## 4 Discussion
To reduce modeling errors of this type, the data quality of the input \(LAI\) should be improved, as discussed in earlier paragraphs. Additionally, more representative values of PFT-specific parameters should be updated from the latest literature or from research datasets such as the TRY Plant Trait Database [151]. Another point to be addressed in future work is the assumption about growth respiration. [PERSON] et al. [152] reviewed existing isotope studies of plant and pointed out a time lag of transport and allocation of carbon from leaves to the root tissues belowground, suggesting that our assumption of almost instantaneous _GPP_ export to root utilization should be converted to a lagged one once measurement data are available to set the delay time range. In addition to temperature and moisture conditions, initial _SOC_ amounts also greatly affected the heterotrophic part of hourly \(R_{e}\). On one hand, active soil biomass pools such as \(\mathit{SOC}_{\mathit{surface}}\)_metabolic_, \(\mathit{SOC}_{\mathit{surface}}\)_micribe_, \(\mathit{SOC}_{\mathit{root}}\)_metabolic_, and \(\mathit{SOC}_{\mathit{soil}}\)_micribe_ reached equilibrium (annual carbon change remained several grams or even larger by our calculations) rapidly during the spin-up. On the other hand, it would take more than one thousand years to stabilize \(\mathit{SOC}_{\mathit{soil}}\)_passive_. However, hourly \(R_{e}\) estimates based on these fully stabilized initial _SOC_ settings were almost twice those of observed ones. Thereby, the number of years for spin-up was shortened to reduce _SOC_ and \(R_{\mathit{heterotrophic}}\) overestimation. To improve this aspect of modeling further, a few aspects must be addressed. As reported by [PERSON] et al. [153] and by [PERSON] et al. [154], both the quantity and quality of litter can be expected to affect the biomass amount in _SOC_ and its decomposition rate. Estimating the hourly litter fall rate more accurately requires good estimation of plant biomass change in foliage, wood, and root parts, as described above. However, plant biomass loss attributable to biogenic volatile organic compound (VOC) emission [152], herbivore consumption [155], and litter loss because of dissolved carbon [64] should be updated or tuned to more accurately estimate _SOC_. From a quality perspective, PFT-specific parameters such as the lignin fraction and the lignin:nitrogen ratio can be further calibrated or adjusted with reference values from the TRY Plant Trait Database [151]. Our \(R_{e}\) modeling only included carbon cycling, and coupling C and N might improve the simulations [85; 87; 88]. Because of the limited availability of _GPP_, \(R_{e}\), and \(NEE\) datasets for Japanese forests and grasslands, some (\(n=5\)) flux tower sites' data outside our study area and the 33\({}^{\circ}\)N-38\({}^{\circ}\)N range were used in model calibration and validation experiments. To improve the representativeness of model calibration, future availability of data from more observational sites is expected to reduce site-specific errors [133]. This point also addresses future application of our sub-daily scale model to provide fine-resolution biogenic CO\({}_{2}\) flux data in an inversion analysis of anthropogenic CO\({}_{2}\) emissions of other megacities, which requires both globally available satellite input datasets and well-tuned model parameters. Furthermore, although biogenic CO\({}_{2}\) fluxes induced by urban forest and greenspace were estimated in our sub-daily scale model, these calculations were based on natural vegetation parameters because of the scarcity of observational data. Such assumptions should be replaced in future work because urban vegetated land might present unique characteristics that are different from natural forests [49; 156].