Association between Australian rainfall and the Southern Annular Mode

INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 7: 19 11 (7) Published online July in Wiley InterScience (www.interscience.wiley.com).137 Association between Australian rainfall and the Southern Annular Mode Belinda Meneghini, a, * Ian Simmonds a and Ian N. Smith b a School of Earth Sciences, The University of Melbourne, Australia b CSIRO Marine and Atmospheric Research, Australia Abstract: In this study, we explore the relationships between seasonal Australian rainfall and the Southern Annular Mode (SAM). We produce two seasonal indices of the SAM: the Antarctic Oscillation Index (AOI), and an Australian regional version (AOIR) using ERA- mean sea-level pressure (MSLP) reanalysis data. The seasonal rainfall data are based on gridded monthly rainfall provided by the Australian Bureau of Meteorology. For the period 195 a significant inverse relationship is found between the SAM and rainfall in southern Australia, while a significant in-phase relationship is found between the SAM and rainfall in northern Australia. Furthermore, widespread significant inverse relationships in southern Australia are only observed in winter, and only with the AOIR. The AOIR accounts for more of the winter rainfall variability in southwest Western Australia, southern South Australia, western and southern Victoria, and western Tasmania than the Southern Oscillation Index. Overall, our results suggest that changes in the SAM may be partly responsible for the current decline in winter rainfall in southern South Australia, Victoria, and Tasmania, but not the long-term decline in southwest Western Australian winter rainfall. Copyright Royal Meteorological Society KEY WORDS Southern Annular Mode; Australian rainfall; El Niño-Southern Oscillation; climate; Antarctic Oscillation Index; Southern Hemisphere variability; southwest Western Australian rainfall; climate change Received October 5; Revised January ; Accepted 3 April 1. INTRODUCTION The Southern Annular Mode (SAM) explains the greatest percentage of the variability in atmospheric circulation in the Southern Hemisphere (SH) extra-tropics on interannual timescales (Visbeck and Hall, ). During the positive (negative) phase of the SAM the pressure tends to be anomalously high (low) in the mid-latitudes and anomalously low (high) in the high-latitudes, and in general between 15 3 S and 5 S the zonal wind is stronger (weaker) and between 3 and 5 S the zonal wind is weaker (stronger). There is known to be a close link between SAM variability and synoptic behaviour (e.g. Rashid and Simmonds,, 5). In southeast South America, Silvestri and Vera (3) have identified significant associations between the SAM and rainfall in winter and late spring. For western South Africa, Reason and Rouault (5) find an inverse relationship between the phase of the SAM and extreme winter rainfall. Winter rainfall has undergone significant reductions in southwest Western Australia (SWWA) from 195 onwards (Li et al., 5), and in southern South Australia, Victoria, and Tasmania (Figure 1) over the past * Correspondence to: Belinda Meneghini, School of Earth Sciences, The University of Melbourne, Victoria 31, Australia. E-mail: b.meneghini@pgrad.unimelb.edu.au several s. The magnitude and duration of the decline in SWWA winter rainfall have created a great deal of interest among researchers and concern among the water authorities and residents of this region. Possible causes are natural variability and global warming (IOCI, ), higher mean sea-level pressure (MSLP) to the west and south of SWWA (Smith et al., ), a weaker African monsoon (Baines, 5), and a positive trend in the SAM (Cai et al., 3; Li et al., 5). Although there is a positive trend in the winter SAM, it is not significant, so it is not clear whether the SAM is responsible for the long-term decrease in SWWA winter rainfall. Both modelling (Cai and Watterson, ) and observational (Cai et al., 3) studies have found the positive phase of the SAM to be associated with below average rainfall in SWWA, owing to fewer extra-tropical cyclones and cold fronts passing through the region. There are other regions of Australia that receive rainfall from extratropical cyclones and cold fronts, such as Victoria and Tasmania; however, the relationship between the SAM and rainfall in these regions has not been examined thoroughly. Our aim in this study is to explore the association between the SAM and seasonal Australian rainfall. Since El Niño-Southern Oscillation (ENSO) has a large effect on Australian rainfall (McBride and Nicholls, 193; Copyright Royal Meteorological Society

