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 projections 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 projections 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 attempt to 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).

The most recent projections for Queensland are based on the modelling completed by the Coupled Model Intercomparison Project Phase 6 (CMIP6) that supported the development of the Sixth Assessment Report (AR6) from the IPCC. However, projections for Queensland based on the previous CMIP5 modelling are also available. Other factsheets on this page explain the differences between CMIP5 and CMIP6, and also provide more details on the climate models used.

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 approaches use output from the Global Climate Model as well as refined elevation, land cover and coastline data to build a Regional Climate Model and significantly improve the spatial resolution of climate change projections. Dynamic downscaling is different to other methods, such as statistical downscaling, which is based on statistical relationships between large- 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 provides 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 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.

 

 

Many different sources of climate data and information have been developed. In many 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 the range of options.

This factsheet distils the large amount of available climate data sources down to a small number of sources most useful to people in Queensland and provides a handy guide to help you select the right dataset for your particular needs.

The Queensland Future Climate Science Program provides climate projections data and information using both CMIP5 and CMIP6 climate models. CMIP refers to the Coupled Model Intercomparison Project, and the latest generation of climate models are part of the sixth phase of the project (CMIP6 for short). In the sections below, the availability of climate projections using these generations of climate models is identified with CMIP5 and CMIP6. For more information on these generations of models, please see factsheet #6 on this page.

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 2050 and 2090 under three emissions scenarios.
https://longpaddock.qld.gov.au/qld-future-climate/regional-sum

The Queensland Future Climate Dashboard provides an easy-to-use, map-based interface for climate projections 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). Projections data are available for a broad range of climate variables (including extreme events), multiple emissions scenarios, seasons and four time horizons (2030, 2050, 2070 and 2090). Please refer to the User Guide for more information on using the Dashboard, and to other factsheets on this page for more information on the climate models, scenarios and key terms used.
https://longpaddock.qld.gov.au/qld-future-climate/dashboard-cmip6

The Queensland Future Climate: Regional Explorer provides easy access to summary tables and time-series charts for several climate variables over selected regions.
https://longpaddock.qld.gov.au/qld-future-climate/regions-cmip6

The Queensland Future Climate: Understanding the data web page explains how climate models work, how Queensland's high-resolution climate projections data were developed, and provides guidance on how to interpret and apply the projections 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 High-resolution projections data pages provide access to gridded datasets for both the CMIP5 and CMIP6 projections, including the individual climate models, all climate variables, and additional time periods (daily, monthly, seasonal). The data are provided in netCDF format and are most appropriate for users with programming skills. Please see factsheet #4 for more information on how to navigate and use these gridded datasets.
https://longpaddock.qld.gov.au/qld-future-climate/data-info/tern-CMIP5/
https://longpaddock.qld.gov.au/qld-future-climate/data-info/tern-CMIP6/

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 than CoastAdapt. 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 projections data for all of Australia. CCiA provides a number of 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, the same range of variables and extreme event indices, nor the same degree of flexibility for accessing data for defined regions and in different formats as 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.climatechangeinaustralia.gov.au/en/projects/esci/

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.

Adaptation planning - This typically follows a multi-step process: assess priority climate risks; identify options to reduce or manage those risks; plan, fund and implement management actions; and monitor and review to improve outcomes. Adaptation plans can vary greatly in their scope, level of complexity and requirements for climate data. CoastAdapt is a great resource for adaptation planning.

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 projections 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. Many of these information sources are designed to be flexible to meet a broad range of user needs, and many of the listed applications can also vary in their scope and data requirements. This is reflected in the matrix with some sources matching a large number of uses.

 

 

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

CMIP5 and CMIP6

CMIP5 and CMIP6 refer to the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP). The Queensland Future Climate resources present information based on both CMIP5 and CMIP6 projections data.

Please refer to the User Guide for information on how to identify and select which CMIP version is being displayed on the Dashboard. A separate factsheet on this page provides more detailed information on the main differences between these 2 phases of CMIP.

Emissions scenarios or greenhouse gas trajectories

Queensland Future Climate presents projections information using multiple emissions scenarios to provide a picture of alternative, plausible climate futures.

A Representative Concentration Pathway (RCP) is a greenhouse gas (GHG) concentration trajectory used in CMIP5 climate models. 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).

The CMIP5 version of the Queensland Future Climate Dashboard presents downscaled data and information for two RCPs:

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 very 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.

Shared Socioeconomic Pathways (SSPs) provide a new way to describe future changes in atmospheric concentrations of GHGs used in CMIP6 models. The SSPs are based on five different narratives that describe the broad trends that could shape global society and economies in the future. These include population growth, economic growth, technological development, education and urbanisation.

The CMIP6 version of the Queensland Future Climate Dashboard presents downscaled data and information for three greenhouse gas scenarios described using SSPs:

SSP1-2.6 - a low emissions scenario in which global emissions are cut but don't reach net zero until after 2050 and warming stabilises at about 1.8°C by 2100.

SSP2-4.5 - a moderate emissions scenarios where emissions remain steady at roughly the same as current levels before starting to fall in the middle of the century and net zero is not reached by 2100, with temperatures rising by about 2.7°C by the end of the century.

SSP3-7.0 - a high emissions scenario where both emissions and temperatures continue to rise, the latter by about 3.6°C by 2100.

Factsheet #6 provides more information on RCPs and SSPs.

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 projections 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 pages.

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 seven specific regional categories for which regional projections are presented are: Local Government Areas, Regional Plan Areas, Bioregions, Major River Basins, Disaster Districts, Natural Resource Management Areas and Locations.

Users can select the region of interest in a drop-down menu to visualise specific future climate projections at a chosen regional scale. Please refer to the User Guide for more information.

Output charts

This interactive plot pane presents summarised information for the selected future climate themes (i.e., Mean Climate, Heatwaves, Extreme Temperature Indices, Extreme Precipitation Indices, Drought indices, Wetness indices and Fire weather indices), for the combination of options selected 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 for CMIP5 products and 1995-2014 for CMIP6 products). The vertical bars represent the range or model spread across the ensemble members (11 for CMIP5 and 15 for CMIP6 downscaled products), while the thick horizontal bars indicate the multi-model averages. The thin horizontal bars denote the individual models (see figure below).

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

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. By changing variables, emission scenarios, seasons and years, a total of nearly 2,000 future climate maps are accessible and can be regionalised across 205 different regions within Queensland.

After selecting the type of region in the drop-down menu (Local Government Areas, Regional Plan Areas, Bioregions, Natural Resource Management regions, Major River Basins, Disaster Districts and Locations), 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 a multi-model ensemble average - i.e., a multi-model average of all the downscaled models with values shown as change relative to the reference period (1986-2005 for CMIP5 products and 1995-2014 for CMIP5 products). The variables Precipitation and Pan Evaporation are also available as percent change (see figure below).

The maps enable user interactivity through a range of action buttons (see figure below). 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 projections data available as gridded data from Queensland Future Climate. Most, but not all, of these variables can also be accessed via the Queensland Future Climate Dashboard, which provides more flexible options for viewing and downloading the data, as well as easier access to regional summary information.

The Queensland Future Climate high-resolution datasets are produced by dynamical downscaling of a range of Global Climate Models (GCMs) developed by a number of institutions as part of the Coupled Model Intercomparison Project (CMIP). Downscaled datasets are available using GCMs from both the 5th (CMIP5) and 6th (CMIP6) phases of CMIP. A separate factsheet (#6) on this page describes the main differences between these two phases of CMIP models.

The CMIP5 and CMIP6 datasets can be accessed using the links in the relevant sections below.

CMIP6 projections

CMIP6 climate models

The CMIP6 high resolution projections are available from https://longpaddock.qld.gov.au/qld-future-climate/data-info/tern-cmip6/.

Table 1. The set of 15 downscaled CMIP6 climate models included in the Queensland Future Climate projections datasets.

CMIP6 model ID	Model name:	Institution(s)	Country of origin:
ACCESS-ESM1.5	Australian Community Climate and Earth System Simulator, version 1.5, CCAM atmospheric model version	CSIRO & BoM	Australia
ACCESS-ESM1.5_oc (run for two variants)	Australian Community Climate and Earth System Simulator, version 1.5, CCAM coupled ocean model version	CSIRO & BoM	Australia
ACCESS_CM2_oc	Australian Community Climate and Earth System Simulator, version 2, CCAM coupled ocean version	CSIRO & BoM	Australia
CMCC-ESM2	Centro Euro-Mediterraneo sui Cambiamenti Climatici Earth System Model, version 2	CMCC	Italy
CNRM-CM6-1-HR	Centre National de Recherches Météorologiques Coupled Global Climate Model, version 6.1, high-resolution	CNRM & CERFACS	France
CNRM-CM6-1-HR_oc	Centre National de Recherches Météorologiques Coupled Global Climate Model, version 6.1, high-resolution, CCAM coupled ocean version	CNRM & CERFACS	France
EC-Earth3	European Community Earth-System Model, version 3	EC	Various European countries
FGOALS-g3	Flexible Global Ocean-Atmosphere-Land System Model, grid point version 3	CAS	China
GFDL-ESM2M	Geophysical Fluid Dynamics Laboratory Earth System Model, version 4	GFDL NOAA	USA
MGISS-E2-2-G	Goddard Institute for Space Studies Model E2.2Gn	GISS NASA	USA
MPI-ESM1-2-LR	Max Planck Institute Earth System Model, version 1.2, low resolution	MPI	Germany
MRI-ESM2-0	Meteorological Research Institute Earth System Model, version 2.0	MRI	Japan
NorESM1-MM	Norwegian Earth System Model, version 2, 1 degree resolution	NCC	Norway
NorESM2-MM_oc	Norwegian Earth System Model, version 2, 1 degree resolution, CCAM coupled ocean version	NCC	Norway

Notes: BoM = Bureau of Meteorology, CMCC = Centro Euro-Mediterraneo sui Cambiamenti Climatici, NCAR = National Centre for Atmospheric Research, CNRM = Centre National de Recherches Météorologiques, EC = European consortium of national meteorological services and research institutes, CAS = Chinese Academy of Sciences, NOAA = National Oceanographic and Atmospheric Administration, NASA = National Aeronautics and Space Administration, MOHC = Met Office Hadley Center, JAMSTEC = Japan Agency for Marine-Earth Science and Technology, MPI = Max Planck Institute, MRI = Meteorological Research Institute, NCC = NorESM Climate Modelling Consortium. CNRM-CM6-1-HR and NorESM2-MM were run in both atmosphere-only and coupled atmosphere-ocean versions, while three variants of the ACCESS-ESM1.5 model were downscaled (r20i1p1f1 and r40i1p1f1 in ocean-coupled form, and r6i1p1f1 in atmosphere-only form).

