## 4 Discussion
However, frequently fluctuated \(LAI\) in evergreen forests can be expected to trigger abnormal amounts of leaf litter fall, which can lead to overestimation of _SOC_ and risks of overestimating hourly \(R_{e}\). In this case, data assimilation of \(LAI\)[135; 136; 137; 138; 139] might provide an alternative for model improvement in future work. Additionally, we recognized that in recent years, satellite-observed SIF has become a promising proxy for _GPP_ because the two variables presented a strong linear relation [140; 141; 142]. Although we already used a SIF-based _GPP_ dataset [126] to assess our modeled _GPP_ results, it was difficult to apply this type of SIF-based _GPP_ algorithm directly at sub-daily scale modeling because of similar challenges discussed in earlier paragraphs: temporally continuous, sub-daily SIF satellite datasets are not yet available. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and the Orbiting Carbon Observatory-3 (OCO-3) aboard the International Space Station (ISS) have diurnal sampling capability to some extent. ECOSTRESS has been used to develop instantaneous _GPP_ estimates for different times of day [143], but neither ECOSTRESS nor OCO-3 could provide temporally continuous observations throughout the day due to the orbit nature of the ISS. Consequently, the diurnal variability of _GPP_ cannot be simulated accordingly [144; 145]. Because _GPP_ modeling in this study was process-based, our last discussion point about photosynthetic CO\({}_{2}\) removal estimation is about PFT-specific parameters. Following [PERSON] [76], we assumed that, in each PFT, the maximal electron transport rate \(J_{\max}\) in photosynthetic reactions was linearly related to the maximal carboxylation rate at 25 \({}^{\circ}\)C \(V_{cmax25}\), where the latter was set as a constant. However, studies such as that by [PERSON] et al. [146] observed a seasonal change in \(V_{\max 25}\), which has been reflected in some global _GPP_ models [147] and which should be included in our future model optimizations. Furthermore, the values of empirical parameters to calculate \(V_{\max 25}\) based \(J_{\max}\) reported by [PERSON] et al. [148] and [PERSON] et al. [149] differ from those reference settings [76] for this study, which should be compared in further sensitivity tests of our _GPP_ modeling. In terms of \(R_{e}\) modeling, hourly \(R_{e}\) calculations in this study considered the effects of biomass amount in all vegetation biomass pools and _SOC_ pools [69; 70; 86; 87; 88] instead of simple \(R_{e}\) correlations to ambient temperature and soil moistures. This consideration is both a benefit and a challenge of our model because below-ground biomass was rarely obtained via satellite observations, if not impossible. Hourly modeled \(R_{e}\) results at the site level suggest that our sub-daily model tended to overestimate biogenic CO\({}_{2}\) emissions, which led to an overestimated natural carbon source at night and underestimated negative biogenic CO\({}_{2}\) flux during the day. Several parts of the hourly \(R_{e}\) model might contribute to such results. To start with, we assumed that live-wood and root biomass were proportional to the foliage biomass in most cases (except for root biomass in DBF) for the allocation of hourly _NPP_, with a fixed specific leaf area (_SLA_) setting [82] in each PFT. Our forest _NPP_ results fluctuate above or below the calculated values presented by [PERSON] et al. [69]: 1200 gC m\({}^{-2}\) year _NPP_ in 1982-2000. These results also exceed the estimates of [PERSON] et al. [150]. In that study, _NPP_ of temperate forest was no more than 500 gC m\({}^{-2}\) year\({}^{-1}\). Occasionally, because the change in biomass was only constrained using the difference of \(LAI\) over time, carbon flow in and out of the plants was not fully balanced. This imbalance can cause errors of maintenance respiration and then biogenic CO\({}_{2}\) emissions.