ENSO signals and their spatial-temporal variation characteristics recorded by the sea-level changes...

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Citation: Yuan L W, Yu Z Y, Xie Z R, et al. ENSO signals and their spatial-temporal variation characteristics recorded by the sea-level changes in the northwest Pacific margin during 1965－2005. Sci China Ser D-Earth Sci, 2009, 52(6): 869-882, doi: 10.1007/s11430-009-0072-5

ENSO signals and their spatial-temporal variation characteristics recorded by the sea-level changes in the northwest Pacific margin during 1965－2005

YUAN LinWang, YU ZhaoYuan, XIE ZhiRen, SONG ZhiYao & LÜ GuoNian School of Geographical Science, Nanjing Normal University, Nanjing 210046, China

Sea-level is closely linked to the Earth's climate and its change is important as a metric for global and regional climate change. Identifying, extracting, and revealing such information through detailed analysis is the prerequisite for understanding the mechanisms of sea-level change. The monthly- av-erage tide-change records reported and examined in this paper are from 10 tide gauge stations dis-tributed in the northwest margin of the Pacific Ocean, registered during the period 1965－2005. In particular, we have utilized the Second Order Blind Identification (SOBI) method to identify and the Empirical Mode Decomposition (EMD) method to extract the El Niño-Southern Oscillation (ENSO) sig-nals imprinted in those tide gauge records; and subsequently, we have investigated the spa-tial-temporal characteristics of the extracted ENSO signals using wavelet analysis methods. The fol-lowing results are obtained: (1) the ENSO events recorded by each tide gauge series are of different types and intensity, which show considerable temporal-spatial variation characteristics, with sea-level responses to ENSO signals remarkably stronger in low latitude areas than in medium-high latitude areas; (2) due to the influences of ocean currents, topographical conditions, and other factors, there exist variations in the type of relationship between the sea-level changes and the recorded ENSO events at different latitudes; (3) sea-level changes can also denote scale-variation characteristics of ENSO events, and particularly, since 1980s, all the tide gauges located south of Kanmen show intense responses to ENSO and the timescale of ENSO events extended gradually from around 4 years to 2－8 years, reflecting variations in the intensity and frequency of ENSO events; and (4) reverse changes of certain scale were noted before and after strong ENSO events recorded by sea-level changes, sup-porting the research findings about the interaction (mutual coupling and superimposition) between the probability of occurrence of strong ENSO events and their time scale.

sea level change, ENSO, SOBI, cross wavelet spectrum, wavelet coherence

Sea-level change, as an indicator of global climate change, is affected by atmosphere, hydrosphere, litho-sphere, and human activities as well as other factors[1]. In recent years, explorations of sea-level change mecha-nisms, which aimed at understanding global sea-level rise, has been focused on interpreting the role of these relative key factors[2]. Considerable research has been conducted on the linkage between sea-level change and greenhouse effects[3], and on possible influences various factors have on sea level change including the polar ice

sheet[4], mountain glaciers, small ice caps[5], volcanism[6]

and land surface runoff[7]. The Intergovernmental Panel on Climate Change (IPCC) report AR4 summaries the progress systematically, and emphasizes global and re- Received September 22, 2008; accepted March 19, 2009 doi: 10.1007/s11430-009-0072-5 †Corresponding author (email: yuanlinwang @njnu.edu.cn) Supported by Special Program for National Basic Research Program of China (Grant No. 2007CB416602), Special Program for National High Technology R & D Pro-gram of China (Grant No. 2009AA12Z205) and Major Program for the Fundamental Research of the Jiangsu Province in University (Grant No. 05KJA17001)

870 Yuan L W et al. Sci China Ser D-Earth Sci | Jun. 2009 | vol. 52 | no. 6 | 869-882

gional characteristics of sea-level change[8]. Significant spatial non-uniform characteristics of the regional sea-level change has been revealed by tide gauges and satellite altimetry[9,10]. Certain researches have also been concerned about the local sea-level change in China’s coastal areas, i.e., in the northwest Pacific Ocean mar-ginal seas[11－13]. Revealing the key factors that con- trolled local sea-level change in different spatial scales may greatly help researchers understand: 1) the cli-mate-sea-level response mechanisms during the process of sea-level variation and 2) the global-regional response mechanisms of sea-level change; and furthermore, it may also promote the capability of future sea-level pro-jection.

