DOI: 10.1002/2016MS000744
Scopus记录号: 2-s2.0-85010845084
论文题名: Improved seasonal prediction using the SINTEX-F2 coupled model
作者: Doi T ; , Behera S ; K ; , Yamagata T
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2016
卷: 8, 期: 4 起始页码: 1847
结束页码: 1867
语种: 英语
英文关键词: Climatology
; Earth (planet)
; Forecasting
; Nickel
; Rain
; Sea ice
; Tropics
; Collaborative framework
; Coupled general circulation models
; Coupled modeling
; Early Warning System
; Indian Ocean dipole
; Industrial activities
; Rainfall distribution
; Seasonal prediction
; Climate models
; climate modeling
; climate prediction
; early warning system
; El Nino
; El Nino-Southern Oscillation
; European Union
; extratropical environment
; general circulation model
; global climate
; Indian Ocean Dipole
; rainfall
; sea ice
; seasonal variation
; Indian Ocean
; Japan
; Pacific Ocean
英文摘要: The SINTEX-F1 Coupled General Circulation Model (CGCM) was developed within the EU-Japan collaborative framework to study global climate variability and its predictability by use of the Earth Simulator. The seasonal prediction system based on the SINTEX-F1 has demonstrated its outstanding performance of predicting El Niño/Southern Oscillation (ENSO) and the Indian Ocean Dipole since 2005. However, there is much room for improvement in predicting extratropical climate variations. To deal with this, a revised CGCM called SINTEX-F2 has been developed; the new system is a high-resolution version with a dynamical sea-ice model. For the tropical climate variations in the Pacific and the Indian Ocean, the SINTEX-F2 preserves the high-prediction skill, and sometimes even shows higher skill especially for strong events, as compared to the SINTEX-F1. In addition, it has turned out that the new system is more skillful in predicting the subtropics, particularly, the Indian Ocean Subtropical Dipole and the Ningaloo Niño. The improvement may contribute to enhancing prediction skills of the regional rainfall distributions and encourage us to develop an early warning system which may be applied for societal and industrial activities. © 2016. The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75852
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: Application Laboratory/Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
Recommended Citation:
Doi T,, Behera S,K,et al. Improved seasonal prediction using the SINTEX-F2 coupled model[J]. Journal of Advances in Modeling Earth Systems,2016-01-01,8(4)