Data

Downscaled Projections of Bioclimatic Indices for Species Distribution Modelling in Australia (1975 - 2099)

Name: Downscaled Projections of Bioclimatic Indices for Species Distribution Modelling in Australia (1975 - 2099)
Published: 2026

Terrestrial Ecosystem Research Network

Toombs, Nathan ; Ma, Shaoxiu ; Chapman, Sarah ; Trancoso, Ralph

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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25901/am36-t566&rft.title=Downscaled Projections of Bioclimatic Indices for Species Distribution Modelling in Australia (1975 - 2099)&rft.identifier=10.25901/am36-t566&rft.publisher=Terrestrial Ecosystem Research Network&rft.description=Climate change is a major factor contributing to biodiversity loss globally and in Australia. Addressing this conservation challenge requires information about present and future species distributions. To improve assessment of future species distributions, we have calculated 19 bioclimatic indices using dynamically and statistically downscaled Coupled Model Intercomparison Project 6 (CMIP6) Global Climate Models (GCMs) over Australia at a 5 km resolution through 1975 – 2099 for three emissions scenarios (SSP1-2.6, SSP2-4.5 and SSP3-7.0). We used the Conformal Cubic Atmospheric Model (CCAM) for dynamical downscaling to 10km, and the Quantile Matching for Extremes (QME) method for statistical downscaling the CCAM output to 5km. This dataset will be useful for the study of climate change impacts on species distributions in Australia.Data CreationBioclimatic indices were calculated from dynamically and statistically downscaled CMIP6-CCAM data for minimum and maximum temperature and precipitation. Bias correction was applied to the downscaled CMIP6-CCAM dataset in two steps. 1) The downscaled CMIP6 outputs were regridded to the SILO 5-km grid using conservative remapping in Climate Data Operators (CDO). 2) SILO was used as the reference dataset to train the Quantile Mapping for Extremes (QME) model over 1975-2014. QME is a tailored univariate quantile-mapping approach designed to better correct biases in extremes while preserving the statistical characteristics of extreme events (Dowdy, A., 2023. A bias correction method designed for weather and climate extremes (No. 87), Bureau Research Report 087, Australian Bureau of Meteorology). The trained QME model was applied to bias-correct future projections for 2015-2100. Bioclimatic indices were derived using the R package dismo, Version 1.3-15.Progress Code: completedMaintenance and Update Frequency: asNeeded&rft.creator=Toombs, Nathan &rft.creator=Ma, Shaoxiu &rft.creator=Chapman, Sarah &rft.creator=Trancoso, Ralph &rft.date=2026&rft.edition=1.0&rft.relation=https://doi.org/10.1088/2752-5295/ae70a5&rft.coverage=The entire Australian continent.&rft.coverage=northlimit=-10; southlimit=-44; westlimit=112; eastLimit=154; projection=EPSG:4326&rft_rights=Creative Commons Attribution 4.0 International Licence http://creativecommons.org/licenses/by/4.0&rft_rights=&rft_rights=1. The user accepts all responsibility and risks associated with the use of this data. 2. The Queensland Government makes no representations or warranties in relation to this data, and, to the extent permitted by law, all warranties relating to accuracy, reliability, completeness, currency or suitability for any particular purpose, and all liability for any loss, damage or costs (including consequential damage) incurred in any way (including but not limited to that arising from negligence) in connection with any use of or reliance on this data are excluded or limited. 3. The user agrees to continually indemnify the State of Queensland (and its officers and employees) against any loss, cost, expense, damage and liability of any kind (including liability in negligence) caused by the use of this data or any product made from this data.&rft_subject=climatologyMeteorologyAtmosphere&rft_subject=environment&rft_subject=biota&rft_subject=AIR TEMPERATURE&rft_subject=EARTH SCIENCE&rft_subject=ATMOSPHERE&rft_subject=ATMOSPHERIC TEMPERATURE&rft_subject=SURFACE TEMPERATURE&rft_subject=RAIN&rft_subject=PRECIPITATION&rft_subject=LIQUID PRECIPITATION&rft_subject=ECOLOGY&rft_subject=BIOLOGICAL SCIENCES&rft_subject=Global Change Biology&rft_subject=OTHER BIOLOGICAL SCIENCES&rft_subject=Model-Derived Gridded data&rft_subject=annual mean temperature&rft_subject=Degree Celsius&rft_subject=mean diurnal range&rft_subject=isothemality&rft_subject=Percent&rft_subject=temperature seasonality&rft_subject=max temperature of warmest month&rft_subject=min temperature of coldest month&rft_subject=temperature annual range&rft_subject=mean temperature of wettest quarter&rft_subject=mean temperature of driest quarter&rft_subject=mean temperature of warmest quarter&rft_subject=mean temperature of coldest quarter&rft_subject=annual precipitation&rft_subject=Millimetre&rft_subject=precipitation of wettest month&rft_subject=precipitation of driest month&rft_subject=precipitation seasonality&rft_subject=precipitation of wettest quarter&rft_subject=precipitation of driest quarter&rft_subject=precipitation of warmest quarter&rft_subject=precipitation of coldest quarter&rft_subject=1 km - < 10 km or approximately .01 degree - < .09 degree&rft_subject=Climate Normal (30-year climatology)&rft_subject=Climate Change&rft_subject=Species Distribution Modelling&rft_subject=Bioclimatic Indices&rft.type=dataset&rft.language=English Access the data

