## 3 Results
### Spatial Pattern of Biome-BGC Model Applicability in China  
In this study, we combined regression analysis with three model accuracy evaluation indices (the R\({}^{2}\), RMSE, and SAE; see Section 2.6) to quantitatively assess the simulation accuracy of the parameter-optimized Biome-BGC model across 10 typical ecosystems in China. We then explored the spatial distribution pattern of the model's applicability across the country. The spatial distribution of the Biome-BGC model's simulation accuracy for GPP (gross primary productivity) and NEP (net ecosystem productivity) at the 10 sites is shown in Figures 5 and 6. As observed from Figures 5 and 6, the R\({}^{2}\) (coefficient of determination) for GPP at all sites ranged from 0.51 to 0.90, while for NEP, it ranged from 0.19 to 0.63. This indicates that the Biome-BGC model exhibited a better fit for GPP than for NEP. The simulation accuracy of GPP based on the Biome-BGC model for 10 typical ecosystems in China shows that the R\({}^{2}\) values for the five forest sites (XSBN, DHS, QYZ, HBGC, and CBS) increased sequentially, with values of 0.51, 0.54, 0.77, 0.81, and 0.90, respectively. This indicates that for forest ecosystems in China, the goodness of fit of the Biome-BGC model gradually improves with increasing latitude (Figure 5). Meanwhile, the MAE values for XSBN, DHS, QYZ, and HBSL were relatively close to each other, while the MAE value for CBS was larger than that of the other four forest sites. In other words, the RMSE and MAE of the five forest sites did not exhibit a specific spatial pattern with latitude. However, when considering the R\({}^{2}\), RMSE, and MAE results together, the accuracy of GPP simulation for Chinese forest ecosystems using the Biome-BGC model gradually increases with increasing latitude. For grassland ecosystems in China, the GOP (goodness of fit) at the five grassland sample sites (DX, HBGL, DL, NMG, and CL) decreased sequentially with increasing latitude, with the R\({}^{2}\) values of 0.88, 0.79, 0.72, 0.60, and 0.60, respectively. This means that the variability pattern of the Biome-BGC model's goodness of fit (GPP) for grassland ecosystems along the latitudinal gradient was opposite to that observed for forest ecosystems. Meanwhile, the MAEs for the five grassland sites (DX, HBGL, DL, NMG, and CL) were 0.29, 0.83, 0.62, 0.87, and 1.31, indicating that as latitude increases, the overall trend in model simulation error for the five grassland sites shows an upward trend. Combining the R\({}^{2}\) and MAE results for the five grassland sites, it is evident that the GPP simulation accuracy of the Biome-BGC model for grassland ecosystems in China gradually decreases with increasing latitude. Similarly, the NEP simulation accuracy for 10 typical Chinese ecosystems based on the Biome-BGC model shows that the R\({}^{2}\) values for the five forest sites (XSBN, QYZ, DHS, HBGC, and CBS) increased sequentially, with values of 0.31, 0.33, 0.35, 0.43, and 0.52, respectively. This indicates that for forest ecosystems in China, the goodness of fit of the Biome-BGC model gradually improves with increasing latitude (Figure 6). Meanwhile, the  
Figure 5: Spatial distribution of accuracy evaluation indicators of GPP. Figure 6: Spatial distribution of accuracy evaluation indicators of NEP. MAE values for XSBN, DHS, QYZ, and HBSL were relatively close to each other, while the MAE value for CBS was larger than that of the other four forest sites. In other words, the RMSE and MAE of the five forest sites did not exhibit a specific spatial pattern with latitude. However, when considering the R\({}^{2}\), RMSE, and MAE results together, the accuracy of NEP simulation for Chinese forest ecosystems using the Biome-BGC model gradually increases with increasing latitude. For grassland ecosystems in China, the GOP (goodness of fit, GOP) at the five grassland sample sites (DX, HBGL, DL, NMG, and CL) decreased sequentially with increasing latitude, with the R\({}^{2}\) values of 0.63, 0.43, 0.19, 0.21, and 0.21, respectively.