地理科学  2018 , 38 (4): 628-635 https://doi.org/10.13249/j.cnki.sgs.2018.04.017

Orginal Article

基于EEMD的太阳活动对印度夏季风的多尺度胁迫分析

李玉霞1234, 陈玲玲5, 江亭桂1234, 罗文134, 林振山1234

1.南京师范大学地理科学学院,江苏 南京210023
2.江苏省环境演变与生态建设重点实验室(南京师范大学),江苏 南京 210023
3.江苏省地理环境演化国家重点实验室培育建设点,江苏 南京 210023
4.江苏省地理信息资源开发与利用协同创新中心,江苏 南京 210023
5.金陵科技学院人文学院,江苏 南京 211169

Multi-scale Modulation Analysis of Solar Activity and Indian Summer Monsoon Based on EEMD Method

Li Yuxia1234, Chen Lingling5, Jiang Tinggui1234, Luo Wen134, Lin Zhenshan1234

1. School of Geography Science, Nanjing Normal University, Nanjing 210023, Jiangsu, China
2. Jiangsu Key Laboratory of Environmental Change and Ecological Construction, Nanjing 210023, Jiangsu, China
3. State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, Jiangsu, China
4. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, Jiangsu, China
5. Jinling Institute of Technology, Nanjing 211169, Jiangsu, China

中图分类号:  P467;P532

文献标识码:  A

文章编号:  1000-0690(2018)04-0628-08

通讯作者:  通讯作者: 林振山,教授。E-mail: linzhenshan@njnu.edu.cn

收稿日期: 2017-04-27

修回日期:  2017-08-20

网络出版日期:  2018-04-20

版权声明:  2018 《地理科学》编辑部 本文是开放获取期刊文献,在以下情况下可以自由使用:学术研究、学术交流、科研教学等,但不允许用于商业目的.

基金资助:  国家自然科学基金项目(41601417),江苏省高校优势学科建设工程资助项目(164320H116)资助

作者简介:

作者简介:李玉霞(1986-),女,河北宣化人,博士后,主要从事自然地理专业研究。E-mail: liyuxia618@126.com

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摘要

选取10.55~2.7 kaBP Qunf洞的Q5石笋δ18O记录作为印度夏季风的代用指标,集合经验模态分解(EEMD)多尺度分析结果揭示了印度夏季风的强弱变化和与亚洲季风减弱事件的一致性,进一步讨论了11 kaBP以来印度夏季风的逐渐增强而后减弱的发展态势。选用同时期的古树年轮14C记录作为太阳活动代用指标,调频、调幅分析结果发现印度夏季风的31 a、70 a和7 860 a尺度分量受太阳活动相应分量的调频胁迫较为显著;同时又揭示出“太阳活动71 a周期-印度夏季风31 a周期”“太阳活动1 379 a周期-印度夏季风142 a周期”和“太阳活动2 376 a周期-印度夏季风733 a周期”三对调幅胁迫。

关键词: EEMD ; 印度夏季风 ; 太阳活动 ; 调频胁迫 ; 调幅胁迫

Abstract

Recent analysis of the multi-scale incidence relation between the Indian summer monsoon(ISM) and the solar activity is relatively poor, which makes the research on the driving mechanism of the ISM cannot go further. To solve this problem, the multi-scale fluctuation characteristics of the ISM was analyzed based on the Ensemble Empirical Mode Decomposition (EEMD) method. Furthermore, based on the amplitude modulation method, the frequency modulation and amplitude modulation forcing by solar activity on ISM is analyzed. Finally, the driving mechanism of ISM was discussed. We selected the δ18O record of Q5 stalagmite from Qunf cave which covers 10.55-2.7 kaBP period as proxy of ISM, the EEMD multi-scale analysis results reveals that the weakened event of ISM is correlated with the Asian monsoon, furthermore, the trend component shows that the development procedure of ISM increased first and then decreased gradually since 11 kaBP. The 14C records of ancient tree rings was selected as solar activity proxy, frequency modulation and amplitude modulation analysis results showed that the ISM components on the scale of 31 years, 70 years and 7 860 years were significantly forced by the frequency modulation of solar activity components on the corresponding scale. Meanwhile, it revealed that there were three pairs of amplitude modulations: the cycles between 71 years of solar activity and 31 years of ISM, the cycles between 1 379 years of solar activity and 142 years of ISM, and the cycles between 2 376 years of solar activity and 733 years of ISM.

