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Projections of southern hemisphere tropical cyclone track density using CMIP5 models

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A recently validated algorithm for detecting and tracking tropical cyclones (TCs) in coarse resolution climate models was applied to a selected group of 12 models from the Coupled Model Intercomparison Project (CMIP5) to assess potential changes in TC track characteristics in the Southern Hemisphere (SH) due to greenhouse warming. Current-climate simulations over the period 1970–2000 are first evaluated against observations using measures of TC genesis location and frequency, as well as track trajectory and lifetime in seven objectively defined genesis regions. The 12-model (12-M) ensemble showed substantial skill in reproducing a realistic TC climatology over the evaluation period. To address potential biases associated with model interdependency, analyses were repeated with an ensemble of five independent models (5-M). Results from both the 12-M and 5-M ensembles were very similar, instilling confidence in the models for climate projections if the current TC-climate relationship is to remain stationary. Projected changes in TC track density between the current- and future-climate (2070–2100) simulations under the Representatives Concentration 8.5 Pathways (RCP8.5) are also assessed. Overall, projection results showed a substantial decrease (~ 1–3 per decade) in track density over most parts of the SH by the end of the twenty-first century. This decrease is attributed to a significant reduction in TC numbers (~ 15–42%) consistent with changes in large-scale environmental parameters such as relative vorticity, environmental vertical wind shear and relative humidity. This study may assist with adaption pathways and implications for regional-scale climate change for vulnerable regions in the SH.

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  1. The TC declaration location was found to best match the timing at which TCs in IBTrACS data first reached a 10-min sustained wind speed of 17 m s− 1.

  2. Note that biases exist in models causing TCs to be detected in the far eastern South Pacific (east of 225°E). These detections are excluded from this cluster analysis to avoid creating an additional arbitrary cluster.

  3. The SPCZ is the dominant climatic feature in the south Pacific where tropical cyclone activities are often spawned, and any biases in the position and orientation of the SPCZ will have implications on tropical cyclones forming in the region.

  4. Note for Region-3 we found a manual split into “eastward” and “westward” more interpretable than those given by cluster analysis. Here, tracks were classified as either “eastward” or “westward” based on their longitude positions at two-thirds of their lifetime in relation to their genesis position. In addition, westward heading model tracks in the South Pacific (Region-7) that do not appear in the observed climatology are separated by an additional cluster (to a total of 3).

  5. Some models produce TCs where they are not observed, particularly in the South Atlantic and eastern South Pacific [east of 135°W]. Storms forming in the eastern South Pacific are more of an issue here as they are nearby to where real TCs are observed to form, creating doubt around the correct simulation of nearby large-scale conditions necessary for TC formation. Therefore, this assessment criteria is put in place to potentially exclude models from the ensemble if more than 5% of a model’s TCs form between [130°W and 60°W].


  • Bell SB, Chand SS, Tory KJ, Turville C (2018) Statistical assessment of the OWZ tropical cyclone tracking scheme in ERA-Interim. J Clim 31:2217–2232

    Article  Google Scholar 

  • Bell GD, Chelliah M (2006) Leading tropical modes associated with interannual and multidecadal fluctuations in north atlantic hurricane activity. J Clim 19:590–612

    Article  Google Scholar 

  • Bi D, Dix M, Marsland S et al (2012) The ACCESS coupled model: description, control climate and evaluation. Aust Meteor Oceanog J CMIP5 Special Issue 63:41–646

    Article  Google Scholar 

  • Brown JR, Moise AF, Colman RA (2012) The South Pacific Convergence Zone in CMIP5 simulations of historical and future climate. Clim Dyn.

    Google Scholar 

  • Camargo SJ (2013) Global and regional aspects of tropical cyclone activity in the CMIP5 models. J Clim 26:9880–9902

    Article  Google Scholar 

  • Camargo SJ, Robertson AW, Gaffney SJ, Smith P, Ghil M (2007) Cluster analysis of typhoon tracks. Part I: General properties. J Clim 20:3635–3653

    Article  Google Scholar 

  • Chand SS, Walsh KJE (2009) Tropical cyclone activity in the Fiji region: Spatial patterns and relationship to large-scale circulation. J Clim 22:3877–3893

    Article  Google Scholar 

  • Chand SS, Tory KJ, Ye H, Walsh KJE (2017) Projected increase in El Niño-driven tropical cyclone frequency in the Pacific. Nature Clim Change 7:123–127.

