Application of Weighted Multi-Model Ensemble Means a Method to Manage Uncertainties between Climate Models

The performance of General Circulation Models (GCMs) should be assessed from region to region based on their ability to simulate temperature and precipitation for the baseline period in each gridded cell. However, the GCMs are associated with different uncertainties related to the climate system, numerical method, parameterization, calibration processes and so on. Therefore, improvement of simple ensemble mean is inevitable and there is a research gap to consider different GCMs in one comprehensive ensemble model, which manage uncertainties between GCMs. Therefore, an ensemble mean model that includes GCMs with more similarity to the climate of the region by considering GCMs based on the simulation weight to the corresponding observed baseline can fill this gap for impact studies of climate change. In this research, a weighted multi-model ensemble means applied to manage the uncertainty between five GCMs from AR4, and seven GCMs from AR5 under their corresponding emission scenarios. The climatic variables were downscaled and generated on a daily basis by LARS-WG. The Δ-changes between observed and future period of minimum temperature, maximum temperature, and precipitation were represented on Boxplots for months May to November. The ranges of uncertainties were different based on months, GCMs and emission scenarios. Overall, the weighted multi-model ensemble means method was able to model the uncertainty of climate models through the contribution of LARS-WG with very good performance for the downscaling and generation of climatic variables for the future period. This method can be used for impact studies of climate change with regard to Agricultural and hydrological models.