中国高精度土地覆盖数据在RegCM4/CLM模式中的引入及其对区域气候模拟影响的分析

基于中国1:100万植被图和1:600万植被区划图, 按照通用陆面模式CLM植被功能型分类标准, 制作了可用于CLM及RegCM4区域气候模式的25 km×25 km分辨率的中国高精度土地覆盖数据(简称VEG数据). 相比CLM默认使用的土地覆盖数据(简称ORG数据), VEG数据不仅能提供更多的土地覆盖局地特征, 还纠正了ORG数据中裸地和农作物的比例偏高、 灌木的比例偏低等误差. 对比使用两套土地覆盖数据的RegCM4多年连续积分结果, 分析了不同土地覆盖分布对气温、 降水等的影响, 并从地表能量收支的角度给出影响机理解释. 结果显示: VEG数据的使用, 使得模式对冬季气温和降水的模拟能力有一定提高, 模式在南部区域偏干偏冷的系统误差有所减弱; 采用VEG数据后, 由于粗糙度、 反照率等下垫面参数的改变及云量的变化, 使得地表能量收支发生显著调整. 青藏高原地区的气温变化与湍流通量和长波辐射的变化有密切的联系, 主要源自粗糙度引起的湍流通量增加、 以及云量引起的向下净长波辐射增加. 而在中国中部和南部, 短波辐射变化更为明显, 它与地表反照率的变化相一致. 基于所制作的土地覆盖数据, 可广泛应用于CLM模式在中国区域的应用之中.

Development of Chinese high resolution land cover data for the RegCM4/CLM and its impact on regional climate simulation

Based on the 1:1 000 000 Vegetation Atlas and 1:6 000 000 Vegetation Regionalization Map of China, a high resolution land cover dataset (VEG) with the CLM classification was developed, which can be used in CLM and RegCM4. Compared to the default land cover data (ORG), the VEG data produced a decreased coverage of bare ground and crop, but an increased coverage of shrub. With higher resolution, this new data can represent the local characteristics of land cover more accurately. A 3-yr continuous integral simulation was used to study the impact of land cover data on surface air temperature, precipitation, and surface energy budget. The results show that the simulation with the VEG data shows a general good performance in describing surface air temperature and precipitation in winter. Most of these improvements are located in South China, which can reduce the dry and cold biases. Using the VEG data not only changes the surface albedo and roughness but also the cloud fraction, and then consequently makes the surface energy budget change. Regional climate change differs by region, as does its mechanism. The temperature change in Tibet Plateau is the result of increasing turbulence flux and downward net long-wave radiation. The increase in surface roughness will enhance surface drag, leading to the increased turbulent heat exchange, and the cloud fraction variations will cause increased thermal warming. However, in Central and South China, the main factor affecting surface air temperature is surface short-wave radiation. It is hopeful that this high resolution land cover dataset will be widely applied in the CLM simulations over China.