基于陆面过程模式CLM4的中国区域植被总初级生产力模拟与评估

植被总初级生产力(Gross Primary Productivity,GPP)决定进入陆地生态系统的初始物质和能量,是陆地碳循环与大气碳库的重要联系纽带。利用陆面过程模式CLM4-CN(Community Land Model version 4 with CarbonNitrogen interactions)模拟和分析中国区域1982~2004年GPP(CLM4_GPP)时空变化特征,并通过与基于观测数据升尺度所得到的MTE_GPP(Model Tree Ensemble,MTE)进行比较,评估CLM4在中国区域GPP的模拟能力,同时探讨了不同土地覆盖资料对GPP的影响。结果表明:(1)CLM4-CN能够较好地刻画中国区域GPP空间分布格局,表现为由东南向西北递减,但在量值上大部分区域尤其是30°N以南地区存在高估,CLM4-CN模拟的GPP多年平均值为13.7 Pg C a-1,而MTE_GPP仅为6.9 Pg C a-1;(2)CLM4-CN可以合理模拟GPP的季节变化(与MTE_GPP相关系数大于0.9),在量值上对温带阔叶落叶林、寒带阔叶落叶林、寒带阔叶落叶灌木、C3极地草地、C3非极地草地和农作物模拟较好(均方根偏差RMSD100 g C m-2 month-1);(3)不同植物功能型CLM4_GPP表现出的年际变率均大于MTE_GPP,仅热带针叶常绿林、寒带阔叶落叶林和C3极地草地的CLM4_GPP与MTE_GPP变化趋势一致;(4)降水是研究时段内控制整个中国区域GPP的主要气候因子,但不同地区存在较大差异;(5)两种不同土地覆盖资料GPP模拟结果的显著差异表明,精确的土地覆盖是准确模拟GPP的重要基础。

Simulation and Evaluation of Gross Primary Productivity in China by Using Land Surface Model CLM4

Gross Primary Productivity (GPP) determines the initial substance and energy in the terrestrial ecosystem, which is an important link between the terrestrial carbon cycle and the atmospheric carbon pool. This study simulates the GPP in China from 1982 to 2004 by using the CLM4-CN (Community Land Model version 4 with Carbon-Nitrogen interactions) and evaluates its capability by comparing it with the MTE (Model Tree Ensemble)_GPP derived from upscaling of FLUXNET eddy covariance observations. We use the results to investigate the effects of different land cover datasets on GPP modeling. CLM4-CN is shown to effectively capture the spatial patterns of the GPP in China, which declines from southeast to northwest. However, the model overestimates the magnitude in most areas, particularly those south of 30°N. The annual GPP in China given by CLM4-CN (CLM4_GPP) is 13.7 PgC a^-1 on average; and that given by MTE_GPP is only 6.9 PgC a^-1. Although the CLM4_GPP and MTE_GPP show similar intra-annual cycles (R>0.9) for different dominant PFTs (Plant Functional Types) in China, the magnitude differs for most PFTs. Inter-annual variability in CLM4_GPP is higher than that in MTE_GPP for all PFTs. In addition, both the products show the same trends for tropical evergreen needleleaf trees, boreal deciduous broadleaf trees, and C3 grass; Differences are shown for the other PFTs. Precipitation is determined to be the main climate factor controlling temporal variation of the GPP in China during the experiment period. By modeling GPP using two different land cover datasets, we determine that different land cover datasets can cause obvious changes in the GPP for most regions in China. Thus, accuracy in the land cover dataset is important for the GPP simulation.