Factsheet 4: Our climate models and variables explained

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 < 10^th 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:

*A screenshot of the gridded data catalog on the TERN portal.*

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.

Last updated: 21 February 2025

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