Editorial Type:
Article
Article Type:
Research Article

The Equatorial Undercurrent and TAO Sampling Bias from a Decade at SEA

William R. Leslie Department of Geology, Oberlin College, Oberlin, Ohio, and Woods Hole Oceanographic Institution, Woods Hole, Massachusetts

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Kristopher B. Karnauskas Woods Hole Oceanographic Institution, Woods Hole, Massachusetts

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Print Publication:
01 Sep 2014
DOI:
https://doi.org/10.1175/JTECH-D-13-00262.1
Page(s):
2015–2025
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Abstract

The NOAA Tropical Atmosphere Ocean (TAO) moored array has, for three decades, been a valuable resource for monitoring and forecasting El Niño–Southern Oscillation and understanding physical oceanographic as well as coupled processes in the tropical Pacific influencing global climate. Acoustic Doppler current profiler (ADCP) measurements by TAO moorings provide benchmarks for evaluating numerical simulations of subsurface circulation including the Equatorial Undercurrent (EUC). Meanwhile, the Sea Education Association (SEA) has been collecting data during repeat cruises to the central equatorial Pacific Ocean (160°–126°W) throughout the past decade that provide useful cross validation and quantitative insight into the potential for stationary observing platforms such as TAO to incur sampling biases related to the strength of the EUC. This paper describes some essential sampling characteristics of the SEA dataset, compares SEA and TAO velocity measurements in the vicinity of the EUC, shares new insight into EUC characteristics and behavior only observable in repeat cross-equatorial sections, and estimates the sampling bias incurred by equatorial TAO moorings in their estimates of the velocity and transport of the EUC. The SEA high-resolution ADCP dataset compares well with concurrent TAO measurements (RMSE = 0.05 m s−1; R2 = 0.98), suggests that the EUC core meanders sinusoidally about the equator between ±0.4° latitude, and reveals a mean sampling bias of equatorial measurements (e.g., TAO) of the EUC’s zonal velocity of −0.14 ± 0.03 m s−1 as well as a ~10% underestimation of EUC volume transport. A bias-corrected monthly record and climatology of EUC strength at 140°W for 1990–2010 is presented.

Corresponding author address: Kristopher B. Karnauskas, Woods Hole Oceanographic Institution, 360 Woods Hole Rd., MS 23, Woods Hole, MA 02544. E-mail: kk@whoi.edu

Abstract

The NOAA Tropical Atmosphere Ocean (TAO) moored array has, for three decades, been a valuable resource for monitoring and forecasting El Niño–Southern Oscillation and understanding physical oceanographic as well as coupled processes in the tropical Pacific influencing global climate. Acoustic Doppler current profiler (ADCP) measurements by TAO moorings provide benchmarks for evaluating numerical simulations of subsurface circulation including the Equatorial Undercurrent (EUC). Meanwhile, the Sea Education Association (SEA) has been collecting data during repeat cruises to the central equatorial Pacific Ocean (160°–126°W) throughout the past decade that provide useful cross validation and quantitative insight into the potential for stationary observing platforms such as TAO to incur sampling biases related to the strength of the EUC. This paper describes some essential sampling characteristics of the SEA dataset, compares SEA and TAO velocity measurements in the vicinity of the EUC, shares new insight into EUC characteristics and behavior only observable in repeat cross-equatorial sections, and estimates the sampling bias incurred by equatorial TAO moorings in their estimates of the velocity and transport of the EUC. The SEA high-resolution ADCP dataset compares well with concurrent TAO measurements (RMSE = 0.05 m s−1; R2 = 0.98), suggests that the EUC core meanders sinusoidally about the equator between ±0.4° latitude, and reveals a mean sampling bias of equatorial measurements (e.g., TAO) of the EUC’s zonal velocity of −0.14 ± 0.03 m s−1 as well as a ~10% underestimation of EUC volume transport. A bias-corrected monthly record and climatology of EUC strength at 140°W for 1990–2010 is presented.

Corresponding author address: Kristopher B. Karnauskas, Woods Hole Oceanographic Institution, 360 Woods Hole Rd., MS 23, Woods Hole, MA 02544. E-mail: kk@whoi.edu
Keywords: Pacific Ocean; Tropics; Currents; Ocean dynamics; Buoy observations; Sampling

1. Introduction

Under way measurements of conductivity–temperature–depth (CTD), as well as horizontal subsurface velocity via acoustic Doppler current profiler (ADCP) have been made routinely on educational cruises of the Sea Education Association (SEA) and archived locally over the past decade. The spatial and temporal characteristics of the SEA cruises provide a unique lens in which to cross validate and better understand several important features concurrently observed by the National Oceanic and Atmospheric Administration (NOAA) Tropical Atmosphere Ocean (TAO) array in the equatorial Pacific Ocean (McPhaden et al. 1998). Given the sampling distribution of SEA cruises and the TAO array, a natural focal point is the Equatorial Undercurrent (EUC), a prominent year-round feature that delivers cold, nutrient-rich water to the eastern Pacific, especially in zones of enhanced upwelling along the equator (Johnson et al. 2001) and the western coasts of the Galápagos and South America (Lukas 1986). The EUC flows eastward along the equatorial thermocline at a rate of approximately 1 m s−1 with seasonal transport exceeding 40 Sverdrups (Sv; 1 Sv ≡ 106 m3 s−1; Johnson et al. 2002). The EUC is both subject to and a major player in ENSO dynamics as evident by the sweeping changes in the tropical ocean circulation and thermohaline structure amidst the subsequent breakdown in the Walker circulation during El Niño (Firing et al. 1983). The EUC is also vitally important to smaller island ecosystems throughout the equatorial Pacific such as Jarvis Island (Gove et al. 2006) and the Gilbert Islands (Karnauskas and Cohen 2012), where the strength of topographic upwelling (and thus delivery of cold, nutrient-rich water) is dependent on EUC velocity. Nonetheless, global climate models do not adequately simulate the EUC (Karnauskas et al. 2012) and may, therefore, suffer in predictions of how the mean circulation and ENSO system may respond to natural and anthropogenic climate forcing, including impacts on ecosystems.

