Summary of interactive discussion groups

1. Summary of interactive discussion groups Topic 2: Is anybody listening Bill Hare with contributions from B.Hezel, M.Hanemann, L.Costa, M.Obersteiner, M.Lüdeke, M.Rounsevell, R.Gudipudi

2. How can attribution of observed impacts best be communicated without sacrificing scientific rigor? (2.3)‏ 2

3. How do we best communicate the magnitude and inevitability of uncertainty in order to support policy makers in dealing with climate-related risks? (2.3)‏ • Be scientifically clear about character and magnitude of uncertainty and decide then how to communicate best • Probability interpretation of frequency statements (e.g.: according to 10% of the models no change will occur – do you want to bet on this? – is that a scientifically acceptable statement?)‏ • uncertainty should be communicated in a way that allows the decision maker at least to compare it with the certainty of other decision-leading projections (demographic, socio-economic etc) 3

4. evaluation of uncertainty -> include (i.e. inform) all stakeholders/potentially affected people (assessment of uncertainty may be very different!)‏ • Perception of uncertainty (risk aversion etc)‏ • Do not communicate “small” uncertainties (what is “small”?)‏ • should the “reasons” for uncertainty also be communicated? • Suggest adaptation measures which are robust against uncertainty 4

5. How can attribution of observed impacts best be communicated without sacrificing scientific rigor? (2.3)‏ 5

6. Structuring discussion: • Character of uncertainty • How to communicate • Michael: • 14:20 • one for the second question • Concentrate on listening, not only uncertainty • the Rumsfelds 6

7. TEEB/Aicher • 14:24 • IPCC is well listened/ discursive leader • Listening ok, but no political impacts • Less uncertainty does not necessarily create political action • -active involvement of potential users- how? in producing the results? In formulating the questions? • Assess & communicate urgency of action • Include ecosystem services into decisions • Scaling: policy reports for national scale, regional, sectoral etc • Social and political demands is starting points • Dialog is more important than uncertainty 7

8. disc. • teeb/ipcc - uncertainty in monetariszation of ecosystems • Who reads the reports? • How much resistance? Here comes uncertainty in. Forest community opposed, but not because of certainty • Lot of people involved – observed political/business impacts? • - teeb gets Anfragen, fairly young 8

9. Felix Creutzer 14:53 • Typology of uncertainty (sender/transmission/receiver)‏ • Impacts: probability distributions/mitigation: action dependent • Sender: • Unc. (quantifyable) → Ambiguity (not quantifyable)‏ • contrasting Opinions of scientists • Transmission: • Media: manufacturing/fragmentation • Receiver: • Lot of bias introduced – belief-systems (CC exists), 2. we can do something, 3. • → selective information stream 9

10. What to do • ex. public health, how to deal with outbreak, doing the vaccination .... overconfidence backfires • Sender: unc. Needs to be reported: interval, not distribution • Media: good scientific journalism / audience relevance • Receiver: audience relevance • Disc. • Who is the audience? • Value/belief systems missing • People tend to forget, repeat the obvious, best uncertainty rep. Depends on reciever • How belief systems are formed – risk and benefits confounded 10

11. Skip media? → fragmentation does • Avoid overconfidence! • Tails mean anything could go • Important: Capacity to block action, start to think strategically • Reaching the public: mixing the abstract with the concrete example • Make clear your role • Tell how you got your scientific results 11

12. Oles 15:29 • Analyze power structures/ data scarcity as an instrument • Skip media, inform civil society and decision makers • Disc. • Bring uncertainty to the peoples interest • Uploading function • Concern about administration, civil society uses it • Who finds out what tools are available? 12

13. TEEB: Uncertainty plays no role • CATHY: inst. Ecology • Template, + socio-political context • Purpose, sender-receiver model old-fashioned!!!! • Who are • General public, politicians,policy makers 13

14. Topic 2, group 3: How do we best communicate the magnitude and inevitability of uncertainty in order to support policy makers in dealing with climate-related risks? 14

15. Propagation of Uncertainty in Climate Change Communication Function Sender Transmitter Receiver 15

16. Politicians Policymakers Civil society Social & political context (India, China, etc) Propagation of Uncertainty in Climate Change Communication Typical actor Sender Science Transmitter Media Receiver 16

17. Uncertainty (quantifyable) Ambiguity (categorial) Opinion Balancing bias „Manufactoring“ Fragmentation Belief systems Politicians Processing capacity Policymakers Vested interests Civil society Social & political context (India, China, etc) Propagation of Uncertainty in Climate Change Communication Sources of distortions: Sender Science Transmitter Media Receiver 17 After Creutzig/Markowitz 2013

18. Uncertainty (quantifyable) Ambiguity (categorial) Opinion Balancing bias „Manufactoring“ Fragmentation Belief systems Politicians Processing capacity Policymakers Vested interests Civil society Reducing Uncertainty Propagation Science Media 18

19. Uncertainty (quantifyable) Ambiguity (categorial) Opinion Balancing bias „Manufactoring“ Fragmentation Belief systems Politicians Processing capacity Policymakers Vested interests Civil society Reducing Uncertainty Propagation Directly addressing policymakers AND civil society (information symmetry!) via open access web-based information Science Media 19 e.g. Kit/Lüdeke 2013

20. Uncertainty (quantifyable) Ambiguity (categorial) Opinion Combining general statements with concrete examples Balancing bias „Manufactoring“ Fragmentation Belief systems Politicians Processing capacity Policymakers Vested interests Civil society Reducing Uncertainty Propagation Directly addressing policymakers AND civil society (information symmetry!) via open access web-based information Science Representation ad-apted to receiver Media Good science journalism 20 e.g. Kit/Lüdeke 2013

21. Uncertainty (quantifyable) Ambiguity (categorial) Opinion Combining general statements with concrete examples Balancing bias „Manufactoring“ Fragmentation Belief systems Politicians Processing capacity Policymakers Vested interests Civil society Reducing Uncertainty Propagation Direct involvement of stakeholders/decision makers into the science process reduces the role of scientific uncertainty as alibi for non-action Science Representation ad-apted to receiver Media Good science journalism 21 e.g. Aicher/Beck 2013

22. Uncertainty (quantifyable) Ambiguity (categorial) Opinion Balancing bias „Manufactoring“ Fragmentation Belief systems/ Politicians Processing capacity/ Policymakers Vested interests Civil society Reducing Uncertainty Propagation Directly addressing policymakers AND civil society (information symmetry!) via open access web-based information sources Science Media Good science journalism 22 e.g. Kit/Lüdeke 2013

23. Uncertainty (quantifyable) Ambiguity (categorial) Opinion Balancing bias „Manufactoring“ Fragmentation Belief systems Politicians Processing capacity Policymakers Vested interests Civil society Propagation of Uncertainty in Climate Change Communication Function Typical actor Sources of distortions: Sender Science Transmitter Media Receiver 23