Abstract

The low-frequency variability of gap winds at the Isthmuses of Tehuantepec and Papagayo is investigated using a 17-yr wind stress dataset merging the remotely sensed observations of Special Sensor Microwave Imager (SSM/I) and Quick Scatterometer (QuikSCAT) satellite sensors. A decadal signal is identified in the Tehuantepec gap winds, which is shown to be related to the Atlantic tripole pattern (ATP). Using linear regression and spectral analysis, it is demonstrated that the low-frequency variability of the Tehuantepec gap winds is remotely forced by the ATP, and the Papagayo gap winds are primarily governed by El Niño–Southern Oscillation (ENSO) with the ATP being of secondary importance.

The Tehuantepec (Papagayo) time series of wind stress anomalies can be better reconstructed when the local cross-isthmus pressure difference and large-scale climate information such as the ATP (ENSO) are included, suggesting that there is important information in the large-scale flow that is not transmitted directly through the background sea level pressure gradient. The geostrophic modulation of the easterly trades in the western Caribbean also serve as a remote driver of the Papagayo gap winds, which is itself not fully independent from ENSO. Finally, it is suggested that precipitation variability in the Inter-Americas region is closely related to the same remote forcing as that of the Tehuantepec gap winds, being the ATP and associated large-scale atmospheric circulation.

1. Introduction

Extending from Mount McKinley in Alaska to Tierra del Fuego at the tip of South America is a series of mountain ranges collectively referred to as the American Cordillera. The important influence of the vast, continental mountain ranges such as the Rockies or the Andes on large-scale circulation and weather patterns is unquestioned, and has been the subject of a vast body of research (e.g., Broccoli and Manabe 1992; Nigam and DeWeaver 1998; Seager et al. 2002). Through Mexico and Central America, the Sierra Madre Occidental and the Central American Cordillera link the North and South American continents. The coastal mountain ranges of southern Mexico and Central America have two appreciable gaps in which the surface elevation drops sharply to within a few meters of sea level: the Isthmus of Tehuantepec in Mexico, separating the Gulf of Mexico from the Pacific Ocean, and the Isthmus of Papagayo in Nicaragua and Costa Rica, separating the Caribbean Sea from the Pacific Ocean. The Central American mountains and their gaps have been shown responsible for key features of eastern tropical Pacific climate, such as the boreal wintertime intertropical convergence zone (ITCZ) being displaced south of the sea surface temperature (SST) maximum associated with the east Pacific warm pool (Xu et al. 2005). Cold air masses and anticyclones moving southward from North America and Canada during boreal winter produce strong wintertime pressure gradients, and through orographic interaction with the Isthmuses of Tehuantepec and Papagayo, form intense low-level wind jets over the east Pacific warm pool (e.g., Hurd 1929; Clarke 1988; Schultz et al. 1997, 1998; Fig. 1). These wind jets have long been known to the shipping and fishing industries, as well as recreational boaters, many of whom can recall encounters with “Tehuantepecers,” or gale-force winds near the Gulf of Tehuantepec that seem to “come out of nowhere.” Tehuantepec gap wind events have been observed to carry sustained wind speeds of 50 m s−1 (Stumpf 1975), equivalent to a category 3 hurricane on the Saffir–Simpson hurricane scale.

Fig. 1.

Climatology of 1° × 1° December–January SSM/I–QuikSCAT (1988–2004) wind stress (dyn cm−2) and NCEP SLP (hPa) in the Inter-Americas region. Yellow boxes denote regions used for calculation of the area-average wind stress time series, and red dots denote locations used for calculation of the cross-isthmus pressure differences at Tehuantepec (T) and Papagayo (P), as discussed in the main text.

Fig. 1.

Climatology of 1° × 1° December–January SSM/I–QuikSCAT (1988–2004) wind stress (dyn cm−2) and NCEP SLP (hPa) in the Inter-Americas region. Yellow boxes denote regions used for calculation of the area-average wind stress time series, and red dots denote locations used for calculation of the cross-isthmus pressure differences at Tehuantepec (T) and Papagayo (P), as discussed in the main text.

Earth-observing satellite remote sensing missions have provided the atmospheric–oceanic science community high spatial and temporal resolution observations of gap winds and the response of the underlying ocean surface. Although gap wind events are episodic, short-duration phenomena (i.e., order of days; Chelton et al. 2000a), they have a strong effect on the surface ocean, which can easily be identified in long-term mean and derivative fields (Chelton et al. 2004). As illustrated in Fig. 2, the Tehuantepec and Papagayo gap winds leave a rich, high-amplitude imprint on the interannual variability of SST and surface chlorophyll concentration in the east Pacific warm pool. Easily identifiable are the Tehuantepec and Papagayo signals [and the Costa Rica Dome, which is not unrelated to the Papagayo gap winds; (e.g., Fiedler 2002; Xie et al. 2005)]. The annual cycle and case studies of specific gap wind events have previously been addressed in detail (e.g., Chelton et al. 2000a, b; Xie et al. 2005), as well as the local dynamical and biological consequences (e.g., Fiedler 1994, 2002; Farber-Lorda et al. 1994).

Fig. 2.

Map of standard deviation of monthly anomalies of 1/12° × 1/12° SeaWiFS (McClain et al. 2004) chlorophyll concentration (mg m−3; shades) and ¼° × ¼° TMI (Kummerow et al. 2000) SST (contours; 1998–2005). SST standard deviation contours are 0.6°, 0.8°, 1.0°, and 1.2°C. The locations of the Tehuantepec (T) and Papagayo (P) mountain gaps are indicated for reference.

Fig. 2.

Map of standard deviation of monthly anomalies of 1/12° × 1/12° SeaWiFS (McClain et al. 2004) chlorophyll concentration (mg m−3; shades) and ¼° × ¼° TMI (Kummerow et al. 2000) SST (contours; 1998–2005). SST standard deviation contours are 0.6°, 0.8°, 1.0°, and 1.2°C. The locations of the Tehuantepec (T) and Papagayo (P) mountain gaps are indicated for reference.

