## 4 Discussion
The responses of plant SLA to environmental changes, as shown in previous studies, have also not been well quantified in terrestrial biosphere models. The SLA vertical variability under the change of light in canopy involved in the CLM4.5 model has not been considered in many other terrestrial biosphere models (Figure 2). Moreover, the variation in SLA with development stages was only simulated in some crop models, such as the WOFOST (World Food Studies) model [52], as shown in Figure 7a. These variations in SLA at different growth stages for both crops and trees (Figure 7b) should be added to terrestrial biosphere models in the future. In addition, SLA also varies with other factors, such as water stress (Figure 8a) and soil nitrogen content (Figure 8b), which need to be paid more attention in terrestrial biosphere models. Figure 6: The differences in SLA values between the observation data in China and the global average in the TRY database. The error bar shows the standard deviation. Abbreviations: NET, needleleaf evergreen tree; NDT, needleleaf deciduous tree; BET, broadleaf evergreen tree; BDT, broadleaf deciduous tree; BES, broadleaf evergreen shrub; BDS, broadleaf deciduous shrub. Figure 7: The changes in SLA values during key development stages (DVS) for different crops (**a**) and observed SLA values in various growth periods (**b**). For (**a**), the dotted lines are the default SLA values at different DVS in the WOFOST model and the solid line is the observed SLA values of summer maize at the Gucheng site in China. DVS values range from \(-0.1\) at sowing to \(0.0\) at emergence, \(1.0\) at flowering, and \(2.0\) at physiological maturity. For (**b**), the different lowercase letters indicate significant differences at the \(0.05\) level. More details about the species can be found in Table S2 [53; 54; 55; 56; 57]. Within a given plant functional type, plants grouped by morphology and structure may better describe the variability in traits [66; 67]. In the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) in CLM5.0, the plant population in each patch is divided first into plant functional types, and each plant type is presented as numerous height classes, according to tree height, diameter, canopy layer, and other variables [68]. Trait-climate relationships have also been analyzed in few modelling studies. For instance, SLA, V\({}_{\text{cmax25}}\), and J\({}_{\text{cmax25}}\) within the PFTs were re-parameterized yearly depending on the local climatic conditions (e.g., MAT, MAP, and soil moisture) in the JSBACH model [69]. [PERSON] et al. used trait-climate relationships to predict the spatial patterns of LMA, N\({}_{\text{mass}}\), and LAI and then simulated vegetation distributions and vegetation responses to climate changes in China using a Gaussian mixture model trained with these trait data [70]. However, the underlying mechanisms behind these trait-climate relationships require more long-term observations, to be able to simulate vegetation responses to future climate change. The collected SLA data in this study mainly distributed in temperate and subtropical forest and grassland ecosystems, SLA observations in boreal ecosystems, woody plants in the Tibetan Plateau region, and crops were relatively few, and need to be supplemented by further research. Different protocols for measuring SLA (e.g., all leaves vs. the topmost leaf, with vs. without petioles) have been used in published studies [15], which may cause bias in data statistics and analysis. The methods, time, and positions of sampling should be standardized in the future to enhance the representativeness of the plant trait database, especially at the region scale. This paper only quantified the impacts of SLA variation on GPP simulation with the CLM4.5 model for four PFTs; its effects in other PFTs and terrestrial biosphere models need to be further investigated.