    Article  Google Scholar 

  • Christensen JH, Krishna Kumar K, Aldrian E et al (2013) Climate phenomena and their relevance for future regional climate change. In: Stocker TF (eds) Climate change 2013: the physical science basis. Contribution of working group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Cambridge University Press, Cambridge

    Google Scholar 

  • Colbert AJ, Soden BJ, Kirtman BP (2015) The impact of natural and anthropogenic climate change on western North Pacific tropical cyclone tracks. J Climate 28:1806–1823

    Article  Google Scholar 

  • Collier MA, Jeffrey SJ, Rotstayn LD et al (2011) The CSIRO-Mk3.6.0 Atmosphere-Ocean GCM: participation in CMIP5 and data publication. In: 19th International Congress on Modelling and Simulation, Perth, Australia, 12–16 December 2011.

  • CSIRO and Bureau of Meteorology (2015) Climate change in Australia (CCiA). Information for Australia’s Natural Resource Management Regions. Technical Report, CSIRO and Bureau of Meteorology, Australia

  • Daloz AS, Camargo SJ, Kossin JP et al (2015) Cluster analysis of down-scaled and explicitly simulated North Atlantic tropical cyclone tracks. J Clim 28:1333–1361.

    Article  Google Scholar 

  • Donner LJ, Wyman BL, Hemler RS et al (2011) The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J Clim 24:3484–3519.

    Article  Google Scholar 

  • Dunkerton TJ, Montgomery MT, Wang Z (2009) Tropical cyclogenesis in a tropical wave critical layer: Easterly waves. Atmos Chem Phys 9:5587–5646

    Article  Google Scholar 

  • Emanuel KA (2013) Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century. Proc Natl Acad Sci USA, 110, 12,219–12,224,

  • Gaffney SJ (2004) Probabilistic curve-aligned clustering and prediction with regression mixture models. Ph.D. thesis, University of California, Irvine, CA, p 281.

  • Gent PR, Danabasoglu G, Donner LJ et al (2011) The community climate system model version 4. J Clim Spec Collect 24:4973–4991

    Article  Google Scholar 

  • Gray WM (1975) Tropical cyclone genesis. Dept. of Atmos. Sci., Paper No.234, Colorado State University, Fort Collins, CO

  • Grose MR, Brown JN, Narsey S, Brown JR, Murphy BF, Langlais C, Gupta AS, Moise AF, Irving DB (2014) Assessment of the CMIP5 global climate model simulations of the western tropical Pacific climate system and comparison to CMIP3. Int J Climatol 34:3382–3399.

    Article  Google Scholar 

  • Horn M, Walsh KJ, Zhao M et al (2014) Tracking scheme dependence of simulated tropical cyclone response to idealized climate simulations. J Clim 27:9197–9213.

    Article  Google Scholar 

  • Jones CD, Hughes JK, Bellouin N et al (2011) The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci Model Dev 4:543–570

    Article  Google Scholar 

  • Kim MIl-Ju, Klotzbach S-H, Chan P JCL (2015) Roles of interbasin frequency changes in the poleward shifts of the maximum intensity location of tropical cyclones. Environ Res Lett 10:104004

    Article  Google Scholar 

  • Klotzbach PJ, Landsea CW (2015) Extremely intense hurricanes: Revisiting Webster et al. (2005) after 10 years. J Clim 28:7621–7629

    Article  Google Scholar 

  • Knapp KR, Kruk MC, Levinson DH, Diamond HJ, Neumann CJ (2010) The international best track archieve for climate stewardship (IBTrACS) unifying tropical cyclone data. Bull Am Meteor Soc 91:363–376

    Article  Google Scholar 

  • Knutson T (2010) Tropical cyclones and climate change. Nat Geosci 3:157–163

    Article  Google Scholar 

  • Knutti R, Masson D, Gettelman A (2013) Climate model genealogy: Generation CMIP5 and how we got there. Geophys Res Lett 40:1194–1199

    Article  Google Scholar 

  • Kossin JP, Emanuel KA, Vecchi GA (2014) The poleward migration of the location of tropical cyclone maximum intensity. Nature 509:349–352.

    Article  Google Scholar 

  • Kossin JP, Emanuel KA, Camargo SJ (2016) Past and projected changes in Western North Pacific tropical cyclone exposure. J Clim 29:5725–5739

    Article  Google Scholar 

  • Moise A, Wilson L, Grose M et al (2015) Evaluation of CMIP3 and CMIP5 models over the Australian region to inform confidence in projections. Aust Meteor Oceanog J 65:19–53.

    Article  Google Scholar 

  • Murakami H, Mizuta R, Shindo E (2012) Future changes in tropical cyclone activity projected by multi-physics and multi-SST ensemble experiments using the 60-km-mesh MRI-AGCM. Clim Dyn 39:2569–2584

    Article  Google Scholar 

  • Nakamura J, Lall U, Kushnir Y, Camargo SJ (2009) Classifying North Atlantic tropical cyclone tracks by mass moments. J Clim 22:5481–5494

    Article  Google Scholar 

  • Nakamura J, Camargo SJ, Sobel AH et al (2017) Western North Pacific tropical cyclone model tracks in present and future climates. J Geo Phys Research 122:1333–1361.

    Google Scholar 

  • Okubo A (1970) Horizontal dispersion of floatable particles in the vicinity of velocity singularities such as convergences. Deep-Sea Res 17:445–454

    Google Scholar 

  • Park D-SR, Ho C-H, Chan JCL, Ha K-J, Kim H-S, Kim J, Kim J-H (2017) Asymmetric response of tropical cyclone activity to global warming over the North Atlantic and western North Pacific from CMIP5 model projections. Sci Rep 7:41354.

    Article  Google Scholar 

  • Ramsay HA, Camargo SJ, Kim D (2012) Cluster analysis of tropical cyclone tracks in the Southern Hemisphere. Clim Dyn 39:897–917.

    Article  Google Scholar 

  • Rathmann NM, Yang S, Kaas E (2014) Tropical cyclones in enhanced resolution CMIP5 experiments. Clim Dyn 42:665–681

    Article  Google Scholar 

  • Riahi K, Rao S, Krey V, Cho C, Chirkov V, Fischer G, Kindermann G, Nakicenovic N, Rafaj P (2011) RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Clim Change 109:33–57.

    Article  Google Scholar 

  • Sanderson B, Knutti R, Caldwell P (2015) A representative democracy to reduce interdependency in a multimodel ensemble. J Clim 28:5171–5194

    Article  Google Scholar 

  • Shen Y, Sun Y, Camargo SJ, Zhong Z (2018) A quantitative method to evaluate tropical cyclone tracks in climate models. J Atmos Ocean Tech (in press)

  • Sugi M, Yoshimura J (2012) Decreasing trend of tropical cyclone frequency in 228-year high-resolution AGCM simulations. Geophys Res Lett 39:L19805.

    Article  Google Scholar 

  • Sugi M, Murakami H, Yoshimura J (2014) Mechanism of the Indian Ocean tropical cyclone frequency changes due to global warming. In: Mohanty UC, Mohapatra M, Singh OP, Bandyopadhyay BK, Rathore LS (eds) Monitoring and prediction of tropical cyclones in the indian ocean and climate change. Springer, Dordrecht, 40–49

    Chapter  Google Scholar 

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteor Soc 93:485–498

    Article  Google Scholar 

  • Tory KJ, Dare RA (2015) Sea surface temperature thresholds for tropical cyclone formation. J Clim 28:8171–8183

    Article  Google Scholar 

  • Tory KJ, Frank WM (2010) Tropical cyclone formation. Chapter 2: Global perspectives on tropical cyclones. In: Chan J, Kepert JD (eds) World Scientific, Second Edition

  • Tory KJ, Ye H (2018) Projected changes of tropical cyclone formation regions in the southern hemisphere. In: 33rd Conference on Hurricanes and Tropical Meteorology, 16–20 April 2018. American Meteorological Society, Ponte Vedra, FL

  • Tory KJ, Dare RA, Davidson NE, McBride JL, Chand SS (2013a) The importance of low-deformation vorticity in tropical cyclone formation. Atmos Chem Phys 13:2115–2132

    Article  Google Scholar 

  • Tory KJ, Chand SS, Dare RA, McBride JL (2013b) The development and assessment of a model-, grid- and basin independent tropical cyclone detection scheme. J Clim 26:5493–5507

    Article  Google Scholar 

  • Tory KJ, Chand SS, McBride JL, Ye H, Dare RA (2013c) Projected changes in late 21st century tropical cyclone frequency in thirteen coupled climate models from the coupled model intercomparison project phase 5. J Clim 26:9946–9959

    Article  Google Scholar 

  • Voldoire A, Sanchez-Gomez E, Salas y Mélia D et al (2012) The CNRM-CM5.1 global climate model: description and basic evaluation. Clim Dyn 40:2091–2121

    Article  Google Scholar 

  • Walsh KJE, Katzfey JJ (2000) The impact of climate change on the poleward movement of tropical cyclone–Like vortices in a regional climate model. J Clim 13:1116–1132

    Article  Google Scholar 

  • Walsh KJE, Fiorino M, Landsea CW, McInnes KL (2007) Objectively determined resolution-dependent threshold criteria for the detection of tropical cyclones in climate models and reanalyses. J Clim 20:2307–2314

    Article  Google Scholar 

  • Walsh KJE, McBride JL, Klotzbach PJ, Balachandran S, Camargo SJ, Holland G, Knutson TR, Kossin JP, Lee TC, Sobel A, Sugi M (2016) Tropical cyclones and climate change. WIREs Clim Change 7:65–89.

    Article  Google Scholar 

  • Wang C, Wu L (2015) Influence of future tropical cyclone track changes on their basin-wide intensity over the western North Pacific: downscaled CMIP5 projections. Adv Atmos Sci 32:613–623

    Article  Google Scholar 

  • Watanabe M, Suzuki T, O’ishi R et al (2010) Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J Clim 23:6312–6335.

    Article  Google Scholar 

  • Weiss J (1991) The dynamics of enstrophy transfer in two-dimensional hydrodynamics. Phys D 48:273–294

    Article  Google Scholar 

  • Wu T, Song L, Weiping L et al (2014) An overview of BCC climate system model development and application for climate change studies. J Meteor Res 28:34–56.

    Google Scholar 

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Thanks to Hamish Ramsay, Paul Gregory and Mike Fiorino for some useful comments to improve the paper. This work is supported through funding from the Earth Systems and Climate Change Hub of the Australian Government’s National Environmental Science Programme (NESP). Samuel Bell is supported by an Australian Government Research Training Program (RTP) Stipend and RTP Fee-Offset Scholarship through Federation University Australia.

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Correspondence to Samuel S. Bell.

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Appendix 1: Robustness of multi-model ensembles

We test the robustness of the 12-M ensemble results as it may be argued that some key assumptions may be violated on the following grounds.

  1. 1.

    Model independence: presence of same family members in the ensemble.

  2. 2.

    Model biases: models simulating TCs where they are not observed in the real world (e.g. eastern South Pacific).

  3. 3.

    Model reliability: ensuring models correctly simulate SH TC formation and track climatology patterns.

  4. 4.

    Sensitivity: exclusion of short lived TCs (< 48-h) in the analysis.

We first address point (4) by repeating the 12-M analysis of Sect. 3.1 with short lived (< 48-h) TCs included. It is found out of the 84 trials (12 models with 7 regions each) there are only two models that indicate changes of sign from a reduction in TC numbers to an increase (BCC-CSM1.1 m changed from a 1% decrease to 4% increase in R2; while CNRM-CM5 changed from a 17% decrease to a 2% increase in R3). Similarly, two other regions changed sign from an increase in TC numbers to a decrease in two other models. Overall, there is no change in the 95% significance of the sign tests for each region. Confidence intervals of percentage change in TCs are also similar as the previous analysis (not shown).

