End-to-end Quantification of Uncertainty for Impacts Prediction (EQUIP)

Lead Research Organisation: University of Exeter
Department Name: Engineering Computer Science and Maths

Abstract

Society is becoming increasingly aware of climate change and its consequences for us. Examples of likely impacts are changes in food production, increases in mortality rates due to heat waves, and changes in our marine environment. Despite such emerging knowledge, precise predictions of future climate are (and will remain) unattainable owing to the fundamental chaotic nature of the climate system and to imperfections in our understanding, our climate simulation models and our observations of the climate system. This situation limits our ability to take effective adaptation actions. However, effective adaptation is still possible, particularly if we assess the level of precision associated with predictions, and thus quantify the risk posed by climate change. Coupled with assessments of the limitations on our knowledge, this approach can be a powerful tool for informing decision makers. Clearly, then, the quantification of uncertainty in the prediction of climate and its impacts is a critical issue. Considerable thought has gone into this issue with regard to climate change research, although a consensus on the best methods is yet to emerge. Climate impacts research, on the other hand, has focussed primarily on a different set of problems: what are the mechanisms through which climate change is likely to affect for example, agriculture and health, and what are the non-climatic influences that also need to be accounted for? Thus the research base for climate impacts is sound, but tends to be less thorough in its quantification of uncertainty than the physical climate change research that supports it. As a result, statements regarding the impacts of climate change often take a less sophisticated approach to risk and uncertainty. The logical next stage for climate impacts research is therefore to learn from the methods used for climate change predictions. Since climate and its impacts both exist within a broader earth system, with many interrelated components, this next stage is not a simple transfer of technology. Rather, it means taking an 'end-to-end' integrated look at climate and its impacts, and assessing risk and uncertainty across whole systems. These systems include not only physical and biological mechanisms, but also the decisions taken by users of climate information. The climate impacts chosen in EQUIP have been chosen to cover this spectrum from end to end. As well as aiding impacts research, end-to-end analyses are also the logical next stage for climate change research, since it is through impacts that society experiences climate change. The project focuses primarily on the next few decades, since this is a timescale of relevance for societies adapting to climate change. It is also a timescale at which our projections of greenhouse gas emissions are relatively well constrained, thus uncertainty is smaller than for, say, the end of the century. Work on longer timescales will also be carried out in order to gain a greater understanding of uncertainty. EQUIP research will build on work to date on the mechanisms and processes that lead to climate change and its impacts, since it is this understanding that forms the basis of predictive power. This knowledge is in the form of observations and experiments (e.g. experiments on crops have demonstrated that even brief episodes of high temperatures near the flowering of the crop can seriously reduce yield) and also simulation models. It is through effective use and combination of climate science and impacts science, and the models used by each community, that we will be able to quantify uncertainty, assess risk, and thus equip society to deal with climate change.

Publications

10 25 50

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Ferro C (2012) A bias-corrected decomposition of the Brier score in Quarterly Journal of the Royal Meteorological Society

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Otto F (2013) On judging the credibility of climate predictions in Climatic Change

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Fricker T (2013) Three recommendations for evaluating climate predictions in Meteorological Applications

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Ferro C (2014) Fair scores for ensemble forecasts Fair Scores for Ensemble Forecasts in Quarterly Journal of the Royal Meteorological Society

 
Description We have described how past climate predictions can be used to inform quantitative judgments about how good predictions of future climate will be. We have also developed ways of measuring the performance of past climate predictions that avoid disadvantages suffered by existing methods.
Exploitation Route Such judgments could help climate predictions to be incorporated more effectively into climate-sensitive planning decisions. These ideas could be used by climate scientists and consumers of climate predictions to obtain justified, quantitative judgments about how good predictions of future climate will be.
Sectors Aerospace, Defence and Marine,Agriculture, Food and Drink,Construction,Energy,Environment,Financial Services, and Management Consultancy,Healthcare,Leisure Activities, including Sports, Recreation and Tourism,Transport

URL http://www.equip.leeds.ac.uk