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  • View in gallery

    The five sectors used to calculate the SIE. The locations of Perth and Melbourne are also indicated.

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    Spatial correlation between cyclone SD and rainfall in (a) Perth and (b) Melbourne for JJA 1979–2003. Areas of correlation above 0.4 are significant at the 95% level, but the whole pattern is shown for clarity. See text for further details.

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    Cyclone (a) SD and (b) DP associated with the five years of highest winter averaged SOI minus the five years of lowest SOI for the period 1979–2003. Years used in the composite: top five (1981, 1988, 1989, 1996, and 1998) and lowest five (1982, 1987, 1993, 1994, and 1997). SD units given in number of systems per 103 (degrees lat2) and DP units given in hPa. Areas significant at 95% level are show by contours.

  • View in gallery

    As in Fig. 3, but for the five years of lowest winter averaged AAO index minus the five years of highest AAOI. Years used in the composite: top five (1979, 1985, 1989, 1993, and 1998) and lowest five (1980, 1981, 1991, 1992, and 1995). The SOI status as used in Fig. 3 is indicated in bold roman for El Niño years and in bold italic for La Niña years.

  • View in gallery

    Cyclone SD for the lower five minus the top five years of SIE for (a) Indian and (b) west Pacific sectors for the period 1979–2003. Years used in the composite: top five Indian Ocean (1982, 1985, 1989, 1993, and 1999), top five west Pacific Ocean (1982, 1983, 1999, 2000, and 2003), lowest five Indian Ocean (1991, 1992, 1997, 2002, and 2003), and lowest five west Pacific Ocean (1980, 1981, 1989, 1991, and 2002). The SOI status as used in Fig. 3 is indicated in bold roman for El Niño years and in bold italic for La Niña years. SD units given in number of systems per 103 (degrees lat2) and DP units given in hPa. Areas significant at 95% level are show by contours.

  • View in gallery

    As in Fig. 5, but for cyclone DP.

  • View in gallery

    As in Fig. 5, but for anticyclone SD.

  • View in gallery

    As in Fig. 5, but for anticyclone DP.

  • View in gallery

    Monthly averaged daily rainfall for Perth and Melbourne and their interannual standard deviations for the period 1979–2003 (mm day−1).

  • View in gallery

    Sea level pressure anomalies associated with the five (a), (c) driest and (b), (d) wettest years in Perth and Melbourne, respectively, for JJA over the period 1948–2004. The contour interval is 1 hPa. Years used in the composite: top five Perth (1955, 1958, 1963, 1964, and 1996); top five Melbourne (1951, 1952, 1981, 1989, and 1991), lowest five Perth (1971, 1979, 1984, 1989, and 1990), and lowest five Melbourne (1948, 1982, 1994, 1997, and 2002). The SOI status as used in Fig. 3 is indicated in bold roman for El Niño years and in bold italic for La Niña years after 1979 when the SIE analysis has been carried out.

  • View in gallery

    Anomalies of (a) Perth rainfall and Indian Ocean SIE and (b) Melbourne rainfall and west Pacific SIE anomalies for JJA 1973–2003. Rainfall is given by dashed lines and SIE is given by solid lines in units of standard deviation. Scatter diagrams showing the correlation and regression lines are given for (c) Perth and (d) Melbourne. For Melbourne (d) the regression is presented for the period 1979–97 (solid line, crosses) and 1998–2003 (dashed line, triangles).

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Southern Hemisphere Synoptic Behavior in Extreme Phases of SAM, ENSO, Sea Ice Extent, and Southern Australia Rainfall

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  • 1 School of Earth Sciences, The University of Melbourne, Victoria, Australia
  • 2 Marine and Atmospheric Research, Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria, Australia
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Abstract

The association between Southern Hemisphere cyclones and anticyclones and the El Niño–Southern Oscillation (ENSO), southern annular mode (SAM), Antarctic sea ice extent (SIE), and rainfall in Perth and Melbourne is explored. Those cities are, respectively, located in the southwestern and southeastern corners of Australia, where substantial decreasing rainfall trends have been observed over the last decades. The need for a more unified understanding of large-scale anomalies in storm indicators associated with the climate features itemized above has motivated this study. The main aim is to identify cyclone-anomalous areas that are potentially important in characterizing continental rainfall anomalies from a hemispheric perspective, focusing on midlatitude Australia. The study covers the “satellite era” from 1979 to 2003 and was conducted for the southern winter when midlatitude rainfall is predominantly baroclinic.

The results indicate a well-organized hemispheric cyclone pattern associated with ENSO, SAM, SIE, and rainfall anomalies. There is a moderate large-scale, high-latitude resemblance between La Niña, negative SAM, and reduced SIE in some sectors. In particular, there is a suggestion that SIE anomalies over the Indian Ocean and Western Australia sectors are associated with a large-scale pattern of cyclone/anticyclone anomalies that is more pronounced over the longitudes of Australia and New Zealand. Spatial correlation analysis suggests a robust link between cyclone density over the sectors mentioned above and rainfall in Perth and Melbourne. Statistical analyses of rainfall and SIE show modest correlations for Perth and weak correlations for Melbourne, generally corroborating the above.

It is proposed that SAM and SIE are part of a complex physical system that is best understood as a coupled mechanism, and that their impacts on the circulation can be seen as partially independent of ENSO. While SAM and SIE have greater influence on the circulation affecting rainfall in the western side of Australia, ENSO is the dominant influence on the eastern half of the country. A contraction of the sea ice seems to be accompanied by a southward shift of high-latitude cyclones, which is also hypothesized to increase downstream cyclone density at midlatitudes via conservation of mass, similarly to what is observed during the extreme positive phase of the SAM. These associations build on previous developments in the literature. They bring a more unified view on high-latitude climate features, and may also help to explain the declining trends in Australian rainfall.

Corresponding author address: Alexandre Bernardes Pezza, School of Earth Sciences, The University of Melbourne, Victoria, 3010, Australia. Email: apezza@unimelb.edu.au

Abstract

The association between Southern Hemisphere cyclones and anticyclones and the El Niño–Southern Oscillation (ENSO), southern annular mode (SAM), Antarctic sea ice extent (SIE), and rainfall in Perth and Melbourne is explored. Those cities are, respectively, located in the southwestern and southeastern corners of Australia, where substantial decreasing rainfall trends have been observed over the last decades. The need for a more unified understanding of large-scale anomalies in storm indicators associated with the climate features itemized above has motivated this study. The main aim is to identify cyclone-anomalous areas that are potentially important in characterizing continental rainfall anomalies from a hemispheric perspective, focusing on midlatitude Australia. The study covers the “satellite era” from 1979 to 2003 and was conducted for the southern winter when midlatitude rainfall is predominantly baroclinic.