11 B. MENEGHINI, I. SIMMONDS AND I. N. SMITH NORTHERN TERRITORY INDIAN OCEAN WESTERN AUSTRALIA QUEENSLAND PACIFIC OCEAN SOUTH AUSTRALIA GREAT AUSTRALIAN BIGHT NEW SOUTH WALES VICTORIA TASMANIA Figure 1. Australia and the surrounding region. Simmonds and Hope, 1997), any relationship between Australian rainfall and the SAM should be put into perspective by comparing it to the relationship between Australian rainfall and ENSO.. DATA AND METHODS The strength and phase of the SAM can be measured by the Antarctic Oscillation Index (AOI). The Gong and Wang (1999) definition of this index is: to produce the most reliable picture of the SH circulation over this period (Marshall, 3). We also make use of ERA- 5 hpa zonal wind and air temperature. A regional AOI (AOIR) was also calculated using Equation (1) but only using data from 9 to 1 E andis shown in Figure 3. We can see from Figures and 3 that the seasonal AOI and AOIR have exhibited positive trends. The correlations between the seasonal AOI and AOIR using raw and detrended data are shown in Table I. The correlations are large, which is not surprising AOI (t) = ( ) mslp S (t) mslp S (season) σ mslp S (season) ( ) mslp 5 S (t) mslp 5 S (season) σ mslp5 S (season) (1) where mslp S (t), mslp S (season) and σ mslp S (season) are the seasonal MSLP, mean of the seasonal MSLP for the season in question and standard deviation of the seasonal MSLP for the season in question at S, respectively, with similar expressions for 5 S. A positive AOI is said to mean that the SAM is in the positive or high phase, while a negative AOI is said to mean that the SAM is in the negative or low phase. Another definition of the AOI is based on the time series of the coefficients of the first mode of variability in seasonal MSLP over 9 S (Thompson et al., ). The two definitions give very similar time series and we chose to use the Gong and Wang definition in this study. The European Centre for Medium Range Weather Forecasts ERA- gridded monthly MSLP dataset (.5.5 resolution) (Simmons and Gibson, ) was used to calculate an AOI for each season (Figure ). The ERA- MSLP reanalysis was chosen for this study as it is thought since the AOI and AOIR both represent aspects of the SAM; however, there is a difference between the AOI and AOIR and this reflects the zonally asymmetric nature of the SAM in the mid-latitudes (Simmonds and King, ). Furthermore, the AOI and AOIR are most similar in summer, and are most different in spring. We also calculated a seasonal Southern Oscillation Index (SOI) using the Troup definition (Troup, 195), and Darwin and Tahiti MSLP from the Australian Bureau of Meteorology. The seasonal SOI time series are shown in Figure. The rainfall dataset we use is from the Australian Bureau of Meteorology, which contains Australian monthly rainfall for January 19 December 3 on a.5.5 grid (Jones and Beard, 199). One way of exploring the relationships between the SAM/ENSO and rainfall is to calculate the Pearson Product-Moment Correlation Coefficient between the relevant index and grid point rainfall. So that we could

SOUTHERN ANNULAR MODE AND AUSTRALIAN RAINFALL 111 y =.3 x 1. y =.9 x. AOI AOI 1955 19 195 197 1975 19 195 199 1995 5 y =. x.9 (c) 1955 19 195 197 1975 19 195 199 1995 5 y =. x. AOI AOI 1955 19 195 197 1975 19 195 199 1995 5 (d) 1955 19 195 197 1975 19 195 199 1995 5 Figure. Time series of the AOI for summer, autumn, (c) winter, and (d) spring, along with the least squares line of best fit. Note: Summer (195, i.e. December 1957 and January/February 195 to December 1 and January/February ), autumn (195, i.e. March/April/May 195 to March/April/May ), winter (195, i.e. June/July/August 195 to June/July/August ), and spring (1957 1, i.e. September/October/November 1957 to September/October/November 1). determine the significance of the correlation at each grid point, we found the effective degrees of freedom (or number of independent data points) in the index and grid point rainfall for each season, and for each grid point we used the smaller of the two as the actual effective degrees of freedom. To determine the significance of the correlations, we compared our correlations to the critical values for a two-tailed test. In this study, we consider correlations at the 95% confidence level to be significant. In many studies, correlations are usually only performed using raw data. For the period we are considering (5 s), correlations performed using raw data will be influenced by the relationship between the two variables on inter-annual and inter-decadal timescales, while correlations performed using detrended data will indicate more the nature of the relationship on inter-annual timescales (Li et al., 5). In this study, data is detrended by finding the equation of the least squares line of best fit to the time series, and subtracting this equation from the data. 3. ANALYSIS In summer there is a tendency for the low phase of the SAM to occur during an El Niño, and for the high phase of the SAM to occur during a La Niña (Carvalho et al., 5). So