## 4 Discussion
### Uncertainties in V\({}_{\text{cmax}}\) Data and Implications for Photosynthesis Simulations  
To examine the accuracy of the LCC-based V\({}_{\text{cmax}}\) products used in this study, we first built an observational dataset of V\({}_{\text{cmax}}\) by compiling field measurements collected at nine sites covering four PFTs (i.e., DBF, EBF, ENF, and GRA) across Europe (Figure 12) [134; 135; 136; 137; 138; 139; 140; 141]. Then, we compared the mean V\({}_{\text{cmax}}\) seasonality derived from the LCC, PFT-specific V\({}_{\text{cmax}}\), and field measurements of V\({}_{\text{cmax}}\) for these sites (Figure 12a-i). The distribution of the monthly mean value of V\({}_{\text{cmax}}\) measurements across the different sites was comparable to that of the corresponding V\({}_{\text{cmax}}\) derived from the LCC (Figure 12k). The monthly averaged V\({}_{\text{cmax}}\) values for site NOIT0-03, derived from the LCC, were well correlated with the field data collected (r = 0.64) during the growing season (i.e., from April to October) (Figure 12l). When we calibrated the V\({}_{\text{cmax}}\) according to the PFTs, the FGM overestimated the V\({}_{\text{cmax}}\) during spring, autumn, and winter at most sites, the same result as that which we obtained for the whole study area. It is worth noting that the V\({}_{\text{cmax}}\) used in this study represents the maximum carboxylation rate at a standardized temperature of 25 \({}^{\circ}\)C (i.e., V\({}_{\rm{cmax25}}\)), which is a proxy for the Rubisco content rather than a realized rate at ambient temperatures. Thus, here, we observed larger V\({}_{\rm{cmax}}\) values (Figure S3a) than the V\({}_{\rm{cmax}}\) rates at the average growing season temperature reported in other studies [142]. Recently, several global-scale V\({}_{\rm{cmax}}\) products of different spatial and temporal resolutions have been distributed [51; 57; 58] with contrasting patterns. Large uncertainties still exist regarding the current V\({}_{\rm{cmax}}\) products, especially in terms of the temporal variations during the growing season [53]. Because a limited quantity of field-measured V\({}_{\rm{cmax}}\) data are available for validation, remote sensing V\({}_{\rm{cmax}}\) products have not yet been fully tested. Although efforts have been made to predict V\({}_{\rm{cmax}}\) on the global scale using remote sensing data [55; 57; 58; 131; 143], the mechanisms driving the spatiotemporal variability in plant photosynthetic production (e.g., environmental acclimation, leaf age effect) are still ongoing [53; 54; 139]. We found that the seasonal pattern of the LCC-derived V\({}_{\rm{cmax}}\) was very similar to that of the LAI. Thus, an alternative strategy for the LCC-based V\({}_{\rm{cmax}}\) is to use a PFT-specific V\({}_{\rm{cmax}}\) and scale it according to the LAI seasonality. This LAI-based V\({}_{\rm{cmax}}\) scaling approach was used for some models (e.g., BESS) [33; 144] and could result in a robust performance, at least regarding the seasonality aspect. The V\({}_{\rm{cmax}}\) change effect on the GPP investigated here highlights the need for detailed studies using multi-source V\({}_{\rm{cmax}}\) datasets on different scales (e.g., site-level field measurements and large-scale remote sensing retrievals). Although we improved European GPP estimations by including the spatiotemporal dynamics of V\({}_{\rm{cmax}}\), there are still some uncertainties regarding the GPP estimated by the FGM. Uncertainties regarding the input parameter datasets are one possible source. Another possible limitation is that the algorithm of the FGM, as presented here, does not use precipitation or soil moisture data directly and implements only VPD, which is partly related to soil moisture. We assumed that VPD would be able to replace soil moisture for the assessment of the influence of drought on GPP. This is an example of how a future model could be improved. Figure 11: Interannual trends in the GPP and LAI scaled by multiple-year means.