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Quantifying uncertainties from additional nitrogen data and processes in a terrestrial ecosystem model with Bayesian probabilistic inversion
Du, Zhenggang1; Zhou, Xuhui1,2; Shao, Junjiong1,2; Yu, Guirui3; Wang, Huimin3; Zhai, Deping1; Xia, Jianyang1,2; Luo, Yiqi2,4,5
2017-03-01
Source PublicationJOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
ISSN1942-2466
Volume9Issue:1Pages:548-565
Corresponding AuthorZhou, Xuhui(xhzhou@des.ecnu.edu.cn)
AbstractSubstantial efforts have recently been made toward integrating more processes to improve ecosystem model performances. However, model uncertainties caused by new processes and/or data sets remain largely unclear. In this study, we explore uncertainties resulting from additional nitrogen (N) data and processes in a terrestrial ecosystem (TECO) model framework using a data assimilation system. Three assimilation experiments were conducted with TECO-C-C (carbon (C)-only model), TECO-CN-C (TECO-CN coupled model with only C measurements as assimilating data), and TECO-CN-CN (TECO-CN model with both C and N measurements). Our results showed that additional N data had greater effects on ecosystem C storage (+68% and +55%) compared with added N processes (+32% and -45%) at the end of the experimental period (2009) and the long-term prediction (2100), respectively. The uncertainties mainly resulted from woody biomass (relative information contributions are +50.4% and +36.6%) and slow soil organic matter pool (+30.6% and -37.7%) at the end of the experimental period and the long-term prediction, respectively. During the experimental period, the additional N processes affected C dynamics mainly through process-induced disequilibrium in the initial value of C pools. However, in the long-term prediction period, the N data and processes jointly influenced the simulated C dynamics by adjusting the posterior probability density functions of key parameters. These results suggest that additional measurements of slow processes are pivotal to improving model predictions. Quantifying the uncertainty of the additional N data and processes can help us explore the terrestrial C-N coupling in ecosystem models and highlight critical observational needs for future studies.
Keyworduncertainty data assimilation carbon-nitrogen coupling model Shannon information index relative information
DOI10.1002/2016MS000687
WOS KeywordAIR CO2 ENRICHMENT ; DATA ASSIMILATION ; CARBON-CYCLE ; BIOGEOCHEMICAL MODELS ; TEMPERATE FOREST ; SOIL ; DYNAMICS ; PHOTOSYNTHESIS ; EQUIFINALITY ; RESPIRATION
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[31370489] ; Shanghai Municipal Education Commission[14ZZ053] ; Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning ; Thousand Young Talents'' Program in China
Funding OrganizationNational Natural Science Foundation of China ; Shanghai Municipal Education Commission ; Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning ; Thousand Young Talents'' Program in China
WOS Research AreaMeteorology & Atmospheric Sciences
WOS SubjectMeteorology & Atmospheric Sciences
WOS IDWOS:000399756400028
PublisherAMER GEOPHYSICAL UNION
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/64576
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhou, Xuhui
Affiliation1.East China Normal Univ, Shanghai Key Lab Urban Ecol Proc & Eco Restorat, Tiantong Natl Field Observat Stn Forest Ecosyst, ECNU UH Joint Translat Sci & Technol Res Inst,Sch, Shanghai, Peoples R China
2.East China Normal Univ, Ctr Global Change & Ecol Forecasting, Shanghai, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resource Res, Beijing, Peoples R China
4.Univ Oklahoma, Dept Microbiol & Plant Biol, Norman, OK 73019 USA
5.Tsinghua Univ, Ctr Earth Syst Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Du, Zhenggang,Zhou, Xuhui,Shao, Junjiong,et al. Quantifying uncertainties from additional nitrogen data and processes in a terrestrial ecosystem model with Bayesian probabilistic inversion[J]. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS,2017,9(1):548-565.
APA Du, Zhenggang.,Zhou, Xuhui.,Shao, Junjiong.,Yu, Guirui.,Wang, Huimin.,...&Luo, Yiqi.(2017).Quantifying uncertainties from additional nitrogen data and processes in a terrestrial ecosystem model with Bayesian probabilistic inversion.JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS,9(1),548-565.
MLA Du, Zhenggang,et al."Quantifying uncertainties from additional nitrogen data and processes in a terrestrial ecosystem model with Bayesian probabilistic inversion".JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 9.1(2017):548-565.
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