The Decadal Reduction of Southeastern Australian Autumn Rainfall since the Early 1990s: A Response to Sea Surface Temperature Warming in the Subtropical South Pacific

ZhongDa Lin State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

Search for other papers by ZhongDa Lin in
Current site
Google Scholar
PubMed
Close
,
Yun Li Business Intelligence and Data Analytics, Western Power, Perth, Western Australia, Australia

Search for other papers by Yun Li in
Current site
Google Scholar
PubMed
Close
,
Yong Liu Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

Search for other papers by Yong Liu in
Current site
Google Scholar
PubMed
Close
, and
AiXue Hu Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado

Search for other papers by AiXue Hu in
Current site
Google Scholar
PubMed
Close
Free access

Abstract

Rainfall in southeastern Australia (SEA) decreased substantially in the austral autumn (March–May) of the 1990s and 2000s. The observed autumn rainfall reduction has been linked to the climate change–induced poleward shift of the subtropical dry zone across SEA and natural multidecadal variations. However, the underlying physical processes responsible for the SEA drought are still not fully understood. This study highlights the role of sea surface temperature (SST) warming in the subtropical South Pacific (SSP) in the autumn rainfall reduction in SEA since the early 1990s. The warmer SSP SST enhances rainfall to the northwest in the southern South Pacific convergence zone (SPCZ); the latter triggers a divergent overturning circulation with the subsidence branch over the eastern coast of Australia. As such, the subsidence increases the surface pressure over Australia, intensifies the subtropical ridge, and reduces the rainfall in SEA. This mechanism is further confirmed by the result of a sensitivity experiment using an atmospheric general circulation model. Moreover, this study further indicates that global warming and natural multidecadal variability contribute approximately 44% and 56%, respectively, of the SST warming in the SSP since the early 1990s.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Zhongda Lin, zdlin@mail.iap.ac.cn

Abstract

Rainfall in southeastern Australia (SEA) decreased substantially in the austral autumn (March–May) of the 1990s and 2000s. The observed autumn rainfall reduction has been linked to the climate change–induced poleward shift of the subtropical dry zone across SEA and natural multidecadal variations. However, the underlying physical processes responsible for the SEA drought are still not fully understood. This study highlights the role of sea surface temperature (SST) warming in the subtropical South Pacific (SSP) in the autumn rainfall reduction in SEA since the early 1990s. The warmer SSP SST enhances rainfall to the northwest in the southern South Pacific convergence zone (SPCZ); the latter triggers a divergent overturning circulation with the subsidence branch over the eastern coast of Australia. As such, the subsidence increases the surface pressure over Australia, intensifies the subtropical ridge, and reduces the rainfall in SEA. This mechanism is further confirmed by the result of a sensitivity experiment using an atmospheric general circulation model. Moreover, this study further indicates that global warming and natural multidecadal variability contribute approximately 44% and 56%, respectively, of the SST warming in the SSP since the early 1990s.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Zhongda Lin, zdlin@mail.iap.ac.cn

1. Introduction

Rainfall in southeastern Australia (SEA) in the austral autumn season [March–May (MAM)] plays an important role in generating annual inflow across the river systems of the region. The autumn rainfall wets the catchments after the dry summer season, and therefore rainfall in the following winter and spring can be efficiently converted to runoff and catchment inflows (Cai and Cowan 2008; Murphy and Timbal 2008). The annual inflow into the southern part (south of 33°S) of the longest Australian river system, the Murray–Darling basin (MDB), is mainly derived from rainfall in SEA (Gallant et al. 2012). As the most important agricultural region, the MDB produces one-third of the Australian food supply and supports over a third of the total gross value of agricultural production. Annual inflow into catchments within the MDB, especially in the southern part, is highly sensitive to autumn rainfall variations (Cai and Cowan 2013). During the two decades in the 1990s and 2000s, the autumn rainfall amounts in SEA decreased remarkably (Fig. 1a). The decadal reduction in autumn rainfall caused a severe drought during 1997–2006 over SEA (Murphy and Timbal 2008) and led to the shortage of surface water resource for irrigation and domestic purposes (Leblanc et al. 2009). Thus, understanding the decadal drought has attracted significant interest and attention, particularly in terms of possible causes and policy options in response to the decrease.

Fig. 1.
Fig. 1.

(a) Time series of March–May (MAM) mean land rainfall total (mm) in SEA south of 33°S and east of 135°E (solid line) from 1900 to 2018 and its interdecadal component (dashed line). (b) The sliding value of the Lepage statistics of the SEARI before and after 9 years. The years 1991 and 2010 (vertical solid red lines) are identified as two changepoint years, with the maximum statistical values exceeding the critical value of 5.99 (horizontal dashed line), indicating that both changepoint years are significant at the 0.05 level. (c) The wavelet analysis of the SEARI, with negative values shaded.

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0686.1

Previous studies have identified a range of factors as potential contributors to the rainfall reduction over SEA. For the sea surface temperature (SST) drivers, Cai et al. (2011) proposed that the positive Indian Ocean dipole (IOD) can trigger an equivalent-barotropic Rossby wave train emanating from the Indian Ocean and induce an anticyclone anomaly across southern Australia, which reduces rainfall over SEA. As such, the increased frequency of positive IOD events during recent years (Cai et al. 2009) may explain a significant portion of the observed rainfall decline across SEA in the late austral winter and spring. However, the strongest rainfall reduction over SEA occurred in the austral autumn when the rainfall–IOD relationship is weak, so that the recent MAM rainfall decline over SEA was not likely induced by a trend in IOD events (Timbal and Hendon 2011).

Another important factor is El Niño–Southern Oscillation (ENSO). However, ENSO cannot explain the recent autumn rainfall decline over SEA due to two facts: 1) the ENSO affects SEA rainfall through the tropical Indian Ocean via the equivalent-barotropic Rossby wave train, a response that is triggered by anomalous divergence associated with the IOD-induced convection anomalies (Cai et al. 2011), and 2) the impact of the ENSO on SEA rainfall is typically most significant in the austral spring because of the significant correlation between the ENSO and the IOD in this season (Ummenhofer et al. 2009; Cai et al. 2011). In addition, Taschetto and England (2009) revealed that the El Niño Modoki events, with a distinct warm SST anomaly centered in the central Pacific and two weaker cold anomalies in the west and east of the Pacific basin (Ashok et al. 2007), may lead to a decrease in SEA rainfall in autumn on an interannual time scale. But these events cannot explain the SEA rainfall decline in autumn, since they show no significant trend (Nicholls 2010). Finally, the SST around northern Australia is strongly correlated with SEA autumn rainfall (Cai and Cowan 2008; Nicholls 2010; Watterson 2010). However, the recent warming of the ocean around northern Australia should actually have led to increased rainfall rather than the observed autumn–winter rainfall decline in SEA (Nicholls 2010).

In addition to the SSTs, the southern annular mode (SAM) is also an important factor that affects SEA rainfall (Hendon et al. 2007; Meneghini et al. 2007; Nicholls 2010; Gallant et al. 2012; Verdon-Kidd et al. 2014). Nicholls (2010) suggested that the strong trend in the SAM is able to explain over 70% of the observed autumn–winter (from March to August) drying trend during 1958–2007. However, the impact of the SAM may only exist in the austral winter (June–August) because a significant inverse relationship between the SAM and SEA rainfall exists in winter but not in autumn (Meneghini et al. 2007; Cai and Cowan 2013). Hendon et al. (2007) suggested that the SAM contributed to much of the observed positive trend of rainfall in SEA during the austral summer season for the period of 1979–2005, and its effect on other seasons was small. Based on model outputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5), Purich et al. (2013) projected a strong positive trend in the austral autumn SAM but little change in rainfall across SEA. Cai and Cowan (2013) also demonstrated that the mean sea level pressure pattern associated with MAM rainfall variability across SEA does not exhibit a SAM-like structure (Fig. 2b in Cai and Cowan 2013) in the period of 1979–2008; in other words, there is no significant correlation between the SAM and rainfall over SEA in the austral autumn. On the other hand, several studies have suggested that interactions between the SAM and other processes can cause the SEA rainfall reduction (Verdon-Kidd and Kiem 2009a; Gallant et al. 2012). However, there is still no definite answer of the significance or quantitative impact of the SAM on the recent SEA autumn rainfall reduction.

