Climate change scenarios for surface temperature in Emilia-Romagna (Italy) obtained using statistical downscaling models |
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Authors: | R Tomozeiu C Cacciamani V Pavan A Morgillo A Busuioc |
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Institution: | (1) ARPA-SIM, Bologna, Italy;(2) National Meteorological Administration, Bucharest, Romania |
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Abstract: | Summary Possible changes of mean climate and the frequency of extreme temperature events in Emilia-Romagna, over the period 2070–2100
compared to 1960–1990, are assessed. A statistical downscaling technique, applied to HadAM3P experiments (control, A2 and
B2 scenarios) performed at the Hadley Centre, is used to achieve this objective. The method applied consists of a multivariate
regression based on Canonical Correlation Analysis (CCA), using as possible predictors mean sea level pressure (MSLP), geopotential
height at 500 hPa (Z500) and temperature at 850 hPa (T850), and as predictands the seasonal mean values of minimum and maximum
surface temperature (Tmin and Tmax), 90th percentile of maximum temperature (Tmax90), 10th percentile of minimum temperature (Tmin10), number of frost days (Tnfd) and heat wave duration (HWD) at the station level. First, the statistical model is optimised and calibrated using NCEP/NCAR
reanalysis to evaluate the large-scale predictors. The observational data at 32 stations uniformly distributed over Emilia-Romagna
are used to compute the local predictands. The results of the optimisation procedure reveal that T850 is the best predictor
in most cases, and in combination with MSLP, is an optimum predictor for winter Tmax90 and autumn Tmin10. Finally, MSLP is the best predictor for spring Tmin while Z500 is the best predictor for spring Tmax90 and heat wave duration index, except during autumn. The ability of HadAM3P to simulate the present day spatial and temporal
variability of the chosen predictors is tested using the control experiments. Finally, the downscaling model is applied to
all model output experiments to obtain simulated present day and A2 and B2 scenario results at the local scale. Results show
that significant increases can be expected to occur under scenario conditions in both maximum and minimum temperature, associated
with a decrease in the number of frost days and with an increase in the heat wave duration index. The magnitude of the change
is more significant for the A2 scenario than for the B2 scenario. |
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