Data

RCP3-PD future climate layers for Australia - 5km resolution

James Cook University
Vanderwal, J
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=info:doi10.25903/h7vm-hz59&rft.title=RCP3-PD future climate layers for Australia - 5km resolution&rft.identifier=10.25903/h7vm-hz59&rft.publisher=James Cook University&rft.description=File format: ascii grid (.asc); zipped (.asc.gz) Extent: continental Australia Grid size: 5km Download size: 4GB Australia-wide RCP3-PD bioclimatic variable spatial layers of 18 Global Circulation Models for decadal timesteps from 2015 to 2085. The RCP 3-PD is developed by the IMAGE modeling team of the Netherlands Environmental Assessment Agency. The emission pathway is representative for scenarios in the literature leading to very low greenhouse gas concentration levels. It is a so-called peak scenario: its radiative forcing level first reaches a value around 3.1 W/m2 mid-century, returning to 2.6 W/m2 by 2100. In order to reach such radiative forcing levels, greenhouse gas emissions (and indirectly emissions of air pollutants) are reduced substantially over time. The final RCP is based on the publication by Van Vuuren et al. (2007).The data associated with the future climate projections included:18 GCMs for each emission scenario;8 time points into the future (10 year intervals from 2015 to 2085); andmonthly min, mean and max temperature, precipitation, sea surface temperatures, wet-day frequency, vapour pressure and cloud cover.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: Annual Mean Temperature Mean Diurnal Range (Mean of monthly (max temp - min temp)) Isothermality (Bioclimatic variable 2/Bioclimatic variable 7) (* 100) Temperature Seasonality (standard deviation *100) Max Temperature of Warmest Month Min Temperature of Coldest Month Temperature Annual Range (Bioclimatic variable 5-Bioclimatic variable 6) Mean Temperature of Wettest Quarter Mean Temperature of Driest Quarter Mean Temperature of Warmest Quarter Mean Temperature of Coldest Quarter Annual Precipitation Precipitation of Wettest Month Precipitation of Driest Month Precipitation Seasonality (Coefficient of Variation) Precipitation of Wettest Quarter Precipitation of Driest Quarter Precipitation of Warmest Quarter Precipitation of Coldest QuarterAll downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (http://www.r-project.org/). 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.Representative Concentration Pathways (RCPs) has been adopted by the IPCC to replace the Special Report on Emissions Scenarios (SRES) used in the AR4 report. The RCP3-PD emission pathway is representative for scenarios leading to very low greenhouse gas concentration levels: peak in radiative forcing at ~ 3 W/m2 before 2100 and decline. RCP3-PD bioclimatic variable spatial layers were developed for Australia (5km grid). Download 4GB zip file containing layers for 18 Global Circulation Models (GCMs) for decadal timesteps from 2015 to 2085.&rft.creator=Vanderwal, J &rft.date=2012&rft.coverage=111.382474194,-7.65207568693 156.734036686,-9.0433910664402 156.030911686,-45.7873591769 109.624661694,-43.7916279233 111.382474194,-7.65207568693&rft.coverage=Continental Australia&rft_rights=&rft_rights=CC BY: Attribution 3.0 AU http://creativecommons.org/licenses/by/3.0/au&rft_subject=Climate Change&rft_subject=Spatial Data / Analysis&rft_subject=Geographic Information System(s) (GIS)&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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Brief description

Representative Concentration Pathways (RCPs) has been adopted by the IPCC to replace the Special Report on Emissions Scenarios (SRES) used in the AR4 report. The RCP3-PD emission pathway is representative for scenarios leading to very low greenhouse gas concentration levels: peak in radiative forcing at ~ 3 W/m2 before 2100 and decline. RCP3-PD bioclimatic variable spatial layers were developed for Australia (5km grid). Download 4GB zip file containing layers for 18 Global Circulation Models (GCMs) for decadal timesteps from 2015 to 2085.

Full description

File format: ascii grid (.asc); zipped (.asc.gz) Extent: continental Australia Grid size: 5km Download size: 4GB Australia-wide RCP3-PD bioclimatic variable spatial layers of 18 Global Circulation Models for decadal timesteps from 2015 to 2085. The RCP 3-PD is developed by the IMAGE modeling team of the Netherlands Environmental Assessment Agency. The emission pathway is representative for scenarios in the literature leading to very low greenhouse gas concentration levels. It is a so-called "peak" scenario: its radiative forcing level first reaches a value around 3.1 W/m2 mid-century, returning to 2.6 W/m2 by 2100. In order to reach such radiative forcing levels, greenhouse gas emissions (and indirectly emissions of air pollutants) are reduced substantially over time. The final RCP is based on the publication by Van Vuuren et al. (2007).

The data associated with the future climate projections included:

  • 18 GCMs for each emission scenario;
  • 8 time points into the future (10 year intervals from 2015 to 2085); and
  • monthly min, mean and max temperature, precipitation, sea surface temperatures, wet-day frequency, vapour pressure and cloud cover.

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/). 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.

Created: 2012-06-25

This dataset is part of a larger collection

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111.38247,-7.65208 156.73404,-9.04339 156.03091,-45.78736 109.62466,-43.79163 111.38247,-7.65208

133.17934919,-26.719717431915

Identifiers
  • Local : 42a4bffce073ec79be4d3d594be2f1b8
  • Local : https://research.jcu.edu.au/data/published/098bbe003be0ef340ee995db123fc143
  • DOI : 10.25903/h7vm-hz59