As described in Supplementary Material to the above paper: The procedure for generating an annual time series of climate variables comprised three steps: First, (MAGICC/SCENGEN 5.3), a coupled gas cycle/aerosol/climate model used in the IPCC Fourth Assessment Report1, was used to generate an annual time series of future climate anomalies (2010 – 2100) using an ensemble of five atmosphere-ocean general circulation models (GCMs). Fordham et al.2 have highlighted the advantages of working within the MAGICC/SCENGEN framework, rather than using GCM data from the Coupled Model Intercomparison Project 3 (CMIP3) archive. We used two strongly contrasting greenhouse gas emission scenarios: a Reference scenario that assumes high CO2 concentration (WRE750;3) and a Policy scenario that assumes CO2 stabilization at about 450 ppm (MiniCAM LEV1;4). GCMs were chosen according to their superior skill in reproducing seasonal precipitation and temperature across North America. Model performance was assessed following already published methods5. The five GCMs were: UKMO-HadCM3 (UK); CGCMA.31(T47) (Canada); MRI-CGCM2.3.2 (Japan); ECHAM5/MPI-OM (Germany); IPSL-CM4 (France). Model terminology follows the CMIP3/AR4 multi-model data archive. Four of these models have been shown elsewhere to have good retrospective skill in reproducing recent climates at a global scale, as well as for North America2. GCM skill assessment results can be quite different depending on the variable considered, the region studied, the month or season examined, or the comparison metric used5. However, ensemble forecasts that include five or more GCMs tend to be more robust to GCM choice6.
Second, climate anomalies were downscaled to an ecologically relevant spatial resolution (~800m x 800m)7, using the “change factor” method, where the low-resolution climate signal (anomaly) from a GCM is added directly to a high-resolution baseline observed climatology (we used PRISM 1971-2000 normals;8,9. Bi-linear interpolation of the GCM data (2.5 x 2.5º longitude/latitude) to a resolution of 0.5 x 0.5º longitude/latitude was used to reduce discontinuities in the perturbed climate at the GCM grid box boundaries2. One advantage of this method is that, by using only GCM change data, it avoids possible errors due to biases in the GCMs baseline (present-day) climate5.
Third, we generated 19 bioclimate variables10 from monthly estimates of minimum temperature, maximum temperature, and mean precipitation generated by the above steps.
- Intergovernmental Panel on Climate Change, Climate Change 2007: Synthesis Report (2007).
- D. A. Fordham, T. M. L. Wigley, M. J. Watts, B. W. Brook, Strengthening forecasts of climate change impacts with multi-model ensemble averaged projections using MAGICC/SCENGEN 5.3, Ecography 35, 4–8 (2012)
- T. M. L. Wigley, R. Richels, J. A. Edmonds, Economic and environmental choices in the stabilization of atmospheric CO2 concentrations, Nature 379, 240–243 (1996).
- T. M. L. Wigley et al., Uncertainties in climate stabilization, Climatic Change 97, 85–121 (2009).
- D. A. Fordham, T. M. L. Wigley, B. W. Brook, Multi-model climate projections for biodiversity risk assessments, Ecological Applications 21, 3317–3331 (2011).
- D. W. Pierce, T. P. Barnett, B. D. Santer, P. J. Gleckler, Selecting global climate models for regional climate change studies, PNAS 106, 8441–8446 (2009).
- C. Seo, J. H. Thorne, L. Hannah, W. Thuiller, Scale effects in species distribution models: implications for conservation planning under climate change, Biol. Lett. 5, 39–43 (2009).
- PRISM Climate Group, Oregon State University, (2006) (available at http://prism.oregonstate.edu).
- M. Hulme, S. C. B. Raper, T. M. L. Wigley, An integrated framework to address climate change (ESCAPE) and further developments of the global and regional climate modules (MAGICC), Energy Policy 23, 347–355 (1995).
- R. J. Hijmans, S. J. Phillips, J. R. Leathwick, J. Elith, R package “dismo”: reference manual (2012) (available at http://cran.r-project.org/web/packages/dismo/dismo.pdf).
Data Service Name: THREDDS
Data Service Infomation: Search Download
Data Access URL: BioClim
Data Service Name: ESGF
Data Service Infomation: Search Project = Bioclim
Data Access URL: BioClim
Richard G. Pearson
Short Name: ADCP_US (Annual Downscaled Climate Projections for the US)
Spatial Coverage: CONUS
Temporal Coverage: 2010-2100
Resolution: ~800 x 800 m
Temporal Resolution: Annual
Total Dataset Size: 36 GB
Individual file size:
Pearson, R.G., Stanton. J.C., Shoemaker, K.T., Aiello-Lammens, M.E., Ersts, P.J.,
Horning, N., Fordham, D.A., Raxworthy, C.J., Ryu, H.Y., McNees, J., & Akçakaya, H.R.
Life history and spatial traits predict extinction risk due to climate change.
Nature Climate Change 4:217-221.
|BIO1||Annual Mean Temperature||C*100|
|BIO2||Mean Diurnal Range (Mean of monthly (max temp - min temp))||C*100|
|BIO4||Temperature Seasonality (standard deviation *100)||C|
|BIO5||Max Temperature of Warmest Month||C*100|
|BIO6||Min Temperature of Coldest Month||C*100|
|BIO7||Temperature Annual Range||C*100|
|BIO8||Mean Temperature of Wettest Quarter||C*100|
|BIO9||Mean Temperature of Driest Quarter||C*100|
|BIO10||Mean Temperature of Warmest Quarter||C*100|
|BIO11||Mean Temperature of Coldest Quarter||C*100|
|BIO13||Precipitation of Wettest Month||mm|
|BIO14||Precipitation of Driest Month||mm|
|BIO15||Precipitation Seasonality (Coefficient of Variation)||mm|
|BIO16||Precipitation of Wettest Quarter||mm|
|BIO17||Precipitation of Driest Quarter||mm|
|BIO18||Precipitation of Warmest Quarter||mm|
|BIO19||Precipitation of Coldest Quarter||mm|
Funding was provided by NASA (Biodiversity Program grant NNX09AK19G to the American museum of Natural History and Stony Brook University) and the Australian Research Council (grants LP0989420, DP1096427and FS110200051 to the University of Adelaide).