## 3 Results  
### Ground Phenology Reference  
We found that Agro-IBIS had the best performance in capturing the seasonal evolution of LAI (_i.e_., smallest MPE between simulated and observed LAI; Table 2) with the onset prescribed as BBRK30 (_i.e_., the DOY when 30% of the buds have broken) and the offset prescribed as LCOLOR20 (_i.e_., the DOY when 20% of the leaves have changed color) (Figure 1). These two metrics represent well the beginning of the increase in LAI in spring and the decrease in autumn. Because L75 and L95 occur around the time when LAI nearly reaches its peak value, which is much later than the leaf onset defined in Agro-IBIS, they were excluded from the analysis. Although LFLAL is consistent with the definition of leaf offset at first sight, our analysis suggests that even LFLAL10 (close to LCOLOR80) is too late to represent the leaf offset. In spring, the simulated LAI accumulates a little slower than the observation, while the simulated LAI generally decreases faster than the observation in autumn, particularly in 2007. Our analysis shows that the model slightly overestimates the peak value of LAI.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**(\%)** & **BBRK10** & **BBRK20** & **BBRK30** & **BBRK40** & **BBRK50** & **BBRK60** & **BBRK70** & **BBRK80** & **BBRK90** \\ \hline LCOLOR10 & 10.46 & 10.69 & 10.51 & 10.79 & 10.92 & 11.04 & 11.03 & 11.31 & 11.45 \\ LCOLOR20 & 9.09 & 9.09 & **8.86** & 9.10 & 9.19 & 9.30 & 9.23 & 9.45 & 9.48 \\ LCOLOR30 & 9.45 & 9.53 & 9.28 & 9.51 & 9.61 & 9.72 & 9.63 & 9.83 & 9.84 \\ LCOLOR40 & 10.22 & 10.26 & 9.98 & 10.15 & 10.22 & 10.32 & 10.20 & 10.38 & 10.36 \\ LCOLOR50 & 11.38 & 11.40 & 11.11 & 11.30 & 11.35 & 11.45 & 11.31 & 11.46 & 11.41 \\ LCOLOR60 & 12.63 & 12.65 & 12.35 & 12.54 & 12.59 & 12.71 & 12.55 & 12.68 & 12.60 \\ LCOLOR70 & 14.21 & 14.22 & 13.91 & 14.10 & 14.15 & 14.26 & 14.10 & 14.21 & 14.11 \\ LCOLOR80 & 15.83 & 15.80 & 15.48 & 15.66 & 15.72 & 15.83 & 15.66 & 15.76 & 15.65 \\ LCOLOR90 & 17.98 & 17.92 & 17.59 & 17.76 & 17.82 & 17.93 & 17.75 & 17.85 & 17.71 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean percentage error between simulated and observed LAI for different simulations.  
Figure 1: Simulated and observed LAI for 1998 (RMSD = 0.33) (**a**); 1999 (RMSD = 0.24) (**b**); 2006 (RMSD = 0.37) (**c**); and 2007 (RMSD = 0.53) (**d**). BBRK30 and LCOLOR20 were used in the Agro-IBIS simulation as leaf onset and offset, respectively. Observed LAI was measured using a LAI2000 sensor at Harvard Forest AmeriFlux site.  
Figure 2: Simulated and observed annual GPP (RMSD = 148.3 g C m\({}^{-2}\)), Re (RMSD = 137.3 g C m\({}^{-2}\)) and NEP (RMSD = 157.7 g C m\({}^{-2}\)). Observation is aggregated from the hourly gap-filled eddy covariance measurements. Simulated GPP was computed within the vegetation dynamics module of Agro-IBIS; Re was computed within both the vegetation dynamics module and belowground carbon cycling module of Agro-IBIS, NEP was computed as the difference between GPP and Re.  
The Agro-IBIS model performed well in simulating annual GPP, Re and NEP (Figure 2). The magnitudes of these variables were reproduced well compared with the eddy covariance measurements. The multi-year average GPP, Re and NEP observations were 1428.2 g C m\({}^{-2}\), 1135.1 g C m\({}^{-2}\), and 293.1 g C m\({}^{-2}\), respectively; while for the Agro-IBIS simulation, they were 1420.1 g C m\({}^{-2}\), 1196.7 g C m\({}^{-2}\), and 223.4 g C m\({}^{-2}\). The model captured the inter-annual variability in GPP relatively well with a correlation of 0.5. In contrast, the correlations for Re and NEP were 0.29 and 0.18, respectively, suggesting the model did not capture the inter-annual variability in Re and NEP, particularly for the last four years of the simulation (Figure 2).