Uncertainty Analysis of Climate Change and Policy Response

Abstract

The MIT Joint Program on the Science and Policy of Global Change is an organization for research, independent policy analysis, and public education in global environmental change. It seeks to provide leadership in understanding scientific, economic, and ecological aspects of this difficult issue, and combining them into policy assessments that serve the needs of ongoing national and international discussions. To this end, the Program brings together an interdisciplinary group from two established research centers at MIT: the Center for Global Change Science (CGCS) and the Center for Energy and Environmental Policy Research (CEEPR). These two centers bridge many key areas of the needed intellectual work, and additional essential areas are covered by other MIT departments, by collaboration with the Ecosystems Center of the Marine Biology Laboratory (MBL) at Woods Hole, and by short-and long-term visitors to the Program. The Program involves sponsorship and active participation by industry, government, and non-profit organizations. To inform processes of policy development and implementation, climate change research needs to focus on improving the prediction of those variables that are most relevant to economic, social, and environmental effects. In turn, the greenhouse gas and atmospheric aerosol assumptions underlying climate analysis need to be related to the economic, technological, and political forces that drive emissions, and to the results of international agreements and mitigation. Further, assessments of possible societal and ecosystem impacts, and analysis of mitigation strategies, need to be based on realistic evaluation of the uncertainties of climate science. This report is one of a series intended to communicate research results and improve public understanding of climate issues, thereby contributing to informed debate about the climate issue, the uncertainties, and the economic and social implications of policy alternatives. Titles in the Report Series to date are listed on the inside back cover.

Ensemble climate predictions using climate models and observational constraints

“…standard deviation). There is nothing inherently wrong with using literature estimates, but the point estimates of uncertain parameters should span the population of interest and not simply be derived from a distribution of mean estimates from different studies (see Webster et al (2003) for further discussion). (Sokolov & Stone 1998;Sokolov et al 2003).…”

“…One crucial aspect of the Webster et al (2003) work is using the joint probability density functions (PDFs) for the climate model parameters controlling S, K v and F aer from Forest et al (2002). The estimation method uses observations of upper air, surface and deep-ocean temperatures for the twentieth century to jointly constrain these climate parameters (qZ(S, K v , F aer )), while including unforced climate variability as a source of uncertainty (Forest et al 2002).…”

“…A sample size of 250 is sufficient to estimate probability distributions for climate outcomes of interest. Further details can be found in Webster et al (2003).…”

Sequential climate change policy

“…Uncertainty analysis in IAMs is becoming more sophisticated, moving beyond point estimates and sensitivity analysis toward studies that propagate exogenous probability distributions of input parameters through to model outputs or that integrate observations to constrain distributions of model parameters. [9][10][11][12][13] Regardless of what form is used to represent uncertain characteristics or knowledge about them, however, if these do not change over time they cannot represent the learning that is central to sequential decision-making. A few studies have adopted alternative methods to study intertemporal climate decisions, including both optimal-control and formal feedback-control methods, [14][15][16] but treatments of these methods to date have also allowed only limited examination of sequential decision-making under uncertainty.…”

Communicating uncertainty in the IPCC’s greenhouse gas emissions scenarios

“…Nevertheless the plausibility, possibility, persistency, and/or probability of the scenarios has been the main topic of dispute in the literature (Allen 2003;Grübler and Nakicenovic 2001;Kriegler and Bruckner 2004;Lempert et al 2004;Lomborg 2001, p280-287;Pittock 2002;Pittock et al 2001;Reilly et al 2001;Schneider 2001;Schneider and Mastrandrea 2005;Webster et al 2003), especially the Castles and Henderson critique (Castles and Henderson 2003a,b;Grübler et al 2004;Nakicenovic et al 2003). In this section, the economic development in the A1 AIM marker scenario (see Fig.…”