EVIDENCE FOR DECADAL VARIABILITY IN SOUTHERN AUSTRALIAN RAINFALL AND RELATIONSHIPS WITH REGIONAL PRESSURE AND SEA SURFACE TEMPERATURE

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 20: (2000) EVIDENCE FOR DECADAL VARIABILITY IN SOUTHERN AUSTRALIAN RAINFALL AND RELATIONSHIPS WITH REGIONAL PRESSURE AND SEA SURFACE TEMPERATURE T.J. ANSELL a, *, C.J.C REASON a, I.N. SMITH b and K. KEAY a a School of Earth Sciences, Uni ersity of Melbourne, Park ille, Australia b CSIRO Atmospheric Research, Aspendale, Australia ABSTRACT A study of decadal variability in winter rainfall over various areas in southern Australia and possible links with regional mean sea level pressure (MSLP) and sea surface temperature (SST) is presented. Newly released historical data sets (GISST 3 and GMSLP 2.1f) are used for this purpose. Emphasis is placed upon southwest Western Australia (SW WA), since this region has apparently experienced a significant winter rainfall decline since the mid-1960s. Cross-spectral, empirical orthogonal function (EOF) and correlation techniques are used to show that there is a statistically significant inverse relationship between regional MSLP and SW WA rainfall on a time scale of 8 9 years. EOF analysis of winter averaged MSLP revealed a mode prominent between Australia and New Zealand and extending into the high southern latitudes, which explained 19.5% of the variance. This pattern appears to reflect a strengthening of the sub-tropical high pressure belt and a shift of the Circumpolar Trough and was found to be significantly correlated with SW WA winter rainfall. Based on the premise that the MSLP rainfall relationship involves changes to the strength and track of the mid-latitude depressions and associated frontal systems that provide the bulk of SW WA winter rainfall, an investigation into coastal regions downstream (Tasmania and coastal South Australia (SA)) was also performed. This also showed a significant relationship between rainfall and regional MSLP on time scales of about 8 9 years. Links between rainfall in these regions and Indo-Pacific Ocean SST were also investigated but found to be less prominent than those for MSLP. This finding is consistent with previous work and possibly reflects the mid-latitude location of the regions considered and the likelihood that winter depressions are more sensitive to the large scale atmospheric circulation than they are to the underlying SST field. Copyright 2000 Royal Meteorological Society. KEY WORDS: Australian rainfall; decadal variability; Indian Ocean; MSLP 1. INTRODUCTION Southwest Western Australia (SW WA) is the home for the great majority of the population in the western half of the continent and is also a very important area for agriculture. Most rainfall occurs during winter in SW WA and there has been concern for some time over an apparent long term decline in falls during this season (e.g. Wright, 1974a,b; Pittock, 1983; Nicholls and Lavery, 1992; Allan and Haylock, 1993; Hennessy et al., 1998). In a comprehensive study of this issue, Allan and Haylock (1993) found evidence for a decadal multidecadal (7 20 year) signal embedded within a longer term fluctuation in SW WA rainfall and that these signals were related to those in regional circulation and, to lesser extent, in sea surface temperature (SST). This is an important finding as it suggests that the low frequency variations in SW WA rainfall may involve more than one component and that better understanding of the large scale atmospheric and ocean circulation may potentially offer some opportunities for improving seasonal and longer term predictions. The analyses of Allan and Haylock (1993) also indicated that changes in the zonal extent of the sub-tropical winter anticyclone over Australia and its intensity, together with modulations of the long * Correspondence to: School of Earth Sciences, University of Melbourne, Parkville 3010, Australia; t.ansell@ earthsci.unimelb.edu.au Copyright 2000 Royal Meteorological Society

