Evaluation of different GCM models and climate change scenarios using LARS_WG model in simulating meteorological data (Case study: Shiraz synoptic station, Fars Province, Iran)

Abstract

-In recent years, the topic of climate change in effect of greenhouse gases increase has been lionized in scientific studies. Hence the prediction and evaluation of meteorological parameters changes in effect of climate change is very important. LARS is a model that generates weather data and predicts weather parameters by downscaling general circulation models (GCM). In this study, in order to evaluate 15 GCM models performance in simulating the meteorological data of Shiraz station synoptic (2011-2012), statistical downscaling of each model under scenarios of approved climate change by the IPCC was performed by LARS model. The parameters of precipitation, radiation, minimum and maximum temperature were tested. The Results showed that for precipitation, downscaling INCM3 model had the best performance in terms of minimum error, under A1B scenario for radiation, , both GFCM21 and CSMK3 models had the best performance in terms of minimum error under A1B and B1 scenarios, respectively. The simulation results of minimum temperature with downscaling FGOALS model under B1 scenario indicated more accuracy than other models. For maximum temperature, both GIAOM and CSMK3 models had the best performance in terms of minimum error under A1B and B1 scenarios, respectively.