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Contested Predictions: The Significance of Modeling to Public Climate Debates
by Karen Akerlof*

“As a result of our inaction, we have three options: mitigation, adaptation, and suffering,” glaciologist Lonnie Thompson recently wrote (2010). In the United States over the past two decades, political response to climate change has been stymied, in part, by arguments that uncertainties in the projections of computer models do not support costly policy prescriptions for reducing greenhouse gas emissions. The ability of computer models to produce accurate information about complex climate processes has been questioned by skeptics (Idso & Singer, 2009). Meanwhile, policymakers have used the controversy and uncertainties to avoid difficult policy decisions, preferring to fund additional research (Lemos & Rood, 2010).

Yet as increasingly variable climates pose challenges to decision makers at all governmental levels, they will likely rely upon climate projections in making policies on decadal and multi-decadal time scales. A National Research Council (NRC) study on advancing climate modeling stated, “Climate models are the foundation for understanding and projecting climate and climate-related changes and are thus critical tools for supporting climate-related decision making” (2010). In this article, I argue for significance of climate modeling and prediction to an understanding of the context of past and present climate debates, and why they will be even more important to future discourses on adaptation policy.

Half a year after James Hansen’s testimony before Congress in which he declared that “with 99 percent confidence” he believed Earth’s temperature was rising (Weart, 2003), President George H. W. Bush took office, pledging to counter the greenhouse effect with the “White House effect” (Weisskopf, 1992). His Chief of Staff John Sununu convinced him otherwise. As Sununu later described, “Our response to their call for policy change in 1989 was to point out that their models should be supported by good science, and that in order to get good science, we would provide a very substantial increase in funding for global climate research” (Sununu, 2009). Twenty years later, the MIT engineer still claims that the “Global Climate Models’ predictions of doom” are used to back up facts that have been “cherry picked” by alarmists. “Since basic hard science is more difficult to bias, they … resort … to modeling.”

Climate models have become “a lightning rod in the climate debate” (Revkin, 2004). In a chapter on “Global Climate Models and Their Limitations,” critics of the Intergovernmental Panel on Climate Change (IPCC) wrote that “scientists working in fields characterized by complexity and uncertainty are apt to confuse the output of models—which are nothing more than a statement of how the modeler believes a part of the world works—with real-world trends and forecasts. Computer climate modelers certainly fall into this trap, and they have been severely criticized for failing to notice that their models fail to replicate real-world phenomena by many scientists … ”  (Idso & Singer, 2009, p. 10).

“The epistemology of modeling is a central focus of climate politics,” Edwards wrote (1999, p. 460). The rhetoric relies on a philosophical argument about the value of theory as opposed to observation. Those who prioritize theory—termed “frontier scientists”—see  models as useful tools, while “high-proof” scientists who place a higher value on observation emphasize model inadequacies such as parameterization. These debates over the use of models presuppose a clear distinction between the three realms of climate science—modeling, theory and observation—that  in fact do not exist (Edwards 1999, 2010). Model parameterizations merge theory with observations, and diverse data sets require models to assimilate them due to differences in sources and calibration.

The eruption of “Climategate” just prior to the December 2009 United Nations climate summit in Copenhagen illustrates the importance of the science of climate modeling to the oratory of climate policy debates and public involvement in the discourse. The Internet distribution of more than a 1,000 hacked e-mails and documents from servers of the Climatic Research Unit (CRU) at the University of East Anglia spurred controversy over perceived lack of public access to the observational data sources used by the models and the politicization of the scientific review and publication process in the field of climatology (Schiermeier, 2009). Polls found that public concern about climate change dropped substantially in the United States after the controversy (Leiserowitz, Maibach, Roser-Renouf, Smith & Dawson, 2010). Britain and Germany underwent similar declines (Rosenthal, 2010).

Complicating matters, the uncertainties both in climate predictions for the next few decades and in longer range projections may widen with the use of new modeling techniques and as additional climate processes and feedbacks are incorporated to produce more realistic simulations (Trenberth, 2010). When only about one-third of the public in the United States currently believes that there is scientific consensus even on whether global warming is occurring (Leiserowitz, Maibach, Roser-Renouf & Smith, 2010), the introduction of additional uncertainties into science reports such as the fifth IPCC assessment, expected in 2013, may further confound the public’s understanding of the issue (Trenberth, 2010). Trenberth notes that the notion that uncertainty in climate model projections could increase even as the science improves may be extremely counterintuitive for lay audiences.

Lemos and Rood have pointed to a conflict between the perceived usefulness of climate predictions, and their usability (2010). Human beings have long been driven to divine the future, only the techniques we use have changed. As computers began to be commercially available in the 1950s, computer models became a primary tool for forecasting, including in both climate science and meteorology (Edwards, 2010). Increases in computing power and understanding of Earth’s climatic processes have led to models with higher resolution and fuller realization of atmospheric, land and ocean systems.