2 1114 T.J. ANSELL ET AL. wave trough to the south southwest of the continent, were linked to the decadal multidecadal variability in SW WA rainfall. This result suggested that changes to the intensity and track of mid-latitude depressions and their associated frontal systems may be involved. Leighton et al. (1997) observed that the time that parent depressions tend to spend in particular locations (i.e. the cyclonicity of an area) is of some importance for SW WA winter rainfall. Increased cyclonicity in the mid-latitude central South Indian Ocean appears unfavourable for SW WA rainfall compared with cyclonicity due south of SW WA. Leighton et al. (1997) showed that annual cyclonicity had declined over this latter region over recent decades, while Smith et al. (submitted) have noted that a reduction in the density of mid-latitude depressions near and to the south of SW WA during winter has accompanied the rainfall decline since the late 1960s. In particular, Smith et al. (submitted) noted that the decline was accompanied by increases in both mean sea level pressure (MSLP) and SSTs over the southern Indian Ocean. They also referred to the difficulty of diagnosing phenomena (e.g. the Antarctic Circumpolar Wave) at mid to high southern latitudes, where the relative sparseness of observations may affect the quality of the data sets involved. While there is no evidence that the long-term rainfall decline in SW WA occurs elsewhere in Australia, it is unclear whether this is the case with decadal scale variability, as identified by Allan and Haylock (1993). If this involves large-scale processes, then we might expect to find evidence in rainfall data from other stations located at similar latitudes. In particular, modulations to the density, strength and track of mid-latitude depressions and associated fronts may modulate rainfall on these time scales in other parts of southern Australia, which also receive predominantly frontal rainfall during winter. Areas such as Tasmania, coastal South Australia and Victoria are obvious possibilities in this regard. The aim of this paper is to investigate whether such modulations of winter rainfall do exist elsewhere in southern Australia. Furthermore, with the recent release of historical data sets for MSLP and SST that are more robust and extensive than those that were available to previous workers, it is now possible to re-examine the broad decadal-scale relationships between SW WA rainfall and regional MSLP/SST, with the aim of isolating these with greater clarity. 2. DATA SETS Rainfall data for SW WA, Tasmania and Victoria were obtained from Hennessy et al. (1998). Australian district and gridded rainfall, on a grid, was obtained from Jones and Beard (1998). The South Australian coastal district 26 was selected for analyses (see Figure 1). The SW WA data initially consisted of district monthly totals from the Lavery et al. (1997) high quality Australian rainfall set, which were later combined into an average (comprising June, July, August (JJA)) for specific regions by Hennessy et al. (1998). It has been rigorously tested for observing practices, the influence of conversion to metric measurements and urban effects such that all stations were found to be consistent (Lavery et al., 1997). Monthly MSLP was obtained from the UKMO/CSIRO global mean sea level pressure (GMSLP, version 2.1f) data set (Basnett and Parker, 1997). GMSLP is a 5 5 global pressure set extending over , and is derived from all land observations and ship of opportunity data. Many of the large data gaps in the Southern Hemisphere prior to the 1950s have been filled. This feature, together with rigorous calibration and testing, has made the GMSLP the most current and accurate data set of its kind (Basnett and Parker, 1997). For SST, the UKMO GISST 3 data set was used, a recent update of the widely recognized GISST 2.2a set (Parker et al., 1995). This is a global monthly gridded (1 1 ) set and spans the period SW WA has a winter rainfall maximum, accounting for over 50% of the annual total, and this rainfall is associated predominantly with frontal systems and associated cloudbands from the northwest. Raw and smoothed SW WA winter rainfall are shown in Figure 2(a). The well-documented apparent decline since the late 1960s is apparent and decadal pulses in the series are also evident. There is some suggestion of a partial recovery during the 1980s and hints of a longer multidecadal signal in the rainfall series. A

