Factsheets

The Queensland Future Climate: Understanding the data web page explains how climate projections work, and provides guidance on how to interpret and apply the projection data to meet your needs. This factsheet includes some extracts from the web page, but you are encouraged to visit the Understanding the data web page to access the full suite of information and animated graphics that explain the processes used to generate the projections, the benefits of Queensland's approach to downscaling, and the assumptions and limitations.

It's important to understand that the climate projection data provided by sites such as Queensland Future Climate are not predictions or forecasts, but are simulations of plausible futures based on a range of assumptions and scenarios.

Climate models

Global climate models are computer simulations of the Earth's climate system that replicate key processes in the atmosphere, oceans, land and ice. These simulations can be used to recreate the past climate or construct projections of the future climate under various assumptions. Climate models have been used to simulate the climate system's response to human-induced increases in greenhouse gases and other factors as summarised in the assessment reports produced by the Intergovernmental Panel on Climate Change (IPCC).

Over 40 global climate models were used to support development of the Fifth Assessment Report (AR5) of the IPCC. Of these, Queensland downscaled 11 models based on their ability to represent historical climate in Australia, ensuring the projected climate change impacts within the selected models were representative of the full ensemble of models.

The most recent projections for Queensland are based on the modelling completed by the Coupled Model Intercomparison Project Phase 5 (CMIP5) organized under the auspices of the World Climate Research Programme.

The benefits of dynamical downscaling

The high-resolution climate change projections available on Queensland Future Climate were produced using a dynamical downscaling approach. Dynamical downscaling means running a high-resolution Regional Climate Model, with output from the Global Climate Model used as input at the boundaries. This is in contrast to other methods, such as statistical downscaling, which is based on simple statistical relationships between large-scale and small-scale variables.

In the dynamical downscaling approach, Queensland used a global variable-resolution climate model called CCAM (Conformal-Cubic Atmospheric Model) developed by CSIRO. The downscaling process consisted of two steps to obtain a spatial resolution of about 10 km over the Queensland region. These high-resolution simulations were completed for the period 1980 to 2099.

Reducing uncertainty

The main advantage of illustrating results from all eleven models is to provide transparency. This is vital when using the data to estimate future climate risk. For example, showing the output from all models allows users to not just look at mean performance, but the full extent of the range of projections. This is more informative than just focusing on the median (50th percentile) and allows output for the upper (90th percentile) and lower (10th percentile) bounds to be considered as well. This allows for more realistic scenario-based testing of a plausible range of future climates.

There are many different sources of climate data and information that have been developed over recent years. In most cases, these have been designed for specific purposes or to suit different audiences and present the information in different ways. As a result, people looking for climate information can feel overwhelmed by the number of different sources of information and confused by the range of options.

This factsheet distils the range of sources down to a small number suitable for a range of uses in Queensland and provides a handy guide to help you select the right one for your particular needs.

A short-list of suitable sources

Climate projections and information

The Queensland Regional Climate Change Impact Summaries (PDF) provide snapshots of climate risks, impacts and responses for the major regions, including climate change projections for 2030 and 2070.
https://www.qld.gov.au/environment/climate/climate-change/resources/science

The Queensland Future Climate Dashboard provides an easy-to-use, map-based interface for climate projection data for Queensland, and enables users to access data for specific regions such as local government areas or major river catchments. The Dashboard provides access to data in different formats to suit different purposes, including simple data summaries and charts, tables to support further analyses and spatial data (shapefile format) to overlay with other datasets in a Geographic Information System (GIS). Projection data are available for a broad range of climate variables (including extreme events), two emissions scenarios or Representive Concentration Pathways (RCP4.5 and RCP8.5), different seasons and four time horizons (2030, 2050, 2070 and 2090). Please see factsheet #3 for more information on the RCPs and other key terms used on the Dashboard.
https://longpaddock.qld.gov.au/qld-future-climate/dashboard

The Queensland Future Climate: Regional Explorer provides easy access to summary tables and time-series charts for all available climate variables over a selected region.
https://longpaddock.qld.gov.au/qld-future-climate/regions

The Queensland Future Climate: Understanding the data web page explains how climate models work, how Queensland's high-resolution climate projection data were developed, as well as guidance on how to interpret and apply the projection data.
https://longpaddock.qld.gov.au/qld-future-climate/understand-data

The Heatwave case study summarises the expected effects of climate change on the frequency and intensity of heatwaves, and potential implications for health, infrastructure, services and industries. It provides information via maps and time-series charts.
https://longpaddock.qld.gov.au/qld-future-climate/adapting/heatwaves/

The Water security case study explores the potential effects of climate change on our water supply and water security, and how these effects can be managed. It provides information via maps and time-series charts.
https://longpaddock.qld.gov.au/qld-future-climate/adapting/water/

The Tropical Cyclone Hazard Dashboard presents information on severe wind hazards associated with tropical cyclones out to 2090, expressed as both Average Recurrence Intervals (ARI) and Annual Exceedance Probabilities (AEP). This presents the data component of the Severe Wind Hazard Assessment for Queensland (SWHA-Q) delivered in partnership with the Queensland Fire and Emergency Services (QFES) and Geoscience Australia. The SWHA-Q aims to better understand the potential impacts of current and future tropical cyclones across Queensland's regions and to better communicate the projected changes in cyclone behaviour across Queensland.
https://longpaddock.qld.gov.au/qld-future-climate/tropical-cyclone/

The CMIP5 High-resolution projection data page provides access to gridded datasets for the 11 individual climate models, all 42 climate variables, and additional time periods (daily, monthly, seasonal). The data are provided in netCDF format and are most appropriate for users with programming and modelling skills. Please see factsheet #4 for more information on how to navigate and apply these gridded datasets.
https://longpaddock.qld.gov.au/qld-future-climate/data-info/tern/

Sea level rise

CoastAdapt provides sea level projections and maps for local government areas, although the map coverage is incomplete. Where available, there are maps for two future emissions scenarios (RCP4.5 and RCP8.5) for 2050 and 2100. CoastAdapt also provides background information on climate-driven coastal hazards and important considerations for risk assessments.
https://coastadapt.com.au/

Coastal Risk Australia provides more coverage and more flexibility in the map displays. In addition to viewing sea level projections for different scenarios, you can manually set the level (in 10cm increments up to 10m) to display on the map, which is useful for exploring the implications of low-likelihood but higher impact levels (e.g. based on the higher range or longer-term information in the latest IPCC report), decision trigger points etc.
https://www.coastalrisk.com.au/home

Canute 3 (CSIRO) provides estimates of the likelihood of extreme sea levels during this century, taking into account climate-related sea level rise as well as the effects of tides, storm surges and wave setup.
https://shiny.csiro.au/Canute3_0/

National climate data sources

The Climate Change in Australia (CCiA) site provides a platform to access climate summary information and projection data for all of Australia. CCiA provides a number of different tools that allow users to explore different aspects of future climate change in different ways. However, CCiA doesn't provide data at the same spatial scale for all variables, the same range of variables and extreme event indices, nor the flexibility of accessing data for defined regions and in different formats available on Queensland Future Climate.
https://www.climatechangeinaustralia.gov.au/

Sector-specific resources and tools

In addition to the general resources listed above, there are some designed specifically to meet the needs of particular sectors such as energy and agriculture. Links to these resources are provided below, but they are not included in the suitability matrix because of their narrow and sector-specific focus.

Electricity Sector Climate Information (ESCI) provides climate and extreme weather information for the electricity sector.
https://www.energy.gov.au/government-priorities/energy-security/electricity-sector-climate-information-esci-project

My Climate View (formerly Climate Services for Agriculture) provides agriculture-relevant historical, seasonal and future climate information for production locations across Australia.
https://myclimateview.com.au/

Common applications

General information - Many people start exploring climate change information out of curiosity or self-education. Others are seeking simple but trustworthy information that can be used in documents like school reports, communication materials, presentations, briefs and regional profiles. Simple summary tables and charts can often meet these needs.

Climate risk assessments - risk assessments vary in the level of detail required and are often performed in sequence, getting more focussed and detailed at each step.

• 1st pass risk assessment (also called a 'scan cycle' in the Climate Compass risk assessment methodology) - an initial or high-level exploration to identify the most relevant climate hazards and risks, to prioritise further work or scope for the other cycles.
• 2nd pass risk assessment ('strategy cycle') - a formal climate risk assessment for a particular entity or activity to develop a strategic climate risk management or adaptation plan.
• 3rd pass risk assessment ('project cycle') - a detailed climate risk assessment that can be used for specific projects, including operational planning and major investment decisions.

