Global Climate Change and Uncertainty

1. Global Climate Change and Uncertainty David B. MacNeill Fisheries Specialist NY Sea Grant Extension SUNY Oswego dbm4@cornell.edu

2. Global Climate Change and Uncertainty Apocalypse Public perceptions Kyoto Heresy Biodiversity Al Gore Greenhouse gases Conspiracy IPCC Junk Science Disaster Human dimensions Polar bears Human behavior Chicanery Tradeoffs Glaciers Climate models Communication Decision-making Adaptation Mitigation Scenarios Policy implementation Social Sciences

3. This Presentation: • Broad-brush overviewof climate change uncertainties, communication etc. from literature sources, extension experience with scientific uncertainty. • Notan indictment of science or an admonishment of scientists, policy makers, government or the lay community!!

4. The uncertainty: What poker hand will I draw next? Understanding the concepts of risk and uncertainty with a deck of cards?? The Dead Man’s Hand: unlucky for Wild Bill Hickok? Therisk:What is the probability of drawing it?(<1%)

5. But, the card deck changes unexpectedly…… Death cards Other cards The Risk ?

6. Some Climate Change Perspectives • A complex, multidisciplinary issue of long-term global consequence, that demands: • Best available information • New assessment, predictive, decision-making tools • A carefully planned extension/outreach strategy • Better PR for science • An opportunity to: • Inform communities: climate science, risks, abatement and science 101 • Assist coastal communities: decision-making

7. Global Climate Model

8. Climate Change Complexity: • Many different disciplines. • Highly uncertain events; outcomes poorly defined. • Interactive anthropogenic and natural events. • Future outcomes sensitive to small changes in current conditions. • Incomplete understanding of climate system. • Imprecise models: feedbacks, interactions, parameter values. • Huge jigsaw puzzle having 10s of thousands of pieces. • Compilation: decades of intensive, international research.

9. Uncertainty leads to those nagging questions • Is climate change real?, are humans responsible? • What are the impacts?, What should we do? • Why: • is science uncertain? • do scientists disagree? change their minds? • don’t scientists always have the answer? • do results contradict?

10. Uncertainty paradigms • Uncertainty is unwelcome, and needs to be avoided. Science must eliminate uncertainty through more and better research. • Uncertainty is undesirable, but unavoidable. Science must estimate and quantify uncertainty as well as possible. • Uncertainty creates opportunities. Science must contribute to more inclusive, understandable discussions. • Uncertainty is an integral part of decision-making. Science must have more societal influence.

11. Communicating Science and UncertaintiesWhy even bother ??? • PR: The process of science. • Restore credibility of science: increased transparency. • Provide accessible information/knowledge to decision-makers. • Decision-making: accurate and collaborative. • Increase public support/involvement: decision-making • Enhance societal abilities: adaptation & mitigation • GCC interactions: science and human ecology

12. Three Arguments for Climate Change • Climate is changing: analyses of many indicators • Human activities have contributed to increases in green house gas emissions • Scientific deliberations and large-scale computer models suggest potential for climate change from anthropogenic influences • High degree of confidence: weight of evidence from expert opinion

13. Is climate really changing? Sub-surface ocean temperatures Surface temperature record Convincing evidence Climate proxies Sea level Sea Ice Glacial record BUT.. Earth’s resiliency? Climate sensitivity Contentious Points Climate cycles Model predictive power Remote sensing calibration Policies: people or nature Climate proxy accuracy Natural vs. anthropogenic Solar activity

14. Seeing is Believing? Muir Glacier Alaska, August 2004. photo by B.F. Molnia Muir Glacier Alaska, August 1940.photo by W.O. Field

15. An exaggerated view….. “Science is sloppy - a collection of useless facts”. “You’re arrogant, out-of touch and have impractical ideas”. “You’ve been wrong before.” “Prove it.” “You just don’t understand.” “It’s too complicated”. “We know what is best.” “It’s not our job to explain it to you”. “We’re scientists, not interpreters”. Scientist Non-scientist Uncertainty

16. Some major challenges • Continuing uncertainties on climate system sensitivity to various feedbacks (e.g., clouds, water vapor, snow). • Several natural modes of climate variability have been identified and described, but their predictability is uncertain. • Need to improve understanding of whether and how human impacts may alter natural climate variability. • Do not yet have confident assessments of the likelihood of abrupt climate changes. • Insufficient understanding of effects of climate variability and change on extreme events. • Limited capabilities at regional scales. • Need better means for identifying, developing, and providing climate information required for policy and resource management decisions.

