The NetCDF files used in this lesson fall into two categories: baseline climate data and a multimodel ensemble (MME) of projected future climate scenarios. The formal name for the future climate scenario data is Multi-model ensemble mean annual / monthly temperature / precipitation anomalies for the period of 2020–39 / 2040–59 / 2060–79 / 2080–99 compared to the model's reference period of 1986–2005 for emissions scenario RCP 2.6 / RCP 4.5 / RCP 6.0 / RCP 8.5.

Each of these NetCDF files contains continuous global data at 1.0 degrees of resolutions, which can be imported into ArcGIS as raster layers, feature layers, or table views with one record for each unique combination of latitude (-90 degrees to 90 degrees) and longitude (-180 degrees to 180 degrees), for a total of 64,800 cells, points, or rows, respectively. When creating layers, use the WGS 1984 geographic coordinate system as the spatial reference.

Baseline data

  • baseline_pr_annual_mean_1986_2005.nc: Mean annual precipitation (mm/yr) from 1986 through 2005 computed by summing the daily precipitation values for each year, and then the mean of those annual sums.
  • baseline_pr_monthly_mean_1986_2005.nc: Mean monthly precipitation (mm/mo) from 1986 through 2005 computed by summing the daily precipitation values for each month of the year, and then finding the mean of those sums across all years for each calendar month.
  • baseline_tas_annual_mean_1986_2005.nc: Mean annual temperature 2 meters above the surface (degrees Celsius) from 1986 through 2005 computed by averaging the daily mean temperatures for each year, and then the mean of those annual averages.
  • baseline_tas_monthly_mean_1986_2005.nc: Mean monthly temperature 2 meters above the surface (degrees Celsius) from 1986 through 2005 computed by averaging the daily mean temperatures for each month of the year, and then finding the mean of those averages across all years for each calendar month.

These baseline datasets are not just observed data; they are reanalysis data, which is a blend of model and observation data to generate a comprehensive record of climate from 1986 through 2005.

Recommended citation: Amman, C., Boehnert, J., and Wilhelmi, O. 2018. "World Climate Data CMIP5 Multi Model Ensemble". Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado. Obtained online <date> at https://learn.arcgis.com/en/projects/explore-future-climate-projections/.

MME data

The naming convention of the MME files contains standard conventions for identifying the data as being CMIP5 anomaly data and being from a MME. The names also contain the variable (pr: precipitation, tas: temperature 2 meters above the surface), statistic (mean or stddev), scenario (rcp26, rcp45, rcp60, and rcp85), and the future 20-year range that the projected anomaly applies to. Anomaly data represents the difference between the baseline climate and the future projection, and thus contains relatively small values. To create a layer of projected future temperatures or precipitation levels, use ArcGIS geoprocessing tools to add the appropriate baseline data to the anomaly.

The following NetCDF files are provided:

