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Forcing of intraseasonal Kelvin waves in the equatorial Pacific

William S. Kessler and Michael J. McPhaden

Pacific Marine Environmental Laboratory, NOAA, Seattle, Washington

Klaus M. Weickmann

Climate Diagnostics Center, NOAA, Boulder, Colorado

J. Geophys. Res., 100(C6), 10,613-10,631 (1995)
This paper is not subject to U.S. copyright. Published in 1995 by the American Geophysical Union.

2. Data Description and Processing

2.1. The TOGA-TAO Buoy Array

The widespread and systematic influence of ENSO on the ocean-atmosphere system led to the initiation of the Tropical Ocean-Global Atmosphere (TOGA) program, a 10-year study of climate variability on seasonal to interannual timescales. One component of the TOGA observing system is the Tropical Atmosphere Ocean (TAO) buoy array, which consists of more than 60 deep ocean moorings arranged in ranks nominally 15 longitude apart across the equatorial Pacific (Figure 1). Observations from these moorings form the principal data set used in this paper. Most of the TOGA-TAO buoys are ATLAS thermistor chain moorings [Hayes et al., 1991b] that measure temperature at the surface and 10 subsurface depths down to 500 m, as well as surface winds, relative humidity, and air temperature. Air temperatures, relative humidities, and water temperatures are sampled 6 times per hour, and daily averages of these are transmitted in real time each day to shore by satellite via Service Argos. Vector winds 3.8 m above the surface are sampled at 2 Hz for 6 min each hour and similarly averaged and transmitted. Zonal and meridional currents, as well as winds and temperatures, are measured by moorings equipped with an Acoustic Doppler Current Profiler (ADCP) at 110W, 140W, 170W, and 165E on the equator. Most of these ADCP buoys telemeter velocity profiles in the upper 250 m and were first deployed in 1990-1991 [McPhaden et al., 1990]. Before 1990-1991, buoys on the equator at 110W, 140W, and 165E were instrumented with mechanical current meters at six to eight depths between the surface and 300 m [McPhaden and McCarty, 1992]. Except for the ADCP moorings, most of the buoys shown in Figure 1 were not deployed until 1991, so only a few time series extend back more than 3 years. The number of buoys in operation has climbed from two that were first deployed in the early 1980s (110W and 140W on the equator), to about 15 in 1988, then to 20 by mid 1991. Since then the number has increased rapidly to more than 60 by mid 1992. Thus a study such as the present one that examines long time histories is partly restricted to the use of a few buoy locations.

Figure 1. The TOGA-TAO buoy array as of September 1993, showing the approximate length of time the various buoys have been in the water. The present study uses principally the long-term thermistor chain buoys and the current meter buoys, which have been operating for 5 years or longer.

The most important advantage of the buoy data over shipboard observational techniques is that their high temporal resolution means that intraseasonal frequencies are not aliased by the ubiquitous high-frequency variability in the ocean [e.g., Hayes, 1982; Hayes and McPhaden, 1992]. However, due to vandalism and instrument failures of various types, the buoy time series are rarely complete, and some method for dealing with data gaps is necessary. The history of instrumentation at the 0, 140W mooring is shown in Figure 2, indicating that the time series at most depths are quite gappy and that the mix of samples changed many times during the decade that a buoy has been in the water at this location. These changes influence the choice of variables available for study. For example, although dynamic height might be most useful for some purposes, there are many periods during which there are no data below 200 m, so either long gaps or a too-shallow reference level would have to be accepted. Similarly, there are several significant (2 to 3 month) gaps in the SST record, which would make it difficult to perform a spectral decomposition on this time series. We use a variety of strategies to deal with these problems. In the present work we focus on changes in the depth of the 20C isotherm, which can almost always be calculated by vertical interpolation, since there is generally sufficient vertical resolution in the thermocline to determine this temperature level accurately. It happens that the equatorial Kelvin waves studied here have a clear expression in the vertical motion of the thermocline, so 20C depth is appropriate for this purpose. In later parts of this paper we show time-longitude sections of 20C depth along the equator. Since there is a high coherence of this variable within at least 2 of the equator, in cases where the equatorial buoy is missing an average of the 2N and 2S buoy was used to fill missing points to construct these diagrams. Similarly, there is no single complete buoy record of winds in the western equatorial Pacific, so to obtain the longest time series possible for the spectral analysis and complex demodulation in section 3 we use the 1-2-1 weighted average of the 2S, 0 and 2N, 165E buoy wind time series, allowing 2S and 2N to fill missing equatorial values when necessary. Of the 2595 total days between the initial deployment at 165E in July 1986 through July 1993, the wind data return from the equatorial buoy was 71%, and for only 2% of the time (48 days at the beginning of 1988) were none of the three near-equatorial stations available.