to have confidence that the SOI does not influence correlations between summer rainfall and the AOI/AOIR or any other seasonal correlations for that matter, and vice versa, one could perform partial correlations. Table I indicates that the AOI and SOI are highly independent in winter and spring, and even though the correlations are significant in summer and autumn, at most only 15% of the variance in the AOI can be explained by the SOI and vice versa. The reverse is true for the correlations between the AOIR and SOI (Table I), with the AOIR and SOI being highly independent in summer and autumn, and significantly correlated in winter and spring. However, at most only 5% of the variance in the AOIR can be explained by the SOI and vice versa. Therefore, ENSO (the SAM) should not have a considerable effect on the relationship between seasonal rainfall and the SAM (ENSO), and in what follows we have chosen not to display results from partial correlation analysis. 3.1. AOI and rainfall As we have discussed above, a powerful method for exploring the relationships between the AOI and rainfall is through correlation analysis. Accordingly we have

11 B. MENEGHINI, I. SIMMONDS AND I. N. SMITH Table I. Correlation between the seasonal SAM and ENSO indices using raw and detrended data. Correlations that are significant at the 95% confidence level are in italics. The seasons are defined as in Figure. r AOI, AOIR r AOI, SOI r AOIR, SOI Raw Detrended Raw Detrended Raw Detrended Summer +.7 +. +.3 +.3 +.1 +.17 Autumn +.79 +.7 +.5 +.3.9 +.7 Winter +.7 +.75 +. +.13.3.3 Spring +.9 +. +. +.3..9 y =.37 x.5 y =.3 x.7 AOIR AOIR 1955 19 195 197 1975 19 195 199 1995 5 (c) 1955 19 195 197 1975 19 195 199 1995 5 y =.3 x 1.5 y =. x.5 AOIR AOIR 1955 19 195 197 1975 19 195 199 1995 5 (d) 1955 19 195 197 1975 19 195 199 1995 5 Figure 3. Time series of the AOIR for summer, autumn, (c) winter, and (d) spring, along with the least squares line of best fit. Note: Summer (195, i.e. December 1957 and January/February 195 to December 1 and January/February ), autumn (195, i.e. March/April/May 195 to March/April/May ), winter (195, i.e. June/July/August 195 to June/July/August ), and spring (1957 1, i.e. September/October/November 1957 to September/October/November 1). calculated the correlation between the seasonal AOI and grid point rainfall using raw and detrended data, and the results are presented in Figure 5. There are significant positive correlations in Western Australia in summer (Figure 5), meaning that when the AOI is positive rainfall will tend to be above average. In summer, rainfall in Western Australia may occur from convection (moist air transported by high-pressure systems in the eastern Indian Ocean and Great Australian Bight is forced to rise by intense surface heating). When the AOI is positive the pressure and temperature tend to be higher in the mid-latitudes (moist air and surface heating result in convection and high rainfall), while a negative AOI is associated with lower pressure and cooler temperatures in the mid-latitudes (the air is probably drier because of the cooler temperatures and surface heating is reduced, hence rainfall is inhibited). Thus, we would expect the correlation between the summer AOI and rainfall in Western Australia to be positive. When detrended data is used (Figure 5) there is a smaller area of Western Australia with significant correlations and there are now significant negative correlations in

SOUTHERN ANNULAR MODE AND AUSTRALIAN RAINFALL 113 3 y =. x +.19 3 y =.1 x + 3. 1 1 SOI SOI 1 3 1955 19 195 197 1975 19 195 199 1995 5 (c) 1 3 1955 19 195 197 1975 19 195 199 1995 5 3 y =.3 x + 5. 3 y =.1 x +.7 1 1 SOI SOI 1 3 1955 19 195 197 1975 19 195 199 1995 5 1 3 1955 19 195 197 1975 19 195 199 1995 5 (d) Figure. Time series of the SOI for summer, autumn, (c) winter, and (d) spring, along with the least squares line of best fit. Note: Summer (195, i.e. December 1957 and January/February 195 to December 1 and January/February ), autumn (195, i.e. March/April/May 195 to March/April/May ), winter (195, i.e. June/July/August 195 to June/July/August ), and spring (1957 1, i.e. September/October/November 1957 to September/October/November 1). southwest Tasmania. All round the main mechanism producing rainfall in western Tasmania is orographic uplift (mid-latitude westerlies transport moisture to the west coast, the mountains force the air to rise and rainfall results). During the high phase of the SAM the midlatitude westerlies tend to be weaker (reduced moisture transport to southwest Tasmania and low rainfall) and during the low phase the mid-latitude westerlies tend to be stronger (increased moisture transport to southwest Tasmania and high rainfall), which is why one would expect negative correlations. The correlations using raw (Figure 5) and detrended (Figure 5) data suggest that