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  • View in gallery

    (a) Map showing the boundaries used to define the Australian TC region used here (0°–36°S, 90°–170°E), as well as the Niño-3.4 (solid line) and Niño-4 (broken line) SST regions. (b) The genesis points of all TCs that formed in the Australian region and immediately to the west and east from 1970/71 to 2005/06.

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    Spatial loading patterns for the first two varimax-rotated PCs of all monthly Pacific SSTAs for 1970–2006. (a) PC1 (30% of total domain variance); (b) PC2 (6% of total domain variance). Solid (broken) isopleths give positive (negative) loadings.

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    Annual cycles of correlation for 1970–2005 between number of November–April TCs in the Australian region and various ENSO-related parameters (Niño-3.4 SST, Niño-4 SST, Darwin SLP, SOI), and Nicholls north Australian SST, for running 3-month periods starting with January–March preceding the TC season and ending with September–November following the TC season. Purple dashed line gives the correlations obtained by Nicholls (1984) for his north Australian SST region for 1964–82. Correlations for 1970–2005 with larger magnitude than the thick dashed (solid) line are significant at the 5% (1%) level according to a two-sided t test (Wilks 1995, 121–122). Core of November–April TC season is shaded in light blue.

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    (a) Spatial distribution of correlation coefficients for 1970–2005 between the number of TCs during November–April and Pacific SSTAs during the preceding August–October. Correlation thresholds for 5% (1%) statistical significance level are r ≥ |0.46| (|0.58|) after reducing the degrees of freedom to account for a lag-1 autocorrelation of 0.28, according to the two-sided t test (Wilks 1995, 121–122). The rectangle north of Australia is the north Australian SST area used by Nicholls (1984), and the rectangles in the central Pacific contain the Niño-4 (broken line) and Niño-3.4 (solid line) SST regions delineated in Fig. 1a. Solid (broken) isopleths indicate positive (negative) correlations with correlations >|0.6| shaded. (b) Same as in (a) but showing partial correlation values with the effect of Niño-3.4 SST removed.

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    Time series for 1970–2005 of the number of November–April TCs in the Australian region (solid line) and Nicholls north Australian SST (dashed line). Overall linear trend is denoted by the solid line through each time series. The vertical solid line marks the end of Nicholls (1984) dataset (1964–82). TC data are obtained from the Bureau of Meteorology’s TC best-track dataset (more information is available online at http://www.bom.gov.au/climate/how).

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    Same as in Fig. 4, but for November–April SST.

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    Same as in Fig. 4, but for November–April 850–200-hPa vertical shear of the zonal wind and with SST regions removed.

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    The mean November–April 200-hPa (blue contours) and 850-hPa (brown contours) zonal winds (m s−1) for (a) 1970–2005 and (b) 11 El Niño seasons as defined in section 4g. (c) The same as in (a) but for 200-hPa minus 850-hPa vertical shear of the zonal wind (blue contours, m s−1) and 850-hPa relative vorticity (red contours; 10−6 s−1 with contour interval of 4). (d) The same as in (c) but for El Niño seasons. Thick solid contours show zero lines and dashed (solid) contours show negative (positive) values. Green shading indicates areas of TC genesis bounded by the 50% KDE contour.

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    Correlation coefficients between Niño-3.4 SST (Fig. 1a) and (a) vertical shear of the zonal wind and (b) 850-hPa relative vorticity for November–April. Solid (broken) isopleths indicate positive (negative) correlations, with correlations >|0.6| shaded.

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    Same as in Fig. 4, but for November–April 850-hPa relative vorticity and with SST regions removed.

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    (a) Time series of the seasonal total number of TCs in the Australian region showing the overall downward linear trend (solid gray line). The solid vertical line divides the 1970–86 and 1986–2005 subsets, with respective linear trends shown by the thick dashed lines. (b) Time series of the seasonal total number of TCs in the Australian region (solid line), as well as the number of intense TCs with minimum central pressure below 965 hPa (dashed line) and number of weak-to-moderate TCs with minimum central pressure above 965 hPa (gray line), for 1970/71–2005/06. Data are obtained from the Bureau of Meteorology’s TC best-track dataset (more information is available online at http://www.bom.gov.au/climate/how).

  • View in gallery

    Location of TC genesis points between 1970/71–2005/06 for (a) all seasons, (b) neutral ENSO seasons, (c) El Niño seasons, and (d) La Niña seasons. Number of TCs and seasons in each category are given in parentheses. Contours enclose areas within which TC genesis had 25%, 50%, and 75% probabilities of occurrence given by the KDEs.

  • View in gallery

    Location of TC genesis points for 1970/71–2005/06 stratified by (a) minimum central pressure greater than or equal to 965 hPa and (b) minimum central pressure less than 965 hPa. The number of TCs in each category is given in parentheses.

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    As in Fig. 4, but with the extension of the correlation patterns into the Atlantic Ocean.

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    Schematic showing the connection between anomalously warm SST in the Niño-3.4 and Niño-4 regions (pink shading) associated with El Niño events and the corresponding atmospheric response: increased 200-hPa zonal westerly winds (blue horizontal arrows) around 15°S resulting in increased vertical shear of the zonal wind, collocated with decreased 850-hPa relative cyclonic vorticity (green) associated with a weakened monsoon trough over the Australian region. Light blue vertical arrows indicate anomalous subsidence over the northern Australian region and anomalous ascent over the central equatorial Pacific.

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Interannual Variability of Tropical Cyclones in the Australian Region: Role of Large-Scale Environment

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  • 1 School of Meteorology, and Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
  • 2 School of Meteorology, University of Oklahoma, Norman, Oklahoma
  • 3 School of Meteorology, and Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
  • 4 School of Meteorology, University of Oklahoma, Norman, Oklahoma
  • 5 Natural Hazards Research, Insurance Australia Group, Sydney, Australia
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Abstract

This study investigates the role of large-scale environmental factors, notably sea surface temperature (SST), low-level relative vorticity, and deep-tropospheric vertical wind shear, in the interannual variability of November–April tropical cyclone (TC) activity in the Australian region. Extensive correlation analyses were carried out between TC frequency and intensity and the aforementioned large-scale parameters, using TC data for 1970–2006 from the official Australian TC dataset. Large correlations were found between the seasonal number of TCs and SST in the Niño-3.4 and Niño-4 regions. These correlations were greatest (−0.73) during August–October, immediately preceding the Australian TC season. The correlations remain almost unchanged for the July–September period and therefore can be viewed as potential seasonal predictors of the forthcoming TC season. In contrast, only weak correlations (<+0.37) were found with the local SST in the region north of Australia where many TCs originate; these were reduced almost to zero when the ENSO component of the SST was removed by partial correlation analysis. The annual frequency of TCs was found to be strongly correlated with 850-hPa relative vorticity and vertical shear of the zonal wind over the main genesis areas of the Australian region. Furthermore, correlations between the Niño SST and these two atmospheric parameters exhibited a strong link between the Australian region and the Niño-3.4 SST. A principal component analysis of the SST dataset revealed two main modes of Pacific Ocean SST variability that match very closely with the basinwide patterns of correlations between SST and TC frequencies. Finally, it is shown that the correlations can be increased markedly (e.g., from −0.73 to −0.80 for the August–October period) by a weighted combination of SST time series from weakly correlated regions.

