基于树轮记录的阿尔泰山中段1798年以来6—7月平均最低气温变化研究

Changes of mean minimum temperature in June—July since 1798 in central Altay Mountain recorded by tree rings

通过分析新疆阿尔泰山中段西伯利亚落叶松树轮宽度年表与气候因子的相关关系证明6—7月平均最低气温是研究区树木径向生长的主控气候因子(r=0.649,P<0.001)。利用线性回归方法构建了阿尔泰山中段1798—2017年6—7月平均最低气温方程,通过计算重建方程方差解释量(41.1%)和利用逐一剔除法对重建方程各项参数进行了稳定性检验,证明了重建方程是稳定可靠的。分析重建的平均最低气温年际变化表明,研究区经历了4个暖期(1906—1919年、1922—1935年、1945—1960年和1997—2017年)和4个冷期(1816—1831年、1840—1867年、1869—1886年和1888—1905年),并且与相邻地区平均气温重建序列冷暖变化阶段一致。基于多窗谱分析结果发现重建6—7月平均最低气温序列存在2a、2.4a、2.8—3a、11a、20—22a左右的变化周期,表明研究区气候变化主要受厄尔尼诺—南方涛动(ENSO)、太阳黑子活动和全球海温变化(SST)驱动。本研究为系统掌握阿尔泰山气候变化规律和科学预测未来气候变化提供了依据。

The analysis of the relationship between the tree ring width chronology of Larix sibirica and climatic factors in central Altay Mountain, Xinjiang indicates that the mean minimum temperature from June to July is the main climatic factors for the radial growth of trees(r=0.649, P<0.001). Mean minimum temperature equation from June to July in the past 220 years in central Altay Mountain was constructed using the linear regression method, and the interpretation of the variance of the reconstructed equation was 41.1%. The leave-one-out method was used to test the stability of the reconstruction equation, and various test parameters proved that the reconstruction equation was stable and reliable. Inter-annual changes in the reconstructed mean minimum temperature indicated that there are four warm periods(1906—1919, 1922—1935, 1945—1960, and 1997—2017) and four cold periods(1816—1831, 1840—1867, 1869—1886, and 1888—1905) in the study area. These are consistent with the cold and warm stages of the reconstruction sequence of the mean temperature in the adjacent area. The reconstructed sequence with change periods of about 2 a, 2.4 a, 2.8—3 a, 11 a, and 20—22 a based on the multi-taper analysis indicates that climate change is mainly driven by the global sea surface temperature(SST), sunspots and ENSO(El Ni1 o-Southern Oscillation) in the study area. The reconstructed mean minimum temperature provides a basis for systematically mastering regularity and scientifically predicting future tendency of climate change in the Altay Mountain.