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

Jemal Ali Mohammed
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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.
埃塞俄比亚上青尼罗河(阿贝)流域 CMIP6 全球气候模型性能评估与排名
使用全球气候模式(GCMs)数据是开展气候科学研究最实际的方法。然而,性能评估和选择合适的gcm是至关重要的。本研究评估了8个CMIP6 gcm对埃塞俄比亚上青尼罗河流域1988 - 2014年年和季节降水的模拟效果。研究采用了相关系数、均方根误差、偏差百分比、克林-古普塔效率和纳什-苏特克利夫效率五个绩效指标。然后将各种pm的分数合并为一个分数,并使用折衷编程(CP)对CMIP6 gcm进行排名。利用空间、泰勒图(TD)和面积平均年度和季节性评估验证了CP的发现。尽管PMs产生了一些相互矛盾的结果,但该研究表明,CP能够一致地评估CMIP6 GCMs。对CMIP6 GCMs与观测数据的区域评价表明,使用CP排名最高的CMIP6 GCMs能够更准确地复制观测到的年和季节降雨量。排名最低的CMIP6 gcm在空间上高估或低估了流域的降雨量。根据CP方法的结果,CMIP6的3种gcm对年降雨量的预测效果最好,分别是BCC-CSM2-MR、MIROC6和NorESM2-MM;在冬季,最佳的CMIP6 gcm是BCC-CSM2-MR、GISS-E2-2-G和EC-Earth3。INM-CM5-0、MIROC6和MRI-ESM2-0在Bega季节排名最高,EC-Earth3、BCC-CSM2-MR和MRI-ESM2-0在Belg季节排名最高。建议使用上述排名的CMIP6 gcm来预测UBNB的降雨特征。此外,研究结果表明,CMIP6 GCMs可以在整个时间尺度上使用一系列pm进行评估,并且可以使用CP等技术来识别性能最好的CMIP6 GCMs。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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