Library Header Image
LSE Research Online LSE Library Services

On the physics of three integrated assessment models

Calel, Raphael and Stainforth, David A. ORCID: 0000-0001-6476-733X (2017) On the physics of three integrated assessment models. Bulletin of the American Meteorological Society, 98 (6). pp. 1199-1216. ISSN 0003-0007

Text - Published Version
Available under License Creative Commons Attribution.

Download (3MB) | Preview
Identification Number: 10.1175/BAMS-D-16-0034.1


Differing physical assumptions are embedded in an important class of integrated assessment models. Reverse-engineering a common description of their underlying physics facilitates inter-comparisons that separate economic and physical uncertainties. Integrated assessment models (IAMs) are the main tools for combining physical and economic analyses to develop and assess climate change policy. Policy makers have relied heavily on three IAMs in particular—DICE, FUND, and PAGE—when trying to balance the benefits and costs of climate action. Unpacking the physics of these IAMs accomplishes four things. Firstly, it reveals how the physics of these IAMs differ, and the extent to which those differences give rise to different visions of the human and economic costs of climate change. Secondly, it makes these IAMs more accessible to the scientific community and thereby invites further physical expertise into the IAM community so that economic assessments of climate change can better reflect the latest physical understanding of the climate system. Thirdly, it increases the visibility of the link between the physical sciences and the outcomes of policy assessments so that the scientific community can focus more sharply on those unresolved questions that loom largest in policy assessments. And finally, in making explicit the link between these IAMs and the underlying physical models, one gains the ability to translate between IAMs using a common physical language. This translation-key will allow multi-model policy assessments to run all three models with physically comparable baseline scenarios, enabling the economic sources of uncertainty to be isolated and facilitating a more informed debate about the most appropriate mitigation pathway.

Item Type: Article
Official URL:
Additional Information: © 2017 American Meteorological Society © CC BY 4.0
Divisions: Grantham Research Institute
Subjects: G Geography. Anthropology. Recreation > GF Human ecology. Anthropogeography
Date Deposited: 27 Oct 2016 11:52
Last Modified: 16 May 2024 02:27
Projects: ES/K006576/1
Funders: Economic and Social Research Council

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics