The cyclonic wind hazard over the Australian region is determined using synthetic tropical cyclone event sets derived from general circulation models (GCMs) to provide guidance on the potential impacts of climate change. Cyclonic wind hazard (defined as the return period wind speed) is influenced by the frequency, intensity and spatial distribution of tropical cyclones, all of which may change under future climate regimes due to influences such as warmer sea surface temperatures and changes in the global circulation. Cyclonic wind hazard is evaluated using a statistical-parametric model of tropical cyclones - the Tropical Cyclone Risk Model (TCRM) - which can be used to simulate many thousands of years of cyclone activity. TCRM is used to generate synthetic tracks which are statistically similar to the input event set - either an historical record or other synthetic event set. After applying a parametric wind field to the simulated tracks, we use the aggregated wind fields to evaluate the return period wind speeds for three IPCC AR4 scenarios, and make comparisons to the corresponding average recurrence interval wind speed estimates for current climate simulations. Results from the analysis of two GCMs are presented and contrasted with hazard estimates based on the historical record of tropical cyclones in the Australian region.

Tropical cyclones present a significant hazard to countries situated in the warm tropical waters of the western Pacific. These severe storms are the most costly and the most common natural disaster to affect this region (World Bank, 2006). The hazards posed by these severe storms include the extreme winds, storm surge inundation, salt water intrusion into ground water supplies, and flooding and landslides caused by the intense rainfall. Despite the high vulnerability of the islands in this region, there have been relatively few previous studies attempting to quantify the hazard from tropical cyclones in this region (i.e. Shorten et al. 2003, Shorten et al. 2005, Terry 2007). Understanding this hazard is also vital for informing climate change adaptation options. This study aims to address the limited understanding of the extreme wind hazard in this region. The wind hazard from tropical cyclones is evaluated for the current climate and projections were made to assess how this hazard may change in the future. The analysis is performed using a combination of historical tracks and downscaled climate models with Geoscience Australia's Tropical Cyclone Risk Model. The work was funded as part of the Pacific Climate Change Science Program (PCCSP), which forms the science component of the International Climate Change Adaptation Initiative (ICCAI), an Australian government initiative designed to meet high priority climate change adaptation needs of vulnerable countries in our region. This study assesses the wind hazard for the fifteen PCCSP partner countries which include 14 islands located in the West Pacific as well as East Timor.

The Regional Tropical Cyclone Hazard for Infrastructure Adaptation to Climate Change project aims to provide improved estimates of tropical cyclone wind hazard in current and future climates, for use in adaptation strategies such as wind speed-based building design criteria. The overarching goal is to make practical recommendations regarding the effect of climate change on tropical cyclones. This is most effectively achieved through evaluating the effect of climate change on extreme return period wind speeds (or severe wind hazard) across tropical Australia. In this manner, the combined effects of changes in frequency, intensity and spatial distribution of tropical cyclone events are integrated into a single quantity. Return period values are used widely in building design standards, and so represent an excellent way of informing adaptation decisions. Preceding components of the project evaluated the performance of existing general circulation models to simulate aspects of the climate important for tropical cyclones. Downscaling methods were applied to these models to create climatological simulations of tropical cyclones for input into Geoscience Australia's statistical-parametric tropical cyclone model. This, in turn, provided new estimates of severe wind hazard in both current and future climates, which may be used to make recommendations for adaptation strategies on a regional basis. Achieving this goal has required a close collaboration between the University of Melbourne, CSIRO Marine and Atmospheric Research (CMAR) and Geoscience Australia. Analysis of the general circulation models and downscaling was undertaken by University of Melbourne. The downscaling was achieved using CMAR's Conformal-Cubic Atmospheric Model (CCAM). This report details the approach used by Geoscience Australia to evaluate severe wind hazard using statistical models, and analyses the effect of climate change on severe wind hazard.

Tropical cyclones pose a significant threat to islands in the tropical western Pacific. The extreme winds from these severe storms can cause extensive damage to housing, infrastructure and food production. As part of the Pacific Climate Change Science Program (PCCSP), Geoscience Australia assessed the wind hazard posed by tropical cyclones for 14 islands in the western Pacific and East Timor. The wind hazard was assessed for both the current climate and for the future climate under the A2 SRES emission scenario. Wind hazard maps were generated using Geoscience Australia's Tropical Cyclone Risk Model (TCRM) that applies a statistical-parametric process to estimate return period wind speeds. To obtain a robust estimate of wind hazard from a short historical track record, TCRM produces several thousand years worth of tracks that are statistically similar to the input track dataset. The model then applies a parametric wind profile to these tracks and fits a Generalized Extreme Value distribution to the maximum wind speeds at each location. To estimate how the hazard may change in the future, tracks of Tropical Cyclone-Like Vortices (TCLVs) detected in dynamically downscaled global climate model are used as input into TCRM. This is performed for four downscaled global climate models for two twenty year periods centered on 1990 and 2090 under the A2 SRES emission scenario. This study provides the first detailed assessment of the current wind hazard for this region, despite the fact that these counties are both highly exposed and vulnerable to these severe storms. The hazard climate projections should be treated with caution due to known deficiencies in the global climate models and poor agreement between models of the hazard projections. However, keeping these limitations in mind, the results suggest that the wind hazard will decrease north of 20º latitude in the South Pacific by 2090.

Meteorological data from the Arcturus (ARA) atmospheric greenhouse gas baseline station. Data includes time stamp (local time), air temperature, relative humidity, wind speed, wind direction, sigma, solar radiation, barometric pressure and rainfall total. Dataset limited to the 1/6/12 to 8/7/12.

The cyclonic wind hazard over the Australian region is determined using synthetic tropical cyclone event sets derived from general circulation models (GCMs) to provide guidance on the potential impacts of climate change. Cyclonic wind hazard is influenced by the frequency, intensity and spatial distribution of tropical cyclones, all of which may change under future climate regimes due to influences such as warmer sea surface temperatures and changes in the global circulation. We evaluate the tropical cyclonic wind hazard using a statisticalparametric model of tropical cyclones - the Tropical Cyclone Risk Model (TCRM) - which can be used to simulate many thousands of years of tropical cyclone activity. TCRM is used to generate synthetic tracks which are statistically similar to the input event set, which can be either an historical record of tropical cyclone activity or a record of tropical cyclone-like vortices identified in general circulation models. A parametric wind field is used to estimate the swath of winds associated with the simulated tracks. The resulting wind fields are then used to evaluate the average recurrence interval wind speeds using extreme value statistics. We present the average recurrence interval wind speeds based on three IPCC AR4 scenarios and draw comparisons with current climate simulations and the historical record.

A model to assess severe wind hazard using climate-simulated wind speeds have been developed at Geoscience Australia (Sanabria and Cechet, 2010a). The model has a num-ber of advantages over wind hazard calculated from observational data: Firstly the use of climate-simulated data makes it possible to assess wind hazard over a region rather than at a recording station. Secondly climate-simulated data allows wind analysts to calculate wind hazard over a long climatology and, more importantly, to consider the impact of cli-mate change on wind hazard. In this paper we discuss model sensitivity to two IPCC scenarios: scenario B1, a low emissions scenario, and scenario A2, a high emissions scenario. Current and future climate is considered. Currently we deal only with gusts associated with synoptic winds (mid-latitude weather systems) as the climate model only provides mean winds at a resolution of 14 km, which does not resolve thunderstorms. MODEL DESCRIPTION The model involves three computationally processes: - Calculation of return period (RP) for gust wind speed using a statistical model; - Extraction of wind speeds from a high resolution climate model; and - A Monte Carlo method to generate synthetic gust speeds based on a convolution of modelled mean speeds and empirical gust factor measurements.

The Tropical Cyclone Scenario Selector Tool (TC SST) provides an interactive application to interrogate the stochastic event catalogue which underpins the 2018 Tropical Cyclone Hazard Assessment (TCHA18). The application allows users to search for TC events in the catalogue based on location and intensity (either TC intensity category, or maximum wind speed), visualise the tracks and the wind fields of those events, and download the data for further analysis.