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Creative Commons Attribution 4.0 International Licence
http://creativecommons.org/licenses/by/4.0

1. The user accepts all responsibility and risks associated with the use of this data. 2. The Queensland Government makes no representations or warranties in relation to this data, and, to the extent permitted by law, all warranties relating to accuracy, reliability, completeness, currency or suitability for any particular purpose, and all liability for any loss, damage or costs (including consequential damage) incurred in any way (including but not limited to that arising from negligence) in connection with any use of or reliance on this data are excluded or limited. 3. The user agrees to continually indemnify the State of Queensland (and its officers and employees) against any loss, cost, expense, damage and liability of any kind (including liability in negligence) caused by the use of this data or any product made from this data.

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Contact Information

Street Address:
Terrestrial Ecosystem Research Network
Building 1019, 80 Meiers Rd
QLD 4068
Australia
Ph: +61 7 3365 9097

[email protected]

Brief description

Climate change is a major factor contributing to biodiversity loss globally and in Australia. Addressing this conservation challenge requires information about present and future species distributions. To improve assessment of future species distributions, we have calculated 19 bioclimatic indices using dynamically and statistically downscaled Coupled Model Intercomparison Project 6 (CMIP6) Global Climate Models (GCMs) over Australia at a 5 km resolution through 1975 – 2099 for three emissions scenarios (SSP1-2.6, SSP2-4.5 and SSP3-7.0). We used the Conformal Cubic Atmospheric Model (CCAM) for dynamical downscaling to 10km, and the Quantile Matching for Extremes (QME) method for statistical downscaling the CCAM output to 5km. This dataset will be useful for the study of climate change impacts on species distributions in Australia.

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Data Creation

Bioclimatic indices were calculated from dynamically and statistically downscaled CMIP6-CCAM data for minimum and maximum temperature and precipitation.

Bias correction was applied to the downscaled CMIP6-CCAM dataset in two steps.

1) The downscaled CMIP6 outputs were regridded to the SILO 5-km grid using conservative remapping in Climate Data Operators (CDO).

2) SILO was used as the reference dataset to train the Quantile Mapping for Extremes (QME) model over 1975-2014. QME is a tailored univariate quantile-mapping approach designed to better correct biases in extremes while preserving the statistical characteristics of extreme events (Dowdy, A., 2023. A bias correction method designed for weather and climate extremes (No. 87), Bureau Research Report 087, Australian Bureau of Meteorology).

The trained QME model was applied to bias-correct future projections for 2015-2100.

Bioclimatic indices were derived using the R package dismo, Version 1.3-15.

Progress Code: completed

Maintenance and Update Frequency: asNeeded

Notes

Credit
We at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging.

The Queensland Future Climate Projections 2 dataset (QldFCP-2) was produced by the Queensland Future Climate Science Program, which aims to support climate adaptation and natural disasters preparedness and was funded by the Queensland Government, Australia (dataset DOI: https://doi.org/10.25914/8fve-1910). Citation: Chapman, S., Syktus, J., Trancoso, R., Thatcher, M., Toombs, N., Wong, K. K-H., Takbash, A., 2023. Earths Future. Evaluation of dynamically downscaled CMIP6-CCAM models over Australia.

Purpose
To provide high-resolution climate data that can be used for species distribution modelling over Australia.

Created: 2025-12-31

Issued: 2026-03-31

Modified: 2026-05-21

Data time period: 1975-01-01 to 2099-12-31

This dataset is part of a larger collection

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text: The entire Australian continent.

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Point-of-truth metadata URL

uri : https://geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/71eefa31-fab2-4a2e-b752-275b14df5467 External Link

Identifiers

URI : geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/71eefa31-fab2-4a2e-b752-275b14df5467
global : 71eefa31-fab2-4a2e-b752-275b14df5467