关于年代际气候变化可能机制的研究

李崇银

doi:10.3878/j.issn.1006-9585.2018.18088

关于年代际气候变化可能机制的研究

doi: 10.3878/j.issn.1006-9585.2018.18088

李崇银^{1, 2,}

1.
国防科技大学气象海洋学院, 南京 211101
2.
中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室, 北京 100029

基金项目:

国家自然科学基金项目 41490642

详细信息

作者简介:
李崇银, 男, 1940年出生, 教授/研究员, 主要从事天气气候变化及其动力学研究。E-mail:lcy@lasg.iap.ac.cn

中图分类号: P463
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- 被引次数: 0
出版历程
- 收稿日期: 2018-07-03
- 网络出版日期: 2018-10-20
- 刊出日期: 2019-01-20

On Possible Mechanisms of Interdecadal Climate Variability

Chongyin LI^{1, 2
,}

1.
Institute of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101
2.
State Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

Funds:

National Natural Science Foundation of China 41490642

摘要

摘要: 年代际气候变化作为年际和月季气候变化的重要背景，往往影响着年际和月季时间尺度的气候及特征。随着科学的发展进步和社会需求的提高，年代际气候变化已成为人们关注的重要问题。作为气候动力学和气候预测研究的重要内容之一，年代际气候变化及其动力学机制的研究在国内外都在蓬勃开展，并取得了不少的成果。本文除简要介绍了中国气候的年代际变化特征，将着重就年代际气候变化的可能机制作一个系统的综合性讨论，内容主要包括全球主要海温变化模态的影响、气候系统相互关系年代际变化的影响、大气行星尺度系统年代际变化的影响，以及太阳活动及火山爆发的影响等。大家知道，年代际气候变化研究十分重要，但也可以看到年代际气候变化的动力学机制却十分复杂，不少问题还没有搞的十分清楚，需要加大力量进行深入研究；我们相信，深入的研究结果必将对年代际气候变化的预测提供可靠的科学依据，进而推动年代际气候变化的业务预测及其能力的提高。
- 年代际气候变化 /
- 可能机制 /
- 主要海温模态 /
- 气候系统相互关系 /
- 太阳活动及火山爆发
Abstract: As an important background of month-seasonal and interannual climate variations, interdecadal climate variation often affects climate features with interannual and month-seasonal time scales. Along with the development and progress of science and the rise of social requirement, interdecadal climate variability has become an important issue that has attracted more attentions. As one of important contents on climate dynamics and climate foreshadow, research has been launched vigorously in the world. Some research achievements have been published. In this paper, we will focus on systematic and comprehensive discussion on possible mechanisms of interdecadal climate variability. The major contents include:Influences of main patterns of ocean temperature in the global; influences of interdecadal variation of climate system relationship; influences of interdecadal variation of the atmospheric system on the planetary scale; and impacts of solar activities and volcano eruptions. As we know, studies on interdecadal climate variability are important, but the dynamic mechanism of interdecadal climate variability is so complicated. There are more problems that still remain unsolved and need further in-depth study. We believe that further in-depth research achievements will be able to provide reliable scientific basis for the foreshadow of interdecadal climate variation, promote professional work of interdecadal climate variation forecast and improve the forecasting capability.
- Interdecadal climate variability /
- Possible mechanism /
- Major patterns of the ocean temperature /
- Climate system relationship /
- Solar activity and volcano eruption

HTML全文

图 1 中国华北地区（北京、天津和保定）夏季（a）降水量距平的时间变化及（b）其功率谱特征（李崇银，2000）

Figure 1. (a) Temperal variation and (b) power spectrum of summer precipitation anomalies in northern China (Beijing, Tianjing, and Baoding) (Li Chongyin, 2000)

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图 2 中国东部夏季降水量EOF第一主分量PC1的（a）方差谱和（b）小波分析结果

Figure 2. (a) Variance spectrum and (b) wavelet analysis of summer preciptation EOF PC1 (Principal Component) in eastern China

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图 3 中国东部冬季气温异常的（a）时间变化及其（b）功率谱特征

