Skilful seasonal prediction of winter wind speeds in China

被引:14
|
作者
Lockwood, Julia F. [1 ]
Thornton, Hazel E. [1 ]
Dunstone, Nick [1 ]
Scaife, Adam A. [1 ,2 ]
Bett, Philip E. [1 ]
Li, Chaofan [3 ]
Ren, Hong-Li [4 ]
机构
[1] Met Off, Hadley Ctr, FitzRoy Rd, Exeter EX1 3PB, Devon, England
[2] Univ Exeter, Coll Engn Math & Phys Sci, Exeter, Devon, England
[3] Chinese Acad Sci, Inst Atmospher Phys, Ctr Monsoon Syst Res, Beijing 100029, Peoples R China
[4] China Meteorol Adm, Natl Climate Ctr, Lab Climate Studies, Beijing, Peoples R China
关键词
Seasonal forecasting; Forecast verification/skill; Wind; Asia; NORTH-ATLANTIC OSCILLATION; ARCTIC OSCILLATION; SOUTHERN CHINA; MODEL; PREDICTABILITY; TEMPERATURE; VARIABILITY; FORECASTS; PRECIPITATION; CONFIGURATION;
D O I
10.1007/s00382-019-04763-8
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
We demonstrate robust skill in forecasting winter (DJF) mean 10 m wind speeds for the period 1992/3-2011/12 over southeastern China and the South China Sea (SE China) and northern-central (NC) China, with correlations exceeding 0.8 and 0.6 respectively. High skill over these regions is seen in two independent initialised ensembles which cover different time periods. The NC China region suffers from a similar signal-to-noise problem as identified in forecasts of the North Atlantic Oscillation, where the model appears to be less predictable than the real world. In SE China, the predictability of wind speeds comes from the model's ability to predict the El Nino Southern Oscillation. In NC China, the wind speed is strongly related to two neighbouring geopotential height anomalies. Cross-validated linear regression models using the above climate indices give similar skill to using the direct model wind predictions in both regions. The model also has significant skill in predicting the strength of the Middle Eastern jet stream, which has previously been shown to be related to winter climate in central China. The model skill demonstrated here may be high enough to develop useful sector-specific seasonal forecasts, for example wind power forecasts for the energy industry, or air quality forecasts for the health sector.
引用
收藏
页码:3937 / 3955
页数:19
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