We use the change-factor method for downscaling because it is an intuitive and computationally efficient technique for downscaling numerous datasets with large spatial extents (Wilby et al., 2004). The method adds a factor of climate change to a fine resolution 20th century observational data set. Downscaled monthly temperature and precipitation means are provided for two time periods, representing the middle (2041-2060 or "2050") and end (2081-2100 or "2090") of the 21st-century.
Climate projections are debiased by subtracting the simulated 20th century (1961-1990 or "1975") monthly means for precipitation and temperature from the 2050 and 2090 monthly means at the native GCM resolution. These monthly 2050-1975 and 2090-1975 change-factors were regridded to 2.5-minute resolution using a cubic spline interpolation, and then added to the 2.5-minute resolution Worldclim 20th century monthly temperature, maximum temperature, minimum temperature, and precipitation means (Hijmans et al., 2005).
The downscaled climate simulations available here are for future-emissions scenarios A1B, A2 and B1, for 24 GCMs, 23 of these GCMs are evaluated in the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) (IPCC, 2007).
Twelve months of variables are tarred and gzipped into a single file for download. The tar.gz file is 100mb in size. Once unzipped each monthly ArcASCII file is 160mb each. The ArcASCII file has a naming convention associated to the dataset properties. Naming convention is as follows:
- S is the scenario code ie sresa1b, sresb1 or sresa2.
- M is the shortened name of the General Circulation Model code.
- yyyy-YYYY is the four digit year at the start of the mean climate period followed by the four digit year at the end of the mean climate period.
- v is either pre for precipitation, tas for temperature, tasmin for minimum temperature, and tasmax for maximum temperature.
- m is the three character month [jan, feb, mar, apr, may, jun, jul, aug, sep, oct, nov, dec]
An example filename is sresa1b_bccr_bcm2_0_5km_2041-2060_debiasedmean_pr_apr.asc. This file is mean April precipitation for period 2041-2060, scenario a1b and model bccr_bcm2_0, Bjerknes Centre for Climate Research, Norway, BCM2.0 Model.
A table for all the models and shortened names is below:
|bccr_bcm2_0||Bjerknes Centre for Climate Research, Norway, BCM2.0 Model|
|cccma_cgcm3_1||Canadian Centre for Climate Modelling and Analysis, CGCM3.1 Model, T47 resolution|
|cccma_cgcm3_1_t63||Canadian Centre for Climate Modelling and Analysis, CGCM3.1 Model, T63 resolution|
|miroc3_2_hires||CCSR/NIES/FRCGC, Japan, MIROC3.2, high resolution|
|miroc3_2_medres||CCSR/NIES/FRCGC, Japan, MIROC3.2, medium resolution|
|csiro_mk3_0||CSIRO Atmospheric Research, Australia, Mk3.0 Model|
|csiro_mk3_5||CSIRO Atmospheric Research, Australia, Mk3.5 Model|
|ukmo_hadcm3||Hadley Centre for Climate Prediction, Met Office, UK, HadCM3 Model|
|ukmo_hadgem1||Hadley Centre for Climate Prediction, Met Office, UK, HadGEM1 Model|
|ingv_echam4||INGV, National Institute of Geophysics and Volcanology, Italy, ECHAM 4.6 Model|
|inmcm3_0||Institute for Numerical Mathematics, Russia, INMCM3.0 Model|
|ipsl_cm4||IPSL/LMD/LSCE, France, CM4 V1 Model|
|iap_fgoals1_0_g||LASG, Institute of Atmospheric Physics, China, FGOALS1.0_g Model|
|mpi_echam5||Max Planck Institute for Meteorology, Germany, ECHAM5 / MPI OM|
|cnrm_cm3||Meteo-France, Centre National de Recherches Meteorologiques, CM3 Model|
|miub_echo_g||Meteorological Institute of the University of Bonn, ECHO-G Model|
|mri_cgcm2_3_2a||Meteorological Research Institute, Japan, CGCM2.3.2a|
|giss_aom||NASA Goddard Institute for Space Studies, C4x3|
|giss_model_e_h||NASA Goddard Institute for Space Studies, ModelE20/HYCOM|
|giss_model_e_r||NASA Goddard Institute for Space Studies, ModelE20/Russell|
|ncar_ccsm3_0||National Center for Atmospheric Research, CCSM3.0|
|ncar_pcm1||National Center for Atmospheric Research, PCM1|
|gfdl_cm2_0||NOAA Geophysical Fluid Dynamics Laboratory, CM2.0 Model|
|gfdl_cm2_1||NOAA Geophysical Fluid Dynamics Laboratory, CM2.1 Model|
The 2.5-minute dataset is -180 to 180 longitude and -60 to 90 latitude. Both datasets are unprojected Geographic with units decimal degrees.
Cellsize: 0.041666669 DD
Number of rows: 3600
Number of Columns: 8640
temperature = average monthly temperature (Degrees Celsius * 10)
maximum temperature = average maximum monthly temperature (Degrees Celsius * 10)
minimum temperature = average minimum monthly temperature (Degrees Celsius * 10)
precipitation = average monthly precipitation (mm)
Downscaled Future Climate Scenarios 2.5-minute resolution, 2009. This is a product of Conservation International based on techniques developed in collaboration with the Department of Geography, Center of Climatic Research, and Land Tenure Center at the University of Wisconsin. Additional support provided by the National Center of Ecological Analysis and Synthesis. http://futureclimates.conservation.org (accessed date)
Notes to User:
This dataset is freely available for academic and other non-commercial use. Redistribution, or commercial use is not allowed without prior permission.
9-22-2009 There is a hole in the temperature data over the Caspian sea. This is inherited from missing data in the Worldclim data.
Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25,1965-1978.
IPCC, 2007: Climate Change 2007: Synthesis report. Contribution of working groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, Pachauri, R.K., Reisinger, A. (Eds.)]. IPCC, Geneva, Switzerland, 104 pp.
Meehl, G. A., Covey, C., Delworth, T., Latif, M., McAvaney, B., Mitchell, J.F.B., Stouffer, R.J., Taylor, K.E., 2007. The WCRP CMIP3 multi-model dataset: A new era in climate change research. Bulletin of the American Meteorological Society 88, 1383-1394.
Wilby, R.L, Charles, S.P., Zorita, E., Timbal, B., Whetton, P., Mearns, L.O., 2004. Guidelines for use of climate scenarios developed from statistical downscaling methods. IPCC Task Group on data and scenario support for Impact and Climate Analysis (TGICA). http://www.ipcc-data.org/guidelines/dgm_no2_v1_09_2004.pdf (accessed 09.29.09).