## 3 Results  
### Sensitivity Analysis Results  
In the sensitivity analysis experiment, the NEE, GPP, and leaf area index (LAI) were used as the model outputs, and the eFAST method was performed on the 19 selected sensitivity parameters (Table 2). Since the parameters with stronger sensitivity contributed most of the influence, we only selected several parameters with the highest sensitivity index for comparison.  
This section includes two numerical experiments: Experiment 1 compared the changes in the sensitivity index from 2010 to 2020. The parameters with the highest sensitivity to NEE and GPP were the same (Figure 1a,b)--namely, the extinction coefficient (k), canopy average specific leaf area (SLA), leaf carbon-to-nitrogen ratio (C: N\({}_{\text{leaf}}\)), fraction of leaf nitrogen in RuBisCO (FLNR), and shaded-to-sunlit specific leaf area ratio (\(\text{SLA}_{\text{shd: sun}}\)). The main difference between the GPP and NEE sensitivity parameters was that GPP was more sensitive to SLA than to k. NEE was much more sensitive to SLA and k than to other parameters (Figure 1c). The other two sensitive parameters of carbon flux were the new fine root carbon to new leaf carbon allocation (FRC: LC) and the new stem carbon to new leaf carbon allocation (SC: LC), which happened to be the parameters with the strongest influence on the LAI. None of the parameters showed large differences in long-term variation, and the differences between the first-order sensitivity index and the total sensitivity index were small.  
On this basis, the sensitivity analysis of Experiment 2 compared the changes in different value ranges. The experiment was based on the ranges of \(\pm\)10% and \(\pm\)20% perturbation of the fixed parameter values in 2010. The results showed that the parameter sensitivity index of the modified parameter value range was quite different from that of the default parameter value range. It was mainly manifested in that the most sensitive parameter for the model output was C: N\({}_{\text{leaf}}\), the sensitivity index of SLA dropped significantly (Figure 2), and there were also differences in the top-ranked parameters. The difference between the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Abbreviation** & **Range** & **Reference** \\ \hline Annual leaf and fine root turnover fraction & LFRT & [0.5, 0.828] & [30] \\ Annual live wood turnover fraction & LWT & [0.56, 0.9] & [18] \\ New fine root C: new leaf C & FRC: LC & [0.545, 1.59] & [18] \\ New stem C: new leaf C & SC: LC & [0.84, 1.56] & [18] \\ New live wood C: new total wood C & LWC: TWC & [0.096, 0.279] & [18] \\ New root C: new stem C & CRC: SC & [0.077, 0.563] & [18] \\ C: N of leaves & C: N\({}_{\text{leaf}}\) & [8.96, 13.44] & Measured \\ C: N of leaf litter & C: N\({}_{\text{lit}}\) & [32.88, 49.32] & [11] \\ C: N of fine roots & C: N\({}_{\text{fr}}\) & [37.92, 56.88] & [11] \\ C: N of dead wood & C: N\({}_{\text{dw}}\) & [240, 360] & [11] \\ Canopy water interception coefficient & W\({}_{\text{int}}\) & [0.0328, 0.0492] & [31] \\ Canopy light extinction coefficient & k & [0.56, 0.8] & [11] \\ All-sided-projected leaf area ratio & \(\text{LAI}_{\text{all:proj}}\) & [1.71, 2.29] & [18] \\ Canopy average specific leaf area & SLA & [13, 26.4] & [18] \\ Ratio of shaded SLA: sunlit SLA & \(\text{SLA}_{\text{shd: sun}}\) & [1.6, 2.2] & [18] \\ Fraction of leaf N in RuBisCO & FLNR & [0.048, 0.072] & [11] \\ Maximum stomatal conductance & \(\text{8}_{\text{smax}}\) & [0.004, 0.006] & [32] \\ Leaf water potential: complete & \(\text{LWP}_{t}\) & [\(-3.9\), \(-1.5\)] & [18] \\ Vapor pressure deficit: complete & \(\text{VPD}_{t}\) & [2300, 4700] & [18] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definitions, abbreviations, ranges, and sources of the 19 parameters of the Biome-BGC model used in the sensitivity analysis.  
parameter sensitivity indices in the range of \(\pm 10\%\) and \(\pm 20\%\) perturbation was small; these parameters were ranked the same for different outputs, such as GPP, NEE, and LAI.