## 6 Discussion and Conclusions  
The performance of the statistical methodology applied to simulate future GPP patterns is dependent on the quality of the data-set analyzed and on its representativeness for the inter-year meteorological variability which can affect forest production. As regards the first issue, the meteorological data layers (temperature, rainfall and radiation) which describe the present climate conditions were produced by a method which has been fully tested by [PERSON] _et al._[15]. Similarly, the capacity of Modified C-Fix to correctly estimate forest production variations in space and time has been assessed in previous investigations [18, 20, 21, 25]. The theoretical basis of this model has also been recently validated by [PERSON] _et al._[34], who indicated that cumulative _fAPAR_ of the growing season derived from space is directly linked to gross carbon uptake in European ecosystems. The length of the data-set analysed (10 years), which is imposed by the availability of Spot-VGT imagery, represents a major constraint for the current investigation. This length is actually suboptimal  
Figure 5: Annual GPP predicted by Modified C-Fix for the five Italian forest types in the environmental scenarios considered (present scenario, climate change, increased atmospheric CO\({}_{2}\) and combination of the two factors). to grasp the temporal variability of the relationships which link weather factors to forest GPP. The period considered, however, includes growing seasons with extreme characteristics, such as that of 2002, quite humid, and that of 2003, exceptionally hot and dry. This should guarantee a sufficiently high representativeness of the regression models found for most possible weather situations which can occur in Italy. As regards the definition of future environmental conditions, the climate data currently considered are the results of a General Circulation Model (GCM) dynamical downscaling. Raw outputs of GCMs, in fact, are not directly suitable for local impact studies since their spatial resolution (\(\sim\)300 km) does not resolve important subscale hydro-meteorological processes [e.g., 35]. Dynamical downscaling, that consists of a high resolution regional climate model (RCM) (20 km in this specific case) nested inside a GCM, provides a more accurate reproduction of local topography and a more realistic simulation of fine scale weather features [36]. Unfortunately, systematic errors in variable simulations, as found comparing RCM to observed data [37, 38], may compromise the direct use of RCM data for impact assessment. The relevance of these systematic errors was currently reduced by expressing the climate change for the 2080-2099 period as difference (temperature) and ratio (rainfall) values with respect to the baseline 1999-2008. The CO\({}_{2}\) concentration corresponding to this future scenario (670 ppm) derives from consolidated theoretical and experimental observations. The enhancements in photosynthetic activity simulated in the current study critically depend on this concentration and on the robustness of Equation 2. This equation was calibrated using the results of various FACE experiments, which should guarantee its applicability to a wide range of ecosystems and environmental situations [21, 39]. In general, the application of the simulation methodology produces plausible results for all forest types considered. The simulated climate scenarios reduce ecosystem production in all cases except for FT 1 (white fir / Norway spruce forests). This increase is in accordance with other works on the same subject which demonstrate that GPP is strictly dependent on mean annual temperature in temperate-humid climates [40]. In contrast, a decrease in productivity similar to that currently observed can be expected for forest ecosystems placed in warmer and drier areas, since in these conditions plants limit photosynthetic activity in order to reduce water loss by transpiration [1, 41].