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Keywords:

  • rainfall reduction;
  • SST variability;
  • atmospheric wave-trains

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Method
  5. 3. Teleconnection Between Rainfall and SST Variability
  6. 4. ITF SST Impacts on May SEA Rainfall
  7. 5. Impacts from the Meridional IO SST Variability
  8. 6. Conclusions
  9. Acknowledgments
  10. References

[1] During 1950–2006, austral autumn rainfall over the south-eastern Australian state of Victoria decreased by about 40% from its long-term seasonal average. The dynamics of this decline are not understood. We show that the reduction predominantly occurs in May, with variations of rainfall linked to two sources of sea surface temperature (SST) variability. A higher SST in the Indonesian Throughflow region, which occurs as the Indo-Pacific system enters into La Niña-like conditions, is associated with a rainfall increase over northern Victoria. An SST variability pattern with a positive anomaly in the central southern Indian Ocean (IO) and a negative anomaly in the subtropical IO are linked to atmospheric wave-trains, with a low pressure center over Victoria, is conducive to a rainfall increase. The weakening of the wave-trains since 1950, and an increase (reduction) in El Niño (La Niña) events, contribute to the rainfall decline. Possible influences from climate change forcing are discussed.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Method
  5. 3. Teleconnection Between Rainfall and SST Variability
  6. 4. ITF SST Impacts on May SEA Rainfall
  7. 5. Impacts from the Meridional IO SST Variability
  8. 6. Conclusions
  9. Acknowledgments
  10. References

[2] There has been a decreasing trend in rainfall over much of southern and eastern Australia during the past 50 years. These trends exhibit strong seasonality and regional differentiations: e.g., in austral winter over the south-west Western Australia (SWWA), and summer over southern Queensland [Cai et al., 2007]. On average, the south-east Australian state of Victoria (Figure 1e) receives more rainfall in winter (June–August, 200 mm) and spring (September–November, 178 mm) than in autumn (March–May, 158 mm), however the reduction trend is greatest in autumn (Figure 1). The autumn rainfall decline is dominated by May (Figures 1c and 1g), which makes up some 50% of the total seasonal reduction. It is manifested through a series of consecutive low rainfall years, predominantly from 1990 onwards. The rainfall reduction, experienced through much of south-east Australia (SEA, south of 33°S, east of 135°E), significantly reduces inflows to the Murray and Darling Rivers, Australia's longest river system [Cai and Cowan, 2008].

image

Figure 1. Time series of Victoria monthly rainfall anomalies for (a) March, (b) April, (c) May, and (d) the austral autumn season. An 11 year running average is also shown (thick black line). (e)–(h) The corresponding pattern of total trend since 1950 (in mm/57yrs).

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[3] The Victoria autumn rainfall reduction is similar to what has occurred over SWWA, where since the late 1960s, winter rainfall has decreased 15% [Cai et al., 2007]. There, the reduction is caused by a southward shift in rainfall and pressure systems, induced by an upward trend of the southern annular mode (SAM) forced by multi-decadal variability, and anthropogenic forcing. We will show that as the SEA rainfall decline occurs in autumn, it is likely that it is driven by different mechanisms to the SWWA rainfall reduction.

2. Data and Method

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Method
  5. 3. Teleconnection Between Rainfall and SST Variability
  6. 4. ITF SST Impacts on May SEA Rainfall
  7. 5. Impacts from the Meridional IO SST Variability
  8. 6. Conclusions
  9. Acknowledgments
  10. References

[4] The observed rainfall data, subjected to extensive quality control, are from the Australian Bureau of Meteorology. Reanalysis data used include an updated version of the Global Sea Ice and Sea Surface Temperature data set [Rayner et al., 1996], and mean sea level pressure (MSLP), 500 mb geopotential heights (Z500), and surface winds from the National Centers for Environmental Prediction [Kalnay et al., 1996]. Monthly and seasonal anomalies are constructed over the period 1950–2006. We examine the teleconnection patterns between Victoria May rainfall and other circulation fields using detrended data. The evolution of these patterns is then invoked to explain the rainfall trend.