Corresponding author address: Hamish A. Ramsay, CIMMS, University of Oklahoma, Sarkeys Energy Center, Rm. 1011, 100 E. Boyd St., Norman, OK 73019-1011. Email: hramsay@rossby.metr.ou.edu

Abstract

This study investigates the role of large-scale environmental factors, notably sea surface temperature (SST), low-level relative vorticity, and deep-tropospheric vertical wind shear, in the interannual variability of November–April tropical cyclone (TC) activity in the Australian region. Extensive correlation analyses were carried out between TC frequency and intensity and the aforementioned large-scale parameters, using TC data for 1970–2006 from the official Australian TC dataset. Large correlations were found between the seasonal number of TCs and SST in the Niño-3.4 and Niño-4 regions. These correlations were greatest (−0.73) during August–October, immediately preceding the Australian TC season. The correlations remain almost unchanged for the July–September period and therefore can be viewed as potential seasonal predictors of the forthcoming TC season. In contrast, only weak correlations (<+0.37) were found with the local SST in the region north of Australia where many TCs originate; these were reduced almost to zero when the ENSO component of the SST was removed by partial correlation analysis. The annual frequency of TCs was found to be strongly correlated with 850-hPa relative vorticity and vertical shear of the zonal wind over the main genesis areas of the Australian region. Furthermore, correlations between the Niño SST and these two atmospheric parameters exhibited a strong link between the Australian region and the Niño-3.4 SST. A principal component analysis of the SST dataset revealed two main modes of Pacific Ocean SST variability that match very closely with the basinwide patterns of correlations between SST and TC frequencies. Finally, it is shown that the correlations can be increased markedly (e.g., from −0.73 to −0.80 for the August–October period) by a weighted combination of SST time series from weakly correlated regions.

Corresponding author address: Hamish A. Ramsay, CIMMS, University of Oklahoma, Sarkeys Energy Center, Rm. 1011, 100 E. Boyd St., Norman, OK 73019-1011. Email: hramsay@rossby.metr.ou.edu

1. Introduction

The influence of large-scale environmental parameters on global interannual variability of tropical cyclone (TC) activity in various TC basins has been a research focus since the late 1970s (e.g., Nicholls 1979, 1984, 1985, 1992; Solow and Nicholls 1990; Gray et al. 1992; Evans and Allan 1992; Gray et al. 1994; Nicholls et al. 1998; Chan 2000; Chan and Liu 2004; Klotzbach and Gray 2004).

TC formation, as documented by Gray (1968, 1975, 1979) and combined into a seasonal genesis index, requires the preexistence of six environmental factors: (i) relatively large values of low-level cyclonic vorticity, (ii) sea surface temperatures (SST) exceeding 26.5°C and a deep thermocline, (iii) weak vertical shear of horizontal winds through a deep-tropospheric layer, (iv) location at least 5° from the equator, (v) conditional instability through a deep-tropospheric layer, and (vi) high values of relative humidity in the lower and middle troposphere. While these parameters are used frequently as predictors of seasonal TC activity (e.g., Chan and Liu 2004; Aiyyer and Thorncroft 2006), Watterson et al. (1995) have shown that Gray’s seasonal genesis index should be used with caution when, for example, it is applied to GCM simulations and some global reanalysis datasets.

The Australian region is unique, as about 50% of all TCs form within approximately 300 km of land (McBride and Keenan 1982), associated with a lower-tropospheric monsoon trough. In contrast, Atlantic and western North Pacific (WNP) Oceans’ TCs almost all form well out to sea. TCs pose a recurring and growing threat to coastal communities throughout the Australian TC region, defined here as the area south of the equator and north of 36°S and between longitudes 90° and 170°E (Fig. 1a), which is slightly larger than the Australian Bureau of Meteorology’s area of responsibility (90°–160°E, south of the equator). The region is climatologically active for TCs, with a typical season average of 12.5 TCs (Dare and Davidson 2004), of which 6 form in the western region, covering the area west of 125°E, 3 form in the northern region from 125° to 142°30′E, and the remaining 3.5 develop in the eastern region east of 142°30′E (Fig. 1a). Approximately 5 TCs cross the coastline each season. Figure 1b shows all TC genesis points for 1970/71–2005/06.

The western Australian region is a major source of raw materials (e.g., iron ore, oil, and natural gas) whose production and supply are seriously disrupted by TCs, making the accurate prediction of TCs’ tracks, intensities, speed of movement, and associated severe weather of paramount importance. Seasonally, at least one TC reaches category 5, the highest category defined by the Australian Bureau of Meteorology (more information is available online at http://www.bom.gov.au/weather/cyclone/index.shtml). The Australian TC classification scheme is based on maximum wind gusts, not maximum sustained winds as used for the Atlantic and WNP basin classifications. Australian TC categories are the following: category 1, maximum gust below 125 km h−1; category 2, maximum gust 125–170 km h−1; category 3, maximum gust 170–225 km h−1; category 4, maximum gust 225–280 km h−1; and category 5, maximum gust above 280 km h−1. On average, one Australian coastal impact of a severe tropical cyclone (category 3 or higher) occurs each season (Dare and Davidson 2004). The 2005–06 Australian region TC season was very active in terms both of the number of coastal impacts of TCs and the greater-than-normal number of high-category TCs.

This study investigates the effects of large-scale environmental parameters on the interannual fluctuations in TC activity in the Australian region, using several observational datasets. We begin with a summary of the most relevant previous research (section 2), and then describe the various datasets, definitions, and methods employed (section 3). The results are presented in section 4 and the principal findings are discussed and summarized in section 5.

2. Review of previous studies

a. Australian region overview

Nicholls (1979) found a link for 1950–75 between interannual variations in TC numbers in the Australian region and Darwin sea level pressure (SLP) averaged over June–August preceding a TC season. He initially obtained a correlation of −0.40 between Darwin SLP averaged over June–August and the total number of TCs the following TC season. However, the highest correlation (−0.48) was found between Darwin winter SLP and the number of early season (October–December) TCs. After correcting for inconsistencies in the observing network over the 25-yr period, especially the introduction of satellite images for locating TCs in the Australian region starting in April 1960, the relationship between Darwin SLP and TC number was found to be even stronger, with a correlation value of about −0.6. Still, the relatively short time series used puts the stability of this relationship into question.

The influence of SST on fluctuations in Australian TC activity also was investigated by Nicholls (1984). The two SST domains he considered were defined as either those characterized by occurrence of El Niño and La Niña events (10°N–10°S, 180°–80°W) or TC genesis in the Australian region (5°–15°S, 120°–160°E; hereinafter termed the “Nicholls north Australian SST region” to distinguish it from the Northern Australian TC region in Fig. 1a). The number of TCs for each TC season was correlated with 3-monthly means of three indices related to El Niño, La Niña, and the associated Southern Oscillation [collectively, El Niño–Southern Oscillation (ENSO)], which were Darwin SLP, east Pacific SST, and the Nicholls north Australian SST, starting with the January–March preceding the TC season and ending with the October–December following the season. For 1964–82, Nicholls found significant correlations between the ENSO-related indices and TC number. The largest correlation was +0.78 between TC number and the area-averaged Nicholls north Australian SST during September–November, which is immediately prior to the TC season.

Evans and Allan (1992) explored the link between ENSO extremes, the Australian monsoon trough, and associated TC activity using observational data for January–February of each of four El Niño (1966, 1973, 1983, 1987) and four La Niña (1971, 1974, 1976, 1989) years. Significant differences in the structure of the monsoon trough and associated TC activity were linked to ENSO phase. The authors found that TCs form closer to the north Australian coast during El Niño years than La Niña years, owing to an equatorward shift of the monsoon trough, warmer SSTs, and weaker vertical wind shear. In contrast, TCs in the southeast Indian Ocean and South Pacific were noted to form away from the coast in El Niño years. Furthermore, the authors showed that TCs were more likely to make landfall in Western Australia and the Northern Territory during El Niño years.

In a later study, Broadbridge and Hanstrum (1998) examined TC activity in part of the western Australian region (south of 10°S, 105°–130°E) for 1960–97 and its relationship to the Troup (1965) Southern Oscillation index (SOI), defined as the difference between the standardized SLP anomalies of Tahiti and Darwin. The correlation between TC number and the September–November SOI preceding the TC season was only +0.32 for their western Australian region, compared to a much larger value of +0.58 for the entire Australian region between 105°–165°E and 5°–32°S (Nicholls 1992). Increases in TC frequency and in landfalling impacts were found for strongly positive SOI values, indicating anomalously low SLP over northern Australia. Furthermore, more landfalling TCs were found to occur when this strongly positive SOI was preceded by a pronounced positive SOI trend from the previous year (at least +10).

b. Western North Pacific basin

Chan et al. (2001) found that both ENSO and the quasi-biennial oscillation (QBO) were important factors associated with the interannual variability of TC activity in the WNP basin. This understanding resulted from development of the so-called Chan–Shi–Lam (CSL) scheme (Chan et al. 1998), based on the projection pursuit regression (PPR) technique of Friedman and Tukey (1974). The three key predictors identified were (i) ENSO region SST, (ii) indices that represent the Asian/WNP circulation from April in the previous year to March of the current year, and (iii) the interannual trend in WNP TC activity. However, a “predictability barrier” was found to exist during the Austral autumn (March–May), making it difficult to capture precursor signals to strong ENSO-related variability. This problem has affected many other ENSO-based regional predictions (e.g., Nicholls 1986; Webster and Yang 1992; Gray et al. 1993).

Chan and Liu (2004) investigated the relationship between typhoon activity in the WNP and large-scale ocean–atmosphere parameters such as SST, vertical shear of the horizontal wind, low-level relative vorticity, and moist static energy. One objective was to explore the respective influences of “local” SST versus nonlocal SST (i.e., Niño-3.4 region) on the interannual TC variability in the WNP basin. The local SST domain was a rectangle around the main WNP TC development region (5°–30°N, 120°E–180°) for the core typhoon season between May and November. TCs that occurred between 1960–2003 were divided into several categories: (i) number of typhoons (a TC with sustained winds >33 m s−1), (ii) ratio of the number of typhoons to the total number of TCs in the same year, and (iii) typhoon destruction potential as given by the sum of the squares of a TC’s maximum wind speed for each 6-h period of its lifespan.

Significant interdecadal variability of typhoons was also noted by Chan and Liu (2004), including active 1960–68 and relatively inactive 1973–88 periods. This interdecadal variation of typhoons was removed by subtracting a fitted fourth-order polynomial from the TC time series. The ratio of typhoons to the total number of TCs (RTY) was negatively correlated with local SST, with the strongest correlation of −0.6 around 20°N, 140°E, though the local area-averaged correlation was only −0.41. Also, typhoon activity showed no significant increase in response to a rise in the area-averaged local SST temperature and, furthermore, the correlations between local SST and various typhoon activity parameters became insignificant after the effect of Niño-3.4 SST was removed using partial correlation analysis. A positive correlation was found between RTY and May–November SST over the central and eastern equatorial Pacific, with r = +0.58 in the Niño-3.4 region, indicating that higher typhoon activity tends to occur during El Niño events. A change in the phase of ENSO was found to have a significant impact on the large-scale atmospheric parameters that modulate TC activity via the so-called atmospheric bridge, defined as the influence of ENSO teleconnections on air–sea interactions over remote oceans (Alexander et al. 2002). Specifically, it was found that the warming of the central and eastern Pacific during El Niño events resulted in weaker vertical wind shear, increased low-level cyclonic vorticity, and higher moist static energy in the southeastern quadrant of the WNP, where many TCs develop. These atmospheric factors combine to produce a favorable environment for TC development and intensification in the WNP.

c. Summary

The current study builds on the approaches used by Nicholls (1984) and Evans and Allan (1992) for the Australian region, and by Chan and Liu (2004) for the WNP. Nicholls (1984) found that the (local) Nicholls north Australian SST averaged over September–November was highly correlated (+0.78) with the number of TCs observed during the following November–April, though he did not consider the possible involvement of Niño-3.4 SST in that association. In contrast, other work (e.g., Holland et al. 1988; Nicholls and Drosdowsky 1999; Chan and Liu 2004) has shown that correlations between TC activity and local SST are substantially reduced after removal of Niño-3.4 SST. One focus of our study, therefore, is to revisit the statistical relationship between mean seasonal TC number and local SST for the Australian region by incorporating the effect of Niño-3.4 SST. A second focus is the dynamical link between the Niño-3.4 SST region and the atmospheric conditions that promote or impede the development of TCs in the Australian region.

3. Data, definitions, and methods

The time period for the datasets was chosen to be consistent with the era in which routine satellite observations were available for the Australian region. Consequently, we have not considered TCs prior to 1970 owing to uncertainties in the quality of the Australian TC “best track” dataset (defined below) in the absence of regular satellite surveillance (Holland 1981; Buckley et al. 2003). This section describes the analytical methods employed and also defines the ENSO parameters involved in the analyses.

a. SST data

The SST data were obtained from the National Oceanic and Atmospheric Administration (NOAA) extended reconstructed SST dataset (Smith and Reynolds 2004) for the period January 1970–December 2006. This dataset was constructed from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) using statistical methods to permit a stable reconstruction where data were sparse. The data have a spatial resolution of 2° longitude by 2° latitude and are for individual months. In addition to these gridded data, monthly SST averages and anomalies for the Niño-3.4 (5°N–5°S, 170°–120°W) and Niño-4 (5°N–5°S, 160°E–150°W) regions (Fig. 1a) were obtained from the NOAA/Climate Prediction Center’s Web site (http://www.cpc.noaa.gov/data/indices). The definition for El Niño (La Niña) was chosen to be consistent with NOAA, which defines El Niño (La Niña) as “a phenomenon in the equatorial Pacific Ocean characterized by a positive (negative) SST departure from normal (for the 1971–2000 base period) in the Niño-3.4 region greater (less) than or equal in magnitude to 0.5°C (0.9°F), averaged over three consecutive months.”

b. Tropical cyclone best-track data

The TC data were extracted from the best-track dataset developed by the Australian Bureau of Meteorology (more information is available online at http://www.bom.gov.au/climate/how) for 1970/71–2005/06. A TC in the Australian region is defined as a tropical disturbance in which 10-min-mean winds reach at least 17 m s−1 (http://www.bom.gov.au) which, as mentioned earlier, is different to the definition of a hurricane or a typhoon (maximum sustained winds >33 m s−1). TCs forming in the Australian TC region (Fig. 1a) were included, as well as those forming immediately outside the region and then subsequently moving into the region (Fig. 1b). Those that formed prior to 1970 were not considered for reasons given above. As Holland (1981, p. 177) pointed out, “during 1969–1979 the quality and quantity of satellite observations increased, radar installations covered the entire east coast and a substantial part of the west coast, and the density of surface observations further improved. Hence, cyclone locations over the whole Australian region probably reached an acceptable level of accuracy in this era.” Finally, because the Australian TC season is spread over two calendar years, the first of these years is used when referring to a particular TC season.

c. Atmospheric data

Daily values of the zonal and meridional winds at 850 and 200 hPa were extracted from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset (Kalnay et al. 1996), which has a horizontal grid spacing of 2.5°, and were used as the basis for deriving the monthly vertical shear of the zonal wind and 850-hPa relative vorticity. The vertical shear of the zonal wind was calculated by subtracting the zonal wind at 850 hPa from the zonal wind at 200 hPa. The vertical shear of the zonal wind was found to match very closely with the magnitude of the total shear, with the additional advantage, noted by DeMaria et al. (2001), of being able to distinguish readily between easterly and westerly shear. The 850-hPa relative vorticity was computed using a centered finite-differencing scheme.

d. Principal component analysis of Pacific SST variability

Principal component analysis (PCA) was used to decompose monthly SST anomaly (SSTA) patterns into dominant modes of spatial variability for the 1970–2006 base period. PCA is a powerful tool because it compresses the data to reduce significantly its dimensionality while still permitting identification and interpretation of spatial and temporal variability patterns. Spatial mode (S mode) PCA is most commonly based on analysis of either the interstation correlation matrix or covariance matrix of a given dataset (Richman 1986). Because we were interested in the most dominant spatial SST modes (i.e., those containing maximum variance), a covariance-based PCA was used in this study rather than a correlation-based PCA. The two leading varimax-rotated (Kaiser 1958) PCs were retained for interpretation after progressive rotation of 2–16 PCs and assessment of the congruence of their loading patterns with the original covariance matrix (Richman and Lamb 1985, their appendix). The North et al. (1982) eigenvalue separation criterion suggested the retention and rotation of only two PCs. This rotated covariance-based PCA was the basis for the analysis of monthly SSTAs over a large section of the Pacific Ocean (100°–70°W, 20°S–50°N) for January 1970–December 2006.

e. Correlation analysis of TC number and environmental parameters

A correlation analysis was performed for 1970–2006 between the number of Australian region TCs for the November–April season and monthly values of SST, vertical shear of the zonal wind, and 850-hPa relative vorticity for the TC season and August–October preseason, over the Pacific domain between 100°E–70°W and 50°N–50°S. For SST only, the analysis was extended eastward over the Atlantic Ocean to 0° longitude using the same latitudinal range. A similar Pacific analysis was performed between the Niño-3.4 SST and both the vertical shear of the zonal wind and 850-hPa relative vorticity. In addition to the gridded data, we also correlated several individual parameters (Niño-3.4 SST, Niño-4 SST, Darwin SLP, SOI, and Nicholls north Australian SST) with the seasonal number of TCs. These correlations were performed for 3-month running periods, starting with January–March preceding the TC season and ending with September–November following the TC season.

A partial correlation analysis (Draper and Smith 1981, 265–266) was made between seasonal TC number and the above large-scale parameters averaged over both the TC season and August–October prior to the start of the season. The partial correlations were used to relate TC numbers to large-scale parameters, while removing the effect of the Niño-3.4 or Niño-4 SST.

f. Kernel density estimation to locate TC genesis

Kernel density estimation (KDE; Bowman and Azzalini 1997, 52–53) was used to objectively identify clusters in the distribution of TC genesis points for 1970–2005. KDE is a nonparametric statistical technique, whereby a density function, or kernel, produces a smooth, continuous estimate of the true density of an empirical distribution. A Gaussian kernel was used to generate KDEs for the two-dimensional TC genesis data; it produced contours that give the probability of a TC genesis point falling within their perimeter. Ramsay and Doswell (2005) provide a detailed discussion of the KDE approach. Here, the 25%, 50%, and 75% probability KDE contours were chosen to document the spatial distribution of TC genesis points.

4. Results

a. Principal component analysis

The varimax-rotated PCA of monthly Pacific SSTAs identified two distinct regions of maximum variance for the calendar year: a major tropical mode and a northwest Pacific mode (Fig. 2). The primary mode reflects tropical and equatorial Pacific variability associated with ENSO, and accounts for 30% of the explained domain variance (Fig. 2a). An almost identical spatial pattern was obtained by Barlow et al. (2001) using monthly SST data for 1945–1993 and the varimax rotation method, though the explained domain variance was somewhat lower (18.3%). PC 2 represents a region of pronounced variability in the northwest-to-north-central Pacific, with a maximum just east of Japan, but accounts for only 6% of the explained domain variance (Fig. 2b). These modes are used below as a reference to examine the relationship between TC frequency in the Australian region and SSTA patterns in the Pacific basin.

b. Correlation between TC activity and SST/ENSO parameters before TC season

The number of November–April TCs was found to be highly correlated with Niño-region SST prior to the start of the TC season. A two-sided t test (Wilks 1995, 121–122) yields a correlation of magnitude 0.46 or greater to be statistically significant at the 95% confidence interval under the standard assumptions of independence and normality. This significance threshold is applied here. The strongest relationship was between TC number and Niño-4 SST (Fig. 3), with correlations peaking in August–October and September–November (−0.73). When the dataset was split into two halves, August–October Niño-4 SST produced correlations of −0.79 and −0.66 for 1970–87 and 1988–2005, respectively. Correlations of at least −0.6 were found for Niño-3.4 SST starting as early as June–August (Fig. 3).

The number of TCs was less correlated (positively) with SST in the Nicholls north Australian region (Fig. 4), peaking at +0.35 during August–October (Figs. 3, 4a) and +0.37 during September–November (Fig. 3). These are substantially less than the +0.78 September–November correlation value obtained by Nicholls (1984) for the same SST region for 1964–82 (Fig. 3). This discrepancy is largely due to the different, longer time period compared to that used by Nicholls (1984). When only the 1970–82 seasons were considered, that is, seasons that overlapped with Nicholls (1984) dataset, we obtained a much higher correlation (+0.95) similar to Nicholls’ correlation for 1964–82. This high correlation is shown by the time series of seasonal TC number and September–November Nicholls north Australian SST in Fig. 5. Peaks and troughs of the two time series are close until the mid-1980s, after which they diverge. The shift in the mid-1980s is possibly due to an improved understanding and resulting categorization of tropical systems, suggesting that the high correlation of +0.95 prior to 1983 possibly is artificial (e.g., Nicholls et al. 1998).

When the effects of Niño-3.4 SST are removed, the partial correlation values show almost no direct relationship between SST in the Nicholls north Australian region and TC activity (Fig. 4b), indicating that most of the TC variability in the Australian region is caused by processes related to the tropical central and eastern Pacific SST. This finding agrees with that of Nicholls and Drosdowsky (1999). The relative maximum of the partial correlation east of Japan (−0.55) corresponds generally to the spatial pattern of varimax-rotated PC 2 (Fig. 2b), suggesting that SST variability there and its associated link with Australian TC activity is independent of the dominant mode of tropical Pacific SST variability associated with ENSO.

The August–October SOI was found to have the second-highest correlation with seasonal TC number (+0.66) after the Niño-4 SST (Fig. 3), with correlation values of greater than +0.6 as early as June–August. The Darwin SLP revealed a similar, albeit weaker correlation pattern, with values peaking at −0.54 for July–September, August–October, and September–November.

c. Correlation between TC activity and SST/ENSO parameters during TC season

Seasonal TC number in the Australian region is strongly related to central and eastern tropical Pacific SST during the November–April TC season, with correlations between −0.6 and −0.7 extending over a large part of the tropical Pacific Ocean (Fig. 6a). When the central and eastern tropical Pacific is anomalously warm (cold) there are fewer (more) than average TCs in the Australian region, confirming previous studies. A second region of strong correlation is south of Japan with correlation coefficients between −0.6 and −0.7 (Fig. 6a). The correlation with Nicholls north Australian SST in the region where many TCs form is much weaker overall, ranging from −0.5 near Indonesia to +0.2 just east of Papua New Guinea. This region appears to be directly affected by increased cloudiness and oceanic upwelling associated with the TC season and TC atmosphere–ocean interaction, respectively, as suggested by the sudden and steady decrease in correlation after the beginning of the TC season (Fig. 3). This decrease also was found by Nicholls (1984). It produces negative correlations that are largest during the end of the TC season and in the months immediately following (Fig. 3). This link between SST and cloudiness was noted by Evans and Allan (1992) in the context of extreme ENSO years.

The large positive SOI correlation obtained for the months preceding the TC season (>+0.6) was found to persist with values greater than +0.55 throughout the TC season before declining precipitously (Fig. 3). Similarly, the strong negative correlations with Darwin SLP established in the preceding July–August (−0.54) persist throughout the TC season, peaking during January–March (−0.62), before decreasing rapidly like the SOI correlation. Low (high) Darwin SLP during the TC season enhances (reduces) Australian TC activity.

The partial correlation analysis revealed a relatively strong SST signal over a region extending from the East China Sea eastward along 30°N to 180° (Fig. 6b). The independence of this signal from the primary tropical ENSO mode (Fig. 2a) is demonstrated by the low correlation (r < +0.1) between this regional SST, centered on 32°N, 160°E, and Niño-3.4 SST (not shown). This area of maximum partial correlation matches closely with the region of maximum SST variance explained by varimax-rotated PC 2 (Fig. 2b), which is uncorrelated with tropical PC 1 and, therefore, is independent (Richman and Lamb 1985).

The large, significant correlations found between SST in the central and eastern equatorial Pacific and Australian seasonal TC number (Figs. 4a, 6a) suggest that atmospheric teleconnections exist between the Niño SST regions and TC activity in the Australian region. Two widely used atmospheric parameters for the diagnosis and prediction of TC genesis are low-level relative vorticity and vertical wind shear through a deep tropospheric layer, both of which have been found to be influenced by fluctuations in SST (e.g., Evans and Allan 1992; Chan and Liu 2004). These links between TC number and atmospheric parameters are investigated below.

d. Correlation between TC activity and vertical wind shear during the TC season

The spatial correlation pattern between seasonal TC number and the vertical shear of the zonal wind during November–April shows a north–south tripole over the central/eastern Pacific, with strong positive correlations (>+0.6) centered on 6°N, and negative correlations of similar magnitude to the north (∼30°N) and south (∼20°S; Fig. 7a). The sign of the tropical correlation reverses toward the west and culminates in a strong maximum negative correlation (−0.73) over the tropical waters north of Australia. This maximum negative correlation region, which lies immediately to the north of the area of maximum TC genesis in Fig. 1b, is overlain by regional maxima of 200-hPa easterlies and 850-hPa monsoon westerlies (Fig. 8a). The negative vertical wind shear is correlated negatively with Australian TC activity (Figs. 7a, 8b), indicating that stronger 200-hPa easterlies and stronger 850-hPa westerlies in that region are associated with increased Australian TC activity. The weakening of this negative correlation southward into the Australian TC genesis region (Fig. 7a) reflects a decrease of both the 200-hPa easterlies and 850-hPa westerlies (Figs. 8a,c), consistent with the low-shear prerequisite for TC genesis.

After removal of the effect of Niño-3.4 SST, the correlation magnitudes decrease substantially over much of the Pacific (Fig. 7b). The previous high correlations between TC number and vertical shear in the subtropics (Fig. 7a, 30°N and 20°S) and central equatorial Pacific are substantially reduced (Fig. 7b). In addition, the region immediately to the north and northwest of Australia between 15°N and 15°S contains lower correlations compared with those shown by the shaded area over that region in Fig. 7a, suggesting that Niño-3.4 SST is modulating the shear in that region via the atmospheric bridge process. Indeed, the observed decrease in the number of TCs during El Niños (e.g., Solow and Nicholls 1990) was associated with increased 200-hPa westerlies and reduced 850-hPa easterlies that combine to produce anomalously strong vertical wind shear in the TC genesis region. This increase in vertical shear is shown by the northward displacement of the 200-hPa zonal wind isotachs over northern Australia in Figs. 8a,b.

Niño-3.4 SST is highly correlated with vertical shear of the zonal wind (∼−0.80) over the central equatorial Pacific, with similar high correlations over the Tropic of Capricorn and Tropic of Cancer east of 180° (Fig. 9a). The dominant vertical shear term is the 200-hPa zonal wind (Figs. 8a,c), which has an average westerly (positive) component over the central/eastern tropical Pacific during November–April. Therefore, Fig. 9a suggests a significant decrease (increase) in the tropical 200-hPa westerlies during the warm (cold) phase of ENSO over the tropical central and eastern Pacific. During very strong El Niño events, such as 1982/83 and 1997/98, the average 200-hPa wind even changes sign from westerly to easterly over the central equatorial Pacific (not shown; more information is available online at http://www.cdc.noaa.gov). Conversely, over the Northern and Southern Hemisphere tropics west of 135°E, the 200-hPa wind develops an anomalous westerly (easterly) component during El Niño (La Niña) events, with weaker 200-hPa easterlies north of 15°S and stronger 200-hPa westerlies south of 15°S. Finally, Fig. 9a shows that the influence of Niño-3.4 SST on the vertical shear of the zonal wind is more pronounced over the Australian TC development region west of 135°E, with correlations greater than +0.6 over a large area, rather than to the east of 135°E where only moderate correlations (∼+0.4) are found. In particular, over the Australian TC genesis region west of 135°E, the zero-shear line in Figs. 8c,d is displaced northward during El Niño events (Fig. 8d) from its mean November–April position (Fig. 8c).

e. Correlation between TC activity and low-level relative vorticity during the TC season

The seasonal frequency of Australian region TCs is strongly positively correlated with 850-hPa relative vorticity, in a region between 2° and 15°S extending from Papua New Guinea eastward into the central and eastern Pacific (Fig. 10a). There are more TCs when anticyclonic vorticity is anomalously high in that region. In contrast, the moderate negative correlations (<−0.4) that extend from the tropical Indian Ocean through northern Queensland, eastward to New Caledonia and Fiji imply that there are more TCs during the active phase of the monsoon trough in the region. This active phase is associated with a second region of negative correlations of similar magnitude over and offshore of northwestern Australia, signifying the importance of the semipermanent heat low there as a vorticity source for TC genesis (McBride and Keenan 1982). The TC–vorticity correlations were preserved in the partial correlation analysis that removed the effect of Niño-3.4 SST, but the correlations were substantially lower (Fig. 10b).

The spatial pattern of correlation between Niño-3.4 SST and relative vorticity is more telling (Fig. 9b). It suggests that during the warm (cold) phase of ENSO, the region of cyclonic vorticity that extends from the area northwest of Australia eastward through northern Queensland and out into the central Pacific is weaker (stronger) than usual, as indicated by the large positive correlations. This is consistent with the earlier detailed observational study of Evans and Allan (1992). The decrease of cyclonic vorticity associated with a weaker monsoon trough during El Niños is seen clearly in Figs. 8c,d. In the Northern Hemisphere tropics immediately north of Australia, the positive correlations (Fig. 9b) indicate the opposite association—relatively large values of cyclonic vorticity are associated with El Niño events as relative vorticity changes sign in the Northern Hemisphere. This WNP result is consistent with several studies for that region (Chan 1985; Chan 2000; Wang and Chan 2002; Chan and Liu 2004) in which the analysis did not extend southward to Australia where the effect is opposite. Finally, in the TC genesis region off the northwest coast of Australia (Fig. 1b), low-level vorticity tends to be less (more) cyclonic during El Niño (La Niña) events (Fig. 8d) consistent with the large positive correlations (>+0.6) there in Fig. 9b, suggesting that TC activity in that region is also dependent on ENSO phase.

f. Summary of correlations between TC number and atmospheric parameters

The interannual variability of TCs in the Australian region is strongly related to fluctuations in the vertical shear of the zonal wind and low-level relative vorticity over the main TC development region (Fig. 8). These parameters, in turn, are influenced by the more slowly varying fluctuations in central and eastern Pacific SST associated with the ENSO cycle. During El Niño events, the 200-hPa westerlies and the 850-hPa easterlies between 10° and 20°S in the Australian region tend to be individually stronger and combine to produce an unfavorable larger vertical shear for TC genesis. The opposite is true for La Niña events. Consistent with Evans and Allan (1992), cyclonic vorticity in the Australian monsoon trough is weaker during El Niños and stronger during La Niñas. These results show that the atmospheric bridge process is important in understanding Australian TC variability. SSTAs in the tropical and eastern equatorial Pacific modify large-scale atmospheric parameters over the Australian region and these atmospheric parameters affect directly the potential for TC genesis.

g. Interannual variability of TCs in the Australian region

Over the 36-yr period from 1970/71 to 2005/06, the seasonal number of all TCs in the Australian region has declined markedly, despite significant interannual variability (Figs. 11a,b). Multidecadal TC variability associated with underlying ENSO variability, as noted by a number of researchers (e.g., Gray et al. 1997; Allan 2000), also is evident in Fig. 11. This multidecadal variability requires care in deciding whether there are trends in the time series. For example, despite the overall downward trend for 1970–2005, a pronounced upward trend is present in the subset 1986–2005 (Fig. 11a). Such differences emphasize the need for care in choosing the end points of time series when attempting to analyze relationships between predictors and TC activity. When stratified by TC intensity, the previously mentioned downward trend is seen only in the frequency of weak to moderate TCs, those with minimum central pressures above 965 hPa. For more intense TCs with central pressures less than 965 hPa (i.e., a category 3 hurricane on the U.S. Saffir–Simpson scale), there was a much smaller downward trend during the period (Fig. 11b). A similar result was found earlier by Nicholls et al. (1998).

We stratified all tropical cyclogenesis points for El Niño and La Niña TC seasons (Fig. 12). Following NOAA, an El Niño/La Niña TC season required a Niño-3.4 SSTA larger than +0.5°C/−0.5°C for any 3-month period during November–April. The seasons categorized as El Niño started in 1972, 1976, 1977, 1982, 1986, 1987, 1991, 1994, 1997, 2002, and 2005, and their La Niña counterparts began in 1970, 1971, 1973, 1974, 1975, 1984, 1988, 1995, 1998, 1999, and 2000. Neutral years are included for completeness. Although there are fewer TCs during El Niños, the genesis locations generally are similar to those for La Niña seasons (Fig. 12).

When all TC genesis points are plotted according to whether their lowest central pressure was above or below 965 hPa, there is a marked difference in genesis location (Fig. 13). Those TCs that attain a central pressure less than 965 hPa show a strong tendency to form over the waters immediately off the northwest coast of Australia, whereas the less intense storms tend to be infrequent in that region and instead form further east, over the Gulf of Carpentaria and east of north Queensland, and also much further to the northwest (south of Sumatra). Furthermore, the less intense TCs tend to have much shorter lifetimes with an average of 5.6 days, compared with 8.5 days for the more intense TCs, because of the fact that TCs forming in and near the Gulf of Carpentaria are more likely to make landfall. These results confirm the earlier findings of Dare and Davidson (2004). No significant differences in genesis location were found when TC intensity was further stratified by ENSO phase (not shown). Furthermore, the seasonal number of severe TCs (<965 hPa) is only weakly correlated with the preceding August–October Niño-4 SST (r = −0.19), whereas the number of weak-to-moderate TCs is much more strongly related to the same preseason SST in that area (r = −0.67). Counterpart correlations with the Nicholls north Australian SST for August–October are significantly lower, being zero for severe TCs and +0.38 for weak–moderate TCs. The above results suggest that the number of severe TCs in the Australian region is only weakly associated with the atmospheric bridge process, making accurate preseason prediction much less likely than for weak–moderate TCs.

h. Other Australian region TC characteristics

Four additional aspects of Australian TCs were explored. Track length was found to be almost independent of ENSO phase, with mean track lengths of 2248 km for El Niño seasons and 2446 km for La Niña seasons. TC duration was only weakly correlated with Niño-3.4/Niño-4 SST for both August–October (+0.18/+0.07) and November–April (+0.32/+0.23), none of which are statistically significant at the 95% confidence level. TC season length correlated significantly (+0.47) with August–October Niño-4 SST, but was not significant for November–April (−0.23). Neither period was correlated significantly with the Niño-3.4 SST, being −0.37 for August–October and −0.26 for November–April. Finally, TC season start and end times were correlated with Niño-3.4 and Niño-4 SST. The TC season started later when the August–October Niño-4 SST region was anomalously warm, with a statistically significant correlation of +0.48, but this relationship was not significant for November–April (+0.30). For Niño-3.4 SST, no significant relationship was found for TC season start dates for both August–October and November–April (+0.38/+0.31). No significant correlations were found between TC season end dates and Niño-3.4/Niño-4 SST—the August–October correlations were −0.1/−0.1 and the November–April correlations were −0.03/+0.05 for Niño-3.4 and Niño-4 SST regions, respectively.

