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6 Characterizing, Quantifying, and Communicating Uncertainty
Pages 129-144

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From page 129...
... This chapter discusses different types of uncertainty related to climate modeling, reviews how uncertainty has been quantified, discusses the complex issue of communicating uncertainty, and, finally, provides findings and recommendations. TYPES OF UNCERTAINTIES IN THE CLIMATE SYSTEM From the point of view of developing projections of long-term climate change from results of climate model simulations, there are three major uncertainties: (1)
From page 130...
... Factors that influence the scale of future anthropogenic emissions include the scale of economic activity, the technologies with which human societies generate and use energy, and the public policy environment in which human activities are conducted. Hence, predicting emissions of GHGs and aerosols requires being able to predict how the entire human world will develop in the future, a truly daunting task fraught with multiple profound uncertainties.
From page 131...
... . Uncertainty in the Climate System Response to Radiative Forcing Climate system uncertainty is explored through the application of global and regional climate models.
From page 132...
... Regional climate models (RCMs) , like general circulation models, are subject to uncertainty related to grid resolution and physics parameterizations but also introduce additional uncertainty associated with the lateral boundaries (including their placement)
From page 133...
... , rely on two important sources of predictability -- processes or variables such as upper ocean heat content and soil moisture that have memory relevant to the ISI time scale, and predictable patterns of variability, such as teleconnection patterns associated with the El Niño/Southern Oscillation, which involve complex dynamics of atmosphere-ocean feedback. Incomplete knowledge of all the relevant long-memory reservoirs, as well as the imperfect ability of models to accurately simulate patterns or modes of variability, and intrinsic loss of predictability due to chaotic behavior of the Earth system, all contribute to uncertainty in ISI predictions.
From page 134...
... . Some uncertainties, such as structural uncertainty due to incomplete or poor representation of processes in climate models, do not readily lend themselves to quantification.
From page 135...
... . With the development of ensembles of regional climate model simulations, methods particularly adapted to that context are emerging (e.g., Deque and Somot, 2010; Sain et al., 2011)
From page 136...
... Hence, it seems likely that structural errors in parameterizations or inadequacies in grid resolution not correctable by parameter tuning are probably a larger driver of systematic errors and projection uncertainty than suboptimal choices of existing uncertain parameters. In this environment, there is a tradeoff between maintaining fluidity of the model development process and the huge investment of computer time needed to apply the rigorous principles of uncertainty quantification and optimization. Some modeling groups, such as the Geophysical Fluid Dynamics Laboratory, are experimenting with some automatic parameter tuning as a routine part of model 136
From page 137...
... ost of the observed increase in global temperatures since the mid-20th century is very likely (i.e., 90% confidence) due to the observed increase in greenhouse gas concentrations." This is primarily due to observation of continuing global warming and many of its anticipated corollaries consistent with the range of climate model predictions.
From page 138...
... Is it for general education, making people aware of important issues, or is it to inspire specific actions regarding managing climate resources, or is it for the sake of shaping the needs for future climate model development? Communications of scientists to scientists about uncertainty are very different from their communication with the lay public.
From page 139...
... A user of climate information will have uncertainty associated with its perception of the process of model evaluation or validation. As discussed above, other sources of uncertainty referred to by climate modelers include boundary conditions, initial conditions, formulation of physics, parametric, numerical formulation, downscaling, and so on.
From page 140...
... To develop effective and consistent communication strategies, social science-based empirical studies are needed. Examples of Current Approaches to Communicating Uncertainty It is hoped that approaches to communicating uncertainty will become much more sophisticated in the coming decades, that the different needs for quantification in different science and policy communities will be well recognized, that means of presenting uncertainties will have greatly advanced so as to match the needs of the particular community, and that more creative ways of communicating uncertainty to the lay public and policy makers alike will be developed.
From page 141...
... There are fledgling activities that have begun to emerge that have focused on effective communication of climate science, such as the Yale Project on Climate Change Communication,2 a nonprofit science and outreach project called Climate Communication,3 and the commentary site RealClimate.4 This effort could also be furthered by more actively engaging the media through agencies dedicated to the reporting of science such as the Society of Environmental Journalists,5 the Yale forum on Climate Change and the Media,6 and Climate Central.7 Although these and other resources (Somerville and Hassol, 2011; Ward, 2008) are starting to become more available, there are very few programs aimed at training climate scientists in lay communication or in targeting groups of scientists or professionals (such as weather forecasters)
From page 142...
... . How important it is to reduce uncertainty of regional climate change depends closely on the approach being used for decision making under uncertainty.
From page 143...
... Nonetheless, important uncertainties remain, particularly regarding climate sensitivity, GHG emissions, and regional details about climate change. As new components of the Earth system are included into models, they may in fact (especially over the short term)
From page 144...
... To facilitate this, the United States should more vigorously support research on uncertainty, including • understanding and quantifying uncertainty in the projection of future cli mate change, including how best to use the current observational record across all time scales; • incorporating uncertainty characterization and quantification more fully in the climate modeling process; • communicating uncertainty to both users of climate model output and decision makers; and • developing deeper understanding on the relationship between uncer tainty and decision making so that climate modeling efforts and charac terization of uncertainty are better brought in line with the true needs for decision making.


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