ENSO events originating in the tropical Pacific area are the strongest signal in the interannual variation of ocean-atmosphere system with global impacts[14,15], and exhibit remarkable regional characteristics in affecting the sea-level changes in different regions[16,17]. Satellite altimetry records can clearly indicate the evolution process of ENSO events and are conducive to ENSO projection[18]. Since 1970s, Wyrtki[19] has emphasized the influence of ENSO on sea-level change, which mo-tivated them to establish the TOGA sea-level center in University of Hawaii in 1980s. Li and Wimbush[20] have studied the sea-level change response to the 1982－1983 ENSO event along the southeast coast of China, and discussed its temporal-spatial evolution process. Based on the tide gauge and satellite altimetry data, Rong et al.[17] have found that ENSO is significantly correlated with the sea-level change at annual scale, showing the intensive infulence of ENSO events on sea-level fluctuations along Chinese coastal seas.

Many methods, for example empirical orthogonal function (EOF), have been widely adopted to uncover the sea-level change characterisics[21]; however, it sometimes yields variation modes without distinct physical significance and are not sufficient to reveal local temporal-spatial characteristics[22]. Wallace et al.[23] have pointed out that the paradigm of a quasi-periodic “ENSO cycle” phase locked with the annual march does not capture the complexity of the evolution of the anomalies. A single modal structure or EOF cannot rep-resent the range of spatial patterns of ocean-atmosphere interaction in the tropical Pacific. Thus, a more inclusive phenomenological description of ENSO is needed[23]. Recently, with the signal analysis methods extending from linear to nonlinear, stationary to non stationary, and

parametric to nonparametric[24], many new methods have been developed, applied, and proved to be significantly superior[25,26]. Blind source seperation methods[27] like SOBI give more robust and accurate analysis re-sults[28], owing to utilizing high-order statistics and greatly weakening the priori hypothesis of original signal. Additionally, integrated time-frequency analysis methods are also developed to promote local and instantaneous time-frequecy analysis ablilities[29,30]. These methods have already been introduced into geo- science research[31,32], and accordingly, they may help shed new light on the characteristics and mechanisms of sea-level change.

Tide gauge and satellite altimetry are the main data sources of sea-level research. High resolution satellite altimetry with global scale has promoted the conf- iguration and procedure reconstruction of recent sea- level change, and can be deployed to discuss the response of sea level to ENSO in non-equatoral Pacific areas. Tide gauge records, which have longer time spans than satellite altimetry data, contain information about regional sea-level variations, action processes, impact pathways, and mechanisms of transmission-coupling[8]. However, tide gauge data are strongly affected by local topography, climate, hydrological process, and human activities. It is more difficult to extract reliable signal components from sparsely located tide gauge series. Due to the complexity and diversity of the factors influencing sea-level change, nonlinearity and quasi-periodic characteristics of ENSO, and mixture of different modes and turbulent noise, the extraction of signals about sea-level change in marginal sea areas and their tempo-spatial characteristics analysis have difficulties.

ENSO events of different intensities occur frequently and are quasi-periodic, and since 1980s both their fre-quency and intensity have changed[33,34]. However, how are these ENSO events with different intensities re-corded by sea-level change in marginal sea areas? How does the regional sea level respond to each ENSO event? And, what are the differences between the impacts of strong and those of weak ENSO events on the sea level? By analyzing different regional sea-level change series at longer time scales, it is possible to interpret the spa-tial-temporal variation characteristics of ENSO events. Here, we will utilize the SOBI method and the EMD method to recognize and extract the ENSO signals from the monthly-average sea-level change series, registered at tide-gauge stations located in the range of 10°18′N to

Yuan L W et al. Sci China Ser D-Earth Sci | Jun. 2009 | vol. 52 | no. 6 | 869-882 871

38°34′N in the northwest Pacific Ocean marginal seas, and then apply wavelet analysis methods on investigat-ing the response and record of the regional sea-level to ENSO events, as well as the temporal-spatial transmis-sion characteristics. The integration of above methods represents a new attempt for interpreting the regional sea-level change in nonlinear, multi-scale of time-space domain, and helps understand the evolutionary process and mechanisms of ENSO, the interaction of global cli-mate and sea-level variation as well as its transmission and differentiation.

1 Data and method

1.1 Data source

Monthly Revised Local Reference (RLR) sea-level data from 10 gauge stations registered during 1965 to 2005 are analyzed here. The 10 stations are located in the northwest Pacific Ocean marginal seas, and the data come from the office for Permanent Service of Mean Sea Level (PSMSL)[35]. The location and the corre-sponding monthly sea-level variation over time at each gauge station are shown in Figure 1, left and right, re-spectively. The Bootstrap Expectation-Maximization

(EMB) method implemented in the Amelia II package[36] is used to interpolate the limited amount of missing data, and then all data are normalized. The Multivariate ENSO Index (MEI)[37,38] from the Earth System Re-search Laboratory of National Oceanic and Atmospheric Administration (NOAA ESRL) of the United States is selected as the ENSO index. Compared with the com-monly used SOI, MEI can better describe the evolution-ary process of ENSO events from occurrence to extinc-tion[39]. The MEI in Figure 2 indicates that there were seven strong ENSO events during 1965－2005, and the MEI also changed significantly around 1975, which may correspond to the adjustment of the sea-level response to the rapid global warming since 1970s.

