1.Qinghai Climate Centre;2.LASG,Institute of Atmospheric Physics,Chinese Academy of Sciences
基于黄河源区8个站点的年平均气温序列，利用集合经验模态分解（Ensemble Empirical Mode Decomposition, EEMD）方法，揭示了以玛多站为代表的黄河源区1953－2017年气温演变的多时间尺度特征，探讨不同时间尺度上的周期振荡对气温变化总体特征的影响程度，分析了黄河源区不同时间尺度的气温变化与海温指数，尤其是与北大西洋多年代际振荡（the Atlantic Multidecadal Oscillation, AMO）间的关系。结果表明：（1）1953年以来黄河源区玛多站年平均气温以0.31℃/10年的变化率表现为明显的增暖趋势，20世纪80年代后期开始转暖，尤其是进入90年代后期变暖更加明显。（2）近65年来，黄河源区年平均气温呈现3a、6a、11a、25a、64a及65a以上时间尺度的准周期变化，其中以准3a和65a以上时间尺度的振荡最显著，准3a的年际振荡在21世纪以前振幅较大，而进入21世纪后年际振荡振幅减弱，65a以上时间尺度的年代际振荡振幅明显加大。（3）1998年气候显著变暖以前，以准3a周期为代表的年际振荡在气温演变过程中占据主导地位，1998年气候显著变暖以后，65a以上时间尺度周期振荡的贡献率增加近5倍，与准3a周期振荡的贡献相当。（4）气温与Nino3.4指数和PDO指数的同期相关均不显著，但当气温领先PDO指数22年时正相关最大且显著，不同于PDO指数，气温原始序列及其三个年代际尺度分量滞后AMO指数3~7年或二者同期时相关性最高，这就意味着AMO对黄河源区气温具有显著影响。（5）AMO的正暖位相对应着包括中国的整个东亚地区偏暖，黄河源区只是受影响区域的一部分，20世纪60年代早期至90年代中后期AMO的负冷位相期、90年代早期至今AMO的正暖位相与黄河源区气温距平序列的负距平、正距平相对应，气温在65a以上时间尺度的变化与AMO指数相关性更高，可见，AMO是影响黄河源区气温变化的一个重要的气候振荡，这种影响主要表现在年代际时间尺度上。
Abstract: Based on the annual averaged surface air temperature data from eight meteorological stations in the source region of the Yellow River using the Ensemble Empirical Mode Decomposition (EEMD) approach, the multi-timescale temperature features of meteorological stations with Madoi as a representative during 1953-2017 and their contributions to the temperature variations are revealed. The correlations between different time-scale temperature oscillations with the SST indices are analyzed, particularly with the Atlantic Multidecadal Oscillation (AMO). The results demonstrated that: (1) a long-term temperature trend was 0.31℃/10a during 1953-2017 in the source region of the Yellow River, and the warming started in the late 1980s and accelerated in the late 1990s. (2) There were 3-year, 6-year, 11-year, 25-year, 64-year and 65-year quasi-cycle oscillations for the temperature during 1953-2017. Among them, the 3-year and 65-year quasi-cycle oscillations were significant. The amplitude of 3-year time-scale oscillation was large before the 21st century and decreased after the 21st century, while the amplitude of 65-year oscillation was enhanced after the 21st century. (3) The 3-year quasi-cycle oscillation occupied a dominant position during the period of 1953-1997, and the contribution of 65-year oscillation increased nearly five times which was equivalent to the contribution of the 3-year oscillation during the rapid warming period since 1998. (4) The correlations between temperature with Nino3.4 and PDO indices were not significant, but the maximum significant correlation was found when the temperature led PDO 22 years. Unlike PDO, the maximum significant correlation was found when AMO led the original temperature and its three inter-decadal components 0 and 3-7 years which supported that AMO had a significant impact on the temperature variation in the source region of the Yellow River. (5) The positive warm phase of AMO corresponded to the warming of the East Asia including China, and the source region of the Yellow River was only a part of that area. The negative cold phase of AMO from the early 1960s to the middle and late 1990s and the positive warm phase of AMO from the early 1990s to the present corresponded to the negative and positive phases of the temperature in the source region of the Yellow River. The AMO highly correlated with the 65-year oscillation. These results supported that AMO was an important climatic oscillation affecting the temperature variation especially on the inter-decadal time scales in the source region of the Yellow River.