Elsevier

Atmospheric Research

Volume 94, Issue 1, September 2009, Pages 37-44
Atmospheric Research

Statistical representation of equatorial waves and tropical instability waves in the Pacific Ocean

https://doi.org/10.1016/j.atmosres.2008.06.002Get rights and content

Abstract

Sea surface height (SSH), sea surface temperature (SST), and surface currents derived from satellite observations are analyzed to investigate signals of equatorial Kelvin and tropical instability waves (TIWs) in the Pacific Ocean. A wavenumber–frequency spectral analysis of SSH and SST anomalies was performed in order to examine their space and time variability. Significant spectral peaks along the dispersion curves of the first baroclinic mode Rossby and Kelvin waves are found in the SSH spectrum, indicating that the analysis can effectively identify the signals of equatorial waves in the upper ocean. A prominent peak in SSH fields at around 33 days and 1500 km wavelength along the Rossby wave dispersion curve is evident, and a similar peak is also found in SST fields. This upper ocean variability on these space and time scales is shown to be associated with TIWs. The spatial structure of 33-day TIWs is further examined based on an analysis of time series filtered in the frequency-wavenumber domain. The phase relationship between SSH, SST, and surface velocity associated with TIWs is described based on a cross-correlation analysis. Also, the interannual variability of TIW activity is compared with that of ENSO, showing a moderate correlation.

Introduction

Equatorially-trapped waves account for a large portion of the intraseasonal variability in the tropical atmosphere and ocean, and play an important role in driving a variety of longer time scale phenomena (e.g., ENSO). Kelvin, Rossby and Mixed Rossby-Gravity waves corresponding to the eigenmodes of the linearized shallow water equations of Matsuno (1966) have been shown to be of particular importance in the equatorial Pacific ocean. While oceanic equatorial waves have been identified in in-situ data (e.g. Johnson and McPhaden, 1993) and in satellite altimeter data (e.g., Miller et al., 1988, Delcroix et al., 1991, Boulanger and Fu, 1996, Chelton et al., 2003) their observed dispersion relationships have not yet been fully described in frequency–wavenumber space. A recent study by Wakata (2007) demonstrated that the dispersion relations of equatorial Rossby and Kelvin waves could be inferred from satellite data by frequency–wavenumber spectra of sea surface height (SSH). However, because his analysis includes the background spectrum, it is difficult to compare the spectral signal with theoretical dispersion curves, especially in the high wavenumber domain. In this study, frequency-wavenumber spectral analysis will first be applied to identify observed dispersion relations of oceanic equatorial waves using sea surface height data measured by satellite altimetry.

It is found that signals of oceanic equatorial Kelvin and Rossby waves are evident in the frequency–wavenumber spectrum of SSH. The same analysis is also conducted using sea surface temperature (SST), showing a prominent spectral peak in both SSH and SST at frequencies and wavenumbers consistent with those of tropical instability waves (TIWs). Further statistical analyses are conducted to isolate TIW oceanic structure and the atmospheric response to TIWs. Interannual variation of TIW activity is also described, and its relation to ENSO is discussed.

Section snippets

Data

Five primary data sets are used in this study. 10-day average SSH data derived from TOPEX altimetry are used to identify signals of equatorial waves and TIWs. Data for the 10-year period of 1993–2002 with horizontal resolution of 1° are analyzed. This period includes a strong ENSO cycle (1997/98), and thus the interannual variation of TIW activity in relation to ENSO can be discussed. Weekly SSTs from the analysis of Reynolds et al. (2002) are used to provide a statistical description of SST

Frequency–wavenumber spectral analysis

Wheeler and Kiladis (1999) demonstrated that wavenumber–frequency spectral analysis is useful for identifying signals of atmospheric equatorial waves, and for isolating their structure. Wavenumber–frequency spectral analysis was also recently used to identify oceanic equatorial waves in numerical model experiments (Shinoda et al., 2008). In this section, the same technique used in Wheeler and Kiladis (1999) is applied to the satellite derived SSH to identify oceanic equatorial waves. In this

Structure and evolution of TIWs

Previous observational studies indicate that TIWs are generally associated with strong fluctuations of SST, SSH and near surface currents (e.g., Duing et al., 1975, Legeckis, 1977, Miller et al., 1985, Musman, 1989, Kennan and Flament, 2000, Polito et al., 2001, Chelton et al., 2003). In this section, the phase relationships between SST, SSH and surface currents are described statistically using long records based on a cross-correlation analysis.

Time series of unfiltered SSH, SST, and surface

Interannual variation of TIWs

The results in previous sections indicate that major features of SST variability associated with TIWs can be well captured by Reynolds SST based on a comparison with those from TMI SST. Because the Reynolds SST covers a much longer period, it is useful to examine the interannual variation of TIW activity inferred by that data set. In this section, interannual variation of TIW activity derived from Reynolds SST is first compared with that from TMI SST and SSH in recent years. Then the longer

Conclusions

Satellite derived SSH, SST and surface velocity are analyzed to examine oceanic equatorial waves and tropical instability waves. Signals of oceanic equatorial waves can be isolated by frequency–wavenumber spectral analysis of SSH fields derived from satellite altimeter measurements. A prominent peak at around a 33 day period and a wavelength of around 1500 km is found to be associated with TIWs, and this peak corresponds to that of 1st meridional mode equatorial Rossby waves with an equivalent

Acknowledgments

The TAO Project Office of NOAA/PMEL provided the mooring time series data. The TMI data are obtained from the Remote Sensing Systems web site. TOPEX data are obtained from the Center for Space Research, University of Texas at Austin. Constructive comments by two reviewers helped improve the original draft of this paper. Toshiaki Shinoda is supported by NOAA CLIVAR-Pacific Grant from Office of Global Programs, NSF Grant OCE-0453046, and the 6.1 project Global Remote Littoral Forcing via Deep

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