3 SOUTHERN AUSTRALIAN RAINFALL, DECADAL VARIABLES 1115 Figure 1. Rainfall regions for SW WA, Tasmania and district 26 in coastal South Australia. The region indicated for district 26 is taken from Jones and Beard (1998) similar picture is observed with Perth MSLP (Figure 2(b)), however, the trend appears to increase. The two are significantly correlated, p= 0.75, at the 99% confidence level. Furthermore, a cross-correlation analysis indicated that the lag zero correlation coefficient was the only significant correlation. 3. RESULTS 3.1. Spectral and cross-spectral analyses Analyses of SW WA rainfall and Perth MSLP for the period conducted by Allan and Haylock (1993) identified a decadal multidecadal signal in the range 7 20 years. The results of spectral analyses for the period using detrended data (linear trend removed) and a Blackman Harris lag window are shown in Figure 3(a) and (b). A spectral analysis of Perth MSLP (Figure 3(a)) displays a significant peak around 8.9 years (quasi-decadal signal) as well as more prominent interannual peaks near 5.5 and 3.2 years. However, in a spectral analysis of SW WA rainfall (Figure 3(b)), only the interannual signal at 3.1 years is significant, although peaks around 5.8, 8.9 and 18 years all stand out from red noise. The analyses suggest that fluctuations in the 7 20 year time scale, as suggested by Allan and Haylock (1993), are the result of the peak near 9 years. As a further test, it was decided to use cross-spectrum analysis of Perth MSLP and SW WA rainfall in an attempt to see at what time scale the relationship between these two variables (that is suggested by Figure 2) is strongest. Similar to the correlation coefficient, the modulus of coherence determined by cross-spectrum analysis measures the strength of the relationship at a particular frequency of two series (Janacek and Swift, 1993). The marginal spectral densities of MSLP (f 11 ( )) and rainfall (f 22 ( )) are estimated by f 11 ( j )= 1 2 W(k)I 11 ( j + k ) k m f 22 ( j )= 1 2 W(k)I 22 ( j + k ) k m where I xx is the periodogram using the standard definition (i.e. Brockwell and Davis, 1991a, Section 11.7; Brockwell and Davis, 1991b, Section 4.2), k =2 k/n, k=0,1,...,[n/2] are the Fourier frequencies in [0, ] and [n/2] is the integer part of n/2, and W is the weight function (a window of length 2m+1) for smoothing the periodogram. The weight function is symmetric by definition, W( j ) =W( j ), j= m 1,...,m, and j= m W( j )=1.

4 1116 T.J. ANSELL ET AL. Figure 2. Raw and smoothed (7 point running mean applied) winter (JJA) (a) SW WA rainfall and (b) Perth MSLP An estimate of the cross-spectrum f 12 ( ), based on the cross-periodogram I 12, is given by f 12 ( j )= 1 2 W(k)I 12 ( j + k ) k m and the estimated absolute coherency spectrum ( ˆ 12 ( j ) ) is ˆ 12 ( j ) = f 12( j ) 1/2 1/2 f 11 ( j )f 22 ( j ) Without smoothing, ˆ 12 ( j ) is equal to unity. Hence, to find an estimate of the coherency that is meaningful, the periodogram must be smoothed (Brockwell and Davis, 1991b). In this study, a Parzen spectral window, corresponding to lag window length M=31 (giving an effective value of m=5 inthe frequency domain) was applied as the weighting function W (see Janacek and Swift, 1993 for appropriate window selection). From this, the coherency was estimated using the program SPEC (Brockwell and Davis, 1991b). The estimated absolute coherency between SW WA rainfall and Perth MSLP is shown in Figure 4. An absolute coherency near one indicates that at that particular frequency there is a strong linear relationship

5 SOUTHERN AUSTRALIAN RAINFALL, DECADAL VARIABLES 1117 Figure 3. Spectral analysis of detrended (a) Perth winter MSLP and (b) SW WA winter rainfall. Frequency given as cycles/year in relation to spectral power. Peaks are indicated for MSLP (a) at 8.9, 5.5 and 3.2 years and for rainfall (b) at 18, 8.9, 5.8 and 3.1 years, respectively. Also shown is the estimated spectral density of a fitted first order autoregressive model (i.e. red noise) with 90% confidence bounds between the sinusoidal components in the two series (Brockwell and Davis, 1991b). One of the highest peaks, around 0.88, occurs at a frequency near cycles/year and to test the significance of the relationship at this frequency, a 100(1 )% confidence interval for ˆ 12 ( j ) can be determined as given by the following formula (Jenkins and Watts, 1969; Brockwell and Davis, 1991b): tanh tanh 1 ( ˆ 12 ( j )) 1 ( /2) a n 2 n, tanh tanh 1 ( ˆ 12 ( j ))+ 1 ( /2) where a n2 = m W 2 (k) and is the percentile of a standard normal distribution (Brockwell and Davis, 1991b). This interval is indicated by the vertical line that intersects the main peak at frequency cycles/year in Figure 4. From this analysis we can conclude that a significant relationship exists between SW WA rainfall and Perth pressure on a time scale of about 8 years. By considering the slope of the phase, the lead or the lag of the relationship can also be measured. For SW WA rainfall and Perth MSLP, the lag a n 2 n

