Selecting representative climate models for climate change impact studies: A case study of the eastern Omo basin

 Selecting representative climate models for climate change impact studies: A case study of the eastern Omo basin

Tekalegn Ayele Woldesenbet^{a b}, Nadir Ahmed Elagib^a

 

^a Institute of Geography, Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany

^b Ethiopian Institute of Water Resources, Addis Ababa University, Addis Ababa, Ethiopia

Abstract

Selecting reliable general climate models (GCMs) is crucial for assessing the impact of climate change in a region. The current study evaluated the performance of 34 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating monthly total rainfall. To this end, the case of eastern Omo basin was considered with observed rainfall data at seven meteorological stations for the period 1985–2014. The corresponding GCM-simulated rainfall data were compared using seven performance metrics. Eight performance metrics were selected under four categories as follow: error metrics (mean bias error, mean absolute error, root mean squared error), model efficiency (Nash and Sutcliffe’s model efficiency, absolute model efficiency, Kling-Gupta model efficiency), indices of agreement (modified index of agreement), and goodness of fit (coefficient of determination). Three key results were obtained: 1) There is no single best overall GCM across the meteorological stations using a single performance metric, 2) No single best GCM was found for a meteorological station using all the performance metrics, and 3) Based on all performance metrics across all the meteorological stations, the best GCMs in order of performance are SAM0-UNICON, TaiESM1, INM_CM5_0, AWI_ESM_1_1_LR, and CMCC_CM2_HR4. The selected GCMs can be used for evaluating the impact of climate change on hydrology, water resource availability, ecological flow regime and for climate adaptation and mitigation strategies.

Keywords: performance metrics, CMIP6, rainfall, climate models, ranking, Omo basin,