Coping with uncertainty in climate change adaptation merging top down and bottom up approaches Dr. Jeroen P. van der Sl

1. COP 15 side event “Adaptive management – merging top down and bottom up”Holland Climate House, Bella Center, Copenhagen, 10 December 2009 Coping with uncertainty in climate change adaptationmerging top down and bottom up approachesDr. Jeroen P. van der Sluijs Copernicus Institute for Sustainable Development and InnovationUtrecht University

2. Statistical uncertainty PROBLEM: Policy makers seem to expect that scientists can calculate such frequencies for2050, 2100, etc.

3. (Giorgi 2005)

5. 3 framings of uncertainty (Van der Sluijs, 2006) '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' • Uncertainty is intrinsic to complex systems: permanent • Uncertainty can be result of new ways of knowledge production • Acknowledge that not all uncertainties can be quantified • Openly deal with deeper dimensions of uncertainty • Tools: Knowledge Quality Assessment “speaking truth to power” vs “working deliberatively within imperfections”

6. Former chairman IPCC on objective to reduce climate uncertainties: • "We cannot be certain that this can be achieved easily and we do know it will take time. Since a fundamentally chaotic climate system is predictable only to a certain degree, our research achievements will always remain uncertain. Exploring the significance and characteristics of this uncertainty is a fundamental challenge to the scientific community." (Bolin, 1994)

7. KNMI 2006

8. Bron: Stern Review

9. Worst case: 1,5 m/eeuw Deltacommissie 65-130 cm 35-85 cm in 2100 NL Later: Sealevel rise till 2100

10. Scenarios can be wrong Statististical uncertainty precipitation According to climateprediction.net versus range KNMI scenarios Winter Summer (Dessai & Van der Sluijs, 2007)

11. Variability in a changing climate: Small shift in mean = big change in frequency of extremes

12. Adaptation under what uncertainty? Planned adaptation • to single scenario of anticipated climate impacts no uncertainty • to single scenario of anticipated climate impacts + to variabilitystatistical uncertainty (without epistemic unc.) • to range of scenario’s of anticipated climate impacts (KNMI 2006 scenario’s)scenario uncertainty • to range of scenario’s of anticipated climate impacts + imaginable climate surprises (MNP Nederland Later)scenario uncertainty + recognized ignorance

13. Decision-making frameworks • Top down approaches • Prevention Principle • IPCC approach • Risk approaches • Bottom up approaches • Precautionary Principle • Engineering safety margin • Anticipating design • Resilience • Adaptive management • Human development approaches • Mixed approaches • Adaptation Policy Framework • Robust decision making (figure: Dessai and Hulme 2004,list: Dessai and Van der Sluijs, 2007)

14. Risk approach (UK-CIP) Eight stages decision framework: • Identify problem and objectives • Establish decision-making criteria • Assess risk • Identify options • Appraise options • Make decision • Implement decision • Monitor, evaluate and review. Flexible characteristics: • cricular • Feedback and iteration • Stages 3, 4 and 5 are tiered. (identify, screen, prioritise and evaluate before more detailed risk assessments and options appraisals are required.) “The risk assessment endpoints should help the decision-maker define levels of risk (probabilities and consequences or impacts) that are acceptable, tolerable or unacceptable”

15. No regrets • Favour adaptation strategies which will yield benefits (for other, less uncertain, policy concerns) regardless of whether or not climate impacts will occur.

16. “Flexible design” Anticipating imaginable surprises

18. Robustness exploration (Dessai, 2005) Problem: Dimensioning of water supply system Additional water required (Ml/d) to maintain levels of service in 2030 under different demand scenarios as a function of regional climate response uncertainty

19. Resilience • If uncertainties about climate change are large, one can still know how the resilience of social-ecological systems can be enhanced • Resilience is the capacity of a system to tolerate disturbance without collapsing into a qualitatively different, usually undesired, state www.resalliance.orgWardekker e.a. 2010 doi:10.1016/j.techfore.2009.11.005 • Principles: • Homeostasis • Omnivory • High flux • Flatness • Buffering • Redundancy

20. Wild Cards / Surprise scenarios • not sufficientely known risks / opportunities • undermine current trends • create new futures • influence our thinking about past & future • give rise to new concepts / new perceptions www.steinmuller.de/media/pdf/WC_GFF.pdf Examples for climate adaptation: - Thermo Haline Circulation shut down - Extreme low river run-off - Long heatwaves and droughts - Extreme storms - Invasive species

21. three types of wild cards (1) extreme forms of expected trends, (2) opposites of expected trends (3) completely new issues (prepared for the wrong impact) Most options remain beneficial under type-1 wildcards. Under type-2 wildcards, options that enhance flexibility and responsiveness remain beneficial Few options protect against type-3 wildcards

22. Synthesis

23. Synthesis

24. Synthesis

25. Concluding remarks • No ‘silver bullet’ for adaptation under uncertainty • Very context dependent • Assess relative importance of: • Statistical uncertainty: predict-then-act • Scenario uncertainty: robustness • Ignorance: resilience & flexibility • Synthesis matrix provides preliminary guidance for analysts

26. Download 2007 rapport: www.nusap.net/adaptation Case studies 2008-2009: - Delta committee (water safety) - Nature / Waddensea - Health impacts Team - Arjan Wardekker MSc - Arie de Jong MSc - Petra Westerlaan - Dr Pita Verweij - Dr. Jeroen van der Sluijs