Produced for discussion by the delegates at IDRC’s workshop, Research on Adaptation to Climate Change in Coastal and Estuarine Systems, Belem, Brazil, 2-4 October, 2013.
Issues of uncertainty are a primary area of mis-communication in climate change science and the confusion impedes effective climate change decisions.
Climate change adaptation should make use of the best available science to identify the nature and location of expected impacts, but at the local scale there are difficulties in interpreting the volumes of climate data that are available from various sources due to the spatial and temporal scales at which these data are reported (Ziervogel and Zermoglio, 2009).
Local estimates of climate change impacts and costs take on a ‘cascade of uncertainty’ (New and Hulme, 2000); uncertainty originates in the range of possible future concentrations of greenhouse gases and is sequentially increased by the possibility of different, and imperfectly understood, ecological, social and institutional responses to the consequences of climate forcing. Much of this uncertainty is innate: whilst knowledge of the interactions between greenhouse gas emissions and global warming is now very good, it is simply not possible to know how people, institutions and the many feedback loops will react to an anthropocentrically perturbed climate or how these reactions will affect future warming.
A systematic approach to determining when and where uncertainty matters represents an on-going challenge for local climate change adaptation efforts, and grounds for further research. In the interim, the issue of how to take good decisions in spite of climate change uncertainty is critical.
Some emerging principles in this regard include:
- Confronted with uncertainty, it is better to know that you don’t know, than to assume that you do when you don’t (or cannot) know. It is possible to plan for uncertainty.
- Reflecting on local knowledge and local records and those data that are applicable at the City-scale can bridge the gap between down-scaled climate models and local decision makers, and improve the decision making process.
- Climate change uncertainty places a greater emphasis on knowledge accumulation and communication and on iterative and flexible decision outcomes. Designing these features into adaptation programmes is important.
- Recognition of uncertainty warrants caution with regards to lock-in of specific decisions and their associated infrastructure and technology (Kemp and Weehuizen, 2005). In the context of climate uncertainty decision making stridency and notions of “climate proofing” verge on hubris.
- Given uncertainty, the “blind use of a single generation of probabilistic impact information raises the possibility of maladaptation” (New et al., 2007). In advancing this approach “Scenarios [conjectures of what might happen in the future] are invaluable in situations where uncertainties might otherwise lead us to think that because we cannot have knowledge that is certain we should proceed in total ignorance” (Cornish, 2004).
- Scenarios do not, however, imply discrete future states and are typically not presented together with probabilities. Recognising this is important to scenario-based approaches leading to a default ‘middle road’ strategy that conceals important information about extremes and variance (Dessai et al., 2009; New et al., 2007; Stainforth et al., 2007).
- In the face of uncertainty, identifying ‘no-regrets’ adaptation options that deliver benefits across and a wide range of possible climate, institutional and socio-economic futures and changing uncertainty estimates, represents an appropriate point of departure (Lempert et al., 2006; Popper et al., 2005). Very often these no-regret options are socio-institutional, and contain the features of reversibility and flexibility that are appropriate to early-stage climate change adaptation.
Cornish, E. (2004) Futuring: The Art of Exploring the Future. Bethesda, Maryland: World Future Society.
Dessai, S. and Hulme, M. (2004) Does climate adaptation policy need probabilities? Climate Policy 4(2):107–128.
Dessai , S., Hulme, M., Lempert, R. and Pielke, Jr. R. (2009) Do we need more precise and accurate predictions in order to adapt to a changing climate? Eos 90(13): 111–112.
Kemp, R. and Weehuizen, R. (2005) Policy Learning: What Does It Mean and How Can We Study It? Innovation in the Public Sector, Oslo: Nordisk Institutt for Studier av Innovasjon, forskning og utdanning – STEP.
Lempert, R.J, Groves, D.G., Popper, S.W. and Bankes, S.C. (2006) A general, analytic method for generating robust strategies and narrative scenarios Management Science 52: 514–528.
New, M. and Hulme, M. (2000) Representing uncertainty in climate change scenarios: a Monte–Carlo approach, Integrated Assessment 1: 203–213. doi:10.1023/A:1019144202120.
New, M., Lopez, A., Dessai, S. and Wilby, R. (2007) Challenges in using probabilistic climate change information for assessments: an example from the water sector, Philosophical Transactions of the Royal Society, 365(1857): 2117–2131.
Popper, S.W., Lempert, R.J. and Bankes, S.C. (2005) Shaping the future, Scientific American 292: 66–71.
Stainforth, D., Downing, T., Washington, R., Lopez, A. and New, M. (2007) Issues in the interpretation of climate model ensembles to inform decisions, Philosophical Transactions of the Royal Society 365: 2163–2177.
Ziervogel, G. and Zermoglio, F. (2009) ‘Challenges in using climate science for developing adaptation strategies in Africa: applying climate science in Africa’, Climate Research, 40: 133–146.
Anton Cartwright is a Mistra Urban Futures Researcher at the African Centre for Cities in Cape Town, South Africa, where his focus is on the green economy. He is an economist with degrees in agriculture, environmental change, and management and economics respectively. He is an associate researcher at the Stockholm Environment Institute and an associate of the Cambridge Programme for Sustainability Leadership. This article is supported by Mistra Urban Futures, a global research and knowledge center in sustainable urban development, funded by the Swedish International Development Agency (SIDA) and the Mistra Foundation for Strategic Development.
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