湖北农业科学 ›› 2022, Vol. 61 ›› Issue (15): 34-41.doi: 10.14088/j.cnki.issn0439-8114.2022.15.006

• 资源·环境 • 上一篇    下一篇

新疆温度精细化时空变化特征分析

阿帕尔·肉孜1, 叶尔克江·霍依哈孜1, 李奇1, 黄健2, 张山清3   

  1. 1.新疆昌吉州气象局,新疆 昌吉 831100;
    2.中国气象局乌鲁木齐沙漠气象研究所,乌鲁木齐 830002;
    3.新疆农业气象台,乌鲁木齐 830002
  • 收稿日期:2022-04-02 出版日期:2022-08-10 发布日期:2022-08-25
  • 通讯作者: 李 奇(1963-),男,高级工程师,主要从事农业气象服务,(电话)0994-8165714(电子信箱)573339528@qq.com。
  • 作者简介:阿帕尔·肉孜(1978-),男,新疆库车人,高级工程师,主要从事应用气象及气候变化研究,(电话)0994-8165713(电子信箱)apar-ek@sohu.com
  • 基金资助:
    国家自然科学基金项目(41775109); 昌吉州科技项目(2020S03); 昌吉回族自治州庭州青年计划项目(2021QN28)

Analysis on temporal and spatial variation characteristics of temperature refinement in Xinjiang

APAR Ruzi1, ERKEJAN Hoyhazi1, LI Qi1, HUANG Jian2, ZHANG Shan-qing3   

  1. 1. Changji Meteorological Bureau of Xinjiang, Changji 831100, Xinjiang, China;
    2. Urumqi Desert Meteorology Institute, China Meteorological Administration, Urumqi 830002, China;
    3. Xinjiang Agro-Meteorological Observatory,Urumqi 830002, China
  • Received:2022-04-02 Online:2022-08-10 Published:2022-08-25

摘要: 基于新疆105个气象站点1961—2018年温度资料,运用气候倾向率、Mann-Kendall检验、小波功率谱分析法以及ArcGIS10.7环境下的反距离加权(IDW)插值技术探究新疆年平均温度的变化趋势、突变特征、变化周期以及精细化时空分布规律。结果表明,新疆各地多年平均温度空间分布呈分布不均的现象,空间分布总体表现为南疆高、北疆低,平原和盆(谷)地高、山区低的格局。近58年新疆各地气温除了库车和阿克陶呈略减少趋势,其他各地均呈波动上升趋势,但有区域性差异,线性增温速率各有不同,其中南疆最小,增温速率为0.025 7 ℃/年,天山山区略大,增温速率为0.031 4 ℃/年,北疆最大,增温速率为0.035 4 ℃/年,增温速率均通过P=0.001显著性检验,重点增长区位于哈密、塔城、伊犁及阿勒泰地区。新疆各地年平均温度增温速率由北向南递减,北部增温速度大于东北部,东北部大于中部,中部大于南部,南部大于东部,寒冷地区增温快于温暖地区。新疆温度均有较强的2.6年左右的主要周期振荡并均通过95%的红噪声检验,各地温度偏高波动周期信号最强的是北疆,其次为天山山区,南疆最小。温度距平在20世纪90年代中期之前除了个别年份都是负距平,90年代中期之后都是正距平;2~8年尺度平均谱显示温度的年际变化在20世纪90年代前较弱、90年代后开始逐渐增强,对应的功率谱温度偏高波动周期信号也较强,这说明新疆气候大约在20世纪90年代中后期出现了普遍而显著地增温转折并发生突变。

关键词: 新疆, 气候倾向率, 突变, 小波功率谱, 反距离权重插值, 均方误差

Abstract: Based on the temperature data of 105 meteorological stations in Xinjiang from 1961 to 2018, using climate tendency rate, Mann-Kendall test, wavelet power spectrum analysis and inverse distance weighted(IDW) interpolation technology under ArcGIS10.7 environment, the change trend, mutation characteristics, change cycle and refined spatial-temporal distribution of annual average temperature in Xinjiang were explored. The results showed that the spatial distribution of annual average temperature in Xinjiang was uneven, and the overall spatial distribution was “high in southern Xinjiang, low in northern Xinjiang; high in plain and basin (Valley), low in mountainous area”. In recent 58 years, except Kuqa and Aktao, the temperature in all parts of Xinjiang showed a fluctuating upward trend, but there were regional differences, and the linear warming rates were different. The lowest warming rate was 0.025 7 ℃/a in southern Xinjiang, the warming rate was 0.031 4 ℃/a in Tianshan Mountains, and the largest warming rate was 0.035 4 ℃/a in northern Xinjiang. The key growth areas were located in Hami, Tacheng, Ili and Altay. The annual average temperature increasing rate in Xinjiang decreased from north to south. The temperature increasing rate in the north was greater than that in the northeast, the northeast was greater than that in the middle, the middle was greater than that in the south, and the south was greater than that in the east. The temperature increasing rate in cold regions was faster than that in warm regions. The main periodic fluctuation of temperature in Xinjiang was about 2.6 years, and all of them pass the 95% red noise test. The strongest periodic signal of high temperature fluctuation was in northern Xinjiang, followed by Tianshan Mountains, and the smallest in southern Xinjiang. Before the mid-1990s, except for a few years, the temperature anomaly was negative, and after the mid-1990s, it was positive. The 2~8a scale average spectrum showed that the interannual variation of temperature was weak before the mid-1990s, and gradually increased after the mid-1990s. The corresponding power spectrum temperature was higher, and the periodic signal of fluctuation was also strong, which indicated that the climate of Xinjiang was in the middle and late 1990s. There was a general and significant turning point of temperature increase and mutation.

Key words: Xinjiang, climate tendency rate, mutation, wavelet power spectrum, inverse distance weighted, mean squared error

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