Detailed hazard analysis - the quantification of climate hazards to enable estimates of exposure and vulnerability can require more detailed information on extreme events under climate change, e.g. projected changes in the frequency, duration and intensity of events relating to extreme heat, rainfall, wind and fire weather. Hazard-specific resources can often provide this kind of information in a variety of formats.

Research and modelling - Researchers and modellers are likely to seek high-resolution projection data at fine time scales and for specific climate models that are known to be appropriate for an application or to enable calculation of specialised indices. Examples include hydrological modelling, bioclimatic modelling and engineering applications.

Strategic policy and planning - Large organisations, including all levels of government, NGOs and private sector organisations, will seek information on changes to climate hazards and risks over strategic timeframes to inform the development of or amendments to policies, regulations, governance structures, decision-making frameworks, operations and procedures that adequately consider the effects of climate change.

Reporting and compliance - Driven by emerging standards for reporting on environmental, social and governance (ESG) performance and financial disclosures of climate risk such as the Task Force on Climate-related Financial Disclosures (TCFD), public and private organisations will need information to demonstrate the assessment and management of climate risks.

Suitability matrix

The suitability matrix below can help match the climate information sources against their ideal applications. Large dark green circles indicate a close match between the source and intended use, and that these would be the recommended climate information sources to use in each case. Smaller lighter green circles indicate that some features of the source may be suitable for that use, but that other options may provide a better match or be easier to use. Empty cells indicate that the source is not a good match for the application, and you will be better served looking elsewhere.

This factsheet explains some of the terminology and concepts used across the Queensland Future Climate resources.

Representative Concentration Pathways

A Representative Concentration Pathway (RCP) is a greenhouse gas (GHG) concentration (not emissions) trajectory adopted by the Intergovernmental Panel on Climate Change (IPCC) for its Fifth Assessment Report (AR5) in 2014. The RCPs superseded the Special Report on Emissions Scenarios (SRES) projections first published in 2000.

These RCPs are based on assumptions about how different human responses may change future emissions of greenhouse gases (not just emissions policy and activities, but other factors including social and economic forces).

Four RCPs that describe different plausible climate futures have been selected for most climate modelling and research, all of which are considered possible depending on the amount of greenhouse gases emitted in the years to come. The four RCPs, namely RCP2.6, RCP4.5, RCP6, and RCP8.5, are labelled after a possible range of radiative forcing values in the year 2100 relative to pre-industrial values (+2.6, +4.5, +6.0 and +8.5 in watts per square metre (W/m2), respectively).

The Queensland Future Climate Dashboard presents downscaled data and information for two greenhouse gas scenarios:

RCP8.5 - a future with little curbing of emissions, with the concentration of GHGs continuing to rise rapidly, reaching 940 ppm by 2100. This scenario represents a high emissions future that would require greater levels of adaptation.

RCP4.5 - GHG concentrations increase steadily until after mid-century, with GHG concentrations stabilizing around 2060 and reaching 540 ppm by 2100. This scenario represents a future of moderate GHG emissions.

CoastAdapt provides a useful explainer for the RCPs.

Expressing the likelihoods of extreme events

The chances of experiencing an extreme event of a certain magnitude are commonly expressed using two different terms.

• The Average Recurrence Interval (ARI) describes the average time period between events equalling or exceeding a given value. (e.g. '1-in-100 years').
• The Annual Exceedance Probability (AEP) describes the probability of an event being equalled or exceeded in any given year, usually expressed as a percentage.

The use of ARIs has been problematic because it suggests a uniform period of time between events. Many people interpret this in a way that reduces their estimate of climate risk. For example, if a location has just experienced a 1-in-100 year event, there is a tendency to believe a similar event won't occur in the foreseeable future.

In contrast, the AEP emphasises there is an equal probability of a specified event occurring in any given year. This description makes it easier to consider events that may be repeated or clustered within relatively short timeframes in response to seasonal drivers, and how the likelihood of events is likely to be influenced by climate change.

The Australian Disaster Resilience Knowledge Hub includes detailed descriptions of these metrics and how to interpret them.

Seasons

In addition to providing projection data on an annual basis, aggregated data is available for calendar seasons:

• Summer (December, January and February, labelled 'djf' in some data products)
• Autumn (March, April and May, or 'mam')
• Winter (June, July and August, or 'jja')
• Spring (September, October and November, or 'son').

In addition, we also provide aggregated information for wet (November to March) and dry (May to September) periods.

Note that for extreme indices, the wet and dry seasons have five months instead of six because the transition months (April and October) are excluded when averaging to preserve the nature of extreme events within the seasons.

Years

The Queensland Government's future climate simulations are continuous from 1980 to 2099. Data available via the Queensland Future Climate Dashboard is provided for four 20-year time slices in which averaged information is presented as:

• 2030 (2020-2039)
• 2050 (2040-2059)
• 2070 (2060-2079)
• 2090 (2080-2099)

Additional time slices, including for the 1986-2005 reference period, are available in gridded format via the links available on the High Resolution Projections Data page.

Regions

The Dashboard can display data defined by a number of different regional categories. The spatial information for regional projections was spatially aggregated from 10km pixel-size grids to specific regions. The following five specific regional categories for which regional projections are presented are: Local Government Areas, Regional Plan Areas, Bioregions, Major River Basins, and Disaster Districts.

Users can select the region of interest to visualise specific future climate projections at regional scale.

Output charts

The interactive plot panes present summarised information for the selected future climate themes (i.e., Mean Climate, Heatwaves, Extreme Temperature Indices, Extreme Precipitation Indices, SPI-drought indices and SPI-wetness indices), for what has been customised in the drop-down menus (i.e., regions, variable, season and year) and for the selected region in the map.

The top plot (green bars) displays the projected future climate across seasons (Annual, Summer, Autumn, Winter, Spring, Wet and Dry) for the region, variable and year of interest.

The bottom plot (purple bars) demonstrates how future climate is expected to change over time using four time slices - 2030 (2020-2039), 2050 (2040-2059), 2070 (2060-2079) and 2090 (2080-2099) for the region, variable and season of interest.

The plots present change in relation to the reference period 1986-2005. The vertical bars represent the range across the 11 regional climate models (also known as model spread), while the thick horizontal bars indicate the multi-model averages. The thin horizontal bars denote the individual models (see Figure 1).

Multi-model averages are calculated at grid-cell basis then spatially aggregated for Queensland's regions.

The values and models represented by horizontal bars can be displayed by hovering over them and a comprehensive table with summary statistics for all models is displayed when clicking over any plot element.

Plots and underlying data are available for download in different formats using the "PDF", "PNG", "CSV" and "XML" buttons under the plots.

Map information

The map panel presents spatial data for the high-resolution climate modelling across Queensland with 10km of spatial resolution. Maps are customisable through climate themes (tabs) and drop-down menus. Maps are available for 205 regions within Queensland.

After selecting the type of region in the drop-down menu (Local Government Areas, Regional Plan Areas, Bioregions, Major River Basins and Disaster Districts), the regional boundaries are displayed on the map. Users can hover over the map to inspect regions by name and select a region of interest. After clicking on a region, the output charts in the right-hand pane of the window will display the summary information for that region.

Each map represents an 11-member ensemble average - i.e., a multi-model average of the 11 downscaled models with values shown as change to reference period 1986-2005. The variables Precipitation and Pan Evaporation are also available as percent change (see Figure 1).

The maps enable user interactivity through a range of action buttons (See Figure 1). Once a map region is selected, grid-cell values can be inspected within the region when hovering over the selected region. An additional button is also available underneath the map to show/hide centred geographic coordinates after selecting a region. Click and drag to move the map and scroll up and down for zoom in and out respectively.

This factsheet applies to the high-resolution projection data available from Queensland Future Climate. Most, but not all, of these variables can also be accessed via the Queensland Future Climate Dashboard.

Climate models

The Queensland Future Climate datasets are produced by dynamical downscaling of a range of General Circulation Models (GCMs) developed by a number of institutions as part of the Coupled Model Intercomparison Project Phase 5 (CMIP5) (Table 1).

Table 1: The set of 11 CMIP5 GCMs included in the Queensland Future Climate projection datasets.

