IMAGEIntegrated Model to Assess the Global Environment.

Atmospheric composition and climate


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Atmospheric composition and climate model (based on MAGICC 6.0) in IMAGE 3.0
Flowchart Atmospheric composition and climate. See also the Input/Output Table on the introduction page.

Key policy issues

  • What would be the impact of global climate change in this century without additional mitigation policies and measures?
  • To what extent would the various scenarios to significantly reduce net greenhouse gas emissions lead to a reduction in climate change?
  • To what extent does the uncertainty of geographical patterns in temperature and precipitation change influence future climate impacts and response strategies?


Climate change is considered to be one of the most important effects of human activities on the environment. Emissions from fossil fuels, industry, land use and land-use change have increased greenhouse gas concentrations and led to almost 1 °C rise in global mean temperature on pre-industrial levels (IPCC, 2007a; IPCC, 2013). The impacts are already visible and are expected to increase in the coming century. Without further action, mean global temperature could increase by 2.6 – 4.8 °C (5-95 percentile, RCP8.5) by the end of this century (IPCC, 2013). Climate change impacts manifest themselves in all world regions, and affect almost all aspects of human activity.

Modelling climate change (changes in temperature and precipitation) is central in global integrated assessments of baseline developments in climate change and of climate policy options. IMAGE uses the simple climate model MAGICC 6.0 (Meinshausen et al., 2011a; Meinshausen et al., 2011b) to simulate the effects of changing greenhouse gas emissions on atmospheric composition, radiative forcing and global mean temperature. MAGICC was used extensively in the Third, Fourth, and Fifth assessment reports of IPCC (Intergovernmental Panel on Climate Change) in assessing a range of greenhouse gas concentration scenarios. Since publication of these reports, MAGICC has been updated in line with results from Atmosphere-Ocean General Circulation Models (AOGCMs).

There is still considerable uncertainty in climate change simulations, as illustrated by differences in results from various AOGCMs, in terms of mean global temperature, and even more so in geographical patterns of surface temperature and precipitation. By adjusting the values of a few of the model parameters, MAGICC6.0 can reproduce time-dependent responses of AOGCMs (Meinshausen et al., 2011a; Meinshausen et al., 2011b). This allows IMAGE to reflect the uncertainty in AOGCM results, and to provide plausible projections of future climate-change feedbacks and impacts.

The analysis of climate impacts and feedbacks requires only location-specific temperature and precipitation changes. Thus, a pattern scaling technique is applied in IMAGE by combining MAGICC results with maps on climate change from the same AOGCMs assessed in AR4 (IPCC, 2007a) and used for calibrating MAGICC. The consistent combination of AOGCM-specific parameter settings for MAGICC and matching geographical patterns of climate change make the dynamic results from IMAGE physically more consistent, and extend the range of uncertainties that can be covered to include future climate change.

Input/Output Table

Input Atmospheric composition and climate component

IMAGE model drivers and variablesDescriptionSource
Cloudiness - grid (historical data) Percentage of cloudiness per month; assumed constant after the historical period CRU database
Number of wet days - grid (historical data) Number of days with a rain event, per month; assumed constant after the historical period CRU database
BC, OC and NOx emissions Emissions of BC, OC and NOx per year. Emissions
CO and NMVOC emissions Emissions from CO and NMVOC. Emissions
CO2 emission from energy and industry CO2 emission from energy and industry. Emissions
Land-use CO2 emissions - grid Land-use CO2 emissions from deforestation, wood harvest, agricultural harvest, bioenergy plantations and timber decay. Carbon cycle and natural vegetation
NEP (net ecosystem production) - grid Net natural exchange of CO2 between biosphere and atmosphere (NPP minus soil respiration), excluding human induced fluxes such as decay of wood products. Carbon cycle and natural vegetation
Non-CO2 GHG emissions (CH4, N2O and Halocarbons) Non-CO2 GHG emissions (CH4, N2O, Halocarbons). Emissions
SO2 emissions SO2 emissions, per source (e.g. fossil fuel burning, deforestation). Emissions
External datasetsDescriptionSource
GCM pattern (temperature, precipitation) - grid Climate change patterns of General Circulation models used to downscale changes in global mean temperature to changes in temperature and precipitation at grid level; default pattern from HadCM3 of the Hadley Centre. AR4 (IPCC) database
MAGICC parameter settings MAGICC 6.0 parameters calibrated to emulate one out of 19 climate models. Meinshausen et al., 2011a
Radiative forcing factors Radiative forcing per greenhouse gas. Forster et al., 2007
Sulphate pattern - grid Patterns of climate change to compute non-linear regional radiative effects of sulphate aerosols.

Output Atmospheric composition and climate component

IMAGE model variablesDescriptionUse
Global mean temperature Average global temperature.
CO2 concentration Atmospheric CO2 concentration.
Precipitation - grid Monthly total precipitation.
Temperature - grid Monthly average temperature.
Ocean carbon uptake Ocean carbon uptake.
Radiative forcing Radiative forcing of greenhouse gases, ozone, and aerosols. Final output
Non-CO2 GHG concentrations Atmospheric concentration of non-CO2 greenhouse gases. Final output
Number of wet days - grid Number of days with a rain event, per month; assumed constant after the historical period
Cloudiness - grid Percentage of cloudiness per month; assumed constant after the historical period

Parts of Atmospheric composition and climate

  1. Introduction page
  2. Model description
  3. Policy issues
  4. Data, uncertainty and limitations
  5. Overview of references