This paper characterizes the high-resolution SEA dataset and applies it toward understanding the nature of the EUC as well as illuminating potential sampling biases in sustained, equatorial observations of the EUC by moored buoys. In the following section, sampling aspects of the SEA data are described and ADCP measurements are compared against TAO observations. In section 3, SEA data are analyzed and compared with previous observations to illuminate some characteristics of the EUC including its spatial and temporal variability. Focusing on SEA cross-equatorial sections, profiles at the equator are compared against those at the core of the EUC in section 4 to address issues concerning the use of TAO measurements to estimate EUC velocity and transport. Finally, a summary and discussion of the scientific implications of this work and future directions are provided in section 5.

2. Description of the SEA dataset and comparison with TAO

a. Temporal and spatial sampling

The SEA dataset used herein draws from 21 “Semester at Sea” cruises of the Sailing School Vessel (SSV) Robert C. Seamans (http://www.sea.edu/ships_crew/seamans) from 2003 to 2012 with a total of 23 equatorial crossings (the equator was crossed twice during two cruises; Fig. 1). The EUC is known to have significant longitudinal and seasonal variations (Johnson et al. 2002), thus subsampling the SEA dataset is necessary to control for those variables. The cruises are clearly separable into two distinct seasons: boreal spring (February–May) and December (Fig. 2a). In figures throughout this paper, we denote spring results as red and December results as blue unless otherwise noted. Furthermore, ADCP data were collected near the equator along three roughly similar cruise tracks (Figs. 1 and 2b). The mean longitudes of equatorial crossings for the three similar tracks are western (158°W), central (144°W), and eastern (129°W). Figures throughout this paper use red, green, and blue to denote the western, central, and eastern tracks, respectively, unless otherwise noted. The entire study area falls within the central Pacific where the EUC is relatively swift; the climatological maximum EUC velocity and transport occurs at 125°W (Johnson et al. 2002). Overall, the 23 SEA equatorial crossings provided a convenient distribution: 13 spring crossings and 10 winter crossings, as well as 10 crossings in the western track, 8 in the central track, and 5 on the eastern track. Further subgroups of five cruises a piece are created based on both comparable season and longitude: west/December (red), central/spring (green), and east/December (blue), enabling us to further isolate the potential confounding variables of season and longitude and facilitate consistency over time as well as providing a fair cross comparison with TAO measurements from a single longitude. More western tracks have been occupied in recent years (Fig. 3). All SEA ADCP data analyzed in this paper are available online at http://hdl.handle.net/1912/6746.

Fig. 1.
Fig. 1.

Overview map of the tropical Pacific Ocean indicating the 21 SEA cruises utilized in this study (black lines), subsets of SEA cruises grouped by the longitude of equatorial crossing (colored ellipses, corresponding to Fig. 2b), and the 140°W TAO mooring also used.

Citation: Journal of Atmospheric and Oceanic Technology 31, 9; 10.1175/JTECH-D-13-00262.1

Fig. 2.
Fig. 2.

Histograms of (a) calendar day and (b) longitude of equatorial crossing for each of the 23 SEA equatorial crossings. (c) Scatterplot of calendar day vs longitude of equatorial crossings with ellipses denoting subgroups with similar longitude and season (west/December, central/spring, and east/December).

Citation: Journal of Atmospheric and Oceanic Technology 31, 9; 10.1175/JTECH-D-13-00262.1

Fig. 3.
Fig. 3.

Scatterplot of decimal year vs longitude of equatorial crossings along with simultaneous daily ADCP measurements from the 0°, 140°W TAO mooring (gray vertical bars).

Citation: Journal of Atmospheric and Oceanic Technology 31, 9; 10.1175/JTECH-D-13-00262.1

b. Comparison with ADCP measurements from the 140°W TAO mooring

In this section, comparisons of SEA and TAO measurements at the equator are used to evaluate the suitability of SEA ADCP measurements made exactly on the equator as a faithful proxy for TAO moored ADCP measurements. The TAO mooring used in this paper is positioned on the equator at 140°W (Figs. 1 and 3). ADCP measurements from this TAO mooring were missing during the periods indicated by breaks in the gray bars (i.e., from 2007 to 2009 and again after 2010).

First, a single, nearly simultaneous and collocated ADCP velocity profile is compared against an equivalent TAO measurement (Fig. 4). The vertical profiles of zonal velocity from SEA and TAO shown in Fig. 4b were taken on 29 May 2009 within 1 h and within 20 km of one another. The comparison reveals excellent agreement between the two datasets with a small offset below the EUC. The difference in maximum equatorial zonal velocity between SEA and TAO (Fig. 4c) is −0.02 m s−1 with an RMSE of 0.05 m s−1 to 250-m depth or 0.01 m s−1 to the EUC core (89-m depth).

Fig. 4.
Fig. 4.

(a) Vertical–meridional section of zonal velocity (m s−1) from SEA cruise on 29 May 2009 clearly indicating the EUC. The black contour line is set at 1.5 m s−1. (b) Vertical profiles of zonal velocity from the same cruise (thick gray) and a nearly collocated and simultaneous (within 1 h and 20 km) measurement from the 140°W TAO mooring (black). (c) The difference in equatorial zonal velocity (SEA − TAO). The RMSE is 0.05 m s−1 to 250-m depth or 0.01 m s−1 to the core of the EUC (89-m depth), and the difference in maximum zonal velocity is −0.02 m s−1.