The results indicate a well-organized hemispheric cyclone pattern associated with ENSO, SAM, SIE, and rainfall anomalies. There is a moderate large-scale, high-latitude resemblance between La Niña, negative SAM, and reduced SIE in some sectors. In particular, there is a suggestion that SIE anomalies over the Indian Ocean and Western Australia sectors are associated with a large-scale pattern of cyclone/anticyclone anomalies that is more pronounced over the longitudes of Australia and New Zealand. Spatial correlation analysis suggests a robust link between cyclone density over the sectors mentioned above and rainfall in Perth and Melbourne. Statistical analyses of rainfall and SIE show modest correlations for Perth and weak correlations for Melbourne, generally corroborating the above.

It is proposed that SAM and SIE are part of a complex physical system that is best understood as a coupled mechanism, and that their impacts on the circulation can be seen as partially independent of ENSO. While SAM and SIE have greater influence on the circulation affecting rainfall in the western side of Australia, ENSO is the dominant influence on the eastern half of the country. A contraction of the sea ice seems to be accompanied by a southward shift of high-latitude cyclones, which is also hypothesized to increase downstream cyclone density at midlatitudes via conservation of mass, similarly to what is observed during the extreme positive phase of the SAM. These associations build on previous developments in the literature. They bring a more unified view on high-latitude climate features, and may also help to explain the declining trends in Australian rainfall.

Corresponding author address: Alexandre Bernardes Pezza, School of Earth Sciences, The University of Melbourne, Victoria, 3010, Australia. Email: apezza@unimelb.edu.au

1. Introduction

Air–sea interactions occurring over the southern high latitudes form a complex system that has been increasingly revealed as a highly interconnected physical mechanism. The scarcity of meteorological data available over the Southern Ocean has greatly inhibited the study of the many interactions taking place between the weather systems and the physical features of the high latitudes. This situation has been significantly improved over the last three decades by the advent of a range of satellite and reanalysis products.

Sea ice for instance has numerous and marked effects on the Southern Hemisphere (SH) climate (Yuan and Martinson 2000, and references therein). Among the most significant of these is the insulation it provides between the ocean and the atmosphere, effectively restricting exchanges of heat, moisture, and momentum. This insulation helps to preserve ocean heat, especially during the polar winter when there is a large difference between the cold atmosphere over the ice and the relatively warm ocean (Raphael 2003).

Another important influence in the role of sea ice is its effect on surface albedo. The albedo of the polar regions is greatly increased as sea ice expands, thereby contributing to keeping the region cool and increasing the pressure gradient in relation to the ice-free regions, which will in turn influence the ice distribution via wind-induced advection. Such mechanisms result in a complex interaction between the sea ice edge and the atmospheric circulation at a number of scales as described in previous works (Simmonds and Budd 1991; Godfred-Spenning and Simmonds 1996; Simmonds 2003b; Parkinson 2004).

Simmonds and Wu (1993) modeled the cyclone response to reductions in winter sea ice concentration in the Antarctic, showing that these could lead to a poleward migration of the low pressure belt around Antarctica. This could have a marked effect on the propagation of frontal systems and therefore would impact on midlatitude rainfall over the SH continents.

In contrast to the Northern Hemisphere, where large decreases in the sea ice have been recently observed (e.g., Rothrock et al. 1999; Serreze et al. 2003, 2007; Stroeve et al. 2005, 2007), the SH ice cover has only modest localized trends (e.g., Watkins and Simmonds 2000; Zwally et al. 2002; Parkinson 2004; Holland and Raphael 2006). While there appears to be little trend in the sea ice extent over the entire pack, there is marked interannual variation at a regional level (Simmonds and Jacka 1995; Simmonds et al. 2005). This is masked when considering the entire pack because of opposing distribution of the anomalies in different longitude sectors (Gloersen et al. 1992; Parkinson 1994; Stammerjohn and Smith 1997; Zwally et al. 2002; Simmonds et al. 2005). Observations also show that the leading mode of the SH ice variability exhibits a dipole structure with anomalies of one sign in the Atlantic sector and of the opposite sign in the Pacific sector associated with wind variations (Baba et al. 2006).

On the atmospheric side of this intricate system the southern annular mode (SAM) and the El Niño–Southern Oscillation (ENSO) represent two important modes of variability (Simmonds 2003a). Both have been shown to generate (and receive feedback from) global impacts (Simmonds and King 2004) via the atmosphere (teleconnections) and via the oceanic system [sea surface temperature (SST), ocean currents, and sea ice extent (SIE)]. Kwok and Comiso (2002) show that the Southern Oscillation index (SOI) is associated with large-scale patterns of climate anomalies in high latitudes that can be observed both in the atmosphere and in the ocean system. They showed that the strongest correlations between SOI and southern polar climate occur in the Bellingshausen, Amundsen, and Ross Seas, with negative phases of SOI (El Niño) being generally associated with higher sea level pressure, warmer temperatures, and less sea ice. A comprehensive review on the links between ENSO and the Antarctic region is presented in Turner (2004).

The SAM is known to generate sea ice variations of interannual and centennial time scales (Parkinson 2004), and it has also been shown to respond at least partially to ENSO (Ciasto and Thompson 2006). However, the long-term increasing trend observed in the Antarctic Oscillation index (AAO) (Marshall 2003), which is representative of the state of the SAM, does not appear to be associated with variations in the sea ice extent (Lefebvre et al. 2004). In fact recent variations in the SAM have been suggested to be primarily driven by ozone depletion (Thompson and Solomon 2002; Roscoe and Haigh 2007). The data also suggest that a dipole-like variation in the ice is led by sea level pressure anomalies in the Amundsen/Bellingshausen Sea (Holland and Raphael 2006). These have been associated, to different degrees, with SAM and ENSO. For instance, recent work has suggested that variability in ENSO may describe at least a quarter of the variance in SAM, in turn giving rise to fluctuations in the SSTs (L’Hereux and Thompson 2006).

As we learn more about these interconnections there is growing evidence that SAM, SIE, and ENSO are part of an integrated mechanism. While SAM is associated with SIE via north–south shifts in the high-latitude pressure gradient, ENSO predominantly reflects the impacts of tropical/extratropical teleconnections. These are key components of a physical system that can explain much of the climate variability observed in the southern high latitudes, including the fraction of continental, midlatitude rainfall anomalies that relies on storm track propagation. This includes a large portion of southern parts of South America, Africa, Australia, and New Zealand.

A key area studied in the paper is the southwestern and southeastern of Australia. These areas have been subject to a markedly decreasing rainfall regime particularly over the last decade (Allan and Haylock 1993; Simmonds and Hope 1997; Smith et al. 2000; Cai et al. 2005; Li et al. 2005; Hope et al. 2006; Timbal et al. 2006; Nicholls 2006) and increasing socioeconomic pressure related to climate change concerns. An extratropical drying trend has also been detected in a more general sense over continental areas in both hemispheres, although subject to high variability (Solomon et al. 2007; Herweijer and Seager 2008). This midlatitude drying pattern is believed to be at least partially associated with climate change mechanisms primarily via changes in the SAM, parts of which could have been induced by ozone losses and global warming. Changes in ENSO and in the Indian Ocean Dipole (IOD) are also believed to play important roles (Simmonds and Hope 1997; Ryan and Hope 2005; Nicholls 2006).

