Source The data was sourced from CSIRO (Victoria) in 2012 by Bob Cechet. It is not known specifically which division of CSIRO, although it is likely to have been the Marine and Atmospheric Research Division (Aspendale), nor the contact details of the person who provided the data to Bob. The data was originally produced by CSIRO for their input into the South-East Queensland Climate Adaptation Research Initiative (SEQCARI). Reference, from an email of 16 March 2012 sent from Bob Cechet to Chris Thomas (Appendix 1 of the README doc stored at the parent folder level with the data), is made to 'download NCEP AVN/GFS files' or to source them from the CSIRO archive. Content The data is compressed into 'tar' files. The name content is separated by a dot where the first section is the climatic variable as outlined in the table format below: Name Translation rain 24 hr accumulated precipitation rh1_3PM Relative humidity at 3pm local time tmax Maximum temperature tmin Minimum temperature tscr_3PM Screen temperature (2 m above ground) at 3pm local time u10_3PM 10-metre above ground eastward wind speed at 3pm local time v10_3PM 10-metre above ground northward wind speed at 3pm local time The second part of the name is the General Circulation Model (GCM) applied: Name Translation gfdlcm21 GFDL CM2.1 miroc3_2_medres MIROC 3.2 (medres) mpi_echam5 MPI ECHAM5 ncep NCEP The third, and final, part of the tarball name is the year range that the results relate to: 1961-2000, 1971-2000, 2001-2040 and 2041-2099 Data format and extent Inside each of the tarball files is a collection of NetCDF files covering each simulation that constitutes the year range (12 simulations for each year). A similar naming protocol is used for the NetCDF files with a two digit extension added to the year for each of the simulations for that year (e.g 01-12). The spatial coverage of the NetCDF files is shown in the bounding box extents as shown below. Max X: -9.92459297180176 Min X: -50.0749073028564 Max Y: 155.149784088135 Min Y: 134.924812316895 The cell size is 0.15 degrees by 0.15 degrees (approximately 17 km square at the equator) The data is stored relative to the WGS 1984 Geographic Coordinate System. The GCMs were forced with the Intergovernmental Panel on Climate Change (IPCC) A2 emission scenario as described in the IPCC Special Report on Emissions Scenarios (SRES) inputs for the future climate. The GCM results were then downscaled from a 2 degree cell resolution by CSIRO using their Cubic Conformal Atmospheric Model (CCAM) to the 0.15 degree cell resolution. Use This data was used within the Rockhampton Project to identify the future climate changes based on the IPCC A2 SRES emissions scenario. The relative difference of the current climate GCM results to the future climate results was applied to the results of higher resolution current climate natural hazard modelling. Refer to GeoCat # 75085 for the details relating to the report and the 59 attached ANZLIC metadata entries for data outputs.

The ultimate purpose of carbon capture and storage is to keep CO2 out of the atmosphere. However there are some scenarios in which leakage to atmosphere may occur. Because of the large and variable level of naturallyoccurring CO2 , and rapid dispersion in the atmosphere, leakage to atmosphere can be difficult to detect from concentration measurements. By using prior information from risk assessments about plausible location of leaks, it is possible to design simple yet effective systems for identifying the location of a leak within a pre-defined area of surveillance. We have designed an inexpensive system of autonomous sensors that can locate leaks of CO2 , and have tested it during a controlled release at the CO2CRC Otway site. The system proved effective and it, and its associated workflow, could be adapted and implemented in a variety of storage settings.

Tropical cyclone Gita impacted the Kingdom of Tonga in February 2018, causing significant damage across the main island of Tongatapu. This dataset is a best estimate of the maximum local gust wind speed across Tongatapu, based on the best-available track information, elevation and land cover data. The data represents the maximum 0.2 second, 10-metre above ground level wind speed at (approximately) 25 metre horizontal resolution. The wind field was generated using: Geoscience Australia's Tropical Cyclone Risk Model - https://github.com/GeoscienceAustralia/tcrm Wind Multipliers code - https://github.com/GeoscienceAusralia/Wind_Multipliers TC Gita track was sourced from the Joint Typhoon Warning Center (http://www.metoc.navy.mil/jtwc/jtwc.html)

There is increasing recognition that minimising methane emissions from the oil and gas sector is a key step in reducing global greenhouse gas emissions in the near term. Atmospheric monitoring techniques are likely to play an important future role in measuring the extent of existing emissions and verifying emission reductions. Geoscience Australia and CSIRO Marine & Atmospheric Research have collected three years of continuous methane and carbon dioxide measurements at their atmospheric composition monitoring station ('Arcturus') in the Bowen Basin, Australia. Methane signals in the Bowen Basin are likely to be influenced by cattle production, landfill, coal production, and conventional and coal seam gas (CSG) production. Australian CSG is typically 'dry' and is characterised by a mixed thermogenic-biogenic methane source with an absence of C3-C6+ alkanes. The range of ?13C isotopic signatures of the CSG is similar to methane from landfill gas and cattle emissions. The absence of standard in-situ tracers for CSG fugitive emissions suggests that having a comprehensive baseline will be critical for successful measurement of fugitive emissions using atmospheric techniques. In this paper we report on the sensitivity of atmospheric techniques for the detection of fugitive emissions for a simulated new CSG field against a three year baseline signal. Simulation of emissions was performed for a 1-year period using the coupled prognostic meteorological and air pollution model TAPM at different fugitive emission rates (i.e. 0.1 - 10 %) and distances (i.e. 10 - 50 km) from the station. Emissions from the simulated CSG field are based on well density, production volumes, and field size typical of CSG fields in Australia. The distributions of the perturbed and baseline signals were evaluated and statistically compared to test for the presence of fugitive methane emissions. In addition, a time series model of the methane baseline was developed in order to generate alternative realizations of the baseline signal. These were used to provide measures of both the likelihood of detecting fugitive emissions at various emission levels and of the false alarm rate. Results of statistical analysis and an indicative minimum fugitive methane emission rate that can be detected using a single monitoring station are presented. Submitted to AGU 2013, San Francisco

The Arcturus greenhouse gas (GHG) monitoring station began operation in July 2010 50 km southeast of Emerald, Queensland. The station was part of a collaborative project between Geoscience Australia (GA) and CSIRO Marine and Atmospheric Research (CMAR) to establish and operate a high precision atmospheric monitoring facility for measurement of baseline greenhouse gases in a geological carbon dioxide capture and storage (CCS) region. The primary purpose of the station was to establish newly developed greenhouse gas monitoring technology and demonstrate best practice for regional baseline atmospheric monitoring appropriate for geological storage of carbon dioxide. An Eddy Covariance (EC) flux tower was installed at the station to compliment baseline atmospheric measurements by providing; supplementary meteorlogical measurements, atmospheric turbulence and stability parameters, the net vertical transport of water vapour and CO₂ to (and from) the surface, establishing the energy, water and carbon balance for the area. The site is located in a semi-arid, subtropical clime with a summer (Dec-Feb) wet season. The site lies on the boundary between pasture to the west, and cropping to the east, split north to south. EC measurements were taken at 10 Hz frequency and used to prepare 30 minute averages. Data was collected for 2.5 years from 10 June 2011 to 31 December 2013. It was processed using standard OzFlux methods, involving rigorous QA/QC to ensure the output of high quality data. For more information on the site location, installation and instrument set-up see the Installation Report for Arcturus (Berko et al., 2012), while for more information on the metadata and data store for the EC and baseline monitoring instruments see the Metadata Report: Arcturus atmospheric greenhouse gas monitoring (Etheridge et al. 2014).

Limited data from emergency services for the April 2015 East Coast Low (ECL) event initially investigated. SES call-out data provides spatial coverage, but does not capture detail of the damage to buildings. EICU data has detailed information, including indicative damage state, but limited spatial coverage. Neither dataset consistently links the damage to the hazard that caused it. Showing that the impact forecasting process adds value beyond the underlying hazard forecasts in this situation is challenging. EICU data can help to calibrate the vulnerability functions applied to model-based hazard forecast data. The SES callout data can help evaluate whether the indicative damage rates for an area are reasonable, through use of a service demand metric. Service demand is the number of callouts compared to the number of buildings for a statistical area (e.g. mesh block, SA1 or local government area). We use the total building count in each area, as the SES callout data does not differentiate between residential and non-residential buildings. It also includes callouts for downed trees or power lines that may not have directly caused structural damage to buildings. Service demand is compared to mesh block-based impact forecast data for the 2015 ECL, using existing heuristic vulnerability functions for severe wind. We recognise these functions are not calibrated against forecast model data, but provide a starting point from which we can establish the workflow while working towards refined vulnerability functions in parallel. The project has sourced EICU and SES post-event survey data, and high-resolution model (reanalysis) data for two additional severe wind and rain events to improve the calibration of the vulnerability functions. Poster presentation at the 2019 AFAC Conference

