Experiences of dealing with uncertainties in practice

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1 School of something FACULTY OF OTHER Experiences of dealing with uncertainties in practice Suraje Dessai Sustainability Research Institute and ESRC Centre for Climate Change Economics and Policy, School of Earth and Environment, University of Leeds, UK Climate Change Impacts, Adaptation and Mitigation (CC-IAM) Research Group, Faculty of Sciences, University of Lisbon, Portugal

2 Outline What is adaptation to climate change? Why is there uncertainty about future climate? Dealing with uncertainty in climate adaptation decisionmaking: long-term water resources planning

3 What is adaptation to climate change? In human systems, the process of adjustment to actual or expected climate and its effects, in order to moderate harm or exploit beneficial opportunities. In natural systems, the process of adjustment to actual climate and its effects; human intervention may facilitate adjustment to expected climate (IPCC SREX, 2012). Complex societal process of activities, actions, decisions and attitudes that reflect existing social norms and processes (Adger et al. 2005) Adaptation to climate change does not happen in isolation multiple actors and multiple stresses and stimuli

4 Climate variability causes damages There is evidence that societies are not well adapted to current climate variability Partial insights about the future open up the possibility of anticipatory/planned adaptation Past emission and inertia mean that adaptation is unavoidable How can we ensure that adaptation measures realise societal benefits now, and over coming decades, despite uncertainty about climate variability and change?

5 Four domains of adaptation research: 1. risk assessment and impact response 2. social vulnerability and adaptive capacity 3. resilience 4. the science of decision making and policy implementation Eakin and Patt 2011 WIREs Climate Change Dessai and Hulme 2004 Climate Policy 4(2), Vulnerability (social) Adaptive capacity Climate adaptation policy Indicators base on: Technology Economic resources Information & skills Infrastructure Equity Institutions Bottom-up approach Top-down approach World development Global greenhouse gases Global climate models Regionalisation Impacts Vulnerability (physical) Past Present Future Global Local

6 Why is there uncertainty about future climate? Future society The cascade of uncertainty GHG emissions Climate model Regional scenario Impact model Local impacts Adaptation responses The envelope of uncertainty Wilby, R. L. and S. Dessai (2010). "Robust adaptation to climate change." Weather 65(7):

7 End-to-end uncertainty quantification Changes in mean river runoff (2xCO2-1xCO2) at the Thames New, M., et al. (2007), Challenges in using probabilistic climate change information for impact assessments: an example from the water sector, Philos T R Soc A, 365(1857),

8 Multiple routes of uncertainty assessment Dessai, S. and J.P. van der Sluijs (2011) Modelling climate change impacts for adaptation assessments, M. Christie, A. Cliffe, P. Dawid and S. Senn (eds.) Simplicity, Complexity and Modelling. Wiley.

9 Uncertainty as a function of prediction lead time Hawkins & Sutton, 2009, BAMS

10 What are the prospects for reducing uncertainty? More research has led to more knowledge but also greater uncertainty The search for objective constraints remains elusive (Allen & Frame 2007) Equifinality: many different model structures and many different parameter sets of a model can produce similar observed behaviour of the system under study (Keith Beven)

11 More fundamental problems Verification and validation of numerical models in the earth sciences is impossible (Naomi Oreskes et al. 1994) Models are heuristic tools and not truth machines (Jerry Ravetz 2003) statements about future climate relate to a never before experienced state of the system; thus it is impossible to either calibrate the model for the forecast regime of interest or confirm the usefulness of the forecasting process (Stainforth et al. 2007)

12 Dealing with uncertainty in climate adaptation decision-making There are significant (deep/severe) uncertainties about how regional climate (and its impacts) will change in the future: a situation in which analysts do not know or cannot agree on (1) models that relate key forces that shape the future, (2) probability distributions of key variables and parameters in these models, and/or (3) the value of alternative outcomes. (Hallegatte et al. 2012). Stationarity is dead (Milly et al. 2008): plan for the unexpected/surprises Flexible and adaptive strategies are more likely to be robust to uncertainty as opposed to static strategies (Hallegatte 2009; Lempert and Groves 2010) Informing adaptation decisions will require new kinds of information and new ways of thinking and learning (NRC, 2009)

13 Uncertainty and climate change adaptation: synthesis

14 Long-term water resources planning in England and Wales Cyclical (every 5 years) 25 year water resources plans on how supply/demand is maintained Highly regulated sector (private water companies; Economic and environmental regulator; government; consumer) Planning based on historical droughts; risk averse? Has incorporated climate change into planning since late 1990s Various uncertainties includes into target headroom : buffer between supply and demand designed to cater for specified uncertainties

15 Third Asset Management Plan (AMP3), UKCIP98 scenarios (Hulme and Jenkins 1998) Factors of change in monthly streamflow and annual groundwater recharge were estimated for various regions of England and Wales (Arnell et al. 1997) These factors were then combined with observed data to produce projected conditions under a changed climate in the 2020s Led to a demand for higher resolution climate change scenarios The Welsh Water company believed that the scenarios were too aggregated for a region as large as Wales (Subak 2000, p. 152)

16 AMP4, Increase in modelling sophistication: from deterministic to probabilistic headroom UKCIP02 scenarios were translated into streamflow and groundwater changes by Arnell (2003a) The impacts of climate change on demand were also being considered (largely through the estimates of Downing et al. 2003). The Environment Agency noted that it does not provide sufficient certainty for a major investment

17 AMP5/PR09, Flow factors were constructed using outputs from six climate models (used in the IPCC Fourth Assessment Report), but also including hydrological model uncertainty EA guidance was more explicit than before, stating that companies should calculate mid, wet and dry scenarios. The mid estimate of climate change impact was incorporated directly in the estimate of deployable output, with the range between the wet and dry scenarios included in headroom (Arnell 2011).