As part of this fundamental interplay discussed above much insight is still needed to better understand the integrated cyclone response to changes in the large-scale modes of variability. We use here this integrated view to explore further the associations between SAM/ENSO/SIE and SH cyclone/anticyclone behavior. In light of the cyclone and anticyclone anomalies we also explore a potential association with southern Australian rainfall over Melbourne and Perth. The paper is structured as follows. Data and methods are discussed in section 2. In section 3a a large-scale spatial pattern of correlation between cyclone density and rainfall is first introduced, followed by a discussion on the cyclone’s association with SAM and ENSO. This is followed by a discussion of cyclone patterns associated with SIE in section 3b. A brief discussion of the impacts associated with anticyclones is discussed in section 3c, followed by comments on the links between the features above and Australian rainfall in section 3d. The last part of the paper is dedicated to exploring statistical relationship between rainfall and sea ice (section 3e) followed by concluding remarks (section 4).

2. Data and methods

The study concentrates on the satellite period (1979–2003), for which the most reliable atmospheric and sea ice data are available. The analyses have been carried out for all seasons but emphasis is given to the SH winter [June–August (JJA)]. Station data of daily rainfall for Perth and Melbourne were obtained from the Australian Bureau of Meteorology (additional information is available online at www.bom.gov.au). The station data received by the bureau have already gone through internal quality control process, and were additionally checked for consistency.

SIE derived from remotely sensed Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) passive microwave data from the National Aeronautics and Space Administration’s (NASA) National Snow and Ice Data Center (NSIDC) is used. This dataset includes daily and monthly averaged sea ice concentrations at a grid cell size of 25 × 25 km. In computing the total sea ice extent, pixels must have an ice concentration of 15% or greater to be included. The total SIE is then calculated by summing up the number of pixels with at least 15% ice concentration multiplied by the total pixel area. Although in practical terms the SIE is also a measurement of area, the NSIDC definition here used has the advantage of being less affected by polynas than the alternative “sea ice area,” which includes only the fraction of each pixel which is effectively covered by ice (Gloersen et al. 1992).

The Melbourne University automatic tracking scheme (Murray and Simmonds 1991; Simmonds et al. 1999) was used to determine the cyclone and anticyclone trajectories and their statistical properties. This algorithm utilizes a totally automatic approach for locating and tracking low and high pressure centers on a sphere based on the Laplacian of the pressure. The scheme was chosen because of its proven reliability in capturing the weather patterns and synoptic climatology of the transient eddy activity in the SH (Jones and Simmonds 1993, 1994; Simmonds et al. 1999; Simmonds and Keay 2000; Pezza and Ambrizzi 2003, 2005; Pezza and Simmonds 2005).

The statistical component of the software comprises a series of calculations based on derived physical properties such as radius, Laplacian of pressure, system velocity, and others. The key cyclone variables used in this work are the system density (SD) and the depth (DP). System density is defined as the average number of cyclones/anticyclones within a reference area of 103 (degrees latitude)2, which is here referred to as a SD unit. The depth is associated with the pressure difference between the “edge” and the center of the system (Simmonds et al. 2003; Lim and Simmonds 2007). The grid values of a DP plot refer to the mean DP of all systems that passed “close” to that grid point during the analysis period. The DP is therefore proportional to the Laplacian of the mean sea level pressure (MSLP), and should not be confused with the central pressure of a cyclone. The DP units are given in hectopascals.

The radius and the depth are related by the equation: DP = 0.25 R2 [2(P)]. This definition of DP based on the Laplacian of the pressure has the additional advantage of being relatively insensitive to artificial trends in the MSLP that might exist in the reanalysis. The inclusion of radius also means that, for a given value of the Laplacian, larger systems will have greater depth. In the case of anticyclones we maintain the use of the word depth, but the variable should be thought as the system “height” (i.e., negative depth).

The tracks and statistical properties were calculated from the MSLP derived from the National Centers for Environment Prediction (NCEP) reanalysis II (NCEP2), which is of superior quality to the first NCEP reanalysis project (see, e.g., Kistler et al. 2001; Kanamitsu et al. 2002). However, given that the mean pressure is well reproduced in both datasets, being arguably better than the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005) over the Australian region (Hope et al. 2006), the NCEP reanalysis I was also used to obtain the MSLP distribution associated with rainfall anomalies based on the longest period available (1948–2004).

Composites of mean cyclone and anticyclone SD and DP were calculated for positive and negative SIE anomalies using the criterion of the top five and bottom five anomalous years for each sector. Our analysis shows that, given the sample size and distribution of anomalies, this criterion is approximately equivalent to choosing anomalous cases away from the mean by one and a half standard deviations. This approach was used rather than an absolute threshold based on standard deviations to ensure the same number of years for each composite. Given the relatively linear cyclone response between the extremes and in order to maximize the physical patterns here discussed the difference maps between top and bottom cases are presented. The Student’s t test corresponding to the difference maps was calculated for all cases (Wilks 1995).

A similar procedure was used to obtain the composites for the state of the SAM and the ENSO using appropriate indices, namely, the AAO (Gong and Wang 1999) and the SOI. The Niño-3.4 index was also used but the results for the composites were similar to those using the SOI. The positive phase of the SAM is associated with a strengthening of the circumpolar trough and an intensification of the westerlies. The technique of partial correlations (Wilks 1995) was used to isolate the association between these three climate features (SAM, ENSO, and SIE) and rainfall in Perth and Melbourne in a statistical sense. Spatial correlations were also calculated between cyclone indicators and rainfall in order to identify the key geographical areas of synoptic influence of cyclone density (and intensity) on Australian rainfall. This is presented as a motivation for the discussions, at the beginning of section 3.

Correlations with SIE were performed based on the extent of the entire pack, as well as for the separate sectors of Weddell, Indian Ocean, west Pacific, Ross Sea, and Bellingshausen/Amundsen Seas as defined by Gloersen et al. (1992). Each sector is 70° wide in longitude with the exception of Weddell, which is 80° wide. The correlations were also performed on both yearly and seasonally averaged data. As shown in Gloersen et al. (1992) these sectors are a good representation of the Southern Ocean’s key circulation areas, given by stationary nonannular wave patterns impinging from the SAM on to the sea ice system recently observed by Lefebvre et al. (2004). Furthermore the key areas of correlation between sea ice and the climate system observed by Kwok and Comiso (2002) have well-defined maxima centered in the sectors defined above. Figure 1 shows the five ice sectors used in this work, with the locations of Perth and Melbourne indicated for reference.