Cai et al. (2012) found that the reduction of SEA autumn rainfall coincides with a poleward expansion of the tropical belt and subtropical dry zone by approximately 2°–3° in the same season. The poleward shift of the tropical belt is associated with the southward shift of Hadley circulation in the Southern Hemisphere under global warming (Kushner et al. 2001; Lu et al. 2007), which may enhance the subtropical ridge (STR) over SEA (Murphy and Timbal 2008) and then cause the decline of the SEA rainfall in MAM (Larsen and Nicholls 2009). Timbal and Drosdowsky (2013) indicated that approximately two-thirds of the observed rainfall decline for the period of 1997–2009 can be accounted for by the strengthening of the ridge. In addition, the poleward shift of the westerlies under global warming also leads to the poleward movement of the midlatitude storm track so that the storm activity largely misses SEA (Frederiksen et al. 2011; Frederiksen et al. 2013). In summary, the observed change over the past few decades is consistent with a poleward shift of the ocean and atmosphere circulation (Cai and Cowan 2013).

As reviewed above, the SEA rainfall decline in recent decades has mostly been treated as a drying trend. On the other hand, the SEA rainfall decline may also be attributable to multidecadal variability (Murphy and Timbal 2008; Gallant et al. 2012; Gergis et al. 2012; Cai et al. 2014; Verdon-Kidd and Kiem 2014; Verdon-Kidd et al. 2014). Gergis et al. (2012) pointed out that there is a 97.1% probability that the decadal rainfall anomaly recorded during 1998–2008 was the worst experienced since 1783 based on multiproxy reconstruction of rainfall variability from the midlatitude region of SEA. In addition, two other drought decades in SEA also occurred from 1895 to 1902 and from 1936 to 1945 (Ummenhofer et al. 2009; Verdon-Kidd and Kiem 2009a; Timbal and Fawcett 2013; Chowdhury et al. 2015). Moreover, the autumn decadal drought in the 1990s and 2000s seems weakened (despite having no break) during the past several years due to the recovery of autumn rainfall in SEA (Fig. 1).

Various efforts were made to understand the mechanisms of the multidecadal variations of the SEA rainfall. Verdon-Kidd and Kiem (2009a) reported that the decadal rainfall reduction in SEA during the period of 1997–2008 is consistent with the decrease in the number of rainy days and the intensity of daily rainfall events, which is likely due to a decrease in the monsoon depression over eastern Australia (Verdon-Kidd et al. 2014). The multidecadal fluctuations of the SEA rainfall have been also linked to the interdecadal Pacific oscillation (IPO) through variations of the South Pacific convergence zone (SPCZ) (Folland et al. 2002) and the magnitude and frequency of the impact of ENSO (Power et al. 1999; Kiem et al. 2003; Kiem and Franks 2004; Verdon-Kidd and Kiem 2009b). However, the mechanisms responsible for the SEA drought are still not fully understood.

This study further investigates the underlying physical process for the recent decadal rainfall reduction in the autumn in SEA. We propose a new mechanism that the increasing SST in the subtropical South Pacific (SSP) could induce the significant decadal decline in the SEA autumn rainfall around the early 1990s. It provides an alternative pathway for global warming causing the SEA decadal drought in the 1990s and 2000s and clarifies the underlying mechanism of the IPO’s impact on the SEA rainfall through the mediating role of the warmer SST in the SSP.

2. Data and methods

The study region (SEA) is selected as the box area south of 33°S and east of 135°E. The averaged autumn (MAM) precipitation totals in SEA during the period of 1900–2018 are downloaded from the Bureau of Meteorology (BoM) website (http://www.bom.gov.au/cgi-bin/climate/change/timeseries.cgi). The observational grid data of monthly Australian precipitation totals during 1900–2010 with a resolution of 0.25° latitude × 0.25° longitude, which were calculated from the Australian Water Availability Project (AWAP) gridded dataset of daily rainfall (Jones et al. 2009), are used in this study. The time series of the BoM SEA rainfall in the austral autumn is almost identical to that based on the AWAP data, with a correlation coefficient r of 0.9992 between them during the 1900–2010 period. But it should be noted that the AWAP data are interpolated from gauges of the BoM gauging network, which may underestimate precipitation in high rainfall area and high mountains, where the BoM gauges are lacking, and overestimate precipitation in low rainfall areas (Beesley et al. 2009; Tozer et al. 2012; King et al. 2013; Chubb et al. 2016). In addition, to reveal rainfall changes in the neighboring seas, especially in the SPCZ, concurrent with the changes in the SEA rainfall, precipitation data derived by the Global Precipitation Climatology Project (GPCP) at a 2.5° × 2.5° grid during the period of 1979–2016 (Huffman et al. 1997; Adler et al. 2003) are also used.

The atmospheric variables, including zonal and meridional winds and vertical pressure velocity during the period of 1948–2018 at a 2.5° × 2.5° grid, are derived from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (Kalnay et al. 1996). The subtropical ridge index (STRI), defined as the local maxima in the monthly surface pressure along zonal profiles for a 5° longitude band (145°–150°E) between 10° and 44°S (Drosdowsky 2005), is used. The SST data at a 1° × 1° grid are derived from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) during the period of 1870–2018 (Rayner et al. 2003). Also used is global-mean surface temperature index, which is constructed using the National Aeronautics and Space Administration’s (NASA’s) Goddard Institute for Space Studies (GISS) surface temperature data and downloaded from the website (https://climate.nasa.gov/vital-signs/global-temperature/).

We employ the Lepage test (Lepage 1971; Yonetani and McCabe 1994) to test significant differences between two samples; this is the same test used by Kwon et al. (2007) and Liu et al. (2011). The Lepage test statistic presented by Yonetani and McCabe (1994) is adopted to denote a combination of standardized Wilcoxon’s and Ansari-Bradley’s statistics (Lepage 1971). If the statistic is greater than the critical value of 5.99, the mean change between two samples is significant at the 0.05 level.

A wavelet method based on the maximum discrete wavelet transform (Percival and Mofjeld 1997) is employed to extract the interdecadal component, with periods being larger than 16 years, from the associated fields. Because the interdecadal components are serially autocorrelated, to test the significance of correlation between them we have adjusted the degrees of freedom using the effective number of degrees of freedom estimated by the modified Chelton method (Li et al. 2012). In addition, the wavelet analysis is employed to detect time evolution of the decadal to interdecadal signals of SEA rainfall with the Morlet as the wavelet basis function (Torrence and Compo 1998).

The Geophysical Fluid Dynamics Laboratory (GFDL) atmospheric general circulation model (AGCM), version 2.1 (AM2.1), is used to investigate the role of SST in influencing Australian rainfall. AM2.1 employs the finite-volume dynamical core (Lin 2004). It has a horizontal resolution of 2° latitude × 2.5° longitude and adopts hybrid coordinate in the vertical direction, with 24 levels ranging from approximately 30 m above the surface up to 3 hPa (approximately 40 km). The land model coupled in AM2.1 is LM2.1, which is based on the land dynamics model described by Milly and Shmakin (2002). A comprehensive description and a model evaluation with prescribed SSTs are given by Anderson et al. (2004) and Delworth et al. (2006).

3. Change in SEA autumn rainfall since the early 1990s

Figure 1a shows the 119-yr time series of autumn SEA rainfall (SEAR) from 1900 to 2018. The 119-yr mean autumn rainfall in SEA is 145 mm. The amount of rainfall is greatly decreased in the 1990s and 2000s. As shown in Fig. 1b, the statistic values of the sliding Lepage test (Lepage 1971; Yonetani and McCabe 1994) are larger than the critical value of 5.99 in the early 1990s, with the maximum in 1991, indicating that the autumn SEAR underwent a significant change in 1991 at the 0.05 level. This manifests one-third of the rainfall reduction post 1990: the 19-yr mean rainfall decreased by approximately 48 mm from 159 mm in the 1972–90 period to 111 mm in the 1991–2009 period.