In March 1999, TC Vance swept through Exmouth, with the eye wall of the cyclone passing directly over the township generating gusts to 267 km/h. Around 10% of residential houses showed structural failure, with some types of housing experiencing significantly greater damage. By revisiting the impacts of TC Vance, we hope to guide thinking of emergency managers and local government on planning for when another category 5 TC strikes Exmouth. Using the best track information provided by the Bureau of Meteorology, we simulate the wind field of TC Vance using Geoscience Australia’s Tropical Cyclone Risk Model (TCRM), incorporating the local effects of topography, terrain and shielding afforded by neighbouring structures. This simulation is validated against observations of peak wind speed recorded at Learmonth Airport and other regional weather stations. The impacts of TC Vance are calculated for the present building stock in Exmouth, which has grown by nearly a third since 1999. Modern residential buildings perform very well, in line with the performance levels established by the wind loading standards for the region. Some groups of older buildings – specifically the U.S. Navy block houses that survived TC Vance unscathed – also perform very well. The analysis shows the town of Exmouth would still suffer substantial impacts, with around 700 buildings likely to suffer moderate to complete damage. This translates to around 1400 people, with at least half of those requiring temporary accommodation in the days and weeks immediately after the cyclone. These types of analysis help to reduce uncertainty and enhances decision-making for emergency services, enabling a more proportional response for rescue, damage assessments and initial recovery at the State, regional and local levels. From a strategic perspective it can also be used to identify and verify current and future capability needs for agencies involved in managing the cyclone hazard. Presented at the Australian Meteorological and Oceanographic Society Annual Meeting and the International Conference on Tropical Meteorology and Oceanography (AMOS-ICTMO 2019) Conference

The world's first satellite-derived mineral maps of a continent, namely Australia, are now publicly available as digital, web-accessible products. The value of this spatially comprehensive mineral information is readily being captured by explorers at terrane to prospect scales. However, potentially even greater benefits can ensue for environmental applications, especially for the Earth's extensive drylands which generate nearly 50% of the world's agricultural production but are most at risk to climate change and poor land management. Here we show how these satellite mineral maps can be used to: characterise soil types; define the extent of deserts; fingerprint sources of dust; measure the REDOX of iron minerals as a potential marine input; and monitor the process of desertification. We propose a 'Mineral Desertification Index' that can be applied to all Earth's drylands where the agriculturally productive clay mineral component is being lost by erosion. Mineral information is fundamental to understanding geology and is important for resource applications1. Minerals are also a fundamental component of soils2 as well as dust eroded from the land surface, which can potentially impact on human health3, the marine environment4 and climate5. Importantly, minerals are well exposed in the world's 'drylands', which account for nearly 50% of Earth's land area6. Here, vegetation cover is sparse to non-existent as a result of low rainfall (P) and high evaporation (E) rates (P/E<0.65). However, drylands support 50% of the world's livestock production and almost half of all cultivated systems6. In Australia, drylands cover 85% of the continent and account for 50% of its beef, 80% of its sheep and 93% of its grain production7. Like other parts of the world, Australia is facing serious desertification of its drylands6. Wind, overgrazing and overstocking are major factors in the desertification process8. That is, the agriculturally productive clay-size fraction of soils (often includes organic carbon) is lost largely through wind erosion, which is acerbated by the loss of any vegetative groundcover (typically dry plant materials). Once clay (and carbon) loss begins, then the related break down of the soil structure and loss of its water holding capacity increases the rate of the degeneration process with the final end products being either exposed rock or quartz sands that often concentrate in deserts.

The Tropical Cyclone Impact Map provides guidance on areas likely to be impacted by severe winds due to tropical cyclones. The impact zones are generated by Geoscience Australia's Tropical Cyclone Risk Model (TCRM), and are based on the tropical cyclone forecast information published by the Bureau of Meteorology's Tropical Cyclone Warning Centres. TCRM applies a 2-dimensional parametric wind field to the forecast track provided by the Bureau of Meteorology, and translates the wind speeds into an indicator of potential damage to housing. Uncertainty in the forecast track is not included in the product.

This dataset contains various code and outputs developed in demonstrating potential methods of generating 3-D topographic (and possibly other) multipliers for use in wind risk modelling activities. The 'wrf' folder contains output and configuration settings Chris Thomas developed in 2010 to test the feasibility of using WRF locally to derive topographic multipliers.