SOUTHWEST WESTERN AUSTRALIAN WINTER RAINFALL AND ITS ASSOCIATION WITH INDIAN OCEAN CLIMATE VARIABILITY

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 20: (2000) SOUTHWEST WESTERN AUSTRALIAN WINTER RAINFALL AND ITS ASSOCIATION WITH INDIAN OCEAN CLIMATE VARIABILITY I.N. SMITH a, *, P. MCINTOSH b, T.J. ANSELL c, C.J.C. REASON c and K. MCINNES a a CSIRO Atmospheric Research, Aspendale, Australia b CSIRO Marine Research, Hobart, Australia c Uni ersity of Melbourne, Park ille, Australia Recei ed 1 September 1999 Re ised 21 June 2000 Accepted 29 June 2000 ABSTRACT Southwest Western Australia (SWWA) has experienced a significant decrease in winter rainfall since the late 1960s. This decrease is unexplained and the resultant problem of reduced water storage has been compounded by the lack of any useful predictive skill at the seasonal time scale. This study uses recent gridded, historical data and simple linear correlation in order to evaluate the importance of links between rainfall and both mean sea level pressure (MSLP) and sea-surface temperature (SST) patterns over the Indian Ocean. The decrease in rainfall is linked to decreases in the density of low-pressure systems in the region and to increases in both MSLP and SST over the southern Indian Ocean. Warmer SSTs and increases in MSLP are associated with the observed long-term changes, but changes in these variables do not explain a great deal of the observed interannual variability. Greenhouse-induced climate change is not regarded as a likely explanation for the observed decrease, however, the existence of links with both MSLP and SSTs suggests the existence of coupled air sea interactions over the southern Indian Ocean which may be relevant at decadal or multi-decadal timescales. A major difficulty with defining any such processes is the relative sparseness of data at high latitudes in the Southern Hemisphere. This should be partly alleviated as more recent high quality data becomes available over time. Copyright 2000 Royal Meteorological Society. KEY WORDS: correlation; empirical orthogonal functions; Indian Ocean; mean sea level pressure; rainfall; sea surface temperatures; southwest Western Australia 1. INTRODUCTION The southwest region of Western Australia (SWWA) is the corner of Western Australia bounded by latitude 30 S and longitude 120 E (see Figure 1). One of the earliest studies involving this region was that by Wright (1974) who compared the two periods and and noted that, while early winter (May July) rainfall for much of this region had increased, late winter (August October) had decreased. Almost a decade later, Pittock (1983) pointed out that May October rainfall in the vicinity of Perth over the period had significantly decreased compared to the period Significant decreases were subsequently confirmed by Nicholls and Lavery (1992) and also Hennessy et al. (1998) in analyses of high quality station data. Because the winter period is the time of year when the bulk of the annual rainfall is recorded, the decrease has impacted on the availability of fresh water particularly during the dry summer months. Water supply authorities and those responsible for designing new water catchments and related infrastructure are, therefore, interested in the probability of the observed long-term decrease persisting, or even becoming more severe into the future. In addition, there is now a greater need for more reliable seasonal rainfall predictions to assist in the management of existing supplies but, as Drosdowsky (1993a) demonstrated, SWWA winter rainfall is characterized by unique * Correspondence to: CSIRO Atmospheric Research, Aspendale, Australia 3195; ins@dar.csiro.au Copyright 2000 Royal Meteorological Society

