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  • View in gallery

    The time-mean structure of the convergence and divergence (s−1) in the SPCZ region from August 1999 to October 2009 for (a) the ERA-Interim data and (b) the QuikSCAT data. Missing data are shown in beige, and landmasses are brown. Positive values are divergence and negative values are convergence.

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    The January composite convergence anomaly (s−1) maps for (a) ERA-Interim and (b) QuikSCAT. July convergence anomaly maps for (c) ERA-Interim and (d) QuikSCAT.

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    Time series of (a) SPCZ convergence strength, (b) convergence area, (c) centroid latitude location, and (d) centroid longitude location for both QuikSCAT (black) and ERA-Interim (gray) data. The horizontal dotted line is the mean for the ERA-Interim data. The two horizontal dashed lines are one standard deviation from the mean value for the ERA-Interim data.

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    Time series of (a) seasonal cycle, (b) interannual anomaly, and (c) intraseasonal anomaly for SPCZ convergence strength for the ERA-Interim dataset. The dotted line is the mean. The two dashed lines are one standard deviation from the mean value.

  • View in gallery

    As in Fig. 4, but for SPCZ convergence area.

  • View in gallery

    As in Fig. 4, but for SPCZ latitudinal centroid.

  • View in gallery

    As in Fig. 4, but for SPCZ longitudinal centroid.

  • View in gallery

    (a) The solid black line is the ERA-Interim DJF-averaged convergence strength (s−1) in the SPCZ region as a function of year. The solid red line is the mean value, and the dashed red lines represent one standard deviation from the mean. (b) A scatterplot of the ERA-Interim DJF-averaged convergence strength (s−1) vs the Niño-3 DJF-averaged values. The yellow diamonds are recorded EP-type El Niño events, the blue circles are recorded CP-type El Niño events, and the yellow line is the best linear fit.

  • View in gallery

    (a) The mean DJF composite nonseasonal anomaly map of convergence strength (s−1) for strong El Niños from 1981 to 2014. (b) The mean DJF composite nonseasonal anomaly map of convergence strength (s−1) for weak El Niños from 1981 to 2014. The blue–red color scale shows all convergence and divergence values that are at least two standard errors from the mean. Locations that do not meet the criteria are masked in gray.

  • View in gallery

    (a) The mean DJF composite nonseasonal anomaly map of convergence strength (s−1) for EP type El Niños from 1981 to 2014. (b) The mean DJF composite nonseasonal anomaly map of convergence strength (s−1) for CP type El Niños from 1981 to 2014. The blue–red color scale shows all convergence and divergence values that are at least 2 standard errors from the mean. Locations that do not meet the criteria are masked in gray.

  • View in gallery

    (a) The DJF composite nonseasonal anomaly map of convergence strength (s−1) for 1991/92. (b) The DJF composite nonseasonal anomaly map of convergence strength (s−1) for 1997/98. The blue–red color scale shows all convergence and divergence values that are at least two standard errors from the mean. Locations that do not meet the criteria are masked in gray.

  • View in gallery

    (a) The mean DJF composite nonseasonal anomaly map of convergence strength (s−1) for all El Niños from 1981 to 2014. (b) The mean DJF composite nonseasonal anomaly map of convergence strength (s−1) for all La Niñas from 1981 to 2014. The blue–red color scale shows all convergence and divergence values that are at least two standard errors from the mean. Locations that do not meet the criteria are masked in gray.

  • View in gallery

    (a) The mean DJF composite nonseasonal anomaly map of convergence strength (s−1) from 1981 to 1998. (b) The mean DJF composite nonseasonal anomaly map of convergence strength (s−1) from 1998 to 2014. The blue–red color scale shows all convergence and divergence values that are at least two standard errors from the mean. Locations that do not meet the criteria are masked in gray.

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Characterization of the Variability of the South Pacific Convergence Zone Using Satellite and Reanalysis Wind Products

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  • 1 College of Earth, Ocean, and Environment, University of Delaware, Newark, Delaware
  • 2 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  • 3 Department of Oceanography, Pusan National University, Busan, South Korea
  • 4 College of Earth, Ocean, and Environment, University of Delaware, and University of Delaware/Xiamen University, Joint Institute of Coastal Research and Management, Newark, Delaware
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Abstract

The variability of the South Pacific convergence zone (SPCZ) is evaluated using ocean surface wind products derived from the atmospheric reanalysis ERA-Interim for the period of 1981–2014 and QuickSCAT for the period of 1999–2009. From these products, indices were developed to represent the SPCZ strength, area, and centroid location. Excellent agreement is found between the indices derived from the two wind products during the QuikSCAT period in terms of the spatiotemporal structures of the SPCZ. The longer ERA-Interim product is used to study the variations of SPCZ properties on intraseasonal, seasonal, interannual, and decadal time scales. The SPCZ strength, area, and centroid latitude have a dominant seasonal cycle. In contrast, the SPCZ centroid longitude is dominated by intraseasonal variability due to MJO influence. The SPCZ indices are all correlated with El Niño–Southern Oscillation indices. Interannual and intraseasonal variations of SPCZ strength during strong El Niño are approximately twice as large as the respective seasonal variations. SPCZ strength depends more on the intensity of El Niño rather than the central-Pacific versus eastern-Pacific type. The change from positive to negative Pacific decadal oscillation (PDO) around 1999 results in a westward shift of the SPCZ centroid longitude, a much smaller interannual swing in centroid latitude, and a decrease in SPCZ area. This study improves the understanding of the variations of the SPCZ on multiple time scales and reveals the variations of SPCZ strength not reported previously. The diagnostics analyses can be used to evaluate climate models to gauge their fidelity.

