DOI: 10.1007/s00382-017-4040-z
Scopus记录号: 2-s2.0-85038116817
论文题名: Potential predictability and forecast skill in ensemble climate forecast: a skill-persistence rule
作者: Jin Y. ; Rong X. ; Liu Z.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2018
卷: 51, 期: 2018-07-08 起始页码: 2725
结束页码: 2742
语种: 英语
英文关键词: AR1 model
; CGCM
; Perfect model
; Predictability
; Seasonal forecast
Scopus关键词: analytical method
; climate modeling
; climate prediction
; ensemble forecasting
; general circulation model
; sea surface temperature
; seasonal variation
; Indian Ocean
; Indian Ocean (Tropical)
; Pacific Ocean
; Pacific Ocean (East)
英文摘要: This study investigates the factors relationship between the forecast skills for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill for sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further proved using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but could be distorted by sampling errors and non-AR1 processes. This study suggests that the so called “perfect skill” is model dependent and cannot serve as an accurate estimate of the true upper limit of real world prediction skill, unless the model can capture at least the persistence property of the observation. © 2017, Springer-Verlag GmbH Germany, part of Springer Nature. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/109081
Appears in Collections: 影响、适应和脆弱性 气候变化事实与影响
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作者单位: Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China; State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China; Atmospheric Science Program, Department of Geography, Ohio State University, Columbus, OH 43210, United States
Recommended Citation:
Jin Y.,Rong X.,Liu Z.. Potential predictability and forecast skill in ensemble climate forecast: a skill-persistence rule[J]. Climate Dynamics,2018-01-01,51(2018-07-08)