Abstract

Distinct pattern of interannual variability in sea surface temperature (SST) in the South Pacific [i.e., the South Pacific subtropical dipole (SPSD)] is examined using outputs from a coupled general circulation model. The SPSD appears as the second empirical orthogonal function (EOF) mode of the SST anomalies in the South Pacific and is associated with a northeast–southwest-oriented dipole of positive and negative SST anomalies in the central basin. The positive and negative SST anomaly poles start to develop during austral spring, reach their peak during austral summer, and gradually decay afterward. Close examination of mixed-layer heat balance yields that the SST anomaly poles develop mainly because warming of the mixed layer by shortwave radiation is modulated by the anomalous mixed-layer thickness. Over the positive (negative) pole, the mixed layer becomes thinner (thicker) than normal and acts to enhance (reduce) the warming of the mixed layer by climatological shortwave radiation. This thinner (thicker) mixed layer may be related to the suppressed (enhanced) evaporation associated with the overlying sea level pressure (SLP) anomalies. Weaker-than-normal surface wind also contributes to the thinner mixed layer in the case of the positive pole. Furthermore, the SLP anomalies are linked with the geopotential height anomalies in the upper troposphere and are associated with a stationary Rossby wave pattern along the westerly jet in the midlatitudes. This suggests that the SLP anomalies that generate the SPSD are not locally excited but remotely induced signals.

1. Introduction

Because of the largest basin in the Southern Hemisphere, the South Pacific plays an important role in the regional and global climate at different time scales. At interannual time scales, climate variations in the South Pacific are dominated by the Pacific–South American (PSA) mode (Karoly 1989) and Antarctic circumpolar wave (ACW; White and Peterson 1996). The PSA mode is characterized by a wave train of geopotential height anomalies in the troposphere extending from the South Pacific to South America and is mainly driven by the interannual sea surface temperature (SST) variability in the tropical Pacific due to the climate modes such as El Niño–Southern Oscillation (ENSO). This PSA pattern, in turn, may induce the SST anomalies in the South Pacific, which propagate southeastward via air–sea interaction and encircle Antarctica along the Antarctic Circumpolar Current as the ACW (Cai and Baines 2001; Venegas 2003). Since the SST anomalies in the South Pacific are the key to these climate modes, clarification of possible mechanisms responsible for the interannual SST variability in the South Pacific is essential for better understanding and accurate prediction of these climate modes.

There are very few studies (Chikamoto et al. 2010; Wang 2010) related to the interannual SST variability over the South Pacific. The lack of the long-term in situ observations may be one key factor for drawing less attention to climate studies in the South Pacific than the other basins in the Southern Hemisphere. As seen from Fig. 1, before 1970s, the in situ observations in the South Pacific were very few and confined mostly to the east of Australia and along a ship track between New Zealand and America. However, after 1980s, the number of the observations considerably increased and this gives us the confidence to look into the SST variability over the South Pacific.

Fig. 1.

Number of observations in the South Pacific for each decade.

Fig. 1.

Number of observations in the South Pacific for each decade.

Among the very few studies, Wang (2010) is the first work that discussed a mechanism of the interannual SST variability in the whole South Pacific. Using various reanalysis datasets, he examined the covariability of SST and surface wind anomalies in the whole region (0°–45°S) of the Southern Hemisphere and found that the dominant mode is associated with a northeast–southwest-oriented dipole pattern of positive and negative SST anomalies in the basin. We call this SST anomaly pattern subtropical dipole after a similar phenomenon in the southern Indian Ocean (Behera and Yamagata 2001). Furthermore, his lead–lag correlation analysis suggests that the dipole SST anomaly may be induced by the dipole latent heat flux anomaly linked with the overlying SLP anomalies. However, since the domain used in his analysis includes the tropical region, the derived subtropical dipole in the South Pacific may be contaminated with the tropical Pacific climate mode such as ENSO. Also, the possible link between the SST and latent heat flux anomalies during the development of the subtropical dipole is not quantitatively examined.

By examining a generation mechanism of the subtropical dipoles in the South Atlantic and southern Indian Oceans, Morioka et al. (2010, 2011, 2012) have recently revealed the significant role of the interannual variations in the mixed-layer thickness in their development phase. Since the mixed layer in the subtropics significantly varies at seasonal and interannual time scales, the effect of mixed-layer thickness variations should be taken into account for examining the SST variability. Here, we hypothesize that a similar mechanism may be relevant to the development of the subtropical dipole in the South Pacific.

In the present study, therefore, we investigate the interannual SST variability in the South Pacific and its generation mechanism, especially focusing on the South Pacific subtropical dipole (SPSD), using outputs from a coupled general circulation model (CGCM) and observational and reanalysis data. The contents of this paper are as follows: The model, data, and methodology are given in the following section. Section 3 provides a comparison of the SPSD between the observation and the model. In section 4, the generation mechanism of the positive SPSD (pSPSD) is discussed by examining a mixed-layer heat balance. Summary and discussion are given by section 5.

2. Model, data, and methodology

a. CGCM and data

The model used in this study is based on version 2 of the Scale Interaction Tropical Experiment-Frontier (SINTEX-F2; Masson et al. 2012; Sasaki et al. 2012) CGCM developed from the SINTEX-F1 CGCM (Luo et al. 2005). The atmospheric component is the ECHAM5 (Roeckner et al. 2003) with 31 vertical levels and a horizontal resolution of spectral T106. The oceanic component of the SINTEX-F2 is based on the Nucleus for European Modeling of the Ocean (NEMO) system (Madec 2008), which consists of the Océan Parallélisé (OPA9) for the ocean part and the Louvain-la-Neuve Sea Ice Model (LIM2; Fichefet and Morales Maqueda 1997) for the sea ice part. The horizontal resolution of the NEMO system is 0.2°–0.5° with the meridional resolution increasing to 0.2° near the poles, and it has 31 vertical levels. The OPA9 is initialized by the monthly climatology of the temperature and salinity in January from the Levitus dataset (Levitus 1982) with no motion. The atmospheric and oceanic fields are exchanged every 2 h with no flux correction by means of version 3 of the Ocean Atmosphere Sea Ice Soil (OASIS3) coupler (Valcke et al. 2004). Then, the SINTEX-F2 is integrated for 100 yr. Considering the oceanic adjustment time to the interannually varying atmospheric forcing, monthly mean outputs from the last 80 yr are used for the present analysis.

For comparison, we use the monthly mean SST data from version 2 of the National Oceanic and Atmospheric Administration (NOAA) optimum interpolation sea surface temperature (OISST v2; Reynolds et al. 2002). It has a horizontal resolution of 1° × 1° and covers the 1982–2011 period. For the subsurface ocean, we use the Argo data during 2001–11 processed by Hosoda et al. (2008). It covers global ocean from 60°S to 70°N and has 1.0° × 1.0° horizontal resolution with 25 vertical levels down to 2000 dbar. We also use the SLP, geopotential height, and horizontal wind at 300 hPa from the National Centers for Environmental Prediction–U.S. Department of Energy (NCEP–DOE) Atmospheric Model Intercomparison Project II (AMIP-II) Reanalysis (R-2; Kanamitsu et al. 2002). It covers the 1982–2011 period with a horizontal resolution of 2.5° × 2.5°.

b. Mixed-layer heat balance

To quantitatively examine the interannual SST variations, we diagnose the interannual anomaly in the tendency of the mixed-layer temperature at each grid,

 
formula

(Qiu and Kelly 1993; Moisan and Niiler 1998). Here, is a deviation from the monthly climatology. The first term on the right-hand side is the contribution from the net surface heat flux, where is the net surface heat flux, is the downward solar insolation penetrating through the mixed-layer bottom (Paulson and Simpson 1977), (=1027 kg m−3) is the density of the seawater, (=4187 J kg−1 K−1) is the specific heat of the seawater, and H is the mixed-layer depth defined as the depth at which temperature is 0.5°C lower than the SST. The second term is the contribution from the horizontal advection, where denotes the horizontal velocity averaged vertically in the mixed layer. The third term is the contribution from the entrainment, where is the temperature difference between the mixed layer and the entrained water. We use the water temperature at 20 m below the mixed-layer base as the temperature of the entrained water (Yasuda et al. 2000). Also, is the entrainment velocity and it is assumed to vanish when it becomes negative (Kraus and Turner 1967). The residual term consists of other oceanic processes such as diffusion and detrainment.