  • cmip5_anomaly_pr_annual_mean_multi-model-ensemble_rcp26_2020-2039.nc
  • cmip5_anomaly_pr_annual_mean_multi-model-ensemble_rcp26_2040-2059.nc
  • cmip5_anomaly_pr_annual_mean_multi-model-ensemble_rcp26_2060-2079.nc
  • cmip5_anomaly_pr_annual_mean_multi-model-ensemble_rcp26_2080-2099.nc
  • cmip5_anomaly_pr_annual_mean_multi-model-ensemble_rcp45_2020-2039.nc
  • cmip5_anomaly_pr_annual_mean_multi-model-ensemble_rcp45_2040-2059.nc
  • cmip5_anomaly_pr_annual_mean_multi-model-ensemble_rcp45_2060-2079.nc
  • cmip5_anomaly_pr_annual_mean_multi-model-ensemble_rcp45_2080-2099.nc
  • cmip5_anomaly_pr_annual_mean_multi-model-ensemble_rcp60_2020-2039.nc
  • cmip5_anomaly_pr_annual_mean_multi-model-ensemble_rcp60_2040-2059.nc
  • cmip5_anomaly_pr_annual_mean_multi-model-ensemble_rcp60_2060-2079.nc
  • cmip5_anomaly_pr_annual_mean_multi-model-ensemble_rcp60_2080-2099.nc
  • cmip5_anomaly_pr_annual_mean_multi-model-ensemble_rcp85_2020-2039.nc
  • cmip5_anomaly_pr_annual_mean_multi-model-ensemble_rcp85_2040-2059.nc
  • cmip5_anomaly_pr_annual_mean_multi-model-ensemble_rcp85_2060-2079.nc
  • cmip5_anomaly_pr_annual_mean_multi-model-ensemble_rcp85_2080-2099.nc
  • cmip5_anomaly_pr_annual_stddev_multi-model-ensemble_rcp26_2020-2039.nc
  • cmip5_anomaly_pr_annual_stddev_multi-model-ensemble_rcp26_2040-2059.nc
  • cmip5_anomaly_pr_annual_stddev_multi-model-ensemble_rcp26_2060-2079.nc
  • cmip5_anomaly_pr_annual_stddev_multi-model-ensemble_rcp26_2080-2099.nc
  • cmip5_anomaly_pr_annual_stddev_multi-model-ensemble_rcp45_2020-2039.nc
  • cmip5_anomaly_pr_annual_stddev_multi-model-ensemble_rcp45_2040-2059.nc
  • cmip5_anomaly_pr_annual_stddev_multi-model-ensemble_rcp45_2060-2079.nc
  • cmip5_anomaly_pr_annual_stddev_multi-model-ensemble_rcp45_2080-2099.nc
  • cmip5_anomaly_pr_annual_stddev_multi-model-ensemble_rcp60_2020-2039.nc
  • cmip5_anomaly_pr_annual_stddev_multi-model-ensemble_rcp60_2040-2059.nc
  • cmip5_anomaly_pr_annual_stddev_multi-model-ensemble_rcp60_2060-2079.nc
  • cmip5_anomaly_pr_annual_stddev _multi-model-ensemble_rcp60_2080-2099.nc
  • cmip5_anomaly_pr_annual_stddev _multi-model-ensemble_rcp85_2020-2039.nc
  • cmip5_anomaly_pr_annual_stddev _multi-model-ensemble_rcp85_2040-2059.nc
  • cmip5_anomaly_pr_annual_stddev _multi-model-ensemble_rcp85_2060-2079.nc
  • cmip5_anomaly_pr_annual_stddev _multi-model-ensemble_rcp85_2080-2099.nc
  • cmip5_anomaly_pr_monthly_mean_multi-model-ensemble_rcp26_2020-2039.nc
  • cmip5_anomaly_pr_monthly_mean_multi-model-ensemble_rcp26_2040-2059.nc
  • cmip5_anomaly_pr_monthly_mean_multi-model-ensemble_rcp26_2060-2079.nc
  • cmip5_anomaly_pr_monthly_mean_multi-model-ensemble_rcp26_2080-2099.nc
  • cmip5_anomaly_pr_monthly_mean_multi-model-ensemble_rcp45_2020-2039.nc
  • cmip5_anomaly_pr_monthly_mean_multi-model-ensemble_rcp45_2040-2059.nc
  • cmip5_anomaly_pr_monthly_mean_multi-model-ensemble_rcp45_2060-2079.nc
  • cmip5_anomaly_pr_monthly_mean_multi-model-ensemble_rcp45_2080-2099.nc
  • cmip5_anomaly_pr_monthly_mean_multi-model-ensemble_rcp60_2020-2039.nc
  • cmip5_anomaly_pr_monthly_mean_multi-model-ensemble_rcp60_2040-2059.nc
  • cmip5_anomaly_pr_monthly_mean_multi-model-ensemble_rcp60_2060-2079.nc
