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太平洋年代际振荡、大西洋年代际振荡和全球变暖对北美地区降水的相对贡献

徐川 张昊 陶丽

徐川, 张昊, 陶丽. 2021. 太平洋年代际振荡、大西洋年代际振荡和全球变暖对北美地区降水的相对贡献[J]. 大气科学, 45(5): 1−21 doi: 10.3878/j.issn.1006-9895.2101.20228
引用本文: 徐川, 张昊, 陶丽. 2021. 太平洋年代际振荡、大西洋年代际振荡和全球变暖对北美地区降水的相对贡献[J]. 大气科学, 45(5): 1−21 doi: 10.3878/j.issn.1006-9895.2101.20228
XU Chuan, ZHANG Hao, TAO Li. 2021. Relative Contributions of Interdecadal Pacific Oscillation, Atlantic Multidecadal Oscillation and Global Warming to the Land Precipitation in North America [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(5): 1−21 doi: 10.3878/j.issn.1006-9895.2101.20228
Citation: XU Chuan, ZHANG Hao, TAO Li. 2021. Relative Contributions of Interdecadal Pacific Oscillation, Atlantic Multidecadal Oscillation and Global Warming to the Land Precipitation in North America [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(5): 1−21 doi: 10.3878/j.issn.1006-9895.2101.20228

太平洋年代际振荡、大西洋年代际振荡和全球变暖对北美地区降水的相对贡献

doi: 10.3878/j.issn.1006-9895.2101.20228
基金项目: 国家重点研发计划项目2016YFA0600402
详细信息
    作者简介:

    徐川,男,1993年出生,博士研究生,主要从事气候变化研究。E-mail: xuchuan1993@126.com

    通讯作者:

    陶丽,E-mail: taoli@nuist.edu.cn

  • 中图分类号: P461

Relative Contributions of Interdecadal Pacific Oscillation, Atlantic Multidecadal Oscillation and Global Warming to the Land Precipitation in North America

Funds: National Key Research and Development Program (Grant 2016YFA0600402)
  • 摘要: 本文研究了1934~2018年期间太平洋年代际振荡(Interdecadal Pacific Oscillation,IPO)、大西洋年代际振荡(Atlantic Multidecadal Oscillation,AMO)以及全球变暖(Global Warming,GW)对北美地区陆地降水年代际变化的相对贡献。首先通过对冬(12~2月)、夏季(6~8月)北美地区的陆地降水与中低纬地区的海表面温度进行奇异值分解分析,得到对北美陆地冬季降水相对贡献较大的主要海温模态为IPO(42.33%)和AMO(23.21%),夏季则为AMO(32.66%)和IPO(21.60%)。其次利用线性回归模型,分析三种信号分别对北美冬、夏季陆地降水的相对贡献及对北美陆地不同区域降水的相对重要性,结果表明AMO对夏季北美陆地降水变化的贡献最大,IPO次之,冬季则相反,GW对冬夏季北美陆地降水都有一定的贡献。夏季期间阿拉斯加地区AMO的贡献最大,约占65.8%,加拿大地区GW的贡献最大,约占44.5%,美国本土及墨西哥地区三者贡献基本一致;冬季期间阿拉斯加和加拿大地区GW的贡献最大,分别为62.3%和44.7%,美国本土和墨西哥地区IPO的贡献最大,分别为47.9%和71.5%。进一步利用信息流方法,验证了IPO、AMO、GW对降水的敏感性区域。最后运用全球大气环流模式ECHAM 4.6进一步确定了太平洋和大西洋海温异常对北美地区陆地降水变化的影响途径,结果表明印度洋海表面温度异常在AMO和IPO对北美陆地降水变化的作用中至关重要。
  • 图  1  1934~2018年6~8月海温与Climatic Research Unit(CRU)陆地降水奇异值分解(Singular Value Decomposition,SVD)分析第一模态。(a)、(b)分别为SVD第一模态海温、降水异类场,打点区域表示通过α=0.10 t检验;(c)为异类场时间系数和AMO指数(AMOI),其中蓝线表示SST时间序列,红线表示降水时间序列,黑线表示AMOI,*、**和***分别代表相关系数(r)通过α=0.10、α=0.05和α=0.01显著性检验

    Figure  1.  First Singular Value Decomposition (SVD) modes between the sea surface temperature (SST) and land precipitation from Climatic Research Unit (CRU) during June, July and August (JJA) of 1934–2018: Spatial pattern of (a) SST and (b) land precipitation; (c) normalized SVD time series of SST (blue lines) and precipitation (red lines). The black line denotes the AMO index (AMOI). The correlation coefficients (r) with *, **, and *** are statistically significant at the 0.10, 0.05, and 0.01 levels, respectively. The areas with dots are statistically significant at α = 0.10 level for (a) and (b)

    图  2  1934~2018年6~8月海温与CRU陆地降水SVD分析第二模态。(a)、(b)分别为SVD第二模态海温、降水异类场,打点区域表示通过α=0.10 t检验;(c)为异类场时间系数和IPO指数(IPOI),其中蓝线表示SST时间序列,红线表示降水时间序列,黑线表示IPOI,*、**和***分别代表相关系数(r)通过α=0.10、α=0.05和α=0.01显著性检验

