海洋水文学

基于 HHT 的吕宋岛西北海域叶绿素浓度及相关环境物理要素的多时间尺度分析*

  • 闫桐 ,
  • 王静
展开
  • 1. 热带海洋环境国家重点实验室 中国科学院南海海洋研究所 , 广东 广州 510301; 2. 中国科学院研究生院 , 北京 100049; 3. 中山大学地理科学与规划学院 , 广东 广州 510275
闫桐 (1984 — ), 男 , 山东省齐河县人 , 博士研究生 , 主要从事海洋卫星遥感资料应用研究。 Email:yantong@scsio.ac.cn

收稿日期: 2011-11-01

  修回日期: 2011-11-01

  网络出版日期: 2011-11-01

基金资助

中国科学院知识创新工程重要方向项目 (KZCX2-EW-204); 国家重点基础研究发展计划项目 (2010CB950401); 卫星海洋环境动力学国家重点实验室开放 (SOED0702); 985 工程科技创新平台资助项目 (105203200400006)

Multi-timescale analysis of chlorophyll and its related physical factors northwest of the Luzon Island based on HHT

  • YAN Dong ,
  • WANG Jing
Expand
  • 1. State Key Laboratory of Tropical Oceanography South China Sea Institute of Oceanology , CAS , Guangzhou 510301, China ; 2. Graduate University of CAS , Beijing 100049, China ; 3. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China

Received date: 2011-11-01

  Revised date: 2011-11-01

  Online published: 2011-11-01

摘要

利用基于集合经验模态分解(EEMD)方法而改进的Hilbert-Huang变换(HHT)方法, 分析了吕宋岛西北海域从1997年9月至2009年7月近12年的月平均遥感叶绿素浓度观测序列以及相关环境物理要素时间序列, 分离出各要素的特征振荡模态(IMFs); 在此基础上以叶绿素与相关要素间具有相同或相近频率的IMF对偶之间相位差余弦值的方差为指标, 探讨了该海域叶绿素浓度与环境场之间的联系。结果表明: 1)海区各研究变量都具有明显的季节和年际振荡特征, 叶绿素准年周期模态方差贡献达81%, 年际变化中准两年振荡是海区诸要素共同的波动类型, 此外叶绿素浓度还具有4年左右周期的振荡。2)除埃克曼抽吸速度在准年周期振荡上与叶绿素浓度显著正相关、Ni? o3区海表温度在准两年周期上有弱的正相关关系外, 其余要素均与叶绿素浓度在不同时间尺度上呈负相关关系。这些结果说明HHT是气候序列多时间尺度分析中的一种有力工具。

本文引用格式

闫桐 , 王静 . 基于 HHT 的吕宋岛西北海域叶绿素浓度及相关环境物理要素的多时间尺度分析*[J]. 热带海洋学报, 2011 , 30(5) : 38 -46 . DOI: 10.11978/j.issn.1009-5470.2011.05.038

Abstract

An improved Hilbert-Huang transform (HHT) based on ensemble empirical mode decomposition (EEMD) is applied to analyze monthly averaged time series of remotely-sensed surface chlorophyll concentration from September 1997 to July 2009 and the relevant hydrological and meteorological factors northwest of the Luzon Island in the South China Sea. On the basis of modes separated by EEMD, we develop a correlation index using the cosine values of the phase differences between a pair of intrinsic mode functions (IMFs), of which one is from chlorophyll and the other is from another variable. Our exploration of the relation between chlorophyll and physical environment shows that a seasonal pattern and an interannual oscillation are common characteristics in the time series. Quasi-annual periodicity accounts for 81% of the total variance of chlorophyll, and there are two other modes (the quasi-biennial oscillation and 4-year period, respectively). Further more, it is revealed that a significant positive correlation between seasonal modes of Ekman pumping velocity and chlorophyll, and a weak positive relationship between quasi-biennial modes of Ni?o3 SST and chlorophyll. Besides, all the other variables show out-of-phase correlation with chlorophyll at different time-scales. These findings demonstrate the usefulness of HHT in climate time series analysis.

参考文献

[1] YODER J A, KENNELLY M A. What have we learned about ocean variabilityfrom satellite ocean color imagers?[J]. Oceanography, 2006, 19(1): 152-171.
[2] TANG DANLING, NI I-H, DANA R K, et al. Remote sensing observations of winter Phytoplankon blooms southwest of the Luzon Strait in the South China Sea [J]. Mar Ecol Prog Ser, 1 999, 191: 43-51.
[3] 赵辉, 齐义泉, 王东晓, 等. 南海叶绿素浓度季节变化及空间分布特征研究 [J]. 海洋学报, 2005, 27 ( 4 ) : 45-52.
[4] PENAFLOR E L, VILLANOY C L, LIU CHO-TENG, et al. Detection of monsoonal phytoplankton blooms in Luzon Strait with MODIS data[J]. Rem Sens Environ, 2007, 109(4): 443- 450.
[5] VANTREPOTTE V, MELIN F. Temporal variability of 10-year global SeaWiFS time-series of phytoplankton chlorophyll a concentration[J]. ICES J Mar Sci, 2009, 66(7): 1547-1556.
[6] NEZLIN N P, LI B AI L IAN. Time-series analysis of remote-sensed chlorophyll and environmental factors in the Santa Monica-San Pedro Basin off Southern California [J]. J Marine Syst, 2003, 39(3-4): 185-202.
[7] HENSON S A, ROBINSON I, Allen J T, et al. Effect of meteorological conditions on interannual variability in timing and magnitude of the spring bloom in the Irminger Basin, North Atlantic [J]. Deep Sea Res, Part I, 2006, 53(10): 1601-1615.
[8] WANG JING, QI YIQUAN, JONES I SF. An analysis of the characteristics of chlorophyll in the Sulu Sea [J]. J Marine Syst, 2006, 59(1-2): 111-119.
[9] ISHII M, KIMOTO M, KACHI M. Historical ocean subsurface temperature analysis with error estimates[J]. Mon Weather Rev, 2003, 131: 51-73.
[10] STEWART R H. Introduction to physical oceanography[M/OL]. Texas A & M Univ, Dept of Oceanogr, 2004[2010-04-07]. . http: //oceanworld. tamu. edu/resources/ocng_textbook/contents. html.
[11] ZHANG CAIYUN, HU CHUANMIN, SHANG SHAOLING, et al. Bridging between SeaWiFS and MODIS for continuity of chlorophyll-a concentration assessments off Southeastern China[J]. Rem Sens Environ, 2006, 102: 250-263.
[12] LEI YAGUO, ZUO M J. Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs[J]. Meas Sci Technol, 2009, 20(12): 125701-125713.
[13] HUANG N E, WU ZHAOHUA. A review on Hilbert-Huang Transform: method and its applications to geophysical studies[J]. Rev Geophys, 2008, 46: RG2006. doi: 10. 1029/2007RG000228.
[14] WU M-C, HU C-K. Empirical mode decomposition and synchrogram approach to cardiorespiratory synchronization[J]. Phys Rev E, 2006, 73: 051917. doi: 10. 1103/PhysRevE. 73. 051917.
[15] PATRICK M C. How do you make a time series sing like a choir? Using the Hilbert-Huang transform to extract embedded frequencies from economic of financial time series[G]. Helsinki: Bank of Finland, 2009: 7-32.
[16] HUANG N E, ZHENG SHEN, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc R Soc Lond, 1998, 454: 903-995.
[17] WU ZHAOHUA, HUANG N E. Ensemble empirical mode decomposition: a noise assisted data analysis method[J]. Adv Adapt Data Anal, 2009, 1(1): 1-41.
[18] CHENG QIAN, FU CONGBIN, WU ZHAOHUA, et al. On the secular of spring onset at Stockholm [J]. Geophys Res Lett, 2009, 36: L12706.
[19] WU ZHAOHUA, HUANG N E. A study of the characteristics of white noise usi n g the empirical mode decomposition method [J]. Proc R Soc Lond, 2004, 460: 1597-1611.
[20] COHEN L. Time-frequency analysis: Theory and applications[M]. Englewood Cliffs, N J: Prentice-Hall, 1995 : 320.
[21] SOLE J, TURIEL A, LLEBOT J E. Using empirical mode decomposition to correlate paleoclimatic time-series[J]. Nat Hazards Earth Syst Sci, 2007, 7: 299-307.
[22] SOLE J, TURIEL A, ESTRADA M, et al. Climatic forcing on hydrography of a Mediterranean bay ( Alfacs Bay )[J]. Continent Shelf Res, 2009, 29(15): 1786-1800.
[23] WU ZHAOHUA, SCHNEIDER E K, KIRTMAN B P, et al. The modulated annual cycle: an alternative reference frame for climate anomalies[J]. Clim Dyn, 2008, 31: 823-841.
[24] WU ZHAOHUA, HUANG N E, LONG S R, et al. On the trend, detrending, and variability of nonlinear and nonstationary time series[J]. PNAS, 2007, 104(38): 14889-14894.
[25] LIN ZHENSHAN, SUN XIAN. Multi-scale analysis of global temperature changes and trend of a drop in temperature in the next 20 years[J]. Meteorol Atmos Phys. , 2007, 95: 115-121.
[26] CHENG XUHUA, QI YIQUAN. Trends of sea level variations in the South China Sea from merged altimetry data[J]. Glob Planet Change, 2007, 57(3-4): 371-382.
[27] LIU K K, CHAO S Y, SHAW P T, et al. Monsoon-forced chlorophyll distribution and primary production in the South China Sea: observations and a numerical study[J]. Deep-Sea Research I, 2002, 49(8): 1387-1412.
[28] SHAW P T, CHAO S Y, LIU K K, et al. Winter upwelling off Luzon in the northeastern South China Sea[J]. J Geophys Res, 1996, 101(C7): 16435-16448.
文章导航

/