Some effort has also been directed toward understanding the large-scale forcing responsible for the interannual variability of the Tehuantepec and Papagayo gap winds. The temporal variability of Tehuantepec gap winds and its association with El Niño–Southern Oscillation (ENSO) was investigated by Romero-Centeno et al. (2003, hereafter RC03). RC03 used what sparse wind observations exist from the southern edge of the Isthmus of Tehuantepec to construct a statistical model for the relationship between the cross-isthmus pressure gradient and Tehuantepec gap wind speed. RC03 then used the statistical model to reconstruct 31 yr of Tehuantepec gap winds. Based on the statistically generated wind record, it was suggested that the monthly Tehuantepec gap winds were stronger during El Niño years and weaker during La Niña years compared with neutral years. However, as the authors note, the difference is only statistically significant for the “weaker gap winds during La Niña” association. Similarly, Palacios and Bograd (2005) established that there was a higher occurrence of oceanic eddies in the east Pacific warm pool during El Niño years than during La Niña years, wherein it was implied a priori that the oceanic eddies were directly related to the gap winds. However, Zamudio et al. (2006) used the same satellite observations and a high-resolution regional ocean model to show that the higher occurrence of eddies in the Gulf of Tehuantepec during El Niño events was equatorially forced (i.e., by downwelling coastally trapped Kelvin waves originating in the equatorial Pacific). Zamudio et al. (2006) also noted instances with high eddy activity, but calm Tehuantepec gap winds. This suggests that the association between ENSO and oceanic eddies in the east Pacific warm pool is not necessarily due to ENSO modulating the Tehuantepec gap winds, but directly forced by ENSO-related ocean waves.

The Isthmuses of Tehuantepec and Papagayo are only separated by approximately 1200 km, and both are in close proximity to the eastern equatorial Pacific Ocean where ENSO is the overwhelmingly dominant driver of climate variability. There are, however, two fundamental differences between the two: the Tehuantepec gap is oriented meridionally and adjacent to the Gulf of Mexico, while the Papagayo gap is oriented zonally and adjacent to the Caribbean Sea. Xie et al. (2005) noted that these orientation differences have implications for the ability of each wind jet to influence oceanographic processes in the eastern Pacific Ocean. The fact that the two gaps are oriented along perpendicular axes should also have implications for their relationship with the large-scale flow, as the background mean pressure gradient is generally meridional. For example, Chelton et al. (2000a) concluded that Tehuantepec gap winds are probably forced solely by weather systems of midlatitude origin, while the Papagayo gap winds can exhibit, in some cases, synchrony with the western Caribbean region.

The paradigm that ENSO controls the low-frequency variability (“low-frequency” in this paper referring to interannual to decadal time scales) of both the Tehuantepec and Papagayo gap winds is the null hypothesis, which is investigated in this paper within the context of current understanding of what processes are responsible for Central American gap winds. The question of possible interactions between gap winds and hydroclimate is also addressed. The datasets used in this paper are described in the next section, the results in section 3, and a summary and discussion of predictability are found in section 4.

2. Data

Unique to this study is the use of a 17-yr merged wind stress dataset derived from Special Sensor Microwave Imager (SSM/I) ocean surface winds and SeaWinds wind vectors. The SSM/I instrument is a passive sensor flown on board Defense Meteorological Satellite Program (DMSP) satellites, and the SeaWinds instrument on board the National Aeronautics and Space Administration (NASA) Quick Scatterometer (QuikSCAT) satellite. Both instruments have a nominal spatial resolution of 25 km. The merged wind stress dataset includes SSM/I using the variational analysis of Atlas et al. (1996) from 1988 to 1999 (version 10) and QuikSCAT from 1999 to 2004, with a smooth conversion from SSM/I to QuikSCAT between July and September 1999. The QuikSCAT dataset was produced using the optimal interpolation method of Bourassa et al. (1999). The SSM/I–QuikSCAT wind stress were regridded to a 1° × 1° horizontal grid with weekly temporal resolution. Time indices of wind stress at the gap exit regions were constructed from the SSM/I–QuikSCAT wind stress by taking the area average for 13°–15°N, 96°–93°W for Tehuantepec, and for 9°–11°N, 89°–86°W for Papagayo (Fig. 1).

The Reynolds et al. (2002) SST analysis (1° × 1°, 1981–present, monthly), Xie and Arkin (1997) Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP; 2.5° × 2.5°, 1979–present, monthly), and sea level pressure (SLP) and 500-hPa geopotential heights and winds from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (Kalnay et al. 1996; 2.5° × 2.5°, 1948–present) are used in this paper for constructing composite anomalies and regressions upon the wind indices described above.

Time indices of the cross-isthmus SLP difference at Tehuantepec (ΔPT) and Papagayo (ΔPP) were computed by taking the absolute magnitude of the difference of SLP between points on either side of each isthmus (illustrated as red dots in Fig. 1), or

 
formula
 
formula

The above points were chosen based on the criteria that they are within a few degrees of the isthmus, and correspond to NCEP–NCAR reanalysis grid points.

Finally, climate indices were obtained from the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) Web site (see online at http://www.cdc.noaa.gov/ClimateIndices/List/).