To illustrate more clearly the time evolution of the interdecadal component, Fig. 1c shows the wavelet components of the SEAR. The SEAR exhibits strong interdecadal variations with the periods of 60–80 years. During the past 119 years, the SEAR experienced two drought periods from the early 1920s to the late 1940s and from the early 1990s to the late 2000s and one wet period in between. Moreover, the deficit of SEAR is more severe during the second drought period in comparison with that in the first drought period (Fig. 1c), which is consistent with the greater SEAR reduction (Fig. 1a) and the larger statistical value of the Lepage test (Fig. 1b) around the early 1990s. In addition, another significant change seems to occur in the late 2000s with the maximum statistical value of the Lepage test in 2010. However, due to the limitation of the SEAR data, the reliability and significance of the change in the late 2000s is still in question. In this study, we focus on the decadal change in the early 1990s. Here, and for the remainder of the paper, we use two equal 19-yr periods, namely, 1972–90 and 1991–2009, to represent the decadal wet and dry epochs before and after the changepoint year 1991. The decadal change is calculated as the difference between the latter period and the former period.

Figure 2 shows the spatial distribution of the decadal rainfall change in Australia in the early 1990s. Rainfall reduction occurred in SEA and extended northward along the eastern coast. The rainfall decline along the eastern coast of Australia is concurrent with the rainfall increase in the SSP, off the northeastern coast of Australia (Fig. 3a). The increased rainfall is situated in the southern rim of the SPCZ, leading to the southward expansion and intensification of the SPCZ after the early 1990s.

Fig. 2.
Fig. 2.

Change in Australian rainfall in MAM between the two 19-yr periods of 1991–2009 and 1972–90. Light and dark shadings denote the significance levels of 0.1 and 0.05, respectively, based on a Student’s t test. The contour interval is 0.5 mm day−1, and the contour of zero is omitted.

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0686.1

Fig. 3.
Fig. 3.

As in Fig. 2, but for (a) GPCP precipitation (shading), (b) 850- and (c) 200-hPa horizontal winds (vectors), (d),(e) their divergent component (vectors) superimposed with horizontal divergence (contours), and (f) 500-hPa vertical pressure velocity (contours). The 1979–90 period, instead of the 1972–90 period, is used for composite GPCP precipitation differences due to the absence of data before 1979. Significance anomalies are dotted in (a) at the 0.1 level and shaded in (d)–(f) with light and dark shadings at the levels of 0.1 and 0.05, respectively, and the thick black arrows are significant at the 0.1 level in (b) and (c). The significances are estimated based on a Student’s t test. The black contour interval is 3 × 10−7 s−1 in (d) and (e) and 0.005 Pa s−1 in (f). The scale for the vectors is plotted in the top-right corner of each panel in (b)–(e). The red and green contours in (a) depict the SPCZ averaged for the 1979–90 and 1991–2009 periods, respectively, with the mean precipitation exceeding 6 mm day−1.

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0686.1

The change of autumn rainfall in SEA is related to an in situ southwesterly wind anomaly and a remarkable anticyclone anomaly over Australia in the lower troposphere (Fig. 3b). The anticyclone anomaly enhances the STR intensity over SEA. The relationship between the stronger STR and the rainfall reduction in SEA is consistent with the result of Timbal and Drosdowsky (2013), who indicated that nearly two-thirds of the observed rainfall decline in the austral autumn for the 1997–2009 period can be accounted for by the strengthening of the ridge. In addition to the anticyclonic anomaly over Australia in the lower troposphere, a significant anticyclonic anomaly is also observed over the SSP in the upper troposphere (Fig. 3c). Related to the two anticyclonic anomalies, wind diverges over the east coast of Australia and converges over the SPCZ in the lower troposphere (Fig. 3d), and an opposite situation occurs in the upper troposphere (Fig. 3e). The divergence–convergence anomalies consist of a regional overturning circulation between the east coast of Australia and the SPCZ. Correspondingly, vertical motion is suppressed over the east coast of Australia and enhanced over the SPCZ (Fig. 3f), resulting in a rainfall reduction extending from SEA to northeastern Australia and a rainfall increase over the SPCZ (Fig. 3a). Therefore, the observed decadal change of SEA autumn rainfall is linked to a regional overturning circulation between the east coast of Australia and the SPCZ. This raises the question of what drives the regional overturning circulation.

4. Linking to SST warming in the SSP

Figure 4a shows the difference of SST in MAM between the dry (1991–2009) and wet (1972–90) periods. It is evident that the SEA autumn rainfall is related to a concurrent SST warming centered in the SSP region after 1991. The significant warming in the SST occurs in each month of the austral autumn (Figs. 4b–d). To investigate the historical significance of the interdecadal relationship between the SEA autumn rainfall and the SST in the SSP, we construct an SSP SST index (SSPI; Fig. 5a) by averaging the SST anomalies in the SSP region (5°–30°S, 165°E–115°W). The correlation between the interdecadal components of the SSPI and the SEA rainfall index (SEARI) is −0.77 during the 1900–2018 period, which is significant at the 0.05 level based on the correlation test with the adjusted degrees of freedom by the modified Chelton method (Li et al. 2012). Corresponding to the interdecadal correlation, the dry period in SEA after 1991 is related to the warmer SST in the SSP after the early 1990s. Moreover, extended back to 1900, the SST in the SSP also reached a peak in the late 1930s when a severe drought occurred in SEA. Result from the wavelet analysis suggests that the SSPI exhibits strong interdecadal variations on the periods of 60–70 years, with two warm phases from the late 1920s to early 1950s and from the early 1990s to the late 2000s (Fig. 5b). The two warm phases are concurrent with the two drought phases of the autumn rainfall in SEA (Fig. 1c). These results indicated that the SST warming in the SSP may have enhanced the decadal droughts in SEA.

Fig. 4.
Fig. 4.

As in Fig. 2, but for SST in (a) MAM, (b) March, (c) April, and (d) May. The contour interval is 0.3°C, and the contour of zero is omitted. The red thick box depicts the subtropical South Pacific (SSP) region.

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0686.1

Fig. 5.
Fig. 5.

(a) Time series of the MAM SST averaged over the SSP region (5°–30°S, 165°E–115°W; red solid line with filled circles) and the SEARI (blue solid line with empty circles) and their interdecadal components (dashed lines). (b) The wavelet analysis of the SSPI, with positive values shaded.

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0686.1

To investigate the role of SST warming in the SSP on the decadal droughts in SEA, the GFDL AM2.1 is employed. Two sets of experiments are performed with the AGCM. The control run is forced with the climatological monthly mean SST averaged over the 1971–2000 period. The sensitivity experiment is forced with the same SST prescribed in the control run plus the monthly warm SST anomalies over the SSP domain in MAM between the periods of 1991–2009 and 1972–90 (Fig. 4). Here, only the warm SST anomalies are added to highlight the impact of the SSP SST warming. Both experiments are integrated for 21 years and the simulations in years 2–21, considered as 20 ensemble members with different initial conditions on 1 March, are used. The difference between the sensitivity and control runs represents the impact of the warmer SST over the SSP in the austral autumn. The atmospheric forcings, such as greenhouse gases, aerosols, and ozone, are fixed at the year-2000 level.

Figure 6 shows the simulated responses to the warm SST anomaly over the SSP in the austral autumn. Rainfall is reduced in SEA (Fig. 6a), similar to the observed decadal rainfall change after 1991 (Fig. 3a). The mean rainfall response in SEA is 0.37 mm day−1 or a total of 34 mm in MAM, which is approximately 70% of the observational rainfall decline of 48 mm after the early 1990s. In addition, the decrease of rainfall in northeastern Australia and the increase of rainfall in the SPCZ are also reproduced. The rainfall responses are related to two anticyclonic anomalies over Australia in the lower troposphere (Fig. 6b) and over the SSP in the upper troposphere (Fig. 6c), respectively, and a regional divergent overturning circulation between eastern Australia and the SSP (Figs. 6d–f). The responses are in good agreement with the observation (Fig. 3). The response of the suppressed rainfall over SEA is related to local anomalies of divergence in the lower troposphere (Fig. 6d), descent (Fig. 6f), and convergence in the upper troposphere (Fig. 6e). In the SPCZ, the response of the enhanced rainfall is related to the lower-tropospheric convergence, ascent, and upper-tropospheric divergence. The similarity in rainfall and atmospheric circulation between the model simulations and the observed decadal change indicates that the SST warming in the SSP since the early 1990s contributes significantly to the rainfall reduction in SEA.