6 1118 T.J. ANSELL ET AL. Figure 4. Estimated absolute coherency for SW WA rainfall and Perth MSLP. Frequency given in cycles/year. A 95% confidence interval is shown for the peak at frequency cycles/year (8 years) was zero. These findings provide evidence of a relationship between SW WA rainfall and Perth MSLP on time scales of around 8 years and are consistent with that suggested by the decadal pulses in Figure 2(a) and previous work (Allan and Haylock, 1993). While generally consistent with Allan and Haylock s findings, the decadal signal in the rainfall and MSLP identified by this work appears to be centred around 7 10 years, rather than the broad 7 20 year signal resolved by Allan and Haylock (1993). Whether a similar relationship between southern Australian rainfall and pressure would also be observed was of particular interest. Spectral analysis of Tasmanian rainfall and MSLP was performed (Figure 5(a) and (b)). Similar to Perth MSLP, the spectral analysis of Tasmanian MSLP revealed a significant peak around 5.5 years, with a strong peak above red noise at 8.9 years. Peaks at both these corresponding frequencies were significant in the spectral analysis of Tasmanian rainfall (Figure 5(b)). Cross-spectrum analysis of both time series was performed and the estimated absolute coherency is shown in Figure 6. A significant relationship between Tasmanian rainfall and pressure is present around 9.8 years (0.102 cycles/year). The phase (not shown) again indicated that there was a lag of zero with this relationship. Similar results were also observed for coastal SA rainfall (district 26) and area averaged MSLP. In the individual spectral analysis, significant peaks were observed for the pressure around 5.5 and 8.7 years (Figure 7(a)) and 8.7 (significant) and 5.2 (not significant, but above red noise) years for the rainfall (Figure 7(b)). Figure 8 shows the estimated absolute coherency for district 26 rainfall and averaged

7 SOUTHERN AUSTRALIAN RAINFALL, DECADAL VARIABLES 1119 Figure 5. Spectral analysis of detrended (a) area averaged Tasmanian winter MSLP and (b) Tasmanian winter rainfall. Frequency given as cycles/year in relation to spectral power. Peaks are indicated for MSLP (a) at 8.9, 5.5 and 3.2 years and for rainfall (b) at 8, 5.5 and 2.7 years, respectively. Also shown is the estimated spectral density of a fitted first order autoregressive model (i.e. red noise) with 90% confidence bounds MSLP. While not as strong as that observed with SW WA and Tasmania, a relationship around 7 8 years is significant between the rainfall and pressure in this south Australian coastal region. These analyses were repeated for Victorian winter rainfall and MSLP, however, no significant results were found Empirical orthogonal function (EOF) analyses In all three regions, a time scale of about 8 years has been highlighted for which a strong relationship exists between regional rainfall and MSLP. EOF analyses of gridded MSLP were employed to try and capture a temporal picture of the dominant modes of large scale variability in this region. JJA averages of the MSLP were computed over the period for the Southern Hemisphere region extending from the equator to high latitudes. Some weighting by area was applied, although the data were not detrended, or normalized. The first EOF and its associated time series are presented in Figure 9(a) and (b); this pattern explains 19.5% of the variance. Surprisingly, this first mode is not the classic El