Figure 3. (a) Temporal variation and (b) power spectrum of temperature anomalies in winter (December–February) in eastern China

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图 4 1948~2010年东亚夏季风指数的时间变化特征

Figure 4. Temporal variation of the East Asian summer monsoon index during 1948−2010

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图 5 1951~2014年110°E~120°E平均30°N以南夏季降量水距平的纬度—时间剖面

Figure 5. Latitude−time cross section of summer precipitation anomalies averaged over (south of 30°N, 110°E−120°E) during 1951−2014

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图 6 北太平洋SST年代际变化的（a、b）25~35年模和（c、d）7~10年模的正位相及负位相模态形势：（a、c）正位相；（b、d）负位相（Li and Xian, 2003）

Figure 6. The patterns of (a, b) 25–35-a and (c, d) 7–10-a interdecadal modes of SSTA (SST anormaly) in the North Pacific: (a, c) Positive phase; (b, d) negative phase (Li and Xian, 2003)

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图 7 对应北太平洋海温25~35年模的正位相（左列）和负位相（右列）形势时（a、b）东亚、（c、d）北美和（e、f）澳大利亚地区的年降水量异常（单位：mm，阴影区域为正距平）分布（Li and Xian, 2003）

Figure 7. Distributions of annual precipitation anomalies (units: mm, shadings are for positive anomalies) in (a, b) East Asia, (c, d) North America, and (e, f) Australia corresponding to positive (left column) and negative (right column) phases of SSTA mode with 25–35-a period in the North Pacific (Li and Xian, 2003)

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图 8 南太平洋月平均海表温度距平（a）EOF第1模态的空间模态特征（单位：℃）及（b）小波局地功率谱的分布（李刚等，2012）

Figure 8. (a) EOF-1 (first EOF mode) pattern of monthly SSTA in the southern Pacific (℃) and (b) the wavelet power spectrum distribution (Li Gang et al., 2012)

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图 9 SPDO指数和我国（a）东北、（b）华北及（c）华南区域降水指数的年代际变化。红线表示去除NPDO影响后的9年滑动平均SPDO指数，黑线和绿线为降水指数9年滑动平均结果，黑线为CRU（Climatic Research Unit）3.10.01降水资料，绿线为GPCC（Global Precipitation Climatology Centre）V6降水资料

Figure 9. Interdecadal variations of the SPDO index and regional precipitation indexes in (a) Northeast China, (b) North China, and (c) South China. The red lines indicate the nine-year running average of SPDO index excluding the NPDO impact; the black and green lines show nine-year-running averages of precipitation indexes using the CRU (Climatic Research Unit) 3.10.01 data and GPCC (Global Precipitation Climatology Centre) V6 data, respectively

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图 10 印度洋（a）9月、（b）10月、（c）11月和（d）12月SST与（25°S~40°S，65°E~80°E）区域平均SST的相关系数分布特征

Figure 10. Correlation coefficient distributions of SST in the Indian Ocean in (a) September, (b) October, (c) November, and (d) December, respectively with SST averaged over (25°S−40°S, 65°E−80°E)

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图 11 南印度洋海温偶极子（a）正、（b）负位相相对应的次年夏季（6~8月）的100 hPa高度场（单位：gpm）（贾小龙和李崇银，2005）

Figure 11. Geopotential height (gpm) at 100 hPa in the subsequent summer (June−August) corresponding to (a) positive and (b) negative phases of the SIOD (Subtropical Indian Ocean Dipole) (Jia Xiaolong and Li Chongyin, 2005)

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图 12 北大西洋冬季（a）SSTA的三极子模态及其（b）指数的功率谱特征（Gu et al., 2009）

Figure 12. (a) The tripolar mode of SSTA in the North Atlantic in the winter and (b) the power spectrum of its index (Gu et al., 2009)

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图 13 经过9~16年带通滤波的梅雨雨量（实线）、梅雨持续时间（虚线）和北大西洋海温三极子模态指数（I_NA，点线）的时间变化（Gu et al., 2009）