2 1914 I.N. SMITH ET AL. Figure 1. Map showing the region referred to as SWWA temporal variability. There is little skill associated with the traditional predictor of seasonal rainfall in Australia, the Southern Oscillation Index (SOI), and more attention has recently been given to the role of sea-surface temperatures (SSTs). It has been long recognized that Pacific Ocean SST anomalies are linked to interannual rainfall variations over much of eastern Australia but rainfall in other regions, such as SWWA, may be independently linked to SST anomalies elsewhere. Pittock (1975) identified two dominant and independent patterns for annual rainfall for the period the first characterized by anomalies concentrated over much of eastern Australia (and linked to the Southern Oscillation), the second characterized by anomalies throughout southern and western Australia contrasting with anomalies over eastern Australia. Nicholls (1989) focused on winter rainfall ( ) and found two similar patterns, one related to the SSTs in the Pacific Ocean (PCP1) and another more dominant pattern (PCP2) related to a dipole-type pattern of SSTs in the Indonesian and Indian Ocean regions. Drosdowsky (1993b) carefully analysed district rainfall over the period and identified similar links between a northwest to southeast winter rainfall pattern and SSTs. It is also worth noting that the correlations he found did not indicate so much a dipole-type structure as a more broad scale structure with links to SSTs in the southern Indian Ocean. Drosdowsky (1993c) investigated the potential for using Indian Ocean SST anomalies in summer and early autumn to predict early winter rainfall for southern and eastern Australia, but found little evidence of any significant links involving SWWA. Smith (1994) also considered the relationship between Australian district rainfall and Indian Ocean SST patterns as revealed by empirical orthogonal function (EOF) analysis, but concluded that there was little predictive skill when using autumn (March May) data to predict winter (June August) rainfall. Drosdowsky and Chambers (1998) performed a more

3 WESTERN AUSTRALIAN WINTER RAINFALL 1915 comprehensive analysis into near-global SST principal component patterns as potential rainfall predictors which again indicated relatively minor skill for the SWWA region. Allan and Haylock (1993), in an analysis of gridded mean sea level pressure (MSLP), SST and cloudiness data over the period noted that, on the decadal to multi-decadal time scale, SWWA winter rainfall was associated with changes in the mid-high latitude longwave atmospheric circulation pattern and also noted that the rainfall decrease also appeared to be linked with Indian Ocean SSTs. In a more recent follow-up study, Ansell et al. (2000), analysed the relationships between rainfall over southern Australia, including SWWA, using much more comprehensive gridded data covering the period Although they focused on variability on time scales between 7 and 20 years, simple correlation also suggested a link between SWWA winter rainfall and SST anomalies at mid (rather than tropical) latitudes, thus distinguishing this region from either the remainder of Western Australia or southeastern Australia. This link was not apparent from the previous studies which dealt with, at most, only 40 years of data and/or because of de-trending (e.g. Smith, 1994). Power et al. (1999) identified links between Australian rainfall and SST variability, which operates on a decadal time scale, but these involve the Pacific Ocean and do not involve the SWWA region. The role of mid- and high-latitude MSLP and SST anomalies in the southern hemisphere is also attracting some attention following the identification of the Antarctic Circumpolar Wave (ACW) (White and Peterson, 1996) and its impacts on New Zealand (White and Cherry, 1999). While SST-rainfall links are attractive, they may not always reflect cause and effect. This appears to be the case with the tropical Indian Ocean SST dipole pattern identified by Nicholls (1989). Although linked to winter rainfall over other parts of the continent, its role as an independent factor is unclear since it does not appear as a distinct mode when analysing SSTs, but only when rainfall is regressed against SSTs. Model studies (Frederiksen et al., 1999) have not revealed any direct link between rainfall and this type of pattern unless the anomalies are made relatively large. It remains possible that both the dipole pattern and the rainfall anomalies are forced by a third factor and this has been demonstrated in an analysis of unforced coupled model experiments in which atmospheric circulation anomalies are primarily responsible (I. Watterson, private communication). Given the limitations of the Southern Oscillation and the absence of any links to the Pacific Ocean, this study attempts to more closely investigate the links between SWWA rainfall and both MSLP and SST historical data over the Indian Ocean as revealed by the previous studies of Allan and Haylock (1993) and Ansell et al. (2000). The approach adopted here is to subject any apparent links to tests for consistency and statistical significance. It is recognized that the quality of the historical data can vary over time where observations have been sparse, particularly towards the remote Southern Ocean, and that this may yield artificial correlations depending on how the data have been processed. We partly account for this effect by sampling different time periods. In particular, we expect that any evidence of links over the full period should also be apparent during the most recent period when data quality can be expected to be high. We also test for robustness by considering whether any links are evident during the adjacent seasons or when lagged by one season. 2. OBSERVED CHANGES IN RAINFALL, CYCLONICITY AND MSLP The rainfall data used in this analysis are based on the high quality daily data set described by Lavery et al. (1997). The data were processed by Hennessy et al. (1998) into regional averages and were subsequently used by Ansell et al. (2000) in an analysis of decadal-scale variability. The SWWA area-averaged rainfall is based on observations from over 30 stations that are reasonably well distributed throughout the region identified in Figure 1 and comprise seasonal averages for each year between 1907 and There has been a significant decrease in winter (JJA) rainfall after the late 1960s (Figure 2). For the period the average rainfall was 314 mm, but in the subsequent years ( ) only one year (1983) exceeded this amount. The average winter rainfall over these subsequent years has been 249