i. Australian region TC activity and North Atlantic SST

A region of SST in the North Atlantic Ocean for August–October was found to be statistically linked with Australian seasonal TC numbers, with correlations of similar magnitude (∼−0.6) to the central Pacific, but over a much smaller area centered on 32°N, 38°W (Fig. 14a). An enlargement of this sensitive North Atlantic SST area still exhibited relatively high (partial) correlations after removal of the effects of Niño-4 SST (∼−0.55, not shown) and Niño-3.4 SST (−0.65, Fig. 14b). This North Atlantic relationship is even stronger than the one obtained for the northwest Pacific centered on 42°N, 152°E (Fig. 4b), and over a broader area. This is not surprising given the recent study by Dong et al. (2006) which found that Atlantic Ocean SST influences both ENSO and the Atlantic Multidecadal Oscillation (AMO) through cross-basin (Atlantic and Pacific) and interhemispheric (North Atlantic and South Atlantic) ocean–atmosphere teleconnections.

j. Increasing correlations by combining SST predictors

Attempts have been made in weather prediction research to increase statistical skill by combining two or more predictors. Thompson (1977) employed an optimal linear combination of two independent predictors, using a least squares minimization of the mean-square error of the combination. Fraedrich and Leslie (1987) extended Thompson’s analysis to include dependent variables. For our study, the 1970–2005 November–April time series of Niño-3.4 SSTA and Niño-4 SSTA were each combined with the November–April SST time series for grid points with highest partial correlations in the Pacific (30°N, 130°E) and in the North Atlantic (34°N, 54°W) basins). The correlations between Niño regions and the above points in the Pacific and North Atlantic basins were found to be less than +0.2. For the Pacific basin, the correlation between the combined tropical and subtropical SST mode time series and the number of Australian TCs during November–April increased to −0.79 (Table 1). Importantly, a similar correlation (−0.78) was obtained using SST data for August–October, prior to the TC season, for which the point of maximum Pacific partial correlation (42°N, 152°E) was displaced to the north and east from its November–April location. When August–October SST data for the North Atlantic were used, correlations increased from −0.62 to −0.76 (Niño-3.4 data) and from −0.73 to −0.80 (Niño-4 data), as shown in Table 1. Interestingly, of all the predictors, the combined Niño-4–Atlantic SST index for August–October produced the highest correlation with November–April Australian TC number (−0.80) and accounted for 64% of the explained variance, underlining the need for a physical understanding of Atlantic SST teleconnections with Australian TCs.

5. Discussion and conclusions

Our results provide strong evidence that SST over the central and eastern tropical Pacific is the main contributing factor to interannual variability of TC activity in the Australian region. Furthermore, the interannual variability of TC number in the Australian region is most highly correlated with SST in the central Pacific, as indicated by the large, stable correlations with the Niño-4 region during August–October (−0.73 for 1970–2005, −0.79 for 1970–87, and −0.66 for 1988–2005). The largest correlations were found several months prior to the start of the Australian TC season, making the central Pacific SST very important for seasonal TC prediction.

Nicholls (1984) suggested that September–November SST for the north Australian region is a good indicator of TC numbers in impending TC seasons, with anomalously warm SST indicating a relatively large number of TCs. Our results suggest this in situ relationship is not as strong as previously thought, owing to the updated, larger dataset used here. Using a subset of TC seasons (1970–82) that overlapped with those of Nicholls (1984), similar large correlations were obtained. The analysis of the Nicholls north Australian SST region showed only a weak positive relationship with TC frequencies, which diminishes after removing the effect of ENSO.

Instead, the dependence of TC activity on north Australian SST was found to be dictated largely by the main tropical mode of Pacific SST variability, as shown in Fig. 2a. This atmospheric bridge between the anomalies of Pacific SST and Australian TC number involves interannual variability of the large-scale atmospheric parameters that are favorable for TC genesis, such as enhanced low-level vorticity and weak vertical shear of the zonal wind. These results are consistent with Chan and Liu’s (2004) work for the WNP basin and the earlier work of Evans and Allan (1992) for the Australian region. Indeed, most interannual variations of TC activity, in both the WNP and the Australian regions, appear to be controlled primarily by ENSO through this atmospheric bridge process.

The monsoon trough is the climatologically preferred position for TC formation globally, aside from the North Atlantic basin for which a zone of convergence occurs over western Africa. During El Niño years, cyclonic vorticity is above average in the central part of the tropical North Pacific, while the monsoon trough in the tropical South Pacific, including the Australian region, is less cyclonic. Over the region where most Australian TCs form (Fig. 1b), warming of the central Pacific coincides with atmospheric factors unfavorable for TC genesis, with stronger vertical shear of the zonal wind during El Niños and weaker low-level cyclonic convergence. Figure 15 shows the salient features of these ocean–atmosphere connections during El Niño events, adding to the findings of Slingo and Annamalai (2000) for the Indian Ocean.

While the total annual number of TCs in the Australian region has apparently declined since 1970, this is not the case for the number of TCs for which central pressure drops to less than 965 hPa. The interannual variability of more intense TCs is far less associated with preseason Niño-4 SST (r = −0.19). Moreover, they form over the warm waters off the northwest coast of Australia, further westward relative to their weaker counterparts, in a region that has become colloquially known as “Cyclone Alley” owing to the relatively large number of landfalling TCs. Favorable large-scale atmospheric parameters for TC formation, and their degree of dependence on ENSO, show considerable variation from east to west across the main TC development region, with El Niño years having relatively high values of tropospheric wind shear over the TC region northwest of Australia, whereas to the east of north Queensland the relationship between vertical wind shear and Niño-3.4 SST is much weaker (r ∼ 0.4).

A final result of this study is that correlations between SST and TC activity are increased by combining SST time series from tropical and subtropical regions that are only weakly correlated. An SST index produced by combining tropical Pacific SST and North Atlantic SST explains 64% of the total variance in TC activity, even several months prior to the TC season. The physical processes underlying such high correlations will be investigated in a later study.

Acknowledgments

This research was supported by funding from the Insurance Australia Group, Sydney, Australia. We thank Dr. Neville Nicholls for valuable discussions about the topic, as well as the two anonymous reviewers for their help in improving the manuscript.

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Fig. 1.
Fig. 1.

(a) Map showing the boundaries used to define the Australian TC region used here (0°–36°S, 90°–170°E), as well as the Niño-3.4 (solid line) and Niño-4 (broken line) SST regions. (b) The genesis points of all TCs that formed in the Australian region and immediately to the west and east from 1970/71 to 2005/06.

