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

王媛媛; 谢正辉; 贾炳浩; 于燕

doi:10.3878/j.issn.1006-9585.2014.13208

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

doi: 10.3878/j.issn.1006-9585.2014.13208

1.
中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室, 北京100029;中国科学院大学, 北京100049
2.
中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室, 北京100029

基金项目: 国家自然科学基金项目91125016、41305066,中国科学院战略性先导科技专项XDA05110102

计量
- 文章访问数: 3458
- HTML全文浏览量: 35
- PDF下载量: 3291
- 被引次数: 0
出版历程
- 收稿日期: 2013-12-02

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

1.
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric and Physics, Chinese Academy of Sciences, Beijing 100029;University of Chinese Academy of Sciences, Beijing 100049
2.
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric and Physics, Chinese Academy of Sciences, Beijing 100029

摘要

摘要: 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 Carbon- Nitrogen 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 PgC a^-1,而MTE_GPP仅为6.9 PgC 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RMSD < 100 gC 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.
- 陆面过程模式 /
- CLM4模式 /
- 植被总初级生产力 /
- MTE_GPP数据
Abstract: 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.
- Land surface model /
- CLM4 model /
- Gross primary productivity /
- MTE_GPP data

HTML全文

参考文献(42)

[1]	Bonan G B, Lawrence P J, Oleson K W, et al. 2011. Improving canopy processes in the community land model version 4 (CLM4) using global flux fields empirically inferred from FLUXNET data [J]. J. Geophys. Res., 116, G02014, doi: 10.1029/2010JG001593.
[2]	Cao M K, Woodward F I. 1998a. Dynamic responses of terrestrial ecosystem carbon cycling to global climate change [J]. Nature, 393: 249-252.
[3]	Cao M K, Woodward F I. 1998b. Net primary and ecosystem production and carbon stocks of terrestrial ecosystems and their responses to climate change [J]. Global Change Biology, 4: 185-198.
[4]	Chen H S, Dickinson R E, Dai Y J, et al. 2011. Sensitivity of simulated terrestrial carbon assimilation and canopy transpiration to different stomatal conductance and carbon assimilation schemes [J]. Climate Dyn., 36: 1037-1054.
[5]	Goetz S J, Prince S D, Goward S N, et al. 1999. Satellite remote sensing of primary production: An improved production efficiency modeling approach [J]. Ecological Modelling, 122: 239-255.
[6]	何勇, 姜允迪, 丹利, 等. 2006. 中国气候、陆地生态系统碳循环研究[M]. 北京: 气象出版社, 66-67. He Yong, Jiang Yundi, Dan Li, et al. 2006. Research of Climate and Terrestrial Ecosystem Carbon Cycle of China (in Chinese) [M]. Beijing: China Meteorological Press, 66-67.
[7]	黄珏, 陈海山, 俞淼. 2013. 1981～2008年中国陆地植被NPP对气候变化响应的敏感性试验 [J]. 大气科学学报, 36 (3): 316-322. Huang Jue, Chen Haishan, Yu Miao. 2013. Sensitivity experiments on response of terrestrial net primary productivity in China to climate change during 1981-2008 [J]. Transactions of Atmospheric Sciences (in Chinese), 36 (3): 316-322.
[8]	Ji J J. 1995. A climate-vegetation interaction model: Simulating physical and biological processes at the surface [J]. Journal of Biogeography, 22: 445-451.
[9]	Ji J J, Yu L. 1999. A simulation study of coupled feedback mechanism between physical and biogeochemical processes at the surface [J]. Chinese Journal of Atmospheric Sciences, 23 (4): 439-448.
[10]	Jung M, Vetter M, Herold M, et al. 2007. Uncertainties of modeling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models [J]. Global Biogeochemical Cycles, 21 (4), GB4021, doi: 10.1029/2006GB002915.
