CR 30:149-160 (2006)  -  doi:10.3354/cr030149

Comparison of temperatures simulated by GCMs, RCMs and statistical downscaling: potential application in studies of future crop development

M. Moriondo*, M. Bindi

Department of Agronomy and Land Management, University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy

ABSTRACT: We evaluated the performance of a general circulation model (HadCM3), a regional circulation model (HadRM3P) and an artificial neural network (ANN), in reproducing daily maximum and minimum temperature (Tmax and Tmin) at site scale (Florence, Italy) for the present climate. The Tmax and Tmin values that were observed and those reproduced by HadCM3, HadRM3P and ANN for both the present and future climate scenarios (IPCC scenarios A2 and B2) were then used as input data in a cropping systems simulation model (CropSyst). In particular, climatic impact on the phenological developmental stages of a summer crop (sunflower Helianthus annuus L.) and winter crop (durum wheat Triticum aestivum L.) were evaluated. In addition, the frequency of extreme climatic events during specific crop phenological stages (i.e. number of events with Tmax and Tmin above and below stressful thresholds) were evaluated. The comparison between observed Tmax and Tmin, values and those produced by HadCM3, HadRM3P and ANN for the present climate, provided evidence for a higher accuracy of the ANN model in simulating these variables. The crop phenological stages and the related extreme climate events were therefore also better reproduced using the ANN climate data. The use of HadCM3 and HadRM3P climate data in climate change impact assessments seemed to result in an overestimation of the impacts (i.e. greater reduction in the length of development phases and greater changes in the frequency of extreme climate events during the most sensitive development stages) compared with those obtained using ANN climate data.


KEY WORDS: Crop development · Crop simulation model · Extreme climate events · Sunflower · Winter wheat


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