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: |
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| 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.
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Climate theme |
Variables |
Daily |
Monthly |
Seasonal (long-term averages) |
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Mean Climate |
Mean Temperature |
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Minimum Temperature |
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Maximum Temperature |
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Precipitation |
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Pan-evaporation |
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Relative Humidity |
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Surface Wind |
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Solar Radiation |
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Bias-corrected Mean Climate |
Bias-corrected Mean Temperature |
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Bias-corrected Minimum Temperature |
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Bias-corrected Maximum Temperature |
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Bias-corrected Precipitation |
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Heatwaves |
Heatwave Peak Temperature |
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Heatwave Frequency |
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Heatwave Duration |
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Maximum Heatwave Duration |
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Extreme Temperature |
Hot Days |
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Hot Nights |
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Warm Spell Duration |
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Cold Spell Duration |
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Cool Nights |
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Very Hot Days |
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Extreme Precipitation |
Maximum 1-day Precipitation |
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Maximum 5-day Precipitation |
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Extreme Wet Day Precipitation |
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Simple Daily Intensity |
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Consecutive Dry Days |
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Consecutive Wet Days |
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SPI-Droughts |
Frequency of Moderate Drought |
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Frequency of Severe Drought |
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Frequency of Extreme Drought |
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Duration of Droughts |
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SPI-wetness |
Frequency of Moderate Wetness |
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Frequency of Severe Wetness |
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Frequency of Extreme Wetness |
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Duration of Wetness |
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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"))