1.2 SOBI method and EMD method for singal extraction

Blind Source Separation (BSS) methods are relatively new signal processing techniques. The main advantage is their ability to filter mixed signals under the condition of limited prior knowledge through statistical character-istics analysis. The SOBI algorithm, which depends on delayed time, mutual covariance or correlation matrix, is one of the generally adopted BSS methods, and uses

Figure 1 (a) Spatial distribution of tide gauge stations; (b) sea level anomaly time series. First two letters of station name were used as series indication. The same below.


Figure 2 MEI time series. Numbers in figure marks start and end year of seven strongest ENSO events.

second order statistics to identify signal characteristics and extract the corresponding components. For signals involving either significant time structure or different spectrum signature, the performance of this method is quite satisfactory[40].

Consider xi (i=1, …, N) as an array of the original observed data from N stations, and si (i=1, …, m) as m sources of influence, and the corresponding matrices X and S stand for the observed and the source matrix. Fur-ther, we also assume that X and S satisfy the equation X=HS+V, where H represents an unknown mixture ma-trix and V stands for additional noise. By applying a combined diagonalization algorithm on the time-delay mutual covariance or correlation matrix, the SOBI method can solve a linear transformation matrix W, such that W≈H −1. Consequently, an approximate solution of the source matrix S can be obtained from S=WX. In the application to the climate-sea level interaction, the mix-ture matrix H can provide information about the trans-mission process and mechanism.

The EMD proposed by Huang et al.[30] is a new method for non-stationary and non-linear signals analy-sis. This method can adaptively yield the base functions from the processed signal itself with which any compli-cated signal can be decomposed into a series of intrinsic mode function (IMF) plus a residual, singling out thus the different scale components in the time sequence and the long-term trends. EMD is a self-adaptive process of decomposition, and it can effectively separate fluctua-tions of different frequencies through gradual and step-wise decomposition, extracting trends and identifying accurately abrupt changes in signals belonging to dif-ferent time scales. By performing comparative analysis using various trend-separation methods, Wu et al.[41] conclude that EMD is one of the most effective methods for trend extraction, attributing to its more logical base and self-adaptive process.

1.3 Wavelet time-frequency analysis methods

Wavelet analysis method is a time-frequency localiza-tion analysis method in which both the time and the fre-quency window can change. It can perform multi-scale filtering of the original signals at predefined scales. Continuous wavelet transforms (CWT) and discrete wavelet transforms (DWT) are the two fundamental transformations of wavelet analysis. In contrast to DWT, CWT has a better performance on the analysis of time-frequency signals. From a mathematical point of view, CWT can be regarded as the projection of the space composed by a cluster of base functions to char-acterize the original signal. For a given input signal x(t) and a mother wavelet function ψ *, the continuous wavelet coefficients at the given scale a, and at the time position b, can be computed as follows:

*,

1( ) d .a bR

t bC x t taa

ψ −⎛ ⎞= ⎜ ⎟⎝ ⎠∫ (1)

It is equivalent to the inner product of the original signal and the mother wavelet function. Therefore, given the range of scale a and position b, the original signal can be decomposed into the time-scale domain deter-mined by a and b, thereby showing the time-scale char-acteristics of the original signal.

Both cross wavelet spectrum and wavelet coherence (WTC) can be applied to investigate the multi-scale in-teraction relationship between two signals[42]. They can provide the distribution of energy resonance and co-variance of two series in the time-frequency space, which helps investigate the coherence and correlation of two signals at different time intervals and scales, and express the phase relationships in the time-scale domain.

Given two time series, Xn and Yn, with corresponding

wavelet transforms, ( )XnW s and ( )Y

nW s , the cross wavelet spectrum can be defined as

*( ) ( ),XY X Yn n nW W s W s= (2)

where *( )YnW s is the complex conjugate of ( )Y

nW s , and hence, one can define the cross-wavelet power as

| ( ) |XYnW s , which reflects the energy resonance of Xn

and Yn in the time-frequency domain. Hence, the com-plex angle can be interpreted as the local relative phase of Xn and Yn in the time-frequency domain.

