Modeling Uncertainty in Integrated Assessment of Climate Change: A Multimodel Comparison

The Nobel Memorial Prize for William D. Nordhaus

“…In Gillingham et al . (), Nordhaus joins forces with several IAM modeling groups in order to push the frontier on multimodel comparisons so as to illuminate key uncertainties afflicting our projections of the future.…”

“…39 For example, Nordhaus routinely reports "Stern Review discounting" scenarios along with standard DICE model results (e.g., Nordhaus, 2017), and has devoted entire book chapters to scholarly engagement with Stern's perspective (e.g., Nordhaus, 2008). Nordhaus has also repeatedly returned to the question of "fat tails" in his analyses of uncertainty and climate change, such as by studying under which parametric and policy scenarios catastrophic risks might arise (e.g., Nordhaus, 2013a;Gillingham et al, 2018). 40 Transcribed from the Nobel Lecture video available from Nobel Media at https://www.…”

“…In recent work, he shines a light both on the prediction errors in his own past work (Nordhaus, 2018a), and on thorny questions about computational complexity and errors in the broader literature (Nordhaus, 2012b). In Gillingham et al (2018), Nordhaus joins forces with several IAM modeling groups in order to push the frontier on multimodel comparisons so as to illuminate key uncertainties afflicting our projections of the future.…”

“…Nordhaus () already devotes three out of eight book chapters to uncertainty. Nordhaus has continually produced dedicated analyses on this topic, including a probabilistic PRICE model extension (Nordhaus and Popp, ), updated uncertainty analyses with new DICE iterations (e.g., Nordhaus, ), and recently also a multimodel comparison together with five other IAMs (Gillingham et al ., ).…”

“…On the one hand, these analyses illuminate the key sources of uncertainty afflicting our predictions of the future. For example, Gillingham et al (2018) find that uncertainty over future economic growth is a significantly larger contributor to uncertainty over model outcomes than uncertainty over the equilibrium climate sensitivity or population growth. Another surprising result of this multimodel comparison is that parametric uncertainty dominates model uncertainty.…”