Modelers (Shukla et al, 2009) and decision makers (Morello, 2010) have suggested that further improvements in modeling—indeed a revolution in the science—will be needed to meet the societal challenges of adapting to climate change. Researchers who have studied the use of seasonal climate forecasts by policymakers have countered that the relationship between improvements in scientific technology and better decision making is not always linear, and reducing uncertainties does not always contribute to improved policy development (Lemos & Dilling, 2007; Lemos & Rood, 2010). “Effective and robust adaptation strategies are not significantly limited by the absence of accurate and precise regional climate predictions. They are limited more by a multitude of technological, institutional, cultural, economic and psychological factors that lie beyond the reach of climate models—and always will,” wrote Hulme and Dessai (2008).

To date there appears to be no systematic evaluation of the treatment of prediction and climate models in public climate debates, even though their use for decision making has been contentious. Similarly, resources developed for journalists and other communicators on climate change may fail to specifically address this topic (McFarling, 2006; Moser & Dilling, 2007; Ward, 2008). “Climate controversies constantly lead into the guts of the infrastructure, inverting it and reviving, over and over again, debates about the origins of numbers” wrote Edwards in his book on climate modeling and global politics (Edwards, 2010, p. 432). The structure of these debates is derived from the nature of the science, its continual “re-interrogation” of past records to enhance the accuracy with which we understand climate processes, and our abilities to forecast the future.

The framing of conflicts over climate policy has been shaped by its origins as a scientific problem and early decisions by politicians to keep it within that sphere, increasing research into its causes and impacts and reducing uncertainties before committing to greenhouse gas reductions (Sarewitz & Pielke, 2000). Thus delving into the “guts” of climate science may further elucidate what it is that we are arguing over, and whether it is material to the decisions that society will need to make.

*Karen Akerlof is a doctoral student in Environmental Science & Public Policy at George Mason University and conducted her master's thesis research on attention to climate models in the media.


References

Edwards, P. N. (1999). Global climate science, uncertainty and politics: Data-laden models, model-fi ltered data. Science as Culture, 8(4), 437-472.

Edwards, P. N. (2010). A vast machine: Computer models, climate data and the politics of global warming. Cambridge, MA: MIT Press.

Hulme, M., & Dessai, S. (2008). Ventures should not overstate their aims just to secure funding. Nature, 453(7198), 979.

Idso, C., & Singer, S. F. (2009). Climate change reconsidered: 2009 report of the Nongovernmental International Panel on Climate Change (NIPCC). Chicago: Heartland Institute.

Leiserowitz, A., Maibach, E., Roser-Renouf, C., & Smith, N. (2010) Climate change in the American Mind: Americans' global warming beliefs and attitudes in June 2010. Yale University and George Mason University. New Haven, CT: Yale Project on Climate Change Communication.

Leiserowitz, A., Maibach, E. W., Roser-Renouf, C., Smith, N., & Dawson, E. (2010). Climategate, public opinion, and the loss of trust. Yale University and George Mason University. New Haven, CT: Yale Project on Climate Change Communication.

Lemos, M. C., & Dilling, L. (2007). Equity in forecasting climate: Can science save the world's poor? Science and Public Policy, 34, 109-116.

Lemos, M. C., & Rood, R. B. (2010). Climate projections and their impact on policy and practice. Wiley Interdisciplinary Reviews: Climate Change, 1(5), 670-682.

McFarling, U. L. (2006). Climate. In D. Blum, M. Knudson, & R. M. Henig (Eds.), A fi eld guide for science writers (2nd ed., pp. 243-250). Oxford, UK: Oxford University Press.

Morello, L. (2010, June 24). Defense experts want more explicit climate models. The New York Times.

Moser, S. C., & Dilling, L. (Eds.). (2007). Creating a climate for change: Communicating climate change and facilitating social change. Cambridge, UK: Cambridge University Press.

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National Research Council. (2010). A national strategy for advancing climate modeling. Washington, DC: National Academy Press.

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Revkin, A. C. (2004, Aug. 31). Computers add sophistication, but don't resolve climate debate. The New York Times, p. F3.

Rosenthal, E. (2010, May 24). Climate fears turn to doubts among Britons. The New York Times. Sarewitz, D., & Pielke, R., Jr. (2000). Breaking the global warming gridlock. The Atlantic Monthly, 286, 54-64.

Schiermeier, Q. (2009). Storm clouds gather over leaked climate e-mails. Nature, 462(26), 397.

Shukla, J., Hagedorn, R., Hoskins, B., Kinter, J., Marotzke, J., Miller, M., Palmer, T., et al. (2009). Revolution in climate prediction is both necessary and possible: A declaration at the world modelling summit for climate prediction. Bulletin of the American Meteorological Society, 90, 175178.

Sununu, J. H. (2009). The politics of global warming. 2009 International Conference on Climate Change, New York.

Thompson, L. (2010). Climate change: The evidence and our options. The Behavior Analyst, 33(2), 153-170.

Trenberth, K. (2010). More knowledge, less certainty. Nature Reports Climate Change, 1002, 20-21.

Ward, B. (2008). Communicating on climate change: An essential resource for journalists, scientists and educators. Narragansett, RI: Metcalf Institute for Marine & Environmental Reporting.

Weart, S. R. (2008). The discovery of global warming. Cambridge, MA: Harvard University Press.

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