Fig. 6.
Fig. 6.

As in Fig. 3, but for responses in GFDL AM2.1 to the prescribed warm SSP SST anomaly in MAM. The red and green contours in (a) depict the SPCZ in the control and sensitivity runs, respectively, with the mean precipitation exceeding 6 mm day−1. (d),(e) The contour interval is 5 × 10−7 s−1.

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0686.1

The above analysis of the SST-forced experiments confirms a mechanism that the warmer SST in the SSP induces a decadal reduction of SEA autumn rainfall through a divergent overturning circulation response. Specifically, the impact of a warmer SSP SST on the SEA rainfall can be interpreted by the associated divergent overturning circulation, which is characterized by the reversal variations of velocity potential over the SSP and Australia in both the upper and lower troposphere (Fig. 7). The warmer SST in the SSP increases local rainfall, resulting in the release of latent heating in the SSP, and thus induces the convergence in the lower troposphere, ascent, and divergence in the upper troposphere over the SSP (Fig. 6). Meanwhile, the upper-tropospheric divergent flow converges toward the west, with convergence over the east coast of Australia and the coupled subsiding motion and divergence in the lower troposphere. As such, rainfall decreases along the east coast of Australia including SEA.

Fig. 7.
Fig. 7.

As in Fig. 6, but for velocity potentials at (a) 200 and (b) 850 hPa. The contour interval is 1 × 105 m2 s−1.

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0686.1

The previous studies have suggested that the climate change–induced poleward shift of ocean–atmosphere circulation in the Southern Hemisphere may shift the position of the subtropical dry zone and cause the recent autumn rainfall decline in SEA in the 1990s and 2000s (Cai et al. 2012; Cai and Cowan 2013). This study, on the other hand, highlights the importance of the SSP SST warming in the SEA rainfall reduction in the austral autumn since the early 1990s. Is there any connection between the SSP SST warming and climate change or is the SSP SST warming after the early 1990s only the result of natural multidecadal variability? In the next, the relative contributions of climate change and natural multidecadal variability on the SSP SST warming after the early 1990s are discussed.

To obtain the climate change–related SST anomalies, we first calculate the leading empirical orthogonal function (EOF) mode of the global MAM SST during the period of 1904–2014 between 60°S and 60°N, in which interannual variability is first removed with a Lanczos low-pass filter of 10 years. The total number of weights of 9 in the Lanczos filter is used, and the first and last 4 years are therefore excluded to eliminate edge effects. The first EOF mode accounts for 55% of the variance and depicts a homogeneous warming signal in the global SST. The corresponding principal component (PC1) is consistent with the time series of the global-mean surface temperature index (figure not shown), with a correlation coefficient of 0.98 between them for the period of 1904–2014. The first EOF mode, therefore, is generally referred to as the global warming mode (e.g., Parker et al. 2007; Zhang 2016). We then reconstruct the global warming–related SST anomalies as the multiplication of the first EOF by the PC1 time series. Accordingly, the total SST anomalies are divided into two components: the component related to the global warming and the residual component due to natural multidecadal variability.

As shown in Fig. 8b, global warming induces a positive SST change in the Pacific after the early 1990s, with the maximum signals in the western Pacific and the midlatitude South Pacific. The mean SST change averaged over the SSP region due to global warming is 0.11°C between the 1991–2009 and 1972–90 periods, which accounts for 44% of the total SST change (0.25°C). Accordingly, natural multidecadal variability (the residual) contributes to the remainder (56%) of the SST warming in the SSP. Unlike the change induced by global warming, the SST change due to the natural multidecadal variability is concentrated mainly in the SSP domain (Fig. 8c). However, it should be noted that the estimation here is based on the assumption that the impacts of global warming and natural multidecadal variability are linearly separable. Therefore, possible interactions between the climate drivers (Gallant et al. 2012) are not considered here, which requires further investigation based on model simulations.

Fig. 8.
Fig. 8.

As in Fig. 4a, but for (a) total SST change, (b) SST change related to global warming, (c) the residual (natural multidecadal variability), and (d) SST change due to the IPO. See text for more details.

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0686.1

Previous studies have suggested a possible impact of the IPO on the SEA rainfall (Power et al. 1999; Folland et al. 2002; Kiem et al. 2003; Kiem and Franks 2004; Verdon-Kidd and Kiem 2009b). We further estimate the contribution from the IPO on the SST change in the SSP. The IPO index (Henley et al. 2015) is calculated from the difference of the residual SST component averaged over the central equatorial Pacific (10°S–10°N, 170°E–90°W) and the average of the residual SST component in the northwest (25°–45°N, 140°E–145°W) and southwest (50°–15°S, 150°E–160°W) Pacific. The lead–lag correlation shows that the correlation coefficient between the IPO index and the SSPI reaches the maxima when the former leads the latter by 10 years. Thus, the IPO-related SST change is reconstructed as the regressed SST pattern of the residual SST data with a lag of 10 years against the IPO index multiplied by 0.35°C, the change of the mean IPO index averaged for between the 1981–99 and 1962–80 periods. The warm SST anomaly related to the IPO (Fig. 8d) exhibits a similar spatial pattern to the residual SST change (Fig. 8c). The IPO-induced mean SST change over the SSP is 0.06°C, which accounts for 24% of the total SST change in the SSP after the early 1990s. In combination with the results that the impact of the IPO on the SEA rainfall could be caused by modulation of the SPCZ (Folland et al. 2002) and that the warm SSP SST affects the SEA rainfall through the SPCZ proposed in this study, we may conclude that the impact of the IPO on the SEA rainfall is probably attributed to the IPO-induced SST anomaly in the SSP.

5. Discussion and conclusions

a. Discussion

1) Relationship with the subtropical ridge

Timbal and Drosdowsky (2013) highlighted that the strengthened STR has a strong influence on the observed decadal rainfall decline during the period of 1997–2009. As shown in Fig. 9a, there is a significant correlation of −0.7 between the interdecadal components of the SEA rainfall and the STR intensity in the austral autumn during the period from 1900 to 2011. As such, the stronger STR leads to the reduction of SEA autumn rainfall, consistent with the results of Timbal and Drosdowsky (2013) and Larsen and Nicholls (2009). However, the question of whether the STR is the ultimate forcing of the SEA drought or it is driven by another factor remains.

Fig. 9.
Fig. 9.

(a),(b) Time series of the interdecadal components of the subtropical ridge intensity index (STRI; blue solid line) and the (a) SEARI (filled line) and (b) SSPI (filled line) during 1900–2011, in which the 1971–2000 mean is subtracted. (c) The wavelet analysis of the STRI, with positive values shaded.

Citation: Journal of Climate 33, 6; 10.1175/JCLI-D-19-0686.1

Figure 9b shows the interdecadal variations of the SSP SST and the STR intensity. The correlation between them is 0.89 during the 1900–2011 period. Moreover, the wavelet analysis of the STR intensity also shows strong interdecadal variations with the periods of 50–70 years (Fig. 9c). The two positive phases, with a stronger STR from the late 1920s to late 1940s and from late 1980s to late 2000s, are in good agreement with the two warm phases of the SSP SST (Fig. 5b) and the two drought phases of the SEA rainfall (Fig. 1c). The relationship between the SSP SST and the STR can be interpreted as a result of the regional overturning circulation induced by the warming SST in the SSP as proposed in this study, with the upward branch in the SSP and the downward branch in eastern Australia. The downward counter increases the surface pressure around Australia and intensifies the STR. We then conclude that the STR intensity change proposed by Timbal and Drosdowsky (2013) and Larsen and Nicholls (2009) is not the ultimate forcing of the SEA drought after the early 1990s but is likely caused by the SSP SST warming. In addition, it is also noted that the phase change to a stronger STR in late 1980s is slightly earlier than the phase changes to a warmer SST in the SSP and a drought in SEA in the early 1990s, suggesting that possible impact of other factors, such as, the effect of the southward shift of Hadley circulation in the Southern Hemisphere under global warming (Kushner et al. 2001; Lu et al. 2007) on the change in the STR.