3. Results

a. Hemispheric associations between ENSO, SAM, and cyclone behavior

To introduce and exemplify the association between cyclone density and rainfall anomalies in a large-scale perspective we have used the Australian stations of Perth and Melbourne, as those are in key geographical locations well exposed to the Indian and Pacific Oceans, respectively, as well as to the Southern Ocean to the south. We have calculated the temporal correlations between rainfall and SD at each grid point for the period JJA 1979–2003, as shown in Fig. 2. The correlations whose magnitude exceeds 0.4 are statistically significant (95% level). A clear large-scale pattern is apparent with areas of maximum correlation appearing to the southwest of Australia and also toward the Indian Ocean in relation to Perth rainfall, whereas a maximum in the southeastern of Australia appears in connection to Melbourne rainfall. Those patterns are significant and suggest that the rainfall anomalies are associated with large-scale changes in cyclone density rather than simply changes in MSLP.

This result builds further on the more traditional views on the links between MSLP and rainfall, and show that the cyclone indicators such as SD have an important large-scale participation in explaining rainfall variability. During the SH winter, climatological values of cyclone SD range between 0.5 and 3 SD units in midlatitudes, increasing to values of about 6 SD units or greater near the Antarctic coast. Typical cyclone DP values range between 4 and 7 hPa in midlatitudes and increase to about 10 hPa near the outer limit of sea ice where the temperature and pressure gradients are very intense (Pezza et al. 2007). The Australian rainfall pattern of relevance for our overall large-scale analyses is discussed in detail in section 3d. In the paragraphs below we show how SD and DP associate with the two fundamental large-scale indicators of circulation, namely, SAM and ENSO.

Figure 3 shows the cyclone SD (Fig. 3a) and DP (Fig. 3b) associated with the five years of highest winter averaged SOI minus the five years of lowest SOI for the period 1979–2003, with statistical significant areas at 95% level indicated. The years used to calculate the SOI composites are indicated in the figure caption. Those were also used as a reference in the other composite figures to indicate the degree of association with the years used to compose the SAM, SIE, and rainfall anomalies (indicated in the captions). This figure gives the observed large-scale pattern that is reinforced during La Niña years, given that the SOI cyclone response has a reasonably linear component between the extremes as observed from the individual composites (figure not shown).

The SD behavior in Fig. 3a shows an increase in the number of cyclones in the Tasman Sea and in the southeast of Australia which is consistent with the notion of more frequent rain-producing storms in the eastern side of Australia when the SOI is positive (Karoly 1989; Power et al. 1999). Figure 3a also shows a hemispheric, quasi-annular pattern of below-average SD around Antarctica, with fewer cyclones during the positive phase also around southern South America. The DP (Fig. 3b) shows stronger cyclones around Australia and in the South Pacific Ocean, areas that have a high degree of significance as discussed in detail below. The southeastern Pacific is known to have a persistent blocking pattern during El Niño years (Renwick 1998), so the result in Fig. 3b reflects more and stronger cyclones during La Niña years in that area. The pattern observed over the Tasman Sea also indicates that not only the frequency of storms is increased during the positive SOI phase, but also their intensity as measured by the DP.

As discussed above, the areas over which the differences differ significantly from zero at the 95% confidence level according to the Student’s t test (Wilks 1995) are indicated by contours in all maps. They show that most cases discussed in the paper are significant in the surroundings of the areas of physical relevance for the systems discussed here in connection with Australian rainfall (Fig. 2; see also section 3d). Although from a statistical view point it is unclear whether the sample is large enough in order to produce a hemispheric, robust pattern of significance, the anomalies appear strong enough to be distinguished from noise from a meteorological view point. Areas lacking significance at the 95% level will generally be significant at the 90% level for at least one of the top or bottom five composites over a broader region (not shown).

The DP composites usually present greater significance, suggesting that the intensity associated with the pressure gradient is a key component for the robustness of the analysis. Notwithstanding the statistical test it is also useful to bring a synoptic climatology perspective to interpret to what extent the results are meaningful. The 10-yr samples associated with both extremes have been calculated based on six-hourly cyclone data and our analysis show that anomalies of about 1 SD unit (or about 1 hPa for DP) are typically well above one standard deviation in most of the midlatitudes. The above is generally valid for all composites presented here. In addition to the features discussed above the anomalies also present a coherent physical pattern with a good degree of large-scale organization, particularly the DP composites. Those features are discussed in more detail next.

Figure 4 presents a similar composite for the SAM phase, considering the five years with lowest winter averaged AAO index minus the five years with the highest AAO index over the 1979–2003 period. The years used to calculate the composites are indicated in the caption, and we have highlighted those cases which satisfy the SOI criteria previously shown in Fig. 3. The cyclone behavior response between the positive and negative phases of the SAM is also reasonably linear around Antarctica (figure not shown), which is not surprising given that its definition is based on the pressure gradient and the consequent northward (or southward) displacement of the storm tracks, with more cyclones around Antarctica when the SAM is in its positive phase, and more cyclones toward midlatitudes when the SAM is in its negative phase.

This figure shows a well-defined pattern with lower cyclone SD around Antarctica (Fig. 4a) and weaker DP (Fig. 4b) in the same area, representing the typical cyclone behavior when the SAM is in the negative phase. The patterns bear some resemblance to the SAM structure, although, a priori, this need not necessarily have been expected given the only modest correlation between MSLP and SD (DP) (Simmonds 2003a). The coincidence between the areas of negative SD and DP indicate that the SAM influences are seen not only in terms of number of storms but also their intensity. This pattern propagates into subtropical and midlatitudes with positive anomalies to about 40°S and negative anomalies further north, describing a reasonably annular pattern that is reminiscent of the SAM itself. Most of the midlatitudes present above-average SD during the negative phase, including the Southern Ocean close to Australia, most of New Zealand, and the southern tip of Africa and South America.

Notwithstanding the statistical significance associated mainly with areas of greater magnitude the overall pattern suggests a sound physical association. There is a clear physical agreement between the cyclone response pattern and what one would expect as a result of the definition of the SAM (Gong and Wang 1999), showing a differential SD (DP) response that builds on the previous results based on MSLP alone. Our large-scale cyclone indicators also agree well with the SAM impacts recently explored for the Australian region (Meneghini et al. 2007; Hendon et al. 2007).