Keywords: EEMD ; Indian summer monsoon ; solar activity ; frequency modulation ; amplitude modulation

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李玉霞, 陈玲玲, 江亭桂, 罗文, 林振山. 基于EEMD的太阳活动对印度夏季风的多尺度胁迫分析[J]. 地理科学, 2018, 38(4): 628-635 https://doi.org/10.13249/j.cnki.sgs.2018.04.017

Li Yuxia, Chen Lingling, Jiang Tinggui, Luo Wen, Lin Zhenshan. Multi-scale Modulation Analysis of Solar Activity and Indian Summer Monsoon Based on EEMD Method[J]. Scientia Geographica Sinica, 2018, 38(4): 628-635 https://doi.org/10.13249/j.cnki.sgs.2018.04.017

全新世以来全球气候并不稳定,呈现出一定的波动,来自海洋、陆地、冰芯等的沉积物中陆续发现了千年、百年和数十年尺度的周期性气候波动和气候事件[1,2,3,4,5,6],并成为近年来国际古气候、古环境领域研究的热点。印度夏季风是亚洲季风环流的重要组成部分,在全球水汽与能量循环中扮演着重要的角色。印度夏季风的强弱通常是以季风季节总雨量的多少来表示[7],而洞穴石笋中δ18O值与降水量成负相关关系,即降水量越大,石笋δ18O值越小[8]。准确诊断和分离石笋记录中不同周期的信号成分将会对印度夏季风特征的认识和驱动机制研究产生深远影响。

1977年,Eddy提出太阳活动的长期变化与全球气候变化之间有很好的正相关[9],为太阳活动与气候变化的直接相关提供了有力证据。Hodell根据云南湖泊沉积资料重建了近五万年的气候记录,表明印度季风在12~8 kaBP期间最为强盛,之后逐渐减弱,响应了太阳辐射的变化[10]。Neff等发现阿曼北部Hoti洞H5石笋高分辨率δ18O记录与树轮14C记录有很好的对应关系,并指出在十年至百年尺度上印度夏季风受太阳活动控制,但早全新世阶段印度夏季风受冰量控制[11]。Agnihotri等研究了阿拉伯海12 ka以来的沉积记录,发现印度夏季风与太阳活动有着良好的匹配关系,即印度夏季风在太阳活动极小期时减弱[12]。此外,很多证据都表明印度夏季风和太阳活动之间存在着很好的相关关系,但多数研究都是从时间序列的直观变化中总结规律,从而进行对比,并没有从不同尺度和不同时段上阐明印度夏季风和太阳活动之间的具体关系。

传统的气候要素时间序列谱分析方法多建立在傅里叶谱分析的基础上,通过傅里叶变换将气候要素时间序列从时域空间转换到频域空间进行分析研究,使得数据的频谱特征得以凸显,但是傅里叶变换只适用于线性的、平稳的系统,无法从非线性、非平稳的地球气候系统中分离某个时间尺度上的变化分量。小波分析方法在保留信号时域特征的同时也具有一定的频域分辨率,且适用于非线性和非平稳数据的多尺度分解,但是小波分解中小波基函数和分解层次的选取具有较大的人为性,易产生虚假谐波。

林振山等用经验模态分解(EMD)方法发现太阳辐射的23 ka波动的包络线(即调幅波而不是传统的调频波)具有明显的100 ka准周期,并指出100 ka的太阳辐射周期信号是对23 ka周期信号的调幅作用,从而揭示了冰期循环100 ka周期也受到岁差而不仅是偏心率的影响[13,14,15]。罗虎明对东亚季风信号做了集合经验模态分解(EEMD),并进一步分析了其分量的调幅波,发现岁差周期不仅是东亚夏季风的主周期,而且对高频分量具有振幅调制作用[16]。然而无论是太阳活动对季风的混频胁迫还是由某时间尺度太阳活动振幅包络线的调幅振荡对亚洲季风的胁迫都是一个崭新的研究领域。

