High Resolution Projections Data - CMIP5

High-resolution downscaled CMIP5 climate change projections for Queensland are available for download from the following link: Terrestrial Ecosystem Research Network (TERN). Eleven CMIP5 Global Climate Models (GCMs) were dynamically downscaled to produce gridded projections with a spatial resolution of 10 km. This dataset is known as QldFCP-1.

Please note: a more up-to-date dataset consisting of dynamically downscaled CMIP6 projections for all of Australia is now available. The projections data available via the Queensland Future Climate Dashboard, Regional Explorer and other online information products are based on these CMIP6 projections. See the CMIP6 High Resolution Projections Data page for more details.

The eleven downscaled CMIP5 GCMs are listed below:

CMIP5 model name: Model name: Institution name(s): Country of origin:
ACCESS1-0 Australian Community Climate and Earth-System Simulator, version 1.0 CSIRO & BoM Australia
ACCESS1-3 Australian Community Climate and Earth-System Simulator, version 1.3 CSIRO & BoM Australia
CCSM4 Community Climate System Model NCAR USA
CNRM-CM5 Centre National de Recherches Météorologiques Coupled Global Climate Model, version 5 CNRM & CERFACS France
CSIRO-Mk3.6 Commonwealth Scientific and Industrial Research Organisation Mark 3.6.0 CSIRO & Qld Govt Australia
GFDL-CM3 Geophysical Fluid Dynamics Laboratory Climate Model, version 3 GFDL NOAA USA
GFDL-ESM2M Geophysical Fluid Dynamics Laboratory Earth System Model with Modular Ocean Model, version 4 component GFDL NOAA USA
HadGEM2 Hadley Centre Global Environment Model, version 2 Met Office Hadley Centre UK
MIROC5 Model for Interdisciplinary Research on Climate, version 5 AORI Japan Japan
MPI-ESM-LR Max Planck Institute Earth System Model, low resolution Max Planck Institute Germany
NorESM1-M Norwegian Earth System Model, version 1 (intermediate resolution) Norwegian Climate Centre Norway

 

Projections are available for both moderate and high-emissions scenarios (RCP4.5 and RCP8.5). Visit the Understanding the data page to learn more about our modelling strategy. A subset of five variables is available at daily time-steps to facilitate other modelling initiatives. Four of them have also been bias-corrected against observations. Eight mean climate variables are available at monthly time-step intervals, while a comprehensive set of 32 metrics is available at the seasonal scale.

The future climate projections at high temporal resolution (e.g., daily scale) were aggregated into 20-year averages with future changes from the 1986-2005 reference period computed as both absolute and percentage change values. The data are available for calendar seasons – i.e., summer (December, January and February), autumn (March, April and May), winter (June, July and August) and spring (September, October and November). In addition, we also provide aggregated information for wet (November to April) and dry (May to October) periods as well as on an annual basis. Modelled climatologies for the reference period 1986-2005 are also available (termed "climatologies").

For additional information about our spatial data products, refer to Queensland Future Climate Datasets documentation.

A set of 32 climate variables is available at TERN as per the table below. Click on “RCP4.5” or “RCP8.5” for direct access to the 11 downscaled CMIP5 models and the ensemble averages for the two emissions scenarios. Data is in Network Common Data Form (NetCDF). It can be easily converted to other grid formats using free software – for an example in R check the bottom of this page.

 

Climate theme

Variables

Daily

Monthly

Seasonal (long-term averages)

Mean Climate

Mean Temperature

RCP4.5

RCP8.5

RCP4.5

RCP8.5

RCP4.5

RCP8.5

Minimum Temperature

RCP4.5

RCP8.5

RCP4.5

RCP8.5

RCP4.5

RCP8.5

Maximum Temperature

RCP4.5

RCP8.5

RCP4.5

RCP8.5

RCP4.5

RCP8.5

Precipitation

RCP4.5

RCP8.5

RCP4.5

RCP8.5

RCP4.5

RCP8.5

Pan-evaporation

RCP4.5

RCP8.5

RCP4.5

RCP8.5

RCP4.5

RCP8.5

Relative Humidity

 

RCP4.5

RCP8.5

RCP4.5

RCP8.5

Surface Wind

 

RCP4.5

RCP8.5

RCP4.5

RCP8.5

Solar Radiation

 

RCP4.5

RCP8.5

RCP4.5

RCP8.5

Bias-corrected Mean Climate

Bias-corrected Mean Temperature

RCP4.5

RCP8.5

 

 

 

Bias-corrected Minimum Temperature

RCP4.5

RCP8.5

 

 

 

Bias-corrected Maximum Temperature

RCP4.5

RCP8.5

 

 

 

Bias-corrected Precipitation

RCP4.5

RCP8.5

 

 

 

Heatwaves

Heatwave Peak Temperature

 

 

 

RCP4.5

RCP8.5

Heatwave Frequency

 

 

RCP4.5

RCP8.5

Heatwave Duration

 

 

RCP4.5

RCP8.5

Maximum Heatwave Duration

 

 

 

RCP4.5

RCP8.5

Extreme Temperature

Hot Days

 

 

 

RCP4.5

RCP8.5

Hot Nights

 

 

RCP4.5

RCP8.5

Warm Spell Duration

 

 

RCP4.5

RCP8.5

Cold Spell Duration

 

 

RCP4.5

RCP8.5

Cool Nights

 

 

RCP4.5

RCP8.5

Very Hot Days

 

 

 

RCP4.5

RCP8.5

Extreme Precipitation

Maximum 1-day Precipitation

 

 

RCP4.5

RCP8.5

Maximum 5-day Precipitation

 

 

RCP4.5

RCP8.5

Extreme Wet Day Precipitation

 

 

RCP4.5

RCP8.5

Simple Daily Intensity

 

 

RCP4.5

RCP8.5

Consecutive Dry Days

 

 

RCP4.5

RCP8.5

Consecutive Wet Days

 

 

 

RCP4.5

RCP8.5

SPI-Droughts

Frequency of Moderate Drought

 

 

 

RCP4.5

RCP8.5

Frequency of Severe Drought

 

 

RCP4.5

RCP8.5

Frequency of Extreme Drought

 

 

RCP4.5

RCP8.5

Duration of Droughts

 

 

 

RCP4.5

RCP8.5

SPI-wetness

Frequency of Moderate Wetness

 

 

 

RCP4.5

RCP8.5

Frequency of Severe Wetness

 

 

RCP4.5

RCP8.5

Frequency of Extreme Wetness

 

 

RCP4.5

RCP8.5

Duration of Wetness

 

 

 

RCP4.5

RCP8.5

 

Converting NetCDF files to the GeoTIFF Format

All high resolution datasets are provided in the NetCDF format, which is the standard used for climate modelling and forecasting. For users who require the GeoTIFF format, these NetCDF files can be readily converted. An example on how this can be achieved in 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"))
Last updated: 30 August 2024