On Possible Mechanisms of Interdecadal Climate Variability
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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。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.
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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)
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)
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)
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)
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
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)
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)
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(INA,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 INA (North Atlantic Tripole index, dotted line)
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)
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)
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)
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)
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)
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)
表 1 SPDO和PDO指数以及去除ENSO信号后的SPDO和PDO指数(SPDOre和PDOre)之间的相关系数
Table 1. Correlation coefficients between SPDO (South Pacific Decadal Oscillation) index, PDO (Pacific Decadal Oscillation) index, SPDOre index, PDOre index (SPDO and PDO indexs after removing the ENSO signal)
NPDO指数 NPDOre指数 SPDO指数 SPDOre指数 NPDO指数 1 — — NPDOre指数 0.88● 1 — — SPDO指数 0.46● 0.22■ 1 — SPDOre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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