3. Results and discussion

The purpose of this paper is to describe the observed low-frequency variability of the Tehuantepec and Papagayo gap winds, and to determine whether the generally accepted causal mechanism of remote forcing of gap winds (i.e., dominated by ENSO) is completely accurate for the gap winds in both the isthmuses of Tehuantepec and Papagayo. To the authors’ knowledge, this constitutes the first analysis of decadal variability or remote forcing other than ENSO in Mexican or Central American gap winds. These results are discussed in sections 3a3c. In addition, the question of interaction between the gap winds and regional hydroclimate is addressed in section 3d, as necessitated by the analysis of remote forcing.

a. Observed low-frequency variability

The results of the weekly wind stress and cross-isthmus ΔP indices for Tehuantepec and Papagayo over the entire 17-yr period 1988–2004 are shown in Fig. 3. Recall that the background SLP gradient is generally meridional, thus the absolute magnitude of ΔPT is always larger than ΔPP. Annually, there is a strong correspondence between the Tehuantepec gap winds and ΔPT, which simply is consistent with the notion that the strong wintertime cross-isthmus ΔP drives the gap winds and is responsible for their marked seasonality. Also evident in the gap winds indices is a decadal signal. The decadal signal is most robust in the Tehuantepec gap winds themselves, with an inflection point in the mid-1990s. The inflection point does not correspond to the merging of SSM/I and QuikSCAT wind stress data, which falls in July 1999. The 1997/98 and 2002/03 El Niño events are indistinguishable from neighboring years in the Tehuantepec gap winds due to the decadal signal, however, they are clearly identifiable at Papagayo.

Fig. 3.

Time series of weekly SSM/I–QuikSCAT (1988–2004) wind stress magnitude (black; dyn cm−2) and cross-isthmus pressure difference (red; ×10 hPa), in the (top) Tehuantepec and (bottom) Papagayo regions as denoted in Fig. 1. The vertical dashed lines denote the boundary between periods discussed in the main text.

Fig. 3.

Time series of weekly SSM/I–QuikSCAT (1988–2004) wind stress magnitude (black; dyn cm−2) and cross-isthmus pressure difference (red; ×10 hPa), in the (top) Tehuantepec and (bottom) Papagayo regions as denoted in Fig. 1. The vertical dashed lines denote the boundary between periods discussed in the main text.

To summarize, the major differences between the low-frequency variability of the Tehuantepec and Papagayo gap winds are that the Tehuantepec gap winds appear to be dominated by a decadal signal, while the Papagayo gap winds are dominated by variability on interannual time scales with a clear influence from ENSO. Diagnosing the low-frequency variability of the Tehuantepec and Papagayo gap winds and remote forcing mechanisms for decadal variability, therefore, is discussed in the following sections.

b. Remote forcing

1) Diagnostic analysis

In this section, a case is presented that the low-frequency variability of the Tehuantepec gap winds is related to a different remote forcing mechanism than that of the Papagayo gap winds. First, to gain a qualitative sense of what large-scale patterns of variability might be related to the Tehuantepec and Papagayo gap winds, linear regression analysis (least squares method) was performed using global SST. Reynolds et al. (2002) SST anomalies (mean climatology removed) were regressed onto the monthly SSM/I–QuikSCAT gap wind anomalies for both Tehuantepec and Papagayo. The results of the linear regression analysis for Tehuantepec and Papagayo are shown in the top panels of Figs. 4 and 5, respectively. Also shown in the bottom panels of Figs. 4 and 5 is the regression of SST onto the Atlantic tripole pattern (ATP) index and Niño-3, respectively. Niño-3 is defined as the area-average SST anomaly for 5°S–5°N, 150°–90°W. The ATP is defined as the leading EOF of SST in the tropical North Atlantic Ocean (Deser and Timlin 1997). Observations by Deser and Blackmon (1993) show the ATP to vary with a period of 12–16 yr. The ATP is also described by Chiang and Vimont (2004) as a meridional mode of coupled ocean–atmosphere variability between surface winds and the meridional SST gradient in the tropical Atlantic Ocean. The most surprising result of the linear regression analysis (Fig. 4) is the lack of an ENSO signal in the Tehuantepec winds. Rather, there is a high degree of similarity between the regression of SST onto the Tehuantepec gap winds and the regression of SST onto the ATP index; the spatial correlation over the domain shown is rs = 0.60. The salient feature in the Atlantic basin is a positive SST anomaly across the tropical North Atlantic. Physical mechanisms for the ATP remotely forcing the Tehuantepec gap winds are discussed in the following section.

Fig. 4.

Reynolds SST anomalies (°C) regressed onto the SSM/I–QuikSCAT (top) Tehuantepec and (bottom) ATP indices.

Fig. 4.

Reynolds SST anomalies (°C) regressed onto the SSM/I–QuikSCAT (top) Tehuantepec and (bottom) ATP indices.

Fig. 5.

Reynolds SST anomalies (°C) regressed onto the SSM/I–QuikSCAT (top) Papagayo and (bottom) Niño-3 indices.

Fig. 5.

Reynolds SST anomalies (°C) regressed onto the SSM/I–QuikSCAT (top) Papagayo and (bottom) Niño-3 indices.

In contrast, the regression of SST onto the Papagayo gap winds (Fig. 5) show a clear ENSO signal. The spatial correlation between the regression of SST onto the Papagayo gap winds and the regression of SST onto Niño-3 is rs = 0.72 over the domain shown, which is consistent with the null hypothesis that ENSO governs the temporal variability of gap winds as it pertains to the Isthmus of Papagayo, but draws a sharp distinction with that of Tehuantepec. The distinction is reconciled with the results of Zamudio et al. (2006) who, as discussed in section 1, established a mechanistic connection between ENSO and oceanic eddies in the Gulf of Tehuantepec, which was not ENSO modulating the Tehuantepec gap winds.

2) Seasonal context of remote forcing

Prior to exploring mechanisms that may help explain the relationships between the modes of climate variability identified in the previous section and the gap winds, it is necessary to first examine some seasonality issues. Since the mean climate spanning from the tropical Pacific to the North Atlantic is quite different depending on season, it is of interest to determine the seasonal phasing of the gap winds and their potential drivers of remote forcing. Shown in Fig. 6 are monthly variance analyses for the ATP, Niño-3, and both the Tehuantepec and Papagayo gap winds. The ATP, as defined by Deser and Timlin (1997), does not have a pronounced season in which there is high variance. There is a peak in wintertime and a much broader peak in summertime, but not a pronounced lack of variance in between. This is in contrast to ENSO (as shown in Fig. 6 by the Niño-3 index), which has a strong peak in boreal wintertime, although variance increases steadily beginning in June. Also according to Fig. 6, the Tehuantepec gap winds are extremely seasonal, with maximum variance in boreal wintertime, while the Papagayo gap winds also peak in wintertime with a secondary maximum in summertime (July–August).

