Description of Data available in the Climate Change Knowledge Portal


Future Climate

     Future information is derived from 14 of the 23 available global circulation models (GCMS), the most comprehensive physically-based models of climate change available and used by the Intergovernmental Panel on Climate Change (IPCC) 4th Assessment Report. The models are useful to illustrate the changing nature of large scale climate dynamics on continental to global scales and form the basis for understanding the human induced changes in climate. Because the resolution of these GCMs varies, they were re-gridded to a common 2o grid as in this MAP

  • Variables
    • Rainfall, and Temperature for all 14 models
    • For 9 of the global circulation models, several statistics are available, including - Frost days, Heat wave duration, Maximum 5 day precipitation, *for 9 models - number of days with rainfall (ppt) >10mm, number of days with ppt > 2 mm, number of days with ppt > 90th percentile, average number of days between rain events,, mean daily rainfall, total monthly rainfall, mean maxT/minT, number, hot days (90th %), cold days (10th %), cold nights (10% of tmin, warm nights (tmin90th %), frost days (Tmin < zero).
  • There are two scenarios that describe future economic growth and energy and they are important tools for understanding the long-term consequences of climate change. Emissions scenarios are often based on different storylines of economic and population growth, energy efficiency, and concern for environmental sustainability. Corresponding levels of emissions of greenhouse gases and other factors may be deduced from these storylines. Future projections are available for two of these storylines:
    • The A2 storyline - is a case of rapid and successful economic development, in which regional average income per capita converge - current distinctions between "poor" and "rich" countries eventually dissolve. Learn more
    • The B1 storyline - The central elements of the B1 future are a high level of environmental and social consciousness combined with a globally coherent approach to a more sustainable development. Heightened environmental consciousness might be brought about by clear evidence that impacts of natural resource use, such as deforestation, soil depletion, over-fishing, and global and regional pollution, pose a serious threat to the continuation of human life on Earth. In the B1 storyline, governments, businesses, the media, and the public pay increased attention to the environmental and social aspects of development. Technological change plays an important role. At the same time, however, the storyline does not include any climate policies, to reflect the SRES terms of reference. Nevertheless, such a possible future cannot be ruled out. Learn More
Future Time Periods
  • Decadal averages for each model across the following time periods:
    • 2010
    • 2020
    • 2030
    • 2040
    • 2050
    • 2060
    • 2070
    • 2080
    • 2090
    • 2100
  • 20 year averages for each model across the following time periods:
    • 2020-2039
    • 2040-2059
    • 2060-2079
    • 2080-2099
Climate Change and projections used in the table. To learn more click here
Historical Climate

Historical data are derived from 4 sources, all quality controlled by leading institutions in the field. To view the historical data, click on the map and under the 'Climate' tab click on the 'Historical data' sub-tab. These are organized below under their potential use as follows:

  • Historical data to understand the seasonal CYCLE
  • Historical data to explore variability at the station level -
  • Historical data to evaluate how well Climate Models capture the regional seasonal cycle
1) Historical data to understand the seasonal CYCLE - click on the Historical data sub-tab

This gridded historical dataset is derived from observational data, and provides quality controlled temperature and rainfall values from thousands of weather stations worldwide, as well as derivative products including monthly climatologies and long term historical climatologies. The dataset is produced by the Climatic Research Unit (CRU) of University of East Anglia (UEA), and reformatted by International Water Management Institute (IWMI). CRU- (Gridded Product). Click here to learn more.

CRU data can be mapped to show the baseline climate and seasonality by month, for specific years, and for rainfall and temperature.





2) Historical data to explore variability at the station level - click on the Historical Variability Analysis Tool sub-tab.

Global Historical Climatology Network (GHCN) - provides station level, quality controlled, observational datasets for temperature and rainfall values from thousands of weather stations worldwide (GHCN), as well as derivative products including monthly climatologies and long term historical climatologies.

The Historical Variability Analysis Tool allows allows a user to investigate the historical variability of precipitation and temperature at various time scales (interannual, decadal, and long-term linear trend) over the 20th century near a user-selected location.

