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Multi-model projections in climate studies are performed to quantifyuncertainty and improve reliability in climate projections. The challengingissue is that there is no unique way to obtain performance metrics, nor isthere any consensus about which method would be the best method of combiningmodels. The goal of this study was to investigate whether combining climatemodel projections by artificial neural network (ANN) approach could improveclimate projections and therefore reduce the range of uncertainty. Theequally-weighted model averaging (the mean model) and single climate modelprojections (the best model) were also considered as references for the ANNcombination approach. Simulations of present-day climate and future projectionsfrom 15 General Circulation Models (GCMs) for temperature and precipitationwere employed. Results indicated that combining GCM projections by the ANNcombination approach significantly improved the simulations of present-daytemperature and precipitation than the best model and the mean model. Theidentity of the best model changed between the two variables and amongstations. Therefore, there was not a unique model which could represent thebest model for all variables and/or stations over the study region. The meanmodel was also not skillful in giving a reliable projection of historicalclimate. Simulation of temperature indicated that the ANN approach had the bestskill at simulating present-day monthly means than other approaches in allstations. Simulation of present-day precipitation, however, indicated that theANN approach was not the best approach in all stations although it performedbetter than the mean model. Multi-model projections of future climateconditions performed by the ANN approach projected an increase in temperatureand reduction in precipitation in all stations and for all scenarios.

Projection Of Temperature And Precipitation For 2020-2100 For Tehran  Region Using Post-processing Of General Circulation Models Output And  Artificial Neural Network Approach