2) Coherent drought in northeastern Australia

As shown in Figs. 2 and 3a, the autumn rainfall reduction is also seen in northeastern Australia, especially along the eastern coast. This rainfall reduction is probably related to the 850-hPa easterly anomaly north of Australia over the region (0°–10°S, 120°–150°E) (Fig. 3b). In fact, Cai and Cowan (2013) suggested that the easterly anomaly enhance the tropical postmonsoonal climatological easterlies in autumn and suppresses rainfall in northeastern Australia. They have demonstrated that after the early 1990s the tropical easterly–induced drought expands poleward from northeastern to southeastern Australia. The enhanced tropical easterly after the early 1990s [Fig. 3b in this study and Fig. 6 of Cai and Cowan (2013)] may lead to rainfall reduction in SEA and contribute to decadal changes in SEA rainfall. However, a reason for the decadal change in the tropical easterly was not given by Cai and Cowan (2013).

This study proposes a mechanism that the decadal change in the tropical easterly and autumn rainfall reduction in northeastern Australia are due to the SST warming in the SSP. In response to the SST warming, autumn rainfall is suppressed in both northeastern and southeastern Australia (Figs. 3a and 6a), and an easterly anomaly over northern Australia, which is related to the anticyclonic anomaly over Australia (Figs. 3b and 6b), is apparent in the lower troposphere. These responses are attributed to the SST-warming-induced regional divergent overturning circulation between the SSP and eastern Australia (Fig. 7).

b. Conclusions

This study identifies that the SEA autumn rainfall experienced a remarkable decadal reduction after 1991. The 19-yr mean rainfall decreases by approximately one-third from 159 mm in the 1972–90 period to 111 mm in the 1991–2009 period. The decadal reduction of SEA autumn rainfall is related to the rainfall deficit in northeastern Australia and the southward expansion and intensification of the South Pacific convergence zone.

The change in the SEA autumn rainfall is attributed to a concurrent SST warming in the SSP. Since the early 1990s, the increasing SST in the SSP has enhanced rainfall to the northwest in the South Pacific convergence zone; the latter triggers a divergent overturning circulation with the subsidence counter over eastern Australia. As such, the subsidence increases surface pressure over Australia, intensifies the subtropical ridge, and thus reduces the rainfall in southeastern and northeastern Australia. The mechanism is further confirmed by the results of the sensitivity experiment with an AGCM. According to the model simulations, the SSP SST warming reduces SEA autumn rainfall by approximately 34 mm, which is 70% of the observed autumn rainfall reduction in SEA after the early 1990s. Moreover, we further estimate the relative contributions of global warming and natural multidecadal variability on the SSP SST warming after the early 1990s based on the observational data. Global warming accounts for 44% of the SSP SST warming between the 1991–2009 and 1972–90 periods, and natural multidecadal variability accounts for 56%, in which the interdecadal Pacific oscillation contributes to 24% of the SSP SST change.

Acknowledgments

The authors thank three anonymous reviewers for their valuable comments and suggestions that have greatly improved the original manuscript. The authors also thank Dr. Bertrand Timbal for providing the STR intensity data. Z. Lin was supported by the Youth Innovation Promotion Association of the Chinese Academy of Sciences (2017105) and the National Natural Science Foundation of China (Grant 41775062). Y. Liu was supported by the National Key Research and Development Program (Grant 2016YFA0600603) and the National Natural Science Foundation of China (Grant 41605058).

REFERENCES

  • Adler, R. F., and Coauthors, 2003: The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 11471167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., and Coauthors, 2004: The new GFDL global atmosphere and land model AM2–LM2: Evaluation with prescribed SST simulations. J. Climate, 17, 46414673, https://doi.org/10.1175/JCLI-3223.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashok, K., S. K. Behera, S. A. Rao, H. Weng, and T. Yamagata, 2007: El Niño Modoki and its possible teleconnection. J. Geophys. Res., 112, C11007, https://doi.org/10.1029/2006JC003798.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beesley, C., A. J. Frost, and J. Zajaczkowski, 2009: A comparison of the BAWAP and SILO spatially interpolated daily rainfall datasets. 18th World IMACS/MODSIM Congress, Cairns, Australia, 38863892.

  • Cai, W., and T. Cowan, 2008: Dynamics of late autumn rainfall reduction over southeastern Australia. Geophys. Res. Lett., 35, L09708, https://doi.org/10.1029/2008GL033727.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, W., and T. Cowan, 2013: Southeast Australia autumn rainfall reduction: A climate-change-induced poleward shift of ocean–atmosphere circulation. J. Climate, 26, 189205, https://doi.org/10.1175/JCLI-D-12-00035.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, W., T. Cowan, and A. Sullivan, 2009: Recent unprecedented skewness towards positive Indian Ocean Dipole occurrences and its impact on Australian rainfall. Geophys. Res. Lett., 36, L11705, https://doi.org/10.1029/2009GL037604.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, W., P. van Rensch, T. Cowan, and H. H. Hendon, 2011: Teleconnection pathways of ENSO and the IOD and the mechanisms for impacts on Australian rainfall. J. Climate, 24, 39103923, https://doi.org/10.1175/2011JCLI4129.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, W., T. Cowan, and M. Thatcher, 2012: Rainfall reductions over Southern Hemisphere semi-arid regions: The role of subtropical dry zone expansion. Sci. Rep., 2, 702, https://doi.org/10.1038/srep00702.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, W., A. Purich, T. Cowan, P. van Rensch, and E. Weller, 2014: Did climate change–induced rainfall trends contribute to the Australian Millennium Drought? J. Climate, 27, 31453168, https://doi.org/10.1175/JCLI-D-13-00322.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chowdhury, R. K., S. Beecham, J. Boland, and J. Piantadosi, 2015: Understanding South Australian rainfall trends and step changes. Int. J. Climatol., 35, 348360, https://doi.org/10.1002/joc.3982.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chubb, T. H., M. J. Manton, S. T. Siems, and A. D. Peace, 2016: Evaluation of the AWAP daily precipitation spatial analysis with an independent gauge network in the Snowy Mountains. J. South. Hemisphere Earth Syst. Sci., 66, 5567, https://doi.org/10.22499/3.6601.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delworth, T. L., and Coauthors, 2006: GFDL’s CM2 global coupled climate models. Part I: Formulation and simulation characteristics. J. Climate, 19, 643674, https://doi.org/10.1175/JCLI3629.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drosdowsky, W., 2005: The latitude of the subtropical ridge over Eastern Australia: The L index revisited. Int. J. Climatol., 25, 12911299, https://doi.org/10.1002/joc.1196.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Folland, C. K., J. A. Renwick, M. J. Salinger, and A. B. Mullan, 2002: Relative influences of the Interdecadal Pacific Oscillation and ENSO on the South Pacific convergence zone. Geophys. Res. Lett., 29, 1643, https://doi.org/10.1029/2001GL014201.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frederiksen, C. S., J. S. Frederiksen, J. M. Sisson, and S. L. Osbrough, 2013: Changes and projections in the annual cycle of the Southern Hemisphere circulation, storm tracks and Australian rainfall. EGU General Assembly, Vienna, Austria, European Geosciences Union, Abstract EGU2013-1101.

  • Frederiksen, J. S., C. S. Frederiksen, S. L. Osbrough, and J. M. Sisson, 2011: Changes in Southern Hemisphere rainfall, circulation and weather systems. 19th Int. Congress on Modelling and Simulation, Perth, Australia, 2712–2718.

