High Resolution Projections Data - CMIP6
The Queensland Government has produced high-resolution climate change projections for Australasia using dynamical downscaling of global climate models (GCMs) from the latest phase of the Coupled Model Intercomparison Project (CMIP6).
Eleven CMIP6 GCMs were selected to produce the downscaled climate change simulations with three models run in multiple configurations, leading to an ensemble of 15 regional climate models. The work includes a 10km resolution dataset of mean climate and climate hazard indices for the whole of Australia, and an hourly 20km resolution dataset for all of Australasia that is compliant with the COordinated Regional climate Downscaling EXperiment (CORDEX) requirements. Both the 10km dataset and CORDEX-compliant 20km dataset can be accessed as gridded netCDF datasets via the National Computational Infrastructure (NCI) for the whole Australasian region. The 10km resolution projections for Queensland are also available for viewing via the Queensland Future Climate Dashboard and Regional Explorer. A paper evaluating these new datasets is also available.
The Conformal Cubic Atmospheric Model (CCAM) was used to dynamically downscale the CMIP6 GCMs at a 10 km resolution for the 1960 – 2100 period for three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5 and SSP3-7.0). Some CCAM simulations were run in atmosphere-only mode and others in ocean-coupled form. For atmosphere only runs, sea surface temperature and sea ice were bias corrected prior to running CCAM. The 10km projections were regridded for a 20km domain to produce the hourly CORDEX compliant downscaled simulations.
The downscaled models are listed below (note that models denoted with _oc represent coupled ocean models):
| CMIP6 model name: | Model name: | Institution name(s): | Ensemble member: | Country of origin: |
|---|---|---|---|---|
| ACCESS-ESM1.5 | Australian Community Climate and Earth System Simulator, version 1.5, CCAM atmospheric model version | CSIRO & BoM | r6i1p1f1 | 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 | r20i1p1f1 & r40i1p1f1 | Australia |
| ACCESS_CM2_oc | Australian Community Climate and Earth System Simulator, version 2, CCAM coupled ocean version | CSIRO & BoM | r2i1p1f1 | Australia |
| CMCC-ESM2 | Centro Euro-Mediterraneo sui Cambiamenti Climatici Earth System Model, version 2 | Centro Euro-Mediterraneo sui Cambiamenti Climatici | r1i1p1f1 | Italy |
| CNRM-CM6-1-HR | Centre National de Recherches Météorologiques Coupled Global Climate Model, version 6.1, high-resolution | CNRM & CERFACS | r1i1p1f2 | 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 | r1i1p1f2 | France |
| EC-Earth3 | European Community Earth-System Model, version 3 | European consortium of national meteorological services and research institutes | r1i1p1f1 | Various |
| FGOALS-g3 | Flexible Global Ocean-Atmosphere-Land System Model, grid point version 3 | Chinese Academy of Sciences | r4i1p1f1 | China |
| GFDL-ESM2M | Geophysical Fluid Dynamics Laboratory Earth System Model, version 4 | GFDL NOAA | r1i1p1f1 | USA |
| MGISS-E2-2-G | Goddard Institute for Space Studies Model E2.2Gn | GISS NASA | r2i1p1f2 | USA |
| MPI-ESM1-2-LR | Max Planck Institute Earth System Model, version 1.2, low resolution | Max Planck Institute | r9i1p1f1 | Germany |
| MRI-ESM2-0 | Meteorological Research Institute Earth System Model, version 2.0 | Meteorological Research Institute | r1i1p1f1 | Japan |
| NorESM1-MM | Norwegian Earth System Model, version 2, 1 degree resolution | Norwegian Climate Centre | r1i1p1f1 | Norway |
| NorESM2-MM_oc | Norwegian Earth System Model, version 2, 1 degree resolution, CCAM coupled ocean version | Norwegian Climate Centre | r1i1p1f1 | Norway |
Note that 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).
The new CMIP6 projections dataset will be referred to as QldFCP-2 (with the original CMIP5 projections being QldFCP-1).