本文利用EEMD方法对印度夏季风信号和太阳活动时间序列进行多尺度分解,从多尺度的角度研究印度夏季风与太阳活动的关系,并进一步探索太阳活动对印度夏季风的调幅胁迫作用,从而更深入地认识全新世印度夏季风的变化规律及其与太阳活动的关系。

1 方法和数据

1.1 集合经验模态分解

集合经验模态分解(EEMD)[17]是一种适用于非线性、非平稳序列的信号分析方法,该方法是在经验模态分解(EMD)[18]的基础上发展起来的。EEMD继承了EMD自适应性等优点,又避免了模态混叠等缺陷,是目前提取时间序列不同尺度信号和趋势的有利方法。将EEMD应用于气候要素时间序列,可以提取可靠、真实的气候变化信号,同时也可以得到气候变化的固有时间尺度。

1.2 调幅波分析

EEMD分解可得到IMF波动分量和趋势分量,其中IMF波动分量是平稳的和非线性的,同时该分量具有显著的缓变波包的特性,意味着不同特征尺度波动的振幅也会随时间变化,因而也具有时域上的局域化特征。传统的谱分析(傅里叶、小波分析等)只能进行频率分析,即调频分析,而EEMD方法则可以同时进行调频分析和调幅分析。求出各IMF波动分量的极大值和极小值,并分别对其做三次样条插值,就可以得到每个分量的上下包络线。该包络线不仅反映了周期波动在特定时段振幅大小变化,也能揭示波动过程在特定时段的振幅突变(由增大变为减小,或由减小变为增大)状况,从而得到相应尺度上的调幅振荡胁迫[13, 14]

1.3 代用数据选取

Qunf洞(54°18′E,17°10′N,海拔650 m)位于阿曼南部,现今此地区处于印度季风降水带上,且年降水量的90%集中于夏季风盛行季节。采集于Qunf洞的Q5石笋高分辨率氧同位素记录很好地反映了从10.55 kaBP到2.7 kaBP的印度夏季风降水量变化[19],从而可用于指示印度夏季风的强弱变化。该记录平均分辨率为6 a,用三次样条插值法对其进行等间距化处理,得到10.55~2.7 kaBP的步长为10 a的786个记录(图1)。

图1   洞穴石笋δ18O和树轮14C曲线

Fig.1   The original data of cave stalagmite δ18O and tree ring 14C

古树年轮中的放射性同位素14C含量变化反映了太阳磁场调节入射地球的宇宙射线通量所引起的大气CO214C含量变化,可以作为太阳活动的代用指标[20]。Stuiver等经过对树轮、大气、海洋等系统的测定和研究也发现树轮14C含量所指示的大气14C产率可以表征太阳活动的变化,树轮记录的14C含量越低,表明当年太阳活动越强烈[21]。用作全新世太阳活动代用资料的树轮14C记录来源于INTCAL98曲线[22],为消除INTCAL98数据中地磁场偶极矩强度长尺度变化,选取窗口为8 000 a,权重为0.99的样条平滑滤波器分离得到去趋势的14C浓度曲线。并根据论文研究时间范围截取11~0 kaBP时段,其分辨率为10 a,共有1 101个样点。

