Variation Characteristics of Spring Low Wind Speed Events and Their Precursor Influencing Factors in Southwest China
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摘要: 中国西南地区风电和水电具有显著的季节互补性，春季是该地区风电资源最丰富的季节，也是水电季节性枯水期。因此，春季低风速事件对该地区电网的安全稳定运行与电力调度具有重要影响，开展该地区春季低风速事件变化特征及气候预测前期信号的研究具有重要意义。本文基于站点观测数据和再分析资料，分析了中国西南地区春季低风速事件的时空变化特征，并探究其年际尺度变化的前期信号和物理机制。结果表明，春季低风速事件的低值区主要集中在川西、滇中东部及贵州西部，其年际变化主要呈现为东西反相位变化的空间模态。进一步的分析表明，该模态的年际变化受前期热带太平洋和北太平洋中纬度海温的共同影响。当前期冬季赤道中太平洋海温偏低、黑潮延伸区海温偏高时，次年春季中南半岛和中国东北地区将受到异常气旋性环流控制，导致西南地区出现异常偏东风和偏北风，减弱该地区的背景西风场，进而有利于低风速事件的发生。因此，前期冬季赤道中太平洋和黑潮延伸区海温可作为预报中国西南地区次年春季风能资源的重要前兆信号。Abstract: Wind power and hydropower in Southwest China (SWC) exhibit strong seasonal complementarity. Spring coincides with the peak of regional wind resources, yet it is also the low-flow season for hydropower. Consequently, spring low wind speed events can significantly disrupt the stable and secure operation of the regional power grid and complicate electricity dispatch. Investigating the spatiotemporal characteristics of these events and identifying precursory signals for their climatic prediction is therefore of great practical importance. Based on in-situ station observations and reanalysis datasets, this study analyzes the spatiotemporal variability of spring low wind speed events in SWC, and further explores the preceding oceanic and atmospheric signals governing their interannual variation. Results show that low wind speed event centers are predominantly located in western Sichuan, central-eastern Yunnan, and western Guizhou. The interannual variation is dominated by the leading spatial mode, which features an east-west out-of-phase dipole structure. Further analysis reveals that this mode is jointly driven by preceding sea surface temperature (SST) anomalies in the tropical Pacific and the mid-latitude North Pacific. Specifically, when the preceding winter SST is anomalously low in the equatorial central Pacific and anomalously high along the Kuroshio Extension, anomalous cyclonic circulations dominate over the Indochina Peninsula and northeastern China in the following spring. These circulation anomalies induce anomalous easterly and northerly winds over SWC, which oppose and weaken the climatological westerlies, thereby favoring the occurrence of low wind speed events. Therefore, the preceding winter SST anomalies in these two key regions can serve as important precursory indicators for spring wind energy resource prediction in SWC.
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图 5 1979—2015年春季低风速事件EOF1（a）和EOF2（c）的空间分布及PC1（b）和PC2（d）的时间序列
图中右上角为对应模态的解释方差。色阶表示特征向量值，b、d中红色和蓝色分别表示正值和负值，b中黑色实线表示标准化区域平均低风速事件的时间序列。
图 6 1979—2015年春季700 hPa平均风场（a，单位：m·s-1）与低风速事件PC1的回归风场（b，单位：m·s-1）
黑实线色表示中国西南地区边界线，红色矢量表示通过α=0.1显著性检验。
图 9 1980—2015年春季700 hPa异常风场（矢量，单位：m·s-1）及位势高度（填色，单位：gpm）对前冬海温指数的回归结果
加粗矢量及阴影区域表示通过α=0.05的显著性检验。
图 10 前冬海温指数异常年份合成的次年春季观测风速概率密度分布（a）；前冬标准化海温指数（黑线）与春季西南地区标准化区域平均低风速事件（红线）的时间序列（b）；春季低风速事件对前冬海温指数的回归系数空间分布（c）
a中红线对应前期冬季海温指数异常偏高年，蓝线对应异常偏低年。c中*表示回归系数通过α=0.05的显著性检验。
表 1 1979—2015年西南地区春季不同低风速阈值下事件序列的相关系数
r (1.5 m·s-1与2.5 m·s-1) r (1.5 m·s-1与3.5 m·s-1) r (2.5 m·s-1与3.5 m·s-1) 原始序列 0.47* 0.61* 0.95* 滤波后序列 0.91* 0.85* 0.94* *代表相关系数r通过α=0.05的显著性检验。 
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