Available Datasets

Sharing data and maps allows the World Bank to better address climate change and the effects on development. It also serves as a knowledge bank and helps NGOs and CSOs around the world leverage this data. More than 15 data sets and maps, which mostly come from the Climate Change Knowledge Portal, are made available here to strengthen evidence-based decision making and build the network of sharing data.

Data can be downloaded in raw formats or embedded as maps. All datasets and maps on this page are published under the World Bank's legal guidelines.

Temperature

Temperature Anomaly (Scenario A2)

This layer depicts the A2 scenario of the predicted change in median annual temperature (degrees Celsius) by the end of year 2100. Scenario A2 assumes a very heterogeneous world with a continuously increasing global population and a regionally oriented economic growth that is more fragmented and slower than other SRES scenarios. See additional notes below.

Source: Climate Change Knowledge Portal

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Temperature Anomaly (Scenario B1)

This layer depicts the B1 scenario of the predicted change in median annual temperature by the end of the 21st century. Scenario B1 assumes a convergent world with a global population that peaks in mid-century and declines thereafter. It assumes rapid changes in economic structures toward a service and information economy with reductions in material intensity and the introduction of clean and resource-efficient technologies. See additional notes below.

Source: Climate Change Knowledge Portal

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Precipitation

Precipitation Anomaly (Scenario A2)

This layer depicts the A2 scenario of the predicted change in total annual precipitation (in mm) by the end of the year 2100. Scenario A2 assumes a very heterogeneous world with a continously increasing global population and a regionally oriented economic growth that is more fragmented and slower than other SRES scenarios. See additional notes below.

Source: Climate Change Knowledge Portal

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Precipitation Anomaly (Scenario B1)

This layer depicts the B1 scenario of the predicted change in total annual precipitation (in mm) by the end of the year 2100. Scenario B1 assumes a very heterogeneous world with a continously increasing global population and a regionally oriented economic growth that is more fragmented and slower than other SRES scenarios. See additional notes below.

Source: Climate Change Knowledge Portal

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Risk & Vulnerability

Affected Croplands

This layer depicts the spatial distribution of large cropland in the year 2000. Data was derived from agricultural inventory data and satellite imagery as described in Ramankutty et al. (2008), "Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000", Global Biogeochemical Cycles, Vol. 22, GB1003, doi:10.1029/2007GB002952.

Source: Department of Geography, McGill University.

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Affected Grasslands

This layer depicts the spatial distribution of grasslands in the year 2000. Data was derived from agricultural inventory data and satellite imagery as described in Ramankutty et al. (2008), "Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000", Global Biogeochemical Cycles, Vol. 22, GB1003, doi:10.1029/2007GB002952.

Source: Department of Geography, McGill University.

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East Africa NDVI Anomaly

This layer depicts the Expedited MODIS (eMODIS) mean anomaly of the Normalized Difference Vegetation Index (NVDI) for the period April 21 - 30, 2011 in East Africa. Mean anomaly represents the difference between the mean NDVI values for the 10-day period for the previous 10 years (2001-2010) and the current year (2011) 10-day period. Negative values represent less vegetation while positive values represent more vegetation.

Source: FEWS NET, USGS, USAID.

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East Africa Food Prices

This layer depicts the market price data collected by the Famine Early Warning System Network (FEWS NET). Market price data is collected at major markets within each country on a monthly basis.

Source: FEWS NET, USAID.

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Flood Hazard

This layer depicts the estimated flood risk derived from a global listing of extreme flood events between 1985 and 2003. Original data was complied by the Dartmouth Flood Observatory, gridded flood risk data was derived by Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).

Source: Center for International Earth Science Information Network (CIESIN).

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Population Exposure to Drought

This layer depicts the population exposure to drought as calculated by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction. Physical exposure to drought was weighted by population density equating to how many people per grid cell were exposed to drought per year. Estimate is based on historical drought data for years 1980 - 2001.

Source: UNEP/GRID

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Change in Agriculture Yields

This layer depicts the predicted change in yields between 2009 - 2050 of 11 major crops (wheat, rice, maize, millet, field pea, sugar beet, sweet potato, soybean, groundnut, sunflower, and rapeseed). The yields-change values are the mean of three emission scenarios across five global climate models, assuming no CO2 fertilization.

