Study on the Global Carbon Assimilation System Based on Multisource Remote Sensing Data
First author:Ju Weimin(1963-), male, Haian County, Jiangsu Province, Professor. Research areas include global change.E-mail:juweimin@nju.edu.cn
Received date: 2016-09-21
Revised date: 2016-11-01
Online published: 2016-11-20
Supported by
Project supported by the National Key Research and Development Program of China“Study on the global carbon assimilation system based on multisource remote sensing data under the national key research and development programs for global change and adaptation”(No.2016YFA0600200)
Copyright
The Paris agreement signed in April, 2016 aims to balance global anthropogenic carbon emissions and terrestrial carbon sinks by the middle of the 21st century. To fulfill this goal, it is necessary to calculate carbon fluxes of different regions reliably. The global carbon assimilation system is an effective technique for achieving this goal. The Ministry of Science and Technology of China supports the project entitled as study on the global carbon assimilation system based on multisource remote sensing data through the national key research and development programs for global change and adaptation during the thirteen-five period. This project will develop synergic inversion techniques for retrieving key parameters of biological and atmospheric cycles and for assimilating multisource remote sensing and ground based data. Then, the high resolution global carbon assimilation system coupled with an ecological model will be constructed. This system is able to assimilate jointly multisource observation data and to optimize key model parameters, photosynthesis and respiration carbon fluxes of global terrestrial ecosystems, and anthropogenic carbon emission fluxes of key regions. This system will be used to study quantitatively the spatial and temporal patterns of carbon fluxes of global terrestrial ecosystems and anthropogenic carbon emission fluxes of key regions and to identify the mechanisms driving the global terrestrial carbon sinks and sources. The outputs of this study will be helpful for the fulfillment of the key research and development programs for global change and adaptation and provide valuable data and technical support for the decision-making in China.
Weimin Ju , Hongliang Fang , Xiangjun Tian , Fei Jiang , Wenfeng Zhan , Yang Liu , Zhengxing Wang , Jianfeng He , Shaoqiang Wang , Shushi Peng , Yongguang Zhang , Yanlian Zhou , Binghao Jia , Dongxu Yang , Yu Fu , Rong Li , Jingxian Liu , Haikun Wang , Guicai Li , Zhuoqi Chen . Study on the Global Carbon Assimilation System Based on Multisource Remote Sensing Data[J]. Advances in Earth Science, 2016 , 31(11) : 1105 -1110 . DOI: 10.11867/j.issn.1001-8166.2016.11.1105
[1] | Ciais P, Sabine C, Bala G, et al.Carbon and other biogeochemical cycles[C]∥Stocker T F, Qin D, Plattner G K, et al, eds.Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York:Cambridge University Press,2013. |
[2] | Peters W, Jacobson A R, Sweeney C, et al.An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker[J].PNAS, 2007,104(48):18 925-18 930. |
[3] | Peters W, Krol M C, van der Werf G R, et al. Seven years of recent European net terrestrial carbon dioxide exchange constrained by atmospheric observations[J]. Global Change Biology,2010,16(4):1 317-1 337. |
[4] | Zhang S, Zheng X, Chen J M, et al.A global carbon assimilation system using a modified ensemble Kalman filter[J]. Geoscientific Model Development, 2015, 8: 805-816, doi:10.5194/gmd-8-805-2015. |
[5] | Yang D, Liu Y, Cai Z,et al.An advanced carbon dioxide retrieval algorithm for satellite measurements and its application to GOSAT observations[J]. Chinese Science Bulletin,2015, 60(23):2 063-2 066. |
[6] | Chevallier F, Palmer P I, Feng L, et al.Toward robust and consistent regional CO2 flux estimates from in situ and spaceborne measurements of atmospheric CO2[J].Geophysical Research Letters, 2014,41:1 065-1 070, doi: 10.1002/2013GL058772. |
[7] | Houweling S, Baker D, Basu S,et al.An intercomparison of inverse models for estimating sources and sinks of CO2 using GOSAT measurements[J]. Journal of Geophysical Research—Atmopshere,2015,120: 5 253-5 266, doi: 10.1002/2014JD022962. |
[8] | Kemp S, Scholze M, Ziehn T,et al.Limiting the parameter space in the Carbon Cycle Data Assimilation System (CCDAS)[J]. Geoscientific Model Development, 2014, 7: 1 609-1 619, doi:10.5194/gmd-7-1609-2014. |
[9] | Scholze M, Kaminski T, Knorr W, et al.Simultaneous assimilation of SMOS soil moisture and atmospheric CO2 in-situ observations to constrain the global terrestrial carbon cycle[J].Remote Sensing of Environment, 2016, 180: 334-345, doi: 10.1016/j.rse.2016.02.058. |
[10] | Parazoo N, Bowman K, Fisher J B,et al.Terrestrial gross primary production inferred from satellite fluorescence and vegetation models[J]. Global Change Biology,2014, 20: 3 103-3 121,doi: 10.1111/gcb.12652. |
[11] | Zhang Y G, Guanter L, Berry J A, et al.Estimation of vegetation photosynthetic capacity from space-based measurement of chlorophyll fluorescence for terrestrial biosphere models[J].Global Change Biology,2014, 20: 3 727-3 742,doi:10.1111/gcb.12664. |
[12] | Koffi E N, Rayner P J, Norton A J, et al.Investigating the usefulness of satellite-derived fluorescence data in inferring gross primary productivity within the carbon cycle data assimilation system[J]. Biogeosciences,2015, 12: 4 067-4 084, doi: 10.5194/bg-12-4067-2015. |
[13] | Zhang S, Yi X, Zheng X, et al.Global carbon assimilation system using a local ensemble Kalman filter with multiple ecosystem models[J]. Journal of Geophysical Research—Biogeosciences, 2014,119, doi:10.1002/2014JG002792. |
[14] | Chen Jingming, Ju Weimin, Liu Ronggao, et al.Remote Sensing and Optimization Calculation Methods of Global Terrestrial Carbon Sinks[M]. Beijing: Science Press, 2015:371. |
[14] | [陈镜明,居为民,刘荣高,等. 全球陆地碳汇的遥感和优化计算方法[M]. 北京:科学出版社,2015:371.] |
[15] | Tian X, Feng X.A non-linear least squares enhanced POD-4DVar algorithm for data assimilation[J].Tellus A, 2015, 67,doi: 10.3402/tellusa.v67.25340. |
[16] | Tian X, Xie Z, Liu Y, et al.A joint data assimilation system (Tan-Tracker) to simultaneously estimate surface CO2 fluxes and 3-D atmospheric CO2 concentrations from observations[J].Atmospheric Chemistry and Physics, 2014, 14:13 281-13 293,doi:10.5194/acp-14-13281-2014. |
[17] | Zhang H F, Chen B Z, van der Laan-Luijkx I T, et al. Net terrestrial CO2 exchange over China during 2001-2010 estimated with an ensemble data assimilation system for atmospheric CO2[J].Journal of Geophysical Research—Atmosphere, 2014, 119(6): 3 500-3 515. |
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