Suraje Dessai - Uncertainty from above and encounters in the middle
•
1 like•1,068 views
Workshop on climate change and uncertainty from below and above, Delhi. http://steps-centre.org/2016/blog/climate-change-and-uncertainty-from-above-and-below/
Recommended
Recommended
More Related Content
Similar to Suraje Dessai - Uncertainty from above and encounters in the middle
Similar to Suraje Dessai - Uncertainty from above and encounters in the middle (20)
More from STEPS Centre
More from STEPS Centre (20)
Recently uploaded
Recently uploaded (20)
Suraje Dessai - Uncertainty from above and encounters in the middle
- 1. Uncertainty from above and encounters in the middle Workshop - Climate Change and Uncertainty from Above and Below 27-28 January, 2016 Conference Room 2, India International Centre, New Delhi Suraje Dessai University of Leeds Centre for Climate Change Economics and Policy
- 6. UK Adaptation Context: Science Met Office Hadley Centre – unified model, Numerical Weather Prediction and Climate Change World-leading status with international collaborations and research substantially contributing to the IPCC assessment reports Small, centralised, network of UK climate science (e.g. NERC) Met Office Hadley Centre has a strong commitment to serve policy priorities Climate Prediction Programme (CPP), funded by Defra and DECC
- 8. UKCP09 projections • First projections designed to treat uncertainties explicitly (Murphy et al. 2009) • More informative but also more complex than previous scenarios (Murphy et al. 2009) • Designed to inform adaptation decisions – “usable science” • Cost £11 million • User Interface • Reviewed by Steering and User group and 5 experts
- 9. Change in temperature (c) UKCP09 outlines the probability of different amounts of change in temperature Probabilityofchange Change in temperature (b) Using many models gives a range of different changes in temperature but no information on which to use Change in temperature (a) UKCIP02 gave a single estimate of change in temperature (a) (b) (c) UKCP09 On interpreting multi-model ensemble outputs: ‘They’re very useful, but they’re ad hoc in construction… They provide no basis to advise users on whether a response “near the middle” should be considered more likely than one “at the edge”, or if the actual response lies outside the multi-model range altogether’ (MOHC Climate Scientist 6 – Interview).
- 10. (a) 10% probability that change in temperature is very likely to be greater than this (c) 90% probability that temperature change is very likely to be less than this (b) 50% probability that change in temperature, also known as the “central estimate”, will likely be in this range UKCP09 provided probabilities measuring how strongly different outcomes for climate change were supported by evidence available at the time (models, observations, understanding). Rise in temperature. (a) (b) (c)
- 11. Bayesian framework to handle uncertainty 'from a methods point of view the goal just seemed right and it was something that should be done.[What] really gives me confidence is the Bayesian framework... we've put our own interpretation on it... but it's all written down in the maths, it's there to debate... you can see it in black-and-white.It's just good science' (Met Office Scientist 3, Interview).
- 12. Change in temperature (c) UKCP09 outlines the probability of different amounts of change in temperature Probabilityofchange Change in temperature (b) Using many models gives a range of different changes in temperature but no information on which to use Change in temperature (a) UKCIP02 gave a single estimate of change in temperature (a) (b) (c) UKCP09 Changing relationship between climate scientists and users (roles and responsibilities) Listening and responding to user demands (higher spatial resolution and quantification of certainty) Complexity of the method has restricteduptake and shifted responsibility onto consultancies Was the science stretched too far (e.g. cascade of uncertainty, user-demand)?
