基于ERA5的逐小时100 m风场数据,利用时间序列K-means聚类方法,将中国沿海冬季风能年际变化划分为四个区域,分别为北中国海(North China Sea,NCS)、东海(East China Sea,ECS)、南海北部(Northern South China Sea,NSCS)及南 海南部(Southern South China Sea,SSCS).四个区域风能的年际变化受不同气候模态的影响,其中NCS风能的年际变化与北极涛动(Arctic Oscillation,AO)有关;ECS风能的年际变化与中部型ENSO及西伯利亚高压有关;SSCS和NSCS的年际变化则和东部型ENSO及大陆高压的南北位置存在联系.鉴于影响各区域风能年际变化的气候模态具有较高的可预测性,进一步评估了多个气候模式对中国沿海风能年际变化的预测技巧.结果表明,气候模式对南中国海的风能年际变化预测技巧更高,这与气候模式对ENSO的高预测技巧有关.气候模式对北方海域风能年际变化的预测技巧较差,这和气候模式不能较好地预测AO和西伯利亚高压有关.
Regional characteristics,causes,and predictions of interannual variations in wind energy along the coast of China
With population growth and socio-economic development,the use of fossil fuels not only impacts the environmental but also highlights its finite nature.Consequently,the quest for environmentally friendly and sus-tainable alternative energy solutions has become urgent.Offshore wind,as an emerging energy source,offers a continuous power supply for China.However,the instability of wind energy's interannual variations can lead to insufficient energy supply for the wind power industry,emphasizing the importance of examining and predicting these variations.In this study,we employed the Time Series K-means clustering method to categorize winter interannual vari-ations of wind energy along the Chinese coast into four regions:the North China Sea(NCS),East China Sea(ECS),Northern South China Sea(NSCS),and Southern South China Sea(SSCS).Subsequently,regression analysis was used to explore the relationship between interannual variations in regional wind energy and large-scale circulation anomalies.We found that interannual variations in NCS are related to the Arctic Oscillation(AO)-related cyclones(Anticyclones)in Northeast China,while those in ECS are associated with the central-type El Niño-Southern Oscillation(ENSO)and the Siberian High.Wind power in both SSCS and NSCS is influ-enced by the eastern-type ENSO-related Philippines cyclones(Anticyclones),with the north-south position of the continental high-pressure system also affecting their interannual variations;when the continental high-pressure system shifts northward(southward),NSCS(SSCS)is mainly affected.Considering the relatively high predictability of climate modes,we evaluated the predictive skill of wind en-ergy along the Chinese coast using five climate models.Regarding climatology predictions,CMCC and JMA o-verestimate wind energy in the southern sea,contrasting with underestimations from NUIST and DWD.SEAS5 a-ligns closely with ERA5.Conversely,in the northern sea,all models except SEAS5 tend to overestimate wind energy.In terms of root mean square error(RMSE)in predictive skill,significant deviations are observed among various models for regions abundant in wind energy resources such as the Taiwan Strait,the Luzon Strait,and areas west of the Nansha Islands.CMCC exhibits the largest prediction error of wind energy resources in the Chi-nese Sea,while the SEAS5 model demonstrates the smallest prediction error.Concerning predictions of interan-nual variations,climate models show higher predictive skill for the wind energy index in the South China Sea,reflecting the models'strong predictive skill for ENSO.However,for northern regions,current climate models face challenges in predicting the influences of climate modes on wind power.This paper elucidates the relationship between the interannual variations of winter wind energy along the Chi-nese coast and large-scale circulation anomalies caused by climate factors.However,it does not delve into the underlying mechanism of this association from the perspective of atmospheric dynamics.Further investigation is needed to explore this intrinsic mechanism.Additionally,the interannual variations of wind energy in other sea-sons along the Chinese coast and their influencing factors also merit further exploration.
Chinese coastal wind energyinterannual variationsArctic oscillationEl Nino-Southern oscillationSiberian Highclimate prediction system
万方数据
维普