Wavelet coherence can be defined as


212

2 21 1

( ( ))( )

( ( ) ) ( ( ) ),

XYn

n X Yn n

S s W sR s

S s W s S s W s

−

− −=

⋅ (3)

where S is a smoothing operator. Note that this definition closely resembles that of the

classical correlation coefficient and thus wavelet coher-ence can be regarded as representing the degree of lo-calized correlation in the time-frequency space.

2 Analysis of sea-level change and separation of ENSO signals

2.1 Signal analysis of sea-level change

With the tide gauge and satellite altimetry data, Church et al.[21] have revealed that global mean sea level is weakly correlated with the SOI, whereas the first EOF component and the SOI index are perfectly comparable. The discrepancy may be due to masking of ENSO signals by regular periodic fluctuations in the sea level. Landerer et al.[43] have studied the differences in the ENSO- response characteristics of different mechanisms of sea- level variations, and the results also imply that the weak correlation between GMSL variations and ENSO events is due to the aforementioned masking effect. Although this is a difficult task, we can analyze the fluctuation process, amplitude, and phase variation of sea-level changes at different time scales by separating and ex-tracting different time-scale signals contained in the sea-level series. Furthermore, ENSO, component signals can also be extracted and then used to analyze the tem-poral-spatial transmission and response of ENSO events.

The SOBI method is introduced to extract sea-level change signals with physical significance. In total, 10 components have been extracted from the SOBI de-composition of anomalies at 10 tide gauge stations. Fig-ures 3 and 4 show their comparison with the MEI and their power spectrum, respectively. From these two fig-ures, we can conclude that both S1 and S2 components represent annual oscillations, but their amplitudes are different. The S4 component reflects a 6-month cycle oscillation, which is superimposed by a 3-month cycle fluctuation. The above three components mainly reveal the influence of astronomical factors. The S3 component reflects the main trend of change but it is superimposed by fluctuations at the scale of 2－6 years, while the S5 component shows a clear negative correlation with the MEI (the correlation coefficient is −0.55). Although the

MEI values are relatively high before 1975, their peaks and troughs are still evident, which can be seen as re-flecting the dominant variability of ENSO signals. The components S6 to S10 correlate, to a certain extent, with the structure of the MEI series, and the power spectrum analysis also shows that these five components corre-spond well with ENSO events at a specific domain. Therefore, these five components may also contain the information about ENSO variations.

2.2 Separation of ENSO signals from sea level records

By summing up all the ENSO-related components (S3, S5, S6, S7, S8, S9, and S10), a new composite series is obtained, which involves trends and various high- frequency components imprinted in sea level data. EMD method is introduced to remove trend (Figure 5(a)) and high-frequency fluctuations at scales less than one year (IMF1 and IMF2 components, yielding the final ENSO series of each station (Figure 5(b)). The correlation co-efficients between the ENSO-signal series, the MEI and the original time series at each tide gauge station are given in Table 1. The MEI coefficient indicates that high correlation and good structural consistency hold for the data recorded at the stations located south of Xiamen, whereas this correlation becomes weak at the stations located north of Xiamen except for the Nisinoomote and Inchon stations. The correlation coefficient between the original series and the ENSO series also presents similar variations. All these indicate that energy attenuation ex-ists in the transmission of ENSO events, as well as com-plex changes in the ENSO signal recorded at tide sta-tions located at high latitudes.

By comparing the ENSO series at different stations with the MEI, it can be seen that the amplitude of ENSO signals presents significant variations at different sta-tions. Their phasing with respect to the MEI is uncertain with time, in addition to the spatial disparity. The higher the latitude, the smaller the amplitude; this reflects the energy and intensity attenuation in the transmission of ENSO from low latitude to high latitude. The phase re-lationship between ENSO series and MEI for the sta-tions located south of Zhapo including the Zhapo station itself, are relatively in-phase, but the phase differences become more significant when the latitude becomes higher, and the phase differences are also evident for different time spans. Therefore, with the wavelet analy-sis, analyzing the scale, energy and phase characteristics between tide-gauge ENSO series and the MEI may help


Figure 3 Comparison between SOBI components and MEI. Black solid Figure 4 Power spectrum of SOBI components.

lines indicate SOBI components and gray dash lines indicate MEI.

Figure 5 Separation of ENSO signals in sea level changes and its comparison with the MEI. (a) Reconstructions (black solid lines) from sum of S3, S5－S10 and their trend extracted by EMD (gray dash lines); (b) the reconstructed ENSO signals with trends and high frequency components removed (black solid lines) and their comparison with the MEI (gray dash lines).


