4.7 Article

Interannual variability of tropical cyclones in the Australian region: Role of large-scale environment

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JOURNAL OF CLIMATE
卷 21, 期 5, 页码 1083-1103

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AMER METEOROLOGICAL SOC
DOI: 10.1175/2007JCLI1970.1

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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 Nino-3.4 and Nino-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 Nino SST and these two atmospheric parameters exhibited a strong link between the Australian region and the Nino-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.

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