in parts of northern and central Western Australia the inter-annual and interdecadal relationships play a role in the SAM-rainfall relationship. Consequently, the SAM contributes to interannual and inter-decadal summer rainfall variability in these regions. In SWWA the inter-annual relationship plays the major role in the SAM-rainfall relationship, therefore the SAM contributes to inter-annual summer rainfall variability there. In autumn significant positive correlations are found in a region spanning northwest Northern Territory, northeast Western Australia and northwest South Australia (Figure 5(c) and (d)). The correlations using raw (Figure 5(c)) and detrended (Figure 5(d)) data are very similar, thus in autumn the SAM-rainfall relationship is mainly due to the inter-annual relationship. There is also a close agreement between the correlations using raw (Figure 5(e)) and detrended (Figure 5(f)) data in winter. Significant positive correlations are found in eastern Australia, while in southern Australia (a region of interest) the only significant correlations are in a very small region on the south coast of Victoria (Figure 5(e) and (f)). Rainfall along the east coast of Australia may occur because of orographic uplift (high-pressure systems in the western Pacific supply moist onshore winds to the east coast, the Great Dividing Range forces the air to rise and this increases the possibility of rainfall occurring). During the high phase of the SAM the pressure is generally higher in the mid-latitudes (moist onshore winds on the east coast of Australia and high rainfall), and during the low phase the pressure tends to be lower in the mid-latitudes (drier onshore winds on the east coast of Australia and low rainfall). This provides a physical explanation for the positive correlations.

11 B. MENEGHINI, I. SIMMONDS AND I. N. SMITH The most widespread significant correlations in spring (Figure 5(g) and (h)) are found in western Tasmania. As with the correlations in autumn and winter, there is a fairly close agreement between the correlations using raw (Figure 5(g)) and detrended (Figure 5(h)) data in spring. In most seasons, the correlation patterns shown in Figure 5 are very similar regardless of whether the data are detrended. Having said this, the area of significant correlations is generally greater when using raw data and this difference is most marked in summer where there is approximately a 3% decrease in the number of grid points with significant correlations when detrended data is used. The tendency for the magnitude of the correlations using raw data to be greater than the magnitude of the correlations using detrended data was expected since correlations performed using raw data may lead to a relationship between the two variables that appears stronger than what it actually is, because of any trends in the data. It then comes as no surprise that the biggest difference between the correlations using raw and detrended data was in summer, as the trend in the AOI is strongest in summer (Figure ). To shed further light on the mechanisms that may be producing the associations between the SAM and Australian rainfall, we have examined the SH dynamic and thermodynamic anomalies associated with the upper and lower quartile values of the AOI. We confine ourselves to showing here the differences between these composites for 5 hpa zonal wind (Figure ) and temperature (Figure ) for winter. We can be confident that the zonal wind and temperature composites do not contain a significant ENSO signal and are a good representation of the SH circulation during the extreme phases of the SAM because of the 1 upper (lower) quartile winter AOI s, only () coincide with upper (lower) quartile winter SOI s or lower (upper) quartile winter SOI s. As would be expected, there is a reasonable amount of zonal symmetry in the differences in zonal wind south of about S, especially in the Indian and Pacific Ocean sectors (Figure ). We can also see a clear SAM signal in temperature in the mid- and high-latitudes, comprising of anomalies of one sign in the high-latitudes over much of Antarctica (negative anomalies), and anomalies of the opposite sign in the mid-latitudes over New Zealand and the Indian and Atlantic Oceans (positive anomalies) (Figure ). The SAM can also account for the temperature anomalies over the Antarctic Peninsula, 1 1....... 5. 5. 3.. 3....... 115 1 15 13 135 1 15 15 155 (c) 115 1 15 13 135 1 15 15 155 1 1.... 5.. 5. 3.. 3.... 115 1 15 13 135 1 15 15 155 (d) 115 1 15 13 135 1 15 15 155 Figure 5. Correlation between total seasonal rainfall and the AOI using raw and detrended data in (a b) summer, (c d) autumn, (e f) winter, and (g h) spring. Contour interval is.1 and stippling indicates the correlations that are significant at the 95% confidence level. The seasons are defined as in Figure.

