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.Lüdecke, M.Rounsevell, R.Gudipudi 1

2. Listening issues…
People are listening but we are not telling them what they want to hear.
As long as impacts are not translated into economic endpoints they are invisible to many policymakers
Uncertainty grows with the length of the information transmission chain….
Translation of issues and messages from local to global scale can be difficult:
Local policy makers “hear” an extreme event now but may not hear noise from the future…

3. Topic 2: Is anybody listening?
How do we improve the construction of damage functions, in order to produce more consistent economic risk assessments?
How do we appropriately address concerns of equity and discounting?

4. Disconnect between damage functions in many IAMs and many biophysical impact models
ICES and PESETA are important exceptions.
Examples
Sea level rise - hard to believe that IAMs now represent best available knowledge on impacts.
Contrast between agricultural damage functions in DICE, FUND and ENVISAGE and findings of (i) crop models and (ii) statistical analyses of the observed effects of weather on crops

5. Existing IAM damage functions not well designed to handle probabilities of, and changes in, extreme
Comes naturally from biophysical impact models.
Combining IAM damage functions with a risk-based approach to decision making for adaptation as well as mitigation would be a valuable improvement

6. Recommendations
Economists need to sit down with bio-physical modellers and learn from them;
Biophysical modellers need to bite the bullet and include economic metrics among the impacts they track.
Need to have a forum where the two communities can have a systematic discourse before the next IPCC assessment

7. Topic 2: Is anybody listening?
How do we best communicate the magnitude and inevitability of uncertainty in order to support policy makers in dealing with climate-related risks?

8. Propagation of uncertainty in climate change communication
Function
Sender
Transmitter
Receiver 8 8

9. Propagation of uncertainty in climate change communication
Social & political context (India, China, etc)
Typical actor
Sender
Science
Transmitter
Media
Politicians
Policymakers
Civil society
Receiver 9 9

10. Propagation of uncertainty in climate change communication
Social & political context (India, China, etc)
Sources of distortions:
Ambiguity (categorial)
Uncertainty (quantifyable)
Opinion
Sender
Science
„Manufactoring“
Balancing bias
Fragmentation
Transmitter
Media
Politicians
Policymakers
Civil society
Processing capacity
Belief systems
Vested interests
Receiver 10
After Creutzig/Markowitz 2013 10

11. Reducing uncertainty propagation
Ambiguity (categorial)
Uncertainty (quantifyable)
Opinion
Science
„Manufactoring“
Balancing bias
Fragmentation
Media
Politicians
Policymakers
Civil society
Processing capacity
Belief systems
Vested interests 11 11

12. Reducing uncertainty propagation: direct communication
Directly addressing policymakers AND civil society
(information symmetry!) via open
access web-based
information
Ambiguity (categorial)
Uncertainty (quantifyable)
Opinion
Science
„Manufactoring“
Balancing bias
Fragmentation
Media
Politicians
Policymakers
Civil society
Processing capacity
Belief systems
Vested interests 12
e.g. Kit/Lüdeke 2013 12

13. Reducing uncertainty propagation: better communication
Directly addressing policymakers AND civil society
(information symmetry!) via open
access web-based
information
Ambiguity (categorial)
Uncertainty (quantifyable)
Opinion
Science
Representation ad-apted to receiver
with concrete examples
Combining general statements
„Manufactoring“
Balancing bias
Fragmentation
Media
Good science
journalism
Politicians
Policymakers
Civil society
Processing capacity
Belief systems
Vested interests 13
e.g. Kit/Lüdeke 2013 13

14. Reducing uncertainty propagation: participation
Direct involvement of stakeholders/decision makers into the science process reduces the role of scientific
uncertainty as alibi for non-action
Ambiguity (categorial)
Uncertainty (quantifyable)
Opinion
Science
Representation ad-apted to receiver
with concrete examples
Combining general statements
„Manufactoring“
Balancing bias
Fragmentation
Media
Good science
journalism
Politicians
Policymakers
Civil society
Processing capacity
Belief systems
Vested interests 14
e.g. Aicher/Beck 2013 14

15. Topic 2: Is anybody listening?
How can the widespread use of global mean temperature as base variable among policy makers be reconciled with a broader approach suggested by science?

