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valuable and applicable climate information about tropical cyclone seasonal
variability, which can help decision making, responses and adaptation in
Australia and Pacific and Indian Ocean island countries. High-quality TC
seasonal prediction also enables to move to a lesser reliance on historical
climatologies which can give misleading information about the to-be-expected
climate conditions and the likelihood of extreme events.
We conclude that for the Australian region the developed statistical models
demonstrate improvement in forecasting skill compared to the currently
employed NCC model. The next step towards improving skill of TC seasonal
prediction in the regions of the Southern Hemisphere will be undertaken through
the direct analysis of outputs from the dynamical climate models such as
POAMA.
Acknowledgement
The research discussed in this paper was conducted with the partial support of
the Pacific-Australia Climate Change Science and Adaptation Planning program
(PACCSAP), which is supported by the Australia Agency for International
Development, in collaboration with the Department of Climate Change and
Energy Efficiency, and delivered by the Bureau of Meteorology and the RMIT
University.
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