A new and potentially skillful seasonal forecast model of tropical cyclone formation [tropical cyclogenesis (TCG)] is developed for the Australian region. The model is based on Poisson regression using the Bayesian approach. Predictor combinations are chosen using a step-by-step predictor selection. The three-predictor model based on derived indices of June-August average convective available potential energy, May-July average meridional winds at 850 hPa (V 850 ), and July-September geopotential height at 500 hPa produces the smallest standard error (se 5 0.36) and root-mean-squared error (RMSE 5 5.20) for the leave-one-out crossvalidated TCG hindcasts over the 40-yr record between 1968/69-2007/08. The corresponding correlation coefficient between observed annual TCG totals and cross-validated model hindcasts is r 5 0.73. Using fourfold cross validation, model hindcast skill is robust, with 85% of the observed seasonal TCG totals hindcast within the model standard deviations. Seasonal TCG totals during ENSO events are typically well captured with RMSE 5 5.14 during El Niñ o, and RMSE 5 6.04 during La Niñ a years. The model is shown to be valuable in hindcasting seasonal TCG totals in the eastern Australian subregion (r 5 0.73) and also provides some skill for the western Australian region (r 5 0.42), while it not useful for the northern region. In summary, the authors find that the three-predictor Bayesian model provides substantial improvement over existing statistical TCG forecast models, with remarkably skillful hindcasts (forecasts) of Australian region and subregional seasonal TCG totals provided one month ahead of the TC season.
We introduce a simple but effective means of removing ENSO-related variations from the Indian Ocean Dipole (IOD) in order to better evaluate the ENSO-independent IOD contribution to Australian climate-specifically here interannual variations in Australian region tropical cyclogensis (TCG) counts. The ENSO time contribution is removed from the Indian Ocean Dipole Mode index (DMI) by first calculating the lagged regression of the DMI on the sea surface temperature anomaly (SSTA) index NINO3.4 to maximum lags of 8 months, and then removing this ENSO portion. The new ENSO-independent time series, DMI NOENSO , correlates strongly with the original DMI at r = 0.87 (significant at [99% level). Despite the strength of the correlation between these series, the IOD events classified based on DMI NOENSO provide important differences from previously identified IOD events, which are more closely aligned with ENSO phases. IOD event composite maps of SSTAs regressed on DMI NOENSO reveal a much greater ENSO-independence than the original DMI-related SSTA pattern. This approach is used to explore relationships between Australian region TCG and IOD from 1968 to 2007. While we show that both the DMI and DMI NOENSO have significant hindcast skill (on the 95% level) when used as predictors in a multiple linear regression model for Australian region annual TCG counts, the IOD does not add any significant hindcast skill over an ENSO-only predictor model, based on NINO4.Correlations between the time series of annual TCG count observations and ENSO ? IOD model cross-validated hindcasts achieve r = 0.68 (significant at the 99% level).
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