Model ID	Model Description	Institution(s)	Country
ACCESS-1.0	Australian Community Climate and Earth-System Simulator, version 1.0	CSIRO, BoM	Australia
ACCESS-1.3	Australian Community Climate and Earth-System Simulator, version 1.3	CSIRO, BoM	Australia
CCSM4	Community Climate System Model, version 4	NCAR	USA
CNRM-CM5	Centre National de Recherches Météorologiques Coupled Global Climate Model, version 5	CNRM	France
CSIRO-Mk3.6.0	Commonwealth Scientific and Industrial Research Organisation Mark 3.6.0	CSIRO-QCCCE	Australia
GFDL-CM3	Geophysical Fluid Dynamics Laboratory Climate Model, version 3	NOAA-GFDL	USA
GFDL-ESM2M	Geophysical Fluid Dynamics Laboratory Earth System Model with Modular Ocean Model, version 4 component	NOAA-GFDL	USA
HadGEM2	Hadley Centre Global Environment Model, version 2 CC	MOHC	UK
MIROC5	Model for Interdisciplinary Research on Climate, version 5	JAMSTEC	Japan
MPI-ESM-LR	Max Planck Institute Earth System Model, low resolution	MPI	Germany
NorESM1-MQ	Norwegian Earth System Model, version 1 (intermediate resolution)	NCC	Norway

Notes: BoM = Bureau of Meteorology, NCAR = National Centre for Atmospheric Research, CNRM = Centre National de Recherches Météorologiques, NOAA = National Oceanographic and Atmospheric Administration, MOHC = Met Office Hadley Center, JAMSTEC = Japan Agency for Marine-Earth Science and Technology, MPI = Max Planck Institute, NCC = NorESM Climate Modelling Consortium.

Climate variables

The following climate variables from the 10km CCAM model (Table 2) have been selected to give an overall understanding of the future climate. The variable code is how these variables are identified in the filenames of downloaded datasets.

Extreme temperature and precipitation indices are adapted from a range of indices defined by the Australian Bureau of Meteorology (http://www.bom.gov.au/climate/change/about/extremes.shtml).

The heatwave indices are adapted from the NARCliM (NSW and ACT Regional Climate Modelling) project (http://www.ccrc.unsw.edu.au/sites/default/files/NARCliM/publications/TechNote5.pdf) and are used to analyse the frequency, severity and duration of heatwave conditions. Bias-corrected temperature data are used in the calculation of these heatwave indices.

For drought and wetness indices, the Standard Precipitation Index (SPI) is used to determine precipitation deficit or excess over a reference period compared to normal conditions over a reference period (using bias-corrected data).

For mean climate indicies, variables are available in daily, monthly and seasonal change format, while only seasonal changes are available for the other climate themes.

Table 2: A summary of the climate variables available from Queensland Future Climate.

Theme	Variable	Variable code	Units	Description
Mean climate	Mean Temperature	tscr_ave	K	Average air temperature
	Minimum Temperature	tminscr	K	Average minimum daily air temperature
	Maximum Temperature	tmaxscr	K	Average maximum daily air temperature
	Precipitation	rnd24	mm	Precipitation total
	Pan-evaporation	epan_ave	mm/day	Average daily class-A pan evaporation
	Relative Humidity	rhscrn	%	Average daily ratio of the water vapour pressure to the saturation vapour pressure (expressed as a percent)
	Surface Wind	u10	m/s	The average daily horizontal surface wind speed
	Solar Radiation	sgdn_ave	W/m2	The average daily solar electromagnetic radiation
Bias-corrected mean climate	Bias-corrected Mean Temperature	tscr_aveAdjust	K	Adjusted average air temperature
	Bias-corrected Minimum Temperature	tminscrAdjust	K	Adjusted average minimum daily air temperature
	Bias-corrected Maximum Temperature	tmaxscrAdjust	K	Adjusted maximum daily air temperature
	Bias-corrected Precipitation	rnd24Adjust	mm	Adjusted precipitation total
Heatwaves	Heatwave Peak Temperature	HWAt	°C	Amplitude of the hottest day in the hottest heatwave event
	Heatwave Frequency	HWF	%	Frequency of heatwave days
	Heatwave Duration	HWD	days	Mean duration of heatwaves
	Maximum Heatwave Duration	HWL	days	Duration of the longest heatwave
Extreme temperature	Hot Days	hd	days	Count of days with maximum temperature > 35 °C
	Hot Nights	hn	days	Count of days with minimum night temperature > 20 °C
	Warm Spell Duration	wsd	days	Count of days with at least 4 consecutive days when daily maximum temperature > 90th percentile
	Cold Spell Duration	csd	days	Count of nights with at least 4 consecutive nights when daily minimum temperature < 10th percentile
	Cool Nights	cn	days	Count of days when minimum temperature < 10th percentile.
	Very Hot Days	vhd	days	Count of days with maximum temperature > 40 °C
Extreme precipitation	Maximum 1-day Precipitation	m1p	mm	Maximum 1-day precipitation total
	Maximum 5-day Precipitation	m5p	mm	Maximum consecutive 5-day precipitation total
	Extreme Wet Day Precipitation	ewdp	mm	Total precipitation when daily precipitation > 99th percentile
	Simple Daily Intensity	sdi	mm	Total precipitation divided by the number of days where daily precipitation ≥ 1 mm
	Consecutive Dry Days	cdd	days	Maximum number of consecutive days with daily precipitation < 1 mm
	Consecutive Wet Days	cwd	days	Maximum number of consecutive days with daily precipitation ≥ 1 mm
SPI-Droughts	Frequency of Moderate Drought	fmd	number of events	Number of events with SPI ranging from -1.00 to -1.49
	Frequency of Severe Drought	fsd	number of events	Number of events with SPI ranging from -1.50 to -1.99
	Frequency of Extreme Drought	fed	number of events	Number of events with SPI less than -2.00
	Duration of Droughts	dd	months	Average number of consecutive months with SPI less than -1.00
SPI-wetness	Frequency of Moderate Wetness	fmf	number of events	Number of events with SPI ranging from +1.00 to +1.49
	Frequency of Severe Wetness	fsf	number of events	Number of events with SPI ranging from +1.50 to +1.99
	Frequency of Extreme Wetness	fef	number of events	Number of events with SPI greater than +2.00
	Duration of Wetness	df	months	Average number of consecutive months with SPI greater than +1.00