CMIP6 climate variables

A large number of climate variables calculated using CMIP6 models are available at different sampling frequencies (hourly, daily and monthly; Table 2).

Table 2. A summary of the CMIP6 climate variables that can be accessed via the National Computational Infrastructure (NCI) where X indicates available frequencies. Note that some of the monthly variables are not available for all models. Fixed variables are those that do not change over time, such as orography.

			Frequency
Name	Units	Long name	Hourly	Daily	Monthly	Fixed
tas	K	Near-Surface Air Temperature	X	X	X
tasmax	K	Daily Maximum Near-Surface Air Temperature		X	X
tasmin	K	Daily Minimum Near-Surface Air Temperature		X	X
pr	kg m-2 s-1	Precipitation	X	X	X
evspsbl	kg m-2 s-1	Evaporation Including Sublimation and Transpiration		X	X
huss	1	Near-Surface Specific Humidity	X	X	X
hurs	%	Near-Surface Relative Humidity	X	X	X
ps	Pa	Surface Air Pressure	X	X	X
psl	Pa	Sea Level Pressure	X	X	X
sfcWind	m s-1	Near-Surface Wind Speed	X	X	X
uas	m s-1	Eastward Near-Surface Wind	X	X	X
vas	m s-1	Northward Near-Surface Wind	X	X	X
clt	%	Total Cloud Cover Percentage	X	X	X
rsds	W m-2	Surface Downwelling Shortwave Radiation	X	X	X
rlds	W m-2	Surface Downwelling Longwave Radiation	X	X	X
clh	%	High Level Cloud Fraction			X
clivi	kg m-2	Ice Water Path			X
cll	%	Low Level Cloud Fraction			X
clm	%	Mid Level Cloud Fraction			X
clwvi	kg m-2	Condensed Water Path			X
evspsblpot	kg m-2 s-1	Potential Evapotranspiration			X
hfls	W m-2	Surface Upward Latent Heat Flux			X
hfss	W m-2	Surface Upward Sensible Heat Flux			X
hus200	1	Specific humidity at 200mb			X
hus500	1	Specific humidity at 500mb			X
hus850	1	Specific humidity at 850mb			X
mrfso	kg m-2	Soil Frozen Water Content			X
mrro	kg m-2 s-1	Total runoff			X
mrros	kg m-2 s-1	Surface runoff			X
mrso	kg m-2	Total Soil Moisture Content			X
prc	kg m-2 s-1	Convective Precipitation			X
prhmax	kg m-2 s-1	Daily Maximum Hourly Precipitation Rate			X
prsn	kg m-2 s-1	Snowfall Flux			X
prw	kg m-2	Water Vapor Path			X
ps	Pa	Surface Air Pressure			X
psl	Pa	Sea Level Pressure			X
rlus	W m-2	Surface Upwelling Longwave Radiation			X
rlut	W m-2	TOA Outgoing Longwave Radiation			X
rsdt	W m-2	TOA Incident Shortwave Radiation			X
rsus	W m-2	Surface Upwelling Shortwave Radiation			X
rsut	W m-2	TOA Outgoing Shortwave Radiation			X
sfcWindmax	m s-1	Daily Maximum Near-Surface Wind Speed			X
snc	%	Snow Area Percentage			X
snd	m	Snow Depth			X
snm	kg m-2 s-1	Surface Snow Melt			X
snw	kg m-2	Surface Snow Amount			X
sund	s	Daily Duration of Sunshine			X
ta200	K	Air temperature at 200mb			X
ta500	K	Air temperature at 500mb			X
ta850	K	Air temperature at 850mb			X
tauu	Pa	Surface Downward Eastward Wind Stress			X
tauv	Pa	Surface Downward Northward Wind Stress			X
ts	K	Surface Temperature			X
ua200	m s-1	Eastward wind at 200mb			X
ua500	m s-1	Eastward wind at 500mb			X
ua850	m s-1	Eastward wind at 850mb			X
va200	m s-1	Northward wind at 200mb			X
va500	m s-1	Northward wind at 500mb			X
va850	m s-1	Northward wind at 850mb			X
zg200	m	Geopotential height at 200mb			X
zg500	m	Geopotential height at 500mb			X
zg850	m	Geopotential height at 850mb			X
zmla	m	Height of boundary layer			X
orog	m	Surface Altitude				X
sfftlaf	%	Percentage of the Grid Cell Occupied by Lake				X
sftlf	%	Percentage of the Grid Cell Occupied by Land				X
soilt		Soil type				X

 

Directory structure and naming convention for CMIP6 projections data

CMIP6 data can be accessed via THREDDS, or via NCI for registered users. Instructions for registering for local access for NCI users are available on the data directory page. The directory structure of the data archive on NCI is:

- CORDEX

- CMIP6

- DD

- domain [AUS-20i/AUS-10i]

- UQ-DES

- model name

- scenario [historical/ssp126/ssp245/ssp370]

- model version

- v1-r1

- frequency [1hr/day/mon]

- variable name [i.e., tas]

- version date [v20231215]

In the directory structure, model version refers to whether CCAM was run in ocean coupled mode (CCAMoc) or atmosphere only mode (CCAM).

As an example, using THREDDS, navigating through the directories to CORDEX/CMIP6/DD/AUS-20i/UQ-DES/ACCESS-CM2/ssp370/r2i1p1f1/CCAMoc-v2112/v1-r1/mon/tasmax/v20231215/ will produce a catalog of files as shown below:

 

 

CMIP5 projections

The Queensland Future Climate CMIP5 datasets are produced by dynamical downscaling of 11 GCMs (Table 3).

The CMIP5 high resolution projections are available from https://longpaddock.qld.gov.au/qld-future-climate/data-info/tern-cmip5/.

Table 3: The set of 11 CMIP5 GCMs included in the Queensland Future Climate projections 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.

CMIP5 climate variables

The following climate variables from the 10km CCAM model (Table 4) 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 based on the official definition for heatwaves in Australia[1] 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 4: A summary of the CMIP5 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

Directory structure and naming convention for CMIP5 data

The links on the CMIP5 High Resolution Projections Data page take you to a catalogue on the Terrestrial Ecosystem Research Network (TERN) for your chosen variable and RCP. Once in the catalogue, 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:

 

Bias correction

Systematic biases such as temperature drifts can occur in GCMs 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. A separate factsheet (#14) on this page describes the bias correction method used.

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.

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/.

 

References

[1] J. Nairn and R. Fawcett, 'Defining heatwaves: heatwave defined as a heat-impact event servicing all community and business sectors in Australia', The Centre for Australian Weather and Climate Research, CAWCR Technical Report 060, 2013. [Online]. Available: https://www.cawcr.gov.au/technical-reports/CTR_060.pdf.

 

 

The effects of climate change will vary in both space and time across Queensland's regions, and understanding this variation is important for assessing climate impacts and informing adaptation decisions.

The Queensland Future Climate Dashboard is a powerful tool for exploring climate projections information for four future time periods and for one climate variable and scenario at a time. The Regional Explorer tool on Queensland Future Climate provides a quick and easy way to access summary information for multiple climate variables at a regional level and with higher temporal frequency. The Regional Explorer provides different options for accessing regional summary data, including user-interactive summary tables and plots that show the change in a selected climate variable over time to 2100.

The Regional Explorer provides access to climate projections information for both CMIP5 and CMIP6 climate models. Please refer to the separate factsheet for more information on these generations of climate models and the different ways of describing emissions scenarios.

The CMIP5 version of the Regional Explorer provides information for two emissions scenarios described using Representative Concentration Pathways (RCP4.5 and RCP8.5). The new CMIP6 version provides information for three emissions scenarios using Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5 and SSP3-7.0).

Users can control the Regional Explorer using an interactive map and drop-down menus, similar to the main Dashboard.

 

What does the Regional Explorer provide?

The Queensland Future Climate Dashboard presents information from high-resolution climate simulations based on a number of climate models until the end of the current century (see Factsheet #4 for a full list of the models available). The Dashboard, while powerful and flexible, presents outputs for just one variable at a time. This approach is very useful for exploring future climate change with a focus on particular hazards at different regional scales.

The Regional Explorer tool makes it easier to view summary information for all climate variables for a defined region which may be very useful in early explorations of potential climate hazards for a defined area (Figure 1).

What information is available?

• summary tables of projected changes in all available climate metrics, presented using the mean and model spread
• timeseries charts of model absolute change over time.