Citation: Journal of Atmospheric and Oceanic Technology 31, 9; 10.1175/JTECH-D-13-00262.1

Second, a comparison of maximum equatorial velocity for the east/December subset is compared against TAO measurements of zonal velocity, which allows for the largest possible comparison of SEA and TAO (N = 5 cruises) while controlling for longitude and season (Fig. 5). In this comparison, values of maximum zonal velocity from both the equator and from the latitude of the EUC core in SEA are compared to the equatorially confined TAO measurement. ADCP velocity profiles from ±0.05° latitude (~5 km) are averaged to form each equatorial measurement. A very strong linear relationship is observed between SEA and TAO equatorial measurements, which indicates that SEA sampling at the equator offers a faithful proxy for an equatorial TAO mooring (RMSE = 0.05 m s−1; R2 = 0.98). The high correlation deteriorates in the comparison between TAO (equatorial) maximum velocity and SEA core maximum velocity (R2 = 0.64), implying that even with the effects of longitudinal and seasonal variations removed, TAO equatorial measurements do not closely capture the variability of the maximum velocity at the EUC core. We note that while one correlation may be significant and the other not, the two correlations may not be significantly different from one another with such small sample sizes. We can, however, conclude that SEA equatorial measurements are a good proxy for TAO equatorial measurements.

Fig. 5.
Fig. 5.

Scatterplots of (a) SEA vs TAO equatorial maximum zonal velocities (R2 = 0.98) and (b) SEA at the EUC core vs TAO equatorial maximum zonal velocities (R2 = 0.64) in east/December subgroup. Note that the nonunity slope and nonzero intercept is due to the slight offset in longitude between the TAO mooring and the eastern group of SEA equatorial crossings.

Citation: Journal of Atmospheric and Oceanic Technology 31, 9; 10.1175/JTECH-D-13-00262.1

Finally, a time series with the 15 cruises grouped by season and longitude is shown to further broaden the scope of comparison to include interannual variability (Fig. 6). With an expected offset due to unavoidable differences in longitudinal sampling, TAO measurements tend to closely follow the SEA equatorial measurements. This confirms that SEA equatorial measurements are in good agreement with TAO moored measurements on an interannual time scale. Further, the consistent offset between the SEA equatorial (dashed) and SEA core (solid) lines show that rarely do equatorial measurements capture the EUC’s maximum zonal velocity. This point is further investigated in section 4.

Fig. 6.
Fig. 6.

Time series comparing (a) maximum zonal velocity (m s−1) and (b) 2D transport (m2 s−1) for the EUC core as measured by SEA (solid lines), the equator as measured by SEA (dashed lines), and the equator as measured by TAO (filled circles). The lines are grouped into west/December (red), central/spring (green), and east/December (blue).

Citation: Journal of Atmospheric and Oceanic Technology 31, 9; 10.1175/JTECH-D-13-00262.1

3. Spatiotemporal variability of the EUC in the SEA dataset

The strength of the EUC is calculated in terms of maximum zonal velocity at the latitude of the EUC core and 2D transport at the same latitude (the vertical integral of a zonal velocity profile as in the one shown in Fig. 4b).1 Figure 7a relates maximum zonal velocity and 2D transport, which shows only a modest correlation (R2 = 0.41). However, relating 2D transport with volume (3D) transport suggests a stronger correlation (R2 = 0.71). This is noteworthy because it suggests that different indicators of EUC strength are not necessarily interchangeable, and especially that maximum zonal velocities is a poor predictor of 2D (and therefore volume) transport. The weak correlation between maximum zonal velocity and transport is not surprising given the varying spatial extent of the EUC as shown by Johnson et al. (2002). Also, given the strong correlation in Fig. 7b, if 2D transport is used as a proxy for volume transport, then purely equatorial measurements might underrepresent both if the equatorial measurement misses the position of the core of the EUC.

Fig. 7.
Fig. 7.

(a) Scatterplots of (a) maximum zonal velocity (m s−1) vs 2D transport (m2 s−1) and (b) 2D transport (m2 s−1) vs volume transport (Sv).

Citation: Journal of Atmospheric and Oceanic Technology 31, 9; 10.1175/JTECH-D-13-00262.1

The EUC is known to shoal from ~200-m depth in the west to <100 m in the east across the Pacific basin, closely following the thermocline (Johnson et al. 2002). Furthermore, the EUC appears weaker and spatially broader in the western Pacific and becomes progressively narrower and faster as it moves eastward with a maximum zonal velocity observed near 125°W (Johnson et al. 2002). To confirm that the SEA dataset captures such spatial and temporal structure of the EUC, Fig. 8 depicts EUC core values of zonal velocity, 2D transport, and depth against both longitude (Figs. 8a,c,e) and the day of the year (Figs. 8b,d,f). The seasonal cycle and longitudinal variation are consistent with previous observational and modeling studies stating the EUC strengthens/shoals during springtime and weakens/deepens during December, and the general strengthening and shoaling is from west to east (Johnson et al. 2002).

Fig. 8.
Fig. 8.

Scatterplots of (a) maximum zonal velocity, (c) 2D transport, and (e) depth of the EUC as a function of the longitude where color denotes season (red for spring and blue for December). (b),(d),(f) As in (a),(c),(e), but as a function of date, where color denotes longitude (red for west, green for central, and blue for east).

Citation: Journal of Atmospheric and Oceanic Technology 31, 9; 10.1175/JTECH-D-13-00262.1

Histograms of EUC core depth and latitude (Fig. 9) further highlight the spatial variability of the EUC. Figure 9a shows a positively skewed distribution of the depth indicating an average depth of 120 m. Furthermore, Fig. 9b shows that the EUC’s core is over twice as likely to be found outside of ±0.125° latitude of the equator than within those bounds. The distribution shown in Fig. 9b also implies that TAO moorings are unlikely to capture the actual core velocity, which further highlights the necessity for a quantitative basis for determining the sampling bias associated with equatorial observations of EUC strength (section 4).

Fig. 9.
Fig. 9.

Histograms of (a) depth (m) and (b) latitude (°) of the EUC core for the 23 SEA equatorial crossings. The mean values are indicated by solid black lines.

Citation: Journal of Atmospheric and Oceanic Technology 31, 9; 10.1175/JTECH-D-13-00262.1

What explains the observed distribution of the latitude of the EUC core? We propose a simple model to explain why the EUC core is more often observed at some position y away from the equator. Consider the simple model:
eq1
where the amplitude A represents the maximum latitudinal meander of the EUC core. Solutions of the model for varying parameter A are shown in Fig. 10a. Figure 10b depicts the corresponding histograms of y for each solution using identical bin widths as in Fig. 9. The sinusoidal function , where x may represent time or zonal distance, appears to provide the best fit to the observations (Fig. 9b), thus suggesting that the EUC meanders in a sinusoidal fashion with an amplitude of 0.4° latitude (or ~44 km) over this longitudinal range.
Fig. 10.
Fig. 10.