Source: Climate Change Knowledge Portal

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Global Weather Stations

This layer depicts point-locations of weather stations a part of the Global Historical Climatology Network (GHCN). GHCN provides station level, quality controlled, observational datasets for temperature and rainfall values from thousands of weather stations worldwide. GHCN also provides derivative products including monthly and long term historical climatologies. See more information about the GHCN below.

Source: Climate Change Knowledge Portal

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Percent GDP Added by Agriculture

This layer depicts the percentage of GDP value added by agriculture mapped to centroid locations within each country. Data was obtained from the World Development Indicators.

Source: World Development Indicators

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Respond to Change

World Bank Agricultural Projects

This layer depicts the locations of all World Bank agricultural based projects currently being implemented. Funding is shown in millions of US dollars.

Source: Climate Change Knowledge Portal

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World Borders

English Labels

This layer depicts international boundaries and country names used by The World Bank. Borders and names used in this layer are used for illustration purposes only do not imply offical endorsement or acceptance by The World Bank.

Source: Climate Change Knowledge Portal

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Credits

The Coupled Model Intercomparison Project Phase 3 (CMIP3) of the World Climate Research Programme (WCRP) has produced a multi-model dataset, which was referenced in the Intergovernmental Panel on Climate Change’s (IPCC) Third and the Fourth Assessment Reports. The outputs from the CMIP3 have been made available through the IPCC and its Data Distribution Centre.

Acknowledgments

We fully acknowledge Global Historical Climatology Network (GHCN), the National Centers for Environmental Prediction (NCEP), the National Center for Atmospheric Research (NCAR), the Climatic Research Unit (CRU) of University of East Anglia (UEA), and the International Water Management Institute (IWMI) for their data sets.

We fully acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP's Working Group on Coupled Modelling (WGCM), for their roles in making available the WCRP CMIP3 multi-model dataset.  Support of this dataset is provided by the Office of Science, U.S. Department of Energy.

We thank the Climate Systems Analysis Group, University of Cape Town for processing the data for the World Bank and International Research Institute, Columbia University, for their analytical support to the portal.

Additional Data Notes

Historical Data Sources

Global Historical Climatology Network (GHCN) is the source of the station level data. GHCN provides quality controlled, observational datasets for temperature and rainfall values from thousands of weather stations worldwide. GHCN also provides derivative products, including monthly and long-term historical climatologies. The Historical Variability Analysis Tool has been developed by the World Bank to allow users to investigate the variability in precipitation and temperature at various time scales over the 20th century near a user-selected location.

National Centers for Environmental Prediction (NCEP) has developed evaluation tools for how well various models capture the historical seasonal cycle of temperature and rainfall. The National Center for Atmospheric Research (NCAR) has been combining satellite and weather station information. In the portal, these data have been modified to provide user-friendly information on rainfall and temperature and regridded to a common 2° grid that matches the global climate models.

Climatic Research Unit (CRU) of the University of East Anglia (UEA) and the International Water Management Institute (IWMI) have provided access to the CRU TS 2.1 Global Climate Dataset. These data have been produced by CRU and reformatted by IWMI. The CRU TS 2.1 Global Climate Dataset is comprised of 1,224 monthly time series of climate variables, including temperature and precipitation, for the period 1901-2002, and covering the global land surface, excluding Antarctica, at 0.5 degrees resolution. These data were downloaded from the CGIAR Consortium for Spatial Information.

Future Climate sources

Projected changes are derived from global circulation models (GCMs) - the most comprehensive physically based models of climate change available, which are referenced by the Intergovernmental Panel on Climate Change (IPCC) in its Third and Fourth Assessment Reports.

Data presented in the portal are derived from 15 global circulation models (GCMs – see background below). Because the resolution of these GCMs varies, they were regridded to a common 2° grid. Data were processed for the portal by the Climate Systems Analysis Group at the University of Cape Town. The processing included identifying and cleaning problem fields where possible; regridding the data files to a common 2° grid; calculation of 10-year and 20-year climatologies for monthly precipitation mean surface air temperature; calculation of derived variables; calculation of multi-model means and 10th and 90th percentiles for future and climatologies; and post-processing data into a GIS format. Data in the portal can be queried at several levels, including country, region, continent, basin, and 3x3° grid aggregations.