3. Teleconnection Between Rainfall and SST Variability

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Method
  5. 3. Teleconnection Between Rainfall and SST Variability
  6. 4. ITF SST Impacts on May SEA Rainfall
  7. 5. Impacts from the Meridional IO SST Variability
  8. 6. Conclusions
  9. Acknowledgments
  10. References

[5] In an attempt to identify a responsible dynamical forcing for SEA rainfall variability, we conduct empirical orthogonal function (EOF) analysis using a correlation matrix on detrended SST anomalies in the Indo-Pacific domain, but are unable to find a dominant mode that is coherent with SEA rainfall. We subsequently conduct correlation analysis between Victoria May rainfall and grid-point circulation fields (Figures 2a and 2d).

image

Figure 2. Map of correlation of Victoria May rainfall with (a) May SST (contour) and winds (vectors) and (d) May grid-point Australian rainfall. (b) and (e) Correlation of SST anomalies in the ITF region (denoted by C: 125°E–130°E, 2°S–7°S) with grid-point SST and with grid-point Australian rainfall. (c) and (f) Correlation of an SST index with grid-point SST and rainfall; the index is referred to as the meridional IO SST index, defined as SST anomalies averaged over a mid-latitude box (denoted by A: 90°E–95°E, 37°S–42°S) minus that averaged over a subtropical latitude box (denoted by B: 90°E–95°E, 19°S–24°S). Areas within dashed lines in Figures 1a1c and areas shaded in Figures 1d1f indicate correlations that are significant at the 95% confidence level. All analyses are conducted using detrended data.

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[6] The Victoria May rainfall has little coherence with rainfall outside of SEA (Figure 2d). It is linked with SST anomalies in three centers: southern midlatitude IO, subtropical IO, and the Banda Sea region of the Indonesian Throughflow (ITF) Passage (denoted as A, B and C respectively in Figure 2a; see figure for definition). A higher SST in the ITF region is conducive to May rainfall across most of eastern Australia, particularly in the south-east (Figures 2b and 2e). The influence is conducted through the familiar northwest cloud-bands similar to those in the winter and spring seasons [Whetton, 1988; Nicholls, 1989]. In a cyclonic circulation pattern, high-moisture flows from the tropics veer westwards rotating northeastward over the Tasman Sea, east of SEA (Figure 2b). Composite analysis using the SST index in the ITF region over positive events greater than one-standard deviation produces a similar rainfall pattern.

[7] There is no suggestion of any linkage with a zonally symmetric SAM-like pattern, as can be seen through correlation analysis of May rainfall with MSLP, which is also reflected in the wind pattern (vectors, Figure 2a). This is further confirmed by correlating May grid-point rainfall with a well established SAM index [Marshall, 2003], which produces no coherence of SAM with SEA rainfall (not shown). In association, analyses using residual anomalies after removing variances associated with the SAM yield almost identical results.

[8] Given that SST variability in the ITF region shows no coherence with variations over the subtropical and midlatitude IO centers (Figure 2b), we will hereafter treat it as a separate source. We treat the two IO sources as an entity using a “meridional IO SST index” defined as the difference in SST anomalies over these two centers (A–B). This index, at its positive phase, represents a heating to the south, producing downstream wave-trains with a cyclonic center over Victoria, as revealed by correlating the index with circulation fields (see section 5).

[9] Next we discuss what controls variability of SSTs in the ITF region and the southern IO using detrended data. We then examine whether the dynamics are consistent with the rainfall decline over SEA using raw data.

4. ITF SST Impacts on May SEA Rainfall

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Method
  5. 3. Teleconnection Between Rainfall and SST Variability
  6. 4. ITF SST Impacts on May SEA Rainfall
  7. 5. Impacts from the Meridional IO SST Variability
  8. 6. Conclusions
  9. Acknowledgments
  10. References

[10] SST variability in the ITF region is intimately involved in the cycle of the El Niño Southern Oscillation (ENSO). Lag-correlation of the time series of May SST anomalies in the ITF region with grid-point SST and MSLP anomalies shows that two months prior (i.e., in March) the Indo-Pacific system is either in a neutral or transition phase from an El Niño event to a La Niña. There is still a basin-scale warming in the IO, associated with negative MSLP anomalies (Figure 3a). Thereafter, SSTs between the eastern IO and the western Pacific increase rapidly, as the region is a “choke point” of the equatorial Indo-Pacific waveguides [Wijffels and Meyers, 2004] associated with a decaying El Niño or a developing La Niña. By May, significantly low MSLP anomalies have developed over the ITF region (Figure 3b), and a structure of wave-trains enacting from the ITF warming region is seen. The low MSLPs extend to SEA with the overall wave-train structure resembling the Pacific-South American (PSA) pattern (Figure 3b). The PSA wave-trains grow into a traditional Southern Oscillation-like pattern by July (Figure 3c), indicating development of a La Niña-like phase. Thus through the SST teleconnection over the ITF center (Figures 2b and 2e), an increase in Victoria May rainfall is associated with a transition into La Niña.