18 Billion pound adaptation needed but current science too old and uncertain

19 Where next? AMP6/PR14 Environment Agency 2012

20 3 paradigms of uncertain risks 'deficit view' Uncertainty is provisional Reduce uncertainty, make ever more complex models Tools: quantification, Monte Carlo, Bayesian belief networks 'evidence evaluation view' Comparative evaluations of research results Tools: Scientific consensus building; multi disciplinary expert panels focus on robust findings 'complex systems view / post-normal view' Uncertainty is intrinsic to complex systems Uncertainty can be result of production of knowledge Acknowledge that not all uncertainties can be quantified Openly deal with deeper dimensions of uncertainty (problem framing indeterminacy, ignorance, assumptions, value loadings, institutional dimensions) Tools: Knowledge Quality Assessment Deliberative negotiated management of risk Jeroen van der Sluijs

21 Two problem framings Top-down scenario, impacts-first approach (left panel) and bottom-up vulnerability, thresholds-first approach (right panel) comparison of stages involved in identifying and evaluating adaptation options under changing climate conditions (IPCC SREX, 2012).

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23 An example of a robust decisionmaking approach Info-Gap decision theory provides an approach to compare the ability of different management options to satisfy system performance criteria over an unbounded range of uncertainty (Ben-Haim 2001) It s a non probabilistic approach Used in many fields from engineering to conservation science Forces preference away from what is optimum for a defined set of circumstance (to optimise) towards what is good enough over a wide range of possible circumstance (to satisfice). The simplicity of being reliant only on the central tendency of parameters to begin an assessment that addresses uncertainty is advantageous

24 Information-gap decision theory for water resources planning Steps A. Build and calibrate the simulation model of the system analysed B. Characterise the uncertainty C. Define the performance criteria An Info-Gap exploration of uncertainty (Hine and Hall 2010) Parameters that are evaluated for uncertainty: 3 related to supply (impact of climate change), 5 related to demand (population changes) and 1 related to cost for electricity. D. Quantify the uncertainty E. Identify Robust Management Strategies Using MCDA Kortleing, B., S. Dessai and Z. Kapelan (2012) Using Information- Gap Decision Theory for Water Resources Planning Under Severe Uncertainty. Water Resources Management (in press).

25 Information-gap decision theory for water resources planning 1. System model (reward function) Measurement of source water -5% Climate change on catchment - 10% Climate change on source yields -Mid to dry Kortleing, B., S. Dessai and Z. Kapelan (in press) Using Information-Gap Decision Theory for Water Resources Planning Under Severe Uncertainty. Water Resources Management. Distribution input Demand forecast + 2.5% + 10% (population &economic growth) Climate change impact on demand + 20% of 1.4%

26 Robustness of strategies Preference reversal Robustness of different management strategies as assessed by a reservoir risk measure (RRM); the product of the probability of the reservoir falling below the drought management curve and the average volume (Ml) of water deficit below this curve.

27 Multi-Criteria Decision Analysis performance evaluation

28 A Framework for Robust Adaptation Observed non-climatic pressures Vulnerability (now) Observed climate variability and change Wilby, R. L. and S. Dessai (2010). "Robust adaptation to climate change." Weather 65(7): Dessai, S. and R. Wilby. How Can Developing Country Decision Makers Incorporate Uncertainty about Climate Risks into Existing Planning and Policymaking Processes? World Resources Report, Washington DC. Social acceptability Technical feasibility Narratives of non-climatic pressures Adaptation principles Performance appraisal Adaptation options A, B, C... Preferred measures B, H, S, W Vulnerability (future) Robust measures B, W Adaptation pathways W then B Economic appraisal Regulatory context Climate change narratives Sensitivity analysis New evidence Monitoring

29 Where next? What kinds of uncertain information work best throughout the adaptation journey? From useful to usable/actionable to valuable What is social status of uncertain techno-scientific knowledge in adaptation to climate change? Advancing Knowledge Systems to Inform Climate Adaptation Decisions Distance from knowledge production MacKenzie s (1990) & Lahsen (2005)

30 Concluding remarks Future climate, climate impacts and society are characterised by deep/severe uncertainty Where uncertainty dominates robust decision-making methods are likely to be more useful to decisionmakers than traditional predict and provide methods Adaptation efforts should not be limited by the lack of reliable foresight about future climate conditions We need a better understanding how uncertain climate knowledge travels across different social worlds. s.dessai@leeds.ac.uk

31 More on uncertainty Uncertainty in Climate Change Research: An Integrated Approach: Handling uncertainty in weather and climate prediction, with application to health, agronomy, hydrology, energy and economics: