How well do integrated assessment models represent non-CO2 radiative forcing?

<p>Integrated Assessment Models (IAMs) are widely used to evaluate the economic costs of climate change, the social cost of carbon and the value of mitigation policies. These IAMs include simple energy balance models (EBMs) to represent the physical climate system and to calculate the timeseries of global mean temperature in response to changing radiative forcing[1]. The EBMs are deterministic in nature which leads to smoothly varying GMT trajectories so for simple monotonically increasing forcing scenarios (e.g. representative concentration pathways (RCPs) 8.5, 6.0 and 4.5) the GMT trajectories are also monotonically increasing. By contrast real world, and global-climate-model-derived, timeseries show substantial inter-annual and inter-decadal variability. Here we present an analysis of the implications of this intrinsic variability for the economic consequences of climate change.</p><p>We use a simple stochastic EBM to generate large ensembles of GMT trajectories under each of the RCP forcing scenarios. The damages implied by each trajectory are calculated using the Weitzman damage function. This provides a conditional estimate of the unavoidable uncertainty in implied damages. It turns out to be large and positively skewed due to the shape of the damage function. Under RCP2.6 we calculate a 5-95% range of -30% to +52% of the deterministic value; -13% to +16% under RCP 8.5. The risk premia associated with such unavoidable uncertainty are also significant. Under our economic assumptions a social planner would be willing to pay 32 trillion dollars to avoid just the intrinsic uncertainty in RCP8.5. This figure rises further when allowance is made for epistemic uncertainty in relation to climate sensitivity. We conclude that appropriate representation of stochastic variability in the climate system is important to include in future economic assessments of climate change.</p><p><br>[1] Calel, R. and Stainforth D.A., &#8220;On the Physics of Three Integrated Assessment Models&#8221;, Bulletin of the American Meteorological Society, 2017.</p><p>&#160;</p>

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Public Outreach and Interactive Learning with En-ROADS Global Energy and Climate Simulator

10.5194/egusphere-egu21-7334 ◽

2021 ◽

Author(s):

Sibel Eker ◽

Lori Siegel ◽

Charles Jones ◽

John Sterman ◽

Florian Kapmeier ◽

...

Keyword(s):

Carbon Cycle ◽

System Dynamics ◽

Integrated Assessment ◽

Radiative Forcing ◽

Climate Model ◽

Climate Impacts ◽

Time Step ◽

Integrated Assessment Models ◽

Carbon Transfer ◽

Outreach And Education

<p>Simple climate models enable not only rapid simulation of a large number of climate scenarios, especially in connection with the integrated assessment models of economy and environment, but also provide chances for outreach and education. En-ROADS, (Energy Rapid Overview and Decision Support)[1], is a publicly available, online policy simulation model designed to complement integrated assessment models for rapid simulation of climate solutions. En-ROADS is a globally aggregated energy-economy-climate model based on a simple climate model, and supports outreach and education about the causes and effects of climate change.&#160; It has an intuitive user interface and runs essentially instantly on ordinary laptops and tablets, providing policymakers, other leaders, educators, and the public with the ability to learn for themselves about the likely consequences of energy and climate policies and uncertainties.</p><p>&#160;</p><p>En-ROADS is a behavioral system dynamics model consisting of a system of nonlinear ordinary differential equations solved numerically from 1990-2100, with a time step of one-eighth year. En-ROADS extends the C-ROADS model, which has been used extensively by officials and policymakers around the world to inform positions of parties to the UNFCCC[2][3]. In En-ROADS&#8217; climate module, the resulting emissions from the energy system, from forestry and land use, and carbon removal technologies, determine the atmospheric concentrations of each GHG, radiative forcing, and climate impacts including global surface temperature anomaly, heat and carbon transfer between the surface and deep ocean, sea level rise, and ocean acidification. It is calibrated to fit historical data of temperature change and carbon cycle elements, as well as the projections within the RCP-SSP framework. Both En-ROADS and C-ROADS are further developed to account for the details of the terrestrial carbon cycle.</p><p>&#160;</p><p>&#160;</p><p>&#160;</p><p>&#160;</p><div><br><div> <p>[1] https://en-roads.climateinteractive.org/scenario.html.</p> </div> <div> <p>[2] Sterman J, Fiddaman T, Franck TR, Jones A, McCauley S, Rice P, et al. Climate interactive: the C-ROADS climate policy model. System Dynamics Review 2013 <strong>28</strong> (3): 295&#8211;305</p> </div> <div> <p>[3] Sterman JD, Fiddaman T, Franck T, Jones A, McCauley S, Rice P, et al. Management flight simulators to support climate negotiations. Environmental Modelling & Software 2013, <strong>44:</strong> 122-135.</p> </div> </div>