8 1120 T.J. ANSELL ET AL. Figure 6. Estimated absolute coherency for Tasmanian rainfall and MSLP. Frequency given in cycles/year. A 95% confidence interval is shown for the peak at frequency cycles/year (9.8 years) Niño Southern Oscillation (ENSO) type pattern, rather it is represented by extrema centred between Australia and New Zealand and at high latitudes. The time series associated with this first mode (Figure 9(b)), indicates a downward trend since the mid-1960s. The more classic ENSO pattern of opposite values in the Indian and Pacific Oceans was resolved in the second EOF (Figure 9(c)), explaining 12.5%. EOF 3, explaining 10.4%, has a zonal dipole structure between the Australian region and the broader Indian (and Pacific) Ocean (Figure 9(e)). Correlations of the first EOF with time series of SW WA, Tasmanian and South Australian district 26 rainfall found a strong positive relationship in each region. The correlation coefficient was 0.53, 0.55 and 0.58 for SW WA, Tasmania and district 26, respectively; all significant at the 99% level. Correlations with the second EOF and regional rainfall were only weakly significant for SW WA. The strong relationship between the first EOF and rainfall in SW WA, Tasmania and district 26 tends to suggest that during winter, the MSLP anomaly pattern represented by EOF 1 is more dominant on rainfall variability for the region than is ENSO. Both spectral and cross-spectral analysis of SW WA, Tasmanian and district 26 rainfall and MSLP (Figures 3 8) revealed strong peaks at around 8 years. Spectral analysis of EOF 1 (not shown) revealed similar strong significant peaks at 8.8 and 3.2 years with a strong, but not significant peak at 5.5 years. This common signal near 8 years and the significant correlation between EOF 1 and rainfall in these three regions suggests that the relationship between MSLP and rainfall may be represented by this first EOF pattern on the quasi-decadal (near 8 year) time scale. To broaden the analyses from these small districts,

9 SOUTHERN AUSTRALIAN RAINFALL, DECADAL VARIABLES 1121 Figure 7. Spectral analysis of detrended (a) area averaged district 26 winter MSLP and (b) district 26 winter rainfall. Frequency given as cycles/year in relation to spectral power. Peaks are indicated for MSLP (a) at 8.7, 5.5 and 3.5 years and for rainfall (b) at 8.7 and 5.2 years, respectively. Also shown is the estimated spectral density of a fitted first order autoregressive model (i.e. red noise) with 90% confidence bounds a correlation of the first EOF was performed with winter averages of Australia-wide gridded rainfall data (Jones and Beard, 1998) (Figure 10). A significant, positive correlation is observed for the SW WA region and for much of the southeast of the continent, including almost all of Tasmania, Victoria and large parts of southeast South Australia. This suggests that the mid-latitude MSLP pattern represented by EOF 1 (Figure 9(a)) is important for rainfall in large portions of southern Australia. The positive correlation indicates that increasing pressure in the mid-latitudes and a shift of the Circumpolar Trough is related to decreases in rainfall in southern Australia. This has been particularly evident in SW WA since the mid-1960s (Allan and Haylock, 1993, Figure 2(a)). No significant decreasing trend has been observed with district 26, nor with Tasmanian winter rainfall however. Only the SW corner of WA and parts of southeast Queensland were significant in a correlation of EOF 2 with Australian gridded rainfall (not shown). There is no significant relationship for SW WA in Figure 11, a correlation of Australian gridded rainfall and EOF 3, however significant regions in large parts of South Australia, southwest Victoria and Tasmania are observed. There appears to be a northwest to

10 1122 T.J. ANSELL ET AL. Figure 8. Estimated absolute coherency for district 26 rainfall and MSLP. Frequency given in cycles/year. A 95% confidence interval is shown for the peak at frequency cycles/year (7.3 years) southeast slant in the correlation pattern across the continent (Figure 11), which would be consistent with the influence of northwest cloudbands. These synoptic features are common in winter (Wright, 1997) and involve an interaction between a mid-latitude system to the south of Australia and a tropical disturbance northwest of the continent. Typically, they result in significant rainfall over the region SST analyses Previous work (Allan and Haylock, 1993; Smith et al., submitted) has observed a relationship between SW WA winter rainfall and SST in the South Indian Ocean. To investigate whether a corresponding link might also exist on the quasi-decadal time scale highlighted above for MSLP, similar EOF analyses of winter averaged (JJA) Indo-Pacific Ocean SST were performed. Since data quality in the high southern latitudes prior to 1950 is quite poor (Smith et al., submitted), only data during the period were used. Some weighting by area was applied and the data were detrended. The first three modes and associated time series are shown in Figure 12. The first mode, explaining 36.2% of the variance, reflects ENSO and was found to be significantly correlated with a JJA average of the SOI (0.69 at the 99% level). Spectral analysis revealed a strongly significant peak at 3.5 years this was the only significant peak. The time series associated with EOF 1 was found to be strongly correlated with Tasmanian and district 26 rainfall, however, not with SW WA. EOF 2 explained 8.7% of the variance; however, it was not significantly correlated with rainfall in any of

11 SOUTHERN AUSTRALIAN RAINFALL, DECADAL VARIABLES 1123 Figure 9. EOF patterns and associated time series (dimensionless units) for Southern Hemisphere MSLP based on winter data, over the period (a) and (b) EOF 1, explaining 19.5% of variance; (c) and (d) EOF 2, explaining 12.5% of variance; (e) and (f) EOF 3, explaining 10.4% of variance

12 1124 T.J. ANSELL ET AL. Figure 10. Correlation of winter (JJA) values of the first EOF time series of Southern hemisphere MSLP with JJA gridded Australian rainfall ( ) the three regions. The time series associated with EOF 3, explaining 6.1% of variance, has interesting changes of sign during the late 1960s and again in the 1980s (Figure 12(f)); the associated spatial pattern is shown in Figure 12(e). The modulations in the EOF 3 time series (Figure 12(f)) are broadly consistent with those in SW WA rainfall (Figure 2(a)) and the two are significantly correlated (r= 0.31 at the 99% level). Cool SST anomalies in the tropical southeast Indian Ocean during the late 1960s late 1980s (as indicated by the positive loadings in Figure 12(f)) would be unfavourable for northwest cloudband development, consistent with the lower winter rainfall totals received during this period (Figure 2(a)). These cloudbands contribute about 25 40% of the cool season rainfall received in southern WA (Wright, 1997). The time loading becomes negative after the late 1980s, indicating warmer SST in the tropical southeast Indian Ocean. These warmer SST would be favourable for the development of northwest cloudbands and may help explain the apparent end of the downward trend in SW WA rainfall around this time (Figure 2(a)). A correlation of EOF 1 and Australian gridded rainfall for winter (Figure 13) indicates a significant relationship over large regions of northeastern Australia, almost all of Tasmania and parts of district 26. EOF 3 is significantly correlated with large parts of the SW WA region, as well as with a broader area extending throughout much of the southern third of WA (Figure 14). However, no significant correlations are observed in southeastern Australia, apart from a small region in western Tasmania and southern NSW. These results suggest that there is some evidence of a relationship between SW WA rainfall and SST in the South Indian Ocean, particularly the tropical southeastern part of this basin. The link between SST and SW WA rainfall does not appear to be as dominant as that observed with MSLP; EOF 3 only

13 SOUTHERN AUSTRALIAN RAINFALL, DECADAL VARIABLES 1125 Figure 11. Correlation of winter (JJA) values of the third EOF time series of Southern hemisphere MSLP with JJA gridded Australian rainfall ( ) explains 6.1% of the total variance. However, these findings are broadly consistent with Allan and Haylock (1993) and Smith et al. (submitted), who found relationships between SW WA rainfall and SST in the tropical southeast and mid-latitude southern Indian Ocean that were less robust than those seen with MSLP. 4. DISCUSSION AND CONCLUSIONS Two objectives were identified in this study of decadal variability in southern Australian rainfall. The first of these was to determine whether more sophisticated methods and newly released historical data sets would enable the broad decadal multidecadal (7 20 year) signal in SW WA rainfall and regional circulation analysed by Allan and Haylock (1993) to be resolved with greater clarity. The second objective was to investigate whether links between rainfall and regional circulation would also be found on these time scales in regions downstream of SW WA (Tasmania, coastal South Australia). A significant negative correlation was observed between winter MSLP and rainfall in SW WA, similar to the earlier studies of Allan and Haylock (1993) and Smith et al. (submitted). The question remains as to whether the apparent decline seen in the raw rainfall data from the mid-1960s with partial recovery around 1980 is mainly associated with decadal or lower frequency modes rather than being a distinct trend. Unlike analyses conducted by Allan and Haylock, individual spectral analysis of SW WA rainfall and Perth MSLP failed to identify a 7 20 year signal. However, cross-spectral analysis revealed a significant relationship between the two series centred around 8 years. The relationship between MSLP

