Projected future climate data

Projected Climate Data


There have been 5 rounds of Climate Change Assessments, each of which differ in the Scenarios used. The 5th Assessment is currently in progress and the first sets of outputs are likely to be produced this year, though are unlikely to be widely available until rather later.  The 4th Assessment results are thus the most recent currently widely available.

There is a huge range of projected climate data from a single Assessment, most of which is available in its basic form from the Worldclim website. A multitude of different climate models have been used to produce climate projections but all have been required by the International Panel for Climate Change (IPPC) to based on one or more of a number of predefined Scenarios which assume different rates of global CO2 increase, which in turn depend on assumptions about future economic development and adoption of low carbon technologies. These are summarisedin the Box below:

The A1 scenario family describes assumes very rapid economic growth, global population peaking in mid-century then falling, and the rapid introduction of new and more efficient technologies. Major themes are convergence among regions, capacity building, and increased cultural and social interactions, with a substantial reduction in regional differences in per capita income. Three A1 groups are distinguished by their technological emphasis: fossil intensive (A1FI), non-fossil energy sources (A1T), or a balance across all sources (A1B).

The A2 family describes a very heterogeneous world. The major assumption is self-reliance and preservation of local identities. Fertility patterns across regions converge very slowly, which results in continuously increasing global population. Economic development is primarily regionally oriented and per capita economic growth and technological change are more fragmented and slower than in other scenarios.
The B1 scenario family describes a convergent world with global population peaking in mid-century then falling, as in the A1 storyline, but with 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. The emphasis is on global solutions to economic, social, and environmental sustainability, including improved equity, but without additional climate initiatives.

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 B1 and A1 storylines. While the scenario is also oriented toward environmental protection and social equity, it focuses on local and regional levels.

Not surprisingly, the different models provide quite startlingly different results, which diverge more and more as the projection range increases.  It is therefore not enough to select one model and process that to provide maps of the likely climate in ten or twenty or more years time. The outputs of several models are needed in the form of an 'ensemble' or average – essentially to produce a consensus prediction with the maxima and minima providing worst and-best case projections. .
Most Global Climate Models (GCM) are produced at a rather coarse spatial resolution which means that they need to be downscaled to be useful for spatial analysis at the continental or regional level. There are a number of sources of downscaled projected climate data, three of which have been obtained by the EDENext DMT. These are from WorldClim, Climond and Future Clim.

Details of Datasets Provided

The Table below summarises the variables acquired for each data set, the source models and assumed scenarios and the years for which data have been downloaded.

Variables Models Scenarios Year

Worldclim Spatial Downscaling (Global 1km): 1KM Monthly Tmin, Tmax, & Precipitation, Including BIOCLIM 19 limWorldCBioclimatic Indicators

HADLEY, CCMA, CSIRO, NIES A2a, A1b, B2a 2020, 2050, 2080
FutureClim Temporal Downscaling (Europe 5km): Monthly Tmin, Tmax, Precipitation, Evapotranspiration, Length of Growing Period MIROC3, ECHAM5, CSIRO3, CNRM A1, A2, B1 2035, 2055, 2085
CLIMOND (Global 5km): 35 Bioclimatic Indicators CSIRO-Mk3, MIROC-H A1b, A2 2030, 2070

Though the global layers for WorldClim and Climond datasets are held in the DMT, the data have been windowed to the EDENext extent. This still represents a very large volume of data which cannot all be made available online, and so a very limited 'taster data set' are provided for direct download. The rest are available on request to the EDENext DMT.


Data from 4 Models and 3 Scenarios are available meaning that there are 12 datasets for each monthly climate parameter and bioclimatic indicator for each projected year. The data are from four IPCC 4th Assessment climate change models: Hadley Centre of the UK Met Office, CSIRO (Commonwealth Scientific and Industrial Research Organisation, Australia) and CCCMA (Canadian Centre for Climate Modelling and Analysis) and CCSR/NEIS (Center for Climate System Research (CCSR). National Institute for Environmental Studies (NIES) , Japan) Three Scenarios (A1b, A2a) and B2a) for three years (2020, 2050 and 2080)  have been process for each model.

EDENext DMT has ensembled the models for each Scenario, for which the data are available on request from the website. Three outputs have been produced for each ensemble: the mean value of the inputs for each pixel, as well as the maximum of all the input values, and the minimum of all the input values.. The first represents a first attempt to provide a consensus average set of projections whilst the  last two products might be taken as the best or worst case for each ensemble

The team has also produced a composite ensemble which combines all three scenarios. From these a set of change images have been produced, calculated as the vale in year YY minus the baseline current value for 1950-2000. The actual calculated value for a particular year can thus be produced by adding the current baseline value to the projected change value.

Each Ensemble has been not only been produced as an average (which may also provide estimates of variability such as standard errors, but also maxima or minima, i.e. the maximum or minimum of the input model and scenario projected values for a particular pixel location. These can be interpreted as the 'best' and 'worst' case projections.

From these different types of ensembles, which represent various projected climate values for each projection year, a change surface can be produced by subtraction of the baseline values.