For gaps that are short compared to the period of the signal of interest, it is possible to trade time resolution for gap filling. Chelton and Davis [1982, Appendix] show how to objectively estimate the value of a running mean in the presence of gaps. Their method has been used to fill small (up to about 10 days) gaps by producing a data series filtered with a 17-day triangle (two successive 9-day running means) filter. This filter has a half-power point at 20.3 days and so fills short gaps while retaining the intraseasonal signals of interest here.

The ability to low-pass filter in the presence of gaps also makes possible the use of complex demodulation on the gappy buoy time series. Complex demodulation [Bloomfield, 1976] is a type of band-pass filter that gives the time variation of the amplitude and phase of a time series in a specified frequency band. It may be preferable to ordinary Fourier techniques when studying a short or gappy record since the result is local in the sense of being determined only by the data in the neighborhood of each particular time realization. Briefly, in complex demodulation the time series is first frequency-shifted by multiplication with e-i t, where is the central frequency of interest. Then the shifted time series is low-pass filtered (using the Chelton and Davis [1982] method if there are gaps), which removes frequencies not near the central frequency. This low pass acts as a band-pass filter when the time series is reconstructed (unshifted). The resulting complex time series can then be expressed as a time-varying amplitude and phase of the variability in a band near the central frequency; that is, in the form h(t) = A(t) cos (t -(t)), where A(t) is the amplitude and (t) the phase for a central frequency , and h(t) is the reconstructed band-passed time series. The phase variation can also be thought of as a temporal compression or expansion of a nearly sinusoidal time series, which is equivalent to a time variation of frequency.

While this study focuses on the 4-year period since mid 1989, there are two sites where 10-year time series of subsurface temperature and velocity have been collected, at 140W and 110W on the equator. These allow examination of the extent to which the past 4 years are typical of the climatology. Figure 2 shows the low-pass filtered (half power at 145 days) depth of the 20C isotherm at 0, 140W. The low-frequency temperature time history until early 1989 is dominated by the El Nio/La Nia seesaw, with the annual cycle barely visible. Since 1989 the character of temperature variability at 140W has changed and a large-amplitude annual cycle of both thermocline depth and SST is evident (Figure 2). Complex demodulation of 20C depth at the annual period (not shown) demonstrates a more than doubling of the amplitude of the annual cycle from less than 10 m in 1984-1985 to more than 20 m since 1990, consistent with the visual impression of Figure 2. The 1991-1992 El Nio appears only as a slightly stronger annual cycle than surrounding years, although the maxima of SST and thermocline depth were as large as during the 1986-1987 event. Alternatively, one might view all the boreal winters since 1989 as having some features similar to a warm event (and note that the Southern Oscillation Index has been low since late 1989). Therefore the period studied may be characterized as having a strong annual cycle at 0, 140W, but this may not be typical of other periods.

Figure 2. Buoy sampling diagram at 0, 140W, showing the history of observations at this location at the various depths sampled. Note that only a few depths have continuous sampling over the 10 years of operation. The overlay of 20C depth (filtered with a 121-day triangle (dark), and 17-day triangle (light)) shows that this value can be reliably calculated by vertical interpolation with no gaps even though temperature at any of the fixed levels would be gappy.

2.2. Outgoing Longwave Radiation

Pentad averages of twice-daily outgoing longwave radiation (OLR) data observed by satellite were used in this study to estimate the location and strength of tropical deep convection. This data set has been the basis for numerous studies of tropical convective activity, in which low values of OLR are assumed to indicate the presence of tall cumulus towers associated with intense convection [Weickmann et al., 1985; Lau and Chan, 1985; Rui and Wang, 1990; Waliser et al., 1993]. The data are obtained from National Oceanic and Atmospheric Administration's polar-orbiting satellites as radiance measurements in an infrared window channel. The window radiance is then converted to a broad-band estimate of the total outgoing longwave radiation [Gruber and Krueger, 1984]. Global measurements are binned into a day and a night observation on a 2 by 2 global grid. Missing data occur both in time and space. The data used here have been interpolated in time and then averaged into 73 pentads per year. Chelliah and Arkin [1992] have documented spurious variability in the OLR data due to different satellite equatorial crossing times and different window channel radiometers. These variations are confined to certain regions, especially those having a large diurnal cycle and should have no impact on our results.


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