2 印度夏季风多尺度分析

2.1 EEMD分解

对Qunf洞Q5石笋δ18O时间序列进行EEMD分解,在原始序列中加入信噪比为0.5的白噪声,取100次EMD分解结果的平均值,可得到8个IMF分量和一个趋势项(res)(图2),即印度夏季风变化存在8个准周期分量和一个趋势项(表1),大致可以分为3个层次:年代尺度31 a和70 a(IMF1和IMF2)、百年尺度142 a、279 a和733 a(IMF3、IMF4和IMF5)、千年尺度1 172 a、7 280 a和7 860 a(IMF6、IMF7和IMF8)。其中千年尺度分量所占比重最大,为27.63%,年代尺度和百年尺度分量所占比重分别为18.28%和16.00%。使用功率谱分析软件REDFIT35[23]进行谱分析,设定WOSA分割参数n50=3,取样窗口iWin=2,窗口函数选用Welch spectrum window。分析结果如图3所示,将其与EEMD结果进行对比可以发现,分解所得的IMF1、IMF4、IMF5、IMF6、IMF7和IMF8均可在谱分析结果中找到对应尺度,仅有IMF2和IMF3两分量在41 a,83 a和232 a三个尺度上存在部分模态混叠,表明EEMD方法已经在一定程度上削弱了模态混叠对分析结果的影响。

图2   Qunf洞Q5石笋δ18O 的EEMD分解结果(‰,VPDB)

Fig.2   EEMD decomposition results of the δ18O record from Qunf Cave Q5 stalagmite (‰,VPDB)

图3   印度夏季风功率谱周期

Fig.3   Power spectrum of Indian summer monsoon

表1   印度夏季风各分量的方差贡献率和准周期

Table 1   The variance contribution and quasi-period of India summer monsoon IMFs

IMF1IMF2IMF3IMF4IMF5IMF6IMF7IMF8res
准周期(a)3170142279733117272807860-
方差贡献率(%)12.35.945.905.274.835.234.5517.837.0
排序345687921

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年代尺度中IMF1分量的方差贡献率仅次于IMF8,是印度夏季风的显著准周期之一,Krishnamurthy和Goswami指出印度夏季风降水存在大约30 a准周期[24,25]。IMF2是准70 a振荡信号,与阿拉伯海沉积资料显示的印度夏季风降水的60 a准周期接近[12];百年尺度中的IMF3(142 a)在10~8 kaBP和6~4 kaBP期间存在两次显著的低振幅和高振幅波动,说明在该尺度上,印度夏季风降水仍然存在不稳定波动,而IMF4、IMF5两个分量则较为稳定,说明印度夏季风存在较为稳定的279 a和733 a周期振荡;千年尺度中IMF7分量和IMF8分量的准周期均在7 000 a左右,Solanki等人指出太阳活动长期变化中可能存在约7 000 a的波动[26],这很可能是印度夏季风7 000 a波动周期的诱因。

2.2 千年以上尺度分量特征分析

将印度夏季风的千年尺度分量(IMF6、IMF7和IMF8)与趋势项叠加(图4),4个分量的贡献率之和为64.71%。图中曲线清楚表明约9 kaBP以后印度夏季风在总体减弱的趋势上呈现出6次显著减弱现象,其中最显著的一次发生在8.2 kaBP左右,之后大约每隔1 000 a出现一次印度夏季风减弱现象。也就是说,在3 kaBP以后依然可能会出现这种每一千年一次的印度夏季风减弱现象。汪永进通过分析中国南部董哥洞石笋记录指出的8次亚洲季风减弱事件[1]中有5次在图4中出现,其余3次由于Qunf洞石笋记录时段的限制未能显示出来,但可推测印度夏季风在3~0 kaBP这3 000 a内也可能每1 000 a出现一次减弱事件。从图2中也明显可以看出分量IMF5、IMF6和IMF7在8.2 kaBP左右存在一个谷点,也就是此时印度夏季风最弱,说明印度夏季风的强弱变化和8.2 ka冷事件之间也存在一定的对应关系。

图4   印度夏季风IMF6、IMF7、IMF8分量和趋势项叠加结果

Fig.4   Combination of IMF6, IMF7, IMF8 and the trend component of the Indian summer monsoon

