地球科学进展 ›› 2014, Vol. 29 ›› Issue (7): 844 -853. doi: 10.11867/j.issn.1001-8166.2014.07.0844

上一篇    下一篇

全球海表温度在不同时间尺度的主模态对比分析
刘鹏 1( ), 江志红 1, 于华英 2, 秦怡 1   
  1. 1. 南京信息工程大学 气象灾害教育部重点实验室,江苏 南京210044
    2. 南京信息工程大学 遥感学院,江苏 南京210044
  • 出版日期:2014-07-10
  • 基金资助:
    国家重点基础研究发展计划项目“东亚季风区年际—年代际气候变率机理与预测研究”(编号:2012CB955204);江苏省博士后科研资助计划项目“不同温盐环流强度背景下北太平洋海表温度年代际振荡的变率研究”(编号:1301137C)资助

A Comparative Analysis of Main Modes of Global-scale Sea Surface Temperature on Multiple Time Scale

Liu Peng 1( ), Jiang Zhihong 1, Yu Huaying 2, Qin Yi 1   

  1. 1.Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Jiangsu, Nanjing, 210044
    2.Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Jiangsu, Nanjing, 210044
  • Online:2014-07-10 Published:2014-07-10

利用1880—2009年HadISST资料,去掉百年全球变暖的信号,研究发现东太平洋、北太平洋和北大西洋都有较强的年际和年代际振荡信号,特别是赤道东太平洋南侧的年代际振荡是不容忽视的。对全球范围的海表温度资料做EOF分析发现,存在3种主要的全球尺度信号,第一模态为太平洋型、第二模态为北大西洋型以及第三模态为赤道中太平洋型。特别指出,第三模态是CP ENSO在全球模态中的表现。这3种模态在年际和年代际尺度都有显著的信号,在无滤波的情况下,3种模态方差贡献之和为34%。在年代际以上时间尺度范围,3种模态方差贡献之和为61%。在各种时间尺度中,这3种信号与全球平均温度都有一定的联系,尤其第一、二模态的影响最为重要,在年代际尺度中,第一、二模态方差贡献之和达到50%。2005年以后全球并没有明显增温,可能与前2个模态同时下降有关。

Using the HadISST data from 1880 to 2009, removed the signal of global warming in one hundred year. The results show that, there were the significant interannual and interdecadal oscillation signal at the eastern Pacific and north Pacific and north Atlantic, especially the decadal oscillation in the south of eastern equatorial Pacific cannot be ignored. We found there are three major global-scale signals by using the Empirical Orthogonal Function (EOF) analysis on global sea surface temperature, the first mode is (ENSO-like/PDO-like) Pacific pattern, the second mode is (AMO-like) the north Atlantic pattern and the third mode is (ENSO Modoki-like/CP ENSO-like) Center Pacific Ocean pattern. In particular, the third mode is the performance of Center Pacific El Niño-Southern Oscillation in the global mode. There are significant signals in interannual and interdecadal scales, in the unfiltered conditions, the three modes can explain 34% of total variance contribution. Above the interdecadal scale, the sum of three modes variance contribution is 61%. In various time scales, the three signals and the average global temperature has a connection, especially the influence of the first and second mode is the most important, in the decadal scale, the sum of the first and second modal variance contribution is 50%. Since 2005, there is no significant signal of global warming may be associated with the simultaneous decline of the first two modes.

中图分类号: 