    Figure  2.  Second SVD modes between the SST and land precipitation from CRU during JJA of 1934–2018: Spatial pattern of (a) SST and (b) land precipitation; (c) normalized SVD time series of SST (blue lines) and precipitation (red lines). The black line denotes the IPO index (IPOI). The correlation coefficients (r) with *, **, and *** are statistically significant at the 0.10, 0.05, and 0.01 levels, respectively. The areas with dots are statistically significant at the 0.10 level for (a) and (b)

    图  3  1934~2018年6~8月海温与CRU陆地降水SVD分析第三模态。(a)、(b)分别为SVD第三模态海温、降水异类场,打点区域表示通过α=0.10 t检验;(c)为异类场时间系数和GW指数(GWI),其中蓝线表示SST时间序列,红线表示降水时间序列,黑线表示GWI,*、**和***分别代表相关系数(r)通过α=0.10、α=0.05和α=0.01显著性检验

    Figure  3.  Third SVD modes between the SST and land precipitation from CRU during JJA of 1934–2018: Spatial pattern of (a) SST and (b) land precipitation; (c) normalized SVD time series of SST (blue lines) and precipitation (red lines). The black line denotes the GW index (GWI). The correlation coefficients (r) with *, **, and *** are statistically significant at the 0.10, 0.05, and 0.01 levels, respectively. The areas with dots are statistically significant at the 0.10 level for (a) and (b)

    图  4  1934~2018年12~2月海温与CRU陆地降水SVD分析第一模态。(a)、(b)分别为SVD第一模态海温、降水异类场,打点区域表示通过α=0.10 t检验;(c)为异类场时间系数和IPOI,其中蓝线表示SST时间序列,红线表示降水时间序列,黑线表示IPOI,*、**和***分别相关系数(r)代表通过α=0.10、α=0.05和α=0.01显著性检验

    Figure  4.  First SVD modes between the SST and land precipitation from CRU during December January February (DJF) of 1934–2018: Spatial pattern of (a) SST and (b) land precipitation; (c) normalized SVD time series of SST (blue lines) and precipitation (red lines). The black line denotes the IPOI. The correlation coefficients (r) with *, **, and *** are statistically significant at the 0.10, 0.05, and 0.01 levels, respectively. The areas with dots are statistically significant at the 0.10 level for (a) and (b)

    图  5  1934~2018年12~2月海温与CRU陆地降水SVD分析第二模态。(a)、(b)分别为SVD第二模态海温、降水异类场,打点区域表示通过90% t检验;(c)为异类场时间系数和AMOI,其中蓝线表示SST时间序列,红线表示降水时间序列,黑线表示AMOI,*、**和***分别代表相关系数(r)通过α=0.10、α=0.05和α=0.01显著性检验

    Figure  5.  Second SVD modes between the SST and land precipitation of CRU during DJF of 1934–2018: Spatial pattern of (a) SST and (b) land precipitation; (c) normalized SVD time series of SST (blue lines) and precipitation (red lines). The black line denotes the AMOI. The correlation coefficients (r) with *, **, and *** are statistically significant at the 0.10, 0.05, and 0.01 levels, respectively. The areas with dots are statistically significant at the 90% level for (a) and (b)

    图  6  1934~2018年12~2月海温与CRU陆地降水SVD分析第三模态。(a)、(b)分别为SVD第三模态海温、降水异类场,打点区域表示通过90% t检验;(c)为异类场时间系数和GWI,其中蓝线表示SST时间序列,红线表示降水时间序列,黑线表示GWI,*、**和***分别代表相关系数(r)通过α=0.10、α=0.05和α=0.01显著性检验

    Figure  6.  Third SVD modes between the SST and land precipitation from CRU during DJF of 1934–2018: Spatial pattern of (a) SST and (b) land precipitation; (c) normalized SVD time series of SST (blue lines) and precipitation (red lines). The black line denotes the GWI. The correlation coefficients (r) with *, **, and *** are statistically significant at the 0.10, 0.05, and 0.01 levels, respectively. The areas with dots are statistically significant at the 90% level for (a) and (b)

    图  8  (a、b)IPOI、(c、d)AMOI和(e、f)GWI对1934~2018年(a、c、e)6~8月和(b、d、f)12~2月CRU降水的线性回归分布(单位:mm month−1)。打点区域为通过α=0.10信度检验区域

    Figure  8.  Regressed land precipitation from CRU (mm month−1) in JJA (left column) and DJF (right column)of 1934–2018 onto the normalized indices of (a, b) IPO, (c, d) AMO, and (e, f) GW. The areas with dots are statistically significant at the 0.10 level

    图  9  1934~2018年IPO、AMO和GW对夏季CRU陆地降水的方差贡献分布。(a)为三者对降水可以解释的方差总贡献,(b)、(c)、(d)分别为IPO、AMO和GW的相对方差贡献比例

    Figure  9.  (a) Total variance contributions of IPO, AMO, and GW to the land precipitation from CRU in JJA of 1934–2018, and the relative contributions of (b) IPO, (c) AMO, and (d) GW

