Dataset

All future climate layers for Australia - 5km resolution

James Cook University
Jeremy James VanDerWal (Associated with, Aggregated by)
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://research.jcu.edu.au/researchdata/published/detail/a06a78f553e1452bcf007231f6204f04&rft.title=All future climate layers for Australia - 5km resolution&rft.identifier=jcu.edu.au/tdh/collection/633b4ccd-2e78-459d-963c-e43d3c8a5ca1&rft.publisher=James Cook University&rft.description=Coarse resolution climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. Although this data is available on http://climascope.wwfus.org, access to all data was facilitated by the collaboration. The data associated with the future climate projections included: • 18 GCMs for each emission scenario; • 9 emission scenarios – 5 representing SRES scenarios and 4 representing RCP scenarios; • 8 time points into the future (10 year intervals from 2015 to 2085); and • annual min, mean and max temperature, among 19 bioclimatic variables. With respect to the emission scenarios, Representative Concentration Pathways (RCPs) has been adopted by the IPCC to replace the Special Report on Emissions Scenarios (SRES) used in the AR4 report (Solomon, Qin et al. 2007); RCPs are to be used in the AR5 IPCC report due in 2014. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at http://climascope.wwfus.org and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; http://www.bom.gov.au/jsp/awap/). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are: 1.Annual Mean Temperature 2.Mean Diurnal Range (Mean of monthly (max temp - min temp)) 3.Isothermality (Bioclimatic variable 2/Bioclimatic variable 7) (* 100) 4.Temperature Seasonality (standard deviation *100) 5.Max Temperature of Warmest Month 6.Min Temperature of Coldest Month 7.Temperature Annual Range (Bioclimatic variable 5-Bioclimatic variable 6) 8.Mean Temperature of Wettest Quarter 9.Mean Temperature of Driest Quarter 10.Mean Temperature of Warmest Quarter 11.Mean Temperature of Coldest Quarter 12.Annual Precipitation 13.Precipitation of Wettest Month 14.Precipitation of Driest Month 15.Precipitation Seasonality (Coefficient of Variation) 16.Precipitation of Wettest Quarter 17.Precipitation of Driest Quarter 18.Precipitation of Warmest Quarter 19.Precipitation of Coldest Quarter All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (http://www.r-project.org/). In summary, 19 climate surfaces for each of 1297 projections (current + 9 emissions scenarios x 18 GCMs * 8 time points) are available at a ~5km resolution for Australia. A recent paper looked at the climate projections under both the old SRES and the new RCPs (Rogelj, Meinshausen et al. 2012) which showed that 1) the RCP range mostly covered the range of SRES projected temperatures (Figure 1), and 2) the temperatures projected for the highest RCP (8.5) was fairly consistent with the highest SRES (A1FI). From this work, and our own preliminary investigations, it appears that we will focus on the RCPs for all future projections, and will limit work done with SRES emission scenarios.Australia-wide bioclimatic variable spatial layers for 9 emissions scenarios using 18 Global Circulation Models for decadal timesteps from 2015 to 2085. File format: ascii grid (.asc); zipped (.asc.gz) Extent: continental Australia Grid size: 5km Download size: from 300mb to 4GB&rft.creator=Jeremy James VanDerWal&rft.date=2012&rft.coverage=Continental Australia&rft.coverage=111.406105293,-5.73174766203 156.757667785,-7.82625574259 156.757667785,-45.6646480471 111.757667793,-45.4184170468 111.406105293,-5.73174766203&rft_rights=CC BY: Attribution 3.0 AU http://creativecommons.org/licenses/by/3.0/au&rft_subject=Climate Change&rft_subject=Geographic Information System(s) (GIS)&rft_subject=Spatial Data / Analysis&rft_subject=Terrestrial Ecology&rft_subject=BIOLOGICAL SCIENCES&rft_subject=ECOLOGY&rft_subject=Climate Change Models&rft_subject=ENVIRONMENT&rft_subject=CLIMATE AND CLIMATE CHANGE&rft.type=dataset&rft.language=English Access the data

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Open Access . If the data is not available via the provided link, please contact an associated party (preferrably the Manager as specified) for access.

