Tropical Cyclone information page

The Tropical Cyclone Hazard Dashboard (TC–) is an interactive visualisation platform for current and future tropical cyclone (TC) wind hazard to support the Severe Wind Hazard Assessment for Queensland (SWHA-Q). It allows users to understand the change in likelihood of TC-related winds across a range of regions and locations in Queensland. The TC– is composed of drop-down menus, maps, plots, and tables whereby users can customise, visualise, and export current and future TC wind hazard information summarised for Queensland’s regions and locations.

The TC– integrates observed TC tracks and the Queensland Future Climate projections to understand the severe wind hazard posed to Queensland. The SWHA-Q and the TC– estimate the current and future wind hazards using the Tropical Cyclone Risk Model (Arthur, 2021) and the regional climate modelling and analysis described below. The modelled outcomes may differ based on assumptions, such as emission scenarios, time horizons and different implementations of the climate system physics. Climate modelling is a rapid progressing field and future updates to science are expected to improve the estimates of wind hazard.

Aims

The SWHA-Q aims to better understand the potential impacts of current and future modelled TCs across Queensland’s regions and better communicate the projected changes in cyclone behaviour across Queensland.

The TC– aims to inform Queenslanders about the outcomes of the SWHA-Q through a user-friendly interface where users can interactively visualize and access current and future modelled TC wind hazard profiles for several regions and locations.

Methods

Assessing the current and future TC hazard across Queensland’s regions and localities required a number of techniques and approaches. High-resolution climate projections were produced for Queensland and their TC tracks were identified, bias-corrected and clustered. Then, a statistical model was fitted for current and future TC wind hazard. TC hazard profiles were then regionalized for Queensland’s regions and locations and presented at an interactive visualisation platform (i.e., the TC–). A brief description of the modelling steps is provided below. For a more detailed overview check the SWHA-Q technical reports (Reports one, two and Executive Summary).

For more information about state-wide risk and hazard assessments visit QFES webpage.

· Historical dataset

The historical simulations were performed based on the IBTrACS (International Best Track Archive for Climate Stewardship; Schreck et al, 2013), which provides global tropical cyclone best track data in a centralized location to aid our understanding of the distribution, frequency, and intensity of tropical cyclones worldwide. The World Meteorological Organization Tropical Cyclone Programme has endorsed IBTrACS as an official archiving and distribution resource for tropical cyclone best track data. The first version of the TC– used modelled TC tracks over the entire Australia following the Tropical Cyclone Hazard Assessment (TCHA; Geoscience Australia). However, it was later updated to a new version based solely on the modelled TC tracks over the Queensland region (refer to SWHA-Q report one for details) to allow better parametrization of the model and optimised representation of specific TC patterns for the state.

· Downscaling climate projections

The underlying data for future assessment was obtained by dynamically downscaling 11 CMIP-5 General Circulation Models using the CSIRO’s Conformal Cubic Atmospheric Model (CCAM; McGregor and Dix, 2008; Thatcher and McGregor, 2011). The CCAM was forced by bias-corrected sea surface temperature (SST) data (Hoffmann et al., 2016). Visit the Understanding the data webpage for a detailed overview of the downscaling methodology. For the sake of future TC risk assessment, two emission scenarios and two time-slices have been considered: RCP 4.5 (moderate emissions scenario), RCP 8.5 (high emissions scenario), mid-century (2041-2060) and end-of-century (2081-2100).

· Identification of TC tracks

The detection and tracking algorithm based on Walsh (1997) works by identifying features in the climate simulations output that resemble TCs, providing information on the location and intensity of Tropical Cyclone-Like Vortices (TCLVs), which can be used as an equivalent dataset to observed tracks for input into stochastic hazard models. The TCLVs were extracted from the downscaled simulations by adapting the thresholds for the resolution of the downscaled climate simulations (Walsh et al., 2013). Detection was done with 6 hourly data from the downscaled climate simulations after data was resampled to 30 km of spatial resolution.

· Bias correction of TCLVs

Intensity of TCLVs (measured by central pressure deficit) were scaled using quantile delta mapping (Cannon et al., 2015) to ensure the distribution of intensity of TCLVs in the reference period (1981-2010) matched the observed distribution from historical records. The relative change in quantiles between reference and future time periods is also conserved.

· Clustering of TCLVs ensembles

To ensure the hazard projections were based on sufficient records of input data, the TCLVs have been merged into two statistical ensembles. The models were grouped based on the frequency of events in the reference period. The aggregation of the 11 CCAM ensembles into two TC ensembles of 100 (i.e., five 20-year climate ensembles) and 120 years (i.e., six 20-year climate ensembles) of TCLV activity provided more robust track statistics for the statistical modelling. The statistical ensemble with five CCAM runs was used because it was based on an updated CCAM version and had smaller bias.

· Statistical modelling of TC wind hazard

The SWHA-Q used Geoscience Australia’s Tropical Cyclone Risk Model (TCRM; Arthur, 2021) to evaluate ARI wind speeds across Australia. The TCRM is a statistical-parametric model based on historical TC activity. The model derives statistical relations for TC behaviour (genesis rate and location, intensity, speed and direction of translation) from best-track datasets, then uses these relations to create a synthetic catalogue based on stochastic sampling, representing 10,000 years of TC activity. The TCRM generates a collection of tracks, and the associated wind fields around each track, which can in turn be used in hazard and impact assessment applications. In addition to the historical assessment, the TCRM was also used with the TCLVs extracted from Queensland’s downscaled climate simulation to evaluate future risk of TCs.

· Regionalization of TC wind hazard for regions and locations

Two different approaches were employed to assess the TC wind hazard across regions and locations: the region-based approach and the point-based approach. The region-based approach utilizes the maximum gust wind speed generated by the associated wind fields of the TC events within a region. The region-based hazard profile indicates the spatially aggregated hazard across an entire region – that is how often a TC event exceeding a given magnitude is expected to occur anywhere within a region such as a Local Government Area (LGA). This approach was used to inform the TC wind hazard for LGAs, regional plan areas, river basins, bio-regions and disaster districts. The region-based hazard profile is useful for broad-scale risk management and preparedness.

The point-based approach utilizes the actual TC winds influencing a certain location represented by a pair of spatial coordinates (latitude and longitude). No spatial aggregation was used. The point-based hazard is more appropriate to inform the hazard profile of specific locations represented by a pair of spatial coordinates such as cities, towns and communities. For both region-based and the point-based approaches, wind speeds were ranked and return intervals were calculated as Average Recurrence Interval (ARI) and Annual Exceedance Probability (AEP). Then, in order to scale future ARI and AEP curves to current curves, the difference between the future climate runs (2041-2060 and 2081-2100) to the reference period (1981-2020) was summed to the current (based on historical TC tracks) curves.

In the example below a synthetic TC event extracted from the future simulations catalogue makes landfall with category 4 wind speed about 45 km south of the city of Mackay. Yet, the city of Mackay and communities on its surrounds have only experienced category 1 wind speed. Conversely, category 5 wind speeds reached the southern border of the Mackay Regional Council area. This illustrates that an entire region is always more likely to experience severe winds than any individual location within the region as the region integrates the probabilities of all locations within it. Therefore, greater hazard should be expected for regions when compared to locations as shown in the Average Recurrence Interval plots.

Point-based regionalization
Region-based regionalization

Point-based regionalization

This selected synthetic TC event made landfall with category 4 winds about 45 km south of the city of Mackay. However, the wind speeds in the city of Mackay were category 1.

City of Mackay

Region-based regionalization

The winds for this same selected synthetic TC event reached category 5 in the south border of the Mackay regional although it made landfall as a category 4 event.

Mackay Regional

Check the current and future hazard profiles for Queensland’s LGAs (region-based) and Queensland’s cities and towns (point-based).

How to interpret the TC wind hazard profiles

The wind hazard profiles provided by the TC– can inform a range of end-users with varying backgrounds. Click on the different personas below to understand how end-users can potentially use the assessment outcomes to make strategic decisions on their sectors.

Emergency management
Research scientist
Policy and environment
Built environment
General public

Jonathon Apted

Emergency management

Local Disaster Group Officer

Background

John is an experienced Disaster Management (DM) Officer and Deputy Local Disaster Coordinator for a medium sized local government area in Central Queensland. He manages all preparedness activities for the Local Disaster Management Group on behalf of the Local Disaster Coordinator.

John is responsible for risk assessment, disaster management (risk-based) planning and resource assignment during an event. He has a strong working relationship with his disaster management group members, and an upward relationship with the District and State Disaster Management Group.

In his work, John may utilise resources under his control to source data and answer questions in relation to all phases of disaster management (prevention, preparedness, response and recovery).

He has DM preparedness and planning responsibilities which include informing his community of the risks they face and how they should best prepare and respond to these risks. Importantly, John has begun to grapple with the issue of climate change and the impact of a changing climate on the frequency and intensity of the hazards his community faces.

Goal as Consumer

John wants to know where he can get the answer to his questions when undertaking preparedness activities such as risk assessments or developing disaster management plans. In addition, he wants to know that he can get the data required to answer his questions in a user-friendly format that is easy to understand and disseminate.

Typically, questions John is seeking to answer could be:

What is the likelihood of an event of a given magnitude or intensity in my area of responsibility? I.e. What is the probability of a Category 3 cyclone impacting my local government area every 30 years (the average lifetime of a mortgage in Queensland)?
How does this probability change when considering the impact of climate change on the hazard?
How does the probability change for those inland communities compared to those coastal communities I am responsible for?
How many people are affected?
Is my community and the life-line infrastructure on which it depends resilient to this hazard?
Do I have the necessary resources to manage a response to an event of this magnitude?
What are the key messages about this hazard I should communicate to all my stakeholders within the community and beyond?
What data sets do I need to answer all or any of the questions above?

Andrew Ramsay

Scientific Researcher

Associate Professor in Environmental Science

Background

Andrew is an associate professor in the School of Environmental Science at North Queensland University. He leads research into a range of climate-related impacts on communities. He oversees a research team that works on understanding environmental, social and economic impacts of weather events on the unique landscape of North Queensland. Andrew also extends that research to areas in the South West Pacific. He manages all preparedness activities for the Local Disaster Management Group on behalf of the Local Disaster Coordinator.

Andrew has a background in numerical weather prediction for severe thunderstorms and has used that experience to investigate the complex interactions of past cyclones with the topography of northern Queensland.

Because of this past experience, Andrew is willing to work with data in a variety of formats and using large computing resources to run complex numerical simulations.

Goal as Consumer

Andrew wants to understand what the implications for climate change are for TCs and the associated consequences for significant environmental assets. Wants to access a range of data sources to help understand the uncertainty associated with projections of TC impacts.

Wants to know that he can access detailed data on TCs in machine-readable formats to integrate into his modelling workflows.

Typically, questions Andrew is seeking to answer could be:

What is the likelihood of an event of a given magnitude or intensity at communities along the Queensland coast, and the relative likelihood between locations?
How does this probability change when considering the impact of climate change on the hazard?
How do these changes affect other storm-related impacts like storm surge or wave impacts on reef systems?
What data sets can I interrogate to explore potential consequences?

Margaret Pritchard

Managing natural ecosystems and biodiversity

Policy officer

Background

Maggie is a policy officer based in the wet tropics. Her workplace responsibilities include strategic acquisitions or management planning for Queensland’s protected areas or private conservation sites.