2026 release of datasets:
In addition to the 10km dataset and CORDEX-compliant 20km dataset mentioned above, the Queensland Future Climate Science Program has released five additional high-resolution climate projection datasets for Australia, providing climate information to support planning, research, and impact assessments:
Gridded downscaled CMIP6 climate projections for Australia based on Shared Socioeconomic Pathways (NetCDF, 10 km resolution):
The Queensland Future Climate Science Program has downscaled CMIP6 climate projections (QldFCP-2) and produced web-based decision support tools such as the Queensland Future Climate Dashboard and Regional Explorer. These tools provide summaries of projected climate metrics (mean climate and extreme indices) for Queensland. To enable users to further explore the underlying data, the Queensland Future Climate Science program has released the gridded NetCDF datasets underpinning these web applications on TERN. The data is available in two formats: 1. Annual time series, and 2. Average climatologies for five 20-year time periods. The dataset covers the entire Australian continent and includes three Shared Socioeconomic Pathway emissions scenarios (SSP1-2.6, SSP2-4.5, and SSP3-7.0). Data is available for all the variables and seasonal options featured in the web tools. This dataset will support a wide range of applications, including climate risk assessments, infrastructure planning, natural resource management, and research into the impacts of climate change on ecosystems, communities, and industries.
Gridded downscaled CMIP6 climate projections for Australia based on Global Warming Levels (NetCDF, 10 km resolution):
The Queensland Future Climate Science Program has also released a gridded downscaled CMIP6 climate projections dataset for Australia using a Global Warming Level (GWL) framework. Unlike projections organised by future emissions scenarios, this dataset presents climate information at specific levels of global warming above the pre-industrial baseline, enabling users to assess potential climate impacts under defined warming thresholds. The dataset is based on the SSP3-7.0 emissions scenario and provides 20-year average climatologies centred on global warming levels of 1.2°C, 1.5°C, 2.0°C, 2.5°C, 3.0°C and 4.0°C above the pre-industrial baseline. Consistent with the SSP-based dataset described above, the data are provided at 10 km spatial resolution across the Australian continent and include the full suite of climate variables and seasonal summaries available through the Queensland Future Climate Dashboard and Regional Explorer.
Regionally averaged CMIP6 climate projections for Australia based on Shared Socioeconomic Pathways (CSV format):
The Queensland Future Climate Science Program has also developed a regionalised climate projections dataset to support applications requiring information at local administrative, catchment or natural resource management scales. This was achieved by spatially averaging Queensland's downscaled CMIP6 climate projections across a range of Australian regional boundaries, including Local Government Areas (LGAs), River Basins, and Natural Resource Management (NRM) regions. The same regionalisation methodology is used to generate the spatially averaged climate projections presented through the Queensland Future Climate Dashboard and Regional Explorer, ensuring consistency between the downloadable datasets and online decision-support tools. The resulting spatially averaged mean-climate and extreme index projections are available in three CSV data formats, each designed to support different user needs and applications.
Gridded downscaled CMIP6 projections for biophysical modelling across Australia (NetCDF, 5 km resolution):
The Queensland Future Climate Science Program has also released a gridded dataset of daily climate variables commonly required for biophysical modelling applications. The product provides six key daily climate variables: maximum temperature, minimum temperature, precipitation, solar radiation, vapour pressure, and pan evaporation. The downscaled CMIP6 model simulations have been bias-corrected to the Scientific Information for Land Owners (SILO) 5 km observational grid using Quantile Mapping for Extremes (QME), a method designed to improve the representation of both average climate conditions and climate extremes. SILO provides nationally consistent daily climate data for Australia from 1889 to the present and is widely used for climate, agricultural, and environmental applications.
Available through the National Computational Infrastructure (NCI) in NetCDF format, the dataset has been designed to support users familiar with SILO-based workflows who require future climate projections at a spatial resolution and format suitable for biophysical modelling. Applications include agricultural productivity assessments, hydrological modelling, ecosystem studies, climate risk assessments, and the development of derived climate indicators and indices.
Gridded downscaled CMIP6 bioclimatic indices for species distribution modelling across Australia (NetCDF, 5 km resolution):
Climate change is a major factor contributing to biodiversity loss in Australia. Addressing this conservation challenge requires information about present and future species distributions. To improve assessment of future species distributions, the Queensland Future Climate Science Program have calculated 19 bioclimatic indices derived from the same downscaled CMIP6 projections and SILO bias-corrected framework used for the biophysical modelling dataset discussed above. These indices, which include measures such as annual temperature range, seasonality, and isothermality, are widely used in ecological and biodiversity modelling.
This dataset enables users to assess potential shifts in species distributions under future climate conditions and supports biodiversity conservation planning, ecological research, and climate adaptation decision-making. The following publication demonstrates the application of these data for modelling the future distribution of the greater glider under climate change.