To examine the details of the contribution from the surface heat flux, we decompose the first term in the right-hand side of Eq. (1) following Morioka et al. (2012),

 
formula

where and the overbar indicates the monthly climatology. The first (second) term in the right-hand side indicates the contribution from the surface heat flux (mixed-layer depth) anomaly. The residual term consists of high frequency variability.

c. Monin–Obukhov depth

In general, when the ocean surface mixed layer becomes shallow, entrainment process does not occur below the mixed layer and the mixed layer changes due mainly to the wind stirring and surface heat flux. To quantify each effect on the mixed-layer depth change, Monin–Obukhov depth is quite useful,

 
formula

(Kraus and Turner 1967; Qiu and Kelly 1993). Here, (=0.5) is a coefficient for the efficiency of wind stirring (Davis et al. 1981) and is the frictional velocity defined by , where ρa (=1.3 kg m−3) is the density of the air, (=0.001 25) is the drag coefficient, and is the wind speed at 10-m height. Also, (=0.000 25) is the thermal expansion coefficient, is the gravity, and is the downward solar insolation (Paulson and Simpson 1977).

Following Morioka et al. (2012), we calculate the interannual anomaly of the Monin–Obukhov depth by

 
formula

where is the effective buoyancy forcing and is the effective penetrative shortwave radiation. The first term (second and third terms) in the right-hand side of Eq. (4) represents contribution from the wind stirring (surface heat flux) anomaly and the fourth term is the residual.

d. Wave activity flux

To describe the propagation of migratory and stationary eddies on a zonally varying basic flow in the troposphere, we evaluate a wave activity flux by Takaya and Nakamura (1997, 2001). It provides a snapshot for three-dimensional propagation of the eddies in a phase-independent manner and is parallel to their local group velocity. The formulation of the wave activity flux is as follows:

 
formula

where , is the horizontal wind, is the streamfunction, is the Coriolis parameter, and is the buoyancy frequency. Here, a variable with a prime indicates the deviation from the monthly climatology.

3. Model validation

Before discussing the generation mechanism of the SPSD, we compare the observed and simulated SPSD to see how well the model reproduces the interannual SST variability in the South Pacific. Fig. 2 shows the second EOF mode of the observed and simulated SST anomalies in the subtropical region of the South Pacific. It should be noted that the first EOF mode shows the ENSO related variations, indicating very high correlation (~0.7) with the Niño-3.4 index defined as SST anomalies averaged between 5°S–5°N and 120°–170°W. On the other hand, the correlation between time series of the principal component of the second EOF mode and the Niño-3.4 index is found to be very low (~0.1). Also, the second EOF mode is sufficiently separated from the first and third EOF modes (North et al. 1982). These suggest that the second EOF mode may not be related to ENSO. The second EOF mode has a northeast–southwest-oriented dipole of positive and negative SST anomalies in the central South Pacific (Fig. 2a), with the positive (negative) SST anomalies to the east (west). This mode accounts for 12.4% of the total variances of all months over the analysis period. This dipole pattern of SST anomalies is also successfully simulated in the model and it explains 11.4% of the variances. Although the simulated positive pole east of New Zealand is twice as strong as the observed one (Fig. 2b), the model seems to reproduce the dipole structure of SST anomalies well, showing a high pattern correlation with the observation (~0.7). Hereafter, we refer to this dipole SST anomaly in the second EOF mode as the SPSD.

Fig. 2.

(a) The second EOF mode of the observed SST anomalies (°C) in the South Pacific. (b) As in (a), but for the model. All months over the analysis period are used. Positive values are shaded.

Fig. 2.

(a) The second EOF mode of the observed SST anomalies (°C) in the South Pacific. (b) As in (a), but for the model. All months over the analysis period are used. Positive values are shaded.

To examine the seasonal dependence of this SPSD, we calculate the standard deviation of the principal component of the second EOF mode for each month (Fig. 3). The observed standard deviation is locked to the seasonal cycle and reaches its peak during austral summer (December–February). Similarly, the model reproduces the observed feature of seasonal locking, but the amplitude during its peak phase is larger than the observed one. The model seems to have a bias of stronger seasonal locking, but it may be partly due to the difference in the analysis period between the observation and the model.

Fig. 3.

The standard deviation of the principal component of the second EOF mode (in Fig. 2) for each month. The solid (dashed) line corresponds to the observation (model).

Fig. 3.

The standard deviation of the principal component of the second EOF mode (in Fig. 2) for each month. The solid (dashed) line corresponds to the observation (model).

The above results enable us to define the SPSD events as years when the principal component of the second EOF mode during the mature phase of austral summer exceeds 0.8 standard deviation. This provides 6 (21) positive events and 6 (18) negative events in the observation (model). The years of observed events are provided in Table 1. Then, we conduct the composite analysis of SST anomalies for the positive SPSD events (Fig. 4). In the observation, the positive and negative SST anomalies in the central South Pacific start to appear during austral spring, reach their peak in austral summer, and gradually decay in austral autumn (Fig. 4a). The positive SST anomaly pole is slightly stronger than the negative one. The similar evolution of the SST anomaly poles is successfully captured in the model (Fig. 4b), and the pattern correlation during austral summer between the observation and the model is very high (~0.8). These results suggest that the model has a sufficient ability in reproducing the location as well as the amplitude of the observed SPSD and would be quite useful for investigating the SST variability associated with the SPSD.

Table 1.

Observed years of the positive and negative SPSD events.

Observed years of the positive and negative SPSD events.
Observed years of the positive and negative SPSD events.
Fig. 4.

Composite of the (a) observed and (b) modeled SST anomalies (°C) during the growth and decay of the positive SPSD. The observed (modeled) anomalies exceeding 90% (95%) confidence level with a two-tailed t test are shaded. The boxes over the positive (negative) SST anomaly poles in the model are defined between 35°–45°S and 155°–175°W (28°–38°S and 115°–135°W).

Fig. 4.

Composite of the (a) observed and (b) modeled SST anomalies (°C) during the growth and decay of the positive SPSD. The observed (modeled) anomalies exceeding 90% (95%) confidence level with a two-tailed t test are shaded. The boxes over the positive (negative) SST anomaly poles in the model are defined between 35°–45°S and 155°–175°W (28°–38°S and 115°–135°W).

4. Generation mechanism of the positive SPSD

Since the mirror image of the generation mechanism is obtained for the negative SPSD, here we only discuss the generation mechanism of the positive SPSD. To investigate the development of the SST anomaly poles during the positive SPSD, we diagnose the tendency anomaly of the mixed-layer temperature averaged over the positive and negative poles in the model using Eq. (1) (Figs. 5a, 6a). Over the positive (negative) pole, the tendency anomaly of the mixed-layer temperature becomes positive (negative) during October(0)–January(1). This is mostly due to the anomalous contribution from the net surface heat flux. Four components of the contribution from the net surface heat flux are shown in Fig. 5b (Fig. 6b). The anomalous contribution from the net surface heat flux is dominated by that from the shortwave radiation. To clarify causes of the largest contribution from the shortwave radiation, the anomalous contribution from the shortwave radiation is decomposed into three terms using Eq. (2), and each term is shown in Fig. 5c (Fig. 6c). The anomalous contribution from the shortwave radiation is mostly explained by that from the second term in the right-hand side of Eq. (2), indicating the significant contribution from the mixed-layer thickness anomaly. In fact, at the positive (negative) pole, the mixed layer becomes thinner (thicker) than normal during October(0)–January(1) in Fig. 5d (Fig. 6d). This thinner (thicker) mixed layer enhances (suppresses) the warming of the mixed layer by the climatological shortwave radiation. Although the mixed-layer depth anomaly reaches its peak in austral spring (Figs. 5d, 6d), the anomalous contribution from the shortwave radiation becomes largest in austral summer. Since the mean mixed-layer thickness in austral spring (~80 m) is thicker than that in austral summer (~20 m), the contribution from the second term in Eq. (2) during austral spring becomes smaller than that in austral summer. This may be responsible for the peak of the anomalous contribution from the shortwave radiation in austral summer. Also, the mean mixed-layer depth greatly differs between the two poles. During the developing phase [November(0)–January(1)], the mean mixed-layer depth over the positive pole is 43 m deeper than 25 m over the negative pole. Although the mixed-layer thickness anomaly over the positive pole is −6.8 m and its amplitude is larger than 2.4 m over the negative pole, the difference in the mean mixed-layer depth makes the magnitude of the contribution from the shortwave radiation almost similar between the two poles. The above results are different from the previous one by Wang (2010), who suggested the important contribution from the latent heat flux anomaly by conducting the lead–lag correlation analysis.