Fig. 6.

Variance as a function of calendar month of the monthly ATP, Niño-3, Tehuantepec gap winds, and Papagayo gap winds time series from January 1988 to December 2004.

Fig. 6.

Variance as a function of calendar month of the monthly ATP, Niño-3, Tehuantepec gap winds, and Papagayo gap winds time series from January 1988 to December 2004.

It is thus reasonable to define a winter season as October–March, and a summer season as July and August. Each of the indices discussed above are stratified according to season, and displayed in Fig. 7. Both the ATP and Niño-3 indices are highly self-correlated over the span of winter to summer. Wintertime ATP is correlated with summertime ATP rt = 0.61, and wintertime Niño-3 is correlated with preceding summertime Niño-3 rt = 0.94 (the summertime Niño-3 index in Fig. 7 has been shifted forward 1 month to line up with the wintertime value). Consistent with the Tehuantepec gap winds being known as a wintertime phenomenon, the Tehuantepec gap wind signal is dominated by its wintertime component (Fig. 7). The general low-frequency signal in the wintertime Tehuantepec gap winds—increasing in the mid-1990s—is consistent with the ATP (Fig. 7, top panel). However, the correspondence between Niño-3 and the Papagayo gap winds appears to be primarily manifested as a projection upon the summertime climate. The exception is the 2002/03 El Niño event, which implies that the season in which ENSO exerts a measurable impact on the gap winds may depend on the individual characteristics of each ENSO event, such as amplitude of phasing with the annual cycle. In addition to low-frequency SST variability in the north Atlantic, several other factors are likely to influence the interannual variability of the Papagayo gap winds, including the Caribbean trade winds, which will be further discussed in section 3c.

Fig. 7.

Seasonally stratified time series of wintertime (October–March; heavy lines) and summertime (July–August; thin lines) ATP, Niño-3, Tehuantepec gap winds, and Papagayo gap winds from 1988 to 2004.

Fig. 7.

Seasonally stratified time series of wintertime (October–March; heavy lines) and summertime (July–August; thin lines) ATP, Niño-3, Tehuantepec gap winds, and Papagayo gap winds from 1988 to 2004.

3) Large-scale mechanisms for ATP remote forcing of the Tehuantepec gap winds

Appearing in the low-frequency evolution of the ATP time series (Fig. 8) is a sharp transition between 1994 and 1995. After roughly a decade in the near-neutral phase of the ATP, came a shift into a full decade of positive ATP. The only exception is a brief (3 months) dip in early 2003. Similarly, as indicated by the dotted lines in all time series shown in this paper, the shift in the ATP roughly corresponds to an inflection point in the decadal signal of the Tehuantepec gap winds. The wintertime (December–January) correlation of the Tehuantepec gap wind index and the ATP is rt = 0.49 (significant at the 95% confidence level, based on a two-tailed Student’s t test).

Fig. 8.

Monthly time series of ATP (January 1978–July 2004) and Niño-3 (Reynolds et al. 2002; December 1981–2004) indices. The vertical dashed line denotes the boundary between periods discussed in the main text.

Fig. 8.

Monthly time series of ATP (January 1978–July 2004) and Niño-3 (Reynolds et al. 2002; December 1981–2004) indices. The vertical dashed line denotes the boundary between periods discussed in the main text.

To contrast the large-scale flow features associated with the periods before and after the ATP shift, composite anomaly fields were constructed for wintertime (December–January) 500-hPa geopotential height and winds before and after the shift (Fig. 9, top and middle panels). Also shown in Fig. 9 (bottom panel) is the linear regression of wintertime 500-hPa geopotential height and wind anomalies onto the ATP index for the full NCEP–NCAR reanalysis period (1949–2003). As discussed in section 1, cold surges and anticyclones moving southward over North America and entering the Gulf of Mexico have been mentioned as one of the possible mechanisms for setting up a pressure gradient conducive to driving strong gap winds. Common to the postshift composite and regressed fields is a large-scale pattern favorable for steering such systems into the Gulf of Mexico. The preshift pattern is symmetric to the postshift pattern; continental systems are steered by the large-scale flow into the North Atlantic without the opportunity to interact with the Sierra Madre Occidental or the Central American Cordillera, and the background northerly flow is not present to contribute to the Tehuantepec gap winds. Furthermore, as the composites and regressions for pressure levels lower in the atmosphere look similar (not shown), it is possible that the large-scale flow contributes to the northerly momentum involved in the Tehuantepec gap winds themselves.

Fig. 9.

Composite wintertime (December–January) 500-hPa geopotential height (m) and wind (m s−1) anomalies for (top) 1978–94 and (middle) 1995–2004; and (bottom) the regression of wintertime 500-hPa geopotential height and winds onto the wintertime Atlantic tripole index from 1949 to 2003.

Fig. 9.

Composite wintertime (December–January) 500-hPa geopotential height (m) and wind (m s−1) anomalies for (top) 1978–94 and (middle) 1995–2004; and (bottom) the regression of wintertime 500-hPa geopotential height and winds onto the wintertime Atlantic tripole index from 1949 to 2003.

