The IPCC as parliament of things Dealing with uncertainty and value commitments in climate simulation 13 March 2012 | Arthur Petersen

IBM Supercomputer European Centre for Medium-Range Weather Forecasts

Warning: take into account uncertainty in climate simulation IPCC 2001: taking into account all uncertainties (including model uncertainty): largest part of warming is ‘likely’ due to anthropogenic greenhouse gases

IPCC 2007: taking into account all uncertainties (including model uncertainty): largest part of warming is ‘very likely’ due to anthropogenic greenhouse gases

de Kwaadsteniet versus van Egmond  de Kwaadsteniet: “Computer simulations are seductive due to their perceived speed, clarity and consistency. However, simulation models are not rigorously compared with data.”  van Egmond: “Policy makers are confronted with incomplete knowledge; task of scientific advisers to report on the current state of knowledge, including uncertainties. Simulation models are indispensable.” 13 March 2012 | Arthur Petersen

Simulation in scientific practice  Definition of computer simulation: “mathematical model that is implemented on a computer and imitates real-world processes”  Functions of simulation: –technique (to investigate detailed dynamics) –heuristic tool (to develop hypotheses, theories) –substitute for an experiment –tool for experimentalists –pedagogical tool 13 March 2012 | Arthur Petersen

Central activities in simulation practice  Formulating the mathematical model (conceptual and mathematical model: ‘ideas’)  Preparing the model inputs (model inputs: ‘marks’)  Implementing and running the model (technical model implementation: ‘things’)  Processing the data and interpreting them (processed output data: ‘marks’) 13 March 2012 | Arthur Petersen

Four claims re. climate simulation 1.Different models give conflicting descriptions of the climate system. 2.There exists no unequivocal methodology for climate simulation. 3.The assumptions in climate simulations are value-laden. 4.Pluralism in climate modelling is an essential requirement both for ‘good’ science and for ‘appropriate’ science advising. 13 March 2012 | Arthur Petersen

Funtowicz and Ravetz, Science for the Post Normal age, Futures, March 2012 | Arthur Petersen

The challenge of post-normal science  Expert advisers should be reflexive  Methods for dealing with uncertainty should merely be considered as tools, not as the solutions  Fear for paralysis in policy making should not be allowed to block communication about uncertainty  Communication with a wider audience about uncertainties is crucial 13 March 2012 | Arthur Petersen

Shifting notions of reliability  Statistical reliability (expressed in terms of probability) –How do you statistically assess climate predictions?  Methodological reliability (expressed qualitatively in terms of weak/strong points) –How do you determine the methodological quality of the different elements in simulation practice, given the purpose of the model?  Public reliability (expressed in terms of public trust) –What determines public trust in modellers? 13 March 2012 | Arthur Petersen

22 Lesson learnt in uncertainty communication (I) 1.Conditional character of probabilistic projections requires being clear on assumptions and potential consequences (e.g. robustness, things left out) 2.Room for further development in probabilistic uncertainty projections: how to deal decently with model ensembles, accounting for model discrepancies 3.There is a role to be played for knowledge quality assessment, as complementary to more quantitative uncertainty assessment 13 March 2012 | Arthur Petersen

23 Lessons learnt in uncertainty communication (II) 4.Recognizing ignorance often more important than characterizing statistical uncertainty 5.Communicate uncertainty in terms of societal/political risks 13 March 2012 | Arthur Petersen

A case of deep uncertainty: adaptation to changes in extreme weather in the Netherlands  Extreme weather events are predominantly associated with the risk of flooding, which is generally considered a government responsibility.  However, future projections for the Netherlands provide a picture which is somewhat more complex. The changes require awareness among society at large.  Yet, individuals and economic sectors have already dealt with the weather for ages and have developed knowledge and behavioural responses with respect to weather extremes. 13 March 2012 | Arthur Petersen

Notions from the policy sciences and social psychology  Articulation (views with respect to extreme weather are not always coherent)  Information (access to information shapes articulation)  Differentiation (different perspectives lead to different assessments of risk and potentials)  Learning by interaction (stakeholders and scientists can learn from interacting, by which preferences for, possibly new, policy options evolve) 13 March 2012 | Arthur Petersen

“Two cold winters don't deny global warming”  Dutch winter coldest since 1996  Questions one may ask: –How 'extreme' was this? –Will this happen less (or more...) often in the future? –Does this fit in the 'Global Warming'-picture? –How to optimally adapt to changes in extremes? 13 March 2012 | Arthur Petersen

