## 4 Discussion
### The Latitude Pattern of Model Applicability and Its Reasons  
Most of the uncertainties in carbon flux simulations are derived from the model structure and input parameters, which lead to biased modeling results. In this study, the optimal values of parameters were calibrated through a uniform model parameterization process, and the spatial variability in the accuracy of the Biome-BGC model simulation productivity was analyzed. We found that the simulation ability of the Biome-BGC model probably has greater differences in the north and south of China. In forest ecosystems, there is a noticeable decline in model simulation accuracy from northern to southern China. This pattern is also observed in the study by [PERSON] [16]. The R\({}^{2}\) value of 0.57 for the original simulated NEP at the HF and CBS sites with higher latitudes is more accurate (with an R\({}^{2}\) value of 0.18) compared to the simulations at the QYZ and DHS sites in southern China. Among the five forest nodes in this paper, the model simulates vegetation productivity with the lowest accuracy in XSBN, which fails to fit well even after parameter optimization. (The R\({}^{2}\) value of GPP is 0.51, and the R\({}^{2}\) value of NEP is 0.31.) From the perspective of the distribution characteristics of forests, this is likely because of the high diversity of species and the high complexity of forest community composition in the south compared to the north. However, the Biome-BGC model lacks generalized default parameters for mixed forests and can only simulate the productivity of idealized pure forests (single PFTs). Due to the unsuitability of the ecophysiological parameters for southern forest vegetation, the model's simulation accuracy is low in the southern regions. The Biome-BGC model exhibits a consistent spatial pattern in its simulation accuracy across typical forest ecosystems in China along a latitudinal gradient. However, latitude inherently influences gradients in temperature and precipitation. In China, annual precipitation and mean annual temperature increase from north to south as latitude decreases. For forest ecosystems, the forests in lower latitude regions are typically located in subtropical or tropical climate zones, characterized by higher average temperatures and greater annual precipitation. Through daily analysis of meteorological data used by the Biome-BGC model for productivity simulations at the XSBN and DHS sites, we found that during periods of extended drought within the peak growing season, the model significantly overestimated evapotranspiration and soil runoff. This led to water stress within the forest ecosystems, negatively impacting the photosynthesis module and consequently reducing the accuracy of GPP and NEP simulations. This phenomenon has also been reported in other studies [60; 61]. In grassland ecosystems, the accuracy of model simulations exhibits a clear increasing trend from northern to southern China. This result may be attributed to the design of most escophysiological processes in the Biome-BGC model, which is referenced from coniferous forest ecosystems [33]. Consequently, the model fails to accurately depict the key ecophysiological processes of grassland ecosystems, thereby limiting its ability to fit the maximum productivity of grasslands. Through the analysis of multiple processes (carbon cycle, nitrogen cycle, and water cycle) within the model, we found that the simulation intensity of the water cycle processes, such as evapotranspiration and outflow, in the Biome-BGC model is greater than the actual situation. This creates a false impression of water stress. As a result, the simulation intensity of photosynthesis in grassland ecosystems is lower than the actual condition, ultimately leading to an error where the simulated maximum productivity at a daily scale during the growing season is lower than reality.