(a) A simple model for 0.1 ≤ A ≤ 1.0, where A represents the maximum latitudinal meander of the EUC core and x may represent time or zonal distance. (b) Corresponding histograms of y for the range of values of A.

Citation: Journal of Atmospheric and Oceanic Technology 31, 9; 10.1175/JTECH-D-13-00262.1

On the equator, where the Coriolis parameter is zero, we may actually expect a broad spectrum of wave energy and other influences on the strength and/or latitudinal position of the EUC. A period of 5–10 days can be visually discerned from relatively short TAO records in the eastern equatorial Pacific based on Karnauskas et al. (2010, their Fig. 1a). Spectral analysis of a daily time series of maximum eastward velocity measured by ADCP at the 140°W equatorial TAO mooring (see Fig. 15a, described in greater detail below) indicates variability with statistically significant power (99% confidence level) occurring within two distinct subannual frequency bands: 3–17 and 50–60 days. Such observed temporal variability could have been interpreted solely as the EUC varying in strength over time, but appears equally likely to be the EUC core meandering about the equator allowing for TAO to momentarily observe values closer to the true maximum core velocity. Given the time scales noted above, several phenomena are implicated such as internal waves (3–15 days; Farrar and Durland 2012), tropical instability waves (15–40 days; Lyman et al. 2007), intraseasonal Kelvin waves (60–75 days; Kessler et al. 1995), and the Madden–Julian oscillation (30–90 days; Madden and Julian 1971).

Finally, we test for dependence of the EUC core latitude on longitude and season. Results suggest that latitudinal variations of the EUC core are more prominent in the west and during boreal spring (Fig. 11). Error bars are set to two standard errors (~95% confidence); these results are not statistically significant, which is likely due to the small sample size and the relatively narrow longitudinal range sampled by SEA. A more comprehensive dataset is clearly needed to establish such second-order dependencies.

Fig. 11.
Fig. 11.

Scatterplots of the absolute latitude of the EUC core vs (a) longitude and (b) calendar day. Color scheme for (a) is west is red, central is green, and east is blue. Color scheme for (b) is red is spring and blue is December.

Citation: Journal of Atmospheric and Oceanic Technology 31, 9; 10.1175/JTECH-D-13-00262.1

4. Estimating the TAO sampling bias using the SEA dataset

The SEA data are strongly correlated with corresponding TAO measurements on the equator and are qualitatively consistent with previous observational descriptions of the tropical Pacific Ocean circulation and structure. Yet, they also provide the essential off-equatorial perspective. In particular, the SEA dataset enables us to quantify the actual EUC core velocity (and transport) when the EUC is not on the equator, which is the majority of the time as shown above (Fig. 9b). Figure 12 compares SEA measurements from the equator with those from the latitude of the EUC core. There is a strong positive linear correlation, but more interesting is the offset between the best-fit and one-to-one lines in both zonal velocity (Fig. 12a) and 2D transport (Fig. 12b). This suggests that the equatorial profiles are systematically undersampling the maximum EUC velocity and transport. For example, the mean maximum (core) EUC velocity estimated by SEA profiles at the latitude of the core is 1.42 m s−1, whereas the mean estimated strictly from SEA equatorial profiles is 1.28 m s−1. This difference, which is both statistically and physically significant, has likely been suspected but not established quantitatively. Since equatorial observations are frequently used to quantify EUC strength as well as validate models and reanalysis products (e.g., Izumo 2005; Karnauskas et al. 2010, 2012; Drenkard and Karnauskas 2014), this knowledge should lead to improved confidence in EUC estimates and, hence, understanding of its role in basin-scale ocean circulation and climate.

Fig. 12.
Fig. 12.

Scatterplots of (a) maximum zonal velocity (m s−1) on the equator vs in the EUC core. (b) As in (a), but for 2D transport (m2 s−1).

Citation: Journal of Atmospheric and Oceanic Technology 31, 9; 10.1175/JTECH-D-13-00262.1

The distribution of biases in maximum zonal velocity and 2D transport further underscores the TAO sampling bias as estimated by SEA measurements. Biases are also calculated and expressed as percentages of their mean values, so as to mitigate the effect of seasonal and longitudinal variations (Fig. 13). The percent bias b of quantity X is calculated as
eq2
where subscript eq denotes the value of X as measured directly on the equator and subscript core denotes the value of X as measured at the latitude of the core of the EUC. The X may be either maximum zonal velocity or 2D transport. Equatorial measurements are consistently underestimating the EUC strength in terms of zonal velocity with a mean bias of −0.14 ± 0.03 m s−1 (−10.38% ± 2.14%), and in terms of 2D transport with a mean bias of −19.04 ± 8.92 m2 s−1 (−9.87% ± 4.37%). Error bars are set to two standard errors of the mean, indicating 95% confidence limits. A nontrivial sampling bias in both zonal velocity and 2D transport is evident, which is germane to the interpretation of equatorial measurements as estimates of the EUC’s actual strength.
Fig. 13.
Fig. 13.

Histograms of (a) bias of maximum zonal velocity and (b) 2D transport (%).

Citation: Journal of Atmospheric and Oceanic Technology 31, 9; 10.1175/JTECH-D-13-00262.1

Comparing the estimated biases in 2D transport with those of maximum zonal velocity (not shown) yields a weak positive correlation, which is consistent with results reported above that transport is not accurately predicted by the maximum zonal velocity alone. Furthermore, no dependence of the sampling bias on core latitude is found, suggesting that the inferred bias does not appear to be simply a function of the distance of the EUC core from the equator. If the EUC in terms of its zonal velocity distribution in the latitude–depth plane was constant, and it simply translated northward and southward relative to the stationary equatorial mooring, then the sampling bias would be dependent only on the latitude of the center of the EUC. Since this is not the case, we hypothesize that there is substantial variability in the structure of the EUC (i.e., the shape and distribution of zonal velocity about the centroid). Sampling biases in maximum zonal velocity and transport are also tested for dependence on longitude and season (Fig. 14). There is no statistically significant dependence of bias on either longitude or season, which again may be limited by the relatively small sample size and longitudinal domain sampled by the SEA dataset. A wider range in longitudes and/or more cruises may further illuminate differences in biases from the western, central, and eastern sections of the broader Pacific basin.

