Climate Change in Australia

Climate information, projections, tools and data

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Data availability

Data Availability

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

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

'Application-ready' future climate data (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 ).

The application-ready data are provided as single-model results from a subset of eight CMIP5 models (that simulate most of the range of projected possible future climates for Australia), plus downscaling where appropriate. This will simplify the range of projection choices and reduce the effort required to manage data in risk assessment processes. The eight models were selected using the Climate Futures approach (TRBox9.1) and other criteria (see TRBox9.2 and Eight model selection ). 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. These data are available as gridded data via the Map Explorer tool and also as station data via the Station Data Download tool.

Single-model gridded change data for the eight model subset are also viewable and downloadable via the Map Explorer tool. Gridded change data from the remaining models are also available for download via the Gridded Data Download tool.

Change data and ‘application-ready’ data are available at temporal and spatial scales to meet user requirements while remaining scientifically robust. Availability is indicated in Table 1 (change data), and Table 2 (Application-ready data) of the Data Delivery brochure (accessed from the Publications Library ).

Data availability

Variables

Variable	Units	Change units	Description
Mean temperature	°C	°C	Average near-surface air temperature. This is analogous to temperature measured in a Stevenson Screen.
Maximum daily temperature	°C	°C	Average daily maximum near-surface air temperature. This is analogous to temperature measured in a Stevenson Screen.
Minimum daily temperature	°C	°C	Average daily minimum near-surface air temperature. This is analogous to temperature measured in a Stevenson Screen.
Rainfall	mm	%	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.
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.
Point potential evapotranspiration	mm	%	Average point potential evapotranspiration, derived from other GCM variables according to the method of Morton (1983).
Wet areal evapotranspiration	mm	%	Average wet areal potential evapotranspiration, derived from other GCM variables according to the method of Morton (1983).
Solar radiation	Wm⁻²	%	Average downwelling short-wave radiation at the Earth’s surface.
Mean wind-speed	ms⁻¹	%	Average near-surface (2 metres) wind speed.
1-in-20 year daily rainfall	mm/day	%	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.
1-in-20 year daily wind speed	ms^-1	%	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.
Hottest day	°C	°C	The daily maximum temperature that can be expected to occur, on average, once per year.
1-in-20 year hottest day	°C	°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.
Coldest day	°C	°C	The daily minimum temperature that can be expected to occur, on average, once per year.
1-in-20 year coldest day	°C	°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.
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.
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.
Sea level	m	m	Mean sea level
Sea level allowance	m	N/A	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.
Sea-surface salinity	g/kg	g/kg	Salinity of the sea surface layer.
Ocean acidification	ΩA	ΩA	Aragonite saturation which is a surrogate for carbonate concentration. See section 8.5 of the Technical Report and the Glossary.
Ocean pH	pH scale	pH change	Acidity of the sea furface. See section 8.5 of the Technical Report.
Sea surface temperature	°C	°C	Average temperature of the sea surface.

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. 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 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. This 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 will be made available at the native grid resolution for each model. In addition, bi-linear interpolation will be undertaken to produce data on a common 5 km grid. Furthermore, for climate variables for which application-ready data will be available, the data will also be bi-linearly interpolated to a 5 km grid (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.

Page updated: 24^th November 2017