4 1916 I.N. SMITH ET AL. Figure 2. Seasonal rainfall totals for SWWA : DJF, MAM, JJA and SON. The heavy curve in each case represents an 11-year running average mm a decrease of 21%. There is no evidence of a similar change in rainfall in summer (DJF), autumn (MAM) or spring (SON). There has been no significant change to this pattern over the most recent years ( ). Yearly inflows to major catchments in the vicinity of Perth between 1975 and 1999 are notable for the absence of any major runoff events that were relatively frequent between 1911 and 1974 (B. Sadler, private communication). Despite the tendency towards low winter rainfall, July 1996 was a relatively wet month and, over a 4-day period (22 25 July), a substantial amount of rain was recorded throughout the region. Figure 3 shows four images (GLOBE Program, 1998) representing 24-h total rainfall amounts over this period based on the National Centre for Environmental Prediction (NCEP)/National Centre for Atmospheric Research (NCAR) reanalysis data. Although the rainfall data are determined by a numerical model based on the assimilation of atmospheric observations, comparisons with direct observations indicate they contain useful information (Kalnay et al., 1996). The images depict the rapid progression of two cold fronts during a period of strong, widespread surface westerly winds. The first front (on the 22nd) appears to interact with tropical cloud over the Indian Ocean, while the second (on the 24th) shows no such interaction. Since cold fronts such as these represent typical winter rain-bearing events for the region, it is possible that the frequency or strength of these fronts has changed over recent decades. To investigate possible changes in the statistics of fronts and their associated low pressure systems on these time scales, the objective technique of Murray and Simmonds (1991a) was used to identify, count and track low pressure anomalies in daily data. This technique for analysing synoptic weather systems has been used in a number of previous studies such as Murray and Simmonds (1991b), Jones and Simmonds (1993a,b) and Sinclair (1994, 1995).

5 WESTERN AUSTRALIAN WINTER RAINFALL 1917 Figure 3. Sequence of 24-h rainfall totals (mm) over the period July 1996 according to NCEP/NCAR reanalysis data. The black shading encloses values greater than 6 mm Similar types of studies (e.g. Nowak and Leighton, 1997) have also been performed which analyse high pressure systems (or anticyclonicity) in the Australian region. The data used in this analysis are the daily, reanalysed gridded data produced by NCEP (Kalnay et al., 1996). These are available on a 2.5 latitude 2.5 longitude global grid and span the years from 1958 to The two decades and represent the years immediately prior to and following the commencement of rainfall decreases. During this time, there was a marked decrease in the incidence of low-pressure systems over the SWWA region and nearby oceans as indicated by the density of low pressure systems (the number of systems counted per degree latitude square) (Figure 4). The average system density in the years subsequent to 1978 (not shown) resembles that for A reduction in extra-tropical cyclonicity (defined as the time in hours during which cyclone centres occupy a 5 square grid box during a given period) is also reported in Plummer et al. (1999). The pattern of differences is consistent with a shift in numbers to the west and southwest and may reflect either a reduction in the number of weather systems passing through the region, or the fact that they travel through the region more rapidly. To clarify this further, the average speed of systems was also examined for the same two decades. This revealed that, over SWWA and the ocean to the southwest, the average speed of systems in the earlier decade was in fact slightly higher than in the decade following. This result suggests that the lower system densities are due to fewer systems rather than a reduction in their speed of movement. The changes in system density are consistent with the observation that average MSLP has increased over this time (Allan and Haylock, 1993). The historical MSLP data set referred to as GMSLP2.1(f) comprises monthly mean values ( ) on a global grid with a horizontal (latitude and longitude) resolution of 5 (Allan et al., 1996; Basnett and Parker, 1997). The reliability of the data depends on the density of available land, island and ship observations which, particularly in the high southern latitudes, is relatively sparse prior to about An increase in average winter MSLP over the SWWA region is a large-scale feature of the southern and Indian Oceans as evidenced by the differences between the