    图  7  1934~2018年IPOI(红线)、AMOI(蓝线)、GWI(黑线)时间序列图。IPOI、AMOI进行9年低通滤波,r表示三个指数间的相关系数

    Figure  7.  Time series of the 9-yr low-passed IPOI (red line), AMOI (blue line), and GWI (black line) from 1934–2018. r is the correlation coefficient between the three indices

    图  10  1934~2018年IPO、AMO和GW对冬季CRU陆地降水的方差贡献分布图。(a)为三者对降水可以解释的方差总贡献,(b)、(c)、(d)分别为IPO、AMO和GW的相对方差贡献比例

    Figure  10.  (a) Total variance contributions of IPO, AMO, and GW to the land precipitation from CRU in DJF of 1934–2018, and the relative contributions of (b) IPO, (c) AMO, and (d) GW

    图  11  (a)IPOI、(c)AMOI、(e)GWI对北美夏半年(5~10月)陆地降水信息流分布;(b)IPOI、(d)AMOI、(f)GWI对北美冬半年(11~4月)陆地降水信息流分布

    Figure  11.  Information flow from (a) IPOI, (c) AMOI, and (e) GWI to the CRU land precipitation in May–October. Information flow from (d) IPOI, (e) AMOI, and (f) GWI to the CRU land precipitation in November–April

    图  12  (a)IPOI和(b)AMOI对SST的回归场(单位:°C)。图中矩形区域表示敏感性试验中所加SST异常的范围

    Figure  12.  Regressed SST (units: °C) onto (a) IPOI and (b) AMOI. The rectangular boxes are the specified SSTA domains

    图  13  (a,b)IPO_TP和(c,d)IPO_TPI敏感性试验(暖试验减去冷试验)中北美夏(左列)、冬(右列)降水异常(单位:mm month−1)和500 hPa风场异常(单位:m s−1)。打点区域为通过α=0.10信度检验区域

    Figure  13.  Simulated precipitation anomalies (units: mm month−1) and 500-hPa wind anomalies (units: m s−1) in JJA (left column) and DJF (right column) for the (a, b) IPO_TP experiment and (c, d) IPO_TPI experiment. The experiment refers to the warm run minus the cold run. The areas with dots are statistically significant at the 0.10 level

    图  15  (a,b)IPOI、(c,d)AMOI对500 hPa夏季(左列)、冬季(右列)风场的线性回归分布

    Figure  15.  Regressed wind (500 hPa) in JJA (left column) and DJF seasons (right column) onto the indices of (a, b) IPO and (c, d) AMO

    图  14  (a,b)AMO_NA和(c,d)AMO_NAI敏感性试验(暖试验减去冷试验)中北美夏(左列)、冬(右列)降水异常和500 hPa风场异常。打点区域为通过α=0.10信度检验区域

    Figure  14.  Simulated precipitation anomalies and 500-hPa wind anomalies in JJA (left column) and DJF (right column) for the (a, b) AMO_NA experiment and (c, d) IPO_NAI experiment. The experiment refers to the warm run minus the cold run. The areas with dots are statistically significant at the 0.10 level

    表  1  IPO、AMO、GW对四个区域夏季陆地降水的总方差贡献和每个因子的相对方差贡献比例

    Table  1.   Total variance contributions of IPO, AMO, and GW to the land precipitation in JJA and the relative contributions of IPO, AMO, and GW

    方差贡献
    IPO+AMO+GWIPOAMOGW
    阿拉斯加34.1%14.2%65.8%20.0%
    加拿大 30.0%30.6%24.9%44.5%
    美国本土22.9%27.6%35.6%36.8%
    墨西哥 24.0%30.6%37.2%32.2%
    下载: 导出CSV

    表  2  IPO、AMO、GW对四个区域冬季陆地降水的总方差贡献和每个因子的相对方差贡献比例

    Table  2.   Total variance contributions of IPO, AMO, and GW to the of land precipitation in DJF and the relative contributions of IPO, AMO, and GW

    方差贡献
    IPO+AMO+GWIPOAMOGW
    阿拉斯加27.0%18.2%19.5%62.3%
    加拿大 29.3%26.5%28.8%44.7%
    美国本土24.7%47.9%25.9%26.2%
    墨西哥 41.3%71.5%16.5%12.0%
    下载: 导出CSV

    表  3  ECHAME4试验的设计方案

    Table  3.   Design scheme of the ECHAM4 model experiments

    试验名称SST 异常区域
    控制试验观测SST气候态
    AMO 试验warm AMO_NA 加上或者减去北大西洋SST正异常
    cold AMO_NA
    warm AMO_NAI加上或者减去北大西洋和印度洋SST正异常
    cold AMO_NAI
    IPO试验warm IPO_TP加上或者减去热带太平洋SST正异常
    cold IPO_TP
    warm IPO_TPI加上或者减去热带太平洋和印度洋SST正异常
    cold IPO_TPI
    下载: 导出CSV
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  • 收稿日期:  2020-11-12
  • 录用日期:  2021-03-29
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