Data

RCP8.5 future climate layers for Australia - 5km resolution

James Cook University
Vanderwal, J
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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:doi10.25903/zd2z-fz05&rft.title=RCP8.5 future climate layers for Australia - 5km resolution&rft.identifier=10.25903/zd2z-fz05&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 RCP8.5 bioclimatic variable spatial layers of 18 Global Circulation Models for decadal timesteps from 2015 to 2085. The RCP 8.5 is developed by the MESSAGE modeling team and the IIASA Integrated Assessment Framework at the International Institute for Applies Systems Analysis (IIASA), Austria. The RCP 8.5 is characterized by increasing greenhouse gas emissions over time representative for scenarios in the literature leading to high greenhouse gas concentration levels. The underlying scenario drivers and resulting development path are based on the A2r scenario detailed in Riahi 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 RCP 8.5 is characterized by increasing greenhouse gas emissions over time: rising radiative forcing pathway leading to 8.5 W/m2 in 2100. RCP8.5 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=111.90256516,-8.52223270989 154.793190152,-10.9475426613 152.683815153,-46.8796350204 107.683815161,-43.40974677 111.90256516,-8.52223270989&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=Geographic Information Systems&rft_subject=Spatial Data&rft_subject=spatial analysis&rft_subject=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 RCP 8.5 is characterized by increasing greenhouse gas emissions over time: rising radiative forcing pathway leading to 8.5 W/m2 in 2100. RCP8.5 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 RCP8.5 bioclimatic variable spatial layers of 18 Global Circulation Models for decadal timesteps from 2015 to 2085. The RCP 8.5 is developed by the MESSAGE modeling team and the IIASA Integrated Assessment Framework at the International Institute for Applies Systems Analysis (IIASA), Austria. The RCP 8.5 is characterized by increasing greenhouse gas emissions over time representative for scenarios in the literature leading to high greenhouse gas concentration levels. The underlying scenario drivers and resulting development path are based on the A2r scenario detailed in Riahi 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: 2012-06-26

Data time period: 31 12 2011 to 31 05 2012

This dataset is part of a larger collection

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111.90257,-8.52223 154.79319,-10.94754 152.68382,-46.87964 107.68382,-43.40975 111.90257,-8.52223

131.2385026565,-27.700933865145

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  • Local : ec957a1bc6f26e2f89ad90805a2f1e00
  • Local : https://research.jcu.edu.au/data/published/bf682e81a45d58d74caf00563699c132
  • DOI : 10.25903/zd2z-fz05