16. Extreme events (for e.g Hurricane Sandy in New York) are triggering actions already at local level.
Though this might be valid from an adaptation point of view, mitigation still needs a broader political commitment
Action responding to climate change at a national level is not neutral because of political polarity and cultural cognition at local level
Long term strategic coordination between scientists and policy makers is found to be an effective way of enhancing climate responsive action at local level (aiming at both mitigation and on a longer run increasing resilience to deal with uncertainities)
The impact community itself is very heterogeneous (natural scientists, social scientists and economists, etc) with quite different objectives. Therefore it will be relevant to focus on “Science for science”

17. Some lessons and conclusions…
Policy recommendations for climate actions on both mitigation and adaptatation differ from each other depending on various local parameters (socio-economic background, projected climate patterns, etc.)
Cross sectoral integration of various climatic impacts can be a more efective way of communication for policy recommendations
Better understanding of possible climate actions on both mitigation and adaptatation at various policy making levels is required
Burden sharing by all coutries globally might bring together for action aganist climate change
Global mean temperature target is irrelevant for actions at local level but regional/spatial climatic variations do play a crucial role
Though it is irrelevant at local level, there exists no better indicator than the current 2 degree target at global level which is tangible, mutually agreeable and achievable)

18. Topic 2: Is anybody listening?
How has impacts research influenced adaptation policy-making thus far?
What has and has not worked at the science policy interface?
How can we ensure a systematic quantification of adaptation options including local knowledge?

19. Lessons…. Doorstep model of implementation does not work
-> Think about an engagement strategy facilitating a co-production and co-design of knowledge
Experiences with institutional and cultural barriers between researchers and policy-makers do not necessarily travel between countries
Build trust with local community and local decision-makers
Stakeholder involvement
→ suitable, effective fair and legitimate adaptation measures
Context specific, streamlined information that is understandable and relevant works
Understand scenarios less as a product rather to be co developed an co- creative process
→ stakeholder perceptions have value, they are often close to the model outputs
Robustness analysis can mitigate uncertainty problem

20. Recommendations, ideas and needs…
Correct framing for policy makers and stakeholders is very important
Start from existing maladapatation to existing climate variabilty…
..then addition explain that climate change will make this worse.
Identify people managing systems right now… they are likely to be the experts to “sell” the information to.
Recurrent need in many contexts for a dedicated science/policy translator/Communicator
Need for new incentives for scientists to go the “extra mile” for co- creation and communication activities (in-house appreciation, new trans-disciplinary journals..)

21. Group 2.2 Raw Material

22. People are listening but we are not telling them what they want to hear.
As long as impacts are not translated into economic endpoints they are invisible to many policymakers.
A disconnect between the damage functions in many IAMs and the biophysical impact models
ICES and PESETA are important exceptions.
Examples
Sea level rise - hard to believe that IAMs now represent best available knowledge on impacts.
Contrast between the agricultural damage functions in DICE, FUND and ENVISAGE
and finding of (i) crop models and (ii) statistical analyses of the observed effects of weather on crop yields.
Recommendations
Economists need to sit down with bio-physical modellers and learn from them;
biophysical modellers need to bite the bullet and include economic metrics among the impacts they track.

23. There exists no forum which provides for a systematic discourse among the two communities. IPCC
doesn’t fill this role. This forum needs to be created before the next IPCC.
The existing IAM damage functions are not well designed to handle probabilities of extreme events and changes in those probabilities. Such probabilities can come naturally from biophysical impact models.
Combining IAM damage functions with a risk-based approach to decision making for adaptation as well as mitigation would be a valuable improvement.
Connecting IAMs with biophysical models is a computational challenge because it involves bridging
different levels of spatial resolution, but this needs to be tackled.

24. Group 2.4 Raw Material

25. Extreme events (for e.g Hurricane Sandy in New York) are triggering actions already at local level.
Though this might be valid from an adaptation point of view, mitigation still needs a broader political commitment
Action responding to climate change at a national level is not neutral because of political polarity and cultural cognition at local level
Long term strategic coordination between scientists and policy makers is found to be an effective way of enhancing climate responsive action at local level (aiming at both mitigation and on a longer run increasing resilience to deal with uncertainities)
The impact community itself is very heterogeneous (natural scientists, social scientists and economists, etc) with quite different objectives. Therefore it will be relevant to focus on “Science for science”