Ogmius Exchange Letter
Response to Clark and Pulwarty’s “Devising Resilient Responses to Potential Climate Change Impacts”
This letter has been triggered by the very timely and most welcome Ogmius Exchange on “decision-making and climate change”. This topic is not only important for research and policy, but to the forthcoming Fourth Assessment Report of the IPCC as well as the strategic planning of the US Climate Change Science Program (CCSP). In this short letter, I argue that there is considerable scope to link probabilities of climate change with climate adaptation decision-making.
In their leading article, Clark and Pulwarty (C&P) give three reasons to scrap probabilities. First, C&P argue that climate models do not estimate the entire range of plausible futures so an approach focused on bounding the scope of the possible futures would be desirable. This is exactly at the core of much of the Bayesian work on constraining climate (and other) parameters (e.g., Forest et al., 2002). Estimating the entire range of plausible futures is practically impossible because of a number of other unquantifiable uncertainties of model prediction (see Dessai and Hulme, 2003) such as the second (ignorance) and third (anticipating human activity) points raised by C&P. However, ignorance of climate feedbacks, land use effects on climate or climate downscaling can benefit from further research, particularly with respect to the representation of uncertainties. Distinguishing between what is known and what is unknown is very useful to the process of focusing research. Though C&P have argued that predicting population growth and other determinants of greenhouse gas emissions is “essentially impossible”, various groups have attempted this (e.g., Lutz et al., 2001; Webster et al., 2002). C&P overlooked to mention that because humans are reflexive, representing uncertainty in terms of probabilities in the context of prediction is impossible. Probabilities will always remain “provisional” hence the need to combine scenario and uncertainty analysis (Dessai and Hulme, 2003).
C&P raise another three issues that need debating. First is the fact that probabilities are not the only way to represent climate change uncertainties. Various other techniques such as specific language or Dempster-Shafer theory are available to represent uncertainty. C&P argue that “probabilistic climate projections can mislead decision-makers”, but this is not the case unless the communication of uncertainty by the researchers is not done appropriately; in any case, probabilities will always remain conditional upon the assumptions taken by the researchers. Finally, Lempert and Schlesinger’s (2000) argument, which C&P support, that policies should be robust, i.e., as flexible as possible, is problematic in a financially constrained world. This is exactly where probabilities can help investments by considering the state-of-the-art knowledge, even if “conditional” and “provisional”. What we need to start researching is how sensitive climate adaptation decisions are to climate change (and other) uncertainties using, for example, a combination of probabilities and scenario analysis. Robust policies will be the ones that are scenario independent.
School of Environmental Sciences,
University of East Anglia, UK
and Tyndall Centre for Climate Change Research, UK
Dessai, S. and M. Hulme (2003). "Does climate policy need probabilities?" Tyndall Centre Working Paper (No. 34.): Available here.
Forest, C. E., P. H. Stone, A. P. Sokolov, M. R. Allen and M. D. Webster (2002). "Quantifying uncertainties in climate system properties with the use of recent climate observations." Science 295(5552): 113-117.
Lempert, R. J. and M. E. Schlesinger (2000). "Robust strategies for abating climate change." Climatic Change 45(3-4): 387-401.
Lutz, W., W. Sanderson and S. Scherbov (2001). "The end of world population growth." Nature 412(6846): 543-545.
Webster, M. D., M. Babiker, M. Mayer, J. M. Reilly, J. Harnisch, R. Hyman, M. C. Sarofim and C. Wang (2002). "Uncertainty in emissions projections for climate models." Atmospheric Environment 36(22): 3659-3670.