In early 2011 a series of natural disasters impacted a large number of communities in Queensland. The flooding in the Brisbane region and the severe wind and storm surge experienced in the tully region caused widespread damage to infrastructure and disrupted both households and businesses. The full recovery costs over the next few years are expected to be considerable and will be a major drain on the resources of all levels of government and the insurance industry.

We highlight the importance of developing and integrating fundamental information at a range of scales (regional to national to local) to develop consistency, gain ownership, and meet the needs of a range of users and decision makers. We demonstrate this with a couple of case studies where we have leveraged national databases and computational tools to work locally to gain ownership of risks and to develop adaptation options. In this sense we endorse the notion of combining top down and bottom up approaches to get the best outcome.

Note that this Record has now been published as Record 2014/050, GeoCat number 78802

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.

Mean monthly and mean annual maximum, minimum & mean temperature grids. The grids show the temperature values across Australia in the form of two-dimensional array data. The mean data are based on the standard 30-year period 1961-1990. Gridded data were generated using the ANU (Australian National University) 3-D Spline (surface fitting algorithm). As part of the 3-D analysis process a 0.025 degree resolution digital elevation model (DEM) was used. The grid point resolution of the data is 0.025 degrees (approximately 2.5km). Approximately 600 stations were used in the analysis over Australia. All input station data underwent a high degree of quality control before analysis, and conform to WMO (World Meteorological Organisation) standards for data quality.

At the request of Prime Minister and Cabinet (PM&C), Geoscience Australia (GA) prepared this report for the purposes of informing a National Security paper that highlights potential national security issues associated with climate change.

Geoscience Australia has created a DVD 'Landsat Metadata Map Ups of Indonesia' for the Indonesian Ministry of Forestry (MoF). The DVD contains Landsat metadata information sourced from USGS and GISTDA for selected years based on the catalogue searches that Geoscience Australia has done to-date. This is one of the action items from the Bali Remote Sensing workshop in February 2009.

The development of climate change adaptation policies must be underpinned by a sound understanding of climate change risk. As part of the Hyogo Framework for Action, governments have agreed to incorporate climate change adaptation into the risk reduction process. This paper explores the nature of climate change risk assessment in the context of human assets and the built environment. More specifically, the paper's focus is on the role of spatial data which is fundamental to the analysis. The fundamental link in all of these examples is the National Exposure Information System (NEXIS) which has been developed as a national database of Australia's built infrastructure and associated demographic information. The first illustrations of the use of NEXIS are through post-disaster impact assessments of a recent flood and bushfire. While these specific events can not be said to be the result of climate change, flood and bushfire risks will certainly increase if rainfall or drought become more prevalent, as most climate change models indicate. The second example is from Australia's National Coastal Vulnerability Assessment which is addressing the impact of sea-level rise and increased storms on coastal communities on a national scale. This study required access to or the development of several other spatial databases covering coastal landforms, digital elevation models and tidal/storm surge. Together, these examples serve to illustrate the importance of spatial data to the assessment of climate change risk and, ultimately, to making informed, cost-effective decisions to adapt to climate change.

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.