图1 海表温度1880—2009年标准差图(单位:K) (a)无滤波;(b)13年以下高频滤波;(c)13年以上低频滤波
Fig.1 The standard deviation of Sea Surface Temperature(SST) during the period 1880-2009 (unit:K) (a) Raw SST; (b) SST with a 13year high pass filter; (c)SST with a 13year low pass filter
图2 海表温度1880—2009年标准差百分比值图 (a)高频滤波SST的标准差除以无滤波的SST标准差;(b)低频滤波SST的标准差除以无滤波的SST标准差
Fig.2 The percentage standard deviation of SST from 1880 to 2009 (a) High pass filter divided by unfiltered; (b)Low pass filter divided by unfiltered
图3 海表温度EOF模态Mode1及其时间系数 (a)无滤波海表温度EOF1模态;(b)低频滤波海表温度EOF1模态;(c)时间系数(细线为图3a的,粗细为图3b的)
Fig.3 The first EOF mode of SST and its time coefficient The first EOF mode of (a)raw SST (b) low-pass filtered SST and (c) the time series of the leading EOF for raw (fine line) and low-pass filtered SST (heavy line)
图4 海表温度EOF模态Mode 2及其时间系数 (a)无滤波海表温度EOF 2模态;(b)低频滤波海表温度EOF 2模态;(c)时间系数(细线为图4a的,粗细为图4b的)
Fig.4 The second EOF mode of SST and its time coefficient The second EOF mode of (a)raw SST (b) low-pass filtered SST and (c) the time series of the leading EOF for raw (fine line) and low-pass filtered SST (heavy line)
图5 海表温度EOF模态Mode 3及其时间系数 (a)无滤波海表温度EOF5模态;(b)低频滤波海表温度EOF 3模态;(c)时间系数(细线为图5a的,粗线为图5b的)
Fig.5 The third EOF mode of SST and its time coefficient[STBZ][WTBZ][HT6SS] The third EOF mode of (a)raw SST (b) low-pass filtered SST and (c) the time series of the leading EOF for raw (fine line) and low-pass filtered SST (heavy line)
表1 无滤波模态与区域海温指数的相关系数
Table 1 The correlation coefficient of unfiltered mode and regional SST index
表2 低频滤波模态与区域海温指数的相关系数
Table 2 The correlation coefficient low-pass filter mode and regional SST index
表3 不同模态的低频型与无滤波型的时间相关系数、及其振幅比例、空间相关系数(时间为1888—2002年,小括号内为1950—2002年)
Table 3 The time correlation coefficients, and its amplitude ratioes, space correlation coefficients of low-pass filter and unfiltered mode in different modes( In the period of 1888-2002, small brackets for 1950-2002)
图6 3种模态时间系数的功率谱分析 无滤波模态(深色)和通过90%的检验值(点线)
Fig.6 The power spectrum of time series of three modes The power spectrum of unfiltered mode (heavy line) and the 90% confidence levels (dotted line ) in figure
图7 3种模态时间系数对全球表面平均温度的超前滞后相关系数 (a),(b)图中虚线为通过99%信度检验,(c)图中虚线为通过90%信度检验, X轴单位:月
Fig.7 The leading or lagged correlation coefficient between time series of three modes and the global average surface temperature Dotted line denote the 99% confidence levels in figure (a) and (b), dotted line denote the 90% confidence levels in figure (c),unit of X axis is month
表4 无滤波型、低频型的三种模态与全球表面平均温度相关系数,时间段为1888~2002年,小括号内为1950—2002年
Table 4 The correlation coefficients between unfiltered and low-pass filter in three modes and the global average surface temperature. In the period of 1888~2002, small brackets for 1950~2002.
[1] Zhang Yuan, Wallace J M, Battisti D S. ENSO-like interdecadal variability: 1900-1993[J].Journal of Climate, 1997, 10(5):1004-1020.
[2] Mantua N J, Hare S R, Zhang Y, et al. A Pacific interdecadal climate oscillation with impacts on salmon production[J]. Bulletin of the American Meteorological Society, 1997, 78(6): 1069-1079.
[3] Power S, Casey T, Folland C, et al. Inter-decadal modulation of the impact of ENSO on Australia[J]. Climate Dynamics, 1999, 15(5): 319-324.
[4] Schubert S, Gutzler D, Wang H, et al. A US CLIVAR project to assess and compare the responses of global climate models to drought-related SST forcing patterns: Overview and results[J]. Journal of Climate, 2009, 22(19): 5251-5272.
[5] Meehl G A, Goddard L, Murphy J, et al. Decadal prediction: Can it be skilful?[J].Bulletin of the American Meteorological Society, 2009,90:1467-1485.