SOUTHERN ANNULAR MODE AND AUSTRALIAN RAINFALL 115 1 1........ 5 3.... 5 3...... (e) 115 1 15 13 135 1 15 15 155 (g) 115 1 15 13 135 1 15 15 155 1 1. 5 3..... 5 3........... (f) 115 1 15 13 135 1 15 15 155 (h) 115 1 15 13 135 1 15 15 155 Figure 5. (Continued)...5..3..1.1..3..5. as during the high phase of the SAM the strengthened high-latitude westerlies are able to transport more heat from over the ocean to the Peninsula (Kwok and Comiso, ). We may assume that the negative temperature anomalies over much of Australia (where we would have expected, at least over southern Australia, positive anomalies) are because of air over higher latitudes being transported north to Australia. On the large-scale it appears that the SAM has a greater influence on zonal wind than on temperature. Figure indicates that around coastal New South Wales and Queensland the easterlies are stronger and the air is warmer or moister during the high phase of the SAM which results in above average winter rainfall, while the reverse occurs during the low phase. So in these regions both the zonal wind and temperature can explain the significant correlations. 3.. Regional AOI and rainfall As the SAM is zonally asymmetric in the mid-latitudes (Simmonds and King, ), its effect on regional climate may be masked when a hemispheric index is used. Thus we calculate the correlations displayed in Figure 5, but using the AOIR. The results are shown in Figure 7. In summer (Figure 7) there are significant correlations in Western Australia, eastern Australia and western Tasmania. These correlations can be explained in the same way the significant correlations between the AOI and rainfall were explained. When detrended data is used (Figure 7) there is a smaller area of Western Australia with significant positive correlations, and slightly stronger significant negative correlations in southwest Tasmania. This is similar to what we observed with the correlation between the summer AOI and rainfall. Summer is actually the season with the closest agreement between the correlation patterns for the AOI and rainfall (Figure 5 and ) and the AOIR and rainfall (Figure 7 and ), because of the AOI and AOIR being most similar in summer (Table I). There are no strong links between the AOIR and rainfall in autumn (Figure 7(c) and (d)), which is very different from the correlation between the autumn AOI and rainfall (Figure 5(c) and (d)). The difference in the correlations must be due to differences in the AOI and

11 B. MENEGHINI, I. SIMMONDS AND I. N. SMITH AOIR. The main rainfall seasons in Australia are summer (northern Australia) and winter (southern Australia), and perhaps the AOIR is more closely associated with the seasonality in synoptic activity and thus rainfall variability than is the AOI. So the correlation between the autumn AOIR and rainfall may represent a truer picture of the synoptic activity-rainfall association than that suggested by the correlation between the autumn AOI and rainfall. The significant correlations in winter (Figure 7(e) and (f)) are predominantly negative and are found in southern New South Wales, Victoria, Tasmania, southern South Australia, and SWWA. Extra-tropical cyclones and cold fronts supply most of southern Australia s winter rainfall and their tracks are largely dictated by the location of the polar front jet (PFJ). More specifically, the PFJ moves south (north) of 5 S when the SAM is in the positive (negative) phase (Limpasuvan and Hartmann, ). So there would be a tendency in southern Australia for dry (wet) winters to occur during the positive (negative) phase of the SAM, hence one would have expected the negative correlations. The correlations using raw (Figure 7(e)) and detrended (Figure 7(f)) data are similar, thus in Victoria, Tasmania, southern South Australia, and SWWA the SAM contributes to inter-annual winter rainfall variability. The current decline in winter rainfall in parts of southeast Australia has already lasted a decade, and it may continue for many more s to become a long-term decline, or it may cease in the near future making it more of a short-term decline. So at this point in time the SAM may play a role in the current decline in winter rainfall in southern South Australia, Victoria, and Tasmania; however, it does not appear to be the cause of the long-term decline in SWWA winter rainfall. The significant association between southern Australian winter rainfall and the AOIR that we see in Figure 7(e) and (f) was not apparent in the correlation between the winter AOI and rainfall shown in Figure 5(e) and (f). This is important because we now see evidence of a significant relationship between the SAM, synoptic activity and southern Australian rainfall, which supports the hypothesis made above that a regional AOI provides a much better representation of the regional circulation and therefore rainfall variability. Thus, it may be preferable to use a regional AOI for studies into the SAM and regional rainfall. Spring exhibits some significant correlations in far north Queensland and Tasmania (Figure 7(g) and (h)). Furthermore, the correlations in western Tasmania are stronger when the AOIR is used. The number of significant correlations between the spring AOI and rainfall (Figure 5(g) and (h)) was also not substantial; however, the biggest difference between the AOI and AOIR is in spring (Table I), so in spring the AOIR should still be superior to the AOI. As with the correlations between the AOI and rainfall, the correlations between the AOIR and rainfall using raw and detrended data are generally similar. Using the AOIR the largest difference between correlations using raw and detrended data is now in autumn, where there is approximately a fivefold increase in the number of grid points with significant correlations when detrended data is used. We may attribute this to the AOIR having the strongest trend in autumn (Figure 3). Given the above discussion, it is of considerable interest to document the difference between the 5 hpa zonal wind (Figure ) and temperature (Figure ) Figure. Difference between the upper and lower quartile winter AOI s in 5 hpa zonal wind with a contour interval of 1. m/s, and 5 hpa air temperature with a contour interval of.5 C forthe Southern Hemisphere. Note: Upper quartile s (19, 197, 197, 1979, 193, 19, 195, 199, 1993, 1997, 199, and 1) and lower quartile s (193, 195, 199, 197, 197, 1975, 1977, 19, 191, 199, 1995, and 199). The upper and lower quartile s are from the raw AOI.

SOUTHERN ANNULAR MODE AND AUSTRALIAN RAINFALL 117 1 1.... 5 3...... 5 3........ 115 1 15 13 135 1 15 15 155 (c) 115 1 15 13 135 1 15 15 155 1 1. 5 3....... 5 3......... 115 1 15 13 135 1 15 15 155 (d) 115 1 15 13 135 1 15 15 155 Figure 7. Correlation between total seasonal rainfall and the AOIR using raw and detrended data in (a b) summer, (c d) autumn, (e f) winter, and (g h) spring. Contour interval is.1 and stippling indicates the correlations that are significant at the 95% confidence level. The seasons are defined as in Figure. during the upper and lower quartile winter AOIR s to help explain the correlations in winter. There is a high degree of zonal symmetry in the zonal wind south of S from the eastern Indian Ocean to the western Pacific Ocean (Figure ), which was also observed in the AOI composite (Figure ). However, in this case the zonal wind anomalies around the Drake Passage are not as zonally symmetric as before, which is reasonable given this composite represents the extreme phases of the SAM in the Australian region. With temperature (Figure ) the anomalies are the same sign as in the AOI composite (Figure ) over the mid-latitude centres of action and Antarctica, so the temperature and therefore the moisture anomalies are fairly similar in the AOI and AOIR composites. Here, as in the AOI composite, the SAM can account for the temperature anomalies over the Antarctic Peninsula. When the high phase of the SAM coincides with an El Niño, enhanced warming may occur over the Antarctic Peninsula (Kwok and Comiso, ). As there is a better correspondence between winter AOIR upper quartile and SOI lower quartile s than in the AOI composite (see following text), this may explain why the temperature anomalies over the Antarctic Peninsula extend west to 15 W. However, of the 1 upper (lower) quartile winter AOIR s, only 5 coincide with lower (upper) quartile winter SOI s, and only () coincide with upper (lower) quartile winter SOI s. Therefore, there is probably no significant ENSO signal in the zonal wind and temperature anomalies. Figure indicates that