Interannual variability and prediction of Northwest Australian tropical cyclones

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Tuesday, 19 January 2010
Kevin H. Goebbert, Valparaiso University, Valparaiso, IN; and L. M. Leslie

Interannual variability of tropical cyclone (TC) activity in Northwest-Australian (NWAUS) basin of the Southeast Indian Ocean (105–135°E) is investigated using 13 metrics. The NWAUS basin averages 5.5 TCs per year, 42 TC days, and 3 TC landfalls. Additionally, a wavelet analysis yields wavelet power maximum in the 4–6 year and the decadal time periods for both yearly TC frequency and TC days. None of the thirteen TC metrics investigated had any significant linear trend. To identify significant correlates, the global atmospheric and oceanic parameters (both classic climate indices and NCEP–NCAR reanalysis data) were correlated with the TC frequency and TC days from the Woodside Petroleum Ltd. NWAUS TC data set. Large correlations were obtained between the NWAUS TC frequency and NCEP–NCAR reanalysis geopotential heights and air temperature. Large correlations were obtained between the NWAUS TC days and geopotential height and the v-component of the wind.

The global teleconnections obtained in this study can be utilized as seasonal predictors for the upcoming TC season in terms of frequency and days with a lead-time of at least three months for TC frequency and two months for TC days. This set of seasonal predictors includes, intra-basin, inter-basin, and cross-hemispheric regions and were unlike previous Australian TC activity studies, which stressed the primacy of standard ENSO parameters. Here it is noted that the traditional Niño 3.4 and Niño 4 regions were not highly correlated with the NWAUS TC activity (|r| < 0.5). No local predictors based on SST, geopotential height, or air temperature resulted from the correlation analysis. The predictors are used in a multiple linear regression model for forecasting the upcoming season's number of TCs and TC days. Both prediction schemes then are compared with forecasts made using persistence, climatology, and random forecasts to determine if they perform better than the reference forecasts. Finally, the scheme has been tested in true predictive mode for the past 4 TC seasons with encouraging skill. 9-->