## 3 Results
### Changes in Vegetation Seasonality Estimations  
The most significant CO\({}_{2}\) uptake by vegetation occurs when their physiological status is the most optimal--coinciding with the highest photosynthetic efficiency--essentially, the peak of the growing season. This makes GPP a suitable variable for estimating the timing of the vegetation's growing season within the year. As previously observed, the shape of the yearly distribution of GPP did not differ significantly by model due to the effect of PAR. Hence, it could be assumed that the estimation of the phenological variables will not differ much between models. However, phenology indicators have to be located based on stable conditions (sunny days); when these conditions are isolated, some displacements and minor changes can be identified in the trends. Starting with the length of the growing season, the displacements between models range between \(\pm\)1-30 days, and CL_1 and CR_3 show the least significant displacements. In the CL site, the RH and WTD's models results, in most cases, predict the shortest growing season, while the NRH model estimates the longest. In contrast, for the CR site, the NRH model estimates the shortest growing season, while the multi-model and the EXP model predict the longest growing season (Table 5). The most similar length of the season to the thermal growing season on average at the CL site is determined by the multi-model results, while for CR, it is the WTD-based model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\mathbf{R^{2}}\) & RMSE & BIAS & \(\boldsymbol{p}\)-Value & AIC & Weighted Stats \\ \hline CL\_C1 & 0.87/0.86 & 1.58/1.66 & 0.07/0.02 & \(5.6\times 10^{-4}/7.0\times 10^{-4}\) & 25.05/25.83 & 1.53/1.59 \\ \hline CL\_C2 & 0.89/0.89 & 1.03/1.12 & \(-0.01/0.01\) & \(2.9\times 10^{-4}/3.4\times 10^{-4}\) & 22.34/23.71 & 1.28/1.38 \\ \hline CL\_C3 & 0.90/0.89 & 0.92/1.06 & 0.04/0.03 & \(7.8\times 10^{-4}/8.2\times 10^{-4}\) & 16.86/18.16 & 1.02/1.12 \\ \hline CR\_C1 & 0.91/0.90 & 0.75/0.77 & \(-0.02/0.01\) & \(1.5\times 10^{-3}/2.2\times 10^{-3}\) & 16.96/17.27 & 1.00/1.04 \\ \hline CR\_C2 & 0.93/0.91 & 0.87/0.97 & 0.04/0.06 & \(6.4\times 10^{-4}/7.8\times 10^{-4}\) & 18.66/19.66 & 1.06/1.14 \\ \hline CR\_C3 & 0.91/0.90 & 0.70/0.76 & 0.03/0.03 & \(2.0\times 10^{-3}/1.5\times 10^{-3}\) & 16.33/17.84 & 0.97/1.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average statistics per plot for the multi-model choice, including the weighted statistics factor calculated from Table 1 (first column) vs. best statistic result from individual models (second column). RMSE and BIAS are expressed with \(\upmu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\). The length of the season is not the only critical factor for determining and indicating changes between model predictions; the amplitude of the GPP seasonal curve is equally important. The amplitude of the season indicates the height of the GPP curve during the growing season or, what is the same, the difference between the median peak values and the median values at the start and end of the season. The rectangular hyperbola modified with temperature factors (TRH and TRH_exp) plus the non-rectangular hyperbola (NRH) present the highest amplitude and, therefore, a steeper curve, while the EXP model estimates the lowest amplitudes and shortest curves for both CR and CL (Table 5). The changes in the shape of the GPP during the growing season per model are the first indication of the possible oscillation of the cumulative GPP of the growing and non-growing seasons. Depending on the model chosen to estimate the annual distribution of GPP, the cumulative values differ by \(\pm\)4-5% in CL and CR sites except for CR_C1, with a standard deviation of \(\pm\)7% of the average cumulative GPP (Table 6). The same occurs in the GPP growing season for CL_1, CL_2, and CR_1 with deviations of \(\pm\)4-5%, while in CL_3, CR_2, and CR_3, the models only differ \(\pm\)2-3%.