In her role, she needs to consider how climate change might affect the decisions she is making now, and in the future, about where to acquire protected areas and/or conservation sites for conserving particular ecosystems or species.

Maggie has an educational background in environmental and biological sciences, she completed her Master’s degree in environmental management and policy development.

Maggie has connections across the sector within Queensland and across Australia and beyond and seeks to ensure her team operates using both best-practice science, and applicable planning and management approaches.

She works both out in the field or at the office, and she maintains good connections with Traditional Custodian groups across Queensland. She needs to explain the basis for her decisions to the lay public and to specialised groups, including the Traditional Custodians.

Goal as Consumer

Maggie wants to understand what the implications of climate change are for current and future protected areas and/or private conservation sites in Queensland, in terms of the associated range of likely consequences for ecosystems and biodiversity.

Similarly, she wants to understand what the implications of climate change are for the land and sea country in Queensland that is currently (or potentially to be) managed by First Nations management groups (e.g., parts of Minjerribah/North Stradbroke Island).

Maggie wants to access a range of trustworthy data sources to help understand the relative uncertainty associated with different climate projections for Queensland, in order to be able to make a sound business case to the management committee for management decisions based on particular climate projections.

She also wants to know whether she can access detailed data on climate projections for certain areas of Queensland in machine-readable formats to integrate into their modelling workflows.

Typically, the questions Maggie seeks to answer could be:

What is the likelihood of exceeding (across varying timeframes 5-30 years) a certain threshold (of relevance for an ecosystem or species) of wind speeds in various locations across Queensland?
Is climate change projected to improve or detract from the habitability of a particular area of Queensland for plant and animal species; and if the effect is negative, in what way, to what extent, for how long, and how often?
How can I better manage ecosystems and species to ensure they are as protected as possible in the face of projected climate change and its potential impacts?
What datasets can I interrogate to explore potential consequences?

Lemon Ade

Built Environment

Building designer, regulator or planner

Background

Lemon is building designer in domestic and small commercial projects. Lemon has many years of consulting experience as designer in South East Queensland.

As an experienced consultant, Lemon has a detailed knowledge of the National Construction Code and associated Australian Standards that dictate, inform and guide major aspects of a building's design. This includes wind load design, durability, safety and amenity aspects of accessibility, and energy efficiency ratings.

Lemon, via the various codes and standards, has a working knowledge of “return periods” and “annual exceedance probability” that is used in rainfall (e.g. roof drainage design) and wind load design.

Lemon needs to be able to communicate all aspects of the design in terms of both code and standards” requirements and associated cost implications to clients and councils. As part of this, Lemon, may source additional information on aspects of climate that can drive design features.

Depending on the design brief, client's wishes, and design philosophy, Lemon may explore how changing climate may impact the building design.

Goal as Consumer

Potential source of localised information on wind “risk” for localities for historical wind record and modelled climate change for “business as usual RCP8.5” and “reduced carbon RCP4.5” scenarios.

However, guidance needs to be provided on differences of the regional and localised wind risk heat maps and plots.

For example, building design return periods (or AEP) for wind loads are derived for the likelihood of wind speed being exceeded with the design lifetime at that location. This is different to likelihood of exceedance for a large regional area.

Information that Lemon could be seeking:

Possible change in AEP of wind speed for different climate change scenarios for a specified location
Comparison of modelled AEP of wind speed between locations
Comparison of modelled AEP scenarios to AS/NZS1170.2
If utilising modelled AEP to inform design choices, will require estimates of uncertainty in modelled predictions as part of submission to client and assessor (and/or local council)

Jessica Mellow

General public

High-school teacher

Background

Jess is a high-school teacher who teaches geography at the Central Coast State High School. She has a broader interest in both human and physical geography and is particularly keen to understand how climate change may affect her community and surrounds.

She also volunteers at a non-profitable organization assisting refugees and traditional communities affected by natural disasters such as droughts, floods and severe winds.

