BioClim – Fine-Scale Climate Scenarios with Annual Time Steps, 2010-2100, for the Contiguous United States

This dataset comprises two climate scenarios for the contiguous United States at a resolution of ~800m x 800m, with annual time slices from 2010 to 2100. Data include nineteen bioclimatic variables that are commonly used in ecological analyses. The data were first used in the following manuscript, where they are described in full:

  • 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.

  • 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.

    1. Intergovernmental Panel on Climate Change, Climate Change 2007: Synthesis Report (2007).
    2. 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)
    3. 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).
    4. T. M. L. Wigley et al., Uncertainties in climate stabilization, Climatic Change 97, 85–121 (2009).
    5. D. A. Fordham, T. M. L. Wigley, B. W. Brook, Multi-model climate projections for biodiversity risk assessments, Ecological Applications 21, 3317–3331 (2011).
    6. 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).
    7. 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).
    8. PRISM Climate Group, Oregon State University, (2006) (available at
    9. 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).
    10. R. J. Hijmans, S. J. Phillips, J. R. Leathwick, J. Elith, R package “dismo”: reference manual (2012) (available at
    Data Access
    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)
    Version: 1
    Format: geotiff
    Spatial Coverage: CONUS
    Temporal Coverage: 2010-2100

    Data Resolution:
    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.

    Variable Description Units
    BIO1 Annual Mean Temperature C*100
    BIO2 Mean Diurnal Range (Mean of monthly (max temp - min temp)) C*100
    BIO3 Isothermality
    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
    BIO12 Annual Precipitation mm
    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).