气候变化领域集成服务门户(CCPortal): Evaluation of the skill of North-American Multi-Model Ensemble (NMME) Global Climate Models in predicting average and extreme precipitation and temperature over the continental USA

CCPortal

DOI	10.1007/s00382-016-3286-1
	Evaluation of the skill of North-American Multi-Model Ensemble (NMME) Global Climate Models in predicting average and extreme precipitation and temperature over the continental USA
	Slater L.J.; Villarini G.; Bradley A.A.
发表日期	2019
ISSN	0930-7575
起始页码	7381
结束页码	7396
卷号	53 期号:12
英文摘要	This paper examines the forecasting skill of eight Global Climate Models from the North-American Multi-Model Ensemble project (CCSM3, CCSM4, CanCM3, CanCM4, GFDL2.1, FLORb01, GEOS5, and CFSv2) over seven major regions of the continental United States. The skill of the monthly forecasts is quantified using the mean square error skill score. This score is decomposed to assess the accuracy of the forecast in the absence of biases (potential skill) and in the presence of conditional (slope reliability) and unconditional (standardized mean error) biases. We summarize the forecasting skill of each model according to the initialization month of the forecast and lead time, and test the models’ ability to predict extended periods of extreme climate conducive to eight ‘billion-dollar’ historical flood and drought events. Results indicate that the most skillful predictions occur at the shortest lead times and decline rapidly thereafter. Spatially, potential skill varies little, while actual model skill scores exhibit strong spatial and seasonal patterns primarily due to the unconditional biases in the models. The conditional biases vary little by model, lead time, month, or region. Overall, we find that the skill of the ensemble mean is equal to or greater than that of any of the individual models. At the seasonal scale, the drought events are better forecast than the flood events, and are predicted equally well in terms of high temperature and low precipitation. Overall, our findings provide a systematic diagnosis of the strengths and weaknesses of the eight models over a wide range of temporal and spatial scales. © 2016, Springer-Verlag Berlin Heidelberg.
英文关键词	Drought; Flood; Model biases; Multi-model ensemble; NMME; Seasonal forecasting
语种	英语
scopus关键词	climate prediction; drought; ensemble forecasting; extreme event; flood; general circulation model; precipitation (climatology); seasonal variation; United States
来源期刊	Climate Dynamics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/145845
作者单位	IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, IA, United States
推荐引用方式 GB/T 7714	Slater L.J.,Villarini G.,Bradley A.A.. Evaluation of the skill of North-American Multi-Model Ensemble (NMME) Global Climate Models in predicting average and extreme precipitation and temperature over the continental USA[J],2019,53(12).
APA	Slater L.J.,Villarini G.,&Bradley A.A..(2019).Evaluation of the skill of North-American Multi-Model Ensemble (NMME) Global Climate Models in predicting average and extreme precipitation and temperature over the continental USA.Climate Dynamics,53(12).
MLA	Slater L.J.,et al."Evaluation of the skill of North-American Multi-Model Ensemble (NMME) Global Climate Models in predicting average and extreme precipitation and temperature over the continental USA".Climate Dynamics 53.12(2019).