## 5 Results
The productivity of this forest type increases of about 3%, while that of the other forest types shows a variable reduction. This reduction is low for deciduous broadleaves (FT 8) and Holm oak (FT 7) forests (-4% and -5% respectively), that are the most adapted to high temperatures, is intermediate for beech (FT 4, -6%), and is more evident for chestnut forests (FT 2, -15%). The last forests are spread on relatively low altitudes (Table 1), but are more sensitive to high temperature than deciduous oaks and Holm oak. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Forest type**} & \multicolumn{2}{c}{**OBSERVED**} & \multicolumn{2}{c}{**FUTURE**} \\ \cline{2-5}  & **Temperature** & **Rainfall** & **Temperature** & **Rainfall** \\  & (\({}^{\circ}\)C) & **(mm)** & (\({}^{\circ}\)C) & **(mm)** \\ \hline FT 1 & 6.0 & 1,463.7 & 8.1 & 1,582.1 \\ FT 2 & 11.1 & 1,537.8 & 12.8 & 1,696.9 \\ FT 4 & 7.6 & 1,983.8 & 9.4 & 2,092.6 \\ FT 7 & 13.3 & 1,011.3 & 15.1 & 1,043.0 \\ FT 8 & 11.9 & 1,177.7 & 13.6 & 1,221.1 \\ \hline \end{tabular}
\end{table} 
Table 2: Mean annual temperature and precipitation found over the five forest types examined for the present and future scenarios. Figure 4: Correlation coefficients found for the five forest types between annual GPP estimated by Modified C-Fix and seasonal temperatures (A) and rainfall (B). \begin{table}
\begin{tabular}{|l|c|c|c|c|c|c|c|c|} \cline{3-10} \multicolumn{1}{c}{} & \multicolumn{3}{c|}{**Temperature**} & \multicolumn{3}{c|}{**Rainfall**} & \\ \cline{2-10} \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{**r\({}^{2}\)**} & \multicolumn{1}{c|}{**Winter**} & \multicolumn{1}{c|}{**Spring**} & \multicolumn{1}{c|}{**Summer**} & \multicolumn{1}{c|}{**Winter**} & \multicolumn{1}{c|}{**Spring**} & \multicolumn{1}{c|}{**Summer**} & \multicolumn{1}{c|}{**Offset**} \\ \hline
**FT1** & 0.718 & 4.433 & 41.139 & –1.898 & 0.147 & –0.323 & 0.065 & 666.87 \\
**FT2** & 0.876 & –11.317 & 46.918 & –98.756 & 0.072 & –0.289 & –0.271 & 2,783.09 \\
**FT4** & 0.521 & –10.656 & 47.212 & –44.719 & 0.153 & 0.033 & –0.092 & 1,240.69 \\
**FT7** & 0.563 & –2.610 & 20.898 & –37.422 & 0.671 & 0.500 & 0.329 & 1,633.60 \\
**FT8** & 0.607 & 5.385 & 23.150 & –50.240 & 0.315 & 0.238 & 0.221 & 1,840.39 \\ \hline \end{tabular}
\end{table} 
Table 3: Determination and regression coefficients and offsets of the multivariate linear models found for the five forest types examined. The models relate annual forest GPP (g C /m\({}^{2}\)/year) to seasonal temperature (\({}^{\circ}\)C) and rainfall (mm), with the exclusion of winter (see text for details). The increased ambient CO\({}_{2}\) concentration yields effects which are summarized in the same histogram (Figure 5). Overall, such concentration leads to quite uniform increases in forest production (about 31%). Deciduous broadleaf (FT 8), chestnut (FT 2) and Holm oak (FT 7) forests are the most sensitive ecosystems, showing increases of about 36%. The lowest increase is found for Norway spruce/white fir forests (FT 1, about 23%). The last simulated scenario, which considers the combined effects of the two factors (climate change \(+\) increased CO\({}_{2}\) concentration), leads to GPP rises which are almost coincident with those brought by the latter factor. The highest GPP increases are found for Norway spruce/white fir (FT 1), deciduous broadleaf (FT 8) and Holm oak (FT 7) (+35%, +37% and 38%, respectively). The GPP increase of beech forests (FT 4) is of 27%, while that of chestnut (FT 2) is 23%.