around southern Australia the mid-latitude westerlies are stronger during the negative phase of the SAM, which results in above average winter rainfall in western Tasmania, while the reverse occurs during the positive phase. The temperature (Figure ), however, cannot explain the correlations in Tasmania or other parts of southern Australia. This may not be all that surprising as southern Australian winter rainfall is derived mainly from extra-tropical cyclones and cold fronts, and therefore rainfall there does not depend greatly on local temperature or moisture availability. Finally, the correlations in western Tasmania are much stronger when the AOIR is used as the mid-latitude zonal wind anomalies are larger in the AOIR composite (i.e. the AOIR can represent much stronger or weaker westerlies, much higher or lower rainfall, and therefore stronger correlations).

11 B. MENEGHINI, I. SIMMONDS AND I. N. SMITH 1 1.... 5 3..... 5 3...... (e) 115 1 15 13 135 1 15 15 155 (g) 115 1 15 13 135 1 15 15 155 1.. 1.... 5 3...... 5 3...... (f) 115 1 15 13 135 1 15 15 155 (h) 115 1 15 13 135 1 15 15 155 Figure 7. (Continued)...7..5..3..1.1..3..5. 3.3. Relative strengths of the associations of the SAM and ENSO with Australian rainfall To be able to see the above associations in a broader perspective, it is useful to compare them to the correlation between the SOI and rainfall over the same period of time. Overall, the correlations between the SOI and rainfall are largely positive and significant correlations cover a large area in all seasons. The strongest correlations are in spring (Figure 9), with significant positive correlations east of 15 E. To determine the regions in Australia where the SAM has a greater influence than ENSO on the rainfall, we produced contour maps of the difference between the magnitude of the correlation between rainfall and the AOIR and the magnitude of the correlation between rainfall and the SOI (Figure 1). The SAM has a greater association with rainfall than does ENSO in summer (northwest Northern Territory, most of Western Australia, parts of South Australia and western Tasmania, Figure 1), winter (SWWA except the far southwest, southern South Australia, much of western and southern Victoria, and western Tasmania, Figure 1(c)) and spring (coastal New South Wales and western Tasmania, Figure 1(d)). It is evident that across all seasons the SAM is more consistently associated with rainfall variability than is ENSO in western Tasmania. Since western Tasmania is subject to westerlies in all seasons and its rainfall is derived from the moisture transported by the westerlies (which we have seen the SAM to have a large effect on), it is not unexpected that the SAM would have a greater influence on the seasonal rainfall there than ENSO. The regions and seasons where the SAM dominates the most over ENSO (in explaining rainfall variability) are southern Western Australia (summer) and the central west coast of Tasmania (spring). One must also appreciate that when ENSO explains more of the seasonal rainfall variability than the SAM does, the magnitude and area of ENSO s dominance is larger, with ENSO s effect being strongest in northern and southeast Australia. This may be one reason why the SOI is utilised in seasonal rainfall prediction (Stone et al., 199).. DISCUSSION AND CONCLUSIONS The strongest correlations between rainfall and the AOIR are negative, and negative correlations are generally

SOUTHERN ANNULAR MODE AND AUSTRALIAN RAINFALL 119 Figure. Difference between the upper and lower quartile winter AOIR s in 5 hpa zonal wind with a contour interval of 1. m/s, and 5 hpa air temperature with a contour interval of.5 C forthe Southern Hemisphere. Note: Upper quartile s (197, 197, 1979, 19, 193, 195, 197, 199, 1993, 1997, 199, and 1) and lower quartile s (195, 19, 195, 19, 197, 197, 1977, 191, 1991, 199, 1995, and 199). The upper and lower quartile s are from the raw AOIR. The region enclosed by the bold lines in is the domain 9 1 E. only found in southern Australia. Thus, in the Australian region the SAM may have a greater effect on extra-tropical cyclones and cold fronts (i.e. the position of the PFJ) and westerlies, than on high-pressure systems and easterlies. As there are no widespread significant correlations in northern