The strong anomalous northerly flow over the Gulf of Mexico toward the Isthmus of Tehuantepec associated with the postshift pattern and the ATP regression (Fig. 9, middle and bottom panels) is also clearly evident in a composite map of surface wind stress for strong Tehuantepec gap winds. After removing the weekly climatology, weeks during which the wind stress anomaly in the Tehuantepec index exceeded a value of 0.8 were included as members of the composite. The result is shown in Fig. 10, and reveals anomalous northerly flow, which is consistent with the large-scale flow pattern indicated in the postshift and ATP regression figures. The northerly flow is topographically funneled by greater Mexico to the west and the Yucatan Peninsula to the east, and the Isthmus of Tehuantepec is the primary, albeit narrow, outlet for the zonally constrained northerly flow. In a similar composite map for strong Papagayo gap winds (not shown), the anomalous northerly flow over the Gulf of Mexico is not present.

Fig. 10.

Composite anomalous SSM/I–QuikSCAT surface wind stress (dyn cm−2) for weeks during which anomalously strong Tehuantepec gap winds were present. Values less than 0.3 dyn cm−2 are shaded gray.

Fig. 10.

Composite anomalous SSM/I–QuikSCAT surface wind stress (dyn cm−2) for weeks during which anomalously strong Tehuantepec gap winds were present. Values less than 0.3 dyn cm−2 are shaded gray.

4) Tropical–extratropical and Pacific–Atlantic interactions

While the Tehuantepec gap winds appear to be associated with the ATP, and the Papagayo gap winds associated with ENSO, interactions between the tropical North Atlantic Ocean and the extratropical atmospheric circulation, as well as interactions between the Pacific and Atlantic basins, may complicate such generalizations. As the wintertime regression of 500-hPa flow onto the ATP (Fig. 9, bottom panel) closely resembles the negative phase of the North Atlantic Oscillation (NAO), we consider the interaction between the extratropical Atlantic atmospheric circulation (i.e., the NAO), and SST in the tropical North Atlantic (i.e., the ATP). The NAO can influence SST anomalies associated with the ATP by modulating the strength of the tropical Atlantic trade winds and thus surface heat fluxes (e.g., Xie and Carton 2004). To illustrate the low-frequency variability of such interactions, the 6-yr low-pass filter of Zhang et al. (1997) was applied to the ATP and NAO indices, and is provided in Fig. 11. The low-pass-filtered ATP and NAO time series are correlated rt = 0.40, although that correlation is not likely to be statistically significant due to the low number of degrees of freedom. Nevertheless, the NAO exhibits a shift in 1995 corresponding to the shift in the ATP that was described in the previous section. Given the spatial and temporal similarities, the ATP could in some sense be described as a low-frequency expression of the NAO, which would be consistent with the mechanism proposed in the previous section. The NAO forcing of tropical North Atlantic SST is strongest in boreal winter. Further details on the NAO and the low-frequency expressions thereof can be found in Curry and McCartney (2001), Eden and Jung (2001), or Czaja and Frankignoul (2002).

Fig. 11.

The 6-yr low-pass-filtered (Zhang et al. 1997) time seriesof ATP (heavy line) and NAO (negative; thin line) indices.

Fig. 11.

The 6-yr low-pass-filtered (Zhang et al. 1997) time seriesof ATP (heavy line) and NAO (negative; thin line) indices.

In addition to influences from the extratropical circulation, the ATP is also subject to the influence of interannual variability in the equatorial Pacific. It has been shown that the tropical North Atlantic responds to ENSO events by a reduction (in the case of El Niño) of the trade winds over the tropical North Atlantic, leading to a delayed warming of SST through reduced evaporative heat flux (Enfield and Mayer 1997). Such response of the tropical North Atlantic to ENSO tends to manifest itself primarily in boreal spring. The GCM results of Saravanan and Chang (2000) corroborate this response by showing that sensible heat fluxes also contribute to the tropical North Atlantic’s warming response. Thus, there is the potential for ENSO to also influence Tehuantepec gap winds, by way of ENSO contributing to the ATP. Given the delayed response mechanism, this somewhat indirect remote forcing mechanism should be strongest in boreal spring, when the Tehuantepec gap winds are dying down from the active winter season. The relatively weak signal found in the eastern equatorial Pacific in the regressions of SST onto the Tehuantepec and ATP indices (Fig. 4) could be a hint of this indirect and delayed role of ENSO in forcing Tehuantepec gap winds.1 Alternatively, this could also be interpreted as consistent with the finding of Schultz et al. (1998) that, from 1900–57, approximately twice as many cold surge events were observed in southern Mexico during cold seasons that correspond to El Niño years than those that correspond to La Niña years. Schultz et al. (1998) did not compare cold surge frequency with Atlantic modes of variability.

c. Spectral decomposition and reconstruction of gap winds

For completeness, the climate and cross-isthmus ΔP indices are shown in Fig. 12 for the entire NCEP–NCAR reanalysis period (1948–2004). With over five decades of data, the overall result is the same. Visually, the ΔPT, ATP, and Tehuantepec gap wind time series (top panel) appear distinctly different than the ΔPP, Niño-3, and Papagayo gap wind time series (bottom panel) in their dominant time scales. To quantitatively confirm this visual impression, a discrete Fourier transform (DFT) was performed on the climate and cross-isthmus ΔP time series. Shown to the right of each time series comparison are the corresponding power spectra (in RMS as a function of period). In the power spectra comparison for the ΔPT, there is a primary spectral peak at the decadal period, and a smaller peak at the ENSO period. This is consistent with the ATP power spectrum, which has a strong peak at the decadal (and one at a period of ∼50 yr, which is caused by the singularly abrupt 1994–95 shift).

Fig. 12.

(top) Monthly time series of the Atlantic tripole index, SSM/I–QuikSCAT wind stress anomalies, anomalous cross-isthmus pressure difference at (left) Tehuantepec and (right) corresponding power spectra (RMS as a function of period computed using discrete Fourier transform). (bottom) As in (top), but for Niño-3 and Papagayo.

Fig. 12.

(top) Monthly time series of the Atlantic tripole index, SSM/I–QuikSCAT wind stress anomalies, anomalous cross-isthmus pressure difference at (left) Tehuantepec and (right) corresponding power spectra (RMS as a function of period computed using discrete Fourier transform). (bottom) As in (top), but for Niño-3 and Papagayo.