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Evaluating process-based integrated assessment models of climate change mitigation

Climatic Change ◽

10.1007/s10584-021-03099-9 ◽

2021 ◽

Vol 166 (1-2) ◽

Author(s):

Charlie Wilson ◽

Céline Guivarch ◽

Elmar Kriegler ◽

Bas van Ruijven ◽

Detlef P. van Vuuren ◽

...

Keyword(s):

Climate Change ◽

Integrated Assessment ◽

Climate Change Mitigation ◽

Evaluation Method ◽

Evaluation Research ◽

Evaluation Framework ◽

Systematic Evaluation ◽

Integrated Assessment Models ◽

Wide Range ◽

Change Mitigation

AbstractProcess-based integrated assessment models (IAMs) project long-term transformation pathways in energy and land-use systems under what-if assumptions. IAM evaluation is necessary to improve the models’ usefulness as scientific tools applicable in the complex and contested domain of climate change mitigation. We contribute the first comprehensive synthesis of process-based IAM evaluation research, drawing on a wide range of examples across six different evaluation methods including historical simulations, stylised facts, and model diagnostics. For each evaluation method, we identify progress and milestones to date, and draw out lessons learnt as well as challenges remaining. We find that each evaluation method has distinctive strengths, as well as constraints on its application. We use these insights to propose a systematic evaluation framework combining multiple methods to establish the appropriateness, interpretability, credibility, and relevance of process-based IAMs as useful scientific tools for informing climate policy. We also set out a programme of evaluation research to be mainstreamed both within and outside the IAM community.

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Scenarios for greenhouse gas emission mitigation: a review of modeling of strategies and policies in integrated assessment models

Environmental Economics and Policy Studies ◽

10.1007/bf03354041 ◽

2000 ◽

Vol 3 (2) ◽

pp. 267-289 ◽

Cited By ~ 10

Author(s):

Ashish Rana ◽

Tsuneyuki Morita

Keyword(s):

Greenhouse Gas ◽

Integrated Assessment ◽

Greenhouse Gas Emission ◽

Gas Emission ◽

Emission Mitigation ◽

Integrated Assessment Models ◽

Greenhouse Gas Emission Mitigation

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The Economics of the Climate

Journal of Economic Literature ◽

10.1257/jel.20151335 ◽

2017 ◽

Vol 55 (3) ◽

pp. 1046-1063 ◽

Cited By ~ 17

Author(s):

Geoffrey Heal

Keyword(s):

Complex Systems ◽

Integrated Assessment ◽

Discount Rates ◽

Risk And Uncertainty ◽

Policy Choices ◽

Integrated Assessment Models ◽

Qualitative Understanding ◽

Climate Problem

I review the economic characteristics of the climate problem, focusing on the choice of discount rates in the presence of a stock externality, risk and uncertainty/ambiguity, and the role of integrated assessment models (IAMs) in analyzing policy choices. I suggest that IAMs can play a role in providing qualitative understanding of how complex systems behave, but are not accurate enough to provide quantitative insights. Arguments in favor of action on climate issues have to be based on aversion to risk and ambiguity and the need to avoid a small but positive risk of a disastrous outcome. ( JEL D61, H43, Q48, Q54, Q58)

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