## 4 Discussion
### 3D-CMCC-PSM Uncertainty in Estimating NEE  
We analyzed 300 random parameterization-sets per site to quantify model assumptions and uncertainty. The model showed different behaviors in different sites, but expectedly consistent across species behaving in a similar functional way. These results may suggest that using functional traits combinations to provide physiological parameters, instead of fixed species-specific ones, may produce still reliable and more general predictions, particularly useful in case of larger spatial/temporal simulations [76; 77; 78]. Using a species-level parameterization, in fact, may result in a too-fine \"resolution\" because: (1) it would require excessive computational resources and a finely-detailed parameterization, usually inaccessible on a broad scale [79]; and (2) the model's rationale in predicting forest structure is mainly driven by competition for resources. However, there are not explicit tradeoffs, positive interactions between different tree cohorts, or intra-specific trait variability, which are fundamental to forecast forest ecosystem structure on long-run simulations [80]. Having fixed species-specific parameters throughout a century would potentially result that only a very reduced amount of species would dominate the different cohorts on landscape to regional scale. According to Figures 6-8, strong uncertainties still reside in timing for the different phenological phases. The biggest source of uncertainty in deciduous stands was driven by the amount of degree days needed to begin the vegetative period. The use of a site-specific thermal sum (GDD) to activate vegetation period is widely used, but proven to be very site sensitive, and not very effective for a regional generalization [81]. On the other hand, the processes triggering bud-burst timing are still partly unknown. Moreover, those models proposing a process-oriented promoter-inhibition rationale are generally over-complex and not prone to spatial generalization [82]. A possible solution in this context is to use remotely-sensed data to train a latitudinal-explicit regression, constraining GDD estimation. 3D-CMCC-PSM showed high uncertainty also in catching the beginning of the senescence phase. The new phenological scheme didn't reduce such uncertainty, since it was still using a photoperiod threshold as the senescence phase trigger [37]. Another strong source of uncertainty in summer GPP may be held by the over-simplicity of soil structure and thus of the soil water routine. Figure 9: Patterns in daily soil respiration (SR) heterotrophic and autotrophic components (gC m\({}^{-2}\) day\({}^{-1}\)). Patterns per DoY (Day of Year) calculated from test-sets on a site level. Microbial respiration average patterns (black-dotted lines) and standard deviation (gray areas). Autotrophic root respiration average patterns (red-dotted lines) and standard deviation (orange areas). First column represents DBF sites, the second ENF, the third EBF. As shown in [37], EC data are prone to high uncertainty. We focused on NEE fluxes to reduce the uncertainty cascade related to NEE partitioning. The next natural step will be reframing the model with a hierarchical Bayesian fashion, to quantify error propagation and parameter uncertainty from the posterior distribution [83]. Daily \(\mathrm{R_{eco}}\) estimation was affected by the cascade of uncertainties related to the calculation of \(R_{\mathrm{A}}\) and heterotrophic respiration, calculated independently. \(R_{\mathrm{A}}\) routine may strongly be influenced by uncertainties in \(R_{\mathrm{M}}\) estimation, which often resulted in \(R_{\mathrm{A}}\) overestimation. The \(R_{\mathrm{M}}\) was in fact simulated by a set of empirical relations, which involve the use of a fixed non-acclimating Q10 factor, whose generality is known to be inaccurate [84]. Moreover, the rationale of [PERSON]'s \(\mathrm{R_{M}}\) calculation [85] is affected by uncertainty in estimating daily increment of N pools, generally estimated by forest ecosystem models as a fixed proportion of daily C increment.