Table 1 Correlation coefficient between ENSO signals in sea level changes, MEI and original seriesa)

MEI Original series Tidal Station

r P

r P Cebu −0.62★ 0.000 0.62★ 0.000

Legaspi −0.45★ 0.000 0.51★ 0.000 Zhapo −0.45★ 0.000 0.24★ 0.000

Xiamen −0.46★ 0.000 0.20★ 0.000 Naha 0.07 0.115 0.30★ 0.000

Kanmen −0.08 0.067 0.20★ 0.000 Nisinoomote −0.19★ 0.000 0.25★ 0.000

Sakai −0.04 0.334 0.09 0.055 Inchon −0.10* 0.026 0.23★ 0.000

Nezugaseki 0.07 0.103 0.15★ 0.001 a) Correlation with * is significant at the 0.05 level (2-tailed); correlation

with ★ is significant at the 0.01 level (2-tailed).

reveal the response of sea-level change to ENSO events and understand its temporal-spatial transmission charac-teristics and mechanism.

3 Temporal-spatial characteristics of ENSO signal recorded by sea-level changes

Variability and possible tele-connections between tide- gauge ENSO signals and MEI are detected by CWT, cross-wavelet and wavelet coherence methods. CWT indicates time-scale characteristics of ENSO signals; the center of alternating positive and negative iso-lines in the time-scale domain reflects the local and dynamic characters in the time-frequency variation of the domi-nant fluctuation components. Cross-wavelet and wavelet coherence analyses can reveal time-scale response char-acteristics at different stations, and phase relationship at different scales can reveal the response differences be-tween different regional sea level and ENSO events with different intensities. The energy resonance and coher-ence continuation in the time axis of different stations uncover the integral structure of the response scale be-tween sea level and ENSO, whereas the extent of influ-ence at a specific time represents the degree of response of sea level to a certain ENSO event, which thus can reflect the variation of action intensity and mechanism during the transmission process of an ENSO event from one tide gauge station to the other.

3.1 Time-scale characteristics of ENSO signals recorded by sea-level change

The results of CWT (Figure 6) show that the time- frequency characteristics of tide-gauge ENSO signals

and MEI are similar for those stations located south of Xiamen, and the recorded ENSO events at those stations are quite consistent. However, there are certain differ-ences in action scales. The predominant action scales at the three stations of Legaspi, Xiamen and Zhapo are around 6 years before 1980, whereas the average cycle of the corresponding MEI is about 4 years during the same period. However, the time-frequency characteris-tics are different for those stations located north of Naha station, as indicated mainly by the difference in the ranges and trends of scale evolution and by the response to strong ENSO events and small scale fluctuations. Data from Legaspi, Xiamen, Kanmen, Sakai, Inchon and Nezugaseki stations show remarkable large scale vari-ability of 8－10 years, but this is not apparent at the rest of stations. Furthermore, data from Naha station show a particular variability, the sea level response to ENSO is not significant before 1980, especially at the scale larger than 6 years, whereas the response intensity has in-creased gradually since 1980, with two dominant fluc-tuation cycles of about 2－4 years and 4－6 years. The location of Naha station may contribute to its peculiarity, which lies on the intersection area of warm and cold ocean currents, with a relatively open topography.

The cross-wavelet analysis results show that the sta-tions located south of Nisinoomote reveal a strong con-tinuity at approximately a 4-year scale, while those northern stations reveal energy resonance at the scale of about 2 years. Wavelet coherence analysis results reveal that Cebu, Xiamen, and Naha stations have a strong co-herence at the large scale of 10－16 years, which re-flects the overall trend of ENSO variation. At the scale of 2－8 years, the data from Cebu station show a con-tinuous, strong coherence in the whole time spans, while data from Legaspi and Zhapo stations show only a re-markable coherence after 1985, with a strong coherence at Naha station concentrated in the period of 1970－1985. At Xiamen station, a strong coherence is mainly at a 4 years scale. For those stations located north of Xia-men, the coherence at ENSO scale is not evident except that at the Nezugaseki station, and the coherence at all other scales shows a plaque distribution. The occurring time of strong coherences corresponds well with the timing of strong ENSO events, showing the trend of progressive increase of ENSO intensity since the end of 1980s.


Figure 6 CWT coefficients of MEI and ENSO signals extracted from each station. y-axis is period (year).

