## 4 Discussion
### Suggestions for Using the Original Biome-BGC Model in China and Future Research Directions  
As described in Section 3.3 of this study, the applicability of the Biome-BGC model has spatial variability in China, where the accuracy of simulations decreases from north to south for woody sites and increases from north to south for nonwoody sites. In other words, the model has high simulation accuracy in high-latitude forests (such as the CBS site) or low-latitude grasslands (such as the DX site) in China. Both the GPP and NEP daily trends are well matched by the model at these sites, even without parameter optimization. Therefore, when using the original Biome-BGC model to simulate forest productivity in northern China or grassland productivity in southern China, we propose that rigorous parameter optimization may be unnecessary to obtain good simulation results. When simulating forest productivity in the south or grassland productivity in the north, sensitivity analysis and parameter optimization probably have little improvement in simulation accuracy because of the limitations of the model structure. It may be necessary to assimilate data or target some modules of the model to improve the accuracy of the simulation. The implication for practical management is that forest (or grassland) management managers can use the original Biome-BGC model when using productivity pairs to assess the growth status and management level in northern forests or southern grasslands in China, while directly using the model would lead to larger errors for estimating the forest productivity in northern China or grassland productivity in southern China. The simulation accuracy of the Biome-BGC model is greatly improved after parameter optimization but is still not excellent in southern forests and northern grasslands. In other words, there is a large promotion space for the simulation accuracy of the model in southern forests and northern grasslands. From a side view, the reason for the lower simulation accuracy of the Biome-BGC model in southern forests and boreal grasslands probably originates from the incompatibility and absence of structural and mechanistic processesin the model. For example, the ecophysiological parameters entered in the Biome-BGC model are in static form, while the previous study revealed that the optimal values of the parameters are variable in time series [11]. Both specific leaf area (SLA) and leaf carbon to nitrogen ratio (C:Nleaf) are important parameters of plant photosynthesis links, whereas they are input to the model in the form of fixed values. As a result, the parameters do not match in temporal resolution and mechanism, thus making the leaf area index (LAI) simulated by the model at the site differ from the actual one. In this regard, a researcher has already input the LAI obtained from remote sensing data into the model using data assimilation and obtained better simulation results [16]. Accordingly, we can modify the parameter with a large variation in the optimal value on the time series, changing the individual input values to a series of input values that vary with month, season, or phenological period. With the improved structure of the model, researchers eliminate the need for data assimilation, which greatly improves the efficiency and accuracy of the simulation and lowers the threshold of use. The future direction is, therefore, to improve the mechanistic process of the Biome-BGC model in more depth and its simulation accuracy in southern forests and northern grasslands in China. Additionally, this study has certain limitations. Due to the limited availability of publicly accessible flux observation data, the number of study sites in this research is relatively small compared to the national flux stations in China. As more national-level flux observation data become available in the future, we will be able to include more study sites, thereby making the spatial pattern of the Biome-BGC model's simulation accuracy more representative and convincing.