Corresponding author address: Autumn Kidwell, College of Earth, Ocean, and Environment, 215 Robinson Hall, University of Delaware, Newark, DE 19716. E-mail: akidwell@udel.edu; xiaohai@udel.edu

Abstract

The variability of the South Pacific convergence zone (SPCZ) is evaluated using ocean surface wind products derived from the atmospheric reanalysis ERA-Interim for the period of 1981–2014 and QuickSCAT for the period of 1999–2009. From these products, indices were developed to represent the SPCZ strength, area, and centroid location. Excellent agreement is found between the indices derived from the two wind products during the QuikSCAT period in terms of the spatiotemporal structures of the SPCZ. The longer ERA-Interim product is used to study the variations of SPCZ properties on intraseasonal, seasonal, interannual, and decadal time scales. The SPCZ strength, area, and centroid latitude have a dominant seasonal cycle. In contrast, the SPCZ centroid longitude is dominated by intraseasonal variability due to MJO influence. The SPCZ indices are all correlated with El Niño–Southern Oscillation indices. Interannual and intraseasonal variations of SPCZ strength during strong El Niño are approximately twice as large as the respective seasonal variations. SPCZ strength depends more on the intensity of El Niño rather than the central-Pacific versus eastern-Pacific type. The change from positive to negative Pacific decadal oscillation (PDO) around 1999 results in a westward shift of the SPCZ centroid longitude, a much smaller interannual swing in centroid latitude, and a decrease in SPCZ area. This study improves the understanding of the variations of the SPCZ on multiple time scales and reveals the variations of SPCZ strength not reported previously. The diagnostics analyses can be used to evaluate climate models to gauge their fidelity.

Corresponding author address: Autumn Kidwell, College of Earth, Ocean, and Environment, 215 Robinson Hall, University of Delaware, Newark, DE 19716. E-mail: akidwell@udel.edu; xiaohai@udel.edu

1. Introduction

The South Pacific convergence zone (SPCZ), a band of low-level convergence of winds and the resultant precipitation and cloudiness, extends from the western Pacific warm pool (WPWP) toward French Polynesia. The SPCZ is the largest and oft-occurring limb of the intertropical convergence zone (ITCZ). During austral summer, the SPCZ produces the largest band of rainfall worldwide. The SPCZ is unique as a convergence zone because of its latitudinal tilt. As a result, the SPCZ can cohabitate the tropics and the subtropics concurrently (Widlansky et al. 2011). The exact size, location, and formation of the SPCZ vary on different time scales. Variations of the wind stress convergence associated with the SPCZ can affect cloudiness and convection, the pattern and rate of precipitation (Cai et al. 2012; Murphy et al. 2014), and tropical cyclone (TC) genesis (e.g., Vincent et al. 2011). Variations of the wind stress curl (Ekman pumping) associated with the SPCZ can influence ocean circulation (Lee and Fukumori 2003; Capotondi 2008; Ganachaud et al. 2014), upper-ocean salinity and stratification (e.g., Delcroix and Hénin 1991; Hasson et al. 2013) in the South Pacific Ocean, and sea level variations (Qu et al. 2008; Widlansky et al. 2014).

An important source of SPCZ variations is El Niño–Southern Oscillation (ENSO). The SPCZ region tends to move northeast during austral summer associated with El Niño events and southwest during austral winter associated with La Niña events. An in-depth study of the SPCZ and ENSO shows a more complex picture. Juillet-Leclerc et al. (2006) studied the relationship between the SPCZ and ENSO from 1908 to 1997. By analyzing a coral skeleton from Fiji as a proxy for sparse sea surface temperature (SST) and sea surface salinity (SSS) measurements prior to 1975, they were able to show that the SPCZ had five migrations associated with El Niño events from 1908 to 1970 and at least the same number during the following 30 years. Their study highlighted that the increased frequency of El Niño events from the 1970s through the 1990s led to increased response in the SPCZ. Vincent et al. (2011) analyzed a 24-yr record of rainfall data from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data and the Global Precipitation Climatology Project (GPCP) product. The study highlights the interannual variability of the orientation of the SPCZ and its effect on TC genesis. During austral summer, the SPCZ band generally has a northwest–southeast orientation. During three austral summers, the SPCZ was zonally oriented. These austral summers were associated with the mature stage of the large El Niño events in 1982/83 and 1997/98 and the weaker El Niño in 1991/1992. The change of the orientation of the SPCZ precipitates a shift in TC location.

The zonally oriented SPCZ that occurred during the three El Niño events were also the subject of a study by Borlace et al. (2014). They refer to the location change as an “extreme swing” and the new orientation as a zonal SPCZ. The study shows how ENSO diversity (i.e., the diversity in ENSO characteristics) influences the SPCZ. ENSO events are often classified into the eastern-Pacific (EP) and central-Pacific (CP) types (e.g., Ashok and Yamagata 2009; Kao and Yu 2009; Kug et al. 2010), although the binary classification might be considered a simplification of a broad spectrum of ENSO diversity and dynamics (Capotondi et al. 2015; Kidwell et al. 2014; Takahashi and Dewitte 2015). Borlace et al. (2014) concluded that zonal SPCZ events could only occur during a strong EP El Niño, regardless of EP or CP classification. The increased occurrence of extreme SPCZ swings, associated with strong El Niño events, is projected to increase under a greenhouse warming scenario that is associated with an increase in extreme El Niño events (Cai et al. 2012; Borlace et al. 2014; Cai et al. 2014).

The SPCZ has also been studied in terms of its visual presence in geostationary satellite images by Haffke and Magnusdottir (2013), who developed SPCZ labels from a statistical model to identify the presence of the SPCZ in individual images. The statistical model captured the range of SPCZ spatiotemporal variability. Haffke and Magnusdottir (2013) used the SPCZ labels to characterize the location and evolution of the SPCZ. They described the SPCZ as having two main axis lines: one in the tropics and another in the subtropics.