  • Gallant, A. J. E., and Coauthors, 2012: Understanding hydroclimate processes in the Murray-Darling Basin for natural resources management. Hydrol. Earth Syst. Sci., 16, 20492068, https://doi.org/10.5194/hess-16-2049-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gergis, J., and Coauthors, 2012: On the long-term context of the 1997–2009 ‘Big Dry’ in South-Eastern Australia: Insights from a 206-year multi-proxy rainfall reconstruction. Climatic Change, 111, 923944, https://doi.org/10.1007/s10584-011-0263-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hendon, H. H., D. W. J. Thompson, and M. C. Wheeler, 2007: Australian rainfall and surface temperature variations associated with the Southern Hemisphere annular mode. J. Climate, 20, 24522467, https://doi.org/10.1175/JCLI4134.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henley, B. J., and Coauthors, 2015: A tripole index for the interdecadal Pacific Oscillation. Climate Dyn., 45, 30773090, https://doi.org/10.1007/s00382-015-2525-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 1997: The Global Precipitation Climatology Project (GPCP) combined precipitation dataset. Bull. Amer. Meteor. Soc., 78, 520, https://doi.org/10.1175/1520-0477(1997)078<0005:TGPCPG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, D. A., W. Wang, and R. Fawcett, 2009: High-quality spatial climate data-sets for Australia. Aust. Meteor. Oceanogr. J., 58, 233248, https://doi.org/10.22499/2.5804.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kiem, A. S., and S. W. Franks, 2004: Multi-decadal variability of drought risk, eastern Australia. Hydrol. Processes, 18, 20392050, https://doi.org/10.1002/hyp.1460.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kiem, A. S., S. W. Franks, and G. Kuczera, 2003: Multi-decadal variability of flood risk. Geophys. Res. Lett., 30, 1035, https://doi.org/10.1029/2002GL015992.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • King, A. D., L. V. Alexander, and M. G. Donat, 2013: The efficacy of using gridded data to examine extreme rainfall characteristics: A case study for Australia. Int. J. Climatol., 33, 23762387, https://doi.org/10.1002/joc.3588.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kushner, P. J., I. M. Held, and T. L. Delworth, 2001: Southern Hemisphere atmospheric circulation response to global warming. J. Climate, 14, 22382249, https://doi.org/10.1175/1520-0442(2001)014<0001:SHACRT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kwon, M., J.-G. Jhun, and K.-J. Ha, 2007: Decadal change in East Asian summer monsoon circulation in the mid-1990s. Geophys. Res. Lett., 34, L21706, https://doi.org/10.1029/2007GL031977.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Larsen, S. H., and N. Nicholls, 2009: Southern Australian rainfall and the subtropical ridge: Variations, interrelationships, and trends. Geophys. Res. Lett., 36, L08708, https://doi.org/10.1029/2009GL037786.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leblanc, M. J., P. Tregoning, G. Ramillien, S. O. Tweed, and A. Fakes, 2009: Basin-scale, integrated observations of the early 21st century multiyear drought in southeast Australia. Water Resour. Res., 45, W04408, https://doi.org/10.1029/2008WR007333.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lepage, Y., 1971: A combination of Wilcoxson’s and Ansari-Bradley’s statistics. Biometrika, 58, 213217, https://doi.org/10.1093/biomet/58.1.213.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Y., J. P. Li, and J. Feng, 2012: A teleconnection between the reduction of rainfall in southwest Western Australia and North China. J. Climate, 25, 84448461, https://doi.org/10.1175/JCLI-D-11-00613.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, S., 2004: A “vertically Lagrangian” finite-volume dynamical core for global models. Mon. Wea. Rev., 132, 22932307, https://doi.org/10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., G. Huang, and R. Huang, 2011: Inter-decadal variability of summer rainfall in Eastern China detected by the Lepage test. Theor. Appl. Climatol., 106, 481488, https://doi.org/10.1007/s00704-011-0442-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, J., G. A. Vecchi, and T. Reichler, 2007: Expansion of the Hadley cell under global warming. Geophys. Res. Lett., 34, L06805, https://doi.org/10.1029/2006GL028443.

    • Search Google Scholar
    • Export Citation
  • Meneghini, B., I. Simmonds, and I. N. Smith, 2007: Association between Australian rainfall and the Southern Annular Mode. Int. J. Climatol., 27, 109121, https://doi.org/10.1002/joc.1370.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Milly, P., and A. Shmakin, 2002: Global modeling of land water and energy balances. Part I: The land dynamics (LaD) model. J. Hydrometeor., 3, 283299, https://doi.org/10.1175/1525-7541(2002)003<0283:GMOLWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murphy, B. F., and B. Timbal, 2008: A review of recent climate variability and climate change in southeastern Australia. Int. J. Climatol., 28, 859879, https://doi.org/10.1002/joc.1627.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nicholls, N., 2010: Local and remote causes of the southern Australian autumn-winter rainfall decline, 1958–2007. Climate Dyn., 34, 835845, https://doi.org/10.1007/s00382-009-0527-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parker, D., C. Folland, A. Scaife, J. Knight, A. Colman, P. Baines, and B. Dong, 2007: Decadal to multidecadal variability and the climate change background. J. Geophys. Res., 112, D18115, https://doi.org/10.1029/2007JD008411.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Percival, D. B., and H. O. Mofjeld, 1997: Analysis of subtidal coastal sea level fluctuations using wavelets. J. Amer. Stat. Assoc., 92, 868880, https://doi.org/10.1080/01621459.1997.10474042.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Power, S., T. Casey, C. Folland, A. Colman, and V. Mehta, 1999: Inter-decadal modulation of the impact of ENSO on Australia. Climate Dyn., 15, 319324, https://doi.org/10.1007/s003820050284.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Purich, A., T. Cowan, S.-K. Min, and W. Cai, 2013: Autumn precipitation trends over Southern Hemisphere midlatitudes as simulated by CMIP5 models. J. Climate, 26, 83418356, https://doi.org/10.1175/JCLI-D-13-00007.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taschetto, A. S., and M. H. England, 2009: El Niño Modoki impacts on Australian rainfall. J. Climate, 22, 31673174, https://doi.org/10.1175/2008JCLI2589.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Timbal, B., and H. Hendon, 2011: The role of tropical modes of variability in recent rainfall deficits across the Murray-Darling Basin. Water Resour. Res., 47, W00G09, https://doi.org/10.1029/2010WR009834.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Timbal, B., and W. Drosdowsky, 2013: The relationship between the decline of southeastern Australian rainfall and the strengthening of the subtropical ridge. Int. J. Climatol., 33, 10211034, https://doi.org/10.1002/joc.3492.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Timbal, B., and R. Fawcett, 2013: A historical perspective on southeastern Australian rainfall since 1865 using the instrumental record. J. Climate, 26, 11121129, https://doi.org/10.1175/JCLI-D-12-00082.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torrence, C., and G. P. Compo, 1998: A practical guide to wavelet analysis. Bull. Amer. Meteor. Soc., 79, 6178, https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tozer, C. R., A. S. Kiem, and D. C. Verdon-Kidd, 2012: On the uncertainties associated with using gridded rainfall data as a proxy for observed. Hydrol. Earth Syst. Sci., 16, 14811499, https://doi.org/10.5194/hess-16-1481-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ummenhofer, C. C., M. H. England, P. C. McIntosh, G. A. Meyers, M. J. Pook, J. S. Risbey, A. Sen Gupta, and A. S. Taschetto, 2009: What causes southeast Australia’s worst droughts? Geophys. Res. Lett., 36, L04706, https://doi.org/10.1029/2008GL036801.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verdon-Kidd, D. C., and A. S. Kiem, 2009a: Nature and causes of protracted droughts in southeast Australia: Comparison between the Federation, WWII, and Big Dry droughts. Geophys. Res. Lett., 36, L22707, https://doi.org/10.1029/2009GL041067.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verdon-Kidd, D. C., and A. S. Kiem, 2009b: On the relationship between large-scale climate modes and regional synoptic patterns that drive Victorian rainfall. Hydrol. Earth Syst. Sci., 13, 467479, https://doi.org/10.5194/hess-13-467-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verdon-Kidd, D. C., and A. S. Kiem, 2014: Synchronicity of historical dry spells in the Southern Hemisphere. Hydrol. Earth Syst. Sci., 18, 22572264, https://doi.org/10.5194/hess-18-2257-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verdon-Kidd, D. C., A. S. Kiem, and R. Moran, 2014: Links between the Big Dry in Australia and hemispheric multi-decadal climate variability—Implications for water resource management. Hydrol. Earth Syst. Sci., 18, 22352256, https://doi.org/10.5194/hess-18-2235-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Watterson, I. G., 2010: Relationships between southeastern Australian rainfall and sea surface temperatures examined using a climate model. J. Geophys. Res., 115, D10108, https://doi.org/10.1029/2009JD012120.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yonetani, T., and G. J. McCabe, 1994: Abrupt changes in regional temperature in the conterminous United States. Climate Res., 4, 1323, https://doi.org/10.3354/cr004013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, L., 2016: The roles of external forcing and natural variability in global warming hiatuses. Climate Dyn., 47, 31573169, https://doi.org/10.1007/s00382-016-3018-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
Save
  • Adler, R. F., and Coauthors, 2003: The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 11471167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., and Coauthors, 2004: The new GFDL global atmosphere and land model AM2–LM2: Evaluation with prescribed SST simulations. J. Climate, 17, 46414673, https://doi.org/10.1175/JCLI-3223.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashok, K., S. K. Behera, S. A. Rao, H. Weng, and T. Yamagata, 2007: El Niño Modoki and its possible teleconnection. J. Geophys. Res., 112, C11007, https://doi.org/10.1029/2006JC003798.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beesley, C., A. J. Frost, and J. Zajaczkowski, 2009: A comparison of the BAWAP and SILO spatially interpolated daily rainfall datasets. 18th World IMACS/MODSIM Congress, Cairns, Australia, 38863892.