To investigate the influence of climate change, the user can select their preferred region, emissions scenario, season and year ‘slice’ (20-year time period).

Regions

The projections data can be defined by several different regional categories. The spatial information for regional projections was spatially aggregated from 10km pixel-size grids to specific regions. The following seven specific regional categories for which regional projections are presented are: Local Government Areas, Regional Plan Areas, Natural Resource Management Areas, Bioregions, Major River Basins, Disaster Districts and point Locations.

Climate themes and variables

The climate variables available are arranged into several themes to help manage the volume of information available:

• Mean Climate: Mean, minimum and maximum temperature, precipitation, pan evaporation, relative humidity, surface wind and solar radiation.
• Heatwaves: Peak temperature (or Peak heat index for the CMIP6 version), frequency, duration and maximum heatwave duration.
• Extreme temperature indices: Hot days, hot nights, very hot days, warm spell duration, cold spell duration and cold nights.
• Extreme precipitation indices: Maximum 1-day precipitation, maximum 5-day precipitation, extreme wet day precipitation, simple daily intensity, consecutive dry days and consecutive wet days.
• SPI- or SPEI-drought indices: Duration of droughts (percent time), frequency of droughts (number of events), percent time in drought (months) and extent of territory in drought (for timeseries only).
• SPI- or SPEI-wetness indices: Duration of wetness (percent time), frequency of wetness (number of events), percent time in wetness (months) and extent of territory in wetness (for timeseries only).
• FFDI: Severe fire risk days, very high fire risk days, high fire risk days, moderate fire risk days, low fire risk days, 95th percentile fire risk days, 99.7th percentile fire risk days.

Seasons

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

• Summer (December, January and February)
• Autumn (March, April and May)
• Winter (June, July and August)
• Spring (September, October and November).

We also provide aggregated information for wet (November to March) and dry (May to September) periods for most climate themes.

Due to the likelihood of individual heatwave events persisting across calendar seasons, heatwave metrics are only presented for the hydrologic year (1 July to 30 June) and wet season (November to April). These periods have been chosen to capture summer heatwave events that stretch across the change in calendar year.

In contrast to other climate themes, the drought and wetness metrics are only presented at the annual scale. These metrics are based on either the Standardised Precipitation Index (SPI; CMIP5 version) or the Standardised Precipitation Evapotranspiration Index (SPEI; CMIP6 version). Given the continuous nature of these indices and the time-series continuity dependence of the methodology, splitting the time-series into calendar seasons could impact the robustness of the methods. Therefore, instead of presenting variation across seasons, we offer three categories of drought and wetness severity for users to choose from: Moderate, Severe and Extreme.

Years

Data available via the Regional Explorer 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).

Emissions scenarios

The amount of future climate change will be highly dependent on the amount of greenhouse gases (GHG) in the atmosphere, including future emissions and sequestration.

The Queensland Future Climate resources present downscaled data and information for different greenhouse gas scenarios, but these are described in different ways for the two versions available.

The CMIP5 (original) version of the Regional Explorer used 2 scenarios described using RCPs:

• RCP4.5 - moderate emissions
• RCP8.5 - very high emissions.

The new CMIP6 version uses 3 scenarios described using SSPs:

• SSP1-2.6 - low emissions
• SSP2-4.5 - moderate emissions
• SSP3-7.0 - high emissions.

Please see factsheets #6 for more information on the RCPs and SSPs

Using the Regional Explorer

The map section of the Regional Explorer allows users to pan and zoom to a region of interest. The summary tables and timeseries charts display the result of the map selection for the currently selected region and climate theme.

Summary tables

By selecting the 'Summary tables' tab, the desired region and climate theme, users will access a summary table (Figure 2) that provides information for all of the climate variables related to the selected climate theme. Values are presented as the mean change values for the ensemble of models. The change values from the reference period (1986-2005 for CMIP5 and 1995-2014 for CMIP6). The range across the multiple regional climate models (also known as model spread) is shown in brackets below the multi-model averages. Values are displayed for a range of seasons and categories which vary depending on the climate theme chosen. The table also includes the reference period values.

Users can download the tables in multiple formats, including PDF, image (PNG), Excel spreadsheet (xlsx), and text (CSV and JSON).

Timeseries charts

The Timeseries charts provide customised visualisations of projected changes in the climate variables for the period 1981 to 2099. Users can select the region, theme, variable, season and emissions scenario to display on the chart via the drop-down menus.

For most climate themes, results are presented as changes in variable relative to the reference period (CMIP5:1986-2005 or CMIP6:1995-2010). However, for the drought and wetness themes, the 'Extent of territory' in drought or wetness are presented as absolute percent of territory in drought or wetness.

The chart is highly customisable - users can select to display just the ensemble mean and range, the mean and range for all the individual models or a comparison of results for all emission scenarios. For the SPI-drought and SPI-wetness climate themes there is also the opportunity to compare results for the three severity categories. In this case, the timeseries is a stacked chart showing only the mean ensemble results for the three categories (not the range).

A table with summary statistics for the model values in specific years can be observed by hovering over the year of interest. When the 'All Models (individual)' button is selected, users can highlight particular models by hovering over the model name in the legend. The selected model will remain highlighted until the user hovers over a different model. Users can hover over the 'Mean' option in the legend to return the chart to the original setting. Models can also be removed from (or added to) the timeseries by clicking on the model name in the legend.

Similar to the summary tables, users can download the plots in multiple formats depending on how the information will be used.

 

 

The Coupled Model Intercomparison Project (CMIP) is a collaborative effort to coordinate international climate modelling activities. The project is currently in its sixth phase, or CMIP6 for short. CMIP6 introduced some important changes in comparison to the previous phase (CMIP5), including how emissions scenarios are described in the models.

The new CMIP6 versions of the Queensland Future Climate resources present climate projections information based on three greenhouse gas (GHG) scenarios described using Shared Socioeconomic Pathways (SSPs). The original CMIP5 version of Queensland Future Climate uses two scenarios described using Representative Concentration Pathways (RCPs).

Why the change?

The latest generation of global climate models used in CMIP6 have introduced SSPs as a new way to describe future changes in atmospheric concentrations of GHGs. The previous generation, CMIP5, described these trajectories using RCPs. A separate factsheet on this page explores the differences between CMIP5 and CMIP6 in more detail.

At the time the Queensland Future Climate Dashboard and other resources were originally released in 2018, CMIP5 models were the latest ones available. Our new resources take advantage of the latest climate science and presents the information based on CMIP6 models and using SSPs for the scenarios.

CMIP5

CMIP5 models informed the previous Fifth Assessment Report (AR5) from the Intergovernmental Panel on Climate Change (IPCC) (released in sections between 2013 and 2014). CMIP5 models considered several different trajectories for future changes in the concentration of atmospheric greenhouse gases (GHGs) described using Representative Concentration Pathways (RCPs).

An RCP describes a trajectory for future GHG concentrations based on assumptions about how different natural processes and human activities may change the rate of emissions (and sequestration) of GHGs.

There are four standard RCPs applied in climate modelling. The number included in the name for each RCP refers to the increase in climate forcing in the year 2100, resulting from the level of atmospheric GHGs (in watts per square meter) – a bigger number indicates more climate warming associated with higher concentrations of GHGs.

• RCP2.6 – describes a pathway to a low GHG concentration where it peaks somewhere in the middle of the century and then declines to 420 parts per million (ppm) as GHG emissions are substantially reduced over time with ambitious mitigation efforts. It is aligned with mitigation efforts aiming to limit the increase of global mean temperature to 2°C.
• RCP4.5 – describes a pathway where GHG concentrations stabilise at 540ppm by 2100.
• RCP6.0 – describes a pathway where GHG concentrations stabilise shortly after 2100 at 660ppm by 2100.
• RCP8.5 – represents a future with little reduction in GHG emissions where GHG concentrations reach 940ppm by 2100.

In their design, no RCP is inherently more or less likely than the others and they are not forecasts. However, an RCP may appear to be more or less likely depending on how policy and other parameters evolve in comparison to their assumptions or how actual GHG concentrations track against the modelled trajectories over time.

Queensland Future Climate provides high-resolution CMIP5 projections based on two RCPs - RCP4.5 to represent a moderate emissions future and RCP8.5 to represent a high emissions future.

 

CMIP6

CMIP6 models represent a new generation of climate models and are highlighted in the most recent Sixth Assessment Report (AR6) from the IPCC (released in sections from 2021 to 2023). In comparison to CMIP5, CMIP6 includes more models based on the latest understanding of ocean and atmospheric processes (such as improved descriptions of cloud processes and biogeochemical cycles). CMIP6 models also feature a wider range of climate sensitivities (the average change in global mean temperature in response to a change in climate forcing). CMIP5 models use a reference period of 1986 to 2005 while CMIP6 models use a reference period of 1995-2014.

CMIP6 models also employ a new way to describe future GHG trajectories using Shared Socioeconomic Pathways (SSPs). The SSPs are based on five different narratives that describe the broad trends that could shape society in the future, including population growth, economic growth, technological development, education and urbanisation. SSPs are intended to span the range of plausible futures from a sustainable future to one of rapid and unconstrained economic growth.

For climate modelling, the SSPs are combined with a range of mitigation targets described using a measure of the resulting increase in ‘climate forcing’ as used for the RCPs. Climate modelling initiatives focus on a set of five combined scenarios.

• SSP1-1.9 – This is the most optimistic of the scenarios where global GHG emissions are cut aggressively to net zero by about 2050. This scenario aligns most closely with the goal of keeping warming below 1.5°C at 2100 (although there may be a bit of ‘overshoot’ where temperatures rise beyond the 1.5°C goal temporarily before dropping below the goal by the end of the century).
• SSP1-2.6 – Under this scenario, global emissions are reduced but don’t reach net zero until after 2050 and warming stabilises at about 1.8°C by 2100.
• SSP2-4.5 – This scenario has emissions steady at roughly the same as current levels before starting to fall in the middle of the century and net zero is not reached by 2100, with temperatures rising by about 2.7°C by the end of the century.
• SSP3-7.0 – Under this scenario, both emissions and temperatures continue to rise, the latter by about 3.6°C by 2100.
• SSP5-8.5 – This high-growth, energy-intensive scenario has emissions roughly doubling by 2050 and global average temperatures rising by about 4.4°C by the end of the century.

The figure below illustrates the projected global mean surface temperature change to 2100 under these five scenarios (relative to the period from 1850 to 1900). Keep in mind that local or regional changes in mean temperature could be different from changes in global mean temperatures, and that climate hazards usually result from extreme events rather than mean values.

Why are there no downscaled SSP5-8.5 projections for Queensland and Australia?

The CMIP6 projections for Queensland do not include data for the SSP5-8.5 scenario. Why not? RCP8.5 was considered to be a good representation of a high range emissions scenario for the CMIP5 suite of models that could be used to inform the plausible upper bounds of climate change for risk assessments. Based on more recent data and improved understanding of how the main drivers of future emissions are likely to change over time, there is a common view among climate scientists that the SSP5-8.5 scenario is very unlikely to be realised, largely because of the changing face of energy generation and rapid electrification in other sectors in response to changing economics. As a result, SSP3-7.0 is now considered to be good representation of a high-end emissions scenario within the CMIP6 suite of models (i.e. because emissions higher than described in SSP3-7.0 are now considered very unlikely).

Based on this, the organisations developing high-resolution downscaled climate projections for Australia agreed to concentrate their resources on the same three scenarios that are expected to cover the most likely range of possible future climates. For more information, please refer to the Climate Projections Roadmap for Australia.

Which is better for your needs?

CMIP6 models can provide advantages in the description of possible future climates, particularly for more sophisticated analyses where the outputs of climate models are used as inputs for further modelling. Examples include regional downscaling simulations or analyses that focus on implications for particular climate hazards or systems (such as extreme events or ecological modelling etc).

However, for the purposes of climate risk assessments and adaptation planning, CMIP6 models are unlikely to provide any substantial differences in relevant climate parameters that would have a material effect on risk classification or decisions on adaptation options.

Both CMIP5 and CMIP6 projections can continue to be applied in climate risk assessments with confidence.

In most cases, CMIP6 projections will be preferred as they represent the latest available climate projections information, and we recommend that all new climate risk assessments, adaptation plans and policies be developed based on CMIP6 projections.

However, there may be cases where the use of CMIP5 projections is warranted. Examples could include:

• reviewing or updating an existing risk assessment or adaptation plan developed using CMIP5 projections
• for compliance with policy or other requirements that specify particular RCPs should be applied
• where the application requires the consideration of a very high emissions scenario even where this may be considered unlikely to eventuate (e.g. for the consideration of low-likelihood, high impact events on long-lived critical infrastructure).

It might not be a good idea to mix CMIP5 and CMIP6 data in the one analysis. There are some differences in the way variables are presented, particularly with different reference periods, that will make interpretation difficult. A climate risk assessment methodology will help you select the most appropriate scenario to use for your application given your interests, decision lifetime, risk tolerance etc. You can find more information on this in the separate factsheet on climate science for risk assessments.

 

More information

High-resolution projections data and information based on CMIP6 projections available here.

High-resolution projections data and information based on CMIP5 projections available here.

Climate Projections Roadmap for Australia and the National Partnership for Climate Projections.

This factsheet was developed with support from the NESP Climate Systems Hub.

 

 

The Queensland Future Climate Dashboard includes information on how climate change will impact mean climate variables, such as temperature, rainfall, wind and humidity. Queensland's climate it highly variable, ranging from tropical wet to arid. Understanding how our future climate will change is crucial for adaptation and preparedness.

Mean climate variables

The mean climate variables are shown in Table 1. The changes in these variables with climate change are available on the Dashboard on an annual basis and for different seasons (summer, winter, autumn, spring, wet and dry seasons), several emissions scenarios (SSP1-2.6, SSP2-4.5 and SSP3-7.0 for CMIP6 and RCP4.5 and RCP8.5 for CMIP5), and for four different time periods (2020-2039, 2040-2059, 2060-2079, 2080-2099).

Table 1: The set of mean climate variables included in the Queensland Future Climate datasets. 

Name	Short name	Description
Minimum Temperature	tasmin	Minimum daily temperature (°C)
Maximum Temperature	tasmax	Maximum daily temperature (°C)
Mean Temperature	tas	Mean daily temperature (°C)
Precipitation	pr	Daily precipitation (mm/d)
Pan evaporation	epan_ave	Daily pan evaporation (mm/d)
FAO Reference Evapotranspiration (CMIP6 only)	FAO	Daily evapotranspiration estimated using the FAO Penman-Monteith equation (mm/d)
Surface wind	sfcWind	Near surface wind speed (m/s)
Solar radiation	sgn_ave	Solar radiation at the Earth's surface
Relative Humidity	hurs	Relative humidity (%)

 

These variables are also available to download as gridded datasets. Gridded CMIP6 projections data can be accessed from the National Computational Infrastructure (NCI) here. Gridded CMIP5 projections data can be accessed from the Terrestrial Ecosystem Research Network here. The gridded data includes data for each individual model, scenario and variable.

The data is available in NetCDF (Network Common Data form) format, the standard for climate data and other multidimensional modelling applications. Further information on differences between CMIP5 and CMIP6, the climate models used and the climate variables provided is available in factsheets #4 and #6.

 

 

What are heatwaves?

Heatwaves are periods of unusually hot weather that can have serious impacts on human health and wellbeing, agriculture and the environment. The Queensland Future Climate Science Program calculates heatwaves using the Excess Heat Factor (EHF), which is the same method used by the Bureau of Meteorology. However, for our latest (CMIP6) estimates instead of using temperature, we use the heat-index, which incorporates temperature and humidity, which is particularly important in tropical regions in Queensland. Using this method, heatwaves are defined as periods of at least 3 days or more where the heat index is high compared to the recent and long-term past.

How are heatwaves analysed in climate projections?

The EHF considers both acclimatisation (the ability to adjust to the change in the heat index from the recent past), and how high the heat index is relative to the 95th percentile of the heat index during the reference period. We used 1981 - 2010 as the calibration period.

The EHF is obtained by combining two indices - the EHI_accl and EHI_sig. The heat acclimatisation index (EHI_accl) is calculated as the average heat index over the past 3 days compared to the average heat index over the previous 30 days (equation 1 below). The heat significance index (EHI_sig) is the average heat index over the past three days compared to the 95th percentile over the calibration period, 1981 - 2010 (equation 2). The EHF is calculated as the product of these two indices (equation 3) where T_i is the heat index on a specific day.

A heatwave occurs when the EHF is 1.0 or more for at least three consecutive days. Based on this definition, we can calculate a time series of the EHF in the climate simulation data that can then be used to derive a number of heatwave metrics depicting its characteristics over a particular season and for different time periods in the future. As heatwaves are more frequent in warmer months (December, January and February), it is important to preserve the continuity of the summer season when representing its characteristics. Therefore, we provide heatwave indices for the 1 July - 30 June hydrological year and the November - April wet season.

The indices available on the Queensland Future Climate Dashboard and Regional Explorer under the Heatwave 'theme' are shown below in Table 1

Table 1: Heatwave indices.

Heatwave Peak Temperature (CMIP5)	Maximum temperature (°C) of the hottest day of the season's hottest heatwave
Heatwave Peak Heat Index (CMIP6)	Maximum heat index (°C) of the hottest day of the hottest heatwave (also known as the heatwave amplitude)
Heatwave Frequency	Number of heatwave days in a season relative to the number of days in the season i.e. (Heatwave days / Number of days in season) * 100%
Heatwave Duration	Average duration of heatwaves in a season (days)
Maximum Heatwave Duration	Length of the longest heatwave in a season (days)

 

The figure below illustrates how heatwaves and their characteristics are identified and extracted from a temperature time-series. For additional information on heatwaves in Queensland, please visit the Heatwaves case study.

Heatwaves on Queensland Future Climate

The Queensland Future Climate Science Program has used state-of-the-art high-resolution downscaled climate simulations from both CMIP5 and CMIP6 global climate models to assess the impact of climate change on heatwaves.

The main tools for viewing climate projections data on the Queensland Future Climate website are the Queensland Future Climate Dashboard and Regional Explorer. Please refer to the user guide for detailed information on how to access and interpret information available from these resources.

Examples of the heatwave information available on the Queensland Future Climate Dashboard and Regional Explorer are shown below.

References

Nairn, J. R., and R. J. B. Fawcett. 2015. The Excess Heat Factor: A Metric for Heatwave Intensity and Its Use in Classifying Heatwave Severity. International Journal of Environmental Research and Public Health 12:227-253.

Trancoso, R., J. Syktus, N. Toombs, D. Ahrens, K. K. H. Wong, and R. Dalla Pozza. 2020. Heatwaves intensification in Australia: A consistent trajectory across past, present and future. Science of the Total Environment 742:140521.

 

 

Droughts are among the costliest climate hazards with negative impacts for agriculture, the environment, and society. There is potential for worse droughts in the future due to changes in rainfall patterns and increasing temperatures under a changing climate. The Queensland Future Climate Science Program provides two indices and three metrics to evaluate changes to future droughts and wetness.

What is a drought?

In comparison to other natural hazards, the onset and severity of a drought are difficult to determine since droughts are characterised by a gradual build-up. It can take many months or years for the full extent and severity of a drought to become apparent. Definitions of a drought can be based on 3 broad concepts of drought:

• Meteorological droughts: relating to extended periods of below average rainfall that is exacerbated through high temperatures and evaporation.
• Hydrological droughts: describing impacts on streamflow and other water systems (e.g., reservoirs or lakes).
• Agricultural droughts: focussing on agricultural impacts, primarily through assessments of soil moisture.

What drought indices did we use?

The Queensland Future Climate Science Program focusses on meteorological droughts and wetness and presents information based on two of the more common meteorological drought indices, namely:

• Standardized Precipitation Index (SPI; McKee et al., 1993)
• Standardized Precipitation Evapotranspiration Index (SPEI; Vicente-Serrano et al., 2010)

SPI is a rainfall-based index, calculated from accumulated rainfall over a set number of months. SPEI is an extension of SPI, and also includes the impacts of increased temperatures through potential-evapotranspiration (PET). Rather than being calculated from accumulated rainfall, SPEI is calculated from the difference between rainfall and PET. It has been argued that SPEI better reflects changes to the overall water budget by considering the effects of changing atmospheric evaporative demand. The Queensland Future Climate Science Program adopts PET derived from the Penman-Monteith equation (Allen et al., 1998).

Both SPI and SPEI were calculated using an accumulation period of 12-months, as this was considered the timeframe most likely to have impacts on various hydrological and agricultural systems. Drought and wetness events are split into three different categories according to the severity (moderate, severe or extreme) as shown in Table 1:

Table 1. SPI and SPEI classification table following McKee et al. (1993)

Metrics relating to the frequency of events, duration of events, and percent time in drought are calculated for each of the drought and wetness categories (extreme, severe, and moderate). These metrics show how drought and wetness characteristics will likely change in the future. A description of these metrics is presented below and an illustrated example provided in Figure 1.

• Frequency of events is the total number of events recorded over a given period.
• Duration of events is the average duration of recorded drought events (in months).
• Percent time in drought/wetness is the total time spent in a category of drought/wetness divided by the total time.

In addition to the three metrics above, timeseries of the extent of territory in drought or wetness (as a percentage) were also calculated for the diffferent severity categories.

Droughts and wetness on Queensland Future Climate

The Queensland Future Climate Science Program has used state-of-the-art high-resolution downscaled climate simulations from both CMIP5 and CMIP6 global climate models to assess the impact of climate change on droughts and wetness. The CMIP5 information is provided using SPI only, while metrics based on both SPI and SPEI are available for CMIP6.

The main tools for viewing climate projections data on the Queensland Future Climate website are the Queensland Future Climate Dashboard and Regional Explorer. Please refer to the user guide for detailed information on how to access and interpret information available from these resources.

Examples of the drought information available on the Queensland Future Climate Dashboard and Regional Explorer are shown below.

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56 (FAO, Rome, Vol. 300, p. D05109).

McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The Relationship of Drought Frequency and Duration to Time Scales. Proceedings of the 8th Conference on Applied Climatology, 17.

Vicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. (2010). A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. Journal of Climate, 23(7), 1696-1718. https://doi.org/10.1175/2009JCLI2909.1.

 

 

Many Australian landscapes are fire prone and have been severely damaged by bushfires in the recent past. Understanding how a changing climate could affect the weather conditions conducive to bushfires is essential for building preparedness.

In Australia, the McArthur Forest Fire Danger Index (FFDI) (McArthur, 1967) is frequently used to quantify the influence of weather on fire risk, with the Australian Bureau of Meteorology (BoM) providing short-term forecasts of FFDI for use by fire authorities for several decades up until the end of 2022 (Sauvage et al., 2024). McArthur defined FFDI to assist foresters in relating weather conditions to the expected fire behaviour. The FFDI uses observations of temperature, relative humidity and wind speed with an estimate of the fuel state to predict fire risk. The fuel state is determined by the 'drought factor' which depends on the daily rainfall and the period of time since the last rain.

Note, unlike other methods such as the Australian Fire Danger Rating System (AFDRS), the McArthur FFDI only assesses the atmospheric or climatic component of wildfire risk. This allows for projections of fire weather while avoiding issues associated with estimating future fuel state under changing land uses and management practices, and also allows for continuity with historical weather data to inform hazard and risk assessments.

The Queensland Future Climate Science Program has used state-of-the-art high-resolution downscaled climate simulations from both CMIP5 and CMIP6 global climate models to assess the impact of climate change on fire weather.

The Queensland Future Climate Dashboard and Regional Explorer offer several options for exploring projections of future fire weather, including:

• high-resolution interactive maps and plots of future changes in FFDI across Queensland for different regions, emission scenarios, seasons and time slices
• table summaries that provide a quick and easy way to access summary FFDI projections at a regional level
• customisable timeseries charts of modelled FFDI anomalies for the period 1981-2100.

FFDI calculation

The Queensland Government calculated the McArthur FFDI using the following equation:

FFDI = 2e^{(0.0338 T + 0.0234 W-0.0345 RH + 0.987 ln(DF)-0.45)}

where the daily maximum temperature at 2m (T), mid-afternoon (3 pm) relative humidity at 2m (RH), and mid-afternoon (3 pm) 10 m wind speed (W) were derived using bias-corrected downscaled climate simulations from the Queensland Future Climate Science Program (see Trancoso et al. 2022 for an example of estimating FFDI using a similar approach).

The drought factor (DF) was based on an estimate of soil moisture deficit (MD) calculated using the Keetch-Byram Drought Index (KBDI), where the KBDI is estimated from daily rainfall and the daily maximum temperature. For a detailed description of the drought factor as well as assessment of the performance of different drought factors see Holgate et al. (2017).

Note that the McArthur FFDI only assesses the atmospheric or climatic component of wildfire risk and does not account for landscape factors such as vegetation characteristics, fuel availability and potential sources of fire ignition.

Seven categories for FFDI were selected to represent a range of fire danger classifications, with indices based on the annual count of days within each of the categories:

1. Severe fire risk days: Seasonal count of days where FFDI ≥ 50
2. Very high fire risk days: Seasonal count of days where 24 ≤ FFDI &lt 50
3. High fire risk days: Seasonal count of days where 12 ≤ FFDI &lt 24
4. Moderate fire risk days: Seasonal count of days where 5 ≤ FFDI &lt 12
5. Low fire risk days: Seasonal count of days where 0 ≤ FFDI &lt 5
6. 95^th Percentile fire risk days: Seasonal count of days with an FFDI greater or equal to the 95^th percentile for the reference period (1995-2014)
7. 99.7^th Percentile fire risk days: Seasonal count of days with an FFDI greater or equal to the 99.726^th percentile for the reference period (1995-2014).

FFDI counts were calculated for each of the downscaled regional climate models for the different seasons (annual, summer, autumn, winter, spring, wet and dry), and several emissions scenarios (RCP4.5 and RCP8.5 for the CMIP5 modelling). A multi-model average of the downscaled models was also calculated.

Results on the Queensland Future Climate website are shown as change relative to a reference period (1986-2005 for the CMIP5 modelling).

Fire weather on Queensland Future Climate

The main tools for viewing climate projections data on the Queensland Future Climate website are the Queensland Future Climate Dashboard and Regional Explorer. Please refer to the user guide for detailed information on how to access and interpret information available from these resources.

Fire weather information is initially available for CMIP5 models only, but CMIP6 information will be released in the near future.

Examples of the FFDI output available on the Queensland Future Climate Dashboard and Regional Explorer are shown below in Figures 1 and 2.

References

Holgate, C.M., van Dijk, A.I.J.M., Cary, G.J., Yebra, M., 2017. Using alternative soil moisture estimates in the McArthur Forest Fire Danger Index. Int. J. Wildland Fire 26, 806. https://doi.org/10.1071/WF16217

Sauvage, S., Fox-Hughes, P., Matthews, S., Kenny, B.J., Hollis, J.J., Grootemaat, S., Runcie, J.W., Holmes, A., Harris, R.M.B., Love, P.T., Williamson, G., 2024. Australian Fire Danger Rating System Research Prototype: a climatology†. Int. J. Wildland Fire 33. https://doi.org/10.1071/WF23144

Trancoso, R., Syktus, J., Salazar, A., Thatcher, M., Toombs, N., Wong, K.K.-H., Meijaard, E., Sheil, D., McAlpine, C.A., 2022. Converting tropical forests to agriculture increases fire risk by fourfold. Environ. Res. Lett. 17, 104019. https://doi.org/10.1088/1748-9326/ac8f5c

 

 

What is extreme precipitation?

Extreme precipitation occurs when there is anomalously high rainfall within a given region or location. Extreme precipitation can lead to flooding with impacts for water supply, agriculture, the environment, emergency services, and infrastructure. The Queensland Future Climate Science Program uses a number of extreme precipitation indices selected from those recommended for use in Australia by the Bureau of Meteorology.

Extreme precipitation indices

The Queensland Future Climate Dashboard and Regional Explorer include the following extreme precipitation indices:

• Maximum 1-day precipitation: Seasonal maximum 1-day precipitation total
• Maximum 5-day precipitation: Seasonal maximum consecutive 5-day precipitation total
• Extremely wet day precipitation: Seasonal total precipitation when daily precipitation > 99th percentile of precipitation for that season in the calibration period (1981 - 2010)
• Simple daily intensity: Seasonal total precipitation divided by the number of wet days (where precipitation ≥ 1 mm) within that season
• Consecutive dry days: Maximum number of consecutive days with daily precipitation &lt 1 mm
• Consecutive wet days: Maximum number of consecutive days with daily precipitation ≥ 1 mm.

These indices are relevant to water managers, agriculture, and emergency services, among other sectors. The maximum 1-day precipitation is relevant to flooding, particularly in small to medium sized catchments, while the maximum 5-day precipitation is more closely linked to flooding in larger catchments.

Changes to the extremely wet day precipitation and the simple daily intensity likewise have implications for the number of flood events, erosion, and agriculture. The number of consecutive wet and dry days are important metrics for evaluating water supplies and for agricultural growing seasons.

Extreme precipitation on Queensland Future Climate

The Queensland Future Climate Science Program has used state-of-the-art high-resolution downscaled climate simulations from both CMIP5 and CMIP6 global climate models to assess the impact of climate change on extreme precipitation.

The main tools for viewing climate projections data on the Queensland Future Climate website are the Queensland Future Climate Dashboard and Regional Explorer. Please refer to the user guide for detailed information on how to access and interpret information available from these resources.

Examples of the information on extreme precipitation available on the Queensland Future Climate Dashboard and Regional Explorer are shown below.

 

 

What are extreme temperatures?

Extreme temperatures occur when it is unusually hot or cold. This can have impacts on human health and well-being, agriculture and infrastructure. The Queensland Future Climate Science Program uses a number of extreme temperature indices selected from those recommended for use in Australia by the Bureau of Meteorology.

Extreme high temperature indices

The Queensland Future Climate Dashboard and Regional Explorer include the following extreme high temperature indices:

• Hot days: The number of days with maximum temperature over 35°C
• Hot nights: The number of nights with minimum temperature over 20°C
• Very hot days: The number of days with maximum temperature over 40°C
• Warm spells: The number of warm spell days within a season/year, where warm spell is defined as a spell of at least 4 consecutive days with temperatures above the 90th percentile of temperatures within that season during the calibration period (1981 - 2010)

These indices are relevant to human health, as hot days and hot nights can increase the chance of heat stress and heat-related illnesses. Consecutive hot days can also be damaging to human health. The risk to human health from prolonged high temperatures also increases pressures on medical services.

High temperatures can also be damaging to agriculture and livestock, and if the temperature is too high for too long, it can damage crops or reduce yield.

Very hot weather can also damage infrastructure, including roads, railways, and cause issues for aircraft.

Extreme cold temperature indices

For extreme cold temperature indices, we have the following available on the Dashboard and Regional Explorer:

• Cold nights: the number of nights with a minimum temperature &lt 5°C.
• Cold spells: The number of cold spell days within a season/year, where cold spell is defined as a spell of at least 4 consecutive days with temperatures below the 10th percentile of temperatures within that season during the calibration period (1981 - 2010).

Cold nights can be damaging to agriculture, with low temperatures able to damage or kill crops. Cold weather can also cause health problems in people, particularly for vulnerable populations such as the elderly or those already suffering from illness.

Extreme temperatures on Queensland Future Climate

The Queensland Future Climate Science Program has used state-of-the-art high-resolution downscaled climate simulations from both CMIP5 and CMIP6 global climate models to assess the impact of climate change on extreme temperatures.

The main tools for viewing climate projections data on the Queensland Future Climate website are the Queensland Future Climate Dashboard and Regional Explorer. Please refer to the user guide for detailed information on how to access and interpret information available from these resources.

Examples of the information on extreme temperature available on the Queensland Future Climate Dashboard and Regional Explorer are shown below.

 

 

Before using climate models to understand the impacts of future climate change, we need to evaluate how well they simulate the present-day climate. This is important to provide confidence that the models capture regional climate features and are suitable for understanding future climate impacts. Model evaluation also highlights areas where future model development can improve. The climate models available on the Queensland Future Climate Dashboard have all been thoroughly evaluated.

This factsheet provides an overview of the evaluation of the downscaled CMIP6 models. A full description is available in this peer-reviewed research paper.

How did we evaluate the models?

Climate models produce a range of different outputs that can be evaluated. As part of our evaluation, we examined mean climate, extreme climate, and seasonal cycles. The dynamically downscaled models were assessed by comparing them to observational data from the Australian Gridded Climate Dataset (AGCD) and to their host global climate model to evaluate whether the downscaling improved the representation of climate. Note that he AGCD is a dataset developed by the Bureau of Meteorology based on daily temperature and precipitation measured at weather stations. We used the period from 1981 to 2010 to evaluate performance and used a number of statistical measures, including bias, correlation, variability, and the Perkins skill score. These statistical measures tell us how similar the mean climate and climate extremes in the models are to the observations and how much the climate varies over time.

We also calculated 'added-value', which is how much improvement there is in performance in the downscaled high-resolution models compared to the coarse-resolution host models.

How well did the models perform?

We found the high-resolution downscaled climate models improved on the performance of the global climate models, with improvements particularly noticable for the extremes and in mountainous and coastal areas. The global climate models generally represent the mean climate well, but are limited when it comes to capturing climate extremes, and downscaled models tend to outperform them. The downscaled models generally show an improvement on extreme high and low temperatures, the number of rainy days, and extreme precipitation. The downscaled models also generally improved on the representation of the seasonal cycle. We found these improvements across all of Australia (Figure 1).

How well did the models perform in Queensland?

In Queensland, the downscaled models added value over the host models in all regions. The largest improvements were found in the South East Queensland region. This evaluation shows that the downscaling of the models improved performance for temperature and precipitation, and this dataset should be particularly useful when looking at climate extremes and when preparing regional climate hazard assessments.

 

 

Climate models provide critical information for us to understand, prepare for and adapt to the future climate. However, climate models are simplifications of the real climate and do not simulate the climate perfectly. In climate science, the mismatch between an observation and data from a climate model is called 'bias'. Biases in climate models are managed by a rigorous process of removing errors from the climate model outputs, commonly referred to as 'bias correction'.

The high-resolution climate change projections available on Queensland Future Climate have undergone an extensive evaluation (see Factsheet #13 - Model Evaluation, for more details). Model evaluation outcomes show that our models represent Australia's historical climate accurately, and improve upon Global Climate Models. The precision of high-resolution climate projections available on Queensland Future Climate is further enhanced by correcting biases for their usage in climate risk assessments (e.g., climate extremes, hydrological hazards, multi-sectoral impact assessments). The data on the Queensland Future Climate Dashboard have all been bias-corrected except for the variables in the Mean Climate Theme. This factsheet explains the bias correction process.

While we bias-correct the data for release on the Dashboard, so it can be used in hazard and risk assessments, we don't bias-correct the data in our scientific publications when our aim is to understand model behaviour.

What is bias correction?

Bias correction is a process of removing biases, or errors, in climate model outputs. This is achieved by 'calibrating' the raw climate model outputs against reliable observational climate data. This process can be as simple as correcting the mean value of a variable, but it can also mean correcting other measures such as variability of a variable and correlation with other variables. Bias correction can be applied to one variable at a time (univariate), or it can be applied to multiple variables at a time (multivariate). Multivariate bias correction methods aim to maintain physical relationships between the different variables and are generally recommended when looking at impacts that depend on multiple variables.

There are many bias correction methods available, and there is no one 'best' method. Different methods may perform better in different locations, seasons, for different climate models, and for different purposes. For this reason, it is important to evaluate different methods before applying them.

Which bias correction method did we use?

Bias correction requires robust, long-term observations for calibrating the modelled climate data. Where possible, we used the Australian Gridded Climate Dataset (AGCD; Jones et al., 2009) developed by the Bureau of Meteorology (BoM) as the source of historical observational climate data used in our bias correction methods. When data were not available from this product, other observational products were applied (further details below). The correction functions applied to the calibration period (1981-2020) are also applied to the future climate of each climate model on an individual grid cell basis. Separate correction functions are derived for every grid cell of the model.

We evaluated several bias correction methods, which were selected based on a review of the literature. Bias correction methods were evaluated for their impact on average bias, daily bias, extremes and the climate change signal. For Queensland Future Climate, we chose the multivariate method, Cannon's N-Method (MBCn; Cannon, 2018), applied on a monthly basis. This method corrects multiple properties of the model output and preserves trends in the climate change signal. We found applying the bias correction method on a monthly basis was better suited for correcting seasonal cycles than applying the method on all months grouped together. Prior to applying MBCn, we also corrected the frequency of wet and dry days, as we found this to be important across Queensland's arid areas.

To maximise the performance of the MBCn under limited computational resources, we tailored the corrections to the climate indices used by the Queensland Future Climate Science Program as follows:

• For extreme temperatures, we corrected maximum and minimum temperature.
• For humid heatwaves, we corrected average temperature and relative humidity.
• For droughts (SPI and SPEI), we corrected precipitation and FAO reference evaporation, which was obtained from the BoM's Australian Water Outlook. This corrected precipitation was also used to calculate extreme precipitation indices.
• For forest fire hazard based on the Forest Fire Danger Index (FFDI), we used the Multi-Source Weather (MSWX; Beck et al., 2022) dataset for the correction of wind and relative humidity, which were bias corrected separately from precipitation and maximum temperature. This was done as wind and relative humidity are taken at 3pm, while daily data was adopted for precipitation and maximum temperature.