The Filename conventions: EDENSXXYYYBIONNCH2k.tif (ED=EDENEXT, ENSXX=EnsembleX, YYYY=Projected year, BIONN =bioclimatic variable number, CH2k=change from year 2000 baseline). EDENSXX2080BIO1CH2K  thus means the change from 2000 to 2080 for bioclimatic variable 1 ( Annual mean temperature)


Climond provides a wider range of climate variables than does  Worldclim, and includes minimum and maximum temperature, morning and afternoon Relative Humidity, Solar radiation, precipitation. and 35 bioclimatic Indicators. Data are provided for two years (2030 and 2070), two scenarios (A1b and A2and two models: CSIRO-MK3 and MIROC_H (a joint collaboraton of CCSR/NIES andFRCGC ). The data are at 5km resolution. The list of Indicators are as follows:

  • Bio01 Annual mean temperature (°C)
  • Bio02 Mean diurnal temperature range (mean(period max-min)) (°C)
  • Bio03 Isothermality (Bio02 ÷ Bio07)
  • Bio04 Temperature seasonality (C of V)
  • Bio05 Max temperature of warmest week (°C)
  • Bio06 Min temperature of coldest week (°C)
  • Bio07 Temperature annual range (Bio05-Bio06) (°C)
  • Bio08 Mean temperature of wettest quarter (°C)
  • Bio09 Mean temperature of driest quarter (°C)
  • Bio10 Mean temperature of warmest quarter (°C)
  • Bio11 Mean temperature of coldest quarter (°C)
  • Bio12 Annual precipitation (mm)
  • Bio13 Precipitation of wettest week (mm)
  • Bio14 Precipitation of driest week (mm)
  • Bio15 Precipitation seasonality (C of V)
  • Bio16 Precipitation of wettest quarter (mm)
  • Bio17 Precipitation of driest quarter (mm)
  • Bio18 Precipitation of warmest quarter (mm)
  • Bio19 Precipitation of coldest quarter (mm)
  • Bio20 Annual mean radiation (W m-2)
  • Bio21 Highest weekly radiation (W m-2)
  • Bio22 Lowest weekly radiation (W m-2
  • Bio23 Radiation seasonality (C of V)
  • Bio24 Radiation of wettest quarter (W m-2)
  • Bio25 Radiation of driest quarter (W m-2)
  • Bio26 Radiation of warmest quarter (W m-2)
  • Bio27 Radiation of coldest quarter (W m-2)
  • Bio28 Annual mean moisture index
  • Bio29 Highest weekly moisture index
  • Bio30 Lowest weekly moisture index
  • Bio31 Moisture index seasonality (C of V)
  • Bio32 Mean moisture index of wettest quarter
  • Bio33 Mean moisture index of driest quarter
  • Bio34 Mean moisture index of warmest quarter
  • Bio35 Mean moisture index of coldest quarter

Full details can be found in  Kriticos, D.J., Webber, B.L., Leriche, A., Ota, N., Macadam, I., Bathols, J. & Scott, J.K. (2011) CliMond: global high resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods in Ecology & Evolution,

he ensembled data provided on  for download  consist of the minimum, maximum and averaged ensembles  for each bioclimatic variable, for each of the two Scenarios, as well as  the two scenarios combined. Changes from the baseline current climate, calculated as the projected minus the baseline value,  have also been provided for each ensemble output. 

The data are held in two zip files, one for each of 2030 an 2070. The FIlename conventions for the files within each archive are as follows edbioNNSSSSYYEE. NN Bioclimatic variable code; SSSS or SS =Scenario, A1b, A2 or A1BA2 (both scenarios); YY Year - 30=2030, 70=2070; EE ensemble type AV average, MN minimum, MX maximum, Files ending with ch75 are change from 1975 baseline calculated as projected value minus baseline.


FutureClim applies temporal as well as spatial downscaling of selected GCM and Scenario outputs to provide 5k resolution surfaces (Jones, Thornton, and Heinke 2009 - Generating Characteristic Daily Weather Data using Downscaled Climate Model Data from the IPCC Fourth Assessment). These can be obtained from the Worldclim website, in the Temporal downscaling section. The datasets are for  2035, 2055, and 2085, for three scenarios (A1, A2 and B1) and four models: CSIRO, MIROC , ECHAM5 (produced by European Centre for Medium-Range Weather Forecasts in Hamburg),and  CNRM (produced by Centre National de Recherches Meteorologiques, Toulouse, France) . The basic parameters are monthly minimum and maximum temperature and precipitation, which have been augmented by two additional variables commissioned by DMT with funding from ECDC in Stockholm as part of the VIMAP project: monthly evapotranspiration and annual length of growing period (an index of agricultural potential).

Ensembles have been produced for each year and scenario, and for all scenarios combined. For each scenario a mean and a standard deviation have been calculated. All are available for download.
File name conventions are as follows: fc_VVVV_YYYY_ens_SS. VVVV= variable, Prec precipitiation, Tmin, tmax = temperature evap, YYYY= year, 2035, 2055, 2085; SS Scenario a1, b1, a2,x

Return to climate data home or:

  1. Details on current climate data available on
  2. Details on projected future climate data available on
  3. A table outlining file naming conventions for climate data files on