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Negotiating uncertainties Jeroen Veraart, Wim Cofino Test case: Expert judgments on Sealevel rise Defining climate proofing and assessing associated uncertainties.

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Presentation on theme: "Negotiating uncertainties Jeroen Veraart, Wim Cofino Test case: Expert judgments on Sealevel rise Defining climate proofing and assessing associated uncertainties."— Presentation transcript:

1 Negotiating uncertainties Jeroen Veraart, Wim Cofino Test case: Expert judgments on Sealevel rise Defining climate proofing and assessing associated uncertainties in coastal zones with scarce freshwater resources

2 Contents Objective PhD research Theories Uncertainty Set up SLR Experiment Results SLR Experiment Further steps… Discussion

3 Objective PhD-research To map levels of (dis)agreement of (un)certainties regarding the freshwater availability for land use by Qualitatively (analysis of cultural concepts), and Quantitatively, with statistical analysis Practical guidelines for negotiating (un)certainties in regional science-policy interfaces related to climate proofing Southwest Delta of the Netherlands To be identified

4 Negotiating uncertainties by IPCC (Swart e.a., 2008) Working group I (climate system ~ uncertainty) Probabilities & climatic processes Setted the scene in the negotiation about uncertainty Implicit about possible cognitive bias (in footnote) Working group II (impacts/adaptation ~ risk) Risks & confidence levels. Explicit about possible cognitive bias in expert judgments Working group III (mitigation ~ human choice) Ignored the developed guidelines Scenario’s

5 Terminologies used in uncertainty negotiating by IPCC Likelihood terminology WG I: Climate change is very likely ≈ >90% probability Footnote: judgmental estimates of confidence Lead authors WG I used in practice a hybrid scale of objective (≈calculated) & subjective probability (≈ expert judgment) Ensures consistency among authors but cognitive bias among readers is still possible Confidence terminology WG II Degree of confidence in being correct Low confidence ≈ about 2 out 10 chance being correct Swart e.a, 2009

6 Negotiating uncertainties by Delta Commission 134p Working group I terminology ~ uncertainty) Probabilities Precise (likely): 1x Qualitative/imprecise(likely) 7x Qualitative/imprecise (unlikely) 5x, very unlikely 2x Uncertainty Qualitative: 23x Quantitative: none Working group II terminology~ risk) Risk: 96x Confidence levels. Qualitative 2x Quantitative 2x (River Rhine) Working group III terminology (mitigation ~ human choice) Scenario’s Climate scenario’s 124x Other scenario’s 57x (mainly emission scenario’s)

7 Uncertainty philosophies in climate science Imprecise Information Precise Information Objective perspective Subjective perspective Earth system Human dimension causality choice observations models Scenario’s Swart e.a., 2008 Likelihood scale Confidence scale Level of agreement & evidence Explanatory factors

8 Entropy Measure of ‘average’ uncertainty (Myung et al., 1996) Measure of level of (dis)agreement amongst experts Minimum Cofino Approach: used to ‘filter’ outliers in time series ≈ risk in expert judgment Maximum Entropy approach: all judgments are equally reliable Entropy is a measure for subjective probability?

9 Quickscan of possible methods for expert judgment Behavorial (Delphi etc), axiomatic or Bayesian Analysis  Bayesian Analysis Clusters or continuous scale?  Continuous (Sealevel rise, Eddies length) Which statistical distribution?  Scheef Hierarchies?  Sample = ESS-CC Comparison with study from Agro-Industry: a probability judgment to a same uncertain event: The future yield of 1 tomato seedling The price of tomatoes in future (4 weeks) Mansholt paper, 2006

10 Set up Sea Level Rise Experiment (questionaire) What will be the sea level rise in 2030/2100/2200? What is the body length of Eddy Moors? (cm) What is the average body length of the ESS group? What is your own body length? (cm) Average (cm) Minimum (cm) Maximum (cm) Chance that you are wrong (%) Explain your (expert) judgment

11 Sea level rise 2030 IDName Confavg 2030min 2030max 2030Expect. value Std 1Erik van Slobbe 60% Aad Sedee 80% Eddy Moors 80% Arnold van Vliet 50% Hasse Goosen 75% Herbert ter Maat 75% Rob Swart 90% Fokke 40% Catharien 30% Judith 40% Rik Leemans 100% Pavel 50% Saskia

12 Sealevel rise experiment: expert judgment Sea level rise 2030(cm) Probability density Erik Aad Eddy Arnold Hasse Herbert Rob Catharien Judith Fokke Rik Pavel Saskia

13 Summed asym. PDF’s for 2030 (2 approaches) Triangulars Min. Entropy approach Pmf1 = 13.7 cm, 51.5% Pmf2 = 29.4 cm, 23.6% Pmf3 = 35 cm, 11.8% Rik = 206, 6.7% Normal Distr. per respondent Min. Entropy approach Pmf1 = 13.7 cm, 71% Pmf2 = 42 cm, 17% Pmf3 = 26 cm, 10% no Rik Arithmic mean = 20 cm ± 13 cm

14 Overlap matrix sealevel rise Catharien 11Rik Leemans 6Herbert ter Maat 3Eddy Moors 1Erik van Slobbe 13Saskia Werners 10Judith Klostermann 5Hasse Goosen 12Pavel Kabat 8Fokke de Jong 7Rob Swart 4Arnold van Vliet 2Aad Sedee

15 The different approaches (2030)

16 Comparison of all expert judgments max min reality

17 Comparison judgment SLR 2100 & 2200 Expert judgment SLR 2100Expert judgment SLR 2200 Delta Commissie Delta Commissie 167cm, 55% 207cm, 19% 222cm, 12% Minent 83cm, 9% 61cm, 20% 69cm, 61%

18 Comparison body lengths estimations Rik, 133cm, 6% 183cm, 25% 183cm, 60% 186cm Eddy JudgmentESS-CC Judgment Minent 176cm, 56% 177cm, 22% 177cm, 9% 180cm Eddies body length is easier to estimate than group length

19 Summary Eddy Judgment ESS-CC Judgment 2030, n-distribution 2030, triangulars2200, triangulars 2100, triangulars Est. Confidence: 64% ±22 Est. Confidence: 53% ±22 Est. Confidence: 39% ±28 Est. Confidence: 74% ±23 Est. Confidence: 74% ±16

20 Overlap matrix judgments: summary 2100, triangulars 2200, triangulars2030, triangulars Eddy Judgment ESS-CC Judgment 2030, n-distribution

21 Further steps: What is the (average) annual minimum amount of rainfall needed (m 3 m -2 yr -1 ) to maintain freshwater supply for sector A,B in region Y under climate change? Compare (expert) judgment regarding freshwater supply from natural resources in region Y under climate change for different stakeholder/expert groups Comparison of regions Method is also applicable to: (beyond scope PhD) map (un(certainties) in the process of valuation of ecosystem services Other ecosystem services

22 Thank you Jeroen Veraart


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