We draw attention to the fact that there is some similarity between the cyclone SD patterns typical of La Niña (Fig. 3a) and the negative phase of SAM (Fig. 4a) with, in particular, negative SD anomalies around Antarctica and positive SD anomalies over midlatitudes. This similarity is observed even though the top and bottom five SAM years presented little association with the years used in the SOI composite as shown in Fig. 4. However, there are important differences between the SOI and the SAM cyclone responses. In the Australian region ENSO has a stronger impact over the Tasman Sea, while the SAM pattern tends to have greater influence on the area to the east of New Zealand. As noted by L’Hereux and Thompson (2006) there is some indication that the variability in ENSO may describe at least parts of the variability in SAM, and therefore the cyclone patterns observed are also a reflection that SAM and ENSO are not totally independent. The complex interactions between SAM, ENSO, and sea ice extent are discussed in the next sections.

b. Cyclone behavior for extremes of sea ice extent

From the discussion presented earlier the SAM and SIE should ideally be seen as intimately related, where feedback processes play an important role. Given the robustness of the SAM it is arguable that most of the influence seen in such system would be from the atmosphere toward the ocean (SIE and SST). It is of value to perform cyclone SD and DP composites for SIE to gain insight into how they differ from those of the SAM. As discussed later in the paper we have documented a modest degree of correlation between certain sea ice sectors and rainfall in southern Australia. The considerations above and particularly the interplay between SAM and SIE discussed in the introduction will be useful for the interpretation of the results discussed below.

Figure 5 shows the cyclone SD anomalies for the five lowest minus the five highest years of SIE over 1979–2003 for the Indian (Fig. 5a) and west Pacific (Fig. 5b) sectors. Again the years used to calculate the composites are indicated in the caption, and those years that satisfied the SOI criteria shown in Fig. 3 are highlighted. The Indian and the west Pacific Ocean sectors were chosen because they are directly upstream of the storms affecting the southwestern and southeastern parts of Australia, respectively (Fig. 1). A statistical analysis of the associations between sea ice extent in those sectors and rainfall in Perth and Melbourne (representing the Australian sectors discussed above) is discussed in sections 3d and 3e.

From Fig. 5a it can be seen that there is a well-defined area of SD below average at high latitudes around Antarctica for most of the Pacific and Indian sectors, accompanied by positive anomalies in the Atlantic sector. The overall structure of the anomalies is much less annular than those observed for the ENSO and the SAM composites discussed earlier, which is an indication that different physical processes may be taking place in the case of sea ice composites. The selection of years subjected to ice extremes are at least partially independent from ENSO and SAM, although it has been observed that there is a dipole-like structure in SIE and other fields which is apparently responsive to the SOI (Kwok and Comiso 2002). There are also local increases of SD around the Antarctic coast in between the main areas of decrease, suggesting a complex mechanism with possible north–south shifts of the wave patterns in the extremes of the SIE. It is possible that parts of this SD increase around Antarctica could be associated with the occurrence of high-latitude mesoscale systems such as polar lows when the SIE is reduced. The SIE pattern for the west Pacific is similar, indicating an overall decrease of cyclone SD around Antarctica when the winter sea ice is contracted.

The large-scale high-latitude decreases discussed above tend to be accompanied by increases in midlatitude cyclone SD in both cases (Figs. 5a,b), resembling the positive SOI and negative SAM phases discussed above for some areas. However, regional differences in several longitudinal locations such as to the south of Australia and New Zealand would have a different impact in terms of storms and fronts affecting the continents. In particular, the composite based on SIE in the Indian sector (Fig. 5a) shows an increase of SD to the southwest of Australia whereas that based on SIE in the west Pacific sector (Fig. 5b) shows an increase in the southeast of the country and New Zealand when the SIE is reduced. Although the statistical significance at 95% level is sparse partially because of the small sample, the areas discussed above are among the strongest anomalies for SH midlatitudes. Those areas present a clear large-scale spatial pattern of correlations with southern Australian rain as discussed in Fig. 2.

From the structure shown in Fig. 5 we hypothesize that there is an increase in the frequency of storms in the southwestern tip of Australia when the sea ice in the Indian sector is less than normal, with the same happening in the eastern coast when the sea ice over the west Pacific sector is less extensive. This, however, does not imply any direct assumption of causality and effect. A suggested interactive physical process is discussed below in light of the mechanisms known for the SAM and ENSO.

It is well known that the anomalies in storm activity discussed in Figs. 3 and 4 related to ENSO and SAM have a pronounced impact on Australian rainfall, with less rain in the east when the SOI is negative (Power et al. 1999) and generally more rain in the west when the SAM is negative (Meneghini et al. 2007; Hendon et al. 2007). Those anomalies are associated with global teleconnective Rossby wave patterns that, in the case of ENSO, can be driven by anomalous convective patterns (Karoly 1989). In the case of SAM they are associated with redistribution of meridional pressure gradient (Thompson and Wallace 2000). The cyclone behavior perspective additionally suggests that the changes in frequency (SD) and intensity (DP) are not necessarily in the same direction locally; that is, an increase in SD is not always associated with an increase in DP, and vice versa. The interplay between both indicators presumably has a marked impact on the frontogenetic activity influencing rainfall (earlier shown in Fig. 2).

For the sea ice case the fact that the greater anomalies are seen downstream of the major sea ice sectors tends to suggest that the presence of sea ice can interact with the cyclone trajectories via changes in the surface temperature and hence pressure gradient, which tends to be intensified over the water–sea ice edge. We hypothesize that contracted sea ice decreases the frequency of cyclones around that sector at high latitudes by decreasing the meridional temperature gradient (and hence the potential for baroclinic instability) related to cyclogenesis. Alternatively this can be interpreted as a poleward shift that agrees with the behavior found by Simmonds and Wu (1993). This mechanism would then increase the cyclone frequency upstream at midlatitudes via conservation of mass, similarly to what is observed when the SAM is negative (cf. Figs. 4a and 5).

The process described above is a reflection of the readjustment of mass when the north–south cells of subsidence are latitudinally shifted. When cyclone activity is reduced (i.e., pressures become higher) at high latitudes, by conservation of mass ascending air in the midlatitudes is increased (i.e., pressures become lower). This seesaw mechanism can be clearly seen in the typical SAM response discussed previously but is also evident for the sea ice composites. The hypothesis above could lead to a potential link between SIE and rainfall anomalies via cyclone/anticyclone behavior; however, it must be noted that the ice is also primarily responsive to the atmosphere as discussed in the introduction, and any explicit causality and effect would be better understood as a set of complex two-way interactions. Although our analysis is carried out for the winter season when no significant trend is observed in the SAM, it is unlikely that variability in sea ice alone could explain the variability in the SAM.

Figure 6 shows the DP anomalies associated with the Indian and west Pacific sectors as in Fig. 5 (five lowest minus five highest SIE years). The composites of DP display stronger patterns than do those for SD. This is reassuring of the physical meaning of the analysis given that the DP portrays the intensity directly associated with large-scale wind anomalies and baroclinicity levels (Pezza et al. 2007). The DP signal also appears more uniformly distributed when compared to the SD, with areas of positive (negative) DP anomalies coinciding with the areas of positive (negative) SD over some regions at midlatitudes particularly for the Indian sector composite (Fig. 6a). This pattern is consistent with the hypothesis of stronger fronts (with deeper cyclones) moving to the southwestern tip of Australia when the SIE is below average in the Indian Ocean, although as discussed earlier no direct causality and effect can be attributed at this stage.