Fig. 14.
Fig. 14.

Distribution of biases in (a),(c) maximum EUC zonal velocity and (b),(d) transport as a function of (a),(b) longitude and (c),(d) calendar day. Error bars indicate ±2 standard errors. Color scheme for (a) and (b) is west is red, central is green, and east is blue. Color scheme for (c) and (d) is red is spring and blue is December.

Citation: Journal of Atmospheric and Oceanic Technology 31, 9; 10.1175/JTECH-D-13-00262.1

Given the estimated bias in EUC velocity and 2D transport noted above with little or no evidence of longitudinal or seasonal variation within the study area, we can create a bias-corrected monthly record and climatology of EUC strength from the ~19 years of observations at the 140°W equatorial TAO mooring (1990–2010 with two approximately year-long gaps). Bias-corrected monthly values X′ are computed as
eq3
where Xeq and b are as defined above. Shown in Figs. 15a and 15c are daily mean, monthly mean, and bias-corrected monthly mean time series of EUC velocity and 2D transport. There are clearly significant daily fluctuations in EUC strength about the monthly means (as discussed in section 3), which are likely a combination of temporal variations in EUC strength and latitudinal position relative to the equator. Correcting for the 10.38% (9.87%) biases at each monthly mean velocity (2D transport) value results in bias-corrected monthly records that correspond closely to (but do not typically exceed) the positive spikes in the daily time series. The 140°W EUC climatology can also be adjusted similarly (Figs. 15b,d), which increases the climatological EUC velocity by 0.09–0.14 m s−1 at the seasonal minimum (February) and by 0.13–0.20 m s−1 at the seasonal maximum (May). The annual mean EUC velocity increases from 1.16 to 1.27–1.33 m s−1 in the bias-corrected climatology.
Fig. 15.
Fig. 15.

(a) Time series of daily mean (thin gray) and monthly mean (thick black) maximum zonal velocity (m s−1) measured by ADCP from the TAO mooring at 0°, 140°W over the period 1990–2010. The bias-corrected monthly mean record (described in section 4) is shown by a thin black line. (b) Monthly climatology (thick line) and bias-corrected monthly climatology (thin lines; dashed lines denote 95% confidence limits on the bias correction) of maximum zonal velocity (m s−1). (c),(d) As in (a),(b), but for 2D EUC transport (m2 s−1; eastward zonal velocity integrated from 35- to 235-m depth).

Citation: Journal of Atmospheric and Oceanic Technology 31, 9; 10.1175/JTECH-D-13-00262.1

5. Conclusions and discussion

This paper directly compares SEA cruise measurements to TAO moorings, when and where available, as well as to previous climatological observations of the EUC (Johnson et al. 2002); proposes a simple model of the EUC’s meander about the equator; and quantitatively estimates a sampling bias in the maximum zonal velocity and transport of the EUC. The major conclusions are as follows.

  • The SEA dataset compares well with TAO measurements and is potentially a very useful resource; the SEA ADCP data focusing on the EUC reproduce well the mean longitudinal and seasonal structure of the EUC relative to prior observational work.

  • Knowledge of the maximum zonal velocity (i.e., at the core of the EUC) does not provide a close estimate of 2D transport at the core of the EUC (R2 = 0.41), but 2D transport at the core of the EUC does provide a fairly reasonable estimate of the full, 3D volume transport of the EUC (R2 = 0.71).

  • The distribution of the latitude of the EUC core throughout the SEA dataset suggests that the EUC core meanders in a sinusoidal fashion about the equator with a meridional scale of ~0.4° latitude (~44 km).

  • On average, an estimate of peak EUC velocity or transport based on an equatorial profile of zonal velocity is biased by −10%.

Based on a high-resolution numerical ocean model experiment capable of resolving the sensitivity of island-scale sea surface temperature (SST) to the strength of the EUC (Karnauskas and Cohen 2012), a 10.4% increase in the time-mean EUC velocity would lead to a 0.5°C cooling of SST on the west side of the Gilbert Islands. Thus, an underestimate of the speed of the EUC of this magnitude also has practical consequences for ecological systems such as coral reefs across the equatorial Pacific.

In theory, the EUC should have a mean tendency to be displaced to the north (south) of the equator in the presence of mean southward (northward) meridional wind stress (Charney and Spiegel 1971). It is important to point out that the longitude where the cross-equatorial (meridional) component of the climatological wind stress changes sign (i.e., from southward in the western Pacific to northward in the eastern Pacific) is ~158°W (Atlas et al. 2011), which is within the domain sampled by the SEA dataset. Thus, it should be expected that the TAO sampling bias estimated here for the central Pacific be low compared to sites in the western or eastern Pacific where the large-scale mean meridional wind stress field is significantly nonzero and thus expected to set up a larger mean tendency for the EUC to be displaced away from the equator. A preliminary survey of hundreds of cross-equatorial sections of zonal velocity measurements by shipboard ADCP not only reveals that the predicted inverse dependence of the mean latitude of the EUC on the mean cross-equatorial wind stress holds true, but that the domain sampled by SEA in fact yields the smallest bias or difference between velocities on the equator and at the actual EUC core. Further analysis with this more spatially comprehensive dataset, the Joint Archive for Shipboard ADCP (Firing 2013), along with numerical models is necessary to confirm the robustness of the bias results presented herein and its dependence on other variables.