Bias correction

Systematic biases such as temperature drifts can occur in General Circulation Models because of the methods required to transform the continuous functions that describe physical processes into discrete data; these biases could have large impacts on future climate projections. Therefore, the temperature and precipitation variables from the 10km CCAM model output are first checked and calibrated against historical observation data from the Australian Water Availability Project (AWAP) and adjusted ('bias corrected') if necessary before further analysis is performed to derive the extreme climate indices based on these variables.

Data format

All gridded data files available on Queensland Future Climate are provided in the Network Common Data Form (NetCDF) format, the standard for climate data and other multidimensional modelling applications. The Climate and Forecast (CF) conventions for data structure and metadata has been followed where feasible to maximise compatibility to third-party software accessing these data files. The file extension is .nc.

Directory structure and naming convention

The links on the High Resolution Projections Data page take you a catalog on the Terrestrial Ecosystem Research Network (TERN) for your chosen variable and RCP. Once in the catalog, you can browse to locate the particular file for the model and time period of interest.

The directory structure and hierarchy is as follows:
[experiment_id]/[data_frequency]/[theme]/[variable] where 'experiment_id' refers to the RCP.

For example:
/RCP45/seasonal/MeanClimate/MaxTemperature

The file names may seem long and complicated, but their contents are easily determined thanks to a consistent naming convention. The files are named according to the following pattern:
[variable code].[absolute or percentage change]_[model]_[RCP].[time period].nc

For example:
tscr_ave.absolute-change.ccam10_ACCESS1-0Q_rcp85.2020-2039_minus_1986-2005.nc
This file is for mean temperature, absolute change, the ACCESS-1.0 model for RCP8.5, for the time period 2020 to 2039 in comparison to the 1986 to 2005 reference period.

rnd24.percentage-change.ccam10_CCSM4Q_rcp45.2080-2099_minus_1986-2005.nc
This file is for the percentage change in precipitation for the time period 2080-2099 relative to the 1986-2005 reference period, using the CCSM4 model for RCP4.5.

For best results, after selecting the file you want, choose the option to download it from the HTTP Server:

Working with netCDF files

The netCDF format is used for multidimensional scientific data and is commonly employed in climatology, meteorology and Geographic Information Systems (GIS). While netCDF is a standard format used in climate modelling, it may need to be imported or converted into other formats for different applications or further analyses.

Using netCDF files in ArcGIS via the multidimension toolbox

ArcGIS (Esri) versions after 9.2 support netCDF files that follow the Climate and Forecast Metadata Conventions and contain rectilinear grids with equally-spaced coordinates. The Multidimension toolbox can be used to create raster layers, feature layers, and table views from netCDF data in ArcMap, or to convert feature, raster, and table data to netCDF.