2.3 趋势项特征分析

EEMD分解所得到最后一个分量为趋势项,代表了数据的演化趋势,可推断数据所指示的物理现象的演化方向。图5所示为印度夏季风信号经过EEMD分解得到的趋势项分量,其贡献率为37.09%,表明了自11 kaBP以来印度夏季风逐渐增强,到约7 kaBP时达到顶峰,之后不断减弱,但减弱速率小于增强速率,且从图中分析可知该减弱态势依然会持续一断时间。

图5   印度夏季风趋势项

Fig.5   The trend component of Indian summer monsoon

3 印度夏季风与太阳活动

对过去11 ka的树轮14C数据序列进行EEMD分解,取信噪比与信号标准差(约为0.25)相当的白噪声引入原数据,100次计算结果的平均作为最终的信号分解结果,共得到9个本征模函数(IMFs)和一个趋势分量(res)。IMF9分量在全新世期间并不存在一个完整周期,同时其贡献率仅为0.09%,相比而言可以忽略不计,因此后面只讨论IMF1~IMF8这8个周期分量。

3.1 太阳活动对印度夏季风的调频胁迫

将印度夏季风分量准周期与太阳活动分量准周期进行对比,如表2所示。印度夏季风和太阳活动存在3个高度一致的准周期(31 a、70 a和7 860 a),上述准周期贡献率相对较高,表明印度夏季风在年代尺度和千年尺度上和太阳活动之间存在很好的对应关系。而其它贡献率相对较小的波动分量则与太阳活动各分量的周期对应性较差。印度夏季风的第7个分量的准周期为7 280 a,与太阳活动的第7个分量的准周期2 376 a存在相当大的差距。以上结果都表明太阳活动的调频胁迫不是决定印度夏季风波动的唯一因素。

表2   印度夏季风和太阳活动各分量准周期对比

Table 2   Component comparison between Indian summer monsoon and solar activity

分量印度夏季风准周期(a)太阳活动准周期(a)
IMF13131
IMF27071
IMF3142161
IMF4279341
IMF5733615
IMF611721397
IMF772802376
IMF878607860

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3.2 太阳活动对印度夏季风的调幅胁迫

对全新世印度夏季风进行多尺度调幅分析,可以进一步深化对印度夏季风的演化规律和驱动机制的理解。求出印度夏季风各波动分量的上下包络线,对包络线在全时段的波动次数取平均,得到对应尺度的平均波动周期(表3)。从季风分量的振幅波包随时间变化的过程(图6),可以看出不同尺度分量的能量演化。

图6   Qunf洞Q5石笋δ18O 的EEMD分量包络线(‰,VPDB)

Fig.6   The envelope lines of EEMD components (IMFs) of the δ18O record from Qunf Cave Q5 stalagmite (‰,VPDB)

表3   印度夏季风分量包络线的显著功率谱周期和太阳活动分量周期对比

Table 3   Envelope lines comparison between Indian summer monsoon and solar activity

分量印度夏季风分量
调频周期(a)
印度夏季风分量调幅周期(a)太阳活动分量
调频周期(a)
IM1318331
IM27027071
IM31421299161
IM42791562341
IM57331960615
IM61172--1397
IM77280--2376

注:“--”表示无结果。

新窗口打开

图6表3可知,印度夏季风分量IMF1~IMF5的调幅波动较为显著,为了更深入研究印度夏季风和太阳活动的关系,将印度夏季风分量IMF1~IMF5的包络线的显著功率谱周期和太阳活动分量周期进行对比(表3),发现印度夏季风分量IMF1、IMF3、IMF5的调幅波周期和太阳活动分量IMF2、IMF6、IMF7的周期具有相似性(图7):季风IMF1分量的调幅波周期(83 a)与太阳IMF2分量准周期(71 a)大体相当,季风IMF3分量的调幅波周期(1 299 a)和太阳分量的IMF6准周期(1 397 a)较为接近,季风分量IMF5的调幅波周期(1 960 a)相似于太阳分量IMF7的准周期(2 376 a)。

图7   印度夏季风IMF1、IMF3、IMF5包络线和太阳活动各分量IMF2、IMF6、IMF7对比

Fig.7   Envelope lines comparison between IMF1, IMF3 and IMF5 of Indian summer monsoon and solar activity component (IMF2, IMF6, IMF7)