Fig. 5.

(a) Time series of composite anomalies of the mixed-layer temperature tendency and its four components (10−7 °C s−1) over the positive SST anomaly pole of the positive SPSD defined in Fig. 4b. (b) As in (a), but for the net surface heat flux term and its four components (10−7 °C s−1) in Eq. (1). (c) As in (a), but for the shortwave radiation term and its three components (10−7 °C s−1) in Eq. (2). (d) As in (a), but for the mixed-layer depth (m). Closed (open) circles show anomalies exceeding the 90% (80%) confidence level using a two-tailed t test. A 3-month running mean is applied to smooth the time series. In (a), the mixed-layer temperature tendency term (thick solid line), the net surface heat flux term (thick dashed line), the horizontal advection term (thick dotted line), the entrainment term (thin solid line), and the residual term (thin dashed line) are shown. In (b), the net surface heat flux term (thick dashed line), the shortwave radiation term (thick solid line), the longwave radiation term (thick dotted line), the latent heat flux term (thin solid line), and the sensible heat flux term (thin dashed line) are shown. In (c), the shortwave radiation term (thick solid line), the first term (thick dashed line), the second term (thick dotted line), and the third term (thin solid line) are shown.

Fig. 5.

(a) Time series of composite anomalies of the mixed-layer temperature tendency and its four components (10−7 °C s−1) over the positive SST anomaly pole of the positive SPSD defined in Fig. 4b. (b) As in (a), but for the net surface heat flux term and its four components (10−7 °C s−1) in Eq. (1). (c) As in (a), but for the shortwave radiation term and its three components (10−7 °C s−1) in Eq. (2). (d) As in (a), but for the mixed-layer depth (m). Closed (open) circles show anomalies exceeding the 90% (80%) confidence level using a two-tailed t test. A 3-month running mean is applied to smooth the time series. In (a), the mixed-layer temperature tendency term (thick solid line), the net surface heat flux term (thick dashed line), the horizontal advection term (thick dotted line), the entrainment term (thin solid line), and the residual term (thin dashed line) are shown. In (b), the net surface heat flux term (thick dashed line), the shortwave radiation term (thick solid line), the longwave radiation term (thick dotted line), the latent heat flux term (thin solid line), and the sensible heat flux term (thin dashed line) are shown. In (c), the shortwave radiation term (thick solid line), the first term (thick dashed line), the second term (thick dotted line), and the third term (thin solid line) are shown.

Fig. 6.

As in Fig. 5, but for the negative SST anomaly pole.

Fig. 6.

As in Fig. 5, but for the negative SST anomaly pole.

Since the mixed layer becomes shallow during austral summer when the SPSD develops, the anomalous mixed-layer thickness results from the anomalous heating/cooling at the sea surface and the mixing due to the anomalous near-surface wind. To examine the relative importance for the thinner (thicker) mixed layer at the positive (negative) pole, composite anomalies of the surface heat flux and near-surface wind are shown in Fig. 7 (Fig. 8). During the development phase of October(0)–January(1), the net surface heat flux anomaly in Fig. 7a (Fig. 8a) becomes positive (negative) because of the latent heat flux anomaly and acts to warm (cool) the ocean, contributing to the generation of the thinner (thicker) mixed layer. Also, the near-surface wind at the positive pole anomalously weakens during this period (Fig. 7b) and contributes to the formation of the thinner mixed layer. On the other hand, at the negative pole, the wind becomes weaker than normal (Fig. 8b) and acts to cause the thinner mixed layer, which contradicts the positive mixed-layer depth anomaly during the developing phase (Fig. 6d). This suggests that at the negative pole, the surface heat flux anomaly plays a dominant role in the mixed-layer thickness anomaly (Fig. 8a). It should be noted that, although the near-surface wind over the negative pole anomalously weakens (Fig. 8b), the specific humidity difference between the ocean and the near-surface atmosphere becomes anomalously large because of the anomalous advection of dry air from the high latitudes. This contributes to the anomalous increase in the evaporation (Fig. 8a).

Fig. 7.