The relation between the SPCZ and the interdecadal Pacific oscillation (IPO) and the Pacific decadal oscillation (PDO) has also been noted (Folland et al. 2002). The PDO is the pattern of ocean–atmospheric climate variability on decadal time scales poleward of 20°N (Mantua et al. 1997). The Pacific-wide expression of the PDO is referred to as the IPO (Folland et al. 2002). Folland et al. (2002) used the mean sea level pressure data as a proxy for the SPCZ location and tracked how the IPO influenced the SPCZ over the past 100 years. They noted that, during the warm phase of both the IPO and Southern Oscillation index (SOI), the location of the maximum of the SPCZ convergence moves northeast.

While recent studies have highlighted certain aspects of the SPCZ, including the variations in its position, tilt, and size (area), the variability of the strength of the SPCZ (typically associated with clouds and rains) has not been adequately documented. Zheng et al. (1997) used three years of vector winds derived from the ERS-1 scatterometer to describe the seasonal variability of the SPCZ strength qualitatively, noting the pronounced seasonal variations as well as interannual changes during the 3-yr period of the study. No quantitative analysis was performed in terms of the SPCZ strength. The short temporal record of the data used in that study is inadequate to characterize the interannual variability. Moreover, intraseasonal variability was not examined. The strength of the SPCZ could have important implications, since the SPCZ is the largest rainband in the Southern Hemisphere and can affect not only the regional climate of northeastern Australia and its neighboring islands but also cyclogenesis. A relatively small change in the SPCZ strength and location can have severe ecological and socioeconomic impacts (Vincent et al. 2011; Glynn et al. 1991). Our study differs from previous studies by utilizing wind convergence rather than precipitation estimates (Cai et al. 2012; Borlace et al. 2014). Wind convergence is what drives convection, cloud, and precipitation in the SPCZ region, so it is arguably more fundamental to diagnose the physics of the SPCZ. The objectives of this study are to examine the variations of SPCZ strength on intraseasonal, seasonal, interannual, and decadal time scales based on wind convergence estimates, to contrast these variations with those of SPCZ area and centroid location, and to investigate the relationships with various climate modes in the tropical Pacific sector. The investigation is highly relevant to the atmospheric aspects of the SPCZ because wind stress convergence affects atmospheric convection, cloud, and rain. The related variation of wind stress curl that forces ocean circulation will be examined in a future study that addresses the oceanic response.

This paper is organized as follows. The atmospheric reanalysis and satellite-based wind products used to estimate the properties (strength, area, and centroid location) of the SPCZ are described in section 2. In section 3, we first evaluate the SPCZ properties estimated from an atmospheric reanalysis wind product using the corresponding estimates inferred from a satellite scatterometer wind product, illustrating the consistency between the two during the satellite observation period (section 3a). We then use the atmospheric reanalysis product that has a longer temporal record to examine the intraseasonal, seasonal, interannual, and decadal variations of the SPCZ properties (section 3b). The relationships of the SPCZ variability with interannual and decadal climate modes in the tropical Pacific sector are discussed in section 3c. The findings are summarized in section 4.

2. Data and methodology

In this study, we use ocean surface wind convergence to infer SPCZ properties, including strength, area, and centroid longitude and latitude. The surface wind convergence was calculated using two datasets: the atmospheric reanalysis ERA-Interim (Dee et al. 2011) and a wind product based on the satellite scatterometer measurements from QuikSCAT. QuikSCAT is a satellite mission of the National Aeronautics and Space Administration (NASA) that measured ocean surface vector winds during the period of August 1999–October 2009.

The ERA-Interim product is a second-generation atmospheric reanalysis produced by the ECMWF. The monthly wind vectors were used for the analysis (ECMWF 2006). The ERA-Interim assimilated ocean surface wind measurements from multiple scatterometers, including ERS-1, ERS-2, and QuikSCAT, as well as a suite of other atmospheric observations. The gridded dataset has a 0.75° spatial resolution. The dataset spans the period from 1979 to the present. The ERA-Interim product has many improvements in the depiction of atmospheric circulation and boundary layer processes compared to the previous generation of reanalysis products (Dee et al. 2011).

Scatterometer observations of ocean-surface vector winds, such as those from QuikSCAT, have provided valuable resources to evaluate atmospheric reanalysis products. The QuikSCAT product used in the analysis is the monthly level-3 product for the period from August 1999 to October 2009, gridded at 1° spatial resolution. The product is distributed by the Physical Oceanography Distributed Active Archive Center (PODAAC) (NASA/PODAAC 2012). The QuikSCAT data are used to evaluate the representation by the ERA-Interim product in terms of the time-mean structure and variations of the SPCZ properties. After establishing the consistency between the two products in terms of SPCZ properties, the longer ERA-Interim product is then used to examine the temporal variations of the SPCZ properties and to investigate the relationships with various climate modes in the tropical Pacific sector, such as the Madden–Julian oscillation, El Niño–Southern Oscillation, and Pacific decadal oscillation.

For this study, the SPCZ region is encompassed by 0°–30°S, 130°E–110°W. The strength of the SPCZ is defined by the surface wind convergence in this region derived from the gridded wind vector product. Given the wind velocity, the divergence or convergence of the wind can be estimated based on the following definition (Zheng et al. 1997; Liu and Xie 2002):
e1
where u and υ are the zonal and meridional components of the surface winds. Positive D corresponds to surface divergence, and a negative value corresponds to surface convergence. The SPCZ strength is defined by the monthly mean, area-weighted average value of convergence within the SPCZ region described above:
e2
where a(x, y) is the area of a grid cell centered at location (x, y), and the spatial summation ∑ is performed over grid cells with D(x, y) < 0 within the SPCZ region. We also examine the total area of convergence, defined as spatial integration of the convergence area within the SPCZ region:
e3
where the spatial summation is performed over grid cells in the SPCZ region associated with D(x, y) < 0.
The centroid location of the SPCZ is described by the following:
e4
e5
where xi is the zonal location of a grid cell, yi is the meridional location of a grid cell, and n represents the number of grid cells being averaged that are associated with convergence (D < 0).

The times series were decomposed into seasonal, interannual, and intraseasonal components. The seasonal anomalies are the monthly climatology referenced to the time mean. The nonseasonal anomalies are the differences between the total anomalies and seasonal anomalies. The nonseasonal anomalies, smoothed by a 1-yr running average filter, yield the interannual anomalies. The difference between the nonseasonal anomalies and the interannual anomalies gives the intraseasonal anomalies. The interannual anomalies also contain decadal variations, although the decadal component is not substantial and is detailed in section 3b.

To compare the SPCZ indices with climate indices of various time scales, we utilize the Pearson correlation coefficient R:
e6
where A and B are the two variables being correlated. The number of observations is N, and μ and σ are the mean and standard deviation of the variables, respectively. To determine the statistical significance of each correlation, we calculate the probability P that there is not a relationship between the data using the Student’s t distribution:
e7
The Student’s t distribution with υ degrees of freedom has a cumulative distribution function (CDF):
e8
where Γ(⋅) is the Gamma function; P is the probability that a single observation from the t distribution with υ degrees of freedom will fall in the interval [−∞, t]. In general, υ is equivalent to N − 2; however, to account for the various time scale filters, each variable has a υ scaled by the filter length:
e9

3. Results

a. QuikSCAT and ERA-Interim comparison

The time-mean structures of convergence and divergence (CD) in the SPCZ region from ERA-Interim and QuikSCAT are shown in Fig. 1, with positive (negative) values indicating divergence (convergence). The ERA-Interim data (Fig. 1a) and the QuikSCAT data (Fig. 1b) were averaged from August 1999 to October 2009, the period over which QuikSCAT delivered vector wind measurements. The time-mean structures for the two datasets show similar large-scale structure. For instance, both datasets capture the tropical and subtropical regimes of the SPCZ as described by Haffke and Magnusdottir (2013). The transition of the tropical and subtropical regimes occurs near 140°W, with the subtropical extension of the SPCZ associated with weaker convergence and having a larger tilt along the northwest–southeast direction.

Fig. 1.
Fig. 1.

The time-mean structure of the convergence and divergence (s−1) in the SPCZ region from August 1999 to October 2009 for (a) the ERA-Interim data and (b) the QuikSCAT data. Missing data are shown in beige, and landmasses are brown. Positive values are divergence and negative values are convergence.

Citation: Journal of Climate 29, 5; 10.1175/JCLI-D-15-0536.1

Figure 2 shows the convergence strength anomaly composites for January and July (i.e., average for the same month of different years during the QuikSCAT period, referenced to the time mean). The SPCZ convergence is stronger during boreal winter (e.g., January; Figs. 2a,b) and weaker during boreal summer (e.g., July; Figs. 2c,d), as evident from the more negative values of convergence anomaly in January. These features are also very similar between ERA-Interim (Figs. 2a,c) and QuikSCAT (Figs. 2b,d).

Fig. 2.
Fig. 2.

The January composite convergence anomaly (s−1) maps for (a) ERA-Interim and (b) QuikSCAT. July convergence anomaly maps for (c) ERA-Interim and (d) QuikSCAT.

Citation: Journal of Climate 29, 5; 10.1175/JCLI-D-15-0536.1

Figure 3 shows the time series of the strength, area, latitudinal SPCZ centroid, and longitudinal SPCZ centroid for both QuikSCAT and ERA-Interim datasets. A more negative SPCZ strength value denotes a stronger convergence in the SPCZ region (Fig. 3a). The QuikSCAT and ERA-Interim show remarkably similar temporal variations in strength, area, and centroid location. The strong similarities are quantified in Table 1, which shows the mean and standard deviation values for each of the SPCZ properties.

Fig. 3.
Fig. 3.

Time series of (a) SPCZ convergence strength, (b) convergence area, (c) centroid latitude location, and (d) centroid longitude location for both QuikSCAT (black) and ERA-Interim (gray) data. The horizontal dotted line is the mean for the ERA-Interim data. The two horizontal dashed lines are one standard deviation from the mean value for the ERA-Interim data.

Citation: Journal of Climate 29, 5; 10.1175/JCLI-D-15-0536.1

Table 1.

Time mean and standard deviation (SD) of various SPCZ diagnostic parameters for ERA-Interim and QuikSCAT.

Table 1.

Although ERA-Interim assimilated the QuikSCAT data, the similarities between ERA-Interim and QuikSCAT for the various diagnostics of the SPCZ are not trivial. This is because a biased atmospheric model could have rejected the QuikSCAT data during the assimilation. The consistency between ERA-Interim and QuikSCAT gives better confidence in using the ERA-Interim product over a longer period (than QuikSCAT) to study the SPCZ changes.

b. Temporal variability of the SPCZ

Visual inspection of Fig. 3 suggests that, for the convergence strength time series (Fig. 3a), the maximum range of interannual swing is larger than that of seasonal variation. The convergence area time series (Fig. 3b) is dominated by seasonal variations. For longitude and latitude of the SPCZ centroid (Figs. 3c,d), there are significant intraseasonal variations, especially for the latitude of the centroid.

To further examine the relative variations of seasonal versus nonseasonal variations (i.e., deviations from the averaged seasonal cycle), the time series of the variability of the strength, area, and centroid location for ERA-Interim were decomposed into their seasonal, interannual, and intraseasonal components, as described in section 2. The seasonal, interannual, and intraseasonal anomalies for the strength, area, latitudinal centroid location, and longitudinal centroid location are shown in Figs. 4, 5, 6, and 7, respectively. Table 2 shows the percentage of variance explained by the seasonal and nonseasonal variations and a breakdown of the nonseasonal variance into intraseasonal and interannual variance.

Fig. 4.
Fig. 4.

Time series of (a) seasonal cycle, (b) interannual anomaly, and (c) intraseasonal anomaly for SPCZ convergence strength for the ERA-Interim dataset. The dotted line is the mean. The two dashed lines are one standard deviation from the mean value.

Citation: Journal of Climate 29, 5; 10.1175/JCLI-D-15-0536.1

Fig. 5.
Fig. 5.

As in Fig. 4, but for SPCZ convergence area.

Citation: Journal of Climate 29, 5; 10.1175/JCLI-D-15-0536.1

Fig. 6.
Fig. 6.

As in Fig. 4, but for SPCZ latitudinal centroid.

Citation: Journal of Climate 29, 5; 10.1175/JCLI-D-15-0536.1

Fig. 7.
Fig. 7.

As in Fig. 4, but for SPCZ longitudinal centroid.

Citation: Journal of Climate 29, 5; 10.1175/JCLI-D-15-0536.1

Table 2.

Variance of the convergence strength, area, and centroid location for the ERA-Interim (ERA) data during the overlapping period of 1999–2009.

Table 2.

Figures 47 and Table 2 together show that the relative contributions from different time scales vary with SPCZ parameters. SPCZ area and centroid latitude are dominated by seasonal variability, especially for the former, with nearly 90% of the variance associated with the seasonal anomalies. The latitudinal and longitudinal centroid components have strikingly different seasonal constituents. The latitudinal component of the centroid location has a seasonal variance of 67%, and the longitudinal component of the centroid location has a seasonal variance of 23%. SPCZ longitude is the only parameter where nonseasonal variability is dominant (mostly owing to intraseasonal variability). The SPCZ strength and longitudinal component of the centroid have intraseasonal variance components of 38% and 51%, respectively. The latitudinal component of the centroid has an intraseasonal component of 26% of the total variance. For SPCZ strength, the seasonal and nonseasonal components have comparable percentages of variance, with the latter mostly accounted for by intraseasonal variability.

The seasonal cycle (Fig. 4) of the SPCZ strength has peak convergence in January and February. Likewise, the SPCZ convergence area (Fig. 5) is the largest during Northern Hemisphere (NH) winter and Southern Hemisphere (SH) summer months. The latitudinal centroid component (Fig. 6) moves south in the NH (SH) winter (summer) months and toward the equator in the boreal (austral) summer (winter). Unlike the other SPCZ indices, the longitudinal centroid component (Fig. 7) exhibits a semiannual east–west migration. The annual signal of the longitudinal component of the variance is less than 25%, suggesting that most of the east–west movement is associated with the semiannual signal. This semiannual signal peaks in December–January and June–July. Semiannual variations of wind stress curl associated with the SPCZ and the related Rossby wave propagations are known to cause semiannual variations of sea level in the southwest tropical Pacific (Qu et al. 2008).

The intraseasonal variability is related to phenomena such as the Madden–Julian oscillation (MJO) (Matthews 2000; Madden and Julian 1971). The intraseasonal variability of each diagnostic parameter supports a relationship to the MJO. The application of a 3-month running average filter reduces each of the diagnostic parameters by at least 40% of their variance, thus indicating the importance of MJO time scales. To further explore this relationship, we compared the intraseasonal component of each SPCZ parameter with the Real-Time Multivariate MJO index (Wheeler and Hendon 2004). This index is a seasonally independent index for monitoring the MJO based upon the first two leading empirical orthogonal functions (EOFs) of the combined fields of near-equatorially averaged 850-hPa zonal wind, 200-hPa zonal wind, and outgoing longwave radiation (OLR) data. The two principal components (PCs) are called the Real-Time Multivariate MJO series 1 (RMM1) and 2 (RMM2). The results of the correlation study are shown in Table 3. Each parameter has a small yet statistically significant correlation with at least one of the MJO indices.

Table 3.

The ERA-Interim-derived weighted convergence strength, convergence area, and centroid location are correlated with Niño-3, Niño-3.4, Niño-4, and SOI, as shown. All R values have an associated P value in parentheses and R values with P values less than 0.05 are in boldface.

Table 3.

Although the percentage of variance explained by the interannual and longer variability is generally smaller than that of the seasonal cycle for the different parameters of the SPCZ, it does not necessarily mean that interannual and longer variability is not important. Interannual anomalies persist over longer time periods than seasonal and intraseasonal anomalies. Moreover, the magnitude of certain interannual events can be comparable to or larger than the magnitude of seasonal variability. These anomalous events account for the large maximum range of the interannual swing relative to the dominant seasonal variation. For example, the magnitudes of the interannual and intraseasonal anomalies of SPCZ strength during the 1997/98 El Niño are 9.0 × 10−7 and 12.0 × 10−7 s−1, which are twice as large as the magnitude of the seasonal variation of the SPCZ strength (5.0 × 10−7 s−1).

Decadal and longer variations are also evident for the SPCZ area and centroid longitude/latitude. The SPCZ centroid latitude has notably smaller fluctuations and a southward shift after 1999 (Fig. 6b). There is a westward shift of the centroid longitude after 1999 (Fig. 7b). There is also a decreasing SPCZ area after 1999 (Fig. 5b). These trends are consistent with the La Niña–like mean conditions after 1999 and the increased frequency of CP-type El Niños (Kug et al. 2010; Capotondi 2013). The nature of this interannual (and longer) variability is discussed in section 3c in association with various climate modes.

c. Relationships to interannual and decadal climate modes

In this section, we discuss the relations of the SPCZ parameters with interannual and decadal climate modes in the Pacific sector. For the interannual variability, the largest variations are in association with the largest El Niño events. Of the diagnostic parameters’ intraseasonal variabilities, only the intraseasonal component of SPCZ strength has peaks in magnitude associated with the largest El Niño events. During the strong El Niño events in 1982/83 and 1997/98, both interannual and intraseasonal anomalies of convergence strength are particularly large (Figs. 4b,c). These are associated with heavy rainfall under the SPCZ, such as near French Polynesia (e.g., Cai et al. 2012). To examine the linkage between SPCZ strength and ENSO, we calculated the correlation coefficients and P values of the interannual components of SPCZ strength, area, and centroid location with various climate indices associated with El Niño (Table 3).

The convergence strength of the SPCZ has a significant negative linear relationship with the Niño-3 index, with a correlation coefficient of −0.49. Physically, this means that a stronger El Niño is associated with a stronger convergence strength (more negative values). This is further explained later on in relation to Figs. 8 and 9. The convergence strength also has a strong (R = 0.45) but statistically significant correlation to the SOI. The SPCZ convergence area has the weakest relationship with Niño-3 and Niño-4, with R values (P values) of 0.09 (0.63) and −0.2 (0.22), respectively. The latitude component has a very strong correlation with all of the listed ENSO-related climate indices. Similarly, the longitude component of the centroid location has a significant correlation with the interannual climate mode indices, but the strongest correlation is with the SOI (R = −0.72), a widely used ENSO index.

Fig. 8.
Fig. 8.

(a) The solid black line is the ERA-Interim DJF-averaged convergence strength (s−1) in the SPCZ region as a function of year. The solid red line is the mean value, and the dashed red lines represent one standard deviation from the mean. (b) A scatterplot of the ERA-Interim DJF-averaged convergence strength (s−1) vs the Niño-3 DJF-averaged values. The yellow diamonds are recorded EP-type El Niño events, the blue circles are recorded CP-type El Niño events, and the yellow line is the best linear fit.

Citation: Journal of Climate 29, 5; 10.1175/JCLI-D-15-0536.1

Fig. 9.
Fig. 9.

(a) The mean DJF composite nonseasonal anomaly map of convergence strength (s−1) for strong El Niños from 1981 to 2014. (b) The mean DJF composite nonseasonal anomaly map of convergence strength (s−1) for weak El Niños from 1981 to 2014. The blue–red color scale shows all convergence and divergence values that are at least two standard errors from the mean. Locations that do not meet the criteria are masked in gray.

Citation: Journal of Climate 29, 5; 10.1175/JCLI-D-15-0536.1

To further examine the association of SPCZ strength with El Niño, the December–February (DJF) average of convergence strength is presented in Fig. 8a. The DJF average, as the peak season for SPCZ, is a good indicator of SPCZ behavior. In the figure, El Niño events are marked by yellow diamonds and blue circles, corresponding to EP- and CP-type El Niños, as defined by Yu et al. (2012). The strongest convergence is found to be associated with the strong El Niño events in 1982/83 and 1997/98, both being EP-type events. The other EP El Niño events, the weaker 1987/88 and 2006/07 events, are associated with convergence values closer to the mean. All CP-type El Niños are connected with convergence strength values that are within one standard deviation of the mean DJF convergence strength. This is further demonstrated by a scatterplot of the DJF convergence strength versus the DJF Niño-3 values (Fig. 8b). The weak and moderate El Niño events are clustered closer to the best-fit linear relationship, while the two anomalous stronger events are clear outliers from the group. CP-type El Niños are typically weak-to-moderate El Niño events (Yu et al. 2011). Therefore, the magnitude of the El Niño is more important than the longitudinal location of the event.

These strong and weak El Niños are associated with different SPCZ spatial structures (Fig. 9). The map of the convergence strength for the strongest El Niños (greater than one standard deviation from the mean convergence strength value) has a large equatorial branch of the SPCZ that extends nearly the entire zonal extent of the basin from 150°E to 110°W, and a smaller subtropical branch stretches diagonally from 12°S, 160°W to 26°S, 110°W. The equatorial branch has a convergence strength exceeding a magnitude of 3 × 10−6 s−1. The subtropical branch is weaker, with a magnitude of 2 × 10−6 s−1. The weak El Niños (all events within one standard deviation of the mean convergence strength value) have a lower magnitude of convergence in the equatorial branch (2 × 10−6 s−1). The equatorial branch also spans a smaller range of longitudes and is confined to the western-central part of the basin (150°E–160°W). During weaker El Niños, the subtropical branch of the SPCZ stretches diagonally from 6°S, 180° to 26°S, 110°W. While the subtropical branch of the SPCZ has a larger coverage area during the weaker El Niños, its magnitude is much lower (<1 × 10−6 s−1). The basinwide convergence pattern for strong El Niño (Fig. 9a) and western-central convergence pattern for weak El Niño (Fig. 9b) correspond to similar patterns of OLR, as reported by Zou et al. (2014) and Chiodi and Harrison (2015).

In terms of the spatial structure of the SPCZ convergence strength, there are only a few differences between the maps for strong El Niños and EP-type El Niños (Figs. 9a and 10a) and the maps for weak El Niños and CP-type El Niños (Figs. 9b and 10b). Of the nine El Niño events, four were labeled as EP-type El Niños and five were labeled as CP-type El Niños. The composite EP-type and CP-type El Niño convergence strength nonseasonal anomaly maps are shown in Figs. 10a and 10b, respectively. The map of strong El Niño convergence strength has a larger equatorial branch, and the subtropical branch has a more latitudinal tilt than EP-type convergence strength nonseasonal anomaly map. The weak El Niño and CP-type El Niño convergence strength nonseasonal anomaly maps have similar equatorial SPCZ branches (magnitudes of 2 × 10−6 s−1) and subtropical branches (magnitudes <1 × 10−6 s−1). The stronger equatorial convergence strength during EP-type and strong El Niños is in good agreement with previous studies that indicate a zonal equatorward SPCZ swing (Vincent et al. 2011; Borlace et al. 2014). Our method illustrates the differences between zonal SPCZ events. Zonal SPCZ events occurred during the El Niño events of 1991/92 and 1997/98. In Fig. 11, we show the convergence strength anomaly during the 1991/92 event (Fig. 11a) and the 1997/98 event (Fig. 11b). The 1991/92 zonal SPCZ event occurred during a weaker El Niño and has a narrower band of equatorial convergence with fewer significant equatorial anomalies (Fig. 11a). The SPCZ convergence strength, with a larger equatorial presence during stronger El Niño events, is associated with a below-normal sea surface height anomaly and more persistent cyclogenesis (Widlansky et al. 2014).

Fig. 10.
Fig. 10.

(a) The mean DJF composite nonseasonal anomaly map of convergence strength (s−1) for EP type El Niños from 1981 to 2014. (b) The mean DJF composite nonseasonal anomaly map of convergence strength (s−1) for CP type El Niños from 1981 to 2014. The blue–red color scale shows all convergence and divergence values that are at least 2 standard errors from the mean. Locations that do not meet the criteria are masked in gray.

Citation: Journal of Climate 29, 5; 10.1175/JCLI-D-15-0536.1

Fig. 11.
Fig. 11.

(a) The DJF composite nonseasonal anomaly map of convergence strength (s−1) for 1991/92. (b) The DJF composite nonseasonal anomaly map of convergence strength (s−1) for 1997/98. The blue–red color scale shows all convergence and divergence values that are at least two standard errors from the mean. Locations that do not meet the criteria are masked in gray.

Citation: Journal of Climate 29, 5; 10.1175/JCLI-D-15-0536.1

We next examine the difference of the spatial pattern of convergence between El Niño and La Niña. The El Niño/La Niña classification is derived from the oceanic Niño index. The oceanic Niño index (ONI) is a standard used by NOAA to identify El Niño strength based upon the SST anomaly in the Niño-3.4 region (5°N–5°S, 120°–170°W; http://www.cpc.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml). From 1981 to 2014, 9 El Niño and 10 La Niña events occurred. Figure 12 shows the composite El Niño and La Niña convergence strength nonseasonal DJF anomaly maps. The convergence composite for El Niño events is associated with a band of strong convergence spanning the equator with a diagonal branch of convergence extending from the tropical to the subtropical South Pacific east of 160°W. The La Niña SPCZ strength composite map shows a reversal of the convergence and divergence patterns from the El Niño composite, with weak convergence in the west extending from the Solomon Island chain toward the southeast into the subtropics.

Fig. 12.
Fig. 12.

(a) The mean DJF composite nonseasonal anomaly map of convergence strength (s−1) for all El Niños from 1981 to 2014. (b) The mean DJF composite nonseasonal anomaly map of convergence strength (s−1) for all La Niñas from 1981 to 2014. The blue–red color scale shows all convergence and divergence values that are at least two standard errors from the mean. Locations that do not meet the criteria are masked in gray.

Citation: Journal of Climate 29, 5; 10.1175/JCLI-D-15-0536.1

The SPCZ parameters show a weak-to-moderate correlation with the PDO, as shown in Table 3. The PDO index is the leading PC of monthly SST anomalies in the North Pacific Ocean, poleward of 20°N (Zhang et al. 1997; Mantua et al. 1997). From 1982 to 1998, the PDO was mostly positive. From 1999 to 2014, the PDO has been strongly negative. Positive/negative PDO phases are associated with weaker/stronger trade winds in the tropical Pacific coupled to warmer/colder SST in the eastern-central equatorial Pacific, which is an important factor in the longitudinal position of the convergence. The SPCZ convergence strength and convergence area have PDO correlations (P values) of −0.503 (0.50) and 0.536 (0.46), respectively. While the correlations for these parameters are high enough to suggest a moderate relationship with the PDO, the correlation alone should not be used to define a strong relationship because of the large P values that result from having too few degrees of freedom. The centroid longitude has migrated westward from the period of 1982–98 to 1999–2014, associated with the switch of the PDO from positive to negative phase. The SPCZ latitude centroid (Fig. 6b) has less variance and an overall southward shift during the negative PDO phase than it did during the preceding positive PDO phase. The convergence area appears to decrease during the negative PDO phase. The differences during the positive and negative PDO phases for SPCZ centroid location and area can be understood as the following. During negative PDO phase, the easterly trade wind in the tropical Pacific is stronger. The stronger trade wind pushes the convective region and thus the SPCZ westward, leading to the more westward centroid longitude. During the negative PDO and westward shift of the SPCZ, meridional swings on interannual time scales are more limited (as is evident from the weaker interannual variation during the negative PDO period) because the tropical branch of the SPCZ is more constrained by the landmasses in the southwestern Pacific, such as Papua New Guinea. The SPCZ area also decreases when the SPCZ is located farther to the west.

We also investigate the composite nonseasonal DJF-average strength anomaly for the positive and negative PDO periods (i.e., 1982–98 and 1999–2014), presented in Figs. 13a and 13b, respectively. These composite maps for the two PDO phases show opposing patterns of convergence and divergence in the SPCZ region. The positive PDO composite is similar to the El Niño composite with equatorial convergence, but with smaller magnitudes. The negative PDO composite is similar to the La Niña composite with a feature of equatorial divergence, with smaller magnitudes than that of the La Niña composite. The major difference between the interannual and decadal composites is the large convergence (negative values) near the equator in the El Niño composite anomaly (Fig. 12a) and lack of decadal convergence anomaly (Fig. 13a) near the equator. For the El Niño composite, the large convergence near the equator was due to the basinwide pattern of the convergence associated with strong El Niño and the convergence in the western-central equatorial Pacific for weaker El Niño. The lack of an equatorial convergence anomaly on decadal time scales is likely a result of the weaker decadal SST changes in comparison with the interannual SST changes in the equatorial zone. In other words, the wind–SST coupling in interannual time scales is more active than that for the decadal time scale near the equatorial zone.

Fig. 13.
Fig. 13.

(a) The mean DJF composite nonseasonal anomaly map of convergence strength (s−1) from 1981 to 1998. (b) The mean DJF composite nonseasonal anomaly map of convergence strength (s−1) from 1998 to 2014. The blue–red color scale shows all convergence and divergence values that are at least two standard errors from the mean. Locations that do not meet the criteria are masked in gray.

Citation: Journal of Climate 29, 5; 10.1175/JCLI-D-15-0536.1

4. Summary and discussion

Ocean surface wind products from the atmospheric reanalysis ERA-Interim during 1981–2014 and scatterometer data from QuikSCAT during 1999–2009 are used to examine the variability of SPCZ strength, area, and centroid location. Previous studies have focused on using a combination of outgoing longwave radiation (OLR) data, precipitation data, and SST (e.g., Widlansky et al. 2011; Matthews 2012). While these studies have provided many insights into the dynamics of the SPCZ, our study presents a more systematic analysis of various SPCZ characteristics from intraseasonal to decadal time scales. The SPCZ characteristics inferred from the ERA-Interim product were found to be consistent with those inferred from QuikSCAT during their overlapping period (1999–2009). The ERA-Interim product is then used to examine the seasonal, interannual, and intraseasonal variations of the SPCZ characteristics.

The variations of SPCZ strength, area, and centroid latitude are dominated by seasonal variability, with the SPCZ area having the strongest seasonal variation. Of these three parameters, the seasonal variations of SPCZ strength and area are primarily associated with the annual cycle. In contrast, the seasonal variation of the SPCZ centroid latitude is dominated by semiannual signal. The SPCZ centroid longitude is the only parameter where the temporal variability is not dominated by seasonal, but by intraseasonal variations. This is consistent with the near-zonal propagation of the MJO pattern. The parameter that has the second strongest intraseasonal variability is the SPCZ strength, with a variance comparable to that of seasonal variability.

Interannual variations of the SPCZ parameters in general have much smaller variance than those on seasonal and intraseasonal time scales. However, the maximum interannual changes for SPCZ strength associated with strong El Niño events are twice as large as the magnitude of seasonal variation. The magnitude of intraseasonal variation of convergence strength during strong El Niño events also exceeds that of seasonal variation, reflecting the influence of a more active MJO during strong El Niño events. The interannual variations of all SPCZ parameters are correlated with ENSO indices. The SPCZ strength depends more on the intensity than on the type of El Niño (i.e., EP vs CP type). However, the strongest El Niño events have been EP-type El Niños, so the distinction between a strong (weak) El Niño and an EP-type (CP type) El Niño is not that dramatic. While there are a limited number of El Niño events in the data record, there is clear evidence of a different response in the SPCZ associated with strong (weak) or EP-type (CP type) El Niño. Borlace et al. (2014) also noted that, via temperature gradients in the SPCZ, zonal SPCZ (zSPCZ) events concurred with EP warming. Van der Wiel et al. (2015) corroborated this in their study that identified a zonally asymmetric SST distribution as the necessary condition for a diagonally oriented SPCZ. Our parameters show that, during reduced SST gradients (i.e., during strong El Niños), there is not only a zSPCZ event, but there is also a larger convergence strength and convergence area. Our results are consistent with the findings by Folland et al. (2002) in terms of the importance of the PDO on the SPCZ, in that analysis of our SPCZ indices shows supporting decadal variations, albeit with a low statistical confidence. During the negative PDO phase (1999–2014), the SPCZ centroid longitude is farther to the west, the SPCZ centroid latitude exhibits fewer interannual swings, and the SPCZ area appears to be smaller compared to those during the positive PDO period (1982–98). These changes during the negative PDO period can be understood as the result of the SPCZ being confined to the western part of the ocean basin by the stronger trade wind, thus limiting its interannual meridional swings and reducing its area.

Our study improves the understanding of the variations of SPCZ characteristics through a systematic investigation of various SPCZ characteristics on intraseasonal, seasonal, interannual, and decadal time scales. In particular, we have examined the SPCZ strength, an aspect that has not been adequately addressed before, except during strong El Niño events. While previous studies have noted the changes in the SPCZ location in conjunction with the PDO before the 1990s (Folland et al. 2002), we documented the changes of the SPCZ characteristics associated with the change of PDO phase around 1999. Climate models in general have difficulty reproducing some of the important features of the SPCZ (IPCC 2001; Jianglong et al. 2004; Ganachaud et al. 2007; Brown et al. 2011; Niznik and Lintner 2013; Brown et al. 2013). A better understanding of the SPCZ dynamics is necessary to reduce the uncertainty of climate change projection associated with the SPCZ (Widlansky et al. 2013). The diagnostics of the different SPCZ parameters described in this study can also be used to evaluate climate models and to assess the reliability of the projected SPCZ changes in response to climate change.

Acknowledgments

We thank the Delaware Space Grant College and Fellowship Program (NASA Grant NNX15AI19H) for financial support. This research was, in part, carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. This study was also carried out with the support of “SaTellite remote sensing on west Antarctic ocean Research: STAR” (Project PE14040) of the Korea Polar Research Institute, Republic of Korea. This research was partially supported by the Natural Science Foundation of China (NSFC-41476007). We thank E. Liao, J. Marks, and M. Shatley for Technical Support and Trouble Shooting (TSTS).

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