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Resolution dependence of tropical cyclone formation in CMIP3 and finer resolution models

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Abstract

Detection of tropical lows is performed in a suite of climate model simulations using objectively-determined detection thresholds that are resolution-dependent. It is found that there is some relationship between model resolution and tropical cyclone formation rate even after the resolution-dependent tropical cyclone detection threshold is applied. The relationship is investigated between model-simulated tropical cyclone formation and a climate-based tropical cyclone Genesis Potential Index (GPI). It is found that coarser-resolution models simulate the GPI better than they simulate formation of tropical cyclones directly. As a result, there appears to be little relationship from model to model between model GPI and the directly-simulated cyclone formation rate. Statistical analysis of the results shows that the main advantage of increasing model resolution is to give a considerably better pattern of cyclone formation. Finer resolution models also simulate a slightly better pattern of GPI, and for these models there is some relationship between the pattern of GPI simulated by each model and that model’s pattern of simulated tropical cyclone formation.

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Acknowledgments

The authors would like to thank the ARC Network for Earth System Science, Woodside Energy, the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Climate Adaptation Flagship and their respective institutions for providing funding for this work. We would also like to thank Deborah Abbs of CSIRO for her detailed comments on an earlier draft of this work. We would like to thank CSIRO for the use of their tropical cyclone detection routine. We would also like to thank Aurel Moise of the Australian Bureau of Meteorology, and Aaron McDonough and Peter Edwards of CSIRO for assistance with obtaining CMIP3 model output.

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Walsh, K., Lavender, S., Scoccimarro, E. et al. Resolution dependence of tropical cyclone formation in CMIP3 and finer resolution models. Clim Dyn 40, 585–599 (2013). https://doi.org/10.1007/s00382-012-1298-z

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