Intraseasonal Atmospheric Variability in the Extratropics and Its Relation to the Onset of Tropical Pacific Sea Surface Temperature Anomalies

Bruce T. Anderson Department of Geography, Boston University, Boston, Massachusetts

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Abstract

Previous research has shown that seasonal-mean boreal winter variations in the subtropical/extratropical sea level pressure and wind stress fields over the central North Pacific are significantly related to the state of the El Niño–Southern Oscillation (ENSO) 12–15 months later. Results presented in this note indicate that boreal winter ENSO events are also preceded by increased intraseasonal variance in the antecedent boreal winter atmospheric circulation patterns over the extratropical central North Pacific as well. Low (high) surface pressure anomalies associated with intraseasonal variability in this region are related to intraseasonal wind stress anomalies that represent a weakening (strengthening) of the trade winds over both the north and south subtropical/tropical Pacific. There is also a concurrent increase (decrease) in the central and eastern subtropical North Pacific sea surface temperatures that projects onto the seasonal-mean SST anomalies that precede mature ENSO events by 9–12 months. Overall these results suggest that similar to seasonal-mean subtropical surface pressure and wind stress fields, enhanced transient variability in the midlatitudes can subsequently induce changes in the atmospheric and oceanic structure of the tropical Pacific that may serve as a precursor to ENSO variability.

Corresponding author address: Bruce T. Anderson, Department of Geography, Boston University, 675 Commonwealth Ave., Boston, MA 02215-1401. Email: brucea@bu.edu

Abstract

Previous research has shown that seasonal-mean boreal winter variations in the subtropical/extratropical sea level pressure and wind stress fields over the central North Pacific are significantly related to the state of the El Niño–Southern Oscillation (ENSO) 12–15 months later. Results presented in this note indicate that boreal winter ENSO events are also preceded by increased intraseasonal variance in the antecedent boreal winter atmospheric circulation patterns over the extratropical central North Pacific as well. Low (high) surface pressure anomalies associated with intraseasonal variability in this region are related to intraseasonal wind stress anomalies that represent a weakening (strengthening) of the trade winds over both the north and south subtropical/tropical Pacific. There is also a concurrent increase (decrease) in the central and eastern subtropical North Pacific sea surface temperatures that projects onto the seasonal-mean SST anomalies that precede mature ENSO events by 9–12 months. Overall these results suggest that similar to seasonal-mean subtropical surface pressure and wind stress fields, enhanced transient variability in the midlatitudes can subsequently induce changes in the atmospheric and oceanic structure of the tropical Pacific that may serve as a precursor to ENSO variability.

Corresponding author address: Bruce T. Anderson, Department of Geography, Boston University, 675 Commonwealth Ave., Boston, MA 02215-1401. Email: brucea@bu.edu

1. Introduction

It is well known that the El Niño–Southern Oscillation (ENSO) phenomenon, which entails a relative warming of the sea surface temperatures (SSTs) over the eastern equatorial Pacific, can provide significant forcing of the overlying atmosphere, resulting in hemispheric-scale circulation changes (e.g., Kumar and Hoerling 1998; Rowell 1998; Trenberth et al. 1998; Zheng et al. 2000; Shukla et al. 2000; Alexander et al. 2002). These large-scale variations in the upper- and lower-level circulation fields in turn can influence various climatological fields in both local and remote regions (Ropelewski and Halpert 1986, 1996; Kiladis and Diaz 1986; Gershunov and Barnett 1998).

Although traditionally defined as a coupled mode of variability of the ocean–atmosphere system in the equatorial Pacific (Philander 1985), the initiation of ENSO events appears to be related to additional precursor fields in the subtropics and extratropics of the Southern Hemisphere (Kidson 1975; Trenberth 1976; Rasmusson and Carpenter 1982; van Loon and Shea 1985), the Northern Hemisphere (Reiter 1978; Lysne et al. 1997; Barnett et al. 1999; Wang 2001; Vimont et al. 2003b; Anderson 2003), or both (Barnett 1985; Trenberth and Shea 1987; Li 1997; Chan and Xu 2000). Taken together, these findings suggest that the ENSO phenomenon may not simply be an oscillation of the equatorial ocean–atmosphere system, and that extratropical processes may play an important role in forcing equatorial ENSO variability.

Studies looking at intraseasonal variations in these subtropical/extratropical atmospheric processes have focused on their role in initiating westerly wind burst activity over the western tropical Pacific, which can subsequently influence the evolution of the ENSO system (e.g., Latif et al. 1988; Moore and Kleeman 1999; McPhaden 1999; Perigaud and Cassou 2000; Fedorov 2002). These studies have found that synoptic-scale extratropical forcing of northerly jets over the far western Pacific may contribute to westerly wind burst activity (Chu 1988; Compo et al. 1999; Yu et al. 2003) and hence ENSO variability.

Other studies have focused on interannual variations in seasonal-mean subtropical/extratropical atmospheric anomalies and their relation to the subtropical trade wind regime, which can also impact the tropical Pacific surface and subsurface temperature structure (Jin 1997; Chan and Xu 2000; Vimont et al. 2001; Meinen and McPhaden 2000; Vimont et al. 2003a, b; McPhaden 2003; Anderson 2004; Anderson and Maloney 2006). These variations in the subtropical pressure patterns and trade wind regime have been associated with underlying SST anomalies (van Loon and Shea 1985; Weisberg and Wang 1997; Wang 2001), remote SST anomalies (Li 1997), and internal atmospheric variability (Vimont et al. 2001; Vimont et al. 2003a, b). On even longer time scales, similar modification of the trade wind regime by midlatitude climate shifts can alter the base state of the equatorial ocean–atmosphere system and precondition it to a particular phase of the ENSO (Reiter 1978; Barnett et al. 1999; Pierce et al. 2000).

Here we intend to show that intraseasonal transient variations in the atmospheric circulations over the tropical/extratropical Pacific, similar to those found on interannual-to-decadal time scales, are also related to the onset of the ENSO system. In section 2, the various datasets used in this study are described. The relation between seasonal sea surface temperature anomalies and antecedent atmospheric intraseasonal variability is discussed in section 3. This section also introduces an index designed to capture this antecedent, ENSO-related intraseasonal variability and investigates atmospheric and oceanic fields related to this index. Findings are summarized in section 4 and are discussed in relation to previous research.

2. Data and methodology

a. Data

The principal dataset used in this investigation is the reanalysis product from the National Centers for Environmental Prediction (NCEP; see acknowledgments). Details about this dataset, including its physics, dynamics, and numerical and computational methods, are discussed in Kalnay et al. (1996) and Kistler et al. (2001). For this paper, we focus on the daily surface fields for the years 1948–2003. These fields are represented at 2.5° resolution in both the meridional and zonal direction, encompassing a total of 144 × 73 grid points. Particular focus will be paid to the sea level pressures because of their integral nature in any air–sea coupling and because of previous studies indicating that they are strong candidates for finding precursor information regarding the development of large-scale SST anomalies (Kidson 1975; Trenberth 1976; van Loon 1984; van Loon and Shea 1985; Barnett 1985; van Loon and Shea 1987; Trenberth and Shea 1987; Wang et al. 1999; Chan and Xu 2000; Wang 2001; Larkin and Harrison 2002; Vimont et al. 2003b; Anderson 2003). We choose to use the reanalysis product owing to its fairly long continuous record (50+ years) and its systematic treatment of observational and numerical data over this entire period, which makes it one of the few long-term datasets available for studying climate change on diurnal-to- interannual time scales and global-to-synoptic spatial scales.

In addition to the reanalyzed atmospheric fields, this study will also examine the time evolution of the seasonal-mean sea surface temperature fields taken from the reanalysis product; a description and evaluation of the reanalysis SST dataset can be found in Hurrell and Trenberth (1999). The data product used here is at triangular-62 resolution (approximately 2° in latitude and longitude), encompassing a total of 192 × 94 grid points. Although the SST data are available daily from the reanalysis, these values represent linear interpolations between monthly mean values and hence cannot be examined on an intraseasonal basis. To obtain estimates of intraseasonal variations in the global SST fields, we archive the Optimal Interpolation Sea Surface Temperature (OISST) version 2 data available through NOAA (see acknowledgments for data availability). These data, which provide weekly mean values for the period 1981–present at a 1° × 1° resolution, are derived from in situ and satellite-based observations using an iterative statistical interpolation scheme originally applied by Reynolds and Smith (1994). A description and evaluation of the newest version of this SST dataset can be found in Reynolds et al. (2002). For this study, we resample the data at 2° × 2° resolution. In addition, we use a simple linear interpolation to produce data at daily resolution; all results are quantitatively the same if a cubic spline interpolation is used instead.

In addition, this paper uses the well-established Niño-3.4 index designed to capture variability in the SST field over the equatorial Pacific. This index is defined as the area-average SST anomalies between 5°N–5°S and 170°–120°W. The version used here is provided by NOAA’s Climate Diagnostics Center (CDC; see acknowledgments for data availability). It is derived from the reanalyzed SST field described above and is therefore consistent in its treatment of observed and analyzed values.

b. Methodology

Throughout this paper, most results will be based upon statistical relationships between anomalous and/or normalized values of various fields. The appendix provides a list of terms and their calculations as referenced in the rest of the paper. The first use of a given term in the manuscript will reference its derivation in the appendix.

In addition, because of the varying time scales of interest, we adopt two methods for testing of significance. For interannual variations of the seasonal-mean fields, we chose the |r| = 0.30 contour as the minimum confidence limit (Fig. 1). We tested this limit explicitly for significance by performing a bootstrap analysis using randomized versions of the predictor time series (while preserving the autocorrelation and spatial-correlation structure of the gridpoint fields) and found that the |r| = 0.30 contour is above the 95% confidence interval across the entire domain (not shown). In addition we did a field significance test by employing the methodology of Livezey and Chen (1983). Monte Carlo simulations, again using randomized versions of the predictor time series and actual values of the gridpoint time series, indicated that the 5% field significance for the |r| = 0.30 level is 6.5% (i.e., less than 5% of the Monte Carlo simulations had more than 6.5% of their grid points with correlations above |r| = 0.3); this value will be used to estimate field significance in the results section.

For the daily fields, because they are non-Gaussian and have intrinsic autocorrelation, following Ebisuzaki (1997) we test explicitly for significance by performing a modified bootstrap analysis using randomized versions of the predictor time series that preserves the autocorrelation structure of the predictor (and predictand) fields. For the reanalyzed data (extending over 53 winter seasons), the |r| = 0.15 contour is above the 99% confidence interval while for the OISST data (extending only over 22 winter seasons), the |r| = 0.15 contour is above the 95% confidence interval (not shown); hence we use the |r| = 0.15 contour as a qualitative discriminator for significance in both cases with the recognition that the level of confidence differs for these two datasets.

3. Results

a. Intraseasonal variability and its relation to ENSO onset

Previous research has shown that there exists a precursor mode of boreal winter sea level pressure (SLP) variability in the central subtropical North Pacific that precedes variations in the January–March ENSO by approximately 12–15 months (Barnett 1985; Trenberth and Shea 1987; Chan and Xu 2000; Vimont et al. 2003b; Anderson 2003). This mode of SLP variability can be captured using a sea level pressure index computed by area-averaging the gridpoint SLP anomalies over 10°–25°N and 150°–175°W (Anderson 2003). The seasonal-mean anomaly of this sea level pressure index (SLPI) from November to March has a significant negative correlation with the boreal winter (January–March) ENSO state 12–15 months later (r = −0.62; Anderson 2007). Our interest in this paper is to further examine similar subtropical/extratropical atmospheric structures preceding ENSO events, particularly with regard to intraseasonal variability and transient atmospheric activity.

To capture intraseasonal atmospheric variations that precede ENSO events, the gridpoint standard deviations of daily sea level pressure anomalies (see appendix), calculated from November to March, are correlated with the January–March Niño-3.4 index one year later (Fig. 1a). The largest correlation values are found in the central North Pacific, slightly north of the region in which the mean SLPI anomalies were located. While the total areal extent for regions in which the correlations are above |r| = 0.30 is fairly small, it makes up 8.0% of the grid points compared with the 5% field significance value of 6.5%. Further corroboration of the intraseasonal variability structure described here (and below) is provided by its similarity to patterns obtained from previous research into intraseasonal atmospheric variability affecting the tropical Pacific region (Plumb 1985; Kiladis and Weickmann 1992; Higgins and Mo 1997; Matthews and Kiladis 1999; Nigam 2003); these similarities will be discussed further in the summary section.

As such, a second index, termed the Intraseasonal Central Pacific Index (ICPI; see appendix), is formed by averaging the normalized daily sea level pressure anomalies (see appendix) from 20° to 45°N and 150° to 175°W. Year-to-year variations of the intraseasonal standard deviation of the daily values of this index from November to March are shown in Fig. 1b. As expected, enhanced intraseasonal variability in the ICPI region is well correlated with the Niño-3.4 index the following boreal winter (r = 0.50); however, it is not significantly correlated with the concurrent ENSO state (r = −0.01) nor the ENSO state from the previous winter (r = −0.13).

In addition, the intraseasonal standard deviation of this index is also negatively correlated with the concurrent value of the seasonal-mean SLPI found farther to the south (r = −0.45), as well as its own seasonal-mean value (r = −0.54); that is, enhanced intraseasonal variability in the ICPI region is associated with lower-than-normal seasonal-mean sea level pressures. This relationship could arise because the seasonal-mean field is affected by a quasi-stationary shift in the location of the storm track, which also affects the location of enhanced intraseasonal variability; alternatively, it could arise because intraseasonal variability is skewed toward transient low pressure anomalies such that enhanced variability is associated with the passage of more low pressure systems. While of interest, a complete study of the multiscalar atmospheric interactions in this region is beyond the scope of this paper.

To aid with further analysis, we calculate the intraseasonal variability spectra of the daily wintertime ICPI anomalies (not shown). Results indicate that the power spectra peak at periods greater than 20 days. As such we will focus only on the low-frequency evolution of the ICPI in order to better characterize its structure and possible relation with tropical variability in the ENSO region. In addition, because enhanced intraseasonal variability of the ICPI is related to decreased seasonal-mean SLP anomalies in the SLPI region (see above), both of which precede positive ENSO events (i.e., El Niños), we will show the intraseasonal anomalies related to decreases in sea level pressures over the ICPI region as well.

b. Evolution of ICPI-related variability

To first characterize the structure and evolution of transient atmospheric variability over the ICPI region, the daily ICPI anomaly is low-pass filtered each year to retain only periods longer than 20 days (see appendix). In the next set of figures, daily atmosphere–ocean anomaly fields are low-pass filtered using the same cutoff period and are lead/lag correlated with the low-pass-filtered daily ICPI anomaly (see appendix). It is important to note that while the evolution shown in these figures is related to the passage of low pressure anomalies over the ICPI region, it is not implied that the evolution follows a quasi-periodic pattern; that is, that anomalies shown here represent one half-period of a 40+ day evolution. In fact the autocorrelation of the unfiltered daily ICPI at lead/lags of more than ±12 days are insignificant (|r| < 0.15) and do not indicate any periodicity in the evolution past these lags (not shown).

Figure 2 shows the low-pass-filtered, daily sea level pressure anomaly field lead/lag correlated with the ICPI, starting from a lead of 12 days prior to the ICPI through the period 10 days after the ICPI. This figure indicates that about 6 days before the ICPI, there is a significant low pressure anomaly centered over the central North Pacific, with additional anomalies found over the Indian subcontinent as well as across North America. This pattern intensifies over the next 4–6 days. In addition, a high pressure anomaly forms over the tropical Pacific basin approximately 2 days after the ICPI. The tropical–extratropical dipole persists over the next 2–4 days, then starts to weaken. Ten days after the ICPI, only the tropical high pressure anomaly signature remains. These results suggest that anomalies associated with the ICPI are first found over the extratropical Pacific and are followed by anomalies in the tropical Pacific that persist even after the initial extratropical anomalies have disappeared.

To see how these intraseasonal low-level atmospheric patterns may dynamically force the intraseasonal and seasonal-mean tropical Pacific sea surface temperature structure, the lead/lag correlations of the low-pass-filtered daily surface wind stress anomalies over the tropical/extratropical Pacific are calculated against the low-pass-filtered daily ICPI anomalies (Fig. 3); here the values are only shown from day −6 to day 8 because there are no significant wind stress anomalies prior to or following this period. Starting 6 days prior to the ICPI, wind stress anomalies are found across the extratropical Pacific; these intensify and then persist over the course of the next 8–10 days. In addition, over the central North Pacific, they begin to extend into the tropical region. About 6 days after the ICPI, the North Pacific extratropical anomalies weaken; however, there is the additional onset of tropical wind stress anomalies in the southern tropical Pacific, coincident with the onset and persistence of the tropical Pacific high pressure anomaly seen in Fig. 2. Comparison of these features with seasonal-mean (November–March) wind stress anomalies, as correlated with mature January–March ENSO events 12–15 months later (Fig. 3i), suggests that the circulation anomalies seen in the intraseasonal fields are centered farther north than are found in the seasonal-mean structure preceding ENSO events (Anderson 2003). However, the weakening of the trade wind anomalies across the subtropical/tropical North Pacific is similar in both sets of maps, as is the weakening of the trades in the tropical South Pacific.

It is expected that these wind stress fields can affect the air–sea interactions and hence SSTs over the Tropics and extratropics of the North Pacific. If we low-pass filter the weekly OISST data, retaining periods longer than 20 days, and correlate the daily anomalies with lead/lagged values of the low-pass-filtered daily ICPI anomalies (Fig. 4), we find that following the onset of the extratropical low pressure anomaly over the central Pacific (day −6), there is the onset of warm SST anomalies extending from the central tropical Pacific to the eastern subtropical Pacific. In addition there is the onset of negative SST anomalies over the central subtropical Pacific. These anomalies tend to persist through the rest of the time period, decaying away at approximately day (12) (not shown). Comparison of these features with seasonal-mean (November–March) SST anomalies, as correlated with mature January–March ENSO events 12–15 months later (Fig. 4l), again indicates that the intraseasonal anomaly fields are similar to the seasonal-mean boreal winter SST features that precede ENSO events, as well as those considered the optimal initial SST structure for ENSO onset (see Fig. 4 of Penland 1996).

4. Summary

The relationship between intraseasonal variations in extratropical atmospheric patterns and interannual variability in equatorial Pacific SSTs is investigated using output from 50+ years of NCEP reanalysis data. Results indicate that within the analyzed system enhanced intraseasonal extratropical atmospheric variability over the central North Pacific is significantly correlated with the development of positive SST anomalies in the central/eastern tropical Pacific 1 year later. Low (high) surface pressure anomalies associated with intraseasonal variability in this region, combined with concurrent high (low) pressure anomalies across the tropical Pacific, are related to a weakening (strengthening) of the subtropical trade winds north and south of the equator, similar to the seasonal-mean trade wind anomalies that precede mature ENSO events by 12–15 months. In addition, there is a concurrent warming (cooling) of the central and eastern subtropical SSTs that also projects onto the optimal seasonal-mean precursor SST pattern that precedes mature ENSO events. These results suggest that similar to seasonal-mean subtropical surface pressure and wind stress fields, enhanced transient variability in the midlatitudes can subsequently induce changes in the atmospheric and oceanic structure of the tropical Pacific that may serve as a precursor to ENSO variability.

It is important to note that we do not argue that the initiation of ENSO events is solely related to extratropical variability over the North Pacific; for instance, intraseasonal variability in the central North Pacific does not appear to be related to either the 1982–83 or the 1997–98 El Niño events, the two largest events over the last half-century (Fig. 1). There is significant documentation of the atmospheric–oceanic conditions preceding the 1997–98 El Niño (e.g., McPhaden 1999), indicating that this event was related to tropical wind burst forcing over the western Pacific. However, it has been shown that tropical wind bursts are not uniquely associated with all ENSO events (e.g., Moore and Kleeman 1999; Perigaud and Cassou 2000; Fedorov 2002; Yu et al. 2003). Here we argue that certain ENSO events may instead be the result of stochastic forcing from outside the tropical regions that appears to be unrelated to westerly wind bursts. For instance, there is no evidence of westerly wind burst activity preceding or during the evolution of the ICPI-related wind stress fields (Fig. 3). In addition, boreal winter westerly wind burst counts, as identified in the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) dataset from 1980 to 2001 (Eisenman et al. 2005; Gebbie et al. 2005, manuscript submitted to J. Atmos. Sci.) are uncorrelated with year-to-year variations in the intraseasonal standard deviation of the ICPI (r = 0.02), suggesting that years with enhanced variability in one forcing mechanism are not the same as years with enhanced variability in the other.

It is also important to note that the ICPI evolution described in section 3 is very similar to previous documentation of the propagation of extratropical intraseasonal anomalies into the central and eastern tropical Pacific region (Plumb 1985; Kiladis and Weickmann 1992; Higgins and Mo 1997; Matthews and Kiladis 1999; Nigam 2003), including a 2–3-week development period; initial anomalies in the western Pacific; an extension and intensification of the low-level wind and pressure patterns over the extratropical/subtropical central Pacific; concurrent weakening of the central North Pacific trade winds; warming of subtropical SSTs; and the subsequent decay over the central tropical Pacific. Indeed, the ICPI discussed here is very likely a surface signature of the extratropical upper-air North Pacific circulation anomalies identified previously (Higgins and Mo 1997; Nigam 2003). Our intent here is not to suggest that the ICPI represents a new mode of intraseasonal atmospheric variability, but to highlight how this variability may be related to surface circulations that can affect the tropical Pacific ENSO state.

While the ICPI has a similar structure and evolution to that associated with persistent North Pacific circulation anomalies, which have been related in part to intrinsic extratropical atmospheric variability (Plumb 1985; Kiladis and Weickmann 1992; Hsu 1996), the atmospheric evolution in this region may also be partly related to the primary mode of intraseasonal variability over the tropical Pacific, namely, the Madden–Julian oscillation (MJO), as suggested by others (Higgins and Mo 1997; Matthews et al. 2004). If a proxy for the daily wintertime MJO time series [developed following the lead of Maloney and Hartmann (2000)] is low-passed filtered in the same manner as the other data and then correlated with the low-pass-filtered ICPI data for lead/lags ranging from ±30 days, results indicate that there is a relation between the MJO and the ICPI 5 to 10 days later with a maximum correlation of r = 0.22. Hence, it appears that the slowly evolving intraseasonal variability of the ICPI may be weakly related to low-frequency intraseasonal variations in the tropical Indian and western Pacific basins associated with the MJO (as suggested by Higgins and Mo 1997; Matthews et al. 2004) as well as to subtropical/extratropical processes (as suggested by Plumb 1985; Kiladis and Weickmann 1992; Hsu 1996; Nigam 2003) although this relationship needs to be verified through further investigations.

Acknowledgments

We thank Eric Maloney for his insightful comments on drafts of this paper. NCEP reanalysis data, climate indices, and NOAA Optimum Interpolation (OI) SST V2 data were provided by the NOAA–CIRES Climate Diagnostics Center, Boulder, Colorado, from their Web sites (http://www.cdc.noaa.gov and http://www.cdc.noaa.gov/ClimateIndices/index.html). Westerly wind burst data were kindly provided by Ian Eisenman from Harvard University.

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  • McPhaden, M. J., 2003: Tropical Pacific Ocean heat content variations and ENSO persistence barriers. Geophys. Res. Lett., 30 .1480, doi:10.1029/2003GL016872.

    • Search Google Scholar
    • Export Citation
  • Meinen, C. S., and M. J. McPhaden, 2000: Observations of warm water volume changes in the equatorial Pacific and their relationship to El Niño and La Niña. J. Climate, 13 , 35513559.

    • Search Google Scholar
    • Export Citation
  • Moore, A. M., and R. Kleeman, 1999: Stochastic forcing of ENSO by the intraseasonal oscillation. J. Climate, 12 , 11991220.

  • Nigam, S., 2003: Teleconnections. Encyclopedia of Atmospheric Sciences, J. Holton, J. Curry, and J. Pyle, Eds., Vol. 6, Elsevier, 2243–2269.

    • Search Google Scholar
    • Export Citation
  • Penland, C., 1996: A stochastic model of IndoPacific sea surface temperature anomalies. Physica D, 98 , 534558.

  • Perigaud, C. M., and C. Cassou, 2000: Importance of oceanic decadal trends and westerly wind bursts for forecasting El Niño. Geophys. Res. Lett., 27 , 389392.

    • Search Google Scholar
    • Export Citation
  • Philander, S. G. H., 1985: El Niño and La Niña. J. Atmos. Sci., 42 , 26522662.

  • Pierce, D. W., T. P. Barnett, and M. Latif, 2000: Connections between the Pacific Ocean Tropics and midlatitudes on decadal timescales. J. Climate, 13 , 11731194.

    • Search Google Scholar
    • Export Citation
  • Plumb, R. A., 1985: On the three-dimensional propagation of stationary waves. J. Atmos. Sci., 42 , 217229.

  • Rasmusson, E. M., and T. H. Carpenter, 1982: Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon. Wea. Rev., 110 , 354384.

    • Search Google Scholar
    • Export Citation
  • Reiter, E. R., 1978: Long-term wind variability in the tropical Pacific, its possible causes and effects. Mon. Wea. Rev., 106 , 324330.

    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., and T. M. Smith, 1994: Improved global sea surface temperature analyses using optimal interpolation. J. Climate, 7 , 929948.

    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15 , 16091625.

    • Search Google Scholar
    • Export Citation
  • Ropelewski, C. F., and M. S. Halpert, 1986: North American precipitation and temperature patterns associated with the El Niño/Southern Oscillation (ENSO). Mon. Wea. Rev., 114 , 23522362.

    • Search Google Scholar
    • Export Citation
  • Ropelewski, C. F., and M. S. Halpert, 1996: Quantifying Southern Oscillation–precipitation relationships. J. Climate, 9 , 10431059.

  • Rowell, D. P., 1998: Assessing potential seasonal predictability with an ensemble of multidecadal GCM simulations. J. Climate, 11 , 109120.

    • Search Google Scholar
    • Export Citation
  • Shukla, J., and Coauthors, 2000: Dynamical seasonal prediction. Bull. Amer. Meteor. Soc., 81 , 25932606.

  • Trenberth, K. E., 1976: Spatial and temporal variations of the Southern Oscillation. Quart. J. Roy. Meteor. Soc., 102 , 639653.

  • Trenberth, K. E., and D. J. Shea, 1987: On the evolution of the Southern Oscillation. Mon. Wea. Rev., 115 , 30783096.