6 1918 I.N. SMITH ET AL. Figure 4. Average JJA low pressure system densities in (number 10 3 per 1 square grid box) for the 10-year periods (a), (b) and their difference (c) decadal averages for and (Figure 5). Note that these two periods are only distinguished by the fact that SWWA winter rainfall decreased significantly after MSLP RAINFALL RELATIONSHIPS Allan and Haylock (1993) showed a strong inverse relationship existed between Perth rainfall and Perth MSLP and this is also apparent when comparing the time series for SWWA rainfall and Perth MSLP (Figure 6). The correlations between winter rainfall and several climate indices for different periods are shown in Table I. Approximately 60% of the total variance in winter rainfall can be explained by fluctuations in Perth MSLP, irrespective of the time period considered. There is also evidence of a simultaneous correlation with the SOI (also shown in Figure 6) which is highly significant (i.e. at the 99% level) over the full period but less so (90%) over the more recent periods. This can explain approximately 10% of the total variance due as much to fluctuations in Tahiti MSLP as to fluctuations in (geographically closer) Darwin MSLP. However, at no time is there any significant relationship with equatorial Pacific SSTs as measured by the NINO3 index.

7 WESTERN AUSTRALIAN WINTER RAINFALL 1919 Figure 5. The difference between average JJA MSLP (hpa) for the 10-year periods and Table I also shows the correlations between winter rainfall and both autumn (MAM) indices and May-only indices. It can be seen that, despite the evidence of simultaneous links, little predictability is evident in the lagged correlations. There is weak evidence of a link with Perth MSLP during MAM and May, but this could simply arise because of persistence of MSLP patterns during the last few days of May and the first few days of June. The data reveal no evidence of any predictability associated with the either the SOI or NINO3. Figure 6. Time series of JJA values for: (a) SWWA rainfall anomalies; (b) MSLP for Perth; and (c) SOI

8 1920 I.N. SMITH ET AL. Table I. Correlations between observed SWWA winter rainfall and Perth MSLP, Darwin MSLP, Tahiti MSLP, the SOI and the NINO3 index MAM May JJA Period: Perth (99%) Darwin (99%) Tahiti (99%) SOI (99%) NINO (90%) Period: Perth (99%) Darwin (90%) Tahiti (95%) SOI (90%) NINO Period: Perth (99%) Darwin Tahiti (95%) SOI (90%) NINO The periods used include the full period ( ), the recent period ( ) and the most recent period ( ). In this and subsequent tables, significance levels are shown in brackets where estimates exceed the 90% level. Levels at or greater than 99% are in bold. The levels correspond to the estimated effective degrees of freedom in each time series. These are, in general, less than the number of points (years) due to varying degrees of persistence (or auto-correlation) within the data. The correlations between SWWA winter rainfall and large-scale MSLP ( ) were shown in Ansell et al. (2000) but are shown here in Figure 7(a) for just the recent period As expected, the strongest relationship is with pressure in the vicinity of SWWA, but significant correlations also occur over a large area extending from almost Madagascar to New Zealand and from the Equator to approximately 50 S. The correlations between winter rainfall and autumn MSLP show that, while correlations are relatively small in the vicinity of SWWA, there are regions over the oceans to the west where apparently significant correlations (up to approximately 0.40) do exist (Figure 7(b)). The 99% significance level associated with a sample size of 45 is nominally 0.37, but will be larger if there is some degree of persistence in the data. If we focus just on the month of May, relatively large correlations (up to approximately 0.50) exist over the Southern Ocean to the west and south (Figure 7(c)). This suggests that pressure anomalies in this region during the prior to winter may potentially provide some indication of expected rainfall. Using Figure 7 as a guide, we extracted box-averaged MSLP anomalies during May for the region between 80 E and 90 E, 40 S and 50 S and correlated these with winter rainfall over different periods (Table II). For the period the correlation is 0.49, indicating that approximately 25% of the total variance in rainfall can be explained by MSLP fluctuations in this region. A simple linear regression fit to the rainfall is shown in Figure 8(a). There is very little persistence in the time series so the fit is estimated to be significant beyond the 99% level, implying that there is a less than 1 chance in 100 of achieving this by chance. Given that the MSLP data base comprises more than 100 individual grid point time series, it could be argued that finding such a fit is not surprising. However the individual grid point time series are not all independent and so the fit is unlikely to be fortuitous. Close examination of Figure 8(a) indicates that the MSLP data reflects variations at relatively long-term time scales, especially the decreases after the late 1960s. The correlation is certainly weaker for both the full period and the more recent period If we consider just the first order differences in both time series