image

Figure 3. Correlation of detrended May ITF SST with detrended MSLP in (a) March, (b) May, and (c) July. (d) Correlation of May SOI and Australian rainfall. (e) Correlation of May SOI and May grid-point MSLP (colour legend as same as Figure 3a). (f) Time series of raw May SOI (with an 11 year running average shown as thick black line). Areas within dashed lines in Figures 3a–3c and 3e and colours greater than 0.26 in Figure 3d indicate correlations that are significant at the 95% confidence level.

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[11] This teleconnection can be confirmed through direct correlation between a well-known ENSO index, the Southern Oscillation Index (SOI) with grid-point rainfall in May (Figure 3d). The SOI-SEA rainfall relationship in May is reminiscent of the correlation pattern between ITF SST and SEA rainfall (Figure 2e), suggesting that the May SOI is dominated by the northern Australia-western Pacific center, which is linked with the SST variability over the ITF region. To confirm this, we correlate detrended May SOI with detrended grid-point MSLP. The correlation pattern (Figure 3e) is indeed similar to the ITF SST-MSLP relationship (Figure 3b). Thus, before the pattern associated with SOI develops into a traditional east-west hemispheric pattern by July, wave-trains enacting from the western Pacific occur, influencing SEA rainfall.

[12] Since the 1976 climate-shift, there have been more El Niño events [Power and Smith, 2007], which are more protracted in terms of duration, than La Niña episodes. The protracted El Niño events tend to show up as consecutive El Niño Modoki events [Ashok et al., 2007]. This disparity in event numbers is a contributing cause for the large number of consecutive years of anomalously low late autumn rainfall, as the Indo-Pacific system spends far less time in a La-Niña development phase. This feature is manifested in the raw May SOI time series which has been trending down since the 1980s, showing stronger negative values (Figure 3f). Furthermore, May SST trends since 1950 shows that relative to warming in the ITF region, a faster rate occurs to the east, north of Papau New Guinea. These features are consistent with a global warming-induced weakening trend in the Walker Circulation during the late 20th and early 21st century [Tanaka et al., 2004; Vecchi et al., 2006; Ashok et al., 2007].

[13] An identical analysis for April highlights a similar evolution of anomalies but weaker in strength. This suggests that a mechanism operates in April similar to that in May, in part contributing to the April rainfall decline.

5. Impacts from the Meridional IO SST Variability

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Method
  5. 3. Teleconnection Between Rainfall and SST Variability
  6. 4. ITF SST Impacts on May SEA Rainfall
  7. 5. Impacts from the Meridional IO SST Variability
  8. 6. Conclusions
  9. Acknowledgments
  10. References

[14] We recall that the SST pattern associated with the meridional IO SST index is identified through a correlation with Victoria May rainfall (Figure 2a). The setting of the wave-trains must generate circulation anomalies in SEA that are conducive to a rainfall increase. Midlatitude heating, like orographic forcing, generates wave-trains downstream [Frederiksen, 1982; Hoskins and Karoly, 1981]. A correlation between the meridional IO SST index with MSLP anomalies (Figure 4a) using detrended data clearly displays wave-trains with a wave-number 4 structure, curving poleward west of the Drake Passage. These wave-trains are barotropic in nature, as seen in the Z500 anomalies (Figure 4b). One important feature is that for a positive phase of the meridional IO SST index, there is a low pressure center over much of southern Australia that provides favourable conditions for rainfall over Victoria. Factors that determine this spatial structure may include the magnitude and structure of the mean flow, and the dissipation of the flow and energy, and this needs further investigation.

image

Figure 4. Correlation of the detrended meridional IO SST index with (a) detrended May MSLP and (b) Z500. (c) EOF2 pattern of raw May Z500 anomalies. (d) Time series of the pattern regression coefficient obtained by regressing raw SST data onto a SST anomaly pattern associated with an increase in Victoria May rainfall (black-line) and a time series of Z500 EOF2 (orange-line). Areas confined by dashed lines in Figures 4a–4b indicate correlations that are significant at the 95% confidence level.

