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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.

Received 21 September 2010; revised 11 December 2010; accepted 3 January 2011; published 5 March 2011. [1] It is well known that the slowly varying oceanic processes provide the primary source for East Asian summer monsoon (EASM) predictability. However, the memory inherent in the land surface state is less well understood or applied toward the EASM prediction. Here we investigate the role of antecedent vegetation conditions over East Asia for the EASM variation and prediction using March, April, May, and spring mean satellite‐sensed Normalized Difference Vegetation Index (NDVI) for the period of 1982–2006. Results show that May vegetation greenness on the southeastern Tibetan Plateau (TP) is most closely linked to the EASM, accounting for about half of the total EASM variance. May vegetation greenness on the southeastern TP has significant and positive correlations with summer rainfall over the southeastern TP, East Asian summer subtropical frontal region, and many areas of northern China. We further discuss the possible physical mechanism explaining our findings. It is proposed that increased TP vegetation greenness enhances surface thermal effects, which subsequently warm atmospheric temperature, as well as strengthen ascending motion, convergence at the lower layers and divergence at the higher layers, and summer monsoon circulation. Finally, a linear regression model is developed to predict the EASM strength by combination of El Nino–Southern Oscillation (ENSO) and the vegetation greenness. Hindcast for the period 1982–2006 shows that the use of the southeastern TP vegetation information can highly improve the EASM prediction skill compared to that using ENSO alone.