这说明印度夏季风在准31 a振荡尺度上,不仅受太阳活动31 a周期振荡的调频影响,也会受到太阳活动71 a准周期振荡的调幅制约;同样在准142 a 振荡尺度上,印度夏季风也受到了太阳活动准161 a周期振荡的调频胁迫和1 379 a周期振荡调幅胁迫的影响;在准733 a振荡尺度上,印度夏季风受到太阳活动准615 a周期振荡的调频胁迫和准2 376 a周期振荡的调幅胁迫共同影响。虽然太阳分量IMF7的准周期(2 376 a)分量没有诱发同尺度的季风振荡,但对低尺度季风分量IMF5却起到了调制振幅的作用。换言之,低尺度季风分量IMF5的周期振荡受到太阳同尺度分量的调控,而振幅却受到太阳分量IMF7的准周期振荡的调控。由此发现太阳大尺度分量不仅可以是同尺度的季风分量振荡的外因之一,还可以是低尺度季风分量调幅振荡的可能外因。

4 结论与讨论

全新世印度夏季风演化具有层次性、阶段性和突变性等诸多特征,表现出一系列的千年、百年、年代际和年际波动。EEMD方法克服了EMD方法的模态混叠缺陷,同时消除了原始时间序列本身可能存在的瞬时噪声,从很大程度上提高了数据分析的准确性。经EEMD分解可得到与各气候事件相一致的多尺度波动,其中千年尺度具有最高的贡献率,千年以上尺度波动与亚洲季风减弱事件具有很好的一致性。趋势项的结果也表明,自11 kaBP以来印度夏季风存在逐渐增强而后减弱的发展态势。

对太阳活动代用资料做EEMD分析,并和印度夏季风EEMD分解结果进行对比,发现印度夏季风贡献率最高的3个分量(31 a、70 a和7 860 a)与太阳活动相应分量的准周期匹配较好,但在142 a~1 172 a和7 280 a尺度上存在不一致,表明印度夏季风波动还受到太阳活动的调幅胁迫。通过对比印度夏季风分量调幅波和太阳活动分量调幅波,揭示出“太阳活动71 a周期-印度夏季风31 a周期”、“太阳活动1 379 a周期-印度夏季风142 a周期”和“太阳活动2 376 a周期-印度夏季风733 a周期”三对调幅胁迫。说明太阳大尺度分量不仅可以是同尺度的季风分量振荡的外因之一,还可以是低尺度季风分量调幅振荡的可能外因。论文的研究为多尺度混频信号驱动机制的揭示提供了新的思路和方法。

The authors have declared that no competing interests exist.


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https://doi.org/10.1029/RS015i006p01195      URL      [本文引用: 1]      摘要

It has become generally accepted during the last year that 14C fluctuations,the so-called "wiggles", observed in wood, dated by its tree rings, do indeedexist. Furthermore the La Jolla measurements show that apart from experimental noise, they do not represent random red noise, but characteristic, recurring features. In 1971, Houtermans found indications for the existence of cyclic components and recent Fourier analyses of all the available data by Neftel and Hartwig show a 200-year component. Cyclic oscillations with other periods appear to be present during limited timeintervals. The character of the oscillations is not harmonic. The time derivative of many fluctuations is remarkably constant and such that the 14C rises by 1 percent in about 20 years and decreases by 1 percent in slightly more than twice that length of time. The properties of the overall radiocarbon record have to be considered in attempts to explain the variations in terms of variations of the cosmic ray-production rate and changes of the geochemical distribution of radiocarbon.
[21] Stuiver M, Braziunas T F.

Sun, ocean, climate and atmospheric 14CO2: an evaluation of causal and spectral relationships

[J]. The Holocene, 1993, 3(4): 289-305.

[本文引用: 1]     

[22] Stuiver M, Reimer P J, Bard E et al.