[11]	Jung M, Reichstein M, Bondeau A. 2009. Towards global empirical upscaling of FLUXNET eddy covariance observations: Validation of a model tree ensemble approach using a biosphere model [J]. Biogeosciences, 6: 2001-2013.
[12]	Kluzek E. 2011. CCSM research tools: CLM4.0 user's guide documentation. Available online at http://www.cesm.ucar.edu/models/cesm1.0/clm/ models/lnd/clm/doc/UsersGuide/book1.html.
[13]	Lawrence P J, Chase T N. 2007. Representing a new MODIS consistent land surface in the community land model (CLM 3.0) [J]. J. Geophys. Res., 112: G01023, doi: 10.1029/2006JG000168.
[14]	李登秋, 周艳莲, 居为民, 等. 2014. 太阳辐射变化对亚热带人工常绿针叶林总初级生产力影响的模拟分析 [J]. 植物生态学报, 38 (3): 219-230. Li Dengqiu, Zhou Yanlian, Ju Weimin, et al. 2014. Modelling the effects of changes in solar radiation on gross primary production in subtropical evergreen needle-leaf plantations [J]. Chinese Journal of Plant Ecology (in Chinese), 38 (3): 219-230.
[15]	梁妙玲, 谢正辉. 2006. 我国气候对植被分布和净初级生产力影响的数值模拟 [J]. 气候与环境研究, 11 (5): 582-592. Liang Miaoling, Xie Zhenghui. 2006. Simulations of climate effects on vegetation distribution and net primary production in China [J]. Climatic and Environment al Research (in Chinese), 11 (5): 582-592.
[16]	Lieth H. 1975. Modeling the primary productivity of the world [C]//Primary Productivity of the Biosphere. Berlin, Heidelberg: Springer, 237-263.
[17]	Liu Y B, Ju W M, He H L, et al. 2013. Changes of net primary productivity in China during recent 11 years detected using an ecological model driven by MODIS data [J]. Frontiers of Earth Science, 7 (1): 112-127.
[18]	Lu J H, Ji J J. 2002. A simulation study of atmosphere-vegetation interactions over the Tibetan Plateau. Part I: Physical fluxes and parameters [J]. Chinese Journal of Atmospheric Sciences, 26 (1): 111-126.
[19]	Lu J H, Ji J J. 2006. A simulation and mechanism analysis of long-term variations at land surface over arid/semi-arid area in North China [J]. J. Geophys. Res., 111 (D9): D09306.
[20]	Mao J F, Thornton P E, Shi X Y, et al. 2012. Remote sensing evaluation of CLM4 GPP for the period 2000-09 [J]. J. Climate, 25 (15): 5327-5342.
[21]	McGuire A D, Melillo J M, Kicklighter D W, et al. 1995. Equilibrium responses of soil carbon to climate change: Empirical and process-based estimates [J]. Journal of Biogeography, 22 (4-5): 785-796.
[22]	McGuire A D, Melillo J M, Kicklighter D W, et al. 1997. Equilibrium responses of global net primary production and carbon storage to doubled atmospheric carbon dioxide: Sensitivity to changes in vegetation nitrogen concentration [J]. Global Biogeochemical Cycles, 11 (2): 173-189.
[23]	Mercado L M, Bellouin N, Sitch S, et al. 2009. Impact of changes in diffuse radiation on the global land carbon sink [J]. Nature, 458: 1014- 1017.
[24]	Oleson K W, Lawrence D M, Bonan G B, et al. 2010. Technical description of version 4.0 of the community land model (CLM) [R]. NCAR Tech. Note NCAR/TN-4781STR, 257 pp.
[25]	Parton W J, Scurlock J M O, Ojima D S, et al. 1993. Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide [J]. Global Biogeochemical Cycles, 7: 785-890.
[26]	Potter C S, Randerson J T, Field C B, et al. 1993. Terrestrial ecosystem production: A process model based on global satellite and surface data [J]. Global Biogeochemical Cycles, 7: 811-841.
[27]	Prince S D, Goward S N. 1995. Global primary production: A remote sensing approach [J]. Journal of Biogeography, 22: 815-835.