[6] Kushnir Y. Interdecadal variations in North Atlantic sea surface temperature and associated atmospheric conditions[J]. Journal of Climate, 1994, 7(1): 141-157.
[7] Delworth T L, Mann M E. Observed and simulated multidecadal variability in the Northern Hemisphere[J]. Climate Dynamics,2000, 16(9): 661-676.
[8] Delworth T L, Zhang R, Mann M E. Decadal to centennial variability of the Atlantic from observations and models[M]∥ Geophysical Monograph Series 173. Washington DC: American Geophysical Union,2007:131-148.
[9] Enfield D B, Mestas Nu ez A M, Trimble P J. The Atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental US[J]. Geophysical Research Letters, 2001, 28(10): 2077-2080.
[10] Knight J R, Folland C K, Scaife A A. Climate impacts of the Atlantic multidecadal oscillation[J]. Geophysical Research Letters, 2006, 33: L17706, doi:10.1029/2006GL026242.
[11] Ma Hao,Wang Zhaomin,Shi Jiuxin. The role of the southern ocean physical processes in global climate system[J]. Advances in Earth Science, 2012, 27(4): 398-412.
[马浩,王召民,史久新. 南大洋物理过程在全球气候系统中的作用[J]. 地球科学进展,2012,27(4): 398-412.]
[12] Wei Fengying, Song Qiaoyun. Spatial distribution of the global sea surface temperature with interdecadal scale and their potential influence on meiyu in middle and lower reaches of Yangtze River[J].Acta Meteorological Sinica, 2005, 63(4): 477-484.
[魏凤英, 宋巧云.全球海表温度年代际尺度的空间分布及其对长江中下游梅雨的影响[J].气象学报, 2005, 63(4): 477-484.]
[13] Jiang Zhihong, Li Jianping, Tu Qipu, et al. Regional characteristics of the decadal and interdecadal variations for global temperature field during the last century[J].Chinese Journal of Atmospheric Sciences, 2004, 28(4): 545-548.
[江志红, 李建平, 屠其璞,等. 20世纪全球温度年代和年代际变化的区域特征[J]. 大气科学, 2004, 28(4): 545-548.]
[14] Xiao Dong, Li Jianping. Main decadal abrupt changes and decadal modes in global sea surface temperature field[J]. Chinese Journal of Atmospheric Sciences, 2007,31(5): 839-854.
[肖栋,李建平.全球海表温度场中主要的年代际突变及其模态[J].大气科学,2007,31(5): 839-854.]
[15] Sun C, Li J, Jin F F, et al. Sea surface temperature inter-hemispheric dipole and its relation to tropical precipitation[J]. Environmental Research Letters, 2013, 8, doi:10.1088/1748-9326/8/4/044006.
[16] Zhang W,Li J, Zhao X. Sea surface temperature cooling mode in the Pacific cold tongue[J]. Journal of Geophysical Research, 2010, 115: C12042, doi:10.1029/2010JC006501.
[17] Liu P, Sui C H. An observational analysis of the oceanic and atmospheric structure of global-scale multi-decadal variability[J]. Advances in Atmospheric Sciences, 2014, 31(2): 316-330.
[18] Messié M, Chavez F. Global modes of sea surface temperature variability in relation to regional climate indices[J]. Journal of Climate, 2011, 24(16): 4314-4331.
[19] Rayner N A, Parker D E, Horton E B, et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century[J]. Journal of Geophysical Research, 2003, 108(D14): 4407, doi: 10.1029/2002JD002670.
[20] Wu Z, Huang N E, Wallace J M, et al. On the time-varying trend in global-mean surface temperature[J]. Climate Dynamics, 2011, 37(3/4): 759-773.
[21] Hansen J, Ruedy R, Glascoe J, et al. GISS analysis of surface temperature change[J]. Journal of Geophysical Research, 1999, 104(D24): 30997-31022.
[22] Xiao Ziniu, Zhong Qi, Yin Zhiqiang, et al. Advances in the research of impact of decadal solar cycle on modern climte[J].Advances in Earth Science, 2013, 28(12):1335-1348.
[肖子牛,钟琦,尹志强,等.太阳活动年代际变化对现代气候影响的研究进展[J]. 地球科学进展,2013,28(12):1335-1348.]
[23] Bretherton C S, Widmann M, Dymnikov V P, et al. The effective number of spatial degrees of freedom of a time-varying field[J]. Journal of Climate, 1999, 12(7): 1990-2009.