For the west Pacific composite, Fig. 6b shows that a pattern of generally negative anomalies is observed elsewhere. This suggests that this sector presents less specific association with the Australian region than does the Indian Ocean. In section 3e we present some statistical results between southern Australian rain and SIE by sectors and, as we shall discuss, the winter correlation pattern between the Indian Ocean sector and Perth rainfall produced a significant association. A tentative physical discussion for this apparent correlation is discussed in light of the robust spatial pattern of gridpoint correlations between cyclone SD and rainfall in Melbourne and Perth originally shown in Fig. 2.

c. Anticyclones

It is of interest to explore the behavior of anticyclones in association with SIE anomalies given their complementary role in terms of large-scale changes associated with north–south shifts in zones of subsidence (Hadley and Ferrel cells). This is presented in Figs. 7 and 8. From Fig. 7 we observe that the main anticyclone association is concentrated in the subtropics and midlatitudes with little signal at high latitudes. The strong signal in the center of Antarctica should be interpreted with caution given the difficulty in attributing a physical meaning to MSLP in areas of very elevated terrain, regardless of having statistical significance.

For the Australian sector there is an intensification of anticyclone activity over subtropical latitudes when the winter SIE is reduced, followed by a decrease further south. This suggests a local northward shift in anticyclone activity, complementary to the apparent increase in midlatitude cyclones discussed in Fig. 5. The analysis of the five top and five bottom average fields suggests that in fact this shift is better described by an increase of anticyclone frequency over subtropical Australia when the SIE is reduced (figure not shown). This is observed for both composites (Figs. 7a,b), but the main signal appears shifted toward the western end of the continent for the Indian composite (Fig. 7a) and toward the eastern half of the country for the west Pacific composite (Fig. 7b).

In other regions of the SH the signal is less strong and not as organized. This may be further evidence that the intensification seen only in the Australian region could be physically associated with SIE, explained by the proximity of the upstream sectors. The DP pattern shown in Fig. 8 also indicates that changes in anticyclone intensity are more uniformly spread toward mid- and high latitudes, being less confined than the SD pattern. From climatological considerations there are relatively few anticyclones seen over high latitudes (Jones and Simmonds 1994). Therefore the DP results discussed above suggest that those we do observe over higher latitudes, as in Fig. 7, are subjected to strong intensity changes.

Although the ENSO and SAM cyclone responses present a more annular structure, the SIE composites also show a certain degree of large-scale organization, which seems to be consistent with potential impacts on storm propagation and rainfall anomalies over midlatitudes. Such organization is stronger in the Australian longitudes for the SIE anomalies based on the Indian and west Pacific sectors. Many of the anomalous areas seen in the SIE composites occur over regions of maximum spatial correlation between SD and rainfall (Fig. 2).

d. Australian rainfall

Figure 9 shows the monthly averaged daily rainfall for Perth and Melbourne for the period 1979–2003, with the interannual standard deviations indicated for each month. It can be seen that the seasonal cycle at the stations is different, with winter maximum in Perth and a more uniform distribution in Melbourne (with weak peaks in autumn and spring). Most of the rain observed in both Perth and Melbourne is associated with baroclinic activity during the winter, with the most important modes being frontal rain, as well as cutoff lows in Melbourne (Pook et al. 2006). The marked winter peak in Perth is also due to the fact that the Indian Ocean semipermanent anticyclone moves to the west during that season. In Melbourne, the winter synoptic pattern is influenced by a strong ridge of cold and stable high pressure that forms over the south of the Australian continent when the radiational cooling is pronounced.

Following the mid-1970s drop in rainfall in southwest Western Australia (SWWA), several studies have investigated the relationship between MSLP and rainfall on climatological time scales in that region with focus on Perth’s rainfall. Allan and Haylock (1993), for example, show that over the period 1886–1989 a strong inverse relationship existed between Perth rainfall and MSLP, with approximately 60% of the total variance in winter rainfall explained by fluctuations in Perth MSLP, irrespective of the time scale considered. Similarly, Smith et al. (2000) calculate the correlation between JJA rainfall and Perth MSLP for the period 1907–94 at −0.83.

More recently the Indian Ocean Climate Initiative (Ryan and Hope 2005) studied the impacts of declining rainfall for water management in the area, for which a comprehensive discussion is presented in Power et al. (2005). England et al. (2006) showed that there is a robust link between rainfall extremes in the region and SST variability in the tropical Indian Ocean and tropical and subtropical Western Australia, following up from the earlier studies of Simmonds and Rocha (1991) and others. Parts of this association can be explained by the state of the IOD alone (Saji et al. 1999; Ashok et al. 2004). Timbal (2004) and Timbal et al. (2006) confirmed the early results of Allan and Haylock (1993), arguing that most of the rainfall decline in southwest Australia can be attributed to a southward shift of the baroclinic zone of low pressure systems. They further suggest that those changes are consistent with greenhouse-induced warming. The results discussed above are consistent with those of Frederiksen and Frederiksen (2007), who showed a reduction in the cyclogenesis activity and meridional temperature gradients associated with weather systems that affect the southwestern of Australia. Pitman et al. (2004) and Timbal and Arblaster (2006) discuss how land cover changes may also have contributed to the drying pattern in addition to the large-scale forcing.

Figure 10 shows the MSLP anomalies associated with the five driest (Figs. 10a,c) and wettest (Figs. 10b,d) winters in Perth and Melbourne, respectively, for JJA over the period 1948–2004. The years used to calculate the composite are indicated in the caption, and those associated with the SOI criteria discussed in Fig. 3 are highlighted. The surface patterns associated with the extremes of winter rain show strong negative anomalies for above-average rain in both cases, and positive anomalies when the rain is low, but most interestingly the maximum of the anomalies are centered almost over Melbourne in Figs. 10c,d but sits toward the middle of the Indian Ocean in Figs. 10a,b. This is suggestive of the southeastern corner of Australia acting as a center of action for anticyclonic anomalies. As discussed in connection to Figs. 7b and 8b such pattern was seen in the SIE composite for the west Pacific sector.

Dry winters in Perth show an anomalous ridge further south around 40°S in the Indian Ocean (Fig. 10a), which is indicative of an anomalous southward displacement of the winter midlatitude trough. The dry case in Melbourne is associated with a ridge of anomalous high pressure with its maximum near Melbourne, with magnitude in excess of +3 hPa.

For the Perth wettest composite (Fig. 10b), although negative anomalies can be seen over most of Australia and toward New Zealand, the largest anomalies are concentrated in the middle of the Indian Ocean, suggesting a strengthening of the midlatitude winter trough over that area. Figure 10d also shows that for enhanced Melbourne rainfall the negative pressure anomalies are concentrated over the Tasman Sea and the broad southeastern sector of Australia, indicating the association of an organized large-scale anomalous pattern approximately of opposite sign to that of the dry composite. Figure 10 also suggests a well-organized hemispheric pattern for both cases which seems to be more annular-like structured in the case of Perth, indicating the importance of the large-scale anomalies for Australian rainfall.

