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Understanding the geographic distribution of tropical cyclone formation for applications in climate models

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Abstract

Projections of Tropical cyclone (TC) formation under future climate scenarios are dependent on climate model simulations. However, many models produce unrealistic geographical distributions of TC formation, especially in the north and south Atlantic and eastern south Pacific TC basins. In order to improve confidence in projections it is important to understand the reasons behind these model errors. However, considerable effort is required to analyse the many models used in projection studies. To address this problem, a novel diagnostic is developed that provides compelling insight into why TCs form where they do, using a few summary diagrams. The diagnostic is developed after identifying a relationship between seasonal climatologies of atmospheric variables in 34 years of ECMWF reanalysis data, and TC detection distributions in the same data. Geographic boundaries of TC formation are constructed from four threshold quantities. TCs form where Emanuel’s Maximum Potential Intensity, \(V_{{PI}}\), exceeds \(40\,\,{\text{ms}}^{{ - 1}}\), 700 hPa relative humidity, \(RH_{{700}}\), exceeds 40%, and the magnitude of the difference in vector winds between 850 and 200 hPa, \(V_{{sh}}\), is less than \(20~\,\,{\text{ms}}^{{ - 1}}\). The equatorial boundary is best defined by a composite quantity containing the ratio of absolute vorticity \((\eta )~\) to the meridional gradient of absolute vorticity (\(\beta ^{*}\)), rather than \(\eta\) alone. \({\beta ^*}\) is also identified as a potentially important ingredient for TC genesis indices. A comparison of detected Tropical Depression (TD) and Tropical Storm (TS) climatologies revealed TDs more readily intensify further to TS where \({V_{PI}}\) is elevated and \({V_{sh}}\) is relatively weak. The distributions of each threshold quantity identify the factors that favour and suppress TC formation throughout the tropics in the real world. This information can be used to understand why TC formation is poorly represented in some climate models, and shows potential for understanding anomalous TC formation behaviour in the real world.

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Notes

  1. This TD, TS breakdown cannot be achieved with observed storms since no globally consistent TD database exists.

  2. In these papers the term TC was used to describe storms of TS intensity or greater.

  3. In Figs. 1a and 5c, d we include storms from the IBTrACS database that exceed 12 \({\rm m}{{\rm s}^{ - 1}}\) or are less than 1000 hPa if no wind data is available to represent TDs, however we expect these to be only a small subset of the true number of TDs due to the observation issues raised here.

  4. The Duvel TD detector was recently applied to the NA basin (Duvel et al. 2017). Although not reported in that paper, they identified a subset of energetic TDs that occurred at a frequency of about 1.5 energetic TDs to every TS (S. Camargo, 2017, Pers. Comm.). This is very similar to our TD to TS detection ratio for the NA basin (Table 4) and suggests our detected TDs are likely to be equivalent to Duvel’s energetic TDs.

  5. Anticyclonic climatological \({\eta _{850}}\)exists near the equator, typically in the summer hemisphere. Using the absolute value of \({\eta _{850}}\) here makes \(\xi\) appear more favourable than it should be, but the values are too small to affect the threshold positions.

  6. The 700 hPa pressure level was chosen for \({\beta ^*}\) since the strongest signal occurs at this level in the Australian region, and very close to this level (650 hPa) in the eastern N Atlantic (Dickinson and Molinari 2000). Two applications of a nine-point smoothing operator have been applied, in which each grid point value takes a quarter of its original value, plus an eighth of each of the nearest grid point values, and a sixteenth of each of the neighbouring diagonal grid point values.

  7. Saturation deficit (Eq. 2) at 700 hPa was tested as an alternative to \(R{H_{700}}\) due to suggestions that it might be a more appropriate moisture variable for comparison between present and future climate scenarios (e.g., Emanuel et al. 2008). However, the best performing (subjectively chosen) \(S{D_{700}}\) threshold values were inferior to their \(R{H_{700}}\) counterparts, and preliminary testing in a selection of CMIP5 models showed poor results.

  8. This threshold is consistent with Korty et al. (2012b) who found (in an ensemble mean of mid-Holocene climate simulations) that for \({{V}_{{PI}}}<50~\,\,{{\rm{m}}}{{{\rm{s}}}^{ - 1}}\) the level of neutral buoyancy is confined to the lower troposphere. This suggests deep convection would be suppressed for these values of \({{V}_{{PI}}}\).

  9. The advective \(\eta\) flux convergence is shown here since HM87 warn against separating the contributions from \(\eta\) advection and convergence due to the potential for large cancellation between the two terms.

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Acknowledgements

Thanks to John McBride and Susana Camargo for very useful suggestions to restructure the paper, and to Savin Chand and an anonymous reviewer for constructive comments. This work has been undertaken as part of the Australian Climate Change Science Programme, funded jointly by the Department of the Environment and Energy, the Bureau of Meteorology and CSIRO. Support was also provided through funding from the Earth System and Climate Change Hub of the Australian Government's National Environmental Science Programme.

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  1. Research and Development Branch, Bureau of Meteorology, GPO Box 1289, Melbourne, VIC, 3001, Australia

    Kevin J. Tory, H. Ye & R. A. Dare

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Correspondence to Kevin J. Tory.

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Tory, K.J., Ye, H. & Dare, R.A. Understanding the geographic distribution of tropical cyclone formation for applications in climate models. Clim Dyn 50, 2489–2512 (2018). https://doi.org/10.1007/s00382-017-3752-4

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