17. Mac’s Uncertainty Concept Model Stochastic (Surprises) Climate System Epistemic (Unknowns) Scientists Science Knowledge Knowledge communication (translation) Non-Scientists Human reflexive(volition) Decisions

18. Mac’s Uncertainty Concept Model Stochastic (Surprises) “To comprehend science as a responsible citizen…both contentand reasoning are essential. The absence of one or the other may produce laughter, but not good science.” Paul Gross. Learning Science: Content with Reason. American Educator Fall 2009: 35-40 Climate System Epistemic (Unknowns) Scientists Science Reasoning Knowledge Knowledge communication (translation) Content Non-Scientists Human reflexive(volition) Decisions

19. Mac’s Uncertainty Concept Model “To comprehend science as a responsible citizen…both contentand reasoning are essential. The absence of one or the other may produce laughter, but not good science.” Paul Gross. Learning Science: Content with Reason. American Educator Fall 2009: 35-40 Surprises Climate System Unknowns Scientists Science Reasoning Knowledge Knowledge communication (translation) Content Non-Scientists Human reflexive(volition) Decisions

20. Different Roles of Science in GCC Policy Pure scientist interpretation Politicians Science arbiter Scientific Knowledge Policy makers Decision making Policy Honest broker opinions Stakeholders ?? Issue advocate Advocacy Roger Pielke Jr.

21. How does science work, anyway? 1. Observe and describe something of interest 2. Make an informed guess about why or how something interesting happens 3. Check out how it (our speculation) stands up to what we know or what information we can get 4. Use our judgment whether to (tentatively) accept it, or change, improve or replace it Susan Haack

22. Addressing uncertainties • Identify • Characterize: source, magnitude • Solicit expert judgments: level of “confidence” • Sensitivity analysis: range of probable model outcomes assessed with model using a range of values various inputs, upper and lower bound • Quantify: probabilistic analysis (Frequentist and Bayesian), probabilistic distributions, deterministic analysis and hybrids • Clarify, document range and distributions • Articulate and communicate: probabilistic and scenarios

23. Warmer, dryer summers Warmer, wetter winters Increased spring flooding Changes in sea/lake levels, water currents, thermal structure Increased storm frequency, severity Droughts, crop loss, famine Invasive species, new or re-emerging pathogens, parasites More hyperthermia deaths Coastal infrastructure/tourism Habitat damage/loss Loss of biodiversity, extinctions? Some predicted impacts of climate change? In-direct Direct • Technological advances • Longer growing seasons • New agriculture/tourism opportunities. • More snow? • Reduced heating costs • Fewer hypothermia deaths

24. What are they really saying? Nature: too complex. Conflicting data. Models: poor predictors. Exaggerated impacts. Doom/gloom vs. facts. Earth’s resiliency. Strategies: cost/benefits? Consensus:evidence supports GCC Less consensus:drivers, impacts, strategies, policies GCC heretics, infidels, skeptics, nay-sayers, cynics, deniers??

25. What is the matter with science?The debate continues…… • Dyson (1993) • Consensus: peer pressure (entrepreneurial science) vs. debate • Public fear drives funding priorities = politicization of science • Science's failure to address global welfare vs. unrealistic expectations • Rubin (2001) • Science is not the sole repository of the truth • Little self-limitation on deliverable truths • Get the facts straight vs. overselling science • Scientific authority fosters hidden agendas that short-circuit debate • Participatory decision making impeded by science education shortfalls • Commoner (1971) • Illusion of scientific objectivity • Grant et al. (2004) • Popper’s vs. psychological v • Benedikter (2004) basic ideologies and mechanisms not fully visible (psychologically) • Malnes (2006) • Mixed messages: duplicity vs. extraneous diversions

26. Classical, Modern & Post-Normal Science • Classical: • Observations • Sense experiments • Subjective judgments • Past experience the Truth! Absolute • predictions • probabilities • possible explanations • disconnected policy • adversarial • communication gaps • Modern / Normal: • Exclusive, remote • Non-interdisciplinary • Experiments/models • Data analysis/interpretation • Hypothesis testing Reductionist, “puzzle-solving” • “Post-Normal” • Inclusive • Natural & social sciences • Complexity/risk/urgency • Systems approach • Cost/benefits • Public debate • shared decision making • problems solving • confidence/trust building • Anti-science perception Precautionary, risk management

27. Classical, Modern & Post-Normal Science • Classical: • Observations • Sense experiments • Subjective judgments • Past experience the Truth! Absolute • predictions • probabilities • possible explanations • disconnected policy • adversarial • communication gaps • Modern / Normal: • Exclusive, remote • Non-interdisciplinary • Experiments/models • Data analysis/interpretation • Hypothesis testing Reductionist, “puzzle-solving” • “Post-Normal” • Inclusive • Natural & social sciences • Complexity/risk/urgency • Systems approach • Cost/benefits • Public debate • shared decision making • problems solving • confidence/trust building • Anti-science perception Precautionary, risk management

28. Perceptions of Science God-like? Elitist? Crusading knight? Mad/evil?

29. “Ultimate source of knowledge/wisdom. Operates in unencumbered, controlled environment. Strives for perfection. Accountable, held to high standard. A creature of our own design, neither good or bad. Powerful, protective, follows orders. Clumsy and dangerous, must be controlled. