Uncertainty as a science policy problem

I would like to add a three points to Linda Mearns’ (2010) insightful and useful review of uncertainty in climate science. First, uncertainty is not just a technical problem. It is also a science policy problem. Mearns rightly asks what priority should be given to uncertainty reduction. But we should explicitly recognize that determining such a priority is not just the purview of scientists. This is a question of resource allocation, and thus a science policy problem. Too often we ignore fundamental questions about the investment of tax dollars in research. What kinds of knowledge should we be seeking from climate science? What kinds of information can we reasonably expect to obtain? Is a marginal increase in model resolution “worth it”? And crucially, what kinds of people and institutions will it take to navigate such decisions in the best interest of society? Second, attitudes toward uncertainty may not be internally consistent. Mearns describes two different “camps” in the scientific community: (1) those who see uncertainty reduction and regional-scale prediction as crucial for dealing with climate change, and (2) those who do not. It makes sense that modelers would fall into the former, and human dimensions researchers into the latter. But oddly enough, the two camps are not always mutually exclusive. My own research (Meyer 2010, 2011) has examined the views of climate science policy decision makers—people who work in the 13 different US federal agencies involved with the Global Change Research Program. I found that most of these individuals embrace the rhetoric of uncertainty management. “No,” they say, “uncertainty cannot always be reduced. It is important to manage and communicate uncertainty.” This seems reasonable and encouraging.