An alternate approach was adopted for parts of the Torres Strait region where there was a lack of AGCD observational data. For regions where data was available, we first fit a function comparing the climate simulations to historic observations, which is calculated separately at every individual grid cell. This function is then applied to correct the historic and future climate simulations. For the Torres Strait region without data available, the bias correction 'function' from the nearest spatial grid cell with observational data available was applied to the spatial grid missing observational data. In this way, we were able to correct the climate model outputs for the whole of Queensland, even in regions where observations were lacking.

References

Beck, H. E., van Dijk, A. I. J. M., Larraondo, P. R., McVicar, T. R., Pan, M., Dutra, E., & Miralles, D. G. (2022). MSWX: Global 3-Hourly 0.1° Bias-Corrected Meteorological Data Including Near-Real-Time Updates and Forecast Ensembles. Bulletin of the American Meteorological Society, 103(3), E710-E732. https://doi.org/10.1175/BAMS-D-21-0145.1

Cannon, A. J. (2018). Multivariate quantile mapping bias correction: An N-dimensional probability density function transform for climate model simulations of multiple variables. Climate Dynamics, 50(1-2), 31-49. https://doi.org/10.1007/s00382-017-3580-6

Jones, D. A., Wang, W., & Fawcett, R. (2009). High-quality spatial climate data-sets for Australia. Australian Meteorlogical and Oceangraphic Journal, 58, 233-248.

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

People new to climate risk assessments often start by collecting all the climate data they can find and quickly get overwhelmed and paralysed by choice. As good as the Queensland Future Climate resources are, they can contribute to this problem by making it easy to access huge quantities of information.

You can avoid this pitfall by following a step-by-step risk assessment process that can help to identify the climate information you actually need to make more effective progress on climate risk management.

This factsheet provides an overview of some key concepts and considerations for climate risk assessments to help you think about where and how you might need to apply the climate data provided by the Queensland Future Climate resources. It concentrates on assessments of physical climate risk for assets, services or systems, and does not consider assessments of transition risk. It does not provide a detailed risk assessment approach or methodology but highlights some things to keep in mind as you explore climate data and risk. Links are provided to a selection of detailed risk assessment methodologies that may suit your needs.

What is a climate risk assessment?

A climate risk assessment is a structured process for estimating the likelihood of future climate hazards and their potential impacts to identify climate-related risks and possible management actions.

Importantly, a risk assessment process provides a way to navigate uncertainty, identify required data and information (climate and non-climate), consider multiple sources of information, and to make appropriate decisions with often imperfect information.

Understanding climate risk

Climate risk occurs at the intersection of a hazard, exposure, vulnerability and response (Figure 1).

A hazard is a natural or human-induced event with the potential to harm. Climate hazards are often described as acute (such as extreme weather events that are expected to become more frequent and or intense under climate change) or chronic (such as sea level rise or shifting climatic zones). It can also be useful to consider compound or coincident hazards that may overlap in time and/or space such as severe winds, flash flooding and storm tide inundation that can occur together with tropical cyclones.

Exposure refers to the presence of people, assets or other values in places that could be affected by a hazard.

Vulnerability is the susceptibility or predisposition to be adversely affected, reflecting a lack of resilience or adaptive capacity. You may see sensitivity and adaptability identified as components of vulnerability.

A Response includes policy or management actions introduced to adapt, mitigate or control risk by reducing exposure and/or vulnerability. However, a management response may not eliminate risk and can still leave some 'residual' risk or even produce new risks for both adaptation and mitigation responses (e.g. maladaptive responses or perverse outcomes). It is common to see definitions of climate risk and variations of figure 1 that exclude the response. However, the design and effectiveness of responses are important considerations in climate policy and can be a major influence of future climate risk for reporting purposes (e.g. financial disclosures), so we've included it here.

An understanding of exposure and vulnerability helps to identify relevant hazards for your system, interests or activities. It can also help to identify particular thresholds at which hazards will have an impact. This then helps to identify what kind of climate data you need, and most importantly, can narrow down or simplify your data search.

Risk assessment tools and frameworks

Multiple climate risk assessments tools, methodologies and frameworks are available, with many focussing on the needs of specific sectors. Most methodologies follow much the same process and align with the International Standard for Risk Management (ISO 31000). However, they may vary in the terminology used or recommended information sources.

Some examples include:

• Climate Compass (CSIRO) - a risk management tool for government agencies that enables the consideration of climate risk within current risk management processes
• National Climate Risk Assessment Methodology - developed by the Australian Government to support the first National Climate Risk Assessment
• CoastAdapt (National Climate Change Adaptation Research Facility) - a resource to support coastal adaptation that includes information for risk assessments and adaptation decision support
• Climate Risk Ready NSW Guide (NSW Government) - public sector climate risk management
• Climate risk management tools for households and small businesses (Queensland Government).

You may prefer to apply a different methodology tailored for your sector, or you could be required to use a particular methodology as directed by internal policy or compliance requirements.

Following one of these formal risk assessment methodologies provides confidence in the decision-making process and provides a way to make progress without being distracted by uncertainty or becoming overwhelmed by the volume of data available. As a result, it's essential to adopt a risk assessment methodology early in your exploration of climate risk to maximise chances of rapid and effective progress.

A common principle in assessing climate risk is to look at the greatest amount of plausible change, not just what's most likely or that is known with high confidence. This enables the consideration of events that may have a low likelihood but would have very high impact should they occur. Another common practice is to consider at least two alternative climate futures that provide plausible upper and lower bounds for a risk assessment, such as different future emissions pathways or scenarios. A risk assessment tool or framework will usually provide guidance on selecting the most appropriate scenarios to use.

Data requirements for climate risk assessments

Climate data is just one of the sources of information that feed into a risk assessment.

Climate projections data, scenarios and storylines can tell you how the physical hazard may change in the future, but you also need to consider what other data you need to assess exposure and vulnerability, and how you can combine this data to calculate risk. This can include information on the design or construction of physical assets, requirements for the reliable provision of services, financial data, socioeconomic data, ecological processes, impacts of historical events, critical hazard thresholds, and links and dependencies such as supply chain vulnerabilities.

It can often be the availability and suitability of these other datasets that can create challenges for a climate risk assessment rather than the availability or uncertainty in climate projections data.

Types of climate risk assessment

Climate risk assessments can vary in the level of detail required and are often performed in sequence, getting more focussed and detailed at each step. A common way to describe these stages is as a 1st, 2nd or 3rd pass risk assessment, but Climate Compass is a little different and describes these levels as 'scan', 'strategy' and 'project' cycles.

A generic risk assessment process

This section describes the key high-level steps in a generic process for a 2nd pass assessment addressing assets or services. A common practice is to conduct a risk assessment in a group setting that brings together people with expertise in different parts of your system to facilitate engagement across an organisation and to ensure multiple perspectives are considered.

Step 1 is where you define the system and boundaries, set your objectives for the system in question, identify your values of interest, level of risk tolerance, set the timeframe for your analysis based on the 'decision lifetime' or 'consequence lifetime' etc.

Step 2 - draw on past events and expert knowledge to identify points of sensitivity and the climate hazards that may be relevant for your interests, including any critical thresholds at which a climate variable or event becomes hazardous for your interests and values.

For this step, it may be useful to explore high-level climate information such as our Regional Climate Change Impact Summaries.

Step 3 - explore relevant climate projection information to look for potential changes that may elevate existing risks or introduce new risks.

For this step, resources with more detailed information such as the Queensland Future Climate Dashboard and Regional Explorer.

Step 4 - calculate risk ratings, identify management responses and prioritise further work.

At the end of this process, you'll have a register of climate risks relevant for your interests and clear decisions on next steps.

A key point is that you only need to start looking at detailed climate projections data in step 3 of this process, after you've already established the context and scope for your assessment and identified material climate hazards and points of climate sensitivity for your interests.

The benefit of doing the steps in this order is that it helps you to narrow down what you might need in the way of detailed climate data that's actually relevant for your system and values. This avoids becoming overwhelmed by the amount of climate data and information available from multiple sources and makes it easier to make progress through the assessment.

Evaluating risk and prioritising responses

The level of risk associated with a hazard or event is determined by combining information on likelihood and consequence to determine a risk rating using a risk matrix similar to the one in Table 1 below (part of step 4 in the process described above). This approach will be familiar to anyone who has done a risk assessment for workplace health and safety or a major project proposal.

Table 1: A typical risk assessment matrix used to determine overall risk ratings based on the likelihood and consequence of a hazard or event (adapted from Climate Compass).

The resulting risk rating determines your course of action, with examples provided in table 2 below where the responses are defined to match a predetermined risk tolerance (which may be defined in an organisation's risk policy).

Table 2: Examples of management responses that may be assigned to risk rating levels.

These tables are examples only - in the first step of the process, you can define the matrix and the required actions or responses to suit your own risk tolerance and strategic objectives. In addition, judgement and expert opinion are often required to supplement hard data when assessing both likelihood and consequence.

Importantly, you can include different levels of the same hazard in your risk matrix. E.g. One level may be likely and with moderate consequences leading to a high risk rating that would require an adaptation plan to be developed. A higher level of the same hazard may be unlikely but would have catastrophic consequences, leading to a medium risk rating that would require ongoing monitoring and review to detect any changes in the risk rating that might then require a different response.

Climate change can present some challenges for a traditional risk matrix approach:

• Traditional risk management relies heavily on past patterns and experience, but there is growing acknowledgement that past climate may not be a reliable indicator of future climate patterns.
• Likelihoods and consequences will both change with time reflecting the changing climate and management responses.
• Some future consequences and impacts can be difficult to envisage in the current environment.
• There may be an increased likelihood of simultaneous or multiple overlapping hazards or events (i.e. non-independent).
• The risk tolerance of different stakeholders within a system may not be the same, even for the same hazards and values.

