DOI: 10.1007/s00382-016-3264-7
Scopus记录号: 2-s2.0-84983551832
论文题名: MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center
作者: Liu X. ; Wu T. ; Yang S. ; Li T. ; Jie W. ; Zhang L. ; Wang Z. ; Liang X. ; Li Q. ; Cheng Y. ; Ren H. ; Fang Y. ; Nie S.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2017
卷: 48, 期: 2017-09-10 起始页码: 3283
结束页码: 3307
语种: 英语
英文关键词: Improved initial condition
; Indian Ocean Dipole
; MJO forecast skill
; Model deficiency
英文摘要: By conducting several sets of hindcast experiments using the Beijing Climate Center Climate System Model, which participates in the Sub-seasonal to Seasonal (S2S) Prediction Project, we systematically evaluate the model’s capability in forecasting MJO and its main deficiencies. In the original S2S hindcast set, MJO forecast skill is about 16 days. Such a skill shows significant seasonal-to-interannual variations. It is found that the model-dependent MJO forecast skill is more correlated with the Indian Ocean Dipole (IOD) than with the El Niño–Southern Oscillation. The highest skill is achieved in autumn when the IOD attains its maturity. Extended skill is found when the IOD is in its positive phase. MJO forecast skill’s close association with the IOD is partially due to the quickly strengthening relationship between MJO amplitude and IOD intensity as lead time increases to about 15 days, beyond which a rapid weakening of the relationship is shown. This relationship transition may cause the forecast skill to decrease quickly with lead time, and is related to the unrealistic amplitude and phase evolutions of predicted MJO over or near the equatorial Indian Ocean during anomalous IOD phases, suggesting a possible influence of exaggerated IOD variability in the model. The results imply that the upper limit of intraseasonal predictability is modulated by large-scale external forcing background state in the tropical Indian Ocean. Two additional sets of hindcast experiments with improved atmosphere and ocean initial conditions (referred to as S2S_IEXP1 and S2S_IEXP2, respectively) are carried out, and the results show that the overall MJO forecast skill is increased to 21–22 days. It is found that the optimization of initial sea surface temperature condition largely accounts for the increase of the overall MJO forecast skill, even though the improved initial atmosphere conditions also play a role. For the DYNAMO/CINDY field campaign period, the forecast skill increases to 27 days in S2S_IEXP2. Nevertheless, even with improved initialization, it is still difficult for the model to predict MJO propagation across the western hemisphere–western Indian Ocean area and across the eastern Indian Ocean–Maritime Continent area. Especially, MJO prediction is apparently limited by various interrelated deficiencies (e.g., overestimated IOD, shorter-than-observed MJO life cycle, Maritime Continent prediction barrier), due possibly to the model bias in the background moisture field over the eastern Indian Ocean and Maritime Continent. Thus, more efforts are needed to correct the deficiency in model physics in this region, in order to overcome the well-known Maritime Continent predictability barrier. © 2016, The Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/53256
Appears in Collections: 过去全球变化的重建
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作者单位: Climate Model Division, National Climate Center, China Meteorological Administration, 46 Zhongguancun Nandajie, Haidian District, Beijing, China; Department of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China; Department of Meteorology, International Pacific Research Center, University of Hawaii at Manoa, Honolulu, HI, United States
Recommended Citation:
Liu X.,Wu T.,Yang S.,et al. MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center[J]. Climate Dynamics,2017-01-01,48(2017-09-10)