An example output from this tool is presented below:



Historical data to evaluate how well Climate Models capture the regional seasonal cycle - click on the 'Projection' sub-tab and click "more". Under the "Projections" sub-tab you can view the variations between different GCM's and National Centers for Environmental Prediction (NCEP) re-analysis data. NCEP offers a way to evaluate how well models capture the historical seasonal cycle of temperature and rainfall, and was developed by the National Center for Atmospheric Research by combining satellite and weather station information. In the portal, it has been modified to provide user-friendly information on rainfall and temperature and regridded to a common 2o grid matching the Global Climate Models.


The historical period for these models is 1980-1999.
Agricultural Impact Data
The datasets provided under were calculated using the latest version of AEZ programs (termed GAEZ 2007) being published by IIASA and FAO. The files were prepared for download on 19 May 2008 represent additions and a partial update of files provided to the World Bank's Climate Change Team in October 2007. To learn more click here
Mitigation Efforts
Energy Sector Management Assistance Program (ESMAP) Low Carbon Growth Studies. To learn more click here
Weather Station Data
The weather stations used here are from the Global Historical Climatology Network (GHCN) beta version 2, a quality controlled temperature and rainfall dataset from thousands of weather stations worldwide (GHCN), as well as derivative products including monthly climatologies and long term historical climatologies . To learn more click here
Malaria Distribution
This layer reflects the level of Malaria's suitability throughout Africa. This dataset was produced by Mapping Malaria Risk in Africa (MARA) and downloaded from IRI's Data Library
Natural Disasters
This layer shows the frequency and distribution of natural disasters as defined in Dilley, M., R.S. Chen, U. Deichmann, A.L. Lerner-Lam, M. Arnold, J. Agwe, P. Buys, O. Kjekstad, B. Lyon, and G. Yetman. 2005. Natural Disaster Hotspots: A Global Risk Analysis. Disaster Risk Management Series, Issue No. 5. The World Bank, Washington, D.C. To learn more click here
If you have any questions about this data, do not hesitate to contact our team.
2 grid MAP
Global Circulation Models (GCMS)
Model Name Modeling Group (Country) Reference
bccr_bcm2.0 Bjerknes Centre for Climate Research (Norway) http://www.bjerknes.uib.no/
ccma_cgcm3.1 Canadian Centre for Climate Modeling and Analysis (Canada) www.cccma.bc.ca.gc.ca
cnrm_cm3.0 Centre National de Recherches Meteorologiques (France) http://www.cnrm.meteo.fr/
csiro_mk3.5 Australia's Commonwealth Scientific and Industrial Research Organisation (Australia) http://www.csiro.au/
gfdl_cm2.1 Geophysical Fluid Dynamics Laboratory (USA) http://gfdl.noaa.gov/
gfdl_cm2.0 Geophysical Fluid Dynamics Laboratory (USA) http://gfdl.noaa.gov/
ingv_echam4.0 NGV, National Institute of Geophysics and Volcanology, Italy, ECHAM 4.6 Model  
inmcm3.0 Institute for Numerical Mathematics (Russia) http://www.inm.ras.ru/
ipsl_cm4.0 Institut Pierre Simon Laplace (France) http://www.ipsl.jussieu.fr/
miub_echo Meteorological Institute, University of Bonn, Germany Meteorological Research Institute of KMA, KoreaModel and Data Groupe at MPI-M, Germany http://www.meteo.uni-bonn.de/
miroc_3.3medres Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC) http://www-pcmdi.llnl.gov/ipcc/model_documentation/MIROC3.2_medres.htm
mpi_echam5 Max-Planck-Institut for Meteorology (Germany) http://www.mpimet.mpg.de/
mri_cgcm2.3.2a Meteorological Research Institute (Japan) http://www.mri-jma.go.jp/Welcome.html
ukmo_hadcm3 Hadley Centre for Climate Prediction, Met Office, UK, HadCM3 Model  
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