Climatic Change

, Volume 103, Issue 3–4, pp 399–422 | Cite as

Uncertainty and risk in climate projections for the 21st century: comparing mitigation to non-intervention scenarios

  • Lorenzo Tomassini
  • Reto Knutti
  • Gian-Kasper Plattner
  • Detlef P. van Vuuren
  • Thomas F. Stocker
  • Richard B. Howarth
  • Mark E. Borsuk


Probabilistic climate projections based on two SRES scenarios, an IMAGE reference scenario and five IMAGE mitigation scenarios (all of them multi-gas scenarios) using the Bern2.5D climate model are calculated. Probability distributions of climate model parameters that are constrained by observations are employed as input for the climate model. The sensitivity of the resulting distributions with respect to prior assumptions on climate sensitivity is then assessed. Due to system inertia, prior assumptions on climate sensitivity play a minor role in the case of temperature projections for the first half of the 21st century, but these assumptions have a considerable influence on the distributions of the projected temperature increase in the year 2100. Upper and lower probabilities for exceeding 2°C by the year 2100 are calculated for the different scenarios. Only the most stringent mitigation measures lead to low probabilities for exceeding the 2°C threshold. This finding is robust with respect to our prior assumptions on climate sensitivity. Further, probability distributions of total present-value damages over the period 2000–2100 for the different scenarios are calculated assuming a wide range of damage cost functions, and the sensitivity of these distributions with respect to the assumed discount rate is investigated. Absolute values of damage costs depend heavily on the chosen damage cost function and discount rate. Nevertheless, some robust conclusions are possible.


Climate Sensitivity Damage Cost Mitigation Scenario SRES Scenario Image Scenario 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Lorenzo Tomassini
    • 1
    • 2
    • 3
  • Reto Knutti
    • 4
  • Gian-Kasper Plattner
    • 2
    • 5
  • Detlef P. van Vuuren
    • 6
  • Thomas F. Stocker
    • 5
  • Richard B. Howarth
    • 7
  • Mark E. Borsuk
    • 8
  1. 1.Swiss Federal Institute of Aquatic Science and TechnologyDuebendorfSwitzerland
  2. 2.Environmental Physics, Institute of Biogeochemistry and Pollutant DynamicsETH ZurichZurichSwitzerland
  3. 3.Max Planck Institute for MeteorologyHamburgGermany
  4. 4.Institute of Atmospheric and Climate ScienceETH ZurichZurichSwitzerland
  5. 5.Climate and Environmental Physics, Physics InstituteUniversity of BernBernSwitzerland
  6. 6.Netherlands Environmental Assessment AgencyBilthovenThe Netherlands
  7. 7.Environmental Studies ProgramDartmouth CollegeHanoverUSA
  8. 8.Thayer School of EngineeringDartmouth CollegeHanoverUSA

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