  • Cai, W., and T. Cowan, 2008: Dynamics of late autumn rainfall reduction over southeastern Australia. Geophys. Res. Lett., 35, L09708, https://doi.org/10.1029/2008GL033727.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, W., and T. Cowan, 2013: Southeast Australia autumn rainfall reduction: A climate-change-induced poleward shift of ocean–atmosphere circulation. J. Climate, 26, 189205, https://doi.org/10.1175/JCLI-D-12-00035.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, W., T. Cowan, and A. Sullivan, 2009: Recent unprecedented skewness towards positive Indian Ocean Dipole occurrences and its impact on Australian rainfall. Geophys. Res. Lett., 36, L11705, https://doi.org/10.1029/2009GL037604.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, W., P. van Rensch, T. Cowan, and H. H. Hendon, 2011: Teleconnection pathways of ENSO and the IOD and the mechanisms for impacts on Australian rainfall. J. Climate, 24, 39103923, https://doi.org/10.1175/2011JCLI4129.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, W., T. Cowan, and M. Thatcher, 2012: Rainfall reductions over Southern Hemisphere semi-arid regions: The role of subtropical dry zone expansion. Sci. Rep., 2, 702, https://doi.org/10.1038/srep00702.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, W., A. Purich, T. Cowan, P. van Rensch, and E. Weller, 2014: Did climate change–induced rainfall trends contribute to the Australian Millennium Drought? J. Climate, 27, 31453168, https://doi.org/10.1175/JCLI-D-13-00322.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chowdhury, R. K., S. Beecham, J. Boland, and J. Piantadosi, 2015: Understanding South Australian rainfall trends and step changes. Int. J. Climatol., 35, 348360, https://doi.org/10.1002/joc.3982.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chubb, T. H., M. J. Manton, S. T. Siems, and A. D. Peace, 2016: Evaluation of the AWAP daily precipitation spatial analysis with an independent gauge network in the Snowy Mountains. J. South. Hemisphere Earth Syst. Sci., 66, 5567, https://doi.org/10.22499/3.6601.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delworth, T. L., and Coauthors, 2006: GFDL’s CM2 global coupled climate models. Part I: Formulation and simulation characteristics. J. Climate, 19, 643674, https://doi.org/10.1175/JCLI3629.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drosdowsky, W., 2005: The latitude of the subtropical ridge over Eastern Australia: The L index revisited. Int. J. Climatol., 25, 12911299, https://doi.org/10.1002/joc.1196.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Folland, C. K., J. A. Renwick, M. J. Salinger, and A. B. Mullan, 2002: Relative influences of the Interdecadal Pacific Oscillation and ENSO on the South Pacific convergence zone. Geophys. Res. Lett., 29, 1643, https://doi.org/10.1029/2001GL014201.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frederiksen, C. S., J. S. Frederiksen, J. M. Sisson, and S. L. Osbrough, 2013: Changes and projections in the annual cycle of the Southern Hemisphere circulation, storm tracks and Australian rainfall. EGU General Assembly, Vienna, Austria, European Geosciences Union, Abstract EGU2013-1101.

  • Frederiksen, J. S., C. S. Frederiksen, S. L. Osbrough, and J. M. Sisson, 2011: Changes in Southern Hemisphere rainfall, circulation and weather systems. 19th Int. Congress on Modelling and Simulation, Perth, Australia, 2712–2718.

  • Gallant, A. J. E., and Coauthors, 2012: Understanding hydroclimate processes in the Murray-Darling Basin for natural resources management. Hydrol. Earth Syst. Sci., 16, 20492068, https://doi.org/10.5194/hess-16-2049-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gergis, J., and Coauthors, 2012: On the long-term context of the 1997–2009 ‘Big Dry’ in South-Eastern Australia: Insights from a 206-year multi-proxy rainfall reconstruction. Climatic Change, 111, 923944, https://doi.org/10.1007/s10584-011-0263-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hendon, H. H., D. W. J. Thompson, and M. C. Wheeler, 2007: Australian rainfall and surface temperature variations associated with the Southern Hemisphere annular mode. J. Climate, 20, 24522467, https://doi.org/10.1175/JCLI4134.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henley, B. J., and Coauthors, 2015: A tripole index for the interdecadal Pacific Oscillation. Climate Dyn., 45, 30773090, https://doi.org/10.1007/s00382-015-2525-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 1997: The Global Precipitation Climatology Project (GPCP) combined precipitation dataset. Bull. Amer. Meteor. Soc., 78, 520, https://doi.org/10.1175/1520-0477(1997)078<0005:TGPCPG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, D. A., W. Wang, and R. Fawcett, 2009: High-quality spatial climate data-sets for Australia. Aust. Meteor. Oceanogr. J., 58, 233248, https://doi.org/10.22499/2.5804.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kiem, A. S., and S. W. Franks, 2004: Multi-decadal variability of drought risk, eastern Australia. Hydrol. Processes, 18, 20392050, https://doi.org/10.1002/hyp.1460.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kiem, A. S., S. W. Franks, and G. Kuczera, 2003: Multi-decadal variability of flood risk. Geophys. Res. Lett., 30, 1035, https://doi.org/10.1029/2002GL015992.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • King, A. D., L. V. Alexander, and M. G. Donat, 2013: The efficacy of using gridded data to examine extreme rainfall characteristics: A case study for Australia. Int. J. Climatol., 33, 23762387, https://doi.org/10.1002/joc.3588.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kushner, P. J., I. M. Held, and T. L. Delworth, 2001: Southern Hemisphere atmospheric circulation response to global warming. J. Climate, 14, 22382249, https://doi.org/10.1175/1520-0442(2001)014<0001:SHACRT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kwon, M., J.-G. Jhun, and K.-J. Ha, 2007: Decadal change in East Asian summer monsoon circulation in the mid-1990s. Geophys. Res. Lett., 34, L21706, https://doi.org/10.1029/2007GL031977.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Larsen, S. H., and N. Nicholls, 2009: Southern Australian rainfall and the subtropical ridge: Variations, interrelationships, and trends. Geophys. Res. Lett., 36, L08708, https://doi.org/10.1029/2009GL037786.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leblanc, M. J., P. Tregoning, G. Ramillien, S. O. Tweed, and A. Fakes, 2009: Basin-scale, integrated observations of the early 21st century multiyear drought in southeast Australia. Water Resour. Res., 45, W04408, https://doi.org/10.1029/2008WR007333.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lepage, Y., 1971: A combination of Wilcoxson’s and Ansari-Bradley’s statistics. Biometrika, 58, 213217, https://doi.org/10.1093/biomet/58.1.213.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Y., J. P. Li, and J. Feng, 2012: A teleconnection between the reduction of rainfall in southwest Western Australia and North China. J. Climate, 25, 84448461, https://doi.org/10.1175/JCLI-D-11-00613.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, S., 2004: A “vertically Lagrangian” finite-volume dynamical core for global models. Mon. Wea. Rev., 132, 22932307, https://doi.org/10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., G. Huang, and R. Huang, 2011: Inter-decadal variability of summer rainfall in Eastern China detected by the Lepage test. Theor. Appl. Climatol., 106, 481488, https://doi.org/10.1007/s00704-011-0442-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, J., G. A. Vecchi, and T. Reichler, 2007: Expansion of the Hadley cell under global warming. Geophys. Res. Lett., 34, L06805, https://doi.org/10.1029/2006GL028443.

    • Search Google Scholar
    • Export Citation
  • Meneghini, B., I. Simmonds, and I. N. Smith, 2007: Association between Australian rainfall and the Southern Annular Mode. Int. J. Climatol., 27, 109121, https://doi.org/10.1002/joc.1370.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Milly, P., and A. Shmakin, 2002: Global modeling of land water and energy balances. Part I: The land dynamics (LaD) model. J. Hydrometeor., 3, 283299, https://doi.org/10.1175/1525-7541(2002)003<0283:GMOLWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murphy, B. F., and B. Timbal, 2008: A review of recent climate variability and climate change in southeastern Australia. Int. J. Climatol., 28, 859879, https://doi.org/10.1002/joc.1627.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nicholls, N., 2010: Local and remote causes of the southern Australian autumn-winter rainfall decline, 1958–2007. Climate Dyn., 34, 835845, https://doi.org/10.1007/s00382-009-0527-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parker, D., C. Folland, A. Scaife, J. Knight, A. Colman, P. Baines, and B. Dong, 2007: Decadal to multidecadal variability and the climate change background. J. Geophys. Res., 112, D18115, https://doi.org/10.1029/2007JD008411.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Percival, D. B., and H. O. Mofjeld, 1997: Analysis of subtidal coastal sea level fluctuations using wavelets. J. Amer. Stat. Assoc., 92, 868880, https://doi.org/10.1080/01621459.1997.10474042.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Power, S., T. Casey, C. Folland, A. Colman, and V. Mehta, 1999: Inter-decadal modulation of the impact of ENSO on Australia. Climate Dyn., 15, 319324, https://doi.org/10.1007/s003820050284.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Purich, A., T. Cowan, S.-K. Min, and W. Cai, 2013: Autumn precipitation trends over Southern Hemisphere midlatitudes as simulated by CMIP5 models. J. Climate, 26, 83418356, https://doi.org/10.1175/JCLI-D-13-00007.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taschetto, A. S., and M. H. England, 2009: El Niño Modoki impacts on Australian rainfall. J. Climate, 22, 31673174, https://doi.org/10.1175/2008JCLI2589.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Timbal, B., and H. Hendon, 2011: The role of tropical modes of variability in recent rainfall deficits across the Murray-Darling Basin. Water Resour. Res., 47, W00G09, https://doi.org/10.1029/2010WR009834.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Timbal, B., and W. Drosdowsky, 2013: The relationship between the decline of southeastern Australian rainfall and the strengthening of the subtropical ridge. Int. J. Climatol., 33, 10211034, https://doi.org/10.1002/joc.3492.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Timbal, B., and R. Fawcett, 2013: A historical perspective on southeastern Australian rainfall since 1865 using the instrumental record. J. Climate, 26, 11121129, https://doi.org/10.1175/JCLI-D-12-00082.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torrence, C., and G. P. Compo, 1998: A practical guide to wavelet analysis. Bull. Amer. Meteor. Soc., 79, 6178, https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tozer, C. R., A. S. Kiem, and D. C. Verdon-Kidd, 2012: On the uncertainties associated with using gridded rainfall data as a proxy for observed. Hydrol. Earth Syst. Sci., 16, 14811499, https://doi.org/10.5194/hess-16-1481-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ummenhofer, C. C., M. H. England, P. C. McIntosh, G. A. Meyers, M. J. Pook, J. S. Risbey, A. Sen Gupta, and A. S. Taschetto, 2009: What causes southeast Australia’s worst droughts? Geophys. Res. Lett., 36, L04706, https://doi.org/10.1029/2008GL036801.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verdon-Kidd, D. C., and A. S. Kiem, 2009a: Nature and causes of protracted droughts in southeast Australia: Comparison between the Federation, WWII, and Big Dry droughts. Geophys. Res. Lett., 36, L22707, https://doi.org/10.1029/2009GL041067.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verdon-Kidd, D. C., and A. S. Kiem, 2009b: On the relationship between large-scale climate modes and regional synoptic patterns that drive Victorian rainfall. Hydrol. Earth Syst. Sci., 13, 467479, https://doi.org/10.5194/hess-13-467-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verdon-Kidd, D. C., and A. S. Kiem, 2014: Synchronicity of historical dry spells in the Southern Hemisphere. Hydrol. Earth Syst. Sci., 18, 22572264, https://doi.org/10.5194/hess-18-2257-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verdon-Kidd, D. C., A. S. Kiem, and R. Moran, 2014: Links between the Big Dry in Australia and hemispheric multi-decadal climate variability—Implications for water resource management. Hydrol. Earth Syst. Sci., 18, 22352256, https://doi.org/10.5194/hess-18-2235-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Watterson, I. G., 2010: Relationships between southeastern Australian rainfall and sea surface temperatures examined using a climate model. J. Geophys. Res., 115, D10108, https://doi.org/10.1029/2009JD012120.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yonetani, T., and G. J. McCabe, 1994: Abrupt changes in regional temperature in the conterminous United States. Climate Res., 4, 1323, https://doi.org/10.3354/cr004013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, L., 2016: The roles of external forcing and natural variability in global warming hiatuses. Climate Dyn., 47, 31573169, https://doi.org/10.1007/s00382-016-3018-6.

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

    (a) Time series of March–May (MAM) mean land rainfall total (mm) in SEA south of 33°S and east of 135°E (solid line) from 1900 to 2018 and its interdecadal component (dashed line). (b) The sliding value of the Lepage statistics of the SEARI before and after 9 years. The years 1991 and 2010 (vertical solid red lines) are identified as two changepoint years, with the maximum statistical values exceeding the critical value of 5.99 (horizontal dashed line), indicating that both changepoint years are significant at the 0.05 level. (c) The wavelet analysis of the SEARI, with negative values shaded.

  • Fig. 2.

    Change in Australian rainfall in MAM between the two 19-yr periods of 1991–2009 and 1972–90. Light and dark shadings denote the significance levels of 0.1 and 0.05, respectively, based on a Student’s t test. The contour interval is 0.5 mm day−1, and the contour of zero is omitted.

  • Fig. 3.

    As in Fig. 2, but for (a) GPCP precipitation (shading), (b) 850- and (c) 200-hPa horizontal winds (vectors), (d),(e) their divergent component (vectors) superimposed with horizontal divergence (contours), and (f) 500-hPa vertical pressure velocity (contours). The 1979–90 period, instead of the 1972–90 period, is used for composite GPCP precipitation differences due to the absence of data before 1979. Significance anomalies are dotted in (a) at the 0.1 level and shaded in (d)–(f) with light and dark shadings at the levels of 0.1 and 0.05, respectively, and the thick black arrows are significant at the 0.1 level in (b) and (c). The significances are estimated based on a Student’s t test. The black contour interval is 3 × 10−7 s−1 in (d) and (e) and 0.005 Pa s−1 in (f). The scale for the vectors is plotted in the top-right corner of each panel in (b)–(e). The red and green contours in (a) depict the SPCZ averaged for the 1979–90 and 1991–2009 periods, respectively, with the mean precipitation exceeding 6 mm day−1.

  • Fig. 4.

    As in Fig. 2, but for SST in (a) MAM, (b) March, (c) April, and (d) May. The contour interval is 0.3°C, and the contour of zero is omitted. The red thick box depicts the subtropical South Pacific (SSP) region.

  • Fig. 5.

    (a) Time series of the MAM SST averaged over the SSP region (5°–30°S, 165°E–115°W; red solid line with filled circles) and the SEARI (blue solid line with empty circles) and their interdecadal components (dashed lines). (b) The wavelet analysis of the SSPI, with positive values shaded.

  • Fig. 6.

    As in Fig. 3, but for responses in GFDL AM2.1 to the prescribed warm SSP SST anomaly in MAM. The red and green contours in (a) depict the SPCZ in the control and sensitivity runs, respectively, with the mean precipitation exceeding 6 mm day−1. (d),(e) The contour interval is 5 × 10−7 s−1.

  • Fig. 7.

    As in Fig. 6, but for velocity potentials at (a) 200 and (b) 850 hPa. The contour interval is 1 × 105 m2 s−1.

  • Fig. 8.

    As in Fig. 4a, but for (a) total SST change, (b) SST change related to global warming, (c) the residual (natural multidecadal variability), and (d) SST change due to the IPO. See text for more details.