## 4 Discussion  
Although current terrestrial biosphere models generally consider the differences in SLA among plant functional types (PFTs), there still remain mismatches in SLA values between model and observations. Our results suggested a remarkable mismatch of SLA between the CLM4.5 model and observations collected in China, especially for tropical broadleaf deciduous tree overestimated by 0.013 m\({}^{2}\)/g (Figure 2). It is necessary for us to revise the SLA values when we use the CLM4.5 model to simulate terrestrial carbon cycle dynamics at the regional scale. Our results also showed that the observed SLA values for China plant were higher than the global average for all PFTs (Figure 6), which is supported by a recent work showing that SLA in Asia was higher than that in Europe and North America by about 0.004 m\({}^{2}\)/g and 0.001 m\({}^{2}\)/g for a given leaf dry matter content of 0.25 g/g [44]. The higher values of SLA in Asia and China might be caused by intense resource competition stress, where the communities are dominated by more acquisitive species with high SLA [45]. Therefore, simply referring to other models or global traits data to set SLA values in a regional gross primary productivity simulation study is unreliable. Furthermore, since the observed SLA data for some plant functional types (e.g., temperate BDT, temperate BET, and C3 grass) in China and around the world do not follow normal distributions [9; 46], the default model values and mean observed values remain uncertain. We recommend using the probability density distribution of observed trait data within a given PFT rather than setting trait parameter values based on the mean values. The SLA among species within a given plant functional type has substantial variability in China and other regions due to differences in the genotype and environmental changes [47; 48], especially climate and soil factors, e.g., temperature, light, precipitation, and soil nutrient [49; 50]. As trait variation was revealed by previous studies as inducing large effects on the simulations of ecosystem productivity (e.g., GPP, NPP, and NEP) and biomass (e.g., vegetation biomass and litter pool) [21; 23; 51], our results also suggested that SLA observation variation could bring great uncertainty to the GPP simulation, especially for temperate NET and temperate BET. As shown in Table 3, the SLA value varying by 43% at the mean observation level could induce a change in the simulated annual GPP by 161 g C m\({}^{-2}\) yr\({}^{-1}\) (7.0%) for temperate NET. It is necessary to describe trait variation within each plant functional type to reduce model uncertainties. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & \multirow{2}{*}{**PFT**} & **Relative** & **CV of SLA** & \multirow{2}{*}{**GPP (\%)**} & \multirow{2}{*}{**Ac (\%)**} & \multirow{2}{*}{**LAI (\%)**} \\  & & **Change** & **(\%)** & & \\ \hline \multirow{3}{*}{QYZ} & temperate & R1 & 42.9 & 7.0 & 43.9 & 14.1 \\  & NET & R2 & 60.0 & 18.5 & 31.0 & 61.7 \\ \multirow{3}{*}{CBS} & temperate & R1 & 50.0 & 6.3 & 30.9 & 16.9 \\  & BDT & R2 & 29.4 & 8.8 & 24.5 & 43.8 \\ \multirow{3}{*}{DHS} & temperate & R1 & 42.9 & 8.0 & 37.3 & 14.7 \\  & BET & R2 & 42.8 & 14.1 & 24.6 & 63.7 \\ \multirow{3}{*}{HBG} & C3 grass & R1 & 57.1 & 3.3 & 57.9 & 48.4 \\  & R2 & 34.3 & 1.6 & 3.3 & 46.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The relative changes in GPP, Ac, and LAI modeled with different SLA values. R1 is the relative changes in modeled GPP, Ac, and LAI caused by the observed SLA variation based on model experiments S2, S3, and S4 (Equation (7)). R2 is the relative changes in modeled GPP, Ac, and LAI caused by the SLA variation across models based on model experiments S1, S5, and S6 (Equation (8)). Abbreviations: PFT, plant functional type; NET, needleleaf evergreen tree; BDT, broadleaf deciduous tree; BET, broadleaf evergreen tree.