An alternative technique for computing power spectra that readily lends itself to understanding the statistical significance of spectral peaks is the multitaper method [MTM; available in the University of California, Los Angeles (UCLA) SSA-MTM Toolkit for Spectral Analysis, useful mathematical discussion in Ghil et al. 2002]. Based on the MTM method, decadal peaks in the ATP spectrum (8 and 11 yr) and ΔPT spectrum (12 yr) are significant at the 99% confidence level. The ΔPT spectrum also has a significant peak at an interannual (2.4 yr) period. In the Niño-3 spectrum, interannual (2 and 5 yr) peaks are significant, while the interannual peak in the ATP spectrum is not. Our spectral analyses therefore support the idea put forth in the previous section: the low-frequency variability of the Tehuantepec gap winds is subject to considerable remote forcing from the ATP, while ENSO plays a more dominant role in the Papagayo gap winds.

RC03 combined a relatively long, continuous record of observed cross-isthmus ΔP at the Isthmus of Tehuantepec with the shorter-duration intervals of wind observations that were available to construct a statistical model for the Tehuantepec gap winds. Here we attempt to reconstruct the Tehuantepec, as well as Papagayo, gap wind time series using multiple linear regression (least squares method). In addition to cross-isthmus ΔP, we incorporate large-scale climate information into the statistical model by using the ATP and ENSO indices as additional predictors. It should also be noted that we are attempting to reconstruct the monthly anomalies, as the inputs to our regression model are themselves anomalies (mean seasonal cycle removed). The general equation for the multiple linear regression model is

 
formula

where y is the predictand, bk is the kth regression coefficient, and xk is the kth predictor. After solving for the regression coefficients, the regression model for the reconstructed Tehuantepec gap winds (VT) using all three predictors is

 
formula

Furthermore, predictors can simply be removed from the regression process one-by-one to test the contribution of that information to the total reconstruction. In these cases, the regression model becomes

 
formula
 
formula

The results of this process are shown in Fig. 13 (left column). The interannual and lower-frequency variability in the Tehuantepec gap wind time series is reproduced reasonably well when all three predictors are used: ΔPT, ATP, and ENSO [temporal correlation between the observed and reconstructed time series is rt = 0.46 (0.59 smoothed) with 5-month centered moving mean]. When ENSO is not included as a predictor in the regression process, the result is relatively unchanged [rt = 0.46 (0.58 smoothed)], confirming that ENSO plays a minor role in governing the low-frequency variability of the Tehuantepec gap winds. When ΔPT is the only predictor (i.e., single linear regression), there is significant loss of fidelity of the time series, particularly in the interannual and lower-frequency variability [rt = 0.33 (0.19 smoothed)], suggesting that there is important information in the large-scale flow that is not transmitted directly through the SLP gradient. All of the aforementioned correlations between observed and reconstructed Tehuantepec gap wind time series are statistically significant at the 99.9% confidence level with the exception of the smoothed reconstruction using only ΔPT.

Fig. 13.

(left) Monthly time series of wind stress anomaly at Tehuantepec from SSM/I–QuikSCAT and that reconstructed via multiple linear regression using (top) ΔPT, ATP, and ENSO; (middle) as in (top), but without ENSO; (bottom) as in (top), but using ΔPT only. (right) Monthly time series of wind stress anomaly at Papagayo from SSM/I–QuikSCAT and that reconstructed via multiple linear regression using (top) ΔPP, ENSO, and ATP; (middle) as in (top) but without ATP; (bottom) as in (top) but using ΔPP only. Linear correlation coefficients are shown in lower-right corners; smoothed coefficients refer to observations and reconstructions to which a 5-month-centered moving mean was applied as discussed in the main text. All time series shown are normalized.

Fig. 13.

(left) Monthly time series of wind stress anomaly at Tehuantepec from SSM/I–QuikSCAT and that reconstructed via multiple linear regression using (top) ΔPT, ATP, and ENSO; (middle) as in (top), but without ENSO; (bottom) as in (top), but using ΔPT only. (right) Monthly time series of wind stress anomaly at Papagayo from SSM/I–QuikSCAT and that reconstructed via multiple linear regression using (top) ΔPP, ENSO, and ATP; (middle) as in (top) but without ATP; (bottom) as in (top) but using ΔPP only. Linear correlation coefficients are shown in lower-right corners; smoothed coefficients refer to observations and reconstructions to which a 5-month-centered moving mean was applied as discussed in the main text. All time series shown are normalized.

The regression model with coefficients for the reconstruction of the Papagayo gap winds (VP) are

 
formula
 
formula
 
formula

The results of the Papagayo reconstruction are presented in Fig. 13 (right column). As expected, the reconstructed Papagayo gap wind time series is optimal when ENSO is included as a predictor in the regression process [rt = 0.31 (0.44 smoothed)], with a minor improvement by including the ATP [rt = 0.33 (0.52 smoothed)], and the least fidelity when ΔPP is the only predictor [rt = 0.26 (0.37 smoothed)].

As previously mentioned, the case studies of Chelton et al. (2000a) suggested that variability in the western Caribbean may, in some cases, be an important factor in driving Papagayo gap wind events. Rather than using the cross-isthmus SLP difference ΔPP, we now consider the use of a sea level pressure gradient in the western Caribbean region. Shown in Fig. 14 (top panel) is the result of a linear regression of SLP monthly anomalies onto zonal wind speed at a point near the western edge of the strong easterly trade winds characteristic of the Caribbean region. From the sharp SLP gradient evident in the regression, we construct an index of a meridional pressure gradient across the Caribbean (ΔPCarib). The zonal wind speed at the chosen point is highly correlated with the ΔPCarib index (rt = 0.92). To obtain a regional picture of the influence of the meridional SLP gradient in the western Caribbean on the zonal wind field, we regress zonal wind on the ΔPCarib index (Fig. 14, middle panel). The analysis suggests that the meridional SLP gradient in the Caribbean can exert an influence not only on the strength of the trade winds in the western Caribbean, but its influence extends over the east Pacific warm pool with a relatively strong signal in the exit region of the Papagayo gap winds.

