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WIREs Clim Change
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Applications of integrated assessment modeling to climate change

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Climate change studies are often interdisciplinary by nature, incorporating many domains of science, economics, and political theory. Integrated assessment (IA) aims to bring diverse scientific, economics and social science expertise together to provide analysis and advice that comprehensively addresses all or at least many aspects of the climate change issue. IA methods have been applied to many areas of climate change providing insights into areas such as optimal timing of emission reductions, weighting of different greenhouse gases, or impacts of biofuel policies. Additionally, IAs have identified key uncertainties that should be priorities of future research, such as the need to understand oceanic heat uptake in order to better constrain climate sensitivity and predict future timing of temperature change. These assessments have also served to establish ongoing communication within the community of researchers, and between researchers and policy makers. In complex scientific issues it is often difficult for policy makers and the public to sort out conflicting scientific views, and an authoritative assessment process can provide consensus views on the issue, accepting that in some cases the “consensus” may be that some aspects of the issue remain unresolved. This review explores the history and applications of these IAs, and identifies avenues for future emphasis. We briefly review the whole field of IAs of climate change, but focus on the role of formal computational frameworks in IA models. WIREs Clim Change 2011 2 27–44 DOI: 10.1002/wcc.93

Figure 1.

Top, IPCC WGI (dashed lines) and revised concentration profiles (WRE, solid lines) for stabilization of CO2 at 350–750 ppmv. Bottom, implied anthropogenic emissions using the model of Wigley. IS92a is shown (thicker line) for comparison. (Reprinted with permission from Ref 39. Copyright 1996 Macmillan Publishers Ltd.)

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Figure 2.

Fossil fuel and industrial CO2 emissions across scenarios (GtC/year). (Reprinted with permission from Ref 40. Copyright 2008 CCSP.)

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Figure 3.

The kernel estimate of the probability density function of the social cost of carbon. Top left: alternative distributional assumptions, top right: sample split according to pure rate of time preference, bottom left: sample split, according to review, bottom right: sample split according to age of study. (Reprinted with permission from Ref 49. 2007 Economics Open‐Access E‐journal.)

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Figure 4.

Risks from climate change by reason for concern‐2001 compared with updated data. Climate change consequences are plotted against increase in global mean temperature (°C) after 1990. Each column corresponds to a specific Reasons For Concern and represents additional outcomes associated with increasing global mean temperature. The color scheme represents progressively increasing levels of risk and should not be interpreted as representing ‘dangerous anthropogenic interference,’ which is a value judgment. The historical period 1900–2000 warmed by ∼0.6°C and led to some impacts. (Reprinted with permission from Ref 55. Copyright 2009 PNAS.)

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Figure 5.

Global land use: (a) reference case—OLSR model, (b) reference case—PCCR model, (c) policy case—OLSR model, and (d) policy case—PCCR model. (Reprinted with permission from Ref 57. Copyright 2007 Berkeley Electronic Press.)

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Figure 6.

The price of CH4 and N2O relative to that of CO2 under alternative constraints on absolute and decadal temperature change. (a and c) Prices of CH4 and N2O relative to that of CO2 when the ceiling is on absolute temperature change. (b and d) The corresponding results when a rate of change constraint is added. (Reprinted with permission from Ref 33. Copyright 2001 Macmillan Publishers Ltd.)

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