- 13. ‘it ends up pushing people towards complete rejection or more dangerously complete acceptance. Imagine if we had a large number of intelligent numerate users who embraced the probabilities, who learnt how to use them, and then realised five years down the line that these are immature probabilities, that the Andes are 1km too short, and we knew this back in 2009. Why would they trust us again?’ (Academic Climate Scientist 7 – Interview). 'There was a feeling that you shouldn't be seen arguing about what we can or can't do on climate change because that'll undermine the need for action. I was sympathetic with that view when UKCP09 started but I'm much less so now. I think the public needs to hear scientific disagreement, especially for things as serious as climate change' (Academic Climate Scientist 5 - Interview). Will users take responsibility? Atmosphere for criticism? Is scientific disagreement in public necessarily bad? Especially with the danger of UEA leaked emails used by the anti-science lobby to cast doubt Next wave of scepticism will come from within the academy
- 14. Is co-producing climate science and decision- making a risk worth taking (for scientists)? • UK government is committed to creating usable science for adaptation decision-making • But scientists have competing priorities • If scientists respond too strongly to user demands they can risk pushing science farther than it’s ready to go (displeasing their peers) • If scientists fail to respond strongly enough they can risk users being unable to apply complex climate information. • Creating usable science is not a neutral activity (Turnhout et al 2016). Rather it’s the contested outcome of intense political struggles over its meaning and application, where new frictions, antagonism, and power concerns are often introduced (Klenk & Meeham 2015).
- 15. Encounters in the middle: robust decision-making and the management of deep uncertainty in climate change adaptation
- 16. Why is there uncertainty about future climate? Future society GHG emissions Climate model Regional scenario Impact model Local impacts Adaptation responses The envelope of uncertainty Thecascadeofuncertainty Wilby and Dessai (2010)
- 17. Uncertain knowledge Future society GHG emissions Climate model Regional scenario Impact model Local impacts Adaptation responses Envelope of uncertainty Thecascadeofuncertainty Adapted from Wilby and Dessai (2010)
- 18. Robust decision-makingand deep uncertainty Robust Decision Making (RDM) is a family of decision analytic methods developed specifically for decisions with long-term consequences and deep uncertainty (Lempert et al. 2006) Deep uncertainty is 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)
- 19. Vulnerability (now) Adaptation options A,B, C.... Preferred measures B,H, S, W Vulnerability (future) Robust measures B,W Adaptation pathways W then B Observed climate variability and change Observed non-climatic pressures Climate change narratives Narratives of non-climatic pressures Social acceptability Technical feasibility Economic appraisal Regulatory context Adaptation principles Sensitivity analysis Performance appraisal New evidence Monitoring A Framework for Robust Adaptation Wilby, R. L. and S. Dessai (2010). "Robust adaptation to climate change." Weather 65(7): 180-185. 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.
- 20. “Top-down” and“bottom-up” 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).
- 21. An example: Thames Estuary 2100 Ranger et al. 2013
- 23. Applying RDM in the Cauvery River Basin in Karnataka 90°0'0"E 90°0'0"E 80°0'0"E 80°0'0"E 70°0'0"E 70°0'0"E 30°0'0"N 30°0'0"N 20°0'0"N 20°0'0"N 10°0'0"N 10°0'0"N 80°0'0"E 80°0'0"E 78°0'0"E 78°0'0"E 76°0'0"E 76°0'0"E 14°0'0"N 14°0'0"N 12°0'0"N 12°0'0"N 10°0'0"N 10°0'0"N 8°0'0"N 8°0'0"N • CRB-K (area: ~35960 sq.km) has a unique combination of characteristics: high groundwater extraction, rapidly expanding cities (Bangalore, Mysore etc), increasing costs for pumping water to urban areas, falling water quality, irrigation expansion and conflict with riparian states • Uncertain future socio-economic changes – Urban expansion and increasing water use – Trade-off between increasing irrigation efficiency and irrigation expansion • Uncertain future climatic conditions • What water management strategies are robust to wide ranges of uncertainty by the 2030s and 2050s?
- 24. Initial reflections of applying RDM in the middle/hybrid space • RDM approaches require specific expertise from analysts and a small number of stakeholders, thus leaning towards the above perspective (perhaps characterised as technocratic) • Tensions between the above and below perspectives, namely: – expertise (scientists and elites versus the public) – temporality (long term versus the now) – representation (the powerful few versus the disempowered many)