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[15] Lag-correlation between the meridional IO SST index and circulation anomalies suggest that the pattern does not propagate eastward, but is quasi-stationary (not shown). It starts in April, peaks in May, decays in June, and disappears by July. The fact that it commences in April suggests that the mechanism for an April rainfall decline is linked to that in May. A detailed study is required to examine the associated dynamics, involving forcing an atmospheric general circulation model with a similar SST anomaly pattern, but this is beyond the scope of the present study. However, an imprint of a positive feedback mechanism is seen: positive MSLP centers are situated to the east of warm SST anomalies, generating poleward wind anomalies, which in turn reinforce the warm SST anomalies [Baines and Cai, 2000].

[16] Since 1950, warming has proceeded throughout much of the southern IO, at vastly different rates for different locations. How has the warming affected the meridional IO SST index? We project raw SST anomalies onto the anomaly pattern associated with the index for each May in the domain of 10°S–50°S, 80°E-120°E to obtain a time series of the pattern regression coefficient (black-line, Figure 4d). The time series displays a downward trend meaning that the IO warming pattern favours a Victoria May rainfall reduction. Further evidence comes from an application of EOF analysis to raw Z500 anomalies in a domain of 20°S–60°S. This second EOF mode (Figure 4c) accounts for 13% of the total variance, sharing a similar pattern to that of Figure 4b. The associated time series (orange-line, Figure 4d) trends downwards, indicating a rising MSLP over Victoria that is not conducive to rainfall there. The two time series in Figure 4d share significant coherence with a correlation of 0.43, suggesting that a coupling between them exists. It is not clear whether the declining EOF2 trend is forced by an IO warming, or vice-versa. Further study is needed.

6. Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Method
  5. 3. Teleconnection Between Rainfall and SST Variability
  6. 4. ITF SST Impacts on May SEA Rainfall
  7. 5. Impacts from the Meridional IO SST Variability
  8. 6. Conclusions
  9. Acknowledgments
  10. References

[17] Victoria May rainfall has linkages to sources of SST variability in the Indo-Pacific system. A higher SST in the ITF region, which occurs as the system enters into a La Niña phase, is associated with a rainfall increase over the northern and central SEA, through an increase in northwest cloudbands. A positive SST anomaly in the southern midlatitude IO in association with a negative anomaly in the subtropical latitudes, represented by a meridional IO SST index, is conducive to a rainfall increase over southern Victoria. The associated wave-trains exhibit a low pressure center over SEA. The decline of the meridional IO SST index since 1950, and the increase in prostrated El Niño events accompanied by a reduction in La Niña episodes contribute to the rainfall decline. Recent studies have suggested that the weakening of the Walker Circulation and the changing ENSO characteristics is in part induced by a climate change forcing [e.g., Power and Smith, 2007; Vecchi et al., 2006]. There is a consensus that the long-term IO warming is at least in part due to global warming [Alory et al., 2007], and the fact that the weakening in the meridional IO SST index is linked to the long-term IO warming suggests a component of climate change is active in the rainfall reduction. A quantification of the relative importance of different forcing factors awaits further investigation.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Method
  5. 3. Teleconnection Between Rainfall and SST Variability
  6. 4. ITF SST Impacts on May SEA Rainfall
  7. 5. Impacts from the Meridional IO SST Variability
  8. 6. Conclusions
  9. Acknowledgments
  10. References

[18] W. Cai and T. Cowan are supported by the Australian Greenhouse Office, the Wealth from Oceans National Research Flagship, and the South East Australia Climate Initiative. We thank an Associate Editor and two reviewers for their helpful comments.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Method
  5. 3. Teleconnection Between Rainfall and SST Variability
  6. 4. ITF SST Impacts on May SEA Rainfall
  7. 5. Impacts from the Meridional IO SST Variability
  8. 6. Conclusions
  9. Acknowledgments
  10. References