9 WESTERN AUSTRALIAN WINTER RAINFALL 1921 Figure 7. Correlations between JJA SWWA rainfall and: (a) grid point JJA MSLP; (b) grid point MAM MSLP; and (c) grid point May MSLP (N. Nicholls, private communication) the correlation is only 0.30, indicating that less than 10% of the year-to-year variation in rainfall can be explained by the MSLP data.

10 1922 I.N. SMITH ET AL. Table II. Correlations between SWWA winter rainfall and May values for box-averaged MSLP and box-averaged SSTs (see Table I for details) Rainfall MSLP (May) SST (May) (80 90 E, S) (60 70 E, S) (99%) 0.27 (95%) (99%) 0.53 (99%) Figure 8. JJA SWWA rainfall anomalies observed (solid line) and fitted (dashed lines) using: (a) grid point May MSLP averaged between 80 and 90 E, 40 and 50 S; and (b) grid point May SST averaged between 60 and 70 E, 25 and 35 S 4. SST RAINFALL RELATIONSHIPS Ansell et al. (2000) found that SWWA rainfall in winter was significantly correlated with winter SSTs over the Indian and Southern Ocean regions over the full period The SST data they analysed is referred to as Global Sea Ice and Sea Surface Temperature (GISST) version 2.2a (see Parker et al., 1995 for details) and comprise monthly values on a global grid with a resolution of 1 1. Here we analyse a more recent version referred to as GISST3. The result of correlating winter rainfall with grid point SSTs for just the recent period similarly reveals significant correlations (approaching 0.60) over the Indian and Southern Ocean region (Figure 9(a)). As was done with MSLP data, lagged correlations were also calculated using autumn

11 WESTERN AUSTRALIAN WINTER RAINFALL 1923 Figure 9. Correlations between JJA SWWA rainfall and: (a) grid point JJA SSTs; (b) grid point MAM SSTs; and (c) grid point May SSTs (MAM) SSTs (Figure 9(b)) and May-only SSTs (Figure 9(c)). There is some indication that the region of greatest correlations, and therefore the anomalies themselves, progress slowly from east to west. A comparison with Figure 7 also indicates that the regions of strongest correlations with SST anomalies are displaced to the west of the regions of strongest MSLP anomalies. One interpretation of these patterns is that the SST anomalies drive the pressure anomalies, but is difficult to entertain because the correlation patterns are not coincident. An alternative explanation, namely that the pressure anomalies drive the SST