A couple of years ago she purchased an investment property located on a floodplain followed by a market devaluation after 2017's floods associated with TC Debbie. The same event affected the school at which she teaches and an entire computer lab has been lost.

Jess is an avid learner and creative problem solver seeking to make a positive contribution to make the world a better place for her children and students.

Goal as Consumer

Jess wants to understand whether her hometown and adjacent First Nations communities might be more prone to tropical cyclones in future under different carbon emission scenarios.

She is also concerned about how the river basin draining her town can handle heavy TC-related rainfall events given the impacts of recent floods and infrastructure damage at her investment property and workplace.

Possible information that Jess could be seeking include:

What are the current and future recurrence intervals of wind speed for different climate change scenarios for her hometown?
What are the current and future recurrence intervals of a TC Debbie-like event for the river basin draining her region?
Which communities are more likely to be affected by TC-related winds by the end of this century?
Which resources she could use to teach her geography students about how climate change affects TC across Queensland”s shoreline?

Visit the Tropical Cyclone Hazard Dashboard to access current and future TC hazard profiles across Queensland’s regions and locations. Read the SWHA-Q technical reports (Reports one, two and Executive Summary) for additional information and assessment details

Disclaimer

The TC- provides access to the SWHA-Q model’s output and was designed to facilitate the visualization of modelled current and future TC wind hazards. It does not supersede building code advice nor imply design values. Users are advised to seek expert opinion to support their interpretation of these results for their intended purpose. Depending on the use-case, a locally specific detailed tropical cyclone hazard and risk analysis may be required to support the specific application.

References

Arthur, W. C. (2021) A statistical-parametric model of tropical cyclones for hazard assessment Natural Hazards and Earth System Science, doi.org/10.5194/nhess-2019-192.

Cannon, A. J., S. R. Sobie, and T. Q. Murdock (2015) Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? Journal of Climate, 28, 6938-6959, 10.1175/jcli-d-14-00754.1.

Hoffmann, P., J. J. Katzfey, J. L. McGregor, and M. Thatcher (2016) Bias and variance correction of sea surface temperatures used for dynamical downscaling. Journal of Geophysical Research Atmospheres, 121, 12,877-812,890, 10.1002/2016jd025383.

McGregor, J., and M. R. Dix (2008) An Updated Description of the Conformal-Cubic Atmospheric Model. High Resolution Numerical Modelling of the Atmosphere and Ocean, K. Hamilton, and W. Ohfuchi, Eds., Springer, 51-75.

Schreck, Carl & National Center for Atmospheric Research Staff (Eds). Last modified 06 Nov 2013. "The Climate Data Guide: IBTrACS: Tropical cyclone best track data." Retrieved from https://climatedataguide.ucar.edu/climate-data/ibtracs-tropical-cyclone-best-track-data.

Siqueira, A., W. C. Arthur, and H. M. Woolf (2014) Evaluation of severe wind hazard from tropical cyclones - current and future climate simulations : Pacific-Australia Climate Change Science and Adaptation Planning Program. Geoscience Australia Record, 47, http://www.ga.gov.au/corporate_data/79681/Rec2014_047.pdf.

Thatcher, M., and J. L. McGregor (2011) A Technique for Dynamically Downscaling Daily-Averaged GCM Datasets Using the Conformal Cubic Atmospheric Model. Monthly Weather Review, 139, 79-95.

Walsh, K. J. E. (1997) Objective Detection of Tropical Cyclones in High-Resolution Analyses. Monthly Weather Review, 125, 1767-1779, 10.1175/1520-0493(1997)125<1767:ODOTCI>2.0.CO;2.

Walsh, K., S. Lavender, E. Scoccimarro, and H. Murakami (2013) Resolution dependence of tropical cyclone formation in CMIP3 and finer resolution models. Climate Dynamics, 40, 585-599, 10.1007/s00382-012-1298-z.

Last updated: 30 August 2024

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