  • cmip5_anomaly_pr_monthly_mean_multi-model-ensemble_rcp60_2080-2099.nc
  • cmip5_anomaly_pr_monthly_mean_multi-model-ensemble_rcp85_2020-2039.nc
  • cmip5_anomaly_pr_monthly_mean_multi-model-ensemble_rcp85_2040-2059.nc
  • cmip5_anomaly_pr_monthly_mean_multi-model-ensemble_rcp85_2060-2079.nc
  • cmip5_anomaly_pr_monthly_mean_multi-model-ensemble_rcp85_2080-2099.nc
  • cmip5_anomaly_pr_monthly_stddev_multi-model-ensemble_rcp26_2020-2039.nc
  • cmip5_anomaly_pr_monthly_stddev_multi-model-ensemble_rcp26_2040-2059.nc
  • cmip5_anomaly_pr_monthly_stddev_multi-model-ensemble_rcp26_2060-2079.nc
  • cmip5_anomaly_pr_monthly_stddev_multi-model-ensemble_rcp26_2080-2099.nc
  • cmip5_anomaly_pr_monthly_stddev_multi-model-ensemble_rcp45_2020-2039.nc
  • cmip5_anomaly_pr_monthly_stddev_multi-model-ensemble_rcp45_2040-2059.nc
  • cmip5_anomaly_pr_monthly_stddev_multi-model-ensemble_rcp45_2060-2079.nc
  • cmip5_anomaly_pr_monthly_stddev_multi-model-ensemble_rcp45_2080-2099.nc
  • cmip5_anomaly_pr_monthly_stddev_multi-model-ensemble_rcp60_2020-2039.nc
  • cmip5_anomaly_pr_monthly_stddev_multi-model-ensemble_rcp60_2040-2059.nc
  • cmip5_anomaly_pr_monthly_stddev_multi-model-ensemble_rcp60_2060-2079.nc
  • cmip5_anomaly_pr_monthly_stddev _multi-model-ensemble_rcp60_2080-2099.nc
  • cmip5_anomaly_pr_monthly_stddev _multi-model-ensemble_rcp85_2020-2039.nc
  • cmip5_anomaly_pr_monthly_stddev _multi-model-ensemble_rcp85_2040-2059.nc
  • cmip5_anomaly_pr_monthly_stddev _multi-model-ensemble_rcp85_2060-2079.nc
  • cmip5_anomaly_pr_monthly_stddev _multi-model-ensemble_rcp85_2080-2099.nc
  • cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp26_2020-2039.nc
  • cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp26_2040-2059.nc
  • cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp26_2060-2079.nc
  • cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp26_2080-2099.nc
  • cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp45_2020-2039.nc
  • cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp45_2040-2059.nc
  • cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp45_2060-2079.nc
  • cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp45_2080-2099.nc
  • cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp60_2020-2039.nc
  • cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp60_2040-2059.nc
  • cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp60_2060-2079.nc
  • cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp60_2080-2099.nc
  • cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp85_2020-2039.nc
  • cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp85_2040-2059.nc
  • cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp85_2060-2079.nc
  • cmip5_anomaly_tas_annual_mean_multi-model-ensemble_rcp85_2080-2099.nc
  • cmip5_anomaly_tas_annual_stddev_multi-model-ensemble_rcp26_2020-2039.nc
  • cmip5_anomaly_tas_annual_stddev_multi-model-ensemble_rcp26_2040-2059.nc
  • cmip5_anomaly_tas_annual_stddev_multi-model-ensemble_rcp26_2060-2079.nc
  • cmip5_anomaly_tas_annual_stddev_multi-model-ensemble_rcp26_2080-2099.nc
  • cmip5_anomaly_tas_annual_stddev_multi-model-ensemble_rcp45_2020-2039.nc
  • cmip5_anomaly_tas_annual_stddev_multi-model-ensemble_rcp45_2040-2059.nc