These results may lend further insight into the integration of observational data with ocean and climate models to understand the strength of the mean ocean circulation, ENSO events, and how they will respond to radiative forcing and other large-scale changes. A greater understanding of how these biases have influenced our estimates of equatorial ocean circulation is necessary to fully understand the extent to which the EUC influences basin-scale and global climate, and thus what to expect from anthropogenic and natural climate variations. Using a similar approach but with vastly more data, one may propose improvements to the global network of oceanographic observation systems, improve the usefulness of equatorial data by accounting for the sampling biases exposed here, and increase confidence in model assessments.

Finally, understanding the mechanisms and time scales for the apparent sinusoidal meander of the EUC could illuminate better ways to understand, model, and predict the behavior of the EUC along with its relationship to atmospheric forcing. For example, apparent ~10% variations in EUC strength observed from the equator that are actually due to the meandering of the EUC away from the mooring (perhaps due to temporal variations in cross-equatorial wind stress or anomalous meridional currents associated with tropical instability waves) clearly have the potential to obfuscate the expected linear relationship between zonal wind stress, the ocean’s zonal pressure gradient, and EUC transport. Such investigations should be process oriented, drawing from more extensive ADCP datasets, autonomous platforms such as gliders, and high-resolution numerical models.

Acknowledgments

The authors are deeply indebted to Prof. Jan Witting of the Sea Education Association (SEA) in Woods Hole, Massachusetts, for generously providing the SEA dataset and valuable guidance over the course of this project. The authors thank the NSF Physical Oceanography program (OCE-1233282) and the WHOI Academic Programs Office for funding.

REFERENCES

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  • Kessler, W. S., McPhaden M. J. , and Weickmann K. M. , 1995: Forcing of intraseasonal Kelvin waves in the equatorial Pacific Ocean. J. Geophys. Res., 100, 10 613–10 631, doi:10.1029/95JC00382.

    • Search Google Scholar
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  • Lukas, R., 1986: The termination of the equatorial undercurrent in the eastern Pacific. Prog. Oceanogr.,16, 63–90, doi:10.1016/0079-6611(86)90007-8.

  • Lyman, J. M., Johnson G. C. , and Kessler W. S. , 2007: Distinct 17- and 33-day tropical instability waves in subsurface observations. J. Phys. Oceanogr., 37, 855–872, doi:10.1175/JPO3023.1.

    • Search Google Scholar
    • Export Citation

  • Madden, R. A., and Julian P. R. , 1971: Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci., 28, 702–708, doi:10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • McPhaden, M. J., and Coauthors, 1998: The Tropical Ocean–Global Atmosphere observing system: A decade of progress. J. Geophys. Res., 103, 14 169–14 240, doi:10.1029/97JC02906.

    • Search Google Scholar
    • Export Citation
1

2D transport is computed as the vertical integral of zonal velocity (eastward only) from the shallowest depth at which the zonal velocity becomes eastward down to the last depth at which the zonal velocity is eastward or 350 m, whichever is shallower [to avoid including, e.g., the Equatorial Intermediate Current (EIC) in the calculation]. Volume (3D) transport is calculated by integrating all zonal velocities >5 cm s−1 between 2°N and 2°S, and shallower than 300 m. These schemes were deemed appropriate based on careful analysis of each of the SEA transects in this region and the sensitivity to minor changes in these parameters is small.

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Cover Journal of Atmospheric and Oceanic Technology
Journal of Atmospheric and Oceanic Technology

  • Atlas, R., Hoffman R. N. , Ardizzone J. , Leidner S. M. , Jusem J. C. , Smith D. K. , and Gombos D. , 2011: A cross-calibrated multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bull. Amer. Meteor. Soc., 92, 157–174, doi:10.1175/2010BAMS2946.1.

    • Search Google Scholar
    • Export Citation

  • Charney, J. G., and Spiegel S. L. , 1971: The structure of wind-driven equatorial currents in homogeneous oceans. J. Phys. Oceanogr., 1, 149–160, doi:10.1175/1520-0485(1971)001<0149:SOWDEC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Drenkard, E. J., and Karnauskas K. B. , 2014: Strengthening of the Pacific Equatorial Undercurrent in the SODA reanalysis: Mechanisms, ocean dynamics, and implications. J. Climate, 27, 2405–2416, doi:10.1175/JCLI-D-13-00359.1.

    • Search Google Scholar
    • Export Citation

  • Farrar, J. T., and Durland T. S. , 2012: Wavenumber–frequency spectra of inertia–gravity and mixed Rossby–gravity waves in the equatorial Pacific Ocean. J. Phys. Oceanogr., 42, 1859–1881, doi:10.1175/JPO-D-11-0235.1.

    • Search Google Scholar
    • Export Citation

  • Firing, E., cited 2013: Notes from Acoustic Doppler Current Profiler Workshop at the National Oceanographic Data Center, May 14–15, 1992. [Available online at http://ilikai.soest.hawaii.edu/sadcp/intro.html.]

  • Firing, E., Lukas R. , Sadler J. , and Wyrtki K. , 1983: Equatorial undercurrent disappears during the 1982–83 El Niño. Science, 222, 1121–1123, doi:10.1126/science.222.4628.1121.

    • Search Google Scholar
    • Export Citation

  • Gove, J. M., Merrifield M. A. , and Brainard R. E. , 2006: Temporal variability of current-driven upwelling at Jarvis Island. J. Geophys. Res., 111, C12011, doi:10.1029/2005JC003161.

    • Search Google Scholar
    • Export Citation

  • Izumo, T., 2005: The equatorial undercurrent, meridional overturning circulation, and their roles in mass and heat exchanges during El Niño events in the tropical Pacific Ocean. Ocean Dyn., 55, 110–123, doi:10.1007/s10236-005-0115-1.

    • Search Google Scholar
    • Export Citation

  • Johnson, G. C., McPhaden M. J. , and Firing E. , 2001: Equatorial Pacific Ocean horizontal velocity, divergence, and upwelling. J. Phys. Oceanogr., 31, 839–849, doi:10.1175/1520-0485(2001)031<0839:EPOHVD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Johnson, G. C., Sloyan B. M. , Kessler W. S. , and McTaggart K. E. , 2002: Direct measurements of upper ocean currents and water properties across the tropical Pacific Ocean during the 1990s. Prog. Oceanogr., 52, 31–61, doi:10.1016/S0079-6611(02)00021-6.

    • Search Google Scholar
    • Export Citation

  • Karnauskas, K. B., and Cohen A. L. , 2012: Equatorial refuge amid tropical warming. Nat. Climate Change, 2, 530–534, doi:10.1038/nclimate1499.

    • Search Google Scholar
    • Export Citation

  • Karnauskas, K. B., Murtugudde R. , and Busalacchi A. J. , 2010: Observing the Galápagos–EUC interaction: Insights and challenges. J. Phys. Oceanogr., 40, 2768–2777, doi:10.1175/2010JPO4461.1.

    • Search Google Scholar
    • Export Citation

  • Karnauskas, K. B., Johnson C. J. , and Murtugudde R. , 2012: An equatorial ocean bottleneck in global climate models. J. Climate, 25, 343–349, doi:10.1175/JCLI-D-11-00059.1.

    • Search Google Scholar
    • Export Citation

  • Kessler, W. S., McPhaden M. J. , and Weickmann K. M. , 1995: Forcing of intraseasonal Kelvin waves in the equatorial Pacific Ocean. J. Geophys. Res., 100, 10 613–10 631, doi:10.1029/95JC00382.

    • Search Google Scholar
    • Export Citation

  • Lukas, R., 1986: The termination of the equatorial undercurrent in the eastern Pacific. Prog. Oceanogr.,16, 63–90, doi:10.1016/0079-6611(86)90007-8.

  • Lyman, J. M., Johnson G. C. , and Kessler W. S. , 2007: Distinct 17- and 33-day tropical instability waves in subsurface observations. J. Phys. Oceanogr., 37, 855–872, doi:10.1175/JPO3023.1.

    • Search Google Scholar
    • Export Citation

  • Madden, R. A., and Julian P. R. , 1971: Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci., 28, 702–708, doi:10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • McPhaden, M. J., and Coauthors, 1998: The Tropical Ocean–Global Atmosphere observing system: A decade of progress. J. Geophys. Res., 103, 14 169–14 240, doi:10.1029/97JC02906.

    • Search Google Scholar
    • Export Citation
  • <sc>Fig</sc>. 1.
    View in gallery
    Fig. 1.

    Overview map of the tropical Pacific Ocean indicating the 21 SEA cruises utilized in this study (black lines), subsets of SEA cruises grouped by the longitude of equatorial crossing (colored ellipses, corresponding to Fig. 2b), and the 140°W TAO mooring also used.

  • <sc>Fig</sc>. 2.
    View in gallery
    Fig. 2.

    Histograms of (a) calendar day and (b) longitude of equatorial crossing for each of the 23 SEA equatorial crossings. (c) Scatterplot of calendar day vs longitude of equatorial crossings with ellipses denoting subgroups with similar longitude and season (west/December, central/spring, and east/December).

  • <sc>Fig</sc>. 3.
    View in gallery
    Fig. 3.

    Scatterplot of decimal year vs longitude of equatorial crossings along with simultaneous daily ADCP measurements from the 0°, 140°W TAO mooring (gray vertical bars).

  • <sc>Fig</sc>. 4.
    View in gallery
    Fig. 4.

    (a) Vertical–meridional section of zonal velocity (m s−1) from SEA cruise on 29 May 2009 clearly indicating the EUC. The black contour line is set at 1.5 m s−1. (b) Vertical profiles of zonal velocity from the same cruise (thick gray) and a nearly collocated and simultaneous (within 1 h and 20 km) measurement from the 140°W TAO mooring (black). (c) The difference in equatorial zonal velocity (SEA − TAO). The RMSE is 0.05 m s−1 to 250-m depth or 0.01 m s−1 to the core of the EUC (89-m depth), and the difference in maximum zonal velocity is −0.02 m s−1.

  • <sc>Fig</sc>. 5.
    View in gallery
    Fig. 5.

    Scatterplots of (a) SEA vs TAO equatorial maximum zonal velocities (R2 = 0.98) and (b) SEA at the EUC core vs TAO equatorial maximum zonal velocities (R2 = 0.64) in east/December subgroup. Note that the nonunity slope and nonzero intercept is due to the slight offset in longitude between the TAO mooring and the eastern group of SEA equatorial crossings.

  • <sc>Fig</sc>. 6.
    View in gallery
    Fig. 6.

    Time series comparing (a) maximum zonal velocity (m s−1) and (b) 2D transport (m2 s−1) for the EUC core as measured by SEA (solid lines), the equator as measured by SEA (dashed lines), and the equator as measured by TAO (filled circles). The lines are grouped into west/December (red), central/spring (green), and east/December (blue).

  • <sc>Fig</sc>. 7.
    View in gallery
    Fig. 7.

    (a) Scatterplots of (a) maximum zonal velocity (m s−1) vs 2D transport (m2 s−1) and (b) 2D transport (m2 s−1) vs volume transport (Sv).

  • <sc>Fig</sc>. 8.
    View in gallery
    Fig. 8.

    Scatterplots of (a) maximum zonal velocity, (c) 2D transport, and (e) depth of the EUC as a function of the longitude where color denotes season (red for spring and blue for December). (b),(d),(f) As in (a),(c),(e), but as a function of date, where color denotes longitude (red for west, green for central, and blue for east).

  • <sc>Fig</sc>. 9.
    View in gallery
    Fig. 9.

    Histograms of (a) depth (m) and (b) latitude (°) of the EUC core for the 23 SEA equatorial crossings. The mean values are indicated by solid black lines.

  • <sc>Fig</sc>. 10.
    View in gallery
    Fig. 10.

    (a) A simple model for 0.1 ≤ A ≤ 1.0, where A represents the maximum latitudinal meander of the EUC core and x may represent time or zonal distance. (b) Corresponding histograms of y for the range of values of A.

  • <sc>Fig</sc>. 11.
    View in gallery
    Fig. 11.

    Scatterplots of the absolute latitude of the EUC core vs (a) longitude and (b) calendar day. Color scheme for (a) is west is red, central is green, and east is blue. Color scheme for (b) is red is spring and blue is December.

