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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, Michael K.1; 39;Heureux, Michelle2 | |
2019-12-01 | |
发表期刊 | CLIMATE DYNAMICS |
ISSN | 0930-7575 |
EISSN | 1432-0894 |
出版年 | 2019 |
卷号 | 53期号:12页码:7497-7518 |
文章类型 | Article |
语种 | 英语 |
国家 | USA; Saudi Arabia |
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 Nino, La Nina 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. |
英文关键词 | ENSO Probabilistic verification Ensemble forecasting |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000495247200023 |
WOS关键词 | EL-NINO ; SEASONAL PRECIPITATION ; SPATIAL EXTENT ; SKILL ; FORECASTS ; SST ; VERSION ; SYSTEM ; CLASSIFICATION ; PREDICTABILITY |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/224305 |
专题 | 环境与发展全球科技态势 |
作者单位 | 1.Columbia Univ, Dept Appl Phys & Appl Math, New York, NY 10027 USA; 2.King Abdulaziz Univ, Ctr Excellence Climate Change Res, Dept Meteorol, Jeddah, Saudi Arabia; 3.Swarthmore Coll, Swarthmore, PA 19081 USA; 4.NOAA, Natl Weather Serv, Natl Centers Environm Predict, Climate Predict Ctr, College Pk, MD USA; 5.Columbia Univ, Earth Inst, Int Res Inst Climate & Soc, New York, NY USA; 6.George Mason Univ, Fairfax, VA 22030 USA; 7.Ctr Ocean Land Atmosphere Studies, Calverton, MD USA |
推荐引用方式 GB/T 7714 | Tippett, Michael K.,39;Heureux, Michelle. Assessing probabilistic predictions of ENSO phase and intensity from the North American Multimodel Ensemble[J]. CLIMATE DYNAMICS,2019,53(12):7497-7518. |
APA | Tippett, Michael K.,&39;Heureux, Michelle.(2019).Assessing probabilistic predictions of ENSO phase and intensity from the North American Multimodel Ensemble.CLIMATE DYNAMICS,53(12),7497-7518. |
MLA | Tippett, Michael K.,et al."Assessing probabilistic predictions of ENSO phase and intensity from the North American Multimodel Ensemble".CLIMATE DYNAMICS 53.12(2019):7497-7518. |
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