The cyclone/anticyclone features discussed in relation to Figs. 3 –8 present strong anomalies over the highly correlated areas shown in Fig. 2, suggesting that at least part of the association between SAM/SOI/SIE and rain comes about via anomalies in the cyclone behavior. This is no surprise, given the fundamental role of synoptic systems and their large-scale shifts as discussed in Hope et al. (2006). The spatial correlations for the DP fields were also calculated as in Fig. 2, and a coherent correlation pattern maximized over the areas discussed above was also observed. A similar structure was also observed for the anticyclones (figures not shown). The next section explores the statistical association between rainfall in Perth and Melbourne and SIE, discussed in light of the results presented above.

e. Statistical relationship between rainfall and sea ice

Table 1 shows the linear correlation coefficients between the SIE for each of the sectors shown in Fig. 1, for the period 1979–2003. Although the main analyses are presented only for the SH winter season, all seasons are shown in Table 1 to put the relationship in perspective with the annual cycle. It is interesting to observe that the relationship between the western Pacific and Indian sectors is maximum and negative during the summer and maximum and positive during autumn (being weak during the remainder of the year), suggesting that nonlinear mechanisms take place throughout the year. As discussed in the following paragraphs, these sectors are the most highly correlated with rain in Melbourne and Perth (respectively) during the winter, a period when they are not correlated between themselves. Our results agree well with those of Carleton (1989) who calculated correlations between sectors for the period 1973–82. He showed variable correlations between sectors and the total ice pack ranging from 0.1 to 0.8 for the annual mean. The distribution of negative correlations in Table 1 is a consequence of a wave structure similar to the one found by Lefebvre et al. (2004). This underlines the need for studying individual associations rather than the total ice pack as a whole.

Tables 2 and 3 show the linear correlation coefficients between rainfall in Perth and Melbourne, respectively, and SIE for each of the studied sectors. The results are shown for all seasons during 1979–2003. From Table 2 it can be observed that correlations between SIE and rainfall in Perth vary according to the sector and season, with the highest value occurring in the Indian Ocean during the winter (−0.49, statistically significant at level 99%). The western Pacific sector is also weakly correlated with rain in Perth depending on the season, but the other sectors do not present any significant association. Perth also exhibits a negative correlation with the total ice pack during March–May (MAM). The Melbourne case presents weak correlation coefficients which are not significant above 95%.

Figure 11 shows the Perth rainfall and Indian Ocean sector SIE anomalies (Fig. 11a) and Melbourne rainfall and west Pacific SIE (Fig. 11b). Both rain and ice series have been normalized in units of standard deviations and the mean removed. The bottom panels show the scatter diagram with the correlation coefficient and regression lines for Perth (Fig. 11c) and Melbourne (Fig. 11d). In the Melbourne case the triangles indicate the years after 1997. This is of climatological interest given the onset of a long-term severe drought in southeastern Australia. Such dry pattern started in the form of a steplike change around 1997 (Gallant et al. 2007; Wardle et al. 2007), and the annual precipitation has not returned to normal since then (as of April 2008).

From Figs. 11a,c it is seen that, as discussed above, the rainfall in Perth has a moderate degree of negative association with the SIE in the Indian Ocean. This pattern is evident for most of the studied period, indicating that the correlations discussed in Table 2 are not merely resulting from a few years of association. For Melbourne (Figs. 11b,d) the association is weaker. A similar pattern as seen for Perth is only evident until the mid-1990s, after which variations in SIE and rain had an apparent change in behavior. Such variability weakens the overall correlation, as earlier shown in Table 3. For the post-1997 period the association becomes positive. Although this apparent change coincides with the beginning of the long-term drought in southeastern Australia, the sample is too small to allow us to make any further assertions at this stage.

One possible low-frequency modulator for the observed changes in the association between SIE and Melbourne rainfall after 1997 could be given by changes in the Antarctic Circumpolar Wave (ACW). Although there is no consensus at present as to whether the ACW describes an independent physical system or if its signature is predominantly a by-product of the atmospheric forcing, some studies have suggested that the ACW can explain at least parts of the variance for the interplay between SST and MSLP (White et al. 2004; White and Simmonds 2006, and references therein).

To refine the statistical analysis and study the potential influences of other effects we performed an analysis of partial correlations between rainfall and sea ice extent isolating the effects of ENSO and SAM, the results of which are shown in Table 4. It is evident that when the influence of ENSO is removed a similar pattern is still observed for Melbourne and Perth (cf. Table 4 with Tables 2 and 3). This shows that, although rain, particularly in Melbourne, is predominantly related with SOI as seen at the bottom of Table 4, a modest relation with SIE cannot be discarded. In terms of SAM when its effects are removed the correlation patterns tend to become less intense, suggesting that SAM is an important driver mechanism particularly in the western side of Australia.

4. Summary and concluding remarks

We analyzed the associations between cyclone and anticyclone behavior and extremes of large-scale climate (air–sea) features of the SH represented by ENSO, SAM, and SIE, and the potential impacts of the complex physical interplay between those and rainfall in Perth and Melbourne. The ice pack around Antarctica was divided in five different regions representing main areas of influence. It was shown that SIE anomalies over the Indian and west Pacific sector are associated with a large-scale pattern of cyclone and anticyclone anomalies that has maximum magnitude for the longitudes of Australia and New Zealand. This is coincident with key areas where cyclone SD has a robust correlation with southern Australia rainfall.

A high-latitude annular-like cyclone structure of anomalies is seen for SAM and SIE, and a somewhat similar but less annular pattern is seen for ENSO. There is some resemblance between La Niña, the negative SAM phase and periods of negative SIE anomalies. The common features discussed above have a large-scale organization but also present important differences locally. Those help to explain how anomalies in baroclinic systems over the Southern Ocean are associated with midlatitude continental rain, as many of the anomalies related to SAM, ENSO, and SIE were observed over areas of maximum correlation between cyclone SD and rainfall.

Statistical analysis between rainfall in Perth and Melbourne and SIE anomalies shows a variable pattern that is stronger between Perth and the Indian Ocean sector during the winter, describing a negative association that is consistent with the observed cyclone/anticyclone anomalies. Although the correlation analysis by itself does not allow us to establish causal links and parts of observed patterns may result from noise associated with low-frequency modulation (Power et al. 1999; Gershunov et al. 2001), the anomalies in the cyclone/anticyclone indicators overlapped to the areas of maximum spatial correlation suggest that at least a limited influence is taking place.

