Climate model performance assessment for MENA domain

This study aims to evaluate the accuracy of global climate models developed under CMIP5 in temperature simulations, specifically for the MENA (Middle East and North Africa) region. Using ERA5 reanalysis data as a reference, the performance of a total of 32 models for the period 2006–2024 was analyzed using various statistical metrics, including correlation coefficient, normalized RMSE, normalized standard deviation, bias, and standard deviation ratio (SDE). All models were first downscaled to resolution, and then interpolated to the ERA5 0.25o grid for comparisons. Three subregions-T¨urkiye, Iran, and Saudi Arabia-were identified, and detailed comparisons were conducted at both the broader MENA scale and the regional level. Visual analyses were supported by bias maps and Taylor diagrams. The thesis also utilized the Ranking Metric (RM Score), which combines multiple metrics to provide a single evaluation metric. Each model was ranked based on its performance based on this combined score. This method prioritizes models with high correlation, low error measures, and normalized standard deviations. The results show that the CESM1-BGC, CCSM4, and CESM1-CAM5 models stand out as the best-performing models, achieving the highest RM scores in many regions, while models such as inmcm4, IPSL-CM5B-LR, BNU-ESM, and GISS-E2-R have the weakest performance with lower scores. Regional differences are also striking: the results suggest that while CMIP5 models are generally successful in capturing large-scale climate trends, model selection based on regional performance is critical for local-level applications.