Eleven city marathon  Marathon has been organized 15 times in the period , in the province of Friesland  How has the chance for holding a marathon changed over the past century?  How will it change in the future? 13 March 2012 | Arthur Petersen

Eleven city marathon Projections for 2050 for four scenarios: once every 18, 29, 55 or 183 years 13 March 2012 | Arthur Petersen

What is the impact of weather extremes, how can we adapt? 13 March 2012 | Arthur Petersen

Bridging the gap between science and policy  Uncertainties with respect to climate change and extreme weather events; knowledge about future is based on models  Need for adaptive governance and for methodology to assess policy options with different, even conflicting, outcomes  Need for indicators of outcomes for evaluating policy options relevant for stakeholders and reliable for scientists 13 March 2012 | Arthur Petersen

Bridging the gap: selected approach Two postdoctoral studies:  Social scientist: engaging with the stakeholders; analysing the process; co-developing adaptation options  Statistician/climate scientist: studying the uncertainty range of climate projections and decadal predictions of weather extremes; co-developing indicators Team of political scientists, statisticians, climate modellers, social scientists 13 March 2012 | Arthur Petersen

Global climate models and regional embedded models Regional model Global model 13 March 2012 | Arthur Petersen

Different sources of uncertainty - Global Source: E. Hawkins & R. Sutton, Bull. of Amer. Meteor. Soc., aug. 2009, March 2012 | Arthur Petersen

Different sources of uncertainty - Regional Source: E. Hawkins & R. Sutton, Bull. of Amer. Meteor. Soc., aug. 2009, March 2012 | Arthur Petersen

Example from the Intergovernmental Panel on Climate Change WG I (2007) “Most of the observed increase in globally averaged temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations 12.” (SPM) 12 Consideration of remaining uncertainty is based on current methodologies. 13 March 2012 | Arthur Petersen

Example from the IPCC WG I 2007 (continued) “Very likely” means a chance >90%. But what kind of probability are we dealing with here? “ assessed likelihood of an outcome or a result” Draft SPM Final SPM “ assessed likelihood, using expert judgement, of an outcome or a result” 13 March 2012 | Arthur Petersen

Importance of identifying high-confidence findings 13 March 2012 | Arthur Petersen

Process: Openness, peer review, supervision  Openness: PBL registration website for possible errors –40 reactions in total; 3 of which relevant for our investigation  Draw on IPCC authors to give feedback  Internal and external peer review  Independent supervision by KNAW Royal Netherlands Academy of Arts and Sciences 13 March 2012 | Arthur Petersen

Quite some risk for losing uncertainty information 13 March 2012 | Arthur Petersen

What can go wrong?  E1 Inaccurate statement –E1a Errors that can be corrected by an erratum (5) –E1b Errors that require a redoing of the assessment of the issue at hand (2)  E2 Inaccurate referencing (3)  C1 Insufficiently substantiated attribution (1)  C2 Insufficiently founded generalization (2)  C3 Insufficiently transparent expert judgment (10)  C4 Inconsistency of messages (2)  C5 Untraceable reference (3)  C6 Unnecessary reliance on grey referencing (2)  C7 Statement unavailable for review (1) 13 March 2012 | Arthur Petersen

Errors and shortcomings in AR4 WG II (8 chapt.) Table SPM.2Additional MajorMinor #S#SMajorMinor#S AfricaC3,C5,C7E1b,C33E1a,C4E1b3 AsiaC2,C31C3,C6E22 Aust & NZE2,C31C1C4,E1a3 EuropeC31E1a,C3(3),C45 L AmericaC21 E1a(2),E2,C5(2), C66 N AmericaC31 Poles IslandsE2,C32 Total#EE1b, E222E1a1E1a(4),E1b, E2(3) 88 Total#CC2(2),C3(2) C5, C7 6C3(4)46C3,C4,C6,C14C3(3),C4, C5(2),C March 2012 | Arthur Petersen

The IPCC: science or politics?  Assessments are social constructs that contain both scientific and political elements  Successful? Depends on ability to connect to climate science and policy  Generally voiced criticism: IPCC not open enough to skeptics 13 March 2012 | Arthur Petersen

The IPCC: science or politics? (II)  Practice: procedures ensure inclusivity; skeptics do have influence; reflexivity on dissensus is moderate (neither low nor high)  Not: “scientific consensus”. But: “policy-relevant assessment acknowledging uncertainty”  Still, the communication of uncertainty can be further improved  The IPCC acts as a Latourian “Parliament of Things” – if only the actors would admit March 2012 | Arthur Petersen