Contact Information

jeremy.vanderwal@jcu.edu.au

ATSIP Building, Centre for Tropical Biodiversity and Climate Change, School of Marine and Tropical Biology, James Cook University, Townsville, QLD, 4811

Brief description

Australia-wide bioclimatic variable spatial layers for 9 emissions scenarios using 18 Global Circulation Models for decadal timesteps from 2015 to 2085. File format: ascii grid (.asc); zipped (.asc.gz) Extent: continental Australia Grid size: 5km Download size: from 300mb to 4GB

Full description

Coarse resolution climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. Although this data is available on http://climascope.wwfus.org, access to all data was facilitated by the collaboration. The data associated with the future climate projections included: • 18 GCMs for each emission scenario; • 9 emission scenarios – 5 representing SRES scenarios and 4 representing RCP scenarios; • 8 time points into the future (10 year intervals from 2015 to 2085); and • annual min, mean and max temperature, among 19 bioclimatic variables. With respect to the emission scenarios, Representative Concentration Pathways (RCPs) has been adopted by the IPCC to replace the Special Report on Emissions Scenarios (SRES) used in the AR4 report (Solomon, Qin et al. 2007); RCPs are to be used in the AR5 IPCC report due in 2014. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at http://climascope.wwfus.org and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; http://www.bom.gov.au/jsp/awap/). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are: 1.Annual Mean Temperature 2.Mean Diurnal Range (Mean of monthly (max temp - min temp)) 3.Isothermality (Bioclimatic variable 2/Bioclimatic variable 7) (* 100) 4.Temperature Seasonality (standard deviation *100) 5.Max Temperature of Warmest Month 6.Min Temperature of Coldest Month 7.Temperature Annual Range (Bioclimatic variable 5-Bioclimatic variable 6) 8.Mean Temperature of Wettest Quarter 9.Mean Temperature of Driest Quarter 10.Mean Temperature of Warmest Quarter 11.Mean Temperature of Coldest Quarter 12.Annual Precipitation 13.Precipitation of Wettest Month 14.Precipitation of Driest Month 15.Precipitation Seasonality (Coefficient of Variation) 16.Precipitation of Wettest Quarter 17.Precipitation of Driest Quarter 18.Precipitation of Warmest Quarter 19.Precipitation of Coldest Quarter All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (http://www.r-project.org/). In summary, 19 climate surfaces for each of 1297 projections (current + 9 emissions scenarios x 18 GCMs * 8 time points) are available at a ~5km resolution for Australia. A recent paper looked at the climate projections under both the old SRES and the new RCPs (Rogelj, Meinshausen et al. 2012) which showed that 1) the RCP range mostly covered the range of SRES projected temperatures (Figure 1), and 2) the temperatures projected for the highest RCP (8.5) was fairly consistent with the highest SRES (A1FI). From this work, and our own preliminary investigations, it appears that we will focus on the RCPs for all future projections, and will limit work done with SRES emission scenarios.

Notes

References: Meinshausen, M., S. C. B. Raper, et al. (2011). "Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – Part 1: Model description and calibration." Atmos. Chem. Phys. 11(4): 1417-1456. Meinshausen, M., S. Smith, et al. (2011). "The RCP greenhouse gas concentrations and their extensions from 1765 to 2300." Climatic Change 109(1): 213-241. Mitchell, T. D. and P. D. Jones (2005). "An improved method of constructing a database of monthly climate observations and associated high-resolution grids." International Journal of Climatology 25(6): 693-712. Rogelj, J., M. Meinshausen, et al. (2012). "Global warming under old and new scenarios using IPCC climate sensitivity range estimates." Nature Clim. Change 2(4): 248-253. Solomon, S., D. Qin, et al. (2007). Climate change 2007: Synthesis Report. Contribution of Working Group I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Summary for Policymakers. Geneva, Intergovernmental Panel on Climate Change (IPCC). VanDerWal, J., L. Beaumont, et al. (2011). climates: Methods for working with weather & climate. Warren, R., S. de la Nava Santos, et al. (2008). "Development and illustrative outputs of the Community Integrated Assessment System (CIAS), a multi-institutional modular integrated assessment approach for modelling climate change." Environmental Modelling & Software 23(5): 592-610.

Created: 25 06 2012

Data time period: 31 12 2014 to 30 12 2085

111.406105293,-5.73174766203 156.757667785,-7.82625574259 156.757667785,-45.6646480471 111.757667793,-45.4184170468 111.406105293,-5.73174766203

134.081886539,-25.698197854565

text: Continental Australia

Identifiers
  • Local : jcu.edu.au/tdh/collection/633b4ccd-2e78-459d-963c-e43d3c8a5ca1