Australia it appears that the SAM does not have a large influence on variability in the Australian monsoon and tropical cyclones. We have identified regions of the continent where there are significant associations between rainfall and the SAM. Having said this, most of the correlations are fairly modest. Therefore, the SAM can only partly explain variability and trends in seasonal Australian rainfall, which means there must be other influences on synoptic activity in the Australian region. The Antarctic Circumpolar Wave is thought to affect extra-tropical cyclones (White et al., ) and southern Australian rainfall (White, ). However, the Antarctic Circumpolar Wave has really only been observed from 195 to 199 (Connolley, ; Simmonds, 3), hence it is probably not responsible for the long-term decline in SWWA winter rainfall or the current decline in winter rainfall in southern South Australia, Victoria, and Tasmania, but it could play a part in inter-annual rainfall variability in southern Australia. It would be of interest to perform a similar study on rainfall of New Zealand, which may reveal even stronger relationships between seasonal rainfall and the SAM since one of the SAM s mid-latitude centres of action is situated over New Zealand. To summarise, there is an inverse relationship between the SAM and rainfall in regions that are affected by extra-tropical cyclones and cold fronts (SWWA, southern South Australia, Victoria, and Tasmania), and westerlies (western Tasmania). While there is an in-phase relationship between the SAM and rainfall in regions where the rainfall is linked to high-pressure systems (Western Australia), and high-pressure systems and easterlies (Queensland and New South Wales). The correlations using raw and detrended data indicate that in most cases the SAM-rainfall relationship is because of the association on inter-annual timescales, and thus for the period examined in this study, the SAM contributes mainly to inter-annual rainfall variability. Therefore, the SAM may play a role in the current decline in winter rainfall in southern South Australia, Victoria, and Tasmania, but if this is just the start of a long-term trend then it may not be due to the SAM, as was noted to be the case in SWWA. Western Tasmania is the main region where the SAM plays a bigger role in seasonal rainfall variability than ENSO. The regional AOI (AOIR) provides a much better representation of the regional circulation and therefore Australian rainfall variability, making it more appropriate for regional rainfall studies than a hemispheric index. We comment that questions have been raised as to the veracity of trends in the SAM that are derived from reanalyses (Simmonds and Keay, ; Marshall, 3; Bengtsson et al., ). It is important to note that although the magnitudes of trends that are derived from reanalyses are generally too large, the positive trends in the seasonal SAM are real (Marshall, 3). Much progress has been made on finding the cause of the positive trends. In one of the most comprehensive investigations undertaken to date, Marshall et al. () established that increased greenhouse gas and anthropogenic aerosolconcentrations, stratospheric ozone depletion, solar variability, and volcanic aerosol concentrations all contributed to the seasonal trends. Therefore,

1 B. MENEGHINI, I. SIMMONDS AND I. N. SMITH 1... 5... 3.... 115 1 15 13 135 1 15 15 155.3..1.1..3..5..7..9 Figure 9. Correlation between total spring rainfall and the SOI using raw data. Contour interval is.1 and stippling indicates the correlations that are significant at the 95% confidence level. Spring is defined as in Figure. 1. 1. 5 3... 5 3..... 115 1 15 13 135 1 15 15 155 (c) 115 1 15 13 135 1 15 15 155 1 1..... 5 3.... 5 3... 115 1 15 13 135 1 15 15 155 (d) 115 1 15 13 135 1 15 15 155.7..5..3..1.1..3..5 Figure 1. Difference between the magnitude of the correlation between seasonal rainfall and the AOIR (using raw data) and the magnitude of the correlation between seasonal rainfall and the SOI (using raw data) in summer, autumn, (c) winter, and (d) spring. Contour interval is.1 and stippling indicates the correlations that are significant at the 95% confidence level. The seasons are defined as in Figure.

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