(a) Time series of composite anomalies of the net surface heat flux and its four components (W m−2) over the positive SST anomaly pole of the positive SPSD defined in Fig. 4b. (b) As in (a), but for the wind speed (m s−1) at 10 m from the ocean surface. Closed (open) circles show anomalies exceeding the 90% (80%) confidence level using a two-tailed t test. A 3-month running mean is applied to smooth the time series. In (a), the net surface heat flux (thick dashed line), the shortwave radiation (thick solid line), the longwave radiation (thick dotted line), the latent heat flux (thin solid line), and the sensible heat flux (thin dashed line) are shown.

Fig. 7.

(a) Time series of composite anomalies of the net surface heat flux and its four components (W m−2) over the positive SST anomaly pole of the positive SPSD defined in Fig. 4b. (b) As in (a), but for the wind speed (m s−1) at 10 m from the ocean surface. Closed (open) circles show anomalies exceeding the 90% (80%) confidence level using a two-tailed t test. A 3-month running mean is applied to smooth the time series. In (a), the net surface heat flux (thick dashed line), the shortwave radiation (thick solid line), the longwave radiation (thick dotted line), the latent heat flux (thin solid line), and the sensible heat flux (thin dashed line) are shown.

Fig. 8.

As in Fig. 7, but for the negative SST anomaly pole.

Fig. 8.

As in Fig. 7, but for the negative SST anomaly pole.

To further quantify each contribution from the wind speed and the surface heat flux anomalies on the mixed-layer thickness anomaly, the interannual anomaly of the Monin–Obukhov depth at each pole during November(0)–January(1) is examined (Table 2). Although the Monin–Obukhov depth anomaly underestimates the mixed-layer depth anomaly (Figs. 5d, 6d), it is mostly explained by the contribution from the surface heat flux anomaly: in particular, the latent heat flux anomaly as suggested in Figs. 7a and 8a. Over the positive SST anomaly pole, the contribution from the wind speed anomaly is also found but relatively small.

Table 2.

The interannual anomaly of the Monin–Obukhov depth (MOD; m) at positive and negative poles during November(0)–January(1) of the positive SPSD and each contribution from the wind stirring, net surface heat flux (NSHF), and residual terms in Eq. (4).

The interannual anomaly of the Monin–Obukhov depth (MOD; m) at positive and negative poles during November(0)–January(1) of the positive SPSD and each contribution from the wind stirring, net surface heat flux (NSHF), and residual terms in Eq. (4).
The interannual anomaly of the Monin–Obukhov depth (MOD; m) at positive and negative poles during November(0)–January(1) of the positive SPSD and each contribution from the wind stirring, net surface heat flux (NSHF), and residual terms in Eq. (4).

The above latent heat flux and near-surface wind anomalies are closely related to the SLP anomalies. Taking into account the oceanic adjustment time to the surface forcing, we calculate composite SLP anomalies during November(0)–January(1) of the positive SPSD (Fig. 9). In the reanalysis data, the positive SLP anomalies southeast of New Zealand exist over the eastern (western) part of the positive (negative) SST anomaly pole during austral summer in Fig. 4a. They may induce anomalous wet northeasterly (dry southeasterly) wind over the positive (negative) pole to suppress (enhance) the near-surface evaporation and weaken (weaken) the near-surface wind. In the model, the positive SLP anomalies southeast of New Zealand are well simulated (Fig. 9b), but their amplitude is twice as large as that in Fig. 9a of the reanalysis data. The positive SLP anomalies gradually weaken and become negative after the SPSD reaches its peak (figure not shown). Therefore, we believe the warm SST anomaly related to the SPSD may act to weaken the anomalous high SLP rather than intensify them. This suggests that the positive SLP anomalies may not be locally induced but remotely forced.

Fig. 9.

Composite of the SLP anomalies (in hPa) during the development of the positive SPSD from (a) the reanalysis data and (b) the model. The anomalies for the reanalysis data (the model) exceeding 90% (95%) confidence level with a two-tailed t test are shaded.

Fig. 9.

Composite of the SLP anomalies (in hPa) during the development of the positive SPSD from (a) the reanalysis data and (b) the model. The anomalies for the reanalysis data (the model) exceeding 90% (95%) confidence level with a two-tailed t test are shaded.