In summary, the ENSO signals recorded at various

tide gauge stations show differences in different time spans and at different scales, and such differences reflect the temporal-spatial differentiation phenomenon of ENSO events of different characteristics and intensities during transmission in the time-space domain. This suggests that tidal gauge records can reveal, to some extents, the overall differentiation characteristics of ENSO variation, but the time-frequency variation of such records also shows significant individual difference. The variation of sea-level wind field, ocean currents, air pressure and thermal energy condition, as well as other

key factors at each gauge station, may all affect the re-sponse of sea-level change to ENSO[44,45]. Moreover, variation of each station is also affected by its geo-graphic location, surrounding topographical conditions, and run-off into sea, etc. For instance, the latitudes of Sakai and Nezugaseki stations are similar to that of In-chon station, but the ENSO intensities show distinct dif-ferences. The sea level response to ENSO at Kanmen station is remarkably weaker than that at the adjacent Xiamen station. The cross-wavelet analysis result of Naha station at the scale of 2－4 years in the period of 1970－1980 is different from those of other stations;


thereby the action of the aforementioned factors in the local, coupled, sea-atmosphere interaction system needs further study.

3.2 Response characteristics of sea-level changes to ENSO events of different intensity

The wavelet analysis results show that the response characteristics of sea-level changes at different stations to ENSO events with different intensities have signifi-cant differences. The three strong ENSO events occurred in 1972－1973, 1982－1983, and 1997－1998, respec-tively, can be detected from almost all of 10 gauge sta-tions records. Thus, it can infer that sea level responds sensitively to strong ENSO events. Furthermore, cross-wavelet analysis results reveal that distinct dis-crepancies in the scale and structure exist in the three strong ENSO events, with the ENSO event in 1997－1998 spanning a much broader scale range. The ENSO signals from low-latitude stations show very good com-parability, from structure to detail, but these are not ob-vious at medium-high latitude stations. The responses at all stations to the 1982－1983 ENSO event are not ob-vious, which may be related to the particularity of the action process of this ENSO event. Water level anomaly at all stations occurred from the summer of 1982 lasting till the end of 1983, which led to the overall declined trend in the sea-level, instead of the violent fluctua-tion[20]. It is significant that the La Niña phenomenon did not occur after the 1982－1983 ENSO event, but a medium intensity ENSO event happened during the pe-riod 1986－1988, together with abnormal Kuroshio Current changes. The abnormal changes in the ocean currents may be the reason why the sea-level responses to the strong 1982－1983 ENSO event were poor[46].

The response differences at various stations to differ-ent ENSO events reveal not only the differences in the inherent structure and characteristics of an ENSO event, but also the effect of local environment at each station. For the 1972－1973 strong ENSO event, the response scale at the Inchon and Nezugaseki stations is 1－2 years, while that at all other stations is 1－4 years. For the 1982－1983 strong ENSO event, the response scale extends to 2－6 years at the stations located south of Naha, while the response at the scale of 2－4 years is more or less absent at Kanmen, Nisinoomote, Sakai and Nezugaseki stations which also recorded a shorter time span than that at low latitude stations. For the 1997－

1998 strong ENSO event, the recorded scales of re-sponse cover basically the range of 1－8 years at Inchon station and at those stations located south of Naha, while the response at the significant scale of 4－8 years is al-most absent at the medium and high latitude stations. Nevertheless, the time-scale range of the influence of this last ENSO event is larger than those of the previous two strong ENSO events.

In general, the effect of strong ENSO events on sea level shows the trend of enlarging the time-scale range of influence, which may reflect the enhanced intensity of the ENSO event itself or of the atmosphere-sea interac-tion. In the three periods of 1976－1981, 1990－1995, and 2001－2005, the MEI shows variation at high fre-quency, indicating the frequent occurrence of weak ENSO events. The cross-wavelet and wavelet coherence in these time periods show that both the continuity of scales and the correspondence are weak. This finding suggests that the sensitivity of sea-level change to weak ENSO events is significantly weaker than that to strong ENSO events.

3.3 Structure characteristics of ENSO signals recorded in sea-level changes

The enhanced tendency of global warming since 1970s triggered the debate on whether the increased intensity and frequency of ENSO events was due to warmer ocean temperatures resulting from global warming[33,34]. The CWT result from our analysis of the MEI shows that the dominant period of ENSO increased from 2－4 years during 1960－1975 to 4－6 years during 1980－1993, which is similar to the conclusion of An and Wang[33]. However, the frequency and intensity of the MEI remain basically unchanged at the scale of around 4 years. Furthermore, the results of the CWT of the ENSO series for each station indicate that the amplitude of the fluctuations at the scale at about 4 years is relatively small, even though at some stations, some specific time spans are missing (e.g. at Naha station) or the transmis-sion process tended to shift to smaller time scales (e.g. at Sakai station), see Figure 6.

