLAS/ICESat data will be combined with empirical algorithm to derive vegetation height. To estimate other key parameters mentioned above, an iteration algorithm with the consideration of canopy density.distribution will be employed. Furthermore, the remote sensing based surface parameters will be introduced in land surface model and regional model, which are expected to make contributions to better simulation of energy and water cycle over East Asia..In addition, the regional high-resolution land surface parameter data set is physically based and model independent. Therefore, it is also applicable to other land-atmosphere interaction studies. Furthermore, the achievements from the project are also expected to help better understanding vegetation-climate interaction and projection of future climate.

陆-气相互作用对不同时、空尺度上的天气和气候过程具有深刻的影响，而陆面过程模式中参数的不确定性是制约其模拟能力的主要因素之一。陆面参数敏感性分析发现，叶面积指数（LAI）、地表粗糙度和零平面位移等是计算地-气通量交换的关键参数。针对以上参数，本项目拟首先对比不同卫星遥感LAI产品在东亚地区的适用性和差异，估算得到总体叶面积指数（TLAI）；其次结合星载激光雷达数据和基于植被碳平衡的经验公式来估算东亚地区植被高度；在此基础上，利用考虑了植被冠层密度分布的迭代方法，根据TLAI和植被高度估算地表粗糙度及零平面位移等关键参数。由此得到的地表植被特征参数将更加符合真实的物理情景，且具有更好的一致性。同时，回避了由于地表非均匀性导致单点率定的参数在区域上扩展应用的难题。在此基础上，将上述关键陆面参数应用到陆面模式和区域模式中，系统评估改进的地表参数能否提高区域模式对东亚地区能量水分循环的模拟能力。

陆-气相互作用对不同时、空尺度上的天气和气候过程具有深刻的影响，而陆面过程模式中参数的不确定性是制约其模拟能力的主要因素之一。参数敏感性分析发现，叶面积指数（LAI）、地表粗糙度和零平面位移等是计算地-气通量交换的关键参数。本项目利用星载激光雷达植被冠层高度产品以及卫星遥感叶面积指数产品在全球范围内逐点优化Enquist植被生长曲线。应用优化后的植被生长曲线和逐月LAI观测，生成全球0.05°植被冠层高度产品（1982-2017）。用估算得到的植被冠层高度和LAI驱动Sellers湍流传输模型，得到全球地表粗糙度和零平面位移以及空气动力学阻抗系数等关键地表参数。这些地表植被特征参数更加符合真实的物理情景，且具有更好的一致性。同时回避了由于地表非均匀性导致单点率定的参数在区域上扩展应用的难题。将上述关键陆面参数应用到陆面模式和区域模拟中，发现使用基于卫星遥感的动力学粗糙度后，显著提高了区域气候模式（WRF-ARW）对地表温度和降水的模拟能力。其中，温度主要受局地辐射传输和地表-大气间湍流交换的影响，对下垫面变化更为敏感。使用新参数后，模式更加准确地描述了地表的空气动力学特征，因此对温度模拟有明显提高。对于我国夏季降水，在季风区带来的降水改进相对较小。但在非季风区域，局地水汽和非绝热加热对降水影响更大，对降水的改进也更显著。近年来，全球植被变绿受到广泛关注。然而，植被冠层变化后带来的空气动力学特征的改变少有关注。我们根据卫星遥感生成的1982-2017年地表粗糙度逐月产品反映了地表粗糙度随植被变绿的动态特征，有望进一步提高对植被变绿带来的气候效应的科学认识。. 目前已发表标注本项目资助的国际期刊学术论文14篇，其中代表性成果(标注项目第一资助) 分别发表于遥感领域和水文气象领域高影响期刊Remote Sens. Environ.以及Hydrol. Earth Syst. Sci..