To examine the remote influence on the positive SLP anomalies, we calculate composite anomalies of geopotential height and wave activity flux at 300 hPa during November(0)–January(1) (Fig. 10). In the reanalysis data, the positive geopotential height anomalies southeast of New Zealand are located just over the positive SLP anomalies and represent equivalent barotropic structure in the troposphere (Fig. 10a). They are associated with the southeastward propagation of the stationary Rossby wave activity flux from the negative geopotential height anomalies southeast of Australia. Although the positive geopotential height anomalies in the model are stronger than those in the reanalysis data, as expected from the positive SLP anomalies (Fig. 9), the southeastward propagation of the stationary Rossby wave activity flux is clearly captured in Fig. 10b. This suggests that the positive SLP anomalies may not be locally excited in the central South Pacific but influenced by the upstream anomalies southeast of Australia associated with the stationary Rossby wave propagation.

Fig. 10.

Composite of the geopotential height anomalies (color shading; m) and wave activity flux (vectors; m2 s−2) at 300 hPa during the development of the positive SPSD from (a) the reanalysis data and (b) the model. The anomalies for the reanalysis data (the model) exceeding 90% (95%) confidence level with a two-tailed t test are shaded.

Fig. 10.

Composite of the geopotential height anomalies (color shading; m) and wave activity flux (vectors; m2 s−2) at 300 hPa during the development of the positive SPSD from (a) the reanalysis data and (b) the model. The anomalies for the reanalysis data (the model) exceeding 90% (95%) confidence level with a two-tailed t test are shaded.

Since the stationary Rossby wave is associated with the equivalent barotropic geopotential height anomalies in the troposphere, the linear barotropic vorticity equation is very useful to diagnose a key feature of this stationary Rossby wave. Using this equation, Hoskins and Karoly (1981) provided the stationary Rossby wavenumber , where is the meridional gradient of the absolute vorticity and is the zonal velocity in the Mercator projection. Fig. 11 shows the stationary Rossby wavenumber, the meridional gradient of the absolute vorticity and the zonal velocity during November(0)–January(1) for the positive SPSD. In the reanalysis data, the maximum of the stationary Rossby wavenumber of 3 and 4 exists in the midlatitudes between 40° and 60°S (Fig. 11a). This corresponds to the meridional gradient of the absolute vorticity of 1.0 × 10−11 m−1 s−1 in the region (Fig. 11c), which is dominated by the curvature of westerly jet in the midlatitudes (Fig. 11e). This westerly jet acts as a waveguide for the stationary Rossby wave. These aspects of the stationary Rossby wave and the westerly jet are successfully simulated in the model (Figs. 11b,d,f). Therefore, the location of the westerly jet may play a crucial role in the stationary Rossby waveguide during the development of the positive SPSD.

Fig. 11.

Stationary Rossby wavenumber (shaded) at 300 hPa during November(0)–January(1) of the positive SPSD from (a) the reanalysis data and (b) the model. (c),(d) As in (a),(b), but for the meridional gradient of the absolute vorticity (10−11 m−1 s−1) at 300 hPa. (e),(f) As in (a),(b), but for the zonal wind (m s−1) at 300 hPa.

Fig. 11.

Stationary Rossby wavenumber (shaded) at 300 hPa during November(0)–January(1) of the positive SPSD from (a) the reanalysis data and (b) the model. (c),(d) As in (a),(b), but for the meridional gradient of the absolute vorticity (10−11 m−1 s−1) at 300 hPa. (e),(f) As in (a),(b), but for the zonal wind (m s−1) at 300 hPa.

5. Summary and discussion

Using the outputs from the CGCM, the generation mechanism of the SPSD is demonstrated in detail for the first time. The SPSD is detected as the second EOF mode of the SST anomalies in the South Pacific, and it has a northeast–southwest-oriented dipole structure of positive and negative SST anomalies in the central basin. The close examination of the mixed-layer heat balance yields that the positive and negative SST anomaly poles develop during austral spring and reach their peak during austral summer, mainly because of the anomalous contribution from the shortwave radiation. Over the positive (negative) pole, the mixed layer becomes thinner (thicker) than normal, enhancing (reducing) the warming of the mixed layer by the climatological shortwave radiation. This thinner (thicker) mixed layer is considered to have a link with the suppressed evaporation and reduced wind (enhanced evaporation) associated with the overlying SLP anomalies. The analysis of wave activity flux in the upper troposphere suggests a remote influence from the upstream geopotential height anomalies southeast of Australia through the southeastward propagation of the stationary Rossby wave activity. The mean state of the westerly jet in the midlatitudes may be responsible for the direction of this Rossby wave response.

The mechanism proposed here is different from the result by Wang (2010), who suggested by the lead–lag correlation analysis that the latent heat flux anomalies directly generate the SST anomaly poles. Wang (2010) did not take into account the effect of the interannual variations in the mixed-layer depth, which plays a key role in the development of the SST anomaly poles. Also, in contrast with other subtropical dipoles in the southern Indian and South Atlantic Oceans, where the positive and negative SST anomaly poles dominate the whole basin (Morioka et al. 2010, 2011, 2012), the dipole SST anomalies of the SPSD are confined to the central part of the South Pacific and is surrounded by SST anomalies with the opposite sign (see Fig. 4). Further analysis reveals that these SST anomalies to the east and west of the SPSD develop in the same manner as those associated with the SPSD and are strongly linked with overlying SLP anomalies (figure not shown). Therefore, the difference in the SST anomaly pattern between the SPSD and other subtropical dipoles may be related to that in the SLP anomaly pattern, which may be strongly linked with the difference in the size of the basin.

However, the origin of the stationary Rossby wave pattern that contributes to the SLP anomaly during the SPSD is still unclear. The wave activity flux southeast of Australia seems to originate from the southern Indian Ocean and partly from the tropical Indian Ocean. Since there exists a strong westerly jet in the southern Indian Ocean (Figs. 11e,f), internal variability of location and/or amplitude of the westerly jet may be a possible source of the Rossby wave (Ding et al. 2012). This may be partly linked with the dominant mode of the atmospheric variability in the mid–high latitudes of the Southern Hemisphere, called southern annular mode (SAM; Limpasuvan and Hartmann 1999). A recent paper by Ding et al. (2012) suggested the SLP anomaly pattern related to the SPSD makes up part of the SAM in the South Pacific. On the other hand, the interannual variability in the tropical Indian Ocean such as the Indian Ocean dipole (Saji et al. 1999) may also excite the Rossby wave, propagating along the great circle and forming the PSA pattern in the South Pacific (Cai et al. 2011). In this direction, further model experiments are required to clarify the possible sources of the SPSD.

Since these results are based on the CGCM outputs, it is important to check whether the mechanism proposed here operates in the real ocean. Using the Argo dataset during 2001–10, the SPSD events are selected to examine its close link with the interannual variations of the mixed-layer depth. Figure 12 shows the SST anomalies during December–February and mixed-layer depth anomalies during November–January for the 2008–09 positive SPSD event. Over the positive (negative) SST anomaly pole located in the central South Pacific, the mixed layer becomes thinner (thicker) than normal. This supports the above mechanism demonstrated from the CGCM and indicates the importance of the mixed-layer variations during the development of the SPSD.

Fig. 12.

(a) The SST anomaly (°C) derived from the Argo data during December 2008–February 2009 of the positive SPSD. (b) As in (a), but for the mixed-layer depth anomaly (m) during November 2008–January 2009. Positive values are shaded.

Fig. 12.

(a) The SST anomaly (°C) derived from the Argo data during December 2008–February 2009 of the positive SPSD. (b) As in (a), but for the mixed-layer depth anomaly (m) during November 2008–January 2009. Positive values are shaded.

This study would provide implications for the in situ observations as well as prediction of the SPSD. More intensive observations of the mixed-layer depth are needed to generate better initial conditions for prediction of the SPSD. Also, a good performance in simulating the interannual variations in the mixed-layer depth, SLP, and westerly jet is required for the CGCMs to realistically reproduce the SPSD. Further studies on the SPSD are necessary for comprehensively understanding the climate variability in the South Pacific.

Acknowledgments

The SINTEX-F2 was run on the Earth Simulator 2 at the Japan Agency for Marine-Earth Science and Technology (JAMSTEC). We thank two anonymous reviewers for their constructive comments. The first author is supported by a research fellowship from the Japan Society for the Promotion of Science (JSPS).

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