Figure 13. Temporal variations of the 9-16-a band-pass filtered Meiyu precipitation (solid line), Meiyu duration (dashed line), and I_NA (North Atlantic Tripole index, dotted line)

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图 14 Nino3指数（实线）、阿拉伯海海温指数（虚线）、南印度洋偶极子指数（点线）、赤道印度洋偶极子指数（点虚线）与全印度降水的21年滑动相关系数的时间变化。两条横线是0.05置信度的相关系数检验线（Wang et al., 2006）

Figure 14. Correlation (based on a 21-a sliding window) of Indian rainfall and Nino3 index (solid line), the SST indices in the Arabian Sea (dashed line), IOD (Indian Ocean Dipole) index (dotted line), and SIOD index (dotted-dashed line). The 5% significant levels are indicated as two horizontal lines (Wang et al., 2006)

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图 15 （a）NAO指数和（b）NPO指数的小波功率谱特征（Li and Li, 2000）

Figure 15. Wavelet spectra of the (a) NAO (North Atlantic Oscillation) index and (b) NPO (North Pacific Oscillation) index (Li and Li, 2000)

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图 16 AO指数的时间变化

Figure 16. Temporal variation of the AO (Arctic Oscillation) index

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图 17 20°N~32.5°N平均夏季低通滤波的200 hPa位势高度距平（单位：gpm）的经度—时间剖面

Figure 17. Longitude−time cross section of low pass filtered summer geopotential height anomalies (gpm) at 200 hPa averaged over 20°N−32.5°N

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图 18 （a）太阳黑子数和（b）江淮梅雨量变化的小波分析结果（潘静等，2010）

Figure 18. Wavelet analyses of (a) the sunspot and (b) the Jiang-Huai Meiyu (Pan Jing et al., 2010)

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图 19 （a）强、（b）弱太阳活动时期所对应的冬季北半球10 hPa位势高度异常场（单位：gpm）的合成形势（潘静等，2010）

Figure 19. Geopotential height anomalies (gpm) at 10 hPa in the winter averaged for (a) high solar activity period and (b) low solar activity period (Pan Jing et al., 2010)

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图 20 太阳活动影响气候变化的可能途径示意图（李崇银，2007）

Figure 20. The sketch map of possible path about influences of the solar activity on the climate (Li Chongyin, 2007)

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图 21 （a）1961~2011年地磁场指数与离地10 m处风速的时间变化（相关系数为0.40，超过99.9%信度），以及（b）1961~2015年地磁场指数与AO指数的时间变化（相关系数为0.347，超过99.9%信度）（金巍等，2017）

Figure 21. Temporal variations of the geomagnetic field index and wind speed at 10 m during 1961−2011 (the correlation coefficient is 0.4, which is at/above the 99.9% confidence level) and AO index during 1961−2015 (the correlation coefficient is 0.347, which is at/above the 99.9% confidence level) (Jin Wei et al., 2017)

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图 22 1880~1980年北半球温度异常与火山爆发。图中虚线是计算的地面温度响应（Kondratyev，1988）

Figure 22. Temperature anomalies in the Northern Hemisphere and volcano eruptions during 1880−1980. The dotted line is computed surface temperature response (Kondratyev, 1988)

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表 1 SPDO和PDO指数以及去除ENSO信号后的SPDO和PDO指数（SPDO_re和PDO_re）之间的相关系数

Table 1. Correlation coefficients between SPDO (South Pacific Decadal Oscillation) index, PDO (Pacific Decadal Oscillation) index, SPDO_re index, PDO_re index (SPDO and PDO indexs after removing the ENSO signal)

	NPDO指数	NPDO_re指数	SPDO指数	SPDO_re指数
NPDO指数	1	—	—
NPDO_re指数	0.88^●	1	—	—
SPDO指数	0.46^●	0.22^■	1	—
SPDO_re指数	0.16^▲	0.20^■	0.62^●	1
注：●、■和▲上标的分别表示通过0.001、0.05和0.1置信度检验。

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