气候变化领域集成服务门户(CCPortal): Assessing probabilistic predictions of ENSO phase and intensity from the North American Multimodel Ensemble

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DOI	10.1007/s00382-017-3721-y
	Assessing probabilistic predictions of ENSO phase and intensity from the North American Multimodel Ensemble
	Tippett M.K.; Ranganathan M.; L’Heureux M.; Barnston A.G.; DelSole T.
发表日期	2019
ISSN	0930-7575
起始页码	7497
结束页码	7518
卷号	53 期号:12
英文摘要	Here we examine the skill of three, five, and seven-category monthly ENSO probability forecasts (1982–2015) from single and multi-model ensemble integrations of the North American Multimodel Ensemble (NMME) project. Three-category forecasts are typical and provide probabilities for the ENSO phase (El Niño, La Niña or neutral). Additional forecast categories indicate the likelihood of ENSO conditions being weak, moderate or strong. The level of skill observed for differing numbers of forecast categories can help to determine the appropriate degree of forecast precision. However, the dependence of the skill score itself on the number of forecast categories must be taken into account. For reliable forecasts with same quality, the ranked probability skill score (RPSS) is fairly insensitive to the number of categories, while the logarithmic skill score (LSS) is an information measure and increases as categories are added. The ignorance skill score decreases to zero as forecast categories are added, regardless of skill level. For all models, forecast formats and skill scores, the northern spring predictability barrier explains much of the dependence of skill on target month and forecast lead. RPSS values for monthly ENSO forecasts show little dependence on the number of categories. However, the LSS of multimodel ensemble forecasts with five and seven categories show statistically significant advantages over the three-category forecasts for the targets and leads that are least affected by the spring predictability barrier. These findings indicate that current prediction systems are capable of providing more detailed probabilistic forecasts of ENSO phase and amplitude than are typically provided. © 2017, The Author(s).
英文关键词	Ensemble forecasting; ENSO; Probabilistic verification
语种	英语
scopus关键词	amplitude; climate modeling; climate prediction; El Nino-Southern Oscillation; ensemble forecasting; probability; North America
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/145836
作者单位	Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY, United States; Department of Meteorology, Center of Excellence for Climate Change Research, King Abdulaziz University, Jeddah, Saudi Arabia; Swarthmore College, Swarthmore, PA, United States; National Oceanic and Atmospheric Administration/National Weather Service/National Centers for Environmental Prediction, Climate Prediction Center, College Park, MD, United States; International Research Institute for Climate and Society, The Earth Institute of Columbia University, Palisades, New York, NY, United States; George Mason University, Fairfax, VA, United States; Center for Ocean-Land-Atmosphere Studies, Calverton, MD, United States
推荐引用方式 GB/T 7714	Tippett M.K.,Ranganathan M.,L’Heureux M.,et al. Assessing probabilistic predictions of ENSO phase and intensity from the North American Multimodel Ensemble[J],2019,53(12).
APA	Tippett M.K.,Ranganathan M.,L’Heureux M.,Barnston A.G.,&DelSole T..(2019).Assessing probabilistic predictions of ENSO phase and intensity from the North American Multimodel Ensemble.Climate Dynamics,53(12).
MLA	Tippett M.K.,et al."Assessing probabilistic predictions of ENSO phase and intensity from the North American Multimodel Ensemble".Climate Dynamics 53.12(2019).