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On the Variability of Winds, Sea Surface Temperature, and Surface Layer Heat Content in the Western Equatorial Pacific

Michael J. McPhaden and Stanley P. Hayes

NOAA/Pacific Marine Environmental Laboratory, Seattle, Washington

Journal of Geophysical Research, 96, supplement, 3331-3342 (1991)
This paper is not subject to U.S. copyright. Published in 1991 by the American Geophysical Union.

4. Structure of Temperature and Wind Variations

Fig. 4. (a) Mean and (b) standard deviation of temperature between 2N and 2S, 165E for December 13, 1986 to April 16, 1987. Contour interval in Figure 4a is 2C (except for the 29C isotherm shown as a dashed line); contour interval in Figure 4b is 0.2C, with contours less than 1C dashed.

Figure 4a shows the mean meridional temperature section between 2N and 2S along 165E for the period from December 1986 to April 1987, which is the longest period of overlapping data at the three mooring sites. Mean SST is near to or greater than 29C during this time. The surface layer is nearly isothermal, with a mean vertical temperature difference in the upper 50-75 m at each of three locations of about 0.2C. The thermocline is centered between 100-200 m, with equatorial weakening indicative of the presence of the Equatorial Undercurrent. Note that during the second half of the record the upper thermocline is weaker and the surface layer is shallower because of an abrupt shoaling of the 24-28C isotherms in late May 1987 (Figure 3).

Figure 4b shows that the largest temperature variations (standard deviation 2C) are found in the thermocline on the equator between 100 and 150 m. Conversely, temperature variations in the surface layer are typically only 0.1-0.3C. Notably, SST variations tend to be larger than those at 50 m, which is a point that will be discussed in detail in section 5.

Figure 5 shows the first two empirical orthogonal eigenfunctions (EOFs) for temperature data summarized in Figure 4. Variations associated with these eigenfunctions are determined by multiplication of the EOFs with their corresponding time series. EOFs are defined to be orthogonal and the time series uncorrelated [Davis, 1976]; as such, they are an efficient means of isolating coherent modes of variability in large data sets.

Fig. 5. (a) Temperature empirical orthogonal eigenfunctions (EOF) mode 1 and (b) mode 2 for the period December 13, 1986, to April 16, 1987. Percent variance explained by each EOF is indicated. Hatching indicates negative eigenvector values.

The first EOF (Figure 5a) accounts for 56% of the temperature variance and is similar in structure to the total variability shown in Figure 4b, that is, the largest variance is on the equator in the thermocline. Variations are in phase in latitude by out of phase between the surface layer and the thermocline. The second EOF (Figure 5b), which accounts for 12% of the variance, shows a latitudinally antisymmetric mode of variability in the thermocline. In combination with meridional wind and velocity measurements from the 165E moorings (including those shown in Figures 2b and 14), Hayes et al. [1988] interpreted this structure in terms of a first baroclinic mode mixed Rossby-gravity wave, with a dominant period near 10 days. Hence in this analysis we will focus mainly on the first EOF, which accounts for most of the temperature variability during the period from December 1986 to April 1987.

We also performed an EOF decomposition of the overlapping wind record from December 1986 to April 1987 in Figure 2. Only the first EOF is shown, since it accounts for 77% (55%) of the variance in the zonal (meridional) directions (Figure 6). The first zonal wind EOF indicates in-phase variations of comparable amplitude across 2N-2S and a low-frequency variation that is similar to that shown in Figure 5a for the first temperature EOF. The cross-correlation coefficient for these two time series (0.54) is significantly nonzero, with 95% confidence. In contrast, the first meridional wind EOF has most of its variance concentrated at 2S, and its correlation with the first temperature EOF is not significant at the 95% level of confidence.

Fig. 6. Mode 1 wind EOFs for the period December 13, 1986, to April 16, 1987, in (a) the zonal direction and (b) the meridional direction. Percent variance explained by each EOF is indicated.

These results indicate that westerly winds (relative to the mean) are associated with warm thermocline temperatures and that easterly winds (relative to the mean) are associated with cold thermocline temperatures, a relationship similar to that expected for Ekman pumping. However, the out-of-phase relationship between surface layer temperature and thermocline temperature shown in Figure 5a suggests that wind-driven vertical advection of the thermocline may not be a significant factor in changing SST. This issue is examined in greater detail in section 5, where the analysis focuses on the longer equatorial mooring record with its finer vertical resolution in the surface layer.

Fig. 7. Time series of daily averaged wind speed (|U|), zonal wind pseudostress (|U|U), wind pseudowork (|U|), SST, and temperature at 50 m (dashed curve) from the current meter mooring at 0, 165E for the period December 13, 1986, to October 14, 1987. Note that 50-m temperatures are off-scale frequently after August 1987 because the upper thermocline has risen to this depth by then (see Figure 3).


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