Model type Quick running climate model that, according to the C-ROADS scientific review board, "reproduces the response properties of state-of-the-art three dimensional climate models...well within the uncertainties of the high resolution models...with sufficient precision to provide useful information" to policy makers and the general public.
Geographic scope Global
Geographic resolution
- 3 regions or emission reduction targets in C-LEARN (3, 7 or 15 regions in C-ROADS)
- Global for deforestation/aforestation targets
Start date 1850
End date 2100
Time step 0.25 year
Data sources
- Country-level CO2 emissions from fossil fuels
Global, Regional, and National Fossil Fuel CO2 Emissions. Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory, U.S. Department of Energy.
- CO2 emissions from changes in land use
Carbon Flux to the Atmosphere from Land-Use Changes 1850-2005 Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory, U.S. Department of Energy.
- GDP and population
Statistics on World Population, GDP and Per Capita GDP, 1-2006 AD Conference Board and Groningen Growth and Development Centre, Total Economy Database.
- Business As Usual CO2 emissions projections
Calibrated to the scenarios in the IPCC's Special Report on Emissions Scenarios (SRES), with the International Energy Agency (IEA)’s World Energy Outlook 2007 allocations between regions.
The C-LEARN/C-ROADS default BAU scenario is the IPCC's A1FI (rapid economic growth, fossil fuel intensive) scenario. On the IPCC scenarios, see "What is the range of GHG emissions in the SRES scenarios and how do they relate to driving forces?" in Summary for Policy Makers section of the SRES.
- Population and GDP projections
Based on the United Nations’ World Population Prospects 2004 forecast and U.S. Energy Information Agency (EIA)'s International Energy Outlook 2008 GDP forecast, respectively.
Approach for addressing risk/uncertainty Model outputs do not show level of uncertainty associated with simulation
Model structure
Return to C-LEARN