12 1924 I.N. SMITH ET AL. anomalies, is based on the fact that Figure 7(a) is consistent with positive rainfall anomalies and low MSLP over SWWA, which would correspond to northerly wind anomalies to the east and southerly wind anomalies to the west. This pattern of anomalous winds would correspond to relatively cool air temperatures to the west, which, in turn, is consistent with Figure 9(a) indicating relatively cool SSTs to the west. The question of cause and effect (SSTs or MSLP) is also not clearly resolved by the lagged correlations. Using Figure 9(c) as a guide, we extracted box-averaged SST anomalies during May for the region E and S and, as was done with box-averaged MSLP anomalies, used these to fit winter rainfall (Figure 8(b)). The correlation over the full period ( ) is a significant 0.27, over the recent period a highly significant 0.53 but, over the most recent period ( ), a non-significant 0.13 (Table II). The weaker correlation over the full period is expected if we assume that the SST data were unreliable early on but, as was found with the MSLP data, the correlations are also weaker over the most recent period. The relatively good correlation over the period occurs because of long-term fluctuations in the data and this is confirmed by the fact that the SST data explain less than 2% of the year-to-year changes. Table II suggests that the correlations with May MSLP are slightly more robust over time than those with SST. This could also be interpreted as being consistent with MSLP driving rainfall and SSTs. However, it is difficult to be conclusive on this point because of uncertainty with data quality over these remote regions. 5. EOF ANALYSIS OF SSTS The use of box-averaged data from fixed locations provides a simple method of analysis, but may be too narrow an approach to use when other means are available for analysing large data sets distributed in space and time. EOF analysis can help identify dominant modes of variability on the large-scale, which are more likely to represent physical processes. The GISST3 monthly data for the full period ( ) were first converted to anomalies by removing the long-term mean annual cycle. Only data between 30 N and 40 S were retained for this analysis since observations throughout the Southern Ocean are very sparse during the early part of the record. No detrending of the data was applied. EOFs were computed separately for both the Indian Ocean and the Pacific Ocean (not shown). The SST data are generally more reliable after the mid-1940s and the period 1980 present is sometimes considered atypical since this marks the advent of satellite-based observations. As a test of the stability of the results, EOFs were calculated for the full period ( ), the intermediate period ( ) and the modern period ( ). In each case, the associated mean annual cycle was removed from the data so that the EOFs were based on monthly anomalies. It was found that there was a great deal of similarity between the EOFs generated using these three periods with the time series for the first EOF in each case essentially identical. The time series for the higher order EOFs using either the full or intermediate periods were also very similar. It was concluded that the leading EOFs calculated for the full period were not greatly affected by data quality in the early period and provide a good representation of the major patterns of variability. The first EOF for the Indian Ocean explains 23% of the variance and is characterized by a broad-scale pattern with maximum values located in the vicinity where the strongest correlations with winter rainfall were identified (Figure 10). The second EOF (10% of the variance) is characterized by a centrally-located warm(north)/cool(south) dipole pattern (Figure 10) while the third and fourth EOFs explain 8% and 7%, respectively, and are mainly characterized by alternating anomalies at sub-tropical and mid-latitudes (Figure 10). EOFs 5 and 6 (not shown) explain only 5% and 4% of the variance. Although the EOFs are linearly independent in terms of variance explained, EOF2 is not that distinct from the subsequent EOFs, which indicates that care should be exercised if attempting to interpret it in terms of any physical processes.

13 WESTERN AUSTRALIAN WINTER RAINFALL 1925 Figure 10. The first four EOF patterns for Indian Ocean SSTs based on monthly data for the period January 1871 December EOF1 (variance explained 23.2%) (upper-left panel), EOF2 (9.9%) (upper-right panel), EOF3 (8%) (lower-left panel) and EOF4 (7.3%) (lower-right panel). The values represent loadings such that the product of the loading and the corresponding EOF amplitude yield temperature anomalies in degrees. Positive loadings are shaded, negative loadings are represented by dashed contours The time series for EOF1, averaged over winter (JJA) values, is characterized by a rising trend which indicates that the whole basin, particularly the southern Indian Ocean, has been relatively warm since the 1960s (Figure 11(a)). These values can are compared with those for global annual average combined land air and SST anomalies (Nicholls et al., 1996). Temperatures have increased by about C since the late nineteenth century and by about C over the past 40 years. EOF1(JJA) closely reflects these trends and the two time series are significantly correlated with r=+0.74 (Figure 11(a)). EOF2(JJA) does not exhibit any strong trends but does exhibit some variability on decadal time scales (Figure 11(b)). Table III shows the correlations between SWWA winter rainfall, winter SOI, and EOFs 1 and 2 (JJA) for both the Indian Ocean and the Pacific Ocean (there were no significant correlations with any of the lower order EOFs). El Niño Southern Oscillation (ENSO) variability is represented by Pacific Ocean EOF1 (not shown) and is significantly correlated with the SOI over all the periods considered. However, neither this EOF nor the second Pacific Ocean EOF (also not shown) is linked to SWWA rainfall. The only evidence of any SST rainfall links is confined to the Indian Ocean where the sign of the correlations with EOFs 1 and 2 indicate that basin-wide warm anomalies tend to be associated with reduced rainfall while a warm(north)/cool(south) pattern tends to be associated with increased rainfall (Figure 10). The links with EOF1 are estimated to be highly significant over the full period ( ), less significant over the recent period ( ) and effectively non-existent (r=0.11) over the most recent period. It is also apparent that, despite representing long-term trends, EOF1 is also consistently linked with the

