JOURNAL ARTICLE

Performance evaluation and ranking of CMIP6 global climate models over upper blue nile (abbay) basin of Ethiopia

Jemal Ali Mohammed

Year: 2024 Journal:   Natural Hazards Research Vol: 5 (1)Pages: 61-74   Publisher: Elsevier BV

Abstract

The use of Global Climate Models (GCMs) data is the most practical way to conduct studies on climate science. However, performance evaluation and the selection of appropriate GCMs are vital. In this research, the effectiveness of eight selected CMIP6 GCMs in simulating the annual and seasonal rainfall observed over the Ethiopian Upper Blue Nile Basin from 1988 to 2014 was assessed. Five performance metrics (PMs) were used in the study: the correlation coefficient, root mean square error, bias percentage, Kling-Gupta efficiency and Nash-Sutcliffe efficiency. The scores of the various PMs were then combined into one, and the CMIP6 GCMs were ranked using Compromised Programming (CP). The findings from the CP were verified using a spatial, Taylor Diagram (TD), and areal average annual and seasonal evaluations. Even though the PMs produced some contradicting results, the study exhibited that CP was capable to evaluate the CMIP6 GCMs consistently. A regional evaluation of the CMIP6 GCMs relative to the observed data revealed that the best-ranked CMIP6 GCMs by using CP were capable to more accurately replicate the observed annual and seasonal rainfall. The lowest-ranking CMIP6 GCMs were found to have either spatially overvalued or undervalued the amount of rainfall over the basin. The best three CMIP6 GCMs for annual rainfall, according to the results of the CP method, are BCC-CSM2-MR, MIROC6, and NorESM2-MM; for the Kiremt season, the best CMIP6 GCMs are BCC-CSM2-MR, GISS-E2-2-G, and EC-Earth3. INM-CM5-0, MIROC6, and MRI-ESM2-0 ranked highest for Bega season, and EC-Earth3, BCC-CSM2-MR, and MRI-ESM2-0 for Belg season. It is recommended using the above-ranked CMIP6 GCMs to predict the characteristics of rainfall in the UBNB. Furthermore, results suggest that the CMIP6 GCMs be evaluated with a range of PMs across the whole temporal scales and that techniques such as CP be used to identify the best-performing CMIP6 GCMs.

Keywords:
Ranking (information retrieval) Structural basin Climatology Geography Environmental science Geology Computer science Paleontology Artificial intelligence

Metrics

7
Cited By
4.02
FWCI (Field Weighted Citation Impact)
62
Refs
0.88
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Climate variability and models
Physical Sciences →  Environmental Science →  Global and Planetary Change
Soil Moisture and Remote Sensing
Physical Sciences →  Environmental Science →  Environmental Engineering
Hydrology and Watershed Management Studies
Physical Sciences →  Environmental Science →  Water Science and Technology

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