基于云杉年轮宽度的天山东部重建降水

Bakhtiyorov Zulfiyor, Yu Ruide, Yang Meilin, Monoldorova Akylai, Aminov Javhar
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引用次数: 1

摘要

我们最近的研究介绍了基于Picea Shrenkiana树木年轮生长的5 - 6月降水重建,跨度为AD 1892-2015。我们使用1952-2012年中国0.25°空间分辨率的三个月气候网格数据参数(平均温度(°C)、总降水量(kg m-2s-1)和总雪水当量(kg m-2)插值到我们的采样点,以突出相关系数。4 ~ 5月和9月的雪水当量与环宽呈显著正相关(约p<0.05),分别为0.411和0.263。与降水的相关性在5 - 6月较高(0.468),说明该地区的限制因子是水分,在树木生长初期感受到水分的影响。3 - 10月与平均气温呈负相关(-0.478),表明气温升高导致蒸发量和蒸腾量增加。我们的重建显示了6个干旱十年(1920年、1930年、1970年、1980年、2000年、2010年)和5个湿润十年(1910年、1940年、1950年、1960年、1990年)。我们的重建证实了全球变化,表明从1991年到现在降水减少。为了体现重建的地理意义,我们对5 - 6月降水重建、仪器网格插值5 - 6月降水和网格化CRU TS 4.0 5 - 6月降水进行了空间相关性分析,结果表明,5 - 6月降水在新疆天山地区显著,穿越蒙古、哈萨克斯坦、吉尔吉斯斯坦和塔吉克斯坦。此外,我们正在进行多锥度光谱分析,并在获得结果后试图将我们的研究与ENSO变率联系起来,高频周期为6.1年(99%),5.1-4.8年(90%),2.7-2.9年(90%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reconstructed Precipitation for the Eastern Tian Shan (China), based on Picea Shrenkiana Tree-Ring Width
Our recent study introduces the reconstruction of May-June precipitation based on Picea Shrenkiana tree-ring growth, spanned AD 1892-2015. We used three monthly climatic gridded data parameters of China which covers with a 0.25° spatial resolution over 1952-2012 (temperature mean (°C), total precipitation (kg m-2s-1) and total snow-water equivalent (kg m-2)) interpolated to our sampling site for highlight correlations coefficients. Snow-water equivalent with ring-width gives us a significant positive correlation (approximately in p<0.05 level) in April-May period (0.411) and September (0.263). With precipitation, correlation is higher in May-June period (0.468) which leads us that limiting factor in this area is water, which is felt during the initial growth of the tree. Results of negative correlation in March-October period (-0.478) with mean temperature, suggest that increase of temperature leads to increase of evaporation and water transpiration. Our reconstruction indicated 6 dry decades (1920, 1930, 1970, 1980, 2000, 2010) and five wet decades (1910, 1940, 1950, 1960, 1990). Our reconstruction confirms to global change, which suggest decreasing precipitation from 1991 until now. To represent our reconstruction geographical meaning, we conducted spatial correlation between our May-June precipitation reconstruction, instrumental gridded interpolated May-June precipitation and gridded CRU TS 4.0 May-June precipitation, which significant in Xinjiang Tian Shan Mountains and goes through Mongolia, Kazakhstan, Kyrgyzstan and Tajikistan. In addition, we are running Multi-taper spectral analysis and after getting results try to connect our study to ENSO variability, with highfrequency cycles 6.1-year (99%), 5.1-4.8-year (90%), 2.7-2.9-years (90%).
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