Fig. 14.

(top) Linear regression of monthly SLP anomalies onto an index of easterly surface zonal wind speed anomalies at 12.5°N, 80°W (open circle; contour interval 0.1 hPa). (middle) Linear regression of monthly easterly zonal wind speed anomalies onto an index of the anomalous pressure difference between 17.5°N, 80°W and 10°N, 80°W [filled squares in top panel; contour interval 0.2 m s−1 beginning with 0.6 m s−1]. (bottom) Linear regression of monthly SLP anomalies onto the Niño-3 index (contour interval 0.05 hPa, using monthly NCEP–NCAR reanalysis data (Kalnay et al. 1996) for the period January 1988–December 2004.

Fig. 14.

(top) Linear regression of monthly SLP anomalies onto an index of easterly surface zonal wind speed anomalies at 12.5°N, 80°W (open circle; contour interval 0.1 hPa). (middle) Linear regression of monthly easterly zonal wind speed anomalies onto an index of the anomalous pressure difference between 17.5°N, 80°W and 10°N, 80°W [filled squares in top panel; contour interval 0.2 m s−1 beginning with 0.6 m s−1]. (bottom) Linear regression of monthly SLP anomalies onto the Niño-3 index (contour interval 0.05 hPa, using monthly NCEP–NCAR reanalysis data (Kalnay et al. 1996) for the period January 1988–December 2004.

This analysis is consistent with the notion that ENSO is a dominant control on the interannual variability of the Papagayo gap winds, since ENSO is capable of modifying the SLP field in such a way that the meridional SLP gradient in the western Caribbean would be affected. To illustrate this dependence, Fig. 14 (bottom panel) shows the regression of monthly SLP anomalies onto the Niño-3 index. During an El Niño, for example, anomalously warm SST in the eastern equatorial Pacific leads to lowered SLP as far east as the coast of South America, which is far enough to increase the meridional pressure gradient across the western Caribbean. In light of the fact that variability in the western Caribbean appears to be capable of contributing to the Papagayo gap winds, we repeat the time series reconstruction, but now incorporating ΔPCarib as a predictor along with ΔPP and Niño-3. The multiple linear regression equation and coefficients thus become

 
formula

The results of the reconstruction that includes the western Caribbean influence are provided in Fig. 15, and are to be compared with the Fig. 13 (middle right panel), which yielded a correlation coefficient of rt = 0.31 (0.44 smoothed). The correlation coefficient when the ΔPCarib predictor is included in the model is rt = 0.41 (0.48 smoothed). This suggests that the improvement is more evident when the higher-frequency variability is not smoothed out. Thus, we conclude that the Papagayo gap winds are still subject to ENSO as the dominant mode of interannual variability, but a clear influence from the western Caribbean contributes to the overall variability—not to ignore that ENSO and ΔPCarib are not independent of one another.

Fig. 15.

Monthly time series of wind stress anomaly at Papagayo from SSM/I–QuikSCAT and that reconstructed via multiple linear regression using ΔPP, ENSO, and ΔPCarib. Linear correlation coefficients are shown in lower-right corners; smoothed coefficients refer to observations and reconstructions to which a 5-month centered moving mean was applied as discussed in the main text. All time series shown are normalized.

Fig. 15.

Monthly time series of wind stress anomaly at Papagayo from SSM/I–QuikSCAT and that reconstructed via multiple linear regression using ΔPP, ENSO, and ΔPCarib. Linear correlation coefficients are shown in lower-right corners; smoothed coefficients refer to observations and reconstructions to which a 5-month centered moving mean was applied as discussed in the main text. All time series shown are normalized.

d. Implications for regional hydroclimate

Prior work on the effects of the ATP (or meridional mode) on precipitation has focused on the tropical Atlantic and Africa. Namely, during the positive phase of the ATP, the ITCZ is displaced toward the warm hemisphere with impacts on rainfall in Sahel Africa and the Nordeste region of Brazil (e.g., Hastenrath 2006; Chiang and Vimont 2004). Furthermore, changes in the large-scale flow due to the NAO have historically been discussed in the context of temperature and precipitation in Europe and the United States (e.g., Hurrell et al. 2003). Given the magnitude of the changes in the Northern Hemisphere wintertime large-scale flow associated with the ATP (e.g., Fig. 9), and its impact on the Tehuantepec gap winds, it is reasonable to ask whether there would be consequences for hydroclimate variability in Mexico and Central America.

Referring to Fig. 9 (bottom panel) and Fig. 10, there is anomalous northerly flow over the Gulf of Mexico, which is favorable for transporting warm, moist air from the surface marine boundary layer onto Mexico and Central America, and dry, continental air to the southeast United States. The moisture transport that would result from such a configuration, coupled with interaction with the topography of the Sierra Madre and Central American Cordillera, is favorable for positive precipitation anomalies in the Isthmus of Tehuantepec, and dry conditions in the southwest United States. Monthly CMAP precipitation anomalies were regressed onto the monthly SSM/I–QuikSCAT Tehuantepec gap wind anomalies, and are shown in Fig. 16. Indeed as a result of the northerly flow over the Gulf of Mexico, driven by a negative NAO-like large-scale circulation anomaly, negative precipitation anomalies are found over the southeast United States and positive precipitation anomalies over the Isthmus of Tehuantepec and a broader region over eastern Central America and the Caribbean islands: Cuba, Jamaica, Hispaniola, and Puerto Rico. This suggests that the remote forcing mechanisms responsible for the low-frequency variability of the Tehuantepec gap winds are also potentially important to understanding hydroclimate variability in the Inter-Americas region.