14 1926 I.N. SMITH ET AL. Figure 11. Time series of Indian Ocean SST EOFs (JJA averages): (a) EOF1 compared with global annual average combined land air and SST anomalies (Nicholls et al., 1996); (b) EOF2. Note that the EOF values are dimensionless and the temperature anomalies have been scaled up by a factor of 10 to assist comparison Table III. Simultaneous correlations between SWWA winter rainfall, winter SOI and SST EOFs for both the Indian and Pacific Ocean basins (see Table I for details) Indian Ocean Pacific Ocean EOF1 EOF2 EOF1 EOF Rainfall 0.37 (99%) 0.33 (99%) SOI 0.25 (98%) (99%) 0.23 (95%) Rainfall 0.38 (95%) 0.41 (98%) SOI 0.45 (99%) (99%) 0.29 (90%) Rainfall (95%) SOI 0.54 (99%) (99%) 0.33 SOI especially over the most recent period. On the other hand, the rainfall links with EOF2 appear to be stronger and far more robust. If a relationship between SSTs and rainfall in winter reflects some physical process, then there should be similar evidence of this relationship in the adjacent seasons. As a further test, we also consider data during the autumn (MAM) and spring (SON) seasons when rainfall is much less, but still represents a

15 WESTERN AUSTRALIAN WINTER RAINFALL 1927 Table IV. Simultaneous and lagged (by 1 season) correlations between seasonal rainfall and the seasonal average values for Indian Ocean SST EOFs 1 and 2 SSTs (see Table I for details) Rainfall Simultaneous Lagged (1 season) EOF1 EOF2 EOF1 EOF2 MAM (95%) 0.20 (90%) MAM (90%) 0.28 MAM JJA (99%) 0.33 (99%) 0.35 (99%) 0.09 JJA (90%) 0.41 (98%) (90%) JJA (95%) SON (99%) (90%) 0.15 SON SON (98%) This Table also includes the JJA correlations shown in Table III. substantial fraction of the annual total (see Figure 2). Table IV shows that EOF1 is consistently negatively correlated with rainfall in all three seasons although the strength and significance varies over time. While the link with winter rainfall is weak during the most recent period, the link with spring rainfall is relatively strong. For the most recent period, the autumn values for EOF1 would not have provided any useful indications about winter rainfall while the winter values may have provided minimal indications about spring rainfall. In the case of EOF2, the only significant correlations are associated with winter rainfall, but the fact that the correlations change sign in the adjacent seasons casts doubts on whether the pattern carries any physical significance. 6. DISCUSSION Over the period , the data reveals several potentially useful links between rainfall and both MSLP and SST data. However, the nature of the correlations between the grid point data and the rainfall time series does not appear to explain a great deal about fluctuations at the interannual time-scale but, instead, appears to indicate links that operate at longer time scales. The data reflect the relatively large changes that took place around the 1960s and this is partly confirmed by the fact that most correlations appear weaker when data over the most recent 25-year period is considered. A study of the SST data using EOF analysis yields patterns of large-scale variability, the leading two of which are also linked to rainfall. The first of these represents a long-term warming trend for the entire basin that reflects global warming observed this century. This pattern is not only linked with rainfall during winter, but also in autumn and spring. The second pattern is also significantly linked to rainfall in winter, but less so in the other seasons. As was found with the grid point data, these links are not robust and do not appear to provide any useful predictive information at the interannual time scale. The problem remains as to what lies behind these links and what is cause and effect with regard to SWWA winter rainfall. There is some indication that MSLP anomalies drive SST anomalies and rainfall at least on interannual time scales. In particular, the correlation patterns shown in Figure 7 and Figure 9 are consistent with anomalous atmospheric circulation and associated air temperatures over the Indian Ocean. It is worth noting that Reason (1999) examined cloud cover and near-surface wind data for the south Indian Ocean and suggested that SST anomalies in this region were primarily forced by the atmosphere. Secondly, the correlation between box-averaged MSLP and rainfall appears more robust over time than that between box-averaged SST and rainfall. Assuming MSLP anomalies are drivers, the problem then becomes one of understanding how these anomalies arise in the first place.

