Phenological models are important tools for planning viticultural practices in the short term and for projecting the impact of climate change on grapevine (Vitis vinifera) in the long term. However, the difficulties in obtaining phenological models which provide accurate predictions on a regional scale prevent them from being exploited to their full potential. The aim of this work was to obtain a robust phenological model for V. vinifera cv. Chardonnay. During calibration of the sub-models for budburst, flowering and veraison we implemented a series of measures to prevent overfitting and to give greater physiological meaning to the models. Among these were the use of experimental information on the response of Chardonnay to forcing temperatures, restriction of parameter space into physiologically meaningful limits prior to calibration, and simplification of the previously selected sub-models. The resulting process-based model had good internal validity and a good level of accuracy in predicting phenological events from external datasets. Model performance was especially high for the prediction of flowering and veraison, and comparison with other models confirmed it as a better predictor of phenology, even in extremely warm years. The modelling study highlighted a different phenological behaviour at the only mountain station, Cembra. We hypothesised that phenotypical plasticity could lead to growth rates adapting to a lower mean temperature, a mechanism not usually accounted for by phenological models.

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In the context of global warming, the general trend towards earlier flowering dates of many temperate tree species is likely to result in an increased risk of damage from exposure to frost. To test this hypothesis, a phenological model of apple flowering was applied to a temperature series from two locations in an important area for apple production in Europe (Trentino, Italy). Two simulated 50-year climatic projections (A2 and B2 of the Intergovernmental Panel on Climate Change--Special Report on Emission Scenarios) from the HadCM3 general circulation model were statistically downscaled to the two sites. Hourly temperature records over a 40-year period were used as the reference for past climate. In the phenological model, the heat requirement (degree hours) for flowering was parameterized using two approaches; static (constant over time) and dynamic (climate dependent). Parameterisation took into account the trees' adaptation to changing temperatures based on either past instrumental records or the downscaled outputs from the climatic simulations. Flowering dates for the past 40 years and simulated flowering dates for the next 50 years were used in the model. A significant trend towards earlier flowering was clearly detected in the past. This negative trend was also apparent in the simulated data. However, the significance was less apparent when the "dynamic" setting for the degree hours requirement was used in the model. The number of frost episodes and flowering dates, on an annual basis, were graphed to assess the risk of spring frost. Risk analysis confirmed a lower risk of exposure to frost at present than in the past, and probably either constant or a slightly lower risk in future, especially given that physiological processes are expected to acclimate to higher temperatures.

Background and Aims:The strong link between climate and grapevine phenology suggests a potentially stronger impact of climate change on viticulture in climate-limited areas, including mountain zones. The aim of this work was to evaluate the potential effects of climate change on grapevine phenology and viticultural suitability in a mountain region. Methods and Results: Climatic projections were applied to phenological models to determine the effect on stages of budburst, flowering and veraison for Vitis vinifera cv. Chardonnay. Calibration and validation of the models had been previously carried out in the same alpine region. The output of the general-circulation climatic model HadCM3, run with two different Intergovernmental Panel on Climate Change emission scenarios, was statistically downscaled to 10 locations in different agricultural sites in Trentino, Italian Alps, some of which are presently unfit for viticulture due to climatic limitations. Results yielded a trend of phenological advance, with interesting differences among phases and sites. Simulated advance was more pronounced at higher elevations, and larger for veraison than for spring phenophases. Conclusions: As a consequence of the considerable warming projected by future climate scenarios, some mountain sites at about 1000 m were expected to fall within areas climatically suitable for viticulture before the end of this century. Nevertheless, noticeable differences from present conditions are not expected within a short timescale. Significance of the Study: These projections, suggesting a more pronounced phenological response at higher elevations, may support the development of adaptation strategies aimed at maintaining the profitability of mountain viticulture even in times of global change. AbbreviationsAOGCM Atmosphere-Ocean Global Circulation Model; DOC Denominazione d'Origine Controllata (Italian 'controlled denomination of origin'); DOY day of the year; GHG green-house gases; IPCC Intergovernmental Panel on Climate Change; SRES Special Report on Emission Scenarios 52 Effects of climate change on grape phenology

Abstract. Model Output Statistics (MOS) refers to a method of post-processing the direct outputs of numerical weather prediction (NWP) models in order to reduce the biases introduced by a coarse horizontal resolution. This technique is especially useful in orographically complex regions, where large differences can be found between the NWP elevation model and the true orography. This study carries out a comparison of linear and non-linear MOS methods, aimed at the prediction of minimum temperatures in a fruit-growing region of the Italian Alps, based on the output of two different NWPs (ECMWF T511-L60 and LAMI-3). Temperature, of course, is a particularly important NWP output; among other roles it drives the local frost forecast, which is of great interest to agriculture. The mechanisms of cold air drainage, a distinctive aspect of mountain environments, are often unsatisfactorily captured by global circulation models. The simplest post-processing technique applied in this work was a correction for the mean bias, assessed at individual model grid points. We also implemented a multivariate linear regression on the output at the grid points surrounding the target area, and two non-linear models based on machine learning techniques: Neural Networks and Random Forest. We compare the performance of all these techniques on four different NWP data sets. Downscaling the temperatures clearly improved the temperature forecasts with respect to the raw NWP output, and also with respect to the basic mean bias correction. Multivariate methods generally yielded better results, but the advantage of using non-linear algorithms was small if not negligible. RF, the best performing method, was implemented on ECMWF prognostic output at 06:00 UTC over the 9 grid points surrounding the target area. Mean absolute errors in the prediction of 2 m temperature at 06:00 UTC were approximately 1.2 • C, close to the natural variability inside the area itself.