Here we hypothesize a unified view where a coupled mechanism SAM/SIE has a greater association with the western side of Australia, while ENSO is more strongly associated with cyclone/anticyclone anomalies associated with precipitation in the eastern side. Areas of reduced ice experience a southward shift of high-latitude cyclones locally followed by a midlatitude increase in downstream sectors via conservation of mass given by changes in patterns of subsidence. The mechanism described above resembles the extreme negative phase of the SAM in the cyclone composites. This suggests that the complex interactions between the key climate (air–sea) features can be thought of as an interconnected SAM/SIE mechanism. While those represent the air–sea interactions between the high-latitude pressure gradient, sea ice positioning, and baroclinicity, ENSO gives the high-latitude impacts of the teleconnective air–sea pattern impinging from equatorial latitudes. Those features further interact on a number of scales, particular the seasonal and interdecadal time scales.

Future research on the intricate relationships between the high-latitude climate (SAM/SIE) and ENSO will improve the current understanding of midlatitude rainfall variability. Some of the areas directly affected are under much stress because of severe drying trends, as Australian economy struggles to adapt to the new scenario. A number of model-simulated climate change scenarios predict a worsening of midlatitude, long-term droughts in the SH over the next decades as global temperatures continue to increase.

Acknowledgments

Parts of this work were made possible with funding from the Australian Research Council and the Antarctic Science Advisory Committee. The authors would like to acknowledge invaluable suggestions made by the anonymous reviewers. We would also like to acknowledge Kevin Keay for helping with the statistical analysis and use of the tracking scheme software. The contribution by one of the coauthors (IS) was partly funded by the Queensland Climate Change Centre of Excellence.

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Fig. 1.
Fig. 1.

The five sectors used to calculate the SIE. The locations of Perth and Melbourne are also indicated.

Citation: Journal of Climate 21, 21; 10.1175/2008JCLI2128.1

Fig. 2.
Fig. 2.

Spatial correlation between cyclone SD and rainfall in (a) Perth and (b) Melbourne for JJA 1979–2003. Areas of correlation above 0.4 are significant at the 95% level, but the whole pattern is shown for clarity. See text for further details.

Citation: Journal of Climate 21, 21; 10.1175/2008JCLI2128.1

Fig. 3.
Fig. 3.

Cyclone (a) SD and (b) DP associated with the five years of highest winter averaged SOI minus the five years of lowest SOI for the period 1979–2003. Years used in the composite: top five (1981, 1988, 1989, 1996, and 1998) and lowest five (1982, 1987, 1993, 1994, and 1997). SD units given in number of systems per 103 (degrees lat2) and DP units given in hPa. Areas significant at 95% level are show by contours.

Citation: Journal of Climate 21, 21; 10.1175/2008JCLI2128.1

Fig. 4.
Fig. 4.

As in Fig. 3, but for the five years of lowest winter averaged AAO index minus the five years of highest AAOI. Years used in the composite: top five (1979, 1985, 1989, 1993, and 1998) and lowest five (1980, 1981, 1991, 1992, and 1995). The SOI status as used in Fig. 3 is indicated in bold roman for El Niño years and in bold italic for La Niña years.

Citation: Journal of Climate 21, 21; 10.1175/2008JCLI2128.1

Fig. 5.
Fig. 5.

Cyclone SD for the lower five minus the top five years of SIE for (a) Indian and (b) west Pacific sectors for the period 1979–2003. Years used in the composite: top five Indian Ocean (1982, 1985, 1989, 1993, and 1999), top five west Pacific Ocean (1982, 1983, 1999, 2000, and 2003), lowest five Indian Ocean (1991, 1992, 1997, 2002, and 2003), and lowest five west Pacific Ocean (1980, 1981, 1989, 1991, and 2002). The SOI status as used in Fig. 3 is indicated in bold roman for El Niño years and in bold italic for La Niña years. SD units given in number of systems per 103 (degrees lat2) and DP units given in hPa. Areas significant at 95% level are show by contours.

Citation: Journal of Climate 21, 21; 10.1175/2008JCLI2128.1

Fig. 6.
Fig. 6.

As in Fig. 5, but for cyclone DP.

Citation: Journal of Climate 21, 21; 10.1175/2008JCLI2128.1

Fig. 7.
Fig. 7.

As in Fig. 5, but for anticyclone SD.

Citation: Journal of Climate 21, 21; 10.1175/2008JCLI2128.1

Fig. 8.
Fig. 8.

As in Fig. 5, but for anticyclone DP.

Citation: Journal of Climate 21, 21; 10.1175/2008JCLI2128.1

Fig. 9.
Fig. 9.

Monthly averaged daily rainfall for Perth and Melbourne and their interannual standard deviations for the period 1979–2003 (mm day−1).

Citation: Journal of Climate 21, 21; 10.1175/2008JCLI2128.1

Fig. 10.
Fig. 10.

Sea level pressure anomalies associated with the five (a), (c) driest and (b), (d) wettest years in Perth and Melbourne, respectively, for JJA over the period 1948–2004. The contour interval is 1 hPa. Years used in the composite: top five Perth (1955, 1958, 1963, 1964, and 1996); top five Melbourne (1951, 1952, 1981, 1989, and 1991), lowest five Perth (1971, 1979, 1984, 1989, and 1990), and lowest five Melbourne (1948, 1982, 1994, 1997, and 2002). The SOI status as used in Fig. 3 is indicated in bold roman for El Niño years and in bold italic for La Niña years after 1979 when the SIE analysis has been carried out.

Citation: Journal of Climate 21, 21; 10.1175/2008JCLI2128.1

Fig. 11.
Fig. 11.

Anomalies of (a) Perth rainfall and Indian Ocean SIE and (b) Melbourne rainfall and west Pacific SIE anomalies for JJA 1973–2003. Rainfall is given by dashed lines and SIE is given by solid lines in units of standard deviation. Scatter diagrams showing the correlation and regression lines are given for (c) Perth and (d) Melbourne. For Melbourne (d) the regression is presented for the period 1979–97 (solid line, crosses) and 1998–2003 (dashed line, triangles).

Citation: Journal of Climate 21, 21; 10.1175/2008JCLI2128.1

Table 1.

Correlations between the SIE for each sector for individual seasons, 1979–2003. Cases with significance above the 95% level are indicated in bold. DJF = December–February; SON = September–November.

Table 1.
Table 2.

Perth rainfall correlations with individual sea ice sectors given by seasons, 1979–2003. Cases with significance above the 95% level are indicated in bold.

Table 2.
Table 3.

As in Table 2, but for Melbourne.

Table 3.
Table 4.

Partial correlations between rainfall in Melbourne and Perth and SIE for each sector removing the effects of (a) ENSO and (b) SAM for the period JJA 1979–2003. Cases with significance of at least 95% are indicated in bold. The total correlations for JJA as shown in Tables 2 and 3 are shown between parentheses. The bottom panel shows partial correlations between Melbourne and Perth rainfall and (c) SAM removing the effects of ENSO and (d) ENSO removing the effects of SAM, for the same period.

Table 4.
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