# Predicted Future Changes in the Mean Seasonal Carbon Cycle Due to Climate Change
## 3 Results  
### Model Validation vs. Fluxnet Data  
The NEE, GPP, and \(\mathrm{R_{eco}}\), as modeled by 3D-CMCC-FEM, exhibit strong correlations with the observed daily and monthly eddy covariance data and for the overall MSC across all five sites (Figures 2 and A1 and Tables A1-A3 in Appendix A). Some slight differences are observed for the daily values for temperate European beech forests of IT-Col and DK-Sor when representing the NEE and \(\mathrm{R_{eco}}\) between the 100 and 200 DoY (Figure 2(1.a-1.c) and Figure 2(5.a-5.c)). Modeled overestimations for GPP of about 5 gC m\({}^{-2}\) day\({}^{-1}\) at the peak of production (\(\sim\)180 DoY) for both the European beech forest at IT-Col (Figure 2(1.b)) and the maritime pine forest at FR-Lbr (Figure 2(2.b)) was observed. The highest correlation coefficients between modeled and observed data were observed for DK-Sor and IT-Col (r = 0.97 and 0.96, respectively) for the daily NEE and GPP (Table A1, Appendix A), while FI-Hyy shows the best correlation for daily \(\mathrm{R_{eco}}\) (r = 0.94) and monthly NEE (r = 0.99) (Tables A2 and A3). At the IT-Col and CZ-Bk1, the model shows the best performances in simulating NEE (RMB = 0.07 and 0.14, respectively--Table A1), while the lowest values were reached by DK-Sor and IT-Col for the GPP (RMB ranging between \(-\)0.06 and 0.55, Table A2). Overall, RMB values for GPP are relatively low across all case studies and time scales. Regarding \(\mathrm{R_{eco}}\) at FI-Hyy and DK-Sor, the model tended to underestimate both daily (RMB of \(-\)0.3 and \(-\)0.42, respectively) and monthly (\(-\)0.29 and \(-\)0.42, respectively). Conversely, at IT-Col, the model exhibited a slight overestimation (RMB daily 0.82 and monthly 0.77, Table A3). The model reports negative NRMSE values for NEE across all time scales, indicating a slight overestimation (values ranging from \(-0.57\) to \(-6.59\)) with the exception of the CZ-Bk1 site for the daily NEE value (1.39), and at the IT-Col site for the monthly NEE value (0.99). Regarding the GPP, (Table A2) DK-Sor showed the lowest NRMSE for daily and monthly values (0.19 and 0.16, respectively). Conversely, at CZ-Bk1 and FR-Lbr forests, the model displays the highest NRMSE for daily (1.21) and monthly (0.40) values, respectively. Last, Table A3 shows the validation results for R\({}_{\rm eco}\). At FR-LBR, the highest accuracy and precision were reported with the lowest NRMSE for daily and monthly values (0.24 and 0.20 respectively). For the other sites, we found almost the same model capability described for the above fluxes (i.e., NEE and GPP) with a lower correlation and a slightly higher error in terms of daily R\({}_{\rm eco}\) at CZ-Bk1 (NRMSE = 1.16). ME exhibits values close to one across all time scales and sites for NEE, with values ranging from \(-0.10\) (FR-LBr) to 0.92 (IT-Col) for daily NEE values and \(-0.15\) (FR-LBr) to 0.95 (IT-Col) for monthly values. The modeled GPP has a similar trend of the observed NEE with values ranging from 0.28 for FR-LBr to 0.97 for DK-Sor for daily values and FR-LBr = 0.34 to DK-Sor = 0.92 for monthly values (Table A2). The R\({}_{\rm eco}\) achieved lower performances than GPP and NEE in terms of ME; Table A3 displays values from 1.17 for IT-Col to 0.75 for FI-Hyy and IT-Col = \(-0.64\) to FI-Hyy = 0.77, for daily and monthly time scale, respectively. Finally, the lowest MAE was found at IT-Col, for NEE (0.67 gC m\({}^{-2}\) day\({}^{-1}\) and 11.42 gC m\({}^{-2}\) month\({}^{-1}\)) and GPP (0.91 gC m\({}^{-2}\) day\({}^{-1}\) and 20.68 gC m\({}^{-2}\) month\({}^{-1}\)), while  
Figure 2: The green lines represent the modeled (**a**) NEE, (**b**) GPP, and (**c**) R\({}_{\rm eco}\) amounts (gC m\({}^{-2}\) day\({}^{-1}\)) per DoY (Day of Year) for the five selected case studies, i.e., (1) Collelongo—IT-Col, (2) Le Bray—FR-Lbr, (3) Hyytialä—FI-Hyy, (4) Bilý Kriz—CZ-Bk1, and (5) Sore—DK-Sor compared to relative observed data (depicted as black dots) from the Fluxnet2015 Dataset [56].