  • <sc>Fig</sc>. 12.
    View in gallery
    Fig. 12.

    Scatterplots of (a) maximum zonal velocity (m s−1) on the equator vs in the EUC core. (b) As in (a), but for 2D transport (m2 s−1).

  • <sc>Fig</sc>. 13.
    View in gallery
    Fig. 13.

    Histograms of (a) bias of maximum zonal velocity and (b) 2D transport (%).

  • <sc>Fig</sc>. 14.
    View in gallery
    Fig. 14.

    Distribution of biases in (a),(c) maximum EUC zonal velocity and (b),(d) transport as a function of (a),(b) longitude and (c),(d) calendar day. Error bars indicate ±2 standard errors. Color scheme for (a) and (b) is west is red, central is green, and east is blue. Color scheme for (c) and (d) is red is spring and blue is December.

  • <sc>Fig</sc>. 15.
    View in gallery
    Fig. 15.

    (a) Time series of daily mean (thin gray) and monthly mean (thick black) maximum zonal velocity (m s−1) measured by ADCP from the TAO mooring at 0°, 140°W over the period 1990–2010. The bias-corrected monthly mean record (described in section 4) is shown by a thin black line. (b) Monthly climatology (thick line) and bias-corrected monthly climatology (thin lines; dashed lines denote 95% confidence limits on the bias correction) of maximum zonal velocity (m s−1). (c),(d) As in (a),(b), but for 2D EUC transport (m2 s−1; eastward zonal velocity integrated from 35- to 235-m depth).

Fig. 1.

Overview map of the tropical Pacific Ocean indicating the 21 SEA cruises utilized in this study (black lines), subsets of SEA cruises grouped by the longitude of equatorial crossing (colored ellipses, corresponding to Fig. 2b), and the 140°W TAO mooring also used.
Fig. 2.

Histograms of (a) calendar day and (b) longitude of equatorial crossing for each of the 23 SEA equatorial crossings. (c) Scatterplot of calendar day vs longitude of equatorial crossings with ellipses denoting subgroups with similar longitude and season (west/December, central/spring, and east/December).
Fig. 3.

Scatterplot of decimal year vs longitude of equatorial crossings along with simultaneous daily ADCP measurements from the 0°, 140°W TAO mooring (gray vertical bars).
Fig. 4.

(a) Vertical–meridional section of zonal velocity (m s−1) from SEA cruise on 29 May 2009 clearly indicating the EUC. The black contour line is set at 1.5 m s−1. (b) Vertical profiles of zonal velocity from the same cruise (thick gray) and a nearly collocated and simultaneous (within 1 h and 20 km) measurement from the 140°W TAO mooring (black). (c) The difference in equatorial zonal velocity (SEA − TAO). The RMSE is 0.05 m s−1 to 250-m depth or 0.01 m s−1 to the core of the EUC (89-m depth), and the difference in maximum zonal velocity is −0.02 m s−1.
Fig. 5.

Scatterplots of (a) SEA vs TAO equatorial maximum zonal velocities (R2 = 0.98) and (b) SEA at the EUC core vs TAO equatorial maximum zonal velocities (R2 = 0.64) in east/December subgroup. Note that the nonunity slope and nonzero intercept is due to the slight offset in longitude between the TAO mooring and the eastern group of SEA equatorial crossings.
Fig. 6.

Time series comparing (a) maximum zonal velocity (m s−1) and (b) 2D transport (m2 s−1) for the EUC core as measured by SEA (solid lines), the equator as measured by SEA (dashed lines), and the equator as measured by TAO (filled circles). The lines are grouped into west/December (red), central/spring (green), and east/December (blue).
Fig. 7.

(a) Scatterplots of (a) maximum zonal velocity (m s−1) vs 2D transport (m2 s−1) and (b) 2D transport (m2 s−1) vs volume transport (Sv).
Fig. 8.

Scatterplots of (a) maximum zonal velocity, (c) 2D transport, and (e) depth of the EUC as a function of the longitude where color denotes season (red for spring and blue for December). (b),(d),(f) As in (a),(c),(e), but as a function of date, where color denotes longitude (red for west, green for central, and blue for east).
Fig. 9.

Histograms of (a) depth (m) and (b) latitude (°) of the EUC core for the 23 SEA equatorial crossings. The mean values are indicated by solid black lines.
Fig. 10.

(a) A simple model for 0.1 ≤ A ≤ 1.0, where A represents the maximum latitudinal meander of the EUC core and x may represent time or zonal distance. (b) Corresponding histograms of y for the range of values of A.
Fig. 11.

Scatterplots of the absolute latitude of the EUC core vs (a) longitude and (b) calendar day. Color scheme for (a) is west is red, central is green, and east is blue. Color scheme for (b) is red is spring and blue is December.
Fig. 12.

Scatterplots of (a) maximum zonal velocity (m s−1) on the equator vs in the EUC core. (b) As in (a), but for 2D transport (m2 s−1).
Fig. 13.

Histograms of (a) bias of maximum zonal velocity and (b) 2D transport (%).
Fig. 14.

Distribution of biases in (a),(c) maximum EUC zonal velocity and (b),(d) transport as a function of (a),(b) longitude and (c),(d) calendar day. Error bars indicate ±2 standard errors. Color scheme for (a) and (b) is west is red, central is green, and east is blue. Color scheme for (c) and (d) is red is spring and blue is December.
Fig. 15.

(a) Time series of daily mean (thin gray) and monthly mean (thick black) maximum zonal velocity (m s−1) measured by ADCP from the TAO mooring at 0°, 140°W over the period 1990–2010. The bias-corrected monthly mean record (described in section 4) is shown by a thin black line. (b) Monthly climatology (thick line) and bias-corrected monthly climatology (thin lines; dashed lines denote 95% confidence limits on the bias correction) of maximum zonal velocity (m s−1). (c),(d) As in (a),(b), but for 2D EUC transport (m2 s−1; eastward zonal velocity integrated from 35- to 235-m depth).
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