## 3 Results  
### Evaluation of Daily, Seasonal, and Annual NEE Estimations  
To evaluate 3D-CMCC-PSM NEE predictions, we compared predicted (MD) daily and monthly NEE time series to EC daily data. The analyses were performed only on the test data (i.e., portions of the series which have not been used for calibration) to avoid any effect of overfitting. The model showed high correlations with observed EC data at all sites for both daily and monthly fluxes, apart from ITCpz site (Table 4). Excluding ITCpz, \(R\) ranged at all sites from 0.65 to 0.84 for daily, and 0.59 to 0.97 for monthly scale. Beech-dominated Deciduous Forests (DBF) performed better than Conifer species (ENF) and evergreen Mediterranean broadleaved forests (EBF). ENF and EBF in FRPue performed similarly on daily scale, for all the statistics used. However, ENF predictability significantly increased on monthly scale (\(R\) ranging between 0.92 and 0.97), while EBF performed worse (\(R\) 0.42 in ITCpz, and 0.59 in FRPue). RMSE on average was 1.92 gC m\({}^{-2}\) day\({}^{-1}\). MAE ranged between 0.96 and 1.78 gC m\({}^{-2}\) day\({}^{-1}\), and on average it decreased almost twice on monthly timescale. MAB showed similar behavior for DBF and ENF. It ranged between 0.39 and 0.56 gC m\({}^{-2}\) day\({}^{-1}\) (0.50 on average) for daily time series. Mediterranean forests resulted as the ones with highest MAB, and showed no significant reduction when predictions were aggregated on monthly scale. Differently from the other simulations, even NSE just improved slightly for ITCpz, and even reduced for FRPue simulation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Statistics** & **Formulation** & **Use and Ranges** \\ \hline Pearson Coefficient & \(r=\frac{\sum_{i}^{n}Y_{i}^{obs}-Y_{i}^{sim}-\frac{\sum_{i}^{obs}-\sum_{i}^{ time}}{n}}{\sum_{i}\left(Y_{i}^{obs}-Y_{i}^{obs}\right)^{2}-\frac{\sum_{i}^{obs}- \sum_{i}^{time}}{n}}\) & Estimation of model’s measure of correlation with EC data [0;1] \\ \hline Nash Sutcliffe efficiency & \(NSE=~{}1-\frac{\sum\left(Y_{i}^{obs}-Y_{i}^{sim}\right)^{2}}{\sum\left(Y_{i}^{obs }-Y_{i}^{obs}\right)^{2}}\) & Estimation of model’s predictability [\(-\infty\);1] \\ \hline Root Mean Square Error & \(RMSE=~{}\sqrt{\frac{\sum\left(Y_{i}^{obs}-Y_{i}^{sim}\right)^{2}}{n}}\) & Estimation of model’s accuracy gC m\({}^{-2}\) day\({}^{-1}\) [0; \(\infty\)] \\ \hline Mean Absolute Bias & \(MAB=\frac{1}{n}\sum_{i}^{n}\left|\frac{Y_{i}^{obs}-Y_{i}^{sim}}{\sigma\left(Y_ {i}^{obs}\right)}\right|\) & Estimation of model’s bias gC m\({}^{-2}\) day\({}^{-1}\) [0; \(\infty\)] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics used for Model’s results validation against Eddy Covariance data. Daily results were aggregated in seasonal series to evaluate seasonal predictability. Daily NEE were averaged to give a time series of mean seasonal NEE (gC m\({}^{-2}\) day\({}^{-1}\)), one value per season, for the duration of the test dataset. Seasonal aggregations showed that 3D-CMCC-PSM poorly performed in predicting seasonal fluxes. NSE was generally negative in summer, with the exclusion of DEHai and FRPue. 3D-CMCC-PSM generally best reproduced NEE dynamics in fall (\(R\) ranging between 0.22 and 0.89). ENF ecosystems showed consistently higher correlation in spring predictions, with R of 0.65 and MAB of 0.62 gC m\({}^{-2}\) day\({}^{-1}\) on average. In the case of evergreen stands, 3D-CMCC-PSM consistently showed poor performance in summer. Expectedly, DBF performed the worst in winter (Table 5). NSE on average resulted positive only in fall for both DBF and EBF, and spring, for ENF stands. We used Taylor diagrams [61] to graphically summarize how closely Daily, Monthly and Annual NEE patterns matched EC observations (Figure 5). 3D-CMCC-PSM performance was generally satisfactory. Daily simulations resulted in all sites but ITCpz being within the \(\pm 1\) normalized standard deviation region. Monthly scale predictions were more consistent with EC data, especially for BDF and ENF sites.