Fig. 16.

CMAP precipitation anomalies (mm day−1) regressed onto SSM/I–QuikSCAT Tehuantepec gap wind index (1988–2004).

Fig. 16.

CMAP precipitation anomalies (mm day−1) regressed onto SSM/I–QuikSCAT Tehuantepec gap wind index (1988–2004).

Although difficult to quantify without higher-resolution observations or a mesoscale model, there is a potential feedback between the wintertime precipitation anomalies and the Tehuantepec gap winds, wherein concentrated convective activity would drive intermittent low-level outflow at high velocity, which could be topographically funneled through the gap at the Isthmus of Tehuantepec and emerge over the Pacific as a gap wind event.

4. Summary and discussion of predictability

The present study is aimed at describing the low-frequency variability of the Tehuantepec and Papagayo gap winds, and to examine the intuitive notion that ENSO would be the primary remote forcing mechanism for both phenomena. The Tehuantepec gap winds appear to be dominated by a decadal signal with a lack of association with ENSO, and the Papagayo gap winds are dominated by variability on interannual time scales with a clear influence from ENSO. The lack of an ENSO signal in the Tehuantepec winds is affirmed by regressing global SST onto the time series of Tehuantepec gap winds. Instead, there is a high degree of similarity with the ATP. The regression of SST onto the Papagayo gap winds show a strong ENSO signal, which is consistent with the assumed relationship between ENSO and the temporal variability of gap winds as it pertains to the Isthmus of Papagayo. Common to the large-scale flow patterns after the 1994–95 shift in the ATP as well as that regressed onto the ATP is a pattern favorable for steering midlatitude systems into the Gulf of Mexico. It is also possible that the large-scale flow contributes to the northerly momentum involved in the Tehuantepec gap winds themselves.

Given the observed effects of the Tehuantepec gap winds on variability in the tropical Pacific, this mechanism represents a direct pathway for Atlantic forcing of Pacific variability. It is also important to note that the Pacific and Atlantic basins are not completely independent from one another, and that the tropical Atlantic Ocean is also subject to remote forcing from the extratropical atmospheric circulation.

Through reconstruction of the Tehuantepec and Papagayo gap wind time series including large-scale climate information, the Tehuantepec gap wind time series is reproduced with higher fidelity when the cross-isthmus pressure difference, ATP and ENSO are included. When ENSO is not included, the result is relatively unchanged, confirming that ENSO plays a minor role in the Tehuantepec gap winds. However, there is significant loss of fidelity of the time series when the ATP is not included in the reconstruction process, suggesting there is important information in the large-scale flow that is not transmitted directly through the background SLP gradient. There is one dominant low-frequency forcing for the Tehuantepec gap winds: the ATP, and apparently multiple remote forcings for the Papagayo gap winds, with ENSO being the most important as it is of highest amplitude. The geostrophic modulation of the easterly trades in the western Caribbean can also act as a remote driver of the Papagayo gap winds, which is itself not fully independent from ENSO.

To the extent that ENSO and the ATP are predictable, these results suggest promise for the predictability of the interannual to decadal variability of the Tehuantepec and Papagayo gap winds. For example, a simple analysis of the ATP–Tehuantepec gap winds paired data between 1988 and 2004 suggests that if the ATP is negative, there is a 78% probability that the monthly mean Tehuantepec gap wind stress magnitude will be less than normal (climatology). Furthermore, if the ATP is negative by at least one standard deviation, it is nearly certain (100% probability) that the Tehuantepec gap winds will be weaker than normal. However, very little predictability appears to be offered from the ATP when the ATP is positive. Conversely, the Papagayo gap winds are apparently more predictable from Niño-3 when ENSO is in a warm phase. Again based on paired data from 1988 to 2004, there is a 76% probability that the Papagayo gap winds will be stronger than normal if Niño-3 is positive by at least one standard deviation. Hence, to the extent that tropical North Atlantic SSTs and ENSO are predictable, some predictability of the Central American gap winds, accompanied by a measure of certainty, which depends on the state of the predictor, can be offered.

One source of uncertainty in the results described in this paper are the errors associated with passive microwave remote sensing and scatterometry, as well as regional reanalyses. In addition, the robustness of the results are somewhat limited by relatively short datasets. However, all of the data used are from continuous, modern records with sufficient length to objectively identify and diagnose a decadal signal within the context of large-scale climate variability.

Finally, analysis of precipitation patterns associated with the ATP suggests that the remote forcing mechanisms responsible for the low-frequency variability of the Tehuantepec gap winds are also related to hydroclimate variability in the Inter-Americas region. Future process studies in the Isthmus of Tehuantepec should consider the potential feedback between local mesoscale processes and the gap winds, as it represents a specific uncertainty concerning hydroclimate variability in a region whose livelihood depends greatly upon rainfall and soil conditions.

Acknowledgments

The authors thank Mr. Eric Hackert for technical assistance with the SSM/I–QuikSCAT wind stress dataset, and Dr. Alfredo Ruiz-Barradas and two anonymous reviewers for valuable input to the manuscript. SSM/I (QuikSCAT) data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science REASoN DISCOVER Project (Ocean Vector Winds Science Team, available online at www.remss.com). This work was supported by the National Oceanic and Atmospheric Administration (NOAA) Pan American Climate Studies (PACS) program through Grant NA17EC1483.

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Footnotes

Corresponding author address: Dr. Kristopher B. Karnauskas, Lamont-Doherty Earth Observatory, Columbia University, 301F Oceanography Bldg., P.O. Box 1000/61, Route 9W, Palisades, NY 10964. Email: krisk@ldeo.columbia.edu

1

This is in contrast to the contribution of the NAO, which should be focused in the wintertime—concurrent with the season during which the gap winds are most active—when the NAO exerts greatest influence on the tropical North Atlantic SST and the steering of continental weather systems.