Citation: Journal of Climate 21, 5; 10.1175/2007JCLI1970.1

Fig. 2.
Fig. 2.

Spatial loading patterns for the first two varimax-rotated PCs of all monthly Pacific SSTAs for 1970–2006. (a) PC1 (30% of total domain variance); (b) PC2 (6% of total domain variance). Solid (broken) isopleths give positive (negative) loadings.

Citation: Journal of Climate 21, 5; 10.1175/2007JCLI1970.1

Fig. 3.
Fig. 3.

Annual cycles of correlation for 1970–2005 between number of November–April TCs in the Australian region and various ENSO-related parameters (Niño-3.4 SST, Niño-4 SST, Darwin SLP, SOI), and Nicholls north Australian SST, for running 3-month periods starting with January–March preceding the TC season and ending with September–November following the TC season. Purple dashed line gives the correlations obtained by Nicholls (1984) for his north Australian SST region for 1964–82. Correlations for 1970–2005 with larger magnitude than the thick dashed (solid) line are significant at the 5% (1%) level according to a two-sided t test (Wilks 1995, 121–122). Core of November–April TC season is shaded in light blue.

Citation: Journal of Climate 21, 5; 10.1175/2007JCLI1970.1

Fig. 4.
Fig. 4.

(a) Spatial distribution of correlation coefficients for 1970–2005 between the number of TCs during November–April and Pacific SSTAs during the preceding August–October. Correlation thresholds for 5% (1%) statistical significance level are r ≥ |0.46| (|0.58|) after reducing the degrees of freedom to account for a lag-1 autocorrelation of 0.28, according to the two-sided t test (Wilks 1995, 121–122). The rectangle north of Australia is the north Australian SST area used by Nicholls (1984), and the rectangles in the central Pacific contain the Niño-4 (broken line) and Niño-3.4 (solid line) SST regions delineated in Fig. 1a. Solid (broken) isopleths indicate positive (negative) correlations with correlations >|0.6| shaded. (b) Same as in (a) but showing partial correlation values with the effect of Niño-3.4 SST removed.

Citation: Journal of Climate 21, 5; 10.1175/2007JCLI1970.1

Fig. 5.
Fig. 5.

Time series for 1970–2005 of the number of November–April TCs in the Australian region (solid line) and Nicholls north Australian SST (dashed line). Overall linear trend is denoted by the solid line through each time series. The vertical solid line marks the end of Nicholls (1984) dataset (1964–82). TC data are obtained from the Bureau of Meteorology’s TC best-track dataset (more information is available online at http://www.bom.gov.au/climate/how).

Citation: Journal of Climate 21, 5; 10.1175/2007JCLI1970.1

Fig. 6.
Fig. 6.

Same as in Fig. 4, but for November–April SST.

Citation: Journal of Climate 21, 5; 10.1175/2007JCLI1970.1

Fig. 7.
Fig. 7.

Same as in Fig. 4, but for November–April 850–200-hPa vertical shear of the zonal wind and with SST regions removed.

Citation: Journal of Climate 21, 5; 10.1175/2007JCLI1970.1

Fig. 8.
Fig. 8.

The mean November–April 200-hPa (blue contours) and 850-hPa (brown contours) zonal winds (m s−1) for (a) 1970–2005 and (b) 11 El Niño seasons as defined in section 4g. (c) The same as in (a) but for 200-hPa minus 850-hPa vertical shear of the zonal wind (blue contours, m s−1) and 850-hPa relative vorticity (red contours; 10−6 s−1 with contour interval of 4). (d) The same as in (c) but for El Niño seasons. Thick solid contours show zero lines and dashed (solid) contours show negative (positive) values. Green shading indicates areas of TC genesis bounded by the 50% KDE contour.

Citation: Journal of Climate 21, 5; 10.1175/2007JCLI1970.1

Fig. 9.
Fig. 9.

Correlation coefficients between Niño-3.4 SST (Fig. 1a) and (a) vertical shear of the zonal wind and (b) 850-hPa relative vorticity for November–April. Solid (broken) isopleths indicate positive (negative) correlations, with correlations >|0.6| shaded.

Citation: Journal of Climate 21, 5; 10.1175/2007JCLI1970.1

Fig. 10.
Fig. 10.

Same as in Fig. 4, but for November–April 850-hPa relative vorticity and with SST regions removed.

Citation: Journal of Climate 21, 5; 10.1175/2007JCLI1970.1

Fig. 11.
Fig. 11.

(a) Time series of the seasonal total number of TCs in the Australian region showing the overall downward linear trend (solid gray line). The solid vertical line divides the 1970–86 and 1986–2005 subsets, with respective linear trends shown by the thick dashed lines. (b) Time series of the seasonal total number of TCs in the Australian region (solid line), as well as the number of intense TCs with minimum central pressure below 965 hPa (dashed line) and number of weak-to-moderate TCs with minimum central pressure above 965 hPa (gray line), for 1970/71–2005/06. Data are obtained from the Bureau of Meteorology’s TC best-track dataset (more information is available online at http://www.bom.gov.au/climate/how).

Citation: Journal of Climate 21, 5; 10.1175/2007JCLI1970.1

Fig. 12.
Fig. 12.

Location of TC genesis points between 1970/71–2005/06 for (a) all seasons, (b) neutral ENSO seasons, (c) El Niño seasons, and (d) La Niña seasons. Number of TCs and seasons in each category are given in parentheses. Contours enclose areas within which TC genesis had 25%, 50%, and 75% probabilities of occurrence given by the KDEs.

Citation: Journal of Climate 21, 5; 10.1175/2007JCLI1970.1

Fig. 13.
Fig. 13.

Location of TC genesis points for 1970/71–2005/06 stratified by (a) minimum central pressure greater than or equal to 965 hPa and (b) minimum central pressure less than 965 hPa. The number of TCs in each category is given in parentheses.

Citation: Journal of Climate 21, 5; 10.1175/2007JCLI1970.1

Fig. 14.
Fig. 14.

As in Fig. 4, but with the extension of the correlation patterns into the Atlantic Ocean.

Citation: Journal of Climate 21, 5; 10.1175/2007JCLI1970.1

Fig. 15.
Fig. 15.

Schematic showing the connection between anomalously warm SST in the Niño-3.4 and Niño-4 regions (pink shading) associated with El Niño events and the corresponding atmospheric response: increased 200-hPa zonal westerly winds (blue horizontal arrows) around 15°S resulting in increased vertical shear of the zonal wind, collocated with decreased 850-hPa relative cyclonic vorticity (green) associated with a weakened monsoon trough over the Australian region. Light blue vertical arrows indicate anomalous subsidence over the northern Australian region and anomalous ascent over the central equatorial Pacific.

Citation: Journal of Climate 21, 5; 10.1175/2007JCLI1970.1

Table 1.

Correlation coefficients between Niño-3.4 and Niño-4 SST and the November–April number of TCs in the Australian region for 1970/71–2005/06, as well as the correlations generated by combining Niño SST with SST in the northwest Pacific (42°N, 152°E for August–October; 30°N, 130°E for November–April) and North Atlantic (32°N, 38°W for August–October; 34°N, 54°W for November–April) as explained in section 4j.

Table 1.
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