Evaluating the skills of the CMIP5 global climate models using multicriteria decision-making analysis in Ethiopia

This study evaluates the skills of 30 CMIP5 GCMs and Multimodel Ensemble (MME) in reproducing characteristics observed precipitation (Pr), minimum (Tmin), maximum (Tmax) temperature over Middle Awash sub-basin (MASB) Ethiopia. The MME climate variables was generated using simple arithmetic mean method. entire analysis performed on raw historical GCM simulations (before bias correction) data for periods 1981–2005 based monthly annual time series seasonal temporal resolutions. considered two approaches. first one an evaluation employing five statistical performance metrics (SPMs), i.e., mean, CV, PBIAS, RSR, r. second approach involves application multicriteria decision-making (MCDM) analysis, adopting three SPMs (PBIAS, r). relative weights were determined by entropy Besides, weighted average compromise programming techniques employed to rank select best-performing GCMs. findings from demonstrate that, a given variable interest, that performs well SPM may fail produce same another scale. Likewise, at different resolutions, perform one-time scale but poorly another. These suggested results relied mainly SPM, scale, formats chosen analysis. Hence, it is critical comprehensively evaluate skill multiple range spatial settings formats. In addition, MCDM proved ensemble GCMs, which provide adequate simulating salient features Pr, Tmin, Tmax concomitantly across MASB, encompass CMCC-CMS, BCC-CSM1.1(m), CMCC-CM, BNU-ESM, CanESM2, MPI-ESM-MR. However, much differently characterizing various scales also its superior potential duplicating area several individual overall attested instead aggregating ranks into one, recommended treat each independently while developing subset ensembling since responds under set conditions. Finally, approaches this will be valuable input subsequent hydrologic studies beyond.