## 5 Conclusions  
Soil respiration has a key role in determining NEE in a deterministic fashion [86]. In general, this work showed how the inclusion of a simplistic soil carbon routine allowed prediction of trends and variability of NEE across the most diffuse European forest ecosystems. Modifications in the phenology scheme produced slight improvements in predicting GPP. However, they were still limited by the correct estimation of bud burst timing, leaf senescence starting point and duration. The use of an optimized parameter-set improved the model's performance only for those sites where the bio-geophysical processes were correctly reproduced. As a matter of fact, we showed how Mediterranean terrestrial forests, which showed lacks in representing some biological and/or physical processes, performed significantly worse than the other modeled sites, regardless of the use of optimized parameters.  
In conclusion, we think that 3D-CMCC-PSM can reliably estimate NEE and \(\mathrm{R_{eco}}\) dynamics in a forest ecosystem, especially in scaling up daily results to monthly NEE averages. We think that 3D-CMCC-PSM is a solid basis to further explore the effects of soil structure on carbon and Water dynamics, especially in Mediterranean systems, and be used as a tool for predicting forest growth and ecosystem services, and address questions related to future scenario forecasting.  
The following are available online at www.mdpi.com/1999-4907/8/6/220/s1, Table S1: Mean and standard deviation of the priors used to sample parameters used for the optimization, Table S2: Values of the parameters used for the 6 simulations, produced by the optimization scheme.  
**Acknowledgments:** S.M. was partially supported by the Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative through Grant GBMF4563 to [PERSON]. This work was partially supported by the Italian Ministry of Education, University and Research and the Italian Ministry of Environment and Protection of Land and Sea under the project GEMINA (n. 232/2011). We finally thanks the University of Tuscia for additional funds provided to the research. A.C. was partially supported by the European Union's Horizon 2020 research and innovation program under grant agreement N. 641816 (CRESCENDD project; [[http://crescendoproject.eu/](http://crescendoproject.eu/)]([http://crescendoproject.eu/](http://crescendoproject.eu/))) and by the \"ALForLab\" (PON30 PE_0024_1) project co-funded by the National Operational Program for Research and Competitiveness (PON R&C) 2007-2013, through the European Regional Development Fund (ERDF) and national resource (Revolving Fund--Cohesion Action Plan (CAP) MIUR. This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboArmeloff, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The ERA-Interim reanalysis data are provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux and AsiaFlux offices. We acknowledge [PERSON] and [PERSON] for providing critical comments, [PERSON] and [PERSON] for insights about the use of Eddy Covariance data, and [PERSON] for his help in generating GIS-based maps.  
[PERSON] conceived the project, designed the experiments, co-developed the model code, performed the simulations, analyzed the data, and wrote the manuscript. [PERSON] contributed in designing the experiment, co-developing the model code, and writing the manuscript. [PERSON], [PERSON] and [PERSON] contributed in writing the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.