On the contrary, for scales larger than 6 years, except Zhapo, Naha, and Nezugaseki stations, the transmission at all other stations presents a significant trend of shift-ing from smaller to larger scales, which is consistent with the variation characteristics of the ENSO events. The above variations are well illustrated by the cross


wavelet spectrum and wavelet coherence shown in Fig-ure 7. In fact, the trend of ENSO changes from shorter scales to longer scales can be found from the cross wavelet spectrum of all stations except the Zhapo, Naha, and Nezugaseki stations. Since 1980s, the same trend of shifting to larger scales can be also seen in the coherence of ENSO series and MEI at the stations located south of Xiamen, indicating an increase in the ENSO intensity. Furthermore, wavelet coherence also shows structure variation of ENSO, which may be due to the different action mechanisms or coupling interaction of ENSO signals during their transmission process in different regions.

As shown in Figures 5(b) and 7(b), the details of the ENSO series extracted from tidal gauge stations located south of Naha station are in a good agreement with that of the MEI, except for Legaspi station where some peaks and valleys in the ENSO signal before 1985 had poor correspondence with the MEI. As for the wavelet coherence, the results indicate that low-latitude stations have strong coherence both at the dominant scale of 2―7 years and at the scale of 10－16 years characterizing long-time trends. On the other hand, for the stations lo-cated north of Kanmen, the coherence is distributed as patches, suggesting that some changes in the structure of ENSO series take place due to stronger effects of other high-frequency components at higher latitudes. Never-theless, it is still possible to discover the overall change of ENSO events from this picture. Since the late 1980s the ENSO series and the MEI were more consistent in details and also there was a trend of increased coherence. In particular, such comparison at Inchon station is more clear than at those medium to high latitude stations, es-pecially at the scale larger than 10 years, whereas a sig-nificant coherence is shown at the scale of 4－8 years during 1993 to 2003 at Nezugaseki station. These find-ings may indicate the particularity of sea-level response to ENSO at these two stations, with respect to the other high-latitude stations. 3.4 Phase characteristics of ENSO signal recorded in sea level Up to now, the researches on phase relations between sea level and ENSO are mostly based on average phase relation between the entire original or filtered sea level series and ENSO index. Chambers et al.[47] suggested that global mean sea level has higher correlation with ENSO events at the scale of 1－5 years, with a lag of

about 3 months with respect to the SOI index. However, Raicich[48] pointed out that the lag of about 5 months existed between tide gauge data and ENSO series based on the data from the south Atlantic Ocean. A complex phase relationship exists both in temporal and spatial fields, which may be due to the complexity of sea-level change and to the time-dependent characteristics of the intensity and frequency of ENSO events. From the en-ergy resonance and coherence relations that passed the significance test, a greater regularity exists in the phase relationship between sea-level changes and ENSO events at low-latitude stations than that at high latitudes. This means that there is a great consistency between ENSO variation and sea-level change at different scales at low-latitude stations, whereas such consistency is greatly reduced at that of higher latitudes. This change in consistency is probably due to the energy attenuation and to the effects of other factors, during their transmis-sion to higher latitudes.

Based on the phase relation of cross wavelet spectrum and wavelet coherence at Cube and Zhapo stations, there was a systematic lag in sea level response to ENSO at the scale of about 4-years before 1990. However, the phasing of both types of events was largely synchro-nized after 1990, which may be related to the strength enhancement of ENSO and may suggest an enhance-ment of the interactions between the global atmosphere and sea level. The relationships at different scales show that, before the three strong ENSO events of 1972－1973, 1982－1983, and 1997－1998, anti-phase rela-tionships can be seen at both the 2 and 4 years scales, appearing as the so-called “phase vortex”. Dickey et al.[49] suggested that ENSO contains two frequency components with center frequencies of 2.4 and 4.2 years, respectively, based on the results from the atmospheric angular momentum (AAM) series. They also pointed out that when the phasing of these two components coin-cides, strong ENSO events may occur. Based on the re-lationship analysis of ENSO, length of Day (LOD) and AAM series, Zheng et al.[50] pointed out that the com-bined superposition of multi-scale atmospheric oscilla-tions including inter-annual, quasi-quadrennial and in-ter-decade as well as other lager scales may result in strong ENSO events. Our research results do support these conclusions, because the ENSO signals extracted from tide gauge data also reveal phase variations at different time scales before and after strong ENSO events.


