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Multi-model projections in climate studies are performed to quantify uncertainty and improve reliability in climate projections. The challenging issue is that there is no unique way to obtain performance metrics, nor is there any consensus about which method would be the best method of combining models. The goal of this study was to investigate whether combining climate model projections by artificial neural network (ANN) approach could improve climate projections and therefore reduce the range of uncertainty. The equally-weighted model averaging (the mean model) and single climate model projections (the best model) were also considered as references for the ANN combination approach. Simulations of present-day climate and future projections from 15 General Circulation Models (GCMs) for temperature and precipitation were employed. Results indicated that combining GCM projections by the ANN combination approach significantly improved the simulations of present-day temperature and precipitation than the best model and the mean model. The identity of the best model changed between the two variables and among stations. Therefore, there was not a unique model which could represent the best model for all variables and/or stations over the study region. The mean model was also not skillful in giving a reliable projection of historical climate. Simulation of temperature indicated that the ANN approach had the best skill at simulating present-day monthly means than other approaches in all stations. Simulation of present-day precipitation, however, indicated that the ANN approach was not the best approach in all stations although it performed better than the mean model. Multi-model projections of future climate conditions performed by the ANN approach projected an increase in temperature and reduction in precipitation in all stations and for all scenarios.
In order to investigate the scope of uncertainty in projections of GCMs for Tehran province, a multi-model projection composed of 15 models is employed. The projected changes in minimum temperature, maximum temperature, precipitation, and solar radia
Any type of non-buoyant material in the ocean is transported horizontally by currents during its sinking journey. This lateral transport can be far from negligible for small sinking velocities. To estimate its magnitude and direction, the material is
Lightning casualties cause tremendous loss to life and property. However, very lately lightning has been considered as one of the major natural calamities which is now studied or monitored with proper instrumentation. The lightning characteristics ov
Grid (1{deg} latitude x 1{deg} longitude) level daily rainfall data over India from June to September for the years 1951 to 2007, generated by India Meteorological Department, was analyzed to build monthly time series of Standardized Precipitation In
Many problems in climate science require the identification of signals obscured by both the noise of internal climate variability and differences across models. Following previous work, we train an artificial neural network (ANN) to identify the year