Dynamical seasonal prediction of Southern African summer precipitation

被引:25
|
作者
Yuan, Chaoxia [1 ]
Tozuka, Tomoki [2 ]
Landman, Willem A. [3 ,4 ]
Yamagata, Toshio [1 ]
机构
[1] JAMSTEC, Applicat Lab, Yokohama, Kanagawa 2360001, Japan
[2] Univ Tokyo, Grad Sch Sci, Dept Earth & Planetary Sci, Tokyo 1130033, Japan
[3] CSIR, ZA-0001 Pretoria, South Africa
[4] Univ Pretoria, Dept Geog Geoinformat & Meteorol, ZA-0002 Pretoria, South Africa
基金
日本科学技术振兴机构;
关键词
Seasonal prediction; Southern African summer precipitation; ENSO; Coupled general circulation model; Relative operating characteristic; Relative operating level; Reliability diagram; SEA-SURFACE TEMPERATURE; RELATIVE OPERATING CHARACTERISTICS; SST DIPOLE EVENTS; INDIAN-OCEAN; PART I; RAINFALL; ENSO; VARIABILITY; MODEL; FORECASTS;
D O I
10.1007/s00382-013-1923-5
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Prediction skill for southern African (16A degrees-33A degrees E, 22A degrees-35A degrees S) summer precipitation in the Scale Interaction Experiment-Frontier coupled model is assessed for the period of 1982-2008. Using three different observation datasets, deterministic forecasts are evaluated by anomaly correlation coefficients, whereas scores of relative operating characteristic and relative operating level are used to evaluate probabilistic forecasts. We have found that these scores for December-February precipitation forecasts initialized on October 1st are significant at 95 % confidence level. On a local scale, the level of prediction skill in the northwestern and central parts of southern Africa is higher than that in northeastern South Africa. El Nio/Southern Oscillation (ENSO) provides the major source of predictability, but the relationship with ENSO is too strong in the model. The Benguela Nio, the basin mode in the tropical Indian Ocean, the subtropical dipole modes in the South Atlantic and the southern Indian Oceans and ENSO Modoki may provide additional sources of predictability. Within the wet season from October to the following April, the precipitation anomalies in December-February are the most predictable. This study presents promising results for seasonal prediction of precipitation anomaly in the extratropics, where seasonal prediction has been considered a difficult task.
引用
收藏
页码:3357 / 3374
页数:18
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