Climate Change in Australia

Climate information, projections, tools and data

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Data availability

Data Availability

Three types of projections data representing a range of climate variables are available through this website:

Projected climate changes (relative to the reference period 1986–2005), based on CMIP5 Global Climate Models (GCMs) judged to perform well over Australia (Model evaluation ) plus dynamic and statistical downscaling where appropriate;

'Application-ready' future climate data based on CMIP5 GCMs (where projected climate change data are applied to 30 years of observed data (1981–2010) for use in detailed risk assessments). Application ready data have been made available where appropriate baseline data existed (See the Data Delivery brochure in the Publications Library ).

Australian Climate Service 'application-ready' future climate data based on CMIP6 GCMs (where projected climate change data are applied to 30 years of observed data (1985–2014).

The application-ready data are provided as single-model results from a subset of CMIP5 or CMIP6 GCMs, plus downscaling where available and appropriate. The use of a subset of GCMs simplifies the range of projection choices and reduces the effort required to manage data in risk assessment processes.

For CMIP5, eight models were selected using the Climate Futures approach (see Technical Report Chapter 5 Box 9.1)and other criteria (see Technical Report Chapter 5 Box 9.2 and Model sub-sets for application-ready data ). For any given region, time period and greenhouse scenario, three of these eight models can be used to represent ‘best case’, ‘worst case’ and ‘maximum consensus’ scenarios.

For CMIP6, the subset of nine models selected for dynamical downscaling over Australia by the Australian Climate Service and the NSW Government’s NARCliM2.0 project was used. These models perform relatively well at simulating relevant aspects of the climate and represent some of the range of changes in annual mean temperature and rainfall over Australia simulated by the full set of ~50 CMIP6 GCMs (Grose et al 2023). For any given region, time period and greenhouse scenario, after analysis of data for all nine GCMs, it should be possible to select a smaller number of models to efficiently sample some of the uncertainty in CMIP6-simulated future climate changes. However, unlike the eight GCMs selected for CMIP5, the nine GCMs selected for CMIP6 were selected based on the suitability of their outputs as inputs to an ensemble of dynamical downscaling simulations. A full analysis of the extent to which the nine CMIP6 GCMs represent the range of uncertainty in changes all key climate variables in different regions of Australia has not yet been undertaken. This means that it is not possible to guarantee that the nine GCMs will include reasonable representations of ‘best case’, ‘worst case’ and ‘maximum consensus’ scenarios.

For CMIP5, the projected climate changes and application-ready data are available from Climate Change in Australia via the Download Datasets page and the Map Explorer tool. Availability of daily, monthly, seasonal and annual data and gridded data, area averages and data for cities and towns is indicated in Table 1 (change data), and Table 2 (application-ready data) of the Data Delivery brochure. Currently, Climate Change in Australia only provides daily gridded application-ready data for CMIP6. These data are available via the Download Datasets page.

The table below summarises the climate variables for which at least some data types are available for CMIP5 and indicates those for which CMIP6 data daily gridded application-ready data are also available.

