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中国降水的年代际变化及全球变暖、IPO、AMO对其的相对贡献

杜佳玉 陶丽 许承宇

杜佳玉,陶丽,许承宇. 2022. 中国降水的年代际变化及全球变暖、IPO、AMO对其的相对贡献. 气象学报,80(5):1-16 doi: 10.11676/qxxb2022.055
引用本文: 杜佳玉,陶丽,许承宇. 2022. 中国降水的年代际变化及全球变暖、IPO、AMO对其的相对贡献. 气象学报,80(5):1-16 doi: 10.11676/qxxb2022.055
Du Jiayu, Tao Li, Xu Chengyu. 2022. Interdecadal variation of land precipitation in China and relative contributions of global warming,IPO and AMO. Acta Meteorologica Sinica, 80(5):1-16 doi: 10.11676/qxxb2022.055
Citation: Du Jiayu, Tao Li, Xu Chengyu. 2022. Interdecadal variation of land precipitation in China and relative contributions of global warming,IPO and AMO. Acta Meteorologica Sinica, 80(5):1-16 doi: 10.11676/qxxb2022.055

中国降水的年代际变化及全球变暖、IPO、AMO对其的相对贡献

doi: 10.11676/qxxb2022.055
基金项目: 国家重点研发计划项目(2016YFA0600402)
详细信息
    作者简介:

    杜佳玉,主要从事气候变化研究。E-mail:1929681708@qq.com

    通讯作者:

    陶丽,主要从事气候变化、台风气候学、季节内振荡等研究。E-mail:taoli@nuist.edu.cn

  • 中图分类号: 461

Interdecadal variation of land precipitation in China and relative contributions of global warming,IPO and AMO

  • 摘要: 虽然中国降水以年际变化为主,但可利用奇异谱分析(SSA,Singular Spectrum Analysis)辨析出10—20 a、20—50 a年代际变化的显著性区域以及 >50 a的长期趋势的显著性区域。本研究通过奇异值分解(SVD,Singular Value Decomposition)、多元线性回归等方法,探究了1934—2018年期间不同海洋模态对6—8月和12—2月中国陆地降水趋势以及年代际振荡的相对贡献。通过对中国降水及中低纬地区海温(SST,Sea Surface Temperature)进行SVD分析发现,不论冬夏,影响中国降水的主要模态是全球变暖(GW,Global Warming),其次是太平洋年代际振荡(IPO,Interdecadal Pacific Oscillation)。利用多元线性回归模型定量评估GW、IPO、大西洋多年代际振荡(AMO,Atlantic Multidecadal Oscillation)对中国不同区域降水的方差贡献及各因子的相对贡献,结果表明:夏季,三者可以解释西北和华北大约30%的年代际降水,其中GW的相对贡献最大、IPO次之;冬季,三者可以解释东北42%、西北和华北30%左右的年代际降水,东北和西北以GW的相对贡献为主、AMO为辅,华北仍以GW影响为主、IPO为辅。

     

  • 图 1  1934—2018年夏 (a、c、e)、冬 (b、d、f) 降水10—20 a (a、b)、20—50 a (c、d)、>50 a (e、f) 方差贡献 (红线表示通过α=0.10的信度检验区域)

    Figure 1.  Percent variance in precipitation in JJA (a,c,e) and DJF (b,d,f) at 10—20-year (a,b),20—50- year (c,d) and secular trend (>50 years;e,f) during 1934—2018 (areas enclosed by redline are statistically significant at the 0.10 level)

    图 2  1934—2018年夏季SVD分析的前三模态 (a、d、g. 第一模态,b、e、h. 第二模态,c、f、i. 第三模态) SST (a—c) 与陆地降水 (d—f) 异类场及其标准化序列 (g—i)(打点(马赛克)表示通过α=0.10的信度t检验;r表示序列间相关系数, **和***分别代表通过α为0.05和0.01的信度检验)

    Figure 2.  The first three SVD modes (a,d,g. model 1,b、e、h. model 2,c,f,i. model 3) between the SST and land precipitation during JJA of 1934—2018 (a—c are heterogeneous correction distribution of SST,d—f are those of land precipitation, are normalized SVD time series (g—i)(the correlation coefficients (r) with **,*** are statistically significant at the 0.05 and 0.01 levels,respectively;the areas with dots (mosaic) are for values statistically significant at the 0.10 level)

    图 3  同图2,但为冬季

    Figure 3.  Same as Fig. 2 but for DJF

    Continued

    Continued

    图 4  1934—2018年GW、IPO、AMO指数时间序列 (IPO、AMO指数进行Lanczos 9年低通滤波,GW指数进行EEMD滤波取其趋势,r表示指数间的相关系数)

    Figure 4.  Time series of 9 years low-passed GW,IPO and AMO indices from 1934—2018 (r is the correlation coefficient between these indices)

    图 5  1934—2018年夏 (a、c、e)、冬 (b、d、f) 季降水对GW (a、b)、IPO (c、d)、AMO (e、f) 指数的线性回归分布 (单位:mm/mon,色阶线上数值为夏季取值范围,色阶线下为冬季取值范围;马赛克表示通过α=0.10的信度t检验)

    Figure 5.  Land precipitation (mm/mon) in JJA (a,c,e) and DJF (b,d,f) regressed onto to the normalized indices of GW (a,b),IPO (c,d) and AMO (e,f) during 1934—2108 (the numbers above the color bar are the value ranges of summer precipitation,those below the color bar are the value ranges of winter precipitation;the areas with mosaic are for values statistically significant at the 0.10 level)

    图 6  1934—2018年GW、IPO、AMO指数对夏季陆地降水的方差贡献分布 (a. 三者对降水可解释方差总贡献, b、c、d分别为GW、IPO和AMO相对方差贡献)

    Figure 6.  (a) Total variance contribution of GW,IPO and AMO to land precipitation in JJA during 1934—2018 and relative contributions of (b) GW,(c) IPO and (d) AMO

    图 7  同图6,但为冬季

    Figure 7.  Same as Fig. 6 but for DJF

    表  1  夏季GW、IPO、AMO对中国各区域降水的总方差贡献及各因子的相对方差贡献

    Table  1.   Domain-averaged total variance contributions by GW,IPO and AMO to the low-frequency variability of land precipitation in JJA over China and relative contributions of IPO,AMO and GW

    GW+IPO+AMO(%)GW(%)IPO(%)AMO(%)
    西北(74°—110°E,34°—49°N)30552421
    西南(79°—110°E,21°—34°N)21422830
    华北(110°—120°E,36°—54°N)32552520
    东北(120°—135°E,39°—54°N)19424018
    华东(116°—122°E,25°—36°N)19352936
    华中(110°—116°E,25°—36°N)15252748
    华南(105°—118°E,21°—25°N)14321454
    下载: 导出CSV

    表  2  同表1,但为冬季

    Table  2.   Same as Table 1 but for DJF

    GW+IPO+AMO(%)GW
    (%)
    IPO
    (%)
    AMO
    (%)
    西北(74°—110°E,34°—49°N)32412633
    西南(79°—110°E,21°—34°N)27324325
    华北(110°—122°E,36°—54°N)29403525
    东北(120°—135°E,39°—54°N)42511732
    华东(116°—122°E,25°—36°N)22325216
    华中(110°—116°E,25°—36°N)13383824
    华南(105°—118°E,21°—25°N)26255223
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-10-25
  • 修回日期:  2022-05-19
  • 网络出版日期:  2022-05-26

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