labs_title

Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901–2005

J. Shao, 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.C. Parazoo, C. Peng, B. Poulter, C.R. Schwalm, X. Shi, R. Sun, F. Tao, H. Tian, Y. Wei, N. Zeng, Q. Zhu and W. Zhuo

Changes in global atmospheric concentrations of carbon dioxide are driven by the difference between net emissions and uptake by land and oceans. This study examines a large number of model-based estimates of net primary productivity and net biome productivity for China, to gain a deeper understanding of carbon cycling within the country. The uncertainty across estimates is found to be large, but the interannual variability is more robust. Structural model differences appear to be the strongest driver of the uncertainty, suggesting that coordinated field observations could help to differentiate among existing estimates.


Figure: Total net primary productivity (NPP) of China, as summarized from a variety of studies and models. The error bars are the standard deviation of annual NPP during the study period (i.e., interannual variability). LUE_NPP indicates models using light use efficiency to simulate NPP; LUE_GPP represents models using light use efficiency to simulate GPP and other modules to simulate autotrophic respiration (Ra).

Abstract

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.

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.C. Parazoo, C. Peng, B. Poulter, C.R. Schwalm, X. Shi, R. Sun, F. Tao, H. Tian, Y. Wei, N. Zeng, Q. Zhu, W. Zhu (2016), "Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901–2005", Journal of Geophysical Research Biogeosciences, 121 (5), 1372-1393, doi:10.1002/2015JG003062.