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Projections of southern hemisphere tropical cyclone track density using CMIP5 models

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Abstract

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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Notes

  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].

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Acknowledgements

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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Appendices

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:

$$OWZ=sgn\left( f \right) \times \left( {\zeta +f} \right) \times \hbox{max} \left[ {\frac{{{\zeta ^2} - ({E^2}+{F^2})}}{{{\zeta ^2}}},0} \right]~$$
(1)

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). https://doi.org/10.1007/s00382-018-4497-4

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