CG-LTDR地表覆盖数据对BCC_AVIM 1.0陆面温度模拟的影响研究

本文将CG-LTDR数据集中的地表覆盖数据产品应用于北京气候中心陆面模式（BCC_AVIM 1.0）中,并通过数值模拟分析不同覆盖类型的数据（冰川、湿地、湖泊、植被PFT）更新对模拟结果的影响。结果表明,新数据对不同地表类型的基本分布特征都有合理描述,但与模式中原有数据的差异明显,表现为冰川比例在格陵兰岛西部地区增加,湿地在大部分地区都减少,湖泊在北美和欧亚大陆中高纬地区的比例减少,但青藏高原及周边地区小幅增加,植被PFT的差异最明显。与采用模式原地表覆盖数据的控制试验相比,新数据引入所致的改变,主要局限于地表覆盖数据改变的区域。冰川数据更新使高纬冰川积雪区和青藏高原的温度降低,湿地数据提高了欧洲和北美主要水区的地面温度,湖泊数据有效降低了亚洲地区的温度,更新植被PFT的影响最广泛,使得南美、南非、东北亚、北美和澳洲大部分地区的温度升高,而中国华南江南地区以及南亚地区的温度降低,但在一些地区的模拟效果降低。数据全部更新引起的温度改变最明显,但并不是所有类型的简单叠加,尤其在地表复杂区域。不同的覆盖类型数据更新,可在一定程度上减少模式对于地表温度的模拟偏差（如格陵兰岛西部和青藏高原地区、欧洲内陆湖区的温度偏高）,因此需适当选用更新的数据。

Impacts of CG-LTDR Land Cover Dataset Updates on the Ground Temperature Simulation with BCC_AVIM 1.0

The land cover (LC) datasets of CG-LTDR was applied in the Beijing Climate Center Land Model (BCC_AVIM 1.0). The impacts of different LC type updates on the ground temperature (Tg) were investigated through several numerical simulations. The results show that the CG-LTDR can reasonably describe the LC features. Compared with the original LC rawdata, the glacier fraction of the new CG-LTDR datasets were extensively increased in the high-latitude regions of the Greenland Island and Europe, as well as the Tibetan Plateau; the fraction of wetland was decreased in the major water body areas of North America and Europe; and the percentage of lake was also majorly decreased in the North American inland water area, but slightly increased around the Tibetan Plateau. The PFT present the largest differences between the new and original datasets. Besides the control runs with the original LC dataset (CTL), five simulations were conducted to compare different impacts of LC types (the glacier, wetland, lake, PFT and all types) on Tg. The changes of Tg due to LC dataset updates majorly constrained in the areas where the LC types (fraction) were modified. With the individual updates of glacier (rGlacier), the simulated Tg was lowered in the high-latitude areas. The simulated Tg with new wetland (rWetland) was increased, while the simulated Tg with the new lake (rLake) datasets were effectively decreased in the Tibetan Plateau. These changes were helpful to improve the model performances on Tg simulations. The most significant and extensive changes among the 4 LC types occurred when updating the PFT (rPFT), which were helpful for reducing the errors in the south and east Asian areas, but enlarged the biases in the other regions. The LC dataset updates of all types (rALL) show the most significant impacts on the Tg simulations, which was not simply the linear sum of the individual updates of LC types, especially in the areas having complex types. Therefore, proper introductions of new CG-LTDR land cover datasets were useful to improve the model performance in Tg simulations.