Using netCDF files in QGIS

The free and open source QGIS can open and display netCDF DATA as either raster or mesh layers. From the QGIS menu, select 'Layer', 'Add Layer', 'Add Mesh Layer', then select the desired .nc file.

Converting netCDF files to the GeoTIFF Format using R

For users who require the GeoTIFF format, these NetCDF files can be readily converted. An example on how this can be achieved using the application R is shown below. Note that the R packages raster and ncdf4 are required for the script below.

# Load required R packages
library(raster)
library(ncdf4)
# Define input and output file paths
input_file <- "rnd24_Asea_ACCESS1-0Q_rcp85_r1i1p1_2005-2024-abs-change-wrt-1986-2005-seasavg-clim_CCAM10km.nc"
output_file <- "sample.tif"
# Read one variable from a NetCDF file
nc <- raster(input_file, varname="rnd24_djf")
# Write variable to a GeoTIFF file
writeRaster(x=nc, filename=output_file, format="GTiff", overwrite=TRUE, options=c("ALPHA=YES"))

This code creates a GeoTIFF file called "sample.tif" for the variable "rnd24_djf" from the specified NetCDF file. Note that this only achieves a basic conversion without a colour palette, and so the image cannot be viewed usefully on common image viewing software such as Windows Photo Viewer.

Converting netCDF files using GDAL

The Geospatial Data Abstraction Library (GDAL) provides support for read and write access to netCDF data. Examples showing how netCDF files can be converted to Esri ArcASCII grids or GeoTIFF images are available at https://longpaddock.qld.gov.au/silo/gridded-data/conversion/.

Hazard assessments

The Queensland State Heatwave Risk Assessment (SHRA) represents the most comprehensive analysis of future climate risk undertaken for a natural hazard risk assessment in Queensland. It is underpinned by a robust scientific basis, enabling all stakeholders including State agencies, disaster management groups, infrastructure owners, town planners and community groups to understand, plan for, and reduce the risks from heatwaves.

The SHRA was developed to provide all stakeholders with clear and consistent information regarding the changing nature of heatwave risk in Queensland. It was a collaborative effort between multiple stakeholders, coordinated through a working group led by Queensland Fire and Emergency Services (QFES), Queensland Health (QH), and the Department of Environment, Science and Innovation (DESI).

The inclusion of long-term climate change projections within the assessment represents a first for hazard specific, emergency management related risk assessments in Australia. This robust scientific basis enhances the assessment and enables State agencies and disaster management groups to inform their planning against current and future heatwave risk.

The Severe Wind Hazard Assessment for Queensland (SWHA-Q), was delivered as a collaborative project between QFES, DES, James Cook University and Geoscience Australia. Developed in response to the 2017 Cyclone Debbie Review, the projects core aim was to provide realistic and tangible information on the potential physical impacts of tropical cyclones on Queensland communities.

The suite of hazard management tools delivered through the project will enable the emergency management sector, local governments, and communities across Queensland to more effectively work through current and future risks posed by cyclones, including long-term strategic risk management strategies.

The Queensland Future Climate Tropical Cyclone Dashboard provides the data component of the SWHA-Q.

Climate Change In Australia

The Climate Change in Australia (CCiA) site provides a platform to access climate summary information and projection data for all of Australia. The CCiA resources were initially developed by CSIRO and the Bureau of Meteorology to support natural resource management planning, but subsequent updates and expansion of the services provided means that it is now much more broadly applicable across all sectors.

CCiA provides a number of different 'Explorers' or tools that allow users to explore different aspects of future climate change in different ways. For example, the Analogues Explorer matches the proposed future climate of a location of interest with the current climate experienced in another location using annual average rainfall and maximum temperature. This can be very useful for people to visualise a possible future based on experience.

The Climate Futures tool is a multi-purpose resource that can assist understanding and application of climate change projections for impact assessment and adaptation planning. This tool includes projections from global and regional climate models as well as statistically downscaled results.

However, CCiA doesn't provide data at the same spatial resolution for all variables, the same range of variables and extreme event indices, nor the flexibility of accessing data for defined regions and in the different formats available on Queensland Future Climate.

Sea level rise (SLR)

Detailed information on SLR projections, including addressing uncertainty and risk, is provided in the 6th Assessment Report (AR6) from the Intergovernmental Panel on Climate Change (IPCC). For example, AR6 includes a likely range of:

• 0.28-0.55m by 2100 under a low emissions scenario
• 0.63-1.01m by 2100 under a high emissions scenario.

AR6 also includes more information on projections of SLR beyond the likely range but that cannot be ruled out; for example, approaching 2m by 2100 and 5m by 2150 under a very high emissions scenario.

The Queensland Government provides information and data on projected levels of SLR via the coastal hazard maps. Other suitable sources for information on future SLR include:

• CoastAdapt (https://coastadapt.com.au/)
• Coastal Risk Australia (https://www.coastalrisk.com.au/home)
• Canute 3 (https://shiny.csiro.au/Canute3_0/).

Historical climate data

Detailed information on historical climate data is available from multiple sources.

SILO (Scientific Information for Land Owners) is a database of Australian climate data from 1889 to yesterday managed by the Queensland Government Department of Environment, Science and Innovation (DESI). It provides daily meteorological datasets for a range of climate variables in ready-to-use formats suitable for biophysical modelling, research and climate applications. SILO provides the climate inputs for other resources available from the Long Paddock site, such as the FORAGE suite of online property reports, as well as the AussieGRASS rainfall and pasture growth outlooks.

The Australian Bureau of Meteorology (BoM) provides access to historical climate data in a variety of formats, including timeseries charts and data for states and major regions.

A good climate risk assessment framework or methodology will provide guidance on how to apply the Queensland climate projection data in climate risk assessments.

Climate risk assessment frameworks

Different climate risk assessment frameworks or methodologies are available, with many focussing on the needs of specific sectors. Most methodologies follow much the same process but may vary in the terminology used or recommended information sources. Most importantly, these frameworks provide a structured process for estimating the level of risk that arises from a combination of a climate hazard, exposure, vulnerability and consequence. A risk assessment framework also provides a way to navigate uncertainty, incorporate multiple sources and types of information, and to make appropriate decisions in the absence of perfect or complete information.

CoastAdapt is a resource developed by the National Climate Change Adaptation Research Facility to support coastal adaptation that includes information for risk assessments and adaptation decision support.

Climate Compass is a general-purpose resource that provides easy-to-follow guidance on how to conduct a climate risk assessment. Climate Compass was developed by CSIRO for Commonwealth Government agencies but is applicable to a broader range of users with one adjustment: Climate Compass refers users to the national climate projection datasets provided by CSIRO and the Bureau of Meteorology. However, the Department of Environment, Science and Innovation (DESI)recommends that all Queensland-based projects should preference the Queensland Future Climate resources.

DESI also provides climate risk management resources to help households and businesses through the climate risk assessment process and support adaptation decisions.

Climate change scenarios

The Queensland Future Climate resources include data for two “Representative Concentration Pathways” (RCPs) that describe scenarios for how atmospheric greenhouse gas concentrations may change over time depending on multiple factors including mitigation policies, population growth, economic development and land use change. RCP4.5 represents a moderate emissions scenario while RCP8.5 represents a relatively high emissions scenario that would lead to greater temperature increases and greater physical climate impacts.

Considering both RCP4.5 and RCP8.5 can provide realistic lower and upper bounds for climate risk assessments.

For further guidance, Climate Compass includes a detailed process for selecting a scenario and climate parameters depending on the scope and objectives of the climate change risk assessment. This includes consideration of the decision lifetime, greatest plausible change, risk tolerance and other relevant factors.

Other data requirements

In addition to climate projection information, a climate risk assessment will usually require additional data to assess the exposure and vulnerability of people, assets and services to climate hazards.

Some examples of additional information may include:

• details on the location and structure of built assets and infrastructure (e.g. building materials, height of key features above ground level etc.)
• requirements for operations and service delivery (e.g. access and safety requirements)
• financial information on maintenance and replacement costs
• links and dependencies that produce indirect or offsite risks (e.g. power, water and telecommunication networks)
• regulatory responsibilities and required levels of service etc.

It will be important to consider these additional data requirements to complete a climate risk assessment.

Adaptation planning

The National Climate Change Adaptation Research Facility (NCCARF) has created numerous resources to support climate change adaptation. A library of practical adaptation guides is available from an online archive, including:

• synthesis summaries for particular climate hazards or systems
• adaptation briefing notes for decision makers in different roles
• policy guidance briefs addressing specific problems
• impact factsheets for different sectors and systems.

Additional information on Queensland Government initiatives supporting adaptation to climate change are available on the Queensland Climate Action website.