Variable	Units	Description	CMIP6 Data
Mean temperature	°C	Average near-surface air temperature. This is analogous to temperature measured in a Stevenson Screen.	No
Maximum daily temperature	°C	Average daily maximum near-surface air temperature. This is analogous to temperature measured in a Stevenson Screen.	Yes
Minimum daily temperature	°C	Average daily minimum near-surface air temperature. This is analogous to temperature measured in a Stevenson Screen.	Yes
Rainfall	mm (changes in %)	Average precipitation reaching the Earth’s surface. In most Australian locations, this is most likely to be rain only, however in some alpine regions, snow may be included.	Yes
Relative humidity	%	Average near-surface relative humidity, derived from other GCM variables. * Some tools provide change values as absolute change (%RH) from the baseline, others (e.g. single-model results) provide change values as the proportional change (%) relative to the baseline. See the explanatory information provided for individual models.	Yes
Point potential evapotranspiration	mm (changes in %)	Average point potential evapotranspiration, derived from other GCM variables according to the method of Morton (1983).	No
Wet areal evapotranspiration	mm (changes in %)	Average wet areal potential evapotranspiration, derived from other GCM variables according to the method of Morton (1983).	No
Solar radiation	Wm⁻² (changes in %)	Average downwelling short-wave radiation at the Earth’s surface.	Yes
Mean wind-speed	ms⁻¹(changes in %)	Average near-surface (2 metres) wind speed.	Yes
1-in-20 year daily rainfall	mm/day (changes in %)	The daily rainfall total that can be expected to occur, on average, once every 20 years. In other words, this rainfall total has a 5% probability of occurring in any given year. Note that the rainfall amount is that simulated to fall between 00:00 and 23:59. Most observed rainfall data are the rain that fell between 09:00 and 08:59 the following day.	No
1-in-20 year daily wind speed	ms^-1 (changes in %)	The daily average wind speed that can be expected to occur, on average, once every 20 years. In other words, this daily average wind speed has a 5% probability of occurring in any given year.	No
Hottest day	°C	The daily maximum temperature that can be expected to occur, on average, once per year.	No
1-in-20 year hottest day	°C	The daily maximum temperature that can be expected to occur, on average, once every 20 years. In other words, this daily maximum temperature has a 5% probability of occurring in any given year.	No
Coldest day	°C	The daily minimum temperature that can be expected to occur, on average, once per year.	No
1-in-20 year coldest day	°C	The daily minimum temperature that can be expected to occur, on average, once every 20 years. In other words, this daily minimum temperature has a 5% probability of occurring in any given year.	No
Time in drought	%	Based on estimates of Standardised Precipitation Index (SPI), the proportion of time with SPI<-1. See Box 7.2.1 of the Technical Report (p.122) for more detail.	No
Fire weather	N/A	Macarthur Forest Fire Danger Index (FFDI). CFFDI is the Cumulative FFDI – the sum of daily FFDI values over a year from July to June. Daily FFDI time series can also be downloaded for selected locations (download site details spreadsheet). See the Technical Report (section 7.8) and Cluster Reports (section 4.10) for more detail.	No
Sea level	m	Mean sea level	No
Sea level allowance	m	The minimum height that structures would need to be raised for the future period so that the expected number of exceedences of that height would remain the same as for the 1986-2005 average sea level conditions. See the Technical Report (section 8.2) for more detail.	No
Sea-surface salinity	g/kg	Salinity of the sea surface layer.	No
Ocean acidification	ΩA	Aragonite saturation which is a surrogate for carbonate concentration. See section 8.5 of the Technical Report and the Glossary.	No
Ocean pH	pH scale	Acidity of the sea furface. See section 8.5 of the Technical Report.	No
Sea surface temperature	°C	Average temperature of the sea surface.	No

Reference: Morton FI (1983) 'Operational estimates of areal evapotranspiration and their significance to the science and practice of hydrology.' Journal of Hydrology 66, 1-76.

DOWNSCALING

In some cases, dynamical or statistical downscaling of information from global climate models (GCMs) offers more detailed information about climate change. Dynamical downscaling involves the use of fine resolution climate models (often using regional climate models: RCMs) which will solve the same physical processes but with a finer resolution, opening up the possibility for a more accurate depiction of these processes, especially in regions with complex topography. Various Australian Federal and State government initiatives have produced projections using dynamical downscaling at a range of spatial resolutions. Statistical downscaling involves applying observed statistical relationships (between large-scale and local climate) to large-scale changes in climate simulated by models, in order to estimate changes at local scales.

However, downscaling doesn’t always provide a superior projection of change for a given region, and there are numerous issues to contend with: selection of GCMs for downscaling, pros and cons of different downscaling methods, representation of the physical processes that drive change, internal consistency of projected changes across multiple variables, as well as practical issues around handling large datasets. Therefore, provision of downscaled data outputs is currently undertaken with advanced users on a case-by-case basis at this stage. Contact us for further information.

CONFIDENCE IN CLIMATE PROJECTIONS

The projection data available on this website should be used in conjunction with information found in the Technical and Cluster Reports and more recent literature incorporating information from CMIP6 (e.g., from the Intergovernmental Panel on Climate Change Sixth Assessment Report, including the associated Atlas, Factsheets, and Grose et al 2020). The Technical Report includes a description of the level of confidence in projections (Chapter 6), which is higher for some models than others, and higher for some climate variables (e.g. regional temperature) than for others (e.g. local rainfall, extreme weather).

Spatial scale

The data are provided at to suit different purposes: NRM super-clusters, NRM clusters, NRM sub-clusters, gridded, and for some cities and towns (see brochure for more information).

The CMIP5 models used in this assessment have an average spatial resolution (spacing between data points) of approximately 180km (ranging from around 67 to 333km). The projected change data is available at the native grid resolution for each model. However, application-ready data for the CMIP5 models is available at a much finer resolution as the process for generating the data includes bi-linear interpolation to the ~5km grid of Bureau of Meteorology observed data (see Figure below).

Figure shows the results of bi-linear interpolation (right) from global climate model output (left). Note that although the data look more detailed and accurate when re-gridded to a finer scale, the process of bi-linear interpolation does not add extra information, and therefore is not more accurate than the coarser resolution data. See also the Common mistakes page.

The nine CMIP6 models for which application-ready data are available have spatial resolutions ranging from around 80 to around 140km. Application-ready data are available on a ~5km grid.

Page updated: 19^th May 2025