Figure 7 Cross wavelet transforms and WTC between the ENSO signal in each station and the MEI. (a) Cross wavelet; (b) wavelet coherence. The 5% significance level against red noise is shown as a thick contour line and COI regions are shown by upward, thin, black envelope lines. The relative phase relationship is shown by arrows (with in-phase pointing left, anti-phase pointing right, and upward arrows indicating that the ENSO signal is ahead of the MEI by 90°). y-axis is period (year).


4 Conclusions and discussion

High frequency variations of sea-level change, espe-cially at the scales of inter-annual to inter-decade, are the keys and also the difficult points in the sea-level re-search. Studies on the record and response of sea level to ENSO events may contribute to understanding the tem-poral-spatial variation characteristics of ENSO. By a combined use of SOBI, EMD and wavelet time- frequency analysis methods, we identify and extract the relevant ENSO signals from the series of 10 tide-gauge stations in the northwest Pacific Ocean marginal seas. We examine the extracted ENSO signals at multi-scale temporal-spatial response and spatial transmission char-acteristics of sea-level changes to ENSO events.

(1) The overall characteristics of ENSO events’ varia-tions can be detected from the tide-gauge records. However, from station to station there are also differ-ences in the ENSO signals recorded in different time spans and at different time scales, which reflect the spe-cific temporal-spatial transmission characteristics of ENSO events with different strengths and characteristics. A significant regional differentiation is present in the ENSO signals recorded in the different tide gauge sea-level changes, showing an overall weakening trend from low latitudes to high latitudes. A similar weakening trend holds also for the comparability of ENSO indices. The inherent strength of ENSO events and local factors like ocean currents and meteorological situations, etc. can all affect the transmission of ENSO signals and lead to the variations in the recording and response of sea level to ENSO events at different gauge stations as re-ported in this paper.

(2) Time-frequency features of ENSO events are re-vealed through a wavelet analysis of the ENSO series extracted from different tidal gauges series and of the MEI. The results show that for all stations the response of the sea level is significant at the dominant scales of ENSO events, with the sea levels at low latitude show-ing a more significant response than those at me-dium-high latitude. In fact, except at Inchon and Ne-zugaseki stations, all other stations at medium-high lati-tudes show only a significant response to strong ENSO events. These observations suggest that the impacts of ENSO events on the sea level at low latitude are more direct and stronger, and that when transmitting from low latitude to high latitude the ENSO signals experienced more coupling processes, leading to distortion or mask-

ing of ENSO signals. (3) As for the scale-variation characteristics of ENSO

events, from the late 1980s, the sea-level response in-creased significantly at the stations located south of Kanmen, and the action scale extended from about 4 years gradually to 2－8 years, which could reflect the change in the intensity and frequency of ENSO events under global warming. The phase relationship between sea-level change and ENSO events also show variations in time, and the mean phasing derived from the entire series is not sufficient to describe the phase change, be-cause the phasing relationships between ENSO and sea level at low latitude are more consistent and regular than those at high latitude, indicating a good agreement in the scales of the two type of events. Furthermore, an anti- phase relationship at both scales of 2 and 4 years was also observed before and after the occurrence of strong ENSO events, which supports the hypothesis that the combined effects at different scales may result in the occurrence of strong ENSO events.

In recent years, the emphasis of sea level research has gradually shifted from global mean sea-level change to regional sea-level response and variation. Regional sea-level change may contain important information about the response mechanisms of sea-level change trends, fluctuations, and abnormal events at larger spa-tial scale or even at the global scale. Therefore, by iden-tifying the components of physical significance at vari-ous scales from the “mixed” series of data, it is possible to investigate the driving mechanisms that control sea-level changes at different scales. Such investigations can then be used to establish models and subsequently conduct simulation experiments, forming the basis both for the inversion of past ENSO events and for projection of future ENSO events.

Although satellite altimetry data have provided more accurate sea-level data with nearly global coverage that helps to widen and deepen the research on sea-level changes, the time span covers by such data series is only since the early 1990s. Tide gauge series cover a much longer time span and also contain implicit information of regional sea-level changes. Consequently, extracting and analyzing useful signals on sea-level changes from sparsely located tide gauge series, at large temporal and spatial scales, can help understand the transmission process and spatial differentiation of climatic events like ENSO in the global sea levels. This, in turn, may not


only facilitate the analysis of the driving factors and transmission process of sea-atmosphere interactions but also provide some clues for a deeper understanding of the variation-process characteristics and internal mecha-

nisms of regional sea-level changes.

We thank Dr. Shulin Xu, and Shaofei Chen, Melissa Rura from The Uni-versity of Texas at Dallas for helping in the discussion and correction on the paper.

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