  • cmip5_anomaly_tas_annual_stddev_multi-model-ensemble_rcp45_2060-2079.nc
  • cmip5_anomaly_tas_annual_stddev_multi-model-ensemble_rcp45_2080-2099.nc
  • cmip5_anomaly_tas_annual_stddev_multi-model-ensemble_rcp60_2020-2039.nc
  • cmip5_anomaly_tas_annual_stddev_multi-model-ensemble_rcp60_2040-2059.nc
  • cmip5_anomaly_tas_annual_stddev_multi-model-ensemble_rcp60_2060-2079.nc
  • cmip5_anomaly_tas_annual_stddev _multi-model-ensemble_rcp60_2080-2099.nc
  • cmip5_anomaly_tas_annual_stddev _multi-model-ensemble_rcp85_2020-2039.nc
  • cmip5_anomaly_tas_annual_stddev _multi-model-ensemble_rcp85_2040-2059.nc
  • cmip5_anomaly_tas_annual_stddev _multi-model-ensemble_rcp85_2060-2079.nc
  • cmip5_anomaly_tas_annual_stddev _multi-model-ensemble_rcp85_2080-2099.nc
  • cmip5_anomaly_tas_monthly_mean_multi-model-ensemble_rcp26_2020-2039.nc
  • cmip5_anomaly_tas_monthly_mean_multi-model-ensemble_rcp26_2040-2059.nc
  • cmip5_anomaly_tas_monthly_mean_multi-model-ensemble_rcp26_2060-2079.nc
  • cmip5_anomaly_tas_monthly_mean_multi-model-ensemble_rcp26_2080-2099.nc
  • cmip5_anomaly_tas_monthly_mean_multi-model-ensemble_rcp45_2020-2039.nc
  • cmip5_anomaly_tas_monthly_mean_multi-model-ensemble_rcp45_2040-2059.nc
  • cmip5_anomaly_tas_monthly_mean_multi-model-ensemble_rcp45_2060-2079.nc
  • cmip5_anomaly_tas_monthly_mean_multi-model-ensemble_rcp45_2080-2099.nc
  • cmip5_anomaly_tas_monthly_mean_multi-model-ensemble_rcp60_2020-2039.nc
  • cmip5_anomaly_tas_monthly_mean_multi-model-ensemble_rcp60_2040-2059.nc
  • cmip5_anomaly_tas_monthly_mean_multi-model-ensemble_rcp60_2060-2079.nc
  • cmip5_anomaly_tas_monthly_mean_multi-model-ensemble_rcp60_2080-2099.nc
  • cmip5_anomaly_tas_monthly_mean_multi-model-ensemble_rcp85_2020-2039.nc
  • cmip5_anomaly_tas_monthly_mean_multi-model-ensemble_rcp85_2040-2059.nc
  • cmip5_anomaly_tas_monthly_mean_multi-model-ensemble_rcp85_2060-2079.nc
  • cmip5_anomaly_tas_monthly_mean_multi-model-ensemble_rcp85_2080-2099.nc
  • cmip5_anomaly_tas_monthly_stddev_multi-model-ensemble_rcp26_2020-2039.nc
  • cmip5_anomaly_tas_monthly_stddev_multi-model-ensemble_rcp26_2040-2059.nc
  • cmip5_anomaly_tas_monthly_stddev_multi-model-ensemble_rcp26_2060-2079.nc
  • cmip5_anomaly_tas_monthly_stddev_multi-model-ensemble_rcp26_2080-2099.nc
  • cmip5_anomaly_tas_monthly_stddev_multi-model-ensemble_rcp45_2020-2039.nc
  • cmip5_anomaly_tas_monthly_stddev_multi-model-ensemble_rcp45_2040-2059.nc
  • cmip5_anomaly_tas_monthly_stddev_multi-model-ensemble_rcp45_2060-2079.nc
  • cmip5_anomaly_tas_monthly_stddev_multi-model-ensemble_rcp45_2080-2099.nc
  • cmip5_anomaly_tas_monthly_stddev_multi-model-ensemble_rcp60_2020-2039.nc
  • cmip5_anomaly_tas_monthly_stddev_multi-model-ensemble_rcp60_2040-2059.nc
  • cmip5_anomaly_tas_monthly_stddev_multi-model-ensemble_rcp60_2060-2079.nc
  • cmip5_anomaly_tas_monthly_stddev_multi-model-ensemble_rcp60_2080-2099.nc
  • cmip5_anomaly_tas_monthly_stddev_multi-model-ensemble_rcp85_2020-2039.nc
  • cmip5_anomaly_tas_monthly_stddev_multi-model-ensemble_rcp85_2040-2059.nc
  • cmip5_anomaly_tas_monthly_stddev _multi-model-ensemble_rcp85_2060-2079.nc
  • cmip5_anomaly_tas_monthly_stddev _multi-model-ensemble_rcp85_2080-2099.nc

About the MME

The MME provides mean and standard deviation values of annual and monthly temperature and precipitation anomalies from 10 models of projected future climates. The 10 models used in the ensemble are from the Intergovernmental Panel on Climate Change (IPCC) for the 5th Assessment Report for emissions scenario RCP 2.6 / RCP 4.5 / RCP 6.0 / RCP 8.5.

The MME mean annual and monthly anomaly values are the average across all the models of their respective average temperature or precipitation anomaly per year or month over the 20 years of the time period relative to each model's reference period of 1986–2005.

The MME also contains standard deviation values showing the amount of variation between the 10 models over the 20 years of the time period relative to each model's reference period of 1986–2005.

Each of the 10 models in the MME are part of the Couple Model Intercomparison Project, Phase 5 (CMIP5). These models present future climate projections for agreed upon scenarios called Representative Concentration Pathways (RCP). CMIP5 provides a framework of climate experiments that reflect different plausible futures under different concentrations of atmospheric greenhouse gas (GHG) RCPs. The RCPs are named according to their level of radiative forcing (enhanced greenhouse effect) that they produce by the year 2100. This MME subsets the future climate projections into four time frames: 2020–39, 2040–59, 2060–79, and 2080–99. The mean values for these time frames represent the mean of all 20 years in the time frame, effectively smoothing out the year-to-year variations that would naturally occur.

The low emissions pathway has a radiative forcing peak around 3 watts per square meter (W/m2) before the year 2100 and is named RCP 2.6. There are two intermediate pathways where the radiative forcing is stabilized at 6 W/m2 (RCP 6.0) and 4.5 W/m2 (RCP 4.5) by 2100. And the high pathways (RCP 8.5) reach 8.5 W/m2 by 2100.

Data processing

Regridding: Each of the 10 models was first regridded to a 1-degree-by-1-degree common grid.

Climatology: The data was originally a monthly time series. Twenty-year intervals were calculated for key time windows: 2020–2039, 2040–2059, 2060–2079, and 2080–2099. Annual averaging was computed on the annual data.

Anomaly calculation: Each time interval can be compared to the present day (1986–2005) to derive the change or anomaly.

Data disclaimer

Any use of this data should acknowledge the contribution of the National Center for Atmospheric Research (NCAR) and the NCAR GIS program. NCAR is sponsored by the National Science Foundation (NSF) and is managed by the University Corporation for Atmospheric Research (UCAR).

The data and information contained in this report is intended for research purposes only. It is provided as is and without representations or warranties of any kind, either expressed or implied. All representations and warranties are disclaimed, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose. Experience has proven that the timeliness, resolution, and manner in which data from these types of reports and models is used does not wholly support the effective or reliable use of the data in making decisions of an immediate or short-term nature that involve the safety of people or property. The user assumes all risk as to the use of the data and information. In no event will UCAR, or any other party who has been involved in the creation, production, or display of this data and information, be liable for damages, whether direct, special, indirect, incidental, or consequential, including loss of profits.

Source models

The 10 models used in the ensemble are from the Intergovernmental Panel on Climate Change (IPCC) for the 5th Assessment Report for emissions scenario RCP 2.6 / RCP 4.5 / RCP 6.0 / RCP 8.5.

Model name Modeling center

ACCESS1.0

Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM), Australia.

This simulation is a historical experiment, simulating the global climate between 1850 and 2006, using historical forcings. The ARC Centre of Excellence for Climate System Science (ARCCSS) has run this particular experiment. This model simulation is part of the contribution of the Australian Community Climate and Earth System Simulator Coupled Model (ACCESS-CM) to CMIP5. There are two versions of the ACCESS-CM, ACCESS1.0 and ACCESS1.3.

ACCESS1.0 uses these components: the UK MetOffice UM atmosphere model, the GFDL MOM4p1 ocean model, the LANL CICE4.1 sea ice model, and the MOSES2 land surface model. The historical experiment is a long-term simulation of the twentieth century climate including all forcing: solar, volcanic, stratospheric aerosol, anthropogenic aerosol, emissions, and greenhouse gas concentrations. Greenhouse gas concentrations include CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, and HFC134a. The simulation covers 1850 to 2005 and branches from the year 351 of the preindustrial control (piControl) experiment. The ensemble member is defined by a triad of integers describing a specific initial condition (r), initialization method (i), and perturbed physics version (p). These are valid only in the contest of a specific model, so r2i1p1 will have the same initialization and physics scheme as the ACCESS1-0 r1i1p1 simulations but a different initial condition.

CanESM2

Canadian Centre for Climate Modelling and Analysis. The second-generation Canadian Earth System Model (CanESM2) consists of the physical coupled atmosphere-ocean model CanCM4 coupled to a terrestrial carbon model (CTEM) and an ocean carbon model (CMOC).

CCSM4

National Center for Atmospheric Research. A coupled climate model for simulating the earth's climate system. Composed of four separate models simultaneously simulating the earth's atmosphere, ocean, land surface, and sea ice, and one central coupler component.

CESM1

Community Earth System Model Contributors. The Community Earth System Model (CESM) is a coupled climate model for simulating the earth's climate system. Composed of four separate models simultaneously simulating the earth's atmosphere, ocean, land surface, and sea ice, and one central coupler component.

CMCC-CM

Centro Euro-Mediterraneo per I Cambiamenti Climatici. The Centro Euro-Mediterraneo sui Cambiamenti Climatici Climate Model (CMCC-CM) is a coupled atmosphere-ocean general circulation model.

CNRM-CM5

Centre National de Recherches Météorologiques/Centre Européen de Recherche et Formation Avancée en Calcul Scientifique (CERFACS). CNRM-CM5 is an Earth System Model designed to run climate simulations. It consists of several existing models designed independently and coupled through the OASIS software developed at CERFACS.

GFDL-CM3

NOAA Geophysical Fluid Dynamics Laboratory. A global circulation model that accounts for characteristics of sea ice, ocean, land cover, elevation, the flow of heat through land and water, cloud type, atmospheric chemistry at multiple altitudes, and the contextualization of the recent climate changes with human-produced forcings as compared to long-term natural climate variability.

HADGEM2-ES

Met Office Hadley Centre (additional HadGEM2-ES realizations contributed by Instituto Nacional de Pesquisas Espaciais). A fully coupled Earth System Model accounting for atmosphere, ocean, sea ice, land surface; HadGEM2-AO plus new hydrology scheme (wetland methane); land ecosystems: dynamic vegetation, soil C, TRIFFID, RothC; ocean ecosystems: NPZD, diatoms, nondiatoms; Diat-HadOCC; Aerosols: Sulphate, BC, OC, dust, sea salt; current aerosol scheme, with some improvements; and tropospheric chemistry: ozone, methane, oxidants—UKCA.

MPI-ESM-MR

Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology). MPI-ESM couples the atmosphere, ocean, and land surface through the exchange of energy, momentum, water, and carbon dioxide. MPI-ESM is freely available to the scientific community and can be accessed with a license on the MPI-M Model distribution website.

MRI-CGCM3

Meteorological Research Institute. MRI-CGCM3 is composed of atmosphere-land, aerosol, and ocean-ice models. Basic experiments for preindustrial control and historical and climate sensitivity are performed with MRI-CGCM3. In the preindustrial control experiment, the model exhibits very stable behavior without climatic drifts, at least in the radiation budget, the temperature near the surface, and the major indices of ocean circulations. The sea surface temperature (SST) drift is sufficiently small, while there is a 1 W/m2 heating imbalance at the surface.