16 1928 I.N. SMITH ET AL. Observations at southern mid-to-high latitudes have revealed signals in both ocean and atmosphere that propagate from west to east and give rise to local anomalies every 4 5 years. This phenomenon is referred to as the Antarctic Circumpolar Wave (ACW) (White and Peterson, 1996). Both Christoph et al. (1998) and Cai et al. (1999) have identified a similar mechanism in coupled model simulations that involve interactions between MSLP anomalies, wind stresses and SSTs. The relationship between the ACW and SWWA rainfall also remains to be resolved, but it is worth noting that the ACW is a relatively recent discovery and is a feature of the post-1982 period when SST and MSLP data at high southern latitudes are regarded as particularly reliable. Further studies focusing on the results of coupled ocean-atmosphere model studies may be particularly important since they are, in principle, capable of simulating this type of phenomenon. Another phenomenon whose role is still not well understood is the so-called High Latitude Mode or Antarctic Oscillation (AO). Ansell et al. (2000) analysed Southern Hemisphere gridded sea level pressure (SLP) data for the period and found two dominant modes in austral winter average values. The first mode represents anomalies throughout the mid-latitude regions across the Indian Ocean and western Pacific, which contrast with anomalies elsewhere and appears independent of the second mode, which represents the Southern Oscillation. These modes are also found in analyses using data from other seasons and are similar to those identified (Karoly et al. 1996) using annual average station data the major difference being that the Southern Oscillation mode dominates the other seasons and the annual average. Importantly, the first mode is significantly correlated with rainfall for much of southern Australia (including SWWA) and also New Zealand. It also exhibits a trend over time but is relatively poorly understood and the implications for predictability remain unclear because the strongest links occur simultaneously. Given the existence of significant fluctuations due to the ACW and the AO, it is likely that continued analyses of good quality high latitude MSLP and SST data over time may reveal more about variability in the southern Indian Ocean and SWWA rainfall than the data sets analysed here. An improved understanding of their roles is also expected from analyses of coupled model experiments. The question of whether winter rainfall will, on average, remain relatively low is difficult to answer. Assuming that, in the long-term, warm SSTs are somehow linked to reduced rainfall, then the question becomes one of judging whether relatively warm SSTs will persist or strengthen. If the warming is linked to increasing levels of CO 2 then it could be argued that the probabilities favour a continuance into the future. A problem with this hypothesis is that significant warming and associated climate impacts are not predicted to occur until well into the 21st century and it is difficult to explain why this region of the globe, almost alone and in only one season, is being impacted to such an extent at such an early stage. It is possible that the low rainfall may represent a multi-decadal fluctuation of the climate system superimposed on a more gradual warming. This could be tested by analysing the results of transient greenhouse climate model simulations in which both fluctuations and warming trends are possible. 7. CONCLUSIONS The observed long-term decrease in SWWA winter rainfall can be linked to decreases in the density of low-pressure systems in the region and to recent increases in MSLP and SSTs over the southern Indian Ocean. The ENSO phenomenon does not explain these changes nor does it provide any useful predictability at the interannual time scale. One explanation for these changes is that they may reflect the early stages of greenhouse-induced climate change. However, this is regarded as unlikely as the sole explanation because of the difficulty in explaining the magnitude, the scale, the regional specificity, the seasonality and the timing of the observed changes compared with the types of changes predicted by climate models. A more likely possibility is that the changes since the late 1960s reflect decadal, or even multi-decadal, fluctuations of the ocean-atmosphere system superimposed on a relatively weak global warming trend. It is difficult to dismiss the links between rainfall and MSLP and SSTs over the Indian Ocean simply

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