Next steps

For hazards that present high or extreme risks, you may need to conduct a more detailed 3rd pass risk assessment, develop an adaptation plan, or perhaps an investment plan or business case to secure funding for the required management actions. These types of plans usually require much more detailed data to support more complex analyses and the methods are beyond the scope of this factsheet. We suggest seeking expert advice before commencing these next steps.

Factsheet #2 provides guidance on how to select a source of climate data and information for a variety of purposes in addition to climate risk assessments.

Key messages

• You will make more rapid progress exploring climate risk if you adopt a formal risk assessment methodology early in the process, and before you start exploring and collecting detailed climate data.
• There's no need to be overwhelmed by the volume and variety of climate data available - the risk assessment process will identify the types of climate data you need to assess the short list of hazards relevant for your system, values and objectives.
• You should have confidence in the risk assessment process as a way to make informed and defendable decisions even if some information is uncertain.

References and more information

CoastAdapt - https://coastadapt.com.au/how-to-pages/how-to-conduct-a-climate-change-risk-assessment.

Climate Compass - https://www.agriculture.gov.au/sites/default/files/documents/climate-compass-climate-risk-management-framework-commonwealth-agencies.pdf.

IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation - https://www.ipcc.ch/site/assets/uploads/2018/03/SREX_Full_Report-1.pdf.

This factsheet was developed in collaboration with the National Environmental Science Program Climate Systems Hub.

Collaboration is an important part of delivering quality climate projections data and services. Within Australia, several organisations produce downscaled, high-resolution regional climate projection information for Australia based on the latest generation of climate models from the 6th phase of the global Coupled Model Intercomparison Project (CMIP6).

This collaboration is coordinated through the National Partnership for Climate Projections to ensure consistency in the models and scenarios used by these multiple teams.

What is the National Partnership for Climate Projections?

The National Partnership for Climate Projections is a voluntary collaboration established to develop a consistent approach to deliver comparable, robust, fit-for-purpose future climate information to assess climate risks and inform adaptation planning.

The Partnership includes state and territory governments, CSIRO, the Bureau of Meteorology, universities, and Australian Government funded initiatives such as the National Environmental Science Program (NESP) Climate Systems Hub and the Australian Climate Service (ACS).

The partnership has three working groups (WGs) that concentrate on key aspects of the multi-faceted work involved in delivering climate projections data and services:

• WG1 - Climate change science and projections
• WG2 - Technology, computing and data
• WG3 - Climate services and user needs.

The Climate Projections Roadmap for Australia

The Climate Projections Roadmap for Australia describes the approach to deliver national future climate information to assess climate risks and inform adaptation planning.

The Roadmap includes activities related to the production (e.g. model selection), climate projections analysis (e.g. hazards assessments), and delivery of projections data and information via climate services. The roadmap also seeks to increase collaboration over the longer term to guide investment in climate research and enabling research infrastructure.

The Roadmap outlines a collaborative approach to bring together existing initiatives from the 4 main climate modelling groups in the Partnership - the Australian Climate Service (CSIRO and the Bureau of Meteorology), the Queensland Future Climate Science Program (Queensland Government and the University of Queensland) and the NSW Government.

Under the Roadmap, these 4 modelling groups will provide CMIP6 projections data for three emissions scenarios (described using Shared Socio-economic Pathways or SSPs) that represent the most likely range of future emissions - SSP1-2.6 as a low-end scenario, SSP2-4.5 as a moderate scenario, and SSP3-7.0 as the high-end emissions scenario. Please refer to factsheet #6 for more information on these SSPs.

Table 1 in the Roadmap outlines the work underway to produce national downscaled climate projections across the main modelling groups, including information on the regional downscaling approach, the models used and scenarios.

Why are there still multiple climate data sources?

You may see organisations within the Partnership release new downscaled climate data products as their modelling work is completed. The partners may also provide their own tailored 'portals' that enable easy access to climate projections data and information for their jurisdictions. For example, under the Partnership, both the Queensland and NSW governments are producing downscaled climate projections datasets that cover all of Australia. These national datasets are usually released as gridded datasets suitable for more technical users. Please see factsheet # 4 for more information and links to access Queensland's gridded datasets.

However, both state governments also produce climate portals designed to meet the needs of users within their states - the Queensland Future Climate resource provides climate projections data and information for Queensland only, while the NSW Government provides climate data and information for NSW and the ACT via AdaptNSW. The Australian Climate Service will produce a climate portal designed to support the National Emergency Management Agency, the National Climate Risk Assessment and the development of the National Adaptation Plan.

Other states and territories also release climate information via portals or reports tailored to the needs of users in their jurisdictions.

The National Environmental Science Program Climate Systems Hub provides a handy guide to the different sources of climate information and how you can pick the right one for your needs.

For people in Queensland, factsheet #2 provides a similar guide to climate resources relevant for Queensland.

This factsheet provides examples of how some people and organisations are using the climate projections data and information available from the Queensland Future Climate site.

Heatwave preparedness and response

Queensland Health is the primary agency that leads preparedness and responses to heatwaves under the State Disaster Management Plan.

Queensland Future Climate is an important resource for Queensland Health and other stakeholders in the heatwave space because it provides an independent and reputable source of information on plausible future climate conditions. For example, this data can be used by university researchers working on the health implications of heatwaves that informs Queensland Health's work.

Projections data provided by Queensland Future Climate supported the State Heatwave Risk Assessment, a collaborative project that also involved the Queensland Fire and Emergency Services. The website also included a heatwaves case study prior to the rapid growth in interest in this area as a multi-sector issue.

The Queensland Future Climate Dashboard provides information on heat in ways that help us understand heatwaves risks, particularly through including more than just average or maximum temperatures. For example, there’s a clear distinction between heatwaves and extreme temperature events, and there’s information on the projected frequency and duration of heatwaves for different future time periods.

The ability to switch between emissions scenarios is helpful depending on the type of analysis or whether we’re most interested in the ‘most likely’ level of change or possible high-impact changes.

The flexibility of the interface depending on a user’s needs and interests is also valuable, including the ability to download shapefiles for spatial analysis and to access summary information filtered by regions like local government areas or disaster districts.

Healthy Places, Healthy People initiative

The Healthy Places, Healthy People initiative led by Queensland Health and the Office of the Queensland Government Architect provides a mechanism to ensure health outcomes are prioritised in built environment design, planning and investment decision making. Queensland Health has a strong interest in influencing state agency and local government authorities to prioritise providing increased access to high-quality natural shade in more places in our communities. High quality and well-planned shade tree planting significantly reduces UV exposure and skin cancer risk and creates cooler and more comfortable environments proven to positively impact physical activity and mental health outcomes.

The Queensland Future Climate Dashboard is a valuable resource for Queensland Health as well as state agencies and local government authorities responsible for planning and delivering built environment infrastructure projects for our communities. Queensland Health uses climate, temperature and heatwave projections data together with relevant health data to influence state agency and local government partners to prioritise natural shade when planning and delivering infrastructure projects given it reduces urban heat impacts significantly and provides multiple health benefits.

Queensland Tourism Resilience Platform

Queensland is the most disaster-affected state in Australia, with the flood event affecting Queensland and New South Wales in 2021-2022 the costliest flood disaster in Australian history resulting in over $4 billion in asset losses.

The tourism industry is a vital economic asset for Queensland. However, it is extremely vulnerable to external disruptions such as disaster events. In a future shaped by climate change, we can expect Queensland to experience higher temperatures, more frequent hot days, harsher fire weather, less frequent but more intense tropical cyclones, rising sea level, and more intense downpours.

These projected changes may have significant implications for the Queensland tourism industry. For example, warmer weather in winter, and uncomfortably hot and prolonged temperatures in summer, may change typical tourism seasons in different regions. Queensland’s tourism businesses are predominantly micro and small-to-medium enterprises that may not have the resources in place to withstand extraordinary weather events.

The Queensland Tourism Resilience Platform supports sustainable and resilient tourism development across Queensland by giving State Government agencies, regional tourism organisations and local councils the tourism, climate change, economic and market data they need to support tourism related investment, risk management and planning.

The Platform was developed to:

• provide a tourism inventory of product, experiences and infrastructure
• provide a regional demand assessment with medium to long-term outlook
• be used as a geospatial platform of tourism assets, allowing a visual lens on disaster risks and external shocks through climate exposure analysis.

Extensive stakeholder engagement informed the design and development of the Platform, and there are now over 300 registered users across 119 unique entities on the Platform.

The Platform is helping users with program design, building industry capability, and developing plans for destination management, business continuity, and disaster risk management.

The Queensland Future Climate Dashboard is a key source of future climate data for the Platform, along with the Cluster Reports from CSIRO and the Bureau of Meteorology. For each region, the Platform provides easy access to summary information for a range of climate variables for 2030 and 2050 sourced from the Dashboard. Information on a subset of hazards to which tourism assets are exposed in each region is provided in short text summaries and as overlays on a regional map.

The Platform allows the climate data to be combined with data on tourism assets from sources such as the Australian Tourism Data Warehouse (ATDW), Trip Advisor, and STR, and with socioeconomic data from the Australian Bureau of Statistics, with modelling completed by SGS Economics.

The Platform was officially launched in December 2023, delivered in partnership with AECOM. The Platform was developed with assistance provided through the jointly-funded Commonwealth-State Disaster Recovery Funding Arrangements (DRFA).

If you would like access to the Platform, please contact tourism@dtis.qld.gov.au for more information.