  • Trenberth, K. E., G. W. Branstator, D. Karoly, A. Kumar, N-C. Lau, and C. Ropelewski, 1998: Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperatures. J. Geophys. Res., 103 , C7. 1429114324.

    • Search Google Scholar
    • Export Citation
  • van Loon, H. L., 1984: The Southern Oscillation. Part III: Associations with the trades and with the trough in the westerlies of the South Pacific Ocean. Mon. Wea. Rev., 112 , 947954.

    • Search Google Scholar
    • Export Citation
  • van Loon, H. L., and D. J. Shea, 1985: The Southern Oscillation. Part IV: The precursors south of 15°S to the extremes of the oscillation. Mon. Wea. Rev., 113 , 20632074.

    • Search Google Scholar
    • Export Citation
  • van Loon, H. L., and D. J. Shea, 1987: The Southern Oscillation. Part VI: Anomalies of sea level pressure on the Southern Hemisphere and of the Pacific sea surface temperature during the development of a warm event. Mon. Wea. Rev., 115 , 370379.

    • Search Google Scholar
    • Export Citation
  • Vimont, D. J., S. Battisti, and A. C. Hirst, 2001: Footprinting: A seasonal connection between the tropics and mid-latitudes. Geophys. Res. Lett., 28 , 39233926.

    • Search Google Scholar
    • Export Citation
  • Vimont, D. J., S. Battisti, and A. C. Hirst, 2003a: The seasonal footprinting mechanism in the CSIRO general circulation models. J. Climate, 16 , 26532667.

    • Search Google Scholar
    • Export Citation
  • Vimont, D. J., J. M. Wallace, and S. Battisti, 2003b: The seasonal footprinting mechanism in the Pacific: Implications for ENSO. J. Climate, 16 , 26682675.

    • Search Google Scholar
    • Export Citation
  • Wang, B., R. Wu, and R. Lukas, 1999: Roles of the western North Pacific wind variation in thermocline adjustment and ENSO phase transition. J. Meteor. Soc. Japan, 77 , 116.

    • Search Google Scholar
    • Export Citation
  • Wang, C., 2001: A unified oscillator model for the El Niño–Southern Oscillation. J. Climate, 14 , 98115.

  • Weisberg, R. H., and C. Z. Wang, 1997: A western Pacific oscillator paradigm for the El Niño–Southern Oscillation. Geophys. Res. Lett., 24 , 779782.

    • Search Google Scholar
    • Export Citation
  • Yu, L., R. A. Weller, and W. T. Liu, 2003: Case analysis of a role of ENSO in regulating the generation of westerly wind bursts in the Western Equatorial Pacific. J. Geophys. Res., 108 .3128, doi:10.1029/2002JC001498.

    • Search Google Scholar
    • Export Citation
  • Zheng, X. G., H. Nakamura, and J. A. Renwick, 2000: Potential predictability of seasonal means based on monthly time series of meteorological variables. J. Climate, 13 , 25912604.

    • Search Google Scholar
    • Export Citation

APPENDIX

Terms and References

Here we present a list of terms and their calculations as referenced in the paper:

  • “Boreal winter” or “winter season” is defined as 1 November through 31 March.

  • “Daily anomalies” (I*) are first calculated by removing the climatological value of I for the given day, thereby removing seasonal trends in the data, resulting in I′; then for each year the mean of I′ for the given winter season is removed in order to remove interannual variations from the statistical calculations:
    i1520-0442-20-5-926-eqa1
  • “Normalized daily anomalies” (Inorm), which are only used in the calculation of the daily ICPI (see below), are calculated by dividing I′ by the standard deviation of I′ across all seasons and years:
    i1520-0442-20-5-926-eqa2

    Note that for the normalized daily anomalies only the climatological value for the given day is removed from the daily values, but not the seasonal-mean value for a given year (as done with the daily anomalies defined above); this allows us to compare changes in the seasonal-mean value of the daily ICPI from one year to the next. In addition, the normalization is constant across years so that changes in the standard deviation of the SLP values for a given year do not affect the calculation of the ICPI.

  • “Intraseasonal standard deviations” [σI*(yr)] of the daily anomalies are calculated by finding the standard deviation of the daily anomalies I* for a given season:
    i1520-0442-20-5-926-eqa3
    “Correlations” of daily time series are calculated by taking the inner product of the daily anomalies of a given field (A*) against the daily anomalies of the regressor (or predictor) index (I*); these values are then normalized by the intraseasonal standard deviations of both for the given year in order to remove variance of the predictor time series from the covariance calculations:
    i1520-0442-20-5-926-eqa4

    These values are then averaged over all years to produce an estimate of the mean correlation value.

  • “Seasonal-mean” anomalies (I+) are calculated by removing the climatological value for the given season from the seasonal-mean value (Ĩ):
    i1520-0442-20-5-926-eqa5
    “Low-pass-filtered” daily time series for a given season (Ilow*) are generated by taking the Fourier transform of the daily anomalies I*. The amplitudes of all spectral coefficients with a period less than 20 days are set to 0, and the time series is then reconstructed using the modified spectral coefficients:
    i1520-0442-20-5-926-eqa6
    The “Intraseasonal Central Pacific Index” (ICPI) time series for a given year is formed by averaging the normalized daily sea level pressure anomalies over the region from 20° to 45°N and 150° to 175°W for each day from 1 November through 31 March:
    i1520-0442-20-5-926-eqa7

    Note that once calculated, the ICPI is treated as a daily value, hence daily anomalies of the ICPI are calculated as ICPI* (not ICPI′)—see above.

Fig. 1.
Fig. 1.

(a) Std dev of daily SLP anomalies from November to March, correlated with the January–March Niño-3.4 index for the following year. Shown here are correlation coefficients. Contour interval is 0.1; minimum contour is ±0.3 (approximately 95% confidence limit). Positive values are shaded. Box shows area-averaging region for the ICPI. (b) Time series of the mean January–March Niño-3.4 index (solid line) for the current year. Also shown is the time series of the boreal winter (November–March) intraseasonal std dev of the daily ICPI anomalies (crosses) shifted forward 12 months (i.e., the 1948 value is plotted in 1949). Daily ICPI anomalies are calculated from the normalized daily SLP anomalies averaged from 20° to 45°N, 150° to 175°W; see (a). Both time series are normalized by their respective interannual std devs.

Citation: Journal of Climate 20, 5; 10.1175/JCLI4036.1

Fig. 2.
Fig. 2.

(a)–(l) Low-pass-filtered daily SLP anomalies correlated with the low-pass-filtered daily ICPI anomaly (see Fig. 1) for different lead/lags relative to the ICPI. Shown here are correlation coefficients. Contour interval is 0.15; minimum contour is ±0.15 (approximately 99% confidence limit using a bootstrapping methodology). Positive values are shaded. Sign convention is such that values are associated with low pressures over the ICPI region.

Citation: Journal of Climate 20, 5; 10.1175/JCLI4036.1

Fig. 3.
Fig. 3.

(a)–(h) Low-pass-filtered daily surface wind stress anomalies correlated with the low-pass-filtered daily ICPI anomaly (see Fig. 1) for different lead/lags relative to the ICPI. Unit vector of r = 0.6 shown in upper-right corner of each panel. Sign convention is such that values are associated with low pressures over the ICPI region. (i) Seasonal-mean wind stress anomalies from November to March, correlated with the January–March Niño-3.4 index one year later. Unit vector of r = 0.6 shown in upper-right corner.

Citation: Journal of Climate 20, 5; 10.1175/JCLI4036.1

Fig. 4.
Fig. 4.

(a)–(k) Low-pass-filtered daily SST anomalies correlated with the low-pass-filtered daily ICPI anomaly (see Fig. 1) for different lead/lags relative to the ICPI. Shown here are correlation coefficients. Contour interval is 0.10; minimum contour is ±0.15 (approximately 95% confidence limit using a bootstrapping methodology). Positive values are shaded. Sign convention such that values are associated with low pressures over the ICPI region. (l) Seasonal-mean SST anomalies from Nov to Mar, correlated with the Jan–Mar Niño-3.4 index one year later.

Citation: Journal of Climate 20, 5; 10.1175/JCLI4036.1

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    • Export Citation
  • Meinen, C. S., and M. J. McPhaden, 2000: Observations of warm water volume changes in the equatorial Pacific and their relationship to El Niño and La Niña. J. Climate, 13 , 35513559.

    • Search Google Scholar
    • Export Citation
  • Moore, A. M., and R. Kleeman, 1999: Stochastic forcing of ENSO by the intraseasonal oscillation. J. Climate, 12 , 11991220.

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    • Search Google Scholar
    • Export Citation
  • Penland, C., 1996: A stochastic model of IndoPacific sea surface temperature anomalies. Physica D, 98 , 534558.

  • Perigaud, C. M., and C. Cassou, 2000: Importance of oceanic decadal trends and westerly wind bursts for forecasting El Niño. Geophys. Res. Lett., 27 , 389392.

    • Search Google Scholar
    • Export Citation
  • Philander, S. G. H., 1985: El Niño and La Niña. J. Atmos. Sci., 42 , 26522662.

  • Pierce, D. W., T. P. Barnett, and M. Latif, 2000: Connections between the Pacific Ocean Tropics and midlatitudes on decadal timescales. J. Climate, 13 , 11731194.

    • Search Google Scholar
    • Export Citation
  • Plumb, R. A., 1985: On the three-dimensional propagation of stationary waves. J. Atmos. Sci., 42 , 217229.

  • Rasmusson, E. M., and T. H. Carpenter, 1982: Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon. Wea. Rev., 110 , 354384.

    • Search Google Scholar
    • Export Citation
  • Reiter, E. R., 1978: Long-term wind variability in the tropical Pacific, its possible causes and effects. Mon. Wea. Rev., 106 , 324330.

    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., and T. M. Smith, 1994: Improved global sea surface temperature analyses using optimal interpolation. J. Climate, 7 , 929948.

    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15 , 16091625.

    • Search Google Scholar
    • Export Citation
  • Ropelewski, C. F., and M. S. Halpert, 1986: North American precipitation and temperature patterns associated with the El Niño/Southern Oscillation (ENSO). Mon. Wea. Rev., 114 , 23522362.

    • Search Google Scholar
    • Export Citation
  • Ropelewski, C. F., and M. S. Halpert, 1996: Quantifying Southern Oscillation–precipitation relationships. J. Climate, 9 , 10431059.

  • Rowell, D. P., 1998: Assessing potential seasonal predictability with an ensemble of multidecadal GCM simulations. J. Climate, 11 , 109120.

    • Search Google Scholar
    • Export Citation
  • Shukla, J., and Coauthors, 2000: Dynamical seasonal prediction. Bull. Amer. Meteor. Soc., 81 , 25932606.

  • Trenberth, K. E., 1976: Spatial and temporal variations of the Southern Oscillation. Quart. J. Roy. Meteor. Soc., 102 , 639653.

  • Trenberth, K. E., and D. J. Shea, 1987: On the evolution of the Southern Oscillation. Mon. Wea. Rev., 115 , 30783096.

  • Trenberth, K. E., G. W. Branstator, D. Karoly, A. Kumar, N-C. Lau, and C. Ropelewski, 1998: Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperatures. J. Geophys. Res., 103 , C7. 1429114324.

    • Search Google Scholar
    • Export Citation
  • van Loon, H. L., 1984: The Southern Oscillation. Part III: Associations with the trades and with the trough in the westerlies of the South Pacific Ocean. Mon. Wea. Rev., 112 , 947954.

    • Search Google Scholar
    • Export Citation
  • van Loon, H. L., and D. J. Shea, 1985: The Southern Oscillation. Part IV: The precursors south of 15°S to the extremes of the oscillation. Mon. Wea. Rev., 113 , 20632074.

    • Search Google Scholar
    • Export Citation
  • van Loon, H. L., and D. J. Shea, 1987: The Southern Oscillation. Part VI: Anomalies of sea level pressure on the Southern Hemisphere and of the Pacific sea surface temperature during the development of a warm event. Mon. Wea. Rev., 115 , 370379.

    • Search Google Scholar
    • Export Citation
  • Vimont, D. J., S. Battisti, and A. C. Hirst, 2001: Footprinting: A seasonal connection between the tropics and mid-latitudes. Geophys. Res. Lett., 28 , 39233926.

    • Search Google Scholar
    • Export Citation
  • Vimont, D. J., S. Battisti, and A. C. Hirst, 2003a: The seasonal footprinting mechanism in the CSIRO general circulation models. J. Climate, 16 , 26532667.

    • Search Google Scholar
    • Export Citation
  • Vimont, D. J., J. M. Wallace, and S. Battisti, 2003b: The seasonal footprinting mechanism in the Pacific: Implications for ENSO. J. Climate, 16 , 26682675.

    • Search Google Scholar
    • Export Citation
  • Wang, B., R. Wu, and R. Lukas, 1999: Roles of the western North Pacific wind variation in thermocline adjustment and ENSO phase transition. J. Meteor. Soc. Japan, 77 , 116.

    • Search Google Scholar
    • Export Citation
  • Wang, C., 2001: A unified oscillator model for the El Niño–Southern Oscillation. J. Climate, 14 , 98115.

  • Weisberg, R. H., and C. Z. Wang, 1997: A western Pacific oscillator paradigm for the El Niño–Southern Oscillation. Geophys. Res. Lett., 24 , 779782.

    • Search Google Scholar
    • Export Citation
  • Yu, L., R. A. Weller, and W. T. Liu, 2003: Case analysis of a role of ENSO in regulating the generation of westerly wind bursts in the Western Equatorial Pacific. J. Geophys. Res., 108 .3128, doi:10.1029/2002JC001498.

    • Search Google Scholar
    • Export Citation
  • Zheng, X. G., H. Nakamura, and J. A. Renwick, 2000: Potential predictability of seasonal means based on monthly time series of meteorological variables. J. Climate, 13 , 25912604.

    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    (a) Std dev of daily SLP anomalies from November to March, correlated with the January–March Niño-3.4 index for the following year. Shown here are correlation coefficients. Contour interval is 0.1; minimum contour is ±0.3 (approximately 95% confidence limit). Positive values are shaded. Box shows area-averaging region for the ICPI. (b) Time series of the mean January–March Niño-3.4 index (solid line) for the current year. Also shown is the time series of the boreal winter (November–March) intraseasonal std dev of the daily ICPI anomalies (crosses) shifted forward 12 months (i.e., the 1948 value is plotted in 1949). Daily ICPI anomalies are calculated from the normalized daily SLP anomalies averaged from 20° to 45°N, 150° to 175°W; see (a). Both time series are normalized by their respective interannual std devs.

  • Fig. 2.

    (a)–(l) Low-pass-filtered daily SLP anomalies correlated with the low-pass-filtered daily ICPI anomaly (see Fig. 1) for different lead/lags relative to the ICPI. Shown here are correlation coefficients. Contour interval is 0.15; minimum contour is ±0.15 (approximately 99% confidence limit using a bootstrapping methodology). Positive values are shaded. Sign convention is such that values are associated with low pressures over the ICPI region.

  • Fig. 3.

    (a)–(h) Low-pass-filtered daily surface wind stress anomalies correlated with the low-pass-filtered daily ICPI anomaly (see Fig. 1) for different lead/lags relative to the ICPI. Unit vector of r = 0.6 shown in upper-right corner of each panel. Sign convention is such that values are associated with low pressures over the ICPI region. (i) Seasonal-mean wind stress anomalies from November to March, correlated with the January–March Niño-3.4 index one year later. Unit vector of r = 0.6 shown in upper-right corner.

  • Fig. 4.

    (a)–(k) Low-pass-filtered daily SST anomalies correlated with the low-pass-filtered daily ICPI anomaly (see Fig. 1) for different lead/lags relative to the ICPI. Shown here are correlation coefficients. Contour interval is 0.10; minimum contour is ±0.15 (approximately 95% confidence limit using a bootstrapping methodology). Positive values are shaded. Sign convention such that values are associated with low pressures over the ICPI region. (l) Seasonal-mean SST anomalies from Nov to Mar, correlated with the Jan–Mar Niño-3.4 index one year later.

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