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Uncertainty Analysis of Climate Change and Policy Response

Author(s): Webster MD, Forest C, Reilly J, Babiker M, Kicklighter D, Mayer M, Prinn R, Sarofim M, Sokolov AP, Stone P, Wang C

Published: December, 2003

Publisher: Climatic Change

DOI: 10.1023/B:CLIM.0000004564.09961.9f

Tags: Policy, Uncertainty, Climate Science

URL: http://www.springerlink.com/content/m43k4n1012v07278/

Abstract: To aid climate policy decisions, accurate quantitative descriptions of the uncertainty in climate outcomes under various possible policies are needed. Here, we apply an earth systems model to describe the uncertainty in climate projections under two different policy scenarios. This study illustrates an internally consistent uncertainty analysis of one climate assessment modeling framework, propagating uncertainties in both economic and climate components, and constraining climate parameter uncertainties based on observation. We find that in the absence of greenhouse gas emissions restrictions, there is a one in forty chance that global mean surface temperature change will exceed 4.9 °C by the year 2100. A policy case with aggressive emissions reductions over time lowers the temperature change to a one in forty chance of exceeding 3.2 °C, thus reducing but not eliminating the chance of substantial warming.


A Methodology for Quantifying Uncertainty in Climate Projections

Author(s): Webster MD, Sokolov AP

Published: September, 2000

Publisher: Climatic Change

DOI: 10.1023/A:1005685317358

Tags: Uncertainty, Climate Science, Climate Modelling

URL: http://rd.springer.com/article/10.1023/A%3A1005685317358#

Abstract: Possible climate change caused by an increase in greenhouse gas concentrations, despite having been a subject of intensive study in recent years, is still very uncertain. Uncertainties in projections of different climate variables are usually described only by the ranges of possible values. For assessing the possible impact of climate change, it would be more useful to have probability distributions for these variables. Obtaining such distributions is usually very computationally expensive and requires knowledge of probability distributions for characteristics of the climate system that affect climate projections. A few studies of this kind have been carried out with energy balance/upwelling diffusion models. Here we demonstrate a methodology for performing a similar study with a 2 dimensional (zonally averaged) climate model that reproduces the behavior of coupled atmosphere/ocean general circulation models more realistically than energy balance models. This methodology involves application of the Deterministic Equivalent Modeling Method to derive functional approximations of the model's probabilistic response.Monte Carlo analysis is then performed on the approximations. An application of the methodology is demonstrated by deriving the uncertainty in surface air temperature change and sea level rise due tothermal expansion of the ocean that result from uncertainties in climate sensitivity and the rate of heat uptake by the deep ocean for a prescribed increase in atmospheric CO2 concentration. We also demonstrate propagation of correlated uncertainties through different models, by presenting results that include uncertainty in projected carbon emissions.


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