Abstract
Model selection over a region known with data scarcity is crucial to get a substitute to study trends, variability and projections. This study was concerned with the comparison of 12 General Circulation Models (GCMs) under CMIP5 and CMIP6 relative to Climate Research Unit (CRU) precipitation in Ethiopia from 1966 to 2005. The comparison between GCMs was made using correlation, root mean squared error (RMSE), bias, skill score, Kling Gupta efficiency (KGE) and Taylor diagram. Comparison revealed that BCC, INM and CanESM during the boreal spring season from March to May (MAM), and CanESM, FGOALS, GFDL and BCC during the boreal summer season from June to August (JJA) under CMIP6 and INM, BCC and FGOALS during MAM and MPI-LR, CMCC and MPI-LR during the boreal autumn season from September to November (SON) under CMIP5 exhibited significantly equal mean value at 5% (p < 0.05) significant level with CRU precipitation. The maximum and minimum deviations were 134.9 mm (positive) from MIROC (CMIP5) and 70.37 mm (negative) from CMCC (CMIP5), respectively. Moreover, the lowest and highest deviations were also observed in JJA. GCMs namely: INM, GFDL, BCC, CMCC, IPSL, MRI and MIROC during MAM, BCC, CMCC, FGOALS, GFDL, INM, IPSL, MIROC and MRI during JJA and ACCESS, FGOALS, INM, MIROC and MPI-LR during SON were best GCMs from CMIP6 in reproducing CRU precipitation in Ethiopia. As a result, INM, MIROC, FGOALS, IPSL and CMCC were models that revealed better performance from CMIP6 compared to CMIP5 in Ethiopia. More number of GCMs from CMIP6 were better than GCMs from CMIP5 in capturing observed precipitation during MAM and JJA. Comparable performance of GCMs from both projects were observed during SON. The study indicated that further analysis need to be carried out to identify the improvement in capturing observed SON and annual precipitation.
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Gobie, B.G., Assamnew, A.D., Habtemicheal, B.A. et al. Comparison of GCMs Under CMIP5 and CMIP6 in Reproducing Observed Precipitation in Ethiopia During Rainy Seasons. Earth Syst Environ (2024). https://doi.org/10.1007/s41748-024-00394-0
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DOI: https://doi.org/10.1007/s41748-024-00394-0