## 3 Results
### Anomalies and Parameters Related Uncertainty  
Figure 6 shows uncertainty associated to random choice of parameters. Overall, uncertainty was expectedly higher in summer and fall. Such increase was particularly clear for deciduous forests, which not only showed wider NEE standard deviation, but also had optimal modeled NEE falling outside standard deviation area.  
DBF sites showed also high uncertainty in estimating the first vegetative day, suggesting that a better representation of winter dormancy effects on budburst dates may significantly improve model's predictability. Uncertainty was generally lower in Mediterranean sites, probably because model's performance was generally lower, and fitting parameters to data would have little effect on performance. 3D-CMCC-PSM uncertainty was generally low for ENF for most of the year, but was generally high when temperatures were higher. Higher uncertainty for warmer days was generally found in DBF sites too, suggesting that 3D-CMCC-PSM was expectedly sensitive to high temperatures for both photosynthesis [62] and respiration [23]; because of such variability, calibrating parameters on data resulted in a significant boost in model's predictions.  
NEE inter-annual variability was generally underestimated by the model. Nevertheless, 3D-CMCC-PSM correctly reproduced 81% of the sign of the anomalies, and residual difference in magnitude was usually less than 0.3 gC m\({}^{-2}\) day\({}^{-1}\). Highest difference in magnitude occurred in ITCol (difference in residuals higher than 0.5 gC m\({}^{-2}\) day\({}^{-1}\) in 5 years out of 12). Highest difference  
Figure 5: Taylor diagrams representing 3D-CMCC-PSM performance in (**a**) daily, (**b**) monthly and (**c**) annual NEE estimation for the test-set. ITCol and DEHai represent DBF (red and green dots); ITRen and FIHyNF (blue and turquoise). ITCpz and FRPue represent EBF (yellow and magenta dots). The closest a simulation lied to the “Ref” point, the better 3D-CMCC-PSM represented NEE patterns. \(X\)- and \(Y\)- axes represent NEE standard deviation (SD): the closest to 1, the better the performance. Simulations with \(R\geq 0.9\), and difference in SD with EC NEE less than 0.5 gC m\({}^{-2}\) day\({}^{-1}\) showed very good performance. Simulations with \(0.75\leq R<0.9\), and difference in SD with EC NEE between 0.5 and 1 gC m\({}^{-2}\) day\({}^{-1}\) showed good performance. Simulations with \(0.35\leq R<0.75\), and difference in SD with EC NEE between 1 and 1.5 gC m\({}^{-2}\) day\({}^{-1}\) showed sufficiently good performance.  
was shown in ITCpz, where the sign was correctly reproduced only once out of 8 years, and having more than 1 gC m\({}^{-2}\) day\({}^{-1}\) of residual difference (Figure 7).  
Figure 6: Model structure-related uncertainty in estimating NEE (gC m\({}^{-2}\) day\({}^{-1}\)) per DoS (Day of Simulation) by a random choice of parameter values from prior distributions. Data represent 300 1-year simulations from randomly extracted parameterization-sets. Average daily simulations (black lines) and standard deviation (grey areas). Red dotted lines represent daily NEE simulation for the optimized parameterization set (Table S2). First column represents DBF sites, the second ENF, the third EBF.  
Figure 7: Inter-annual variability (IAV) for the test time-series (gC m\({}^{-2}\) month\({}^{-1}\)). Observed IAV in gray boxes, simulated IAV in orange. First column represents DBF sites, the second ENF, the third EBF.