Probabilistic Seasonal Prediction of Summer Rainfall over East China Based on Multi-Model Ensemble Schemes

被引:8
作者
Li Fang [1 ]
机构
[1] Chinese Acad Sci, Inst Atmospher Phys, Beijing 100029, Peoples R China
来源
ACTA METEOROLOGICA SINICA | 2011年 / 25卷 / 03期
基金
中国国家自然科学基金;
关键词
multi-model ensemble; uncertainty; probability density function; seasonal prediction; rainfall; CLIMATE; FORECASTS; CALIBRATION; SYSTEM;
D O I
10.1007/s13351-011-0304-4
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
The skill of probability density function (PDF) prediction of summer rainfall over East China using optimal ensemble schemes is evaluated based on the precipitation data from five coupled atmosphere-ocean general circulation models that participate in the ENSEMBLES project. The optimal ensemble scheme in each region is the scheme with the highest skill among the four commonly-used ones: the equally-weighted ensemble (EE), EE for calibrated model-simulations (Cali-EE), the ensemble scheme based on multiple linear regression analysis (MLR), and the Bayesian ensemble scheme (Bayes). The results show that the optimal ensemble scheme is the Bayes in the southern part of East China; the Cali-EE in the Yangtze River valley, the Yangtze-Huaihe River basin, and the central part of northern China; and the MLR in the eastern part of northern China. Their PDF predictions are well calibrated, and are sharper than or have approximately equal interval-width to the climatology prediction. In all regions, these optimal ensemble schemes outperform the climatology prediction, indicating that current commonly-used multi-model ensemble schemes are able to produce skillful PDF prediction of summer rainfall over East China, even though more information for other model variables is not derived.
引用
收藏
页码:283 / 292
页数:10
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