Modeling Uncertainty in Climate Change: A Multi-Model Comparison

Joint Program Report
Modeling Uncertainty in Climate Change: A Multi-Model Comparison
Gillingham, K., W. Nordhaus, D. Anthoff, G. Blanford, V. Bosetti, P. Christensen, H. McJeon, J. Reilly and P. Sztorc (2015)
Joint Program Report Series, 47 p.

Report 290 [Download]

Abstract/Summary:

The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity. It estimates the pdfs of key output variables, including CO2 concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insights on tail events.

Citation:

Gillingham, K., W. Nordhaus, D. Anthoff, G. Blanford, V. Bosetti, P. Christensen, H. McJeon, J. Reilly and P. Sztorc (2015): Modeling Uncertainty in Climate Change: A Multi-Model Comparison. Joint Program Report Series Report 290, 47 p. (http://globalchange.mit.edu/publication/16235)
  • Joint Program Report
Modeling Uncertainty in Climate Change: A Multi-Model Comparison

Gillingham, K., W. Nordhaus, D. Anthoff, G. Blanford, V. Bosetti, P. Christensen, H. McJeon, J. Reilly and P. Sztorc

Report 

290
47 p.
2016

Abstract/Summary: 

The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity. It estimates the pdfs of key output variables, including CO2 concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insights on tail events.