## 3 Results
### Calibration of Optimal Values for Sensitive Ecophysiological Parameters  
In this study, we utilized a literature review and the PEST method to calibrate the optimal values for sensitivity-related ecophysiological parameters of the Biome-BGC model across 10 representative forest and grassland ecosystems. We gathered and reviewed a substantial amount of research on these parameters, prioritizing the observed values reported in the literature for calibration. For parameters not covered in the literature, we applied the PEST method to determine the optimal values (results are shown in Supplementary Materials S4). During the parameter optimization process, carbon flux observations from 2007 at these 10 typical vegetation sites in China were used as the benchmark for model validation. Following parameter optimization, carbon flux observations from 2008 were employed to assess the simulation accuracy of the model. The results, illustrating the extent of improvement in the simulation accuracy of the Biome-BGC model at these 10 sites post-optimization, are shown in Figure 4.  
Figure 4: The accuracy evaluation indexes of the original Biome-BGC model compared to the accuracy evaluation indexes after parameter optimization. The left of (**a–f**) shows the change in accuracy index values before and after parameter optimization; the right of (**a–f**) shows the magnitude of the change in accuracy index before and after parameter optimization.  
Overall, the regression analysis of the simulated and observed carbon fluxes from the Biome-BGC model shows that the R\({}^{2}\) (coefficient of determination) for both GPP (gross primary productivity) and NEP (net ecosystem productivity) improved following parameter optimization, while the RMSE and MAE of GPP and NEP decreased at most observation stations. This indicates that parameter optimization enhanced the simulation accuracy of the Biome-BGC model for both GPP and NEP at these 10 stations. Specifically, for the GPP simulation, the site with the most significant R\({}^{2}\) improvement after parameter optimization was HBSL, where the R\({}^{2}\) increased from 0.38 to 0.81, reflecting an improvement of 53.09%. The DHS site showed the largest reduction in the RMSE and MAE, with decreases of 65.42% and 68.50%, respectively. For the NEP simulation, HBSL again exhibited the greatest R\({}^{2}\) improvement, with a remarkable increase of 99.84%. The XSBN site had the largest reductions in the RMSE and MAE, with decreases of 55.32% and 57.35%, respectively.  
The increase in the R\({}^{2}\) indicates that, after parameter optimization, the Biome-BGC model can more accurately simulate the temporal variations in productivity (including GPP and NEP). The decrease in the RMSE and MAE suggests that parameter optimization brings the Biome-BGC model's simulation results closer to the observed values. For instance, as shown in Figure 4, the regression analysis of GPP after parameter optimization demonstrates a significant improvement in the accuracy of the model's time-series simulations at the HBSL, XSBN, and DHS sites. Additionally, the numerical error in GPP simulations at the QYZ, DHS, and XSBN sites is greatly reduced. In summary, parameter optimization has enhanced the simulation accuracy of the Biome-BGC model at the vast majority of the 10 sites examined in this study.