Uncertainty analysis of terrestrial net primary productivity and net biome...

Shao, J., X. Zhou, Y. Luo, G. Zhang, W. Yan, J. Li, B. Li, L. Dan, J. B. Fisher, Z. Gao, Y. He, D. Huntzinger, A. K. Jain, J. Mao, J. Meng, A. M. Michalak, N. Parazoo, C. Peng, B. Poulter, C. R. Schwalm, X. Shi, R. Sun, F. Tao, H. Tian, Y. Wei, N. Zeng, Q. Zhu, and W. Zhu (2016), Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901-2005, J. Geophys. Res., 121, 1372-1393, doi:10.1002/2015JG003062.

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 3.35 ± 1.25 and 0.14 ± 0.094 Pg C yr−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 0.012 Pg C yr−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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Carbon Cycle & Ecosystems Program (CCEP)