To address points (1)–(3), we assess each model subject to the following criteria.

  1. i.

    Detected model TCs should be within ± 50% of observed climatology [0°–225°E]

  2. ii.

    Less than 5% of a model’s TCs should form in the eastern South Pacific (between [230°–300°E])Footnote 5

  3. iii.

    Spatial density correlations between model TC tracks and observed climatology should be above 0.85.

  4. iv.

    Cluster analysis should produce similar clusters to the observed climatology (as in Fig. 2a).

In order to properly address these concerns (i–iv), a decision was made to create a new ensemble that only contains the models that best meet the above criteria. Knutti et al. (2013) derived a ‘family tree’ of CMIP5 models, documenting their similarities and highlighting how code sharing between institutions is behind some interdependency. Here, only one model from each family (e.g., Table A1) are allowed to be part of the new ensemble, to ensure independence is retained. Results for each model (Fig. A1; Tables A1, A2) show that the models ACCESS1.0, CCSM4, MIROC5, BCC-CSM1.1M and CSIRO-MK3.6 meet all four criteria, and are therefore first choice members of the new ensemble. The B-tier includes ACCESS1.3 and HadGEM2-ES but as they share common characteristics with ACCESS1.0, these are not included in the ensemble. The three GFDL models all have similar errors from the criteria assessed, with GFDL-ESM2m managing the best score of “C”. However, given that the emphasis of this ensemble is on track related performance, errant steering in the South Pacific makes this model unsatisfactory to include. Therefore, only the five A-tier models are used to form the 5-model ensemble (5-M).

Appendix 2: OWZ detection and tracking

The OWZ detection system consists of six parameters (Table B1): minimum thresholds of OWZ at the 850- and 500 hPa levels, relative humidity (RH) at the 950- and 700 hPa levels, specific humidity (SpH) at the 950 hPa level and a maximum threshold of vertical wind shear (VWS) between 850- and 200 hPa. The OWZ variable is a low deformation vorticity parameter used to identify regions favourable for TC formation at the centre of a semi-closed circulation (i.e. a ‘marsupial pouch’; Dunkerton et al. 2009), within the lower- to mid-troposphere. More precisely, it is the product of absolute vorticity and the Okubo-Weiss parameter (Okubo 1970; Weiss 1991) normalised by the vertical components of relative vorticity squared such that:

$$OWZ=sgn\left( f \right) \times \left( {\zeta +f} \right) \times \hbox{max} \left[ {\frac{{{\zeta ^2} - ({E^2}+{F^2})}}{{{\zeta ^2}}},0} \right]~$$

where \(f\) is the Coriolis parameter, \(\zeta =\frac{{\partial v}}{{\partial x}} - \frac{{\partial u}}{{\partial y}}\) the vertical component of relative vorticity, \(E=\frac{{\partial u}}{{\partial x}} - \frac{{\partial v}}{{\partial y}}\) the stretching deformation, and \(F=\frac{{\partial v}}{{\partial x}}+\frac{{\partial u}}{{\partial y}}\) the shearing deformation.

The OWZ detection and tracking scheme is concisely summarized in five dot points below, with further detail accessible in other studies (Tory et al. 2013a; Bell et al. 2018).

  1. a.

    Each 1° × 1° grid point is assessed based on the initial threshold values of each OWZ-Detector parameter every 12-h.

  2. b.

    When at least two neighbouring grid points satisfy the initial thresholds of each OWZ-Detector parameter, these points are considered to represent a single circulation at that point in time.

  3. c.

    The circulations from step (b) are linked through time by estimating their position in relation to the circulation’s expected position based on an averaged 4° × 4° steering wind at 700 hPa.

  4. d.

    Tracks are terminated when no circulation match is found in the next two time-steps within a generous (~ 350 km) latitude dependent radius.

  5. e.

    The core thresholds are then applied to each storm track, and if they are satisfied for 48-h, a TC is declared.

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Bell, S.S., Chand, S.S., Tory, K.J. et al. Projections of southern hemisphere tropical cyclone track density using CMIP5 models. Clim Dyn 52, 6065–6079 (2019).

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