[28]	朴世龙, 方精云, 郭庆华. 2001. 1982～1999 年我国植被净第一性生产力及其时空变化[J]. 北京大学学报 (自然科学版), 37 (4): 563-569. Piao Shilong, Fang Jingyun, Guo Qinghua. 2001. Terrestrial net primary production and its spatio-temporal patterns in China during 1982-1999 [J]. Acta Scientiarum Naturalium Universitatis Pekinensis (in Chinese), 37 (4): 563-569.
[29]	Qian T T, Dai A G, Trenberth K E, et al. 2006. Simulation of global land surface conditions from 1948 to 2004: Part I: Forcing data and evaluations [J]. Journal of Hydrometeorology, 7: 953-975.
[30]	冉有华, 李新, 卢玲. 2009. 四种常用的全球1 km土地覆盖数据中国区域的精度评价 [J]. 冰川冻土, 31 (3): 490-500. Ran Youhua, Li Xin, Lu Ling. 2009. Accuracy evaluation of the four remote sensing based land cover products over China [J]. Journal of Glaciology and Geocryology (in Chinese), 31 (3): 490-500.
[31]	Ran Y H, Li X, Lu L, et al. 2012. Large-scale land cover mapping with the integration of multi-source information based on the dempster-shafer theory [J]. International Journal of Geographical Information Science, 26 (1): 169-191.
[32]	Running S W, Hunt E R. 1993. Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models [C]// Ehleringer J R. Scaling Physiological Processes: Leaf to Globe. San Diego, CA: Academic Press, 141-158.
[33]	Sitch S, Smith B, Prentice I C, et al. 2003. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model [J]. Global Change Biology, 9 (2): 161-185.
[34]	陶波, 李克让, 邵雪梅, 等. 2003. 中国陆地净初级生产力时空特征模拟[J]. 地理学报, 58 (3): 372-380. Tao Bo, Li Kerang, Shao Xuemei, et al. 2003. Temporal and spatial pattern of net primary production of terrestrial ecosystems in China [J]. Acta Geographica Sinica (in Chinese), 58 (3): 372-380.
[35]	Uchijima Z, Seino H. 1985. Agroclimatic evaluation of net primary productivity of natural vegetations I. Chikugo model for evaluating net primary productivity [J]. Journal of Agricultural Meteorology, 40: 343- 353.
[36]	王鹤松, 贾根锁, 冯锦明, 等. 2010. 我国北方地区植被总初级生产力的空间分布与季节变化 [J]. 大气科学, 34 (5): 882-890. Wang Hesong, Jia Gensuo, Feng Jinming, et al. 2010. Spatial distribution and seasonality of gross primary production in northern China [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 34 (5): 882-890.
[37]	Wang Y P, Law R M, Pak B. 2010. A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere [J]. Biogeosciences, 7: 2261-2282.
[38]	Yuan W P, Liu S G, Zhou G S, et al. 2007. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes [J]. Agricultural and Forest Meteorology, 143: 189-207.
[39]	Zaehle S, Sitch S, Smith B, et al. 2005. Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics [J]. Global Biogeochemical Cycles, 19 (3): doi: 10.1029/2004GB002395.
[40]	Zhao M S, Heinsch F A, Nemani R R, et al. 2005. Improvements of the MODIS terrestrial gross and net primary production global data set [J]. Remote Sensing of Environment, 95: 164-176.
[41]	朱文泉, 陈云浩, 徐丹, 等. 2005. 陆地植被净初级生产力计算模型研究进展 [J]. 生态学杂志, 24 (3): 296-300. Zhu Wenquan, Chen Yunhao, Xu Dan, et al. 2005. Advances in terrestrial net primary productivity (NPP) estimation models [J]. Chinese Journal of Ecology (in Chinese), 24 (3): 296-300.
[42]	朱文泉, 潘耀忠, 阳小琼, 等. 2007. 气候变化对中国陆地植被净初级生产力的影响分析 [J]. 科学通报, 52 (21): 2535-2541. Zhu Wenquan, Pan Yaozhong, Yang Xiaoqiong, et al. 2007. Analysis of the effects of climate change on net primary production of terrestrial vegetation in China [J]. Chinese Science Bulletin (in Chinese), 52 (21): 2535-2541.