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https://doi.org/10.1016/j.dynatmoce.2022.101317
Copy DOIJournal: Dynamics of Atmospheres and Oceans | Publication Date: Aug 6, 2022 |
Citations: 12 |
Measuring the simulation skill of regional climate models (RCMs) is vital in selecting the best performing model that can be used for climate change studies. To that end, the performance of eleven Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa RCMs were evaluated against observed datasets from 1991 to 2005 over Gidabo river basin (GRB), main Ethiopian rift valley. RCMs’ outputs were evaluated using coefficient of variation (CV), percent of bias (PBIAS), Root Means Square Error (RMSE), Pearson's correlation coefficient (r), revised R-squared (RR2), Taylor Skill Score (TSS), Mann-Kendall (MK) trend test and Sen’s slope estimator. The results confirm the difference of RCMs in capturing the annual and seasonal climate variables. In relation to the spatial pattern of the rainfall, RACMO22T (EC-EARTH) strongly reproduced the mean annual rainfall. CCLM4–8 (MPI) and mean ensemble reproduced the annual patterns of the observed rainfall despite the fact with varying rainfall amounts reproduced. The seasonal rainfall pattern was satisfactorily captured by RACMO22T (EC-EARTH), CCLM4–8 (MPI) and REMO2009 (MPI). The agreement between the observed and modeled rainfall is superior in CCLM4–8 (MPI) and RACMO22T (EC-EARTH) at station level. CRCM5 (MPI) satisfactory replicated the patterns of both minimum and maximum temperature. RACMO22T (EC-EARTH) showed best performance in simulating annual and seasonal rainfall trends in GRB. In overall, models that performs better in replicating the observed climatology include RACMO22T (EC-EARTH), CCLM4–8 (MPI), CRCM5 (MPI), CCLM4–8 (CNRM), and REMO2009 (EC-EARTH). The study underscored the use of the mean ensemble of model simulation did not always guarantee better agreement with observation than individual models. Therefore prior to climate impact study, it is advisable to correct the systematic bias and employ the multi-model ensemble of best performing models for climate change impact and adaptation studies in the GRB.
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