14 1126 T.J. ANSELL ET AL. Figure 12. EOF patterns and associated time series (dimensionless units) for Indo-Pacific region SST based on winter data, over the period (a) and (b) EOF 1, explaining 36.2% of variance; (c) and (d) EOF 2, explaining 8.7% of variance; (e) and (f) EOF 3, explaining 6.1% of variance

15 SOUTHERN AUSTRALIAN RAINFALL, DECADAL VARIABLES 1127 Figure 13. Correlation of winter (JJA) values of the first EOF time series of Indo-Pacific SST with JJA gridded Australian rainfall ( ) and rainfall on longer time scales was not as strong and hence focus in this study has been placed on the quasi-decadal signal around 8 9 years. Much of the evidence concerning the winter rainfall variability in SW WA has concentrated on circulation changes (Allan and Haylock, 1993; Smith et al., submitted) and trends in cyclonicity (Leighton et al., 1997). Since the synoptic processes involved are also relevant to other areas of southern Australia, it was decided to investigate rainfall MSLP relationships for Tasmania, coastal South Australia and Victoria. Cross-spectral analysis of rainfall and MSLP for both Tasmania and district 26 (coastal South Australia) revealed strong relationships peaking around 7 9 years, consistent with that observed in SW WA. No significant relationships were found for Victoria however. EOF analysis of southern hemisphere MSLP yielded a leading mode that was strongly positively correlated with rainfall in both SW WA and southeastern Australia. Spectral analysis of this leading mode revealed a significant peak of 8.8 years, which together with the correlation results, strongly suggests that this spatial pattern may be important for southern Australian winter rainfall variability on quasi-decadal time scales. The spatial pattern represented extrema centred between Australia and New Zealand and at high latitudes and may reflect, in part, the low frequency changes in MSLP over recent decades observed by Allan et al. (1995) and Reason (2000). These changes in MSLP between the mid and high latitudes appear to involve a strengthening of the sub-tropical high pressure belt and a shift of the Circumpolar Trough, as has been found in other studies (e.g. Jones and Allan, 1998; Reason et al., 1998). Figure 9(a) is very similar to the 7 20 year bandpass filtered composite MSLP anomaly for dry SW WA winters presented in Allan and Haylock (1993). However, in this study this pattern has been resolved to a narrower quasi-decadal time scale and has also been shown to be influential for southeastern Australian rainfall as well.

16 1128 T.J. ANSELL ET AL. Figure 14. Correlation of winter (JJA) values of the third EOF time series of Indo-Pacific SST with JJA gridded Australian rainfall ( ) Compared to the links with MSLP, those between SW WA rainfall and SST appear to be weak. Rainfall in SW WA was only significantly correlated with EOF 3, which explains 6.1% of the variance. Spectral analysis of this EOF 3 pattern failed to reveal any significant signal on decadal time scales, however, EOF 3 was found to be significantly related to the decadal MSLP EOF 1 pattern (r= 0.32). Allan and Haylock (1993) resolved a similar pattern using 7 20 year bandpass filtered data and related SST anomalies in the southeast Indian Ocean to SW WA rainfall via cloudband development. The pattern resolved in this study explained a relatively small amount of the variance, however it may still influence SW WA rainfall to some extent through modulations in cloudband activity, as suggested by Allan and Haylock. Thus, it appears that MSLP and regional atmospheric circulation are more important for SW WA decadal rainfall variability than is SST. This is not a surprising result, considering that rainfall in SW WA is mainly associated with frontal activity and that the track and intensity of these systems and their parent depressions are likely to be significantly influenced by changes in the large scale MSLP distribution. For regions in the southeast, such as Tasmania and district 26 in coastal South Australia, there is a more obvious link between rainfall and SST, as indicated by Figure 13. Having observed a significant link between southern Australian decadal rainfall variability and MSLP, and hence by inference regional atmospheric circulation, it would be of benefit to extend these analyses to those of air temperature, wind speed, humidity and heat fluxes, important parameters in any assessment of modulations in the climate system. Any proposed mechanisms alluded to in this study are thus restrained somewhat by the lack of such analyses. In response to this caveat, future work is now moving towards considering mid to upper level atmospheric and sub-surface oceanographic data.

17 SOUTHERN AUSTRALIAN RAINFALL, DECADAL VARIABLES 1129 Certainly for the recent period, where data quality is more comprehensive, such analysis may provide further information regarding the complex SST, MSLP and rainfall relationship in the region. Such analysis could then contribute towards the long term objective of developing predictive capability for low frequency rainfall variability over Australia. ACKNOWLEDGEMENTS The authors would like to thank firstly Rob Allan, Bob Leighton and Gary Meyers for useful discussions and secondly an anonymous reviewer who offered valuable advice regarding cross-spectral analysis. They also thank Kevin Hennessy and David Jones, who kindly provided us with the respective rainfall data sets. This report is based on the BSc (Hons) thesis of the first author. REFERENCES Allan RJ, Haylock MR Circulation features associated with the winter rainfall decrease in southwestern Australia. Journal of Climate 6: Allan RJ, Lindesay J, Reason CJC Multidecadal variability in the climate system over the Indian Ocean region during the austral summer. Journal of Climate 8: Basnett TA, Parker DE Development of the global mean sea level pressure data set GMSLP version 2, Prediction and research. UK Meteorological Office, Climate Research Technical Note 79, 16 pp. Brockwell PJ, Davis RA. 1991a. Time Series: Theory and Methods (2nd edn). Springer: New York. Brockwell PJ, Davis RA. 1991b. ITSM: An Interacti e Time Series Modelling Package for the PC. Springer: New York. Hennessy KJ, Suppiah R, Page CM Australian rainfall changes, Australian Meteorological Magazine 48: Janacek G, Swift L Time Series: Forecasting, Simulation, Applications. Ellis Horwood: UK. Jenkins GM, Watts DG Spectral Analysis and its Applications. Holden-Day: San Francisco, CA. Jones DA, Beard G Verification of Australian monthly district rainfall totals using high resolution gridded analyses. Australian Meteorological Magazine 47: Jones PD, Allan RJ Climatic change and long-term climatic variability. In Meteorology of the Southern Hemisphere, Karoly D, Vincent D (eds). American Meteorology Society: Boston, MA; Lavery B, Joung G, Nicholls N An extended high-quality historical rainfall dataset for Australia. Australian Meteorological Magazine 46: Leighton RM, Keay K, Simmonds I Variations in annual cyclonicity across the Australian region for the 29-year period and relationships with annual Australian rainfall. In Climate Prediction for Agricultural and Resource Management: Australian Academy of Science Conference, Canberra, 6 8 May, 1997, Munro RK, Leslie L (eds). Bureau of Resource Management: Canberra; Nicholls N, Lavery B Australian rainfall trends during the twentieth century. International Journal of Climatology 12: Parker DE, Jackson M, Horton EB The GISST 2.2 sea surface temperature and sea-ice climatology. Climate Research Technical Note CRTN63, UK Meteorological Office, Bracknell, Berkshire, UK, 35 pp. Pittock AB Recent climatic change in Australia: implications for a CO 2 warmed earth. Climate Change 5: Reason CJC Multidecadal climate variability in the subtropics/midlatitude of the Southern Hemisphere Oceans. Tellus 52A: Reason CJC, Allan R, Lindesay J Climate variability on decadal time scales: mechanisms and implications for climate change. Palaeoclimates 3(1 3): Wright PB. 1974a. Seasonal rainfall in southwestern Australia and the general circulation. Monthly Weather Re iew 102: Wright PB. 1974b. Temporal variations in seasonal rainfalls in southwestern Australia. Monthly Weather Re iew 102: Wright WJ Tropical extratropical cloudbands and Australian rainfall: I. Climatology. International Journal of Climatology 17: