## 4 Discussion  
### 3D-CMCC-PSM Predictability in Estimating NEE  
In general, the inclusion of a simplistic SOC routine resulted in a reliable estimation of daily and monthly NEE patterns. While daily and monthly patterns are consistent with EC data, seasonal patterns showed non-negligible misrepresentations, which resulted in negative NSE in most of the cases. This inconsistency may be driven by the strong seasonality in both \(\mathrm{R_{eco}}\) and GPP [63], which positively affects correlation between EC data and MD results. NEE patterns during summer and fall were much more consistent with measured ones, than winter and spring patterns. During these seasons the biases appeared mostly affected by estimation of \(\mathrm{R_{eco}}\). The scarcity of the model in representing EBF C fluxes was especially attributable to GPP predictions. 3D-CMCC-PSM and 3D-CMCC FEM inability in predicting GPP in ITCpz and FRPue sites, denoted the necessity to better represent the relations between Mediterranean forests and environmental factors [64; 65]. In FRPue the model well reproduced spring, summer and fall NEE. On the other hand, it showed a bias of around 1 gC m\({}^{-2}\) day\({}^{-1}\) in winter, suggesting it was missing some particularly important seasonal processes. For example, evergreen phenology still didn't consider secondary or continuous growth. Thus, species like _Quercus ilex_, which exhibit secondary gem sprouts in fall [54], have fresh leaves and mild temperatures to guarantee photosynthetic activity in fall and winter, partly explaining 3D-CMCC FEM and 3D-CMCC-PSM systematic underestimation. ITCpz showed the same pattern. However, differently from FRPue, it poorly performed also in early spring and summer, especially for GPP patterns. This poor performance was expected, because of the physical characteristics of the site. In fact, 3D-CMCC FEM soil water dynamics routine was still simplistic, and to date, such other similar models have not included any effect of water table dynamics. On the contrary, ITCpz is characterized with the presence of a shallow groundwater table, which seems to reduce water stress in early summer [37]. Moreover, we used average daily meteo data collected from the Eddy Covariance stations, and initialized simulations using average structural information found in literature. Uncertainty about those data was potentially high, and could have dramatically affected 3D-CMCC-PSM results. Summer NEE misrepresentation in DBF was probably affected by the assumption that LAI and photosynthetic capacity reach their maximum in early summer, at the same time. On the contrary, maximum photosynthetic capacity may be reached in late summer, and varies across the canopy. Without taking this into account, GPP could be overestimated up to 40% [66]. Notwithstanding, comparing model outputs with published works [67; 68], these defects are common also for other PBMs. Seasonal patterns showed that the model consistently misrepresented NEE in winter, suggesting that \(\mathrm{R_{eco}}\) still needs to be improved. Especially for DBF sites (e.g., DEHai), winter \(\mathrm{R_{eco}}\) was mostly driven by RH. RH was exponentially affected by soil temperatures and especially moisture [69], which are calculated by the model, and could be over-fluctuating in winter. Moreover, EC data are prone to random noise [70], whose relative impact on performance metrics may be relatively larger. Interestingly, annual predictions suggested reasonably high performance of 3D-CMCC-PSM, despite these seasonal inconsistencies. This suggests that biases are usually consistent within a season, but have different signs across seasons (Figure 8), resulting in a compensating effect at coarser time scale. It was not always possible to individually validate the different components of respiration. Since EC Reco was not measured but inferred, evaluation metrics should be interpreted as a general ability of 3D-CMCC-PSM in predicting it.