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A mechanistic growth model was used to evaluate the mean yield and yield variability of grapevine Vjtis vinifera L. under current and future climates. The model used was previously validated using field experiment data. The effect of elevated CO2 on grapevine growth was also considered. Adaptation of 2 varieties (Sangiovese and Cabernet Sauvignon) to scenarios of increased CO2 and climate change, and potential changes in agricultural risk (i.e. inter-seasonal variability), were examined. Before testing the effect of climate scenarios, we analysed the sensitivity of modelled grapevine yield to arbitrary changes in the 3 driving variables (temperature, solar radiation and COz). The results showed the model to be more sensitive to changes in CO2 concentration and temperature than to changes in radiation. Analyses made using transient GCM (general circulation model) scenarios (UKTR and GFDL) showed different changes in mean fruit dry matter for the different scenarios, whereas mean total dry matter, and fruit and total dry matter variability, were predicted to increase under almost all the scenarios. Predictions based on equilibrium scenarios (UKLO and UKHI) gave similar results. For Sangiovese, vancty adaptation analysis suggested a better adaptation in terms of mean production, but a worse adaptation in terms of yield variab~l~ty.

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The availability of daily meteorological data extended over wide areas is a common requirement for modeling vegetation processes on regional scales. The present paper investigates the applicability of a pan-European data set of daily minimum and maximum temperatures and precipitation, E-OBS, to drive models of ecosystem processes over Italy. Daily meteorological data from a 10 yr period (2000 to 2009) were first downscaled to 1 km spatial resolution by applying locally calibrated regressions to a digital elevation model. The original and downscaled E-OBS maps were compared with meteorological data collected at 10 ground stations representative of different eco-climatic conditions. Additional tests were performed for the same sites to evaluate the effects of driving a model of vegetation processes, BIOME-BGC, with measured and estimated weather data. The tests were carried out using 10 BIOME-BGC versions characteristic for local vegetation types (Holm oak, other oaks, chestnut, beech, plain/hilly conifers, mountain conifers, Mediterranean macchia, olive trees, and C3 and C4 grasses). The experimental results indicate that the applied downscaling performs best for maximum temperatures, which is the most decisive factor for driving BIOME-BGC simulation of vegetation production. The downscaled data set is particularly suitable for the modeling of forest ecosystem processes, which could be further improved by the use of information obtained from remote sensing imagery.

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