Tran Van Ty, Le Hai Tri, Nguyen Van Tho, Nguyen Van Toan, Giap Minh Nhat, N. Downes, Pankaj Kumar, Huỳnh Vương Thu Minh
{"title":"Evaluating the Performance of CMIP6 GCMs to Simulate Precipitation and Temperature Over the Vietnamese Mekong Delta","authors":"Tran Van Ty, Le Hai Tri, Nguyen Van Tho, Nguyen Van Toan, Giap Minh Nhat, N. Downes, Pankaj Kumar, Huỳnh Vương Thu Minh","doi":"10.3233/jcc230013","DOIUrl":null,"url":null,"abstract":"This study evaluates the performance of simulated precipitation and maximum and minimum temperatures in the historical runs of the Climate Model Intercomparison Project Phase 6 (CMIP6) for the Vietnamese Mekong Delta (VMD). The precipitation, as well as maximum and minimum temperatures outputs from 16 general circulation models (GCMs), were compared with observations from 12 stations for the period 1980–2014, using a set of statistical metrics, namely, normalised root mean square error (NRMSE), percentage of bias (PBIAS), Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), and volumetric efficiency (VE). Finally, ranking (total score - TS) was carried out and the probability distribution function (PDF) and Taylor diagram were used to confirm rankings. The results show that different statistical indicators reveal variation ranking order of the 16 GCMs. Based on RS ranking, it is indicated that each simulation GCM performed differently under the different metrics and no single model performed best for all metrics. The top five highest ranked GCMs based on TS were HadGEM3-GC31-LL, ACCESS-CM2, CanESM5, NESM3 and CanESM5-CanOE for precipitation; and CNRM-CM6-1, CNRM-ESM2-1, GFDL-ESM4, NESM3 and INM-CM5-0 for the maximum; and CNRM-CM6-1, CNRM-ESM2-1, GFDL-ESM4, NESM3 and INM-CM5-0 for minimum temperatures, respectively. We also observed an underestimation of precipitation and an overestimation of temperature over the study area. The TS method demonstrates efficiency to aggregate the multi-model ensemble GCMs based on different statistical indicators which were sometimes contradictory. The findings from this study provide useful guidance in the selection of GCMs for climate change applications in the VMD.","PeriodicalId":43177,"journal":{"name":"Journal of Climate Change","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Climate Change","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/jcc230013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
This study evaluates the performance of simulated precipitation and maximum and minimum temperatures in the historical runs of the Climate Model Intercomparison Project Phase 6 (CMIP6) for the Vietnamese Mekong Delta (VMD). The precipitation, as well as maximum and minimum temperatures outputs from 16 general circulation models (GCMs), were compared with observations from 12 stations for the period 1980–2014, using a set of statistical metrics, namely, normalised root mean square error (NRMSE), percentage of bias (PBIAS), Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), and volumetric efficiency (VE). Finally, ranking (total score - TS) was carried out and the probability distribution function (PDF) and Taylor diagram were used to confirm rankings. The results show that different statistical indicators reveal variation ranking order of the 16 GCMs. Based on RS ranking, it is indicated that each simulation GCM performed differently under the different metrics and no single model performed best for all metrics. The top five highest ranked GCMs based on TS were HadGEM3-GC31-LL, ACCESS-CM2, CanESM5, NESM3 and CanESM5-CanOE for precipitation; and CNRM-CM6-1, CNRM-ESM2-1, GFDL-ESM4, NESM3 and INM-CM5-0 for the maximum; and CNRM-CM6-1, CNRM-ESM2-1, GFDL-ESM4, NESM3 and INM-CM5-0 for minimum temperatures, respectively. We also observed an underestimation of precipitation and an overestimation of temperature over the study area. The TS method demonstrates efficiency to aggregate the multi-model ensemble GCMs based on different statistical indicators which were sometimes contradictory. The findings from this study provide useful guidance in the selection of GCMs for climate change applications in the VMD.