期刊
JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES
卷 121, 期 5, 页码 1372-1393出版社
AMER GEOPHYSICAL UNION
DOI: 10.1002/2015JG003062
关键词
China; interannual variability; model structure; net primary productivity; net biome productivity; uncertainty
资金
- National Natural Science Foundation of China [31370489]
- Program for Professor of Special Appointment (Eastern Scholar) at the Shanghai Institutions of Higher Learning
- national Thousand Young Talents Program in China
- NASA ROSES [NNX10AG01A, NNH10AN681]
- U. S. Department of Energy (DOE), Office of Science, Biological, and Environmental Research
- DOE [DE-AC05-00OR22725]
- U. S. DOE [DE-AC05-76RLO1830]
- NASA Interdisciplinary Science Program
- NASA Land Cover/Land Use Change Program (LCLUC)
- NASA Terrestrial Ecology Program
- NASA Atmospheric Composition Modeling and Analysis Program
- NSF Dynamics of Coupled Natural-Human System Program, Decadal and Regional Climate Prediction using Earth System Models
- DOE National Institute for Climate Change Research
- USDA AFRI Program
- EPA STAR Program
- Office of Science of the U. S. Department of Energy [DE-AC0205CH11231]
- National Science Foundation [OCI-0725070, ACI-1238993]
- state of Illinois
- National Basic Research Program of China [2013CB956602]
Despite the importance of net primary productivity (NPP) and net biome productivity (NBP), estimates of NPP and NBP for China are highly uncertain. To investigate the main sources of uncertainty, we synthesized model estimates of NPP and NBP for China from published literature and the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The literature-based results showed that total NPP and NBP in China were 3.351.25 and 0.140.094PgCyr(-1), respectively. Classification and regression tree analysis based on literature data showed that model type was the primary source of the uncertainty, explaining 36% and 64% of the variance in NPP and NBP, respectively. Spatiotemporal scales, land cover conditions, inclusion of the N cycle, and effects of N addition also contributed to the overall uncertainty. Results based on the MsTMIP data suggested that model structures were overwhelmingly important (>90%) for the overall uncertainty compared to simulations with different combinations of time-varying global change factors. The interannual pattern of NPP was similar among diverse studies and increased by 0.012PgCyr(-1) during 1981-2000. In addition, high uncertainty in China's NPP occurred in areas with high productivity, whereas NBP showed the opposite pattern. Our results suggest that to significantly reduce uncertainty in estimated NPP and NBP, model structures should be substantially tested on the basis of empirical results. To this end, coordinated distributed experiments with multiple global change factors might be a practical approach that can validate specific structures of different models.
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