Intcal98 radiocarbon age calibration, 24000-0 cal BP

[J]. Radiocarbon, 1998, 40(3): 1041-1083.

https://doi.org/10.1029/97RS03183      URL      [本文引用: 1]      摘要

INTCAL98 Radiocarbon Age Calibration, 24,000–0 cal BP - Volume 40 Issue 3 - Minze Stuiver, Paula J. Reimer, Edouard Bard, J. Warren Beck, G. S. Burr, Konrad A. Hughen, Bernd Kromer, Gerry McCormac, Johannes Van Der Plicht, Marco Spurk
[23] Schulz M, Mudelsee M.

REDFIT: estimating red-noise spectra directly from unevenly spaced paleoclimatic time series

. Computers & Geosciences, 2002, 28(3):421-426.

https://doi.org/10.1016/S0098-3004(01)00044-9      URL      [本文引用: 1]      摘要

Paleoclimatic time series are often unevenly spaced in time, making it difficult to obtain an accurate estimate of their red-noise spectrum. A Fortran 90 program (REDFIT) is presented that overcomes this problem by fitting a first-order autoregressive (AR1) process, being characteristic for many climatic processes, directly to unevenly spaced time series. Hence, interpolation in the time domain and its inevitable bias can be avoided. The program can be used to test if peaks in the spectrum of a time series are significant against the red-noise background from an AR1 process. Generated and paleoclimatic time series are used to demonstrate the capability of the program.
[24] Krishnamurthy V, Goswami B N.

Indian monsoon-ENSO relationship on interdecadal timescale

[J]. Journal of Climate, 2000, 13(3): 579-595.

[本文引用: 1]     

[25] Goswami B N, Madhusoodanan M S, Neema C P et al.

A physical mechanism for North Atlantic SST influence on the Indian summer monsoon

[J]. Geophysical Research Letters, 2006, 33(2): L02706.

https://doi.org/10.1029/2005GL024803      URL      [本文引用: 1]      摘要

A link between the Atlantic Multidecadal Oscillation (AMO) and multidecadal variability of the Indian summer monsoon rainfall is unraveled and a long sought physical mechanism linking Atlantic climate and monsoon has been identified. The AMO produces persistent weakening (strengthening) of the meridional gradient of tropospheric temperature (TT) by setting up negative (positive) TT anomaly over Eurasia during northern late summer/autumn resulting in early (late) withdrawal of the south west monsoon and persistent decrease (increase) of seasonal monsoon rainfall. On inter-annual time scales, strong North Atlantic Oscillation (NAO) or North Annular mode (NAM) influences the monsoon by producing similar TT anomaly over Eurasia. The AMO achieves the interdecadal modulation of the monsoon by modulating the frequency of occurrence of strong NAO/NAM events. This mechanism also provides a basis for explaining the observed teleconnection between North Atlantic temperature and the Asian monsoon in paleoclimatic proxies.
[26] Solanki S K, Usoskin I G, Kromer B et al.

Unusual activity of the Sun during recent decades compared to the previous 11,000 years

[J]. Nature, 2004, 431(7012): 1084-1087.

https://doi.org/10.1038/nature02995      URL      PMID: 15510145      [本文引用: 1]      摘要

Direct observations of sunspot numbers are available for the past four centuries, but longer time series are required, for example, for the identification of a possible solar influence on climate and for testing models of the solar dynamo. Here we report a reconstruction of the sunspot number covering the past 11,400 years, based on dendrochronologically dated radiocarbon concentrations. We combine physics-based models for each of the processes connecting the radiocarbon concentration with sunspot number. According to our reconstruction, the level of solar activity during the past 70 years is exceptional, and the previous period of equally high activity occurred more than 8,000 years ago. We find that during the past 11,400 years the Sun spent only of the order of 10% of the time at a similarly high level of magnetic activity and almost all of the earlier high-activity periods were shorter than the present episode. Although the rarity of the current episode of high average sunspot numbers may indicate that the Sun has contributed to the unusual climate change during the twentieth century, we point out that solar variability is unlikely to have been the dominant cause of the strong warming during the past three decades.

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