**Acknowledgements**

**Figure 10.** Climatological number of TCs as a function of month for observations (solid black line), POAMA (blue dashed line) and JMA/MRI-CGCM (red dashed line) in the Australian (top panel) and South Pacific Ocean (bottom

By design JMA/MRI-CGCM yields annual totals of TCs for NDJFMA close to climatology for both basins and in the Australian region a similar degree of variability to POAMA is captured, demonstrated by a correlation value of 0.48. In the South Pacific JMA/MRI-CGCM fairs less

Ensemble mean monthly TC climatologies for each basin and model are shown compared with

Both models capture the monthly variability in the Australian region well, although neither model represents the peak value correctly. In the South Pacific, POAMA performs well,

In summary, POAMA and JMA/MRI-CGCM both represent the large-scale environment relevant to TCs reasonably well, although possible deficiencies exist in the Australian region. The monthly TC climatologies in both models are reasonably realistic. Both models capture some of the inter-annual variability in the Australian region, although POAMA performs better

however, JMA/MRI-CGCM peaks a month too early and drops off too quickly.

panel) regions.

well at capturing the variability.

244 Recent Developments in Tropical Cyclone Dynamics, Prediction, and Detection

observations in **Figure 10**.

The research discussed in this paper was conducted with support of the Pacific Climate Change Science Program and Pacific-Australia Climate Change Science and Adaptation Planning program. D. Jones, R. Fawcett, J. Chan, R. de Wit, J. Apajee, Y. Wang, K. Shelton, A. Charles, T. Nakaegawa, J. Wijnands and G. Qian contributed to this research.