Background on Global Circulation Models and Scenarios

Global Circulation Models are a numerical representation of the climate system based on the physical, chemical, and biological properties of its components, their interactions and feedback processes, and accounting for all or some of its known properties. Climate models are applied, as a research tool, to study and simulate the climate, but also for operational purposes, including monthly, seasonal, and interannual climate predictions (IPCC glossary, IPCC 2007, 2001 – Synthesis Reports). Various GCMs have been developed by different research groups and organizations over the years. The outputs from all of these have been made available through the IPCC and its Data Distribution Centre.

Scenarios: Emissions of greenhouse gases and aerosols due to human activities change the composition of the atmosphere. Future emissions of greenhouse gases and aerosols are determined by driving forces such as population, socio-economic development, and technological change, and hence are highly uncertain. Scenarios are alternative images of how the future might unfold and are an appropriate tool with which to analyze how driving forces may influence future emission outcomes and to assess the associated uncertainties. The scenarios, developed by the Intergovernmental Panel on Climate Change (IPCC) in its Special Report on Emission Scenarios (SRES) consist of six scenario groups, based on narrative storylines. The SRES scenarios do not include additional climate initiatives and no probabilities are assigned by the IPCC. They encompass four combinations of demographic change, social and economic development, and broad economic developments (A1B, A2, B1, B2). Two further scenario groups, A1FI and A1T, explicitly explore alternative energy technology developments to A1B. The resulting emissions of the greenhouse gases – especially carbon dioxide, methane, nitrous oxides and sulphur dioxide which leads to the production of sulfate aerosols (IPCC glossary, IPCC 2007, 2001 – Synthesis Reports).

Illustrative Storylines are sometimes referenced to help user understand the long-term consequences of climate change. The IPCC includes the following descriptions for the families of storylines and illustrative scenarios:

A1. The A1 storyline and scenario family describes a future world of very rapid economic growth, global population that peaks in mid-century and declines thereafter, and the rapid introduction of new and more efficient technologies. Major underlying themes are convergence among regions, capacity building and increased cultural and social interactions, with a substantial reduction in regional differences in per capita income. The A1 scenario family develops into three groups that describe alternative directions of technological change in the energy system. The three A1 groups are distinguished by their technological emphasis: fossil intensive (A1FI), non-fossil energy sources (A1T), or a balance across all sources (A1B) (where balanced is defined as not relying too heavily on one particular energy source, on the assumption that similar improvement rates apply to all energy supply and end-use technologies).

A2. The A2 storyline and scenario family describes a very heterogeneous world. The underlying theme is self-reliance and preservation of local identities. Fertility patterns across regions converge very slowly, which results in continuously increasing population. Economic development is primarily regionally oriented and per capita economic growth and technological change more fragmented and slower than other storylines.

B1. The B1 storyline and scenario family describes a convergent world with the same global population, that peaks in mid-century and declines thereafter, as in the A1 storyline, but with rapid change in economic structures toward a service and information economy, with reductions in material intensity and the introduction of clean and resource efficient technologies. The emphasis is on global solutions to economic, social and environmental sustainability, including improved equity, but without additional climate initiatives.

B2. The B2 storyline and scenario family describes a world in which the emphasis is on local solutions to economic, social and environmental sustainability. It is a world with continuously increasing global population, at a rate lower than A2, intermediate levels of economic development, and less rapid and more diverse technological change than in the A1 and B1 storylines. While the scenario is also oriented towards environmental protection and social equity, it focuses on local and regional levels.

Information in this screening tool does not constitute legal or scientific advice or service. The World Bank (the International Bank for Reconstruction and Development and the International Development Association) makes no warranties or representations, express or implied, as to the accuracy or reliability of this tool or the data contained therein. A user of this tool should seek qualified expert for specific diagnosis and analysis of a particular project. Any use thereof or reliance thereon is at the sole and independent discretion and responsibility of the user. The maps shown on the tool have not been produced by the World Bank. No conclusions or inferences drawn from the tool or relating to any aspect of any should be attributed to the World Bank, its Board of Governors or Executive Directors, its Management, or any of its member countries. The boundaries, colors, denominations, and other information shown on any map in the tool do not imply any judgment or endorsement on the part of the World Bank concerning the delimitation or the legal status of any territory or boundaries. In no event will the World Bank be liable for any form of damage arising from the application or misapplication of the tool, any maps, or any associated materials.