大尺度驱动因子对新疆极端冷事件的单一与耦合影响

全球变暖背景下，偶发极端冷事件产生的重大灾害损失不容忽视。探究区域极端冷事件的大尺度驱动因子的耦合影响，对预估和应对气候变化产生的极端灾害具有重要意义。本文基于新疆1961—2016年53个气象站点的逐日气温资料，通过反距离加权等方法对极端冷事件的时空演变特征进行分析；利用交叉小波变换对6个极端冷指数与大尺度驱动因子——北极涛动（AO）、北大西洋涛动（NAO）和厄尔尼诺-南方涛动（ENSO）进行多尺度分析；使用参数假设检验对大尺度驱动因子单一/耦合模态下的冷指数变化进行统计学显著性检验，随后对大尺度环流机制进行距平合成分析。结果表明：年均冷指数在时间尺度上均有显著性变化，新疆气温有明显的变暖趋势；空间尺度上冷指数在北疆、东疆和伊犁河谷地区的变化幅度远大于其他区域，存在空间差异性。AO、NAO与冷指数的相关性较强，ENSO与冷指数相关关系最弱但存在明显的时滞效应，大尺度驱动因子对极端冷指数的总体影响程度为AO>NAO>ENSO。单一模态下，极端冷事件在AO负位相、NAO负位相和La Ni?a事件期间易发生。耦合模态下，EI Ni?o-AO正位相和EI Ni?o-NAO正位相配置下冷日日数偏多；EI Ni?o-NAO负位相配置时极端低温值更小；La Ni?a-AO负位相和La Ni?a-NAO正位相时极端冷事件发生的可能性更大。EI Ni?o（La Ni?a）事件对AO（NAO）有一定的调制作用。新疆极端冷事件更易出现在La Ni?a-AO负位相、La Ni?a-NAO正位相时期，成因与亚欧大陆中高纬度位势异常导致冷空气路径偏西、乌拉尔阻塞加强与偏北气流影响新疆有关。

Individual and coupled influences of large-scale driving factors on extreme cold events in Xinjiang

The extreme cold events that occur occasionally ought to be valued under the trend of global warming. Meanwhile， exploring the influences of large-scale driving factors on extreme cold events in Xinjiang is of great significance to predict and cope with disasters caused by climate change. Based on extreme cold indices which are calculated by daily air surface temperature data from 53 meteorological stations covering the period from 1961 to 2016. Temporal and spatial characteristics of extreme cold events in Xinjiang have been analyzed using linear regression analysis and Inverse Distance Weighted， it is found that the average extreme cold indices have a significant change in spatial point， this means the temperature of Xinjiang has a increasing trend. In the spatial-scale， the variation range of cold indices in northern Xinjiang， eastern Xinjiang and Ili River Valley have dramatic change rather than other region. Cross wavelet transform are used in order to analyze the relationship between extreme cold indices， including frost days（FD）， ice days（ID）， daily minimum air tempreture （TNn）， maximum of daily maximum tempreture （TXn）， cold nights（TN10p）， cold days（TX10p）， and large-scale driving factors ［Arctic Oscillation（AO）， North Atlantic Oscillation（NAO）， EI Ni?o and the Southern Oscillation（ENSO）］， the result shows that AO and NAO have strong relevance with extreme cold indices， the overall influence of large-scale driving factors on extreme cold indices is AO>NAO>ENSO. Using parametric hypothesis tests verify statistically significant changes in the cold indices characteristics from one phase to another of each oscillation and also in coupled phases. During the individual factor driving modes， the extreme cold events have occurred easier on AO negative phase， NAO negative phase， and La Ni?a event. During the coupled factors driving modes， the number of cold days are more on EI Ni?o-AO positive phase and EI Ni?o-NAO positive phase， and the lower temperature values of extreme cold events would be smaller on EI Ni?o-NAO negative phase. Extreme cold events are more likely to occur during the La Ni?a-AO negative phase and La Ni?a-NAO positive phase. The EI Ni?o （La Ni?a） event has a modulating effect on the AO （NAO）. To explore the mechanism for the coupled modes， the atmospheric circulation systems are discussed via anomalies composite analysis by using National Centers for Environmental Prediction（NCEP） reanalysis data. The extreme cold events in Xinjiang are more likely to occur during the La Ni?a-AO negative phase and La Ni?a-NAO positive phase， and the causes are related to the westward path of cold air due to the anomalous mid and high latitude potential in Eurasia， the strengthening of Ural blocking， and the northerly airflow affecting Xinjiang.