[24] Choi J, An S I, Yeh S W, et al. ENSO-Like and ENSO-Induced tropical Pacific decadal variability in CGCMs[J]. Journal of Climate, 2013, 26(5): 1485-1501.
[25] Deser C, Phillips A S, Alexander M A. Twentieth century tropical sea surface temperature trends revisited[J]. Geophysical Research Letters, 2010, 37(10), doi:10.1029/2010GL043321.
[26] Liu Z. Dynamics of interdecadal climate variability: A historical perspective[J]. Journal of Climate, 2012, 25(6): 1963-1995.
[27] Zhang Wenjun, Wang Lei, Xiang Baoqiang, et al. Impacts of two types of La Nia on the NAO during boreal winter[J]. Climate Dynamics, 2014, doi:10.1007/s00382-014-2155-z.
[28] Weng H, Ashok K, Behera S K, et al. Impacts of recent El Nio Modoki on dry/wet conditions in the Pacific rim during boreal summer[J]. Climate Dynamics, 2007, 29(2/3): 113-129.
[29] Ashok K, Yamagata T. Climate change: The El Nio with a difference[J]. Nature, 2009, 461(7263): 481-484.
[30] Kao H Y, Yu J Y. Contrasting eastern-Pacific and central-Pacific types of ENSO[J]. Journal of Climate, 2009, 22(3): 615-632.
[31] Yu J Y, Zou Y, Kim S T, et al. The changing impact of El Nio on US winter temperatures[J]. Geophysical Research Letters, 2012, 39(15), doi:10.1029/2012GL052483.
[32] Chung P H, Li T. Interdecadal relationship between the mean state and El Nio types[J]. Journal of Climate, 2013, 26(2): 361-379.
[33] Banholzer S, Donner S. The influence of different El Nio types on global average temperature[J]. Geophysical Research Letters, 2014, 41(6): 2093-2099.
[34] Chen J, Del Genio A D, Carlson B E, et al. The spatiotemporal structure of twentieth-century climate variations in observations and reanalyses. Part I: Long-term trend[J]. Journal of Climate, 2008, 21(11): 2611-2633.
[35] Chen A, Del Genio D, Carlson B E, et al. The spatiotemporal structure of twentieth-century climatevariations in observations and reanalyses. Part II: Pacific pandecadal variability[J]. Journal of Climate,2008,21: 2634-2649.
[36] Li J, Sun C, Jin F F. NAO implicated as a predictor of Northern Hemisphere mean temperature multidecadal variability[J]. Geophysical Research Letters, 2013, 40(20): 5497-5502.
[37] Ting M, Kushnir Y, Seager R, et al. Forced and internal twentieth-century sst trends in the north atlantic[J]. Journal of Climate,2009, 22(6): 1469-1481.
[38] Kosaka Y, Xie S P. Recent global-warming hiatus tied to equatorial Pacific surface cooling[J]. Nature, 2013, 501(7467): 403-407.
[1] 黄强,陈子燊,唐常源,李绍峰. 珠江流域重大干旱事件时空发展过程反演研究[J]. 地球科学进展, 2019, 34(10): 1050-1059.
[2] 郭准, 周天军. IAP近期际气候预测系统海洋初始化试验中海表温度和层积云的关系[J]. 地球科学进展, 2017, 32(4): 373-381.
[3] 韩振宇, 吴波, 辛晓歌. BCC_CSM1.1气候模式对全球海表温度年代际变化的回报能力评估[J]. 地球科学进展, 2017, 32(4): 396-408.
[4] 叶晓燕, 陈崇成, 罗明. 东亚夏季降水与全球海温异常的年代际变化关系[J]. 地球科学进展, 2016, 31(9): 984-994.
[5] 蒲俊兵, 蒋忠诚, 袁道先, 章程. 岩石风化碳汇研究进展:基于IPCC 第五次气候变化评估报告的分析[J]. 地球科学进展, 2015, 30(10): 1081-1090.
[6] 张宏芳,潘留杰,侯建忠,李明娟. 陕西暖季雷暴的主模态及其可能的影响机制[J]. 地球科学进展, 2013, 28(9): 1025-1035.
[7] 郑晓东,鲁帆,马静. 汉江流域降水多时间尺度特性及其与环流因子的相关性分析[J]. 地球科学进展, 2013, 28(5): 618-626.
[8] 杨秋明,宋娟,李熠,谢志清,黄世成,钱玮. 全球大气季节内振荡对长江流域持续暴雨影响的研究进展[J]. 地球科学进展, 2012, 27(8): 876-884.
[9] 万国江,郑向东,Lee H N,Bai Z G,万恩源,王仕禄,杨伟,苏菲,汤洁,王长生,黄荣贵,刘鹏. 黔中气溶胶传输的210Pb和7Be示踪:II.月及年时间尺度的剖析[J]. 地球科学进展, 2010, 25(5): 505-514.
[10] 万国江,郑向东,Lee H N,Bai Z G,万恩源,王仕禄,杨伟,苏菲,汤洁,王长生,黄荣贵,刘鹏. 黔中气溶胶传输的 210Pb和 7Be示踪:Ⅰ.周时间尺度的解释[J]. 地球科学进展, 2010, 25(5): 492-504.
[11] 陈文,魏科. 大气准定常行星波异常传播及其在平流层影响东亚冬季气候中的作用[J]. 地球科学进展, 2009, 24(3): 272-285.
[12] 陈燕,齐清文,杨桂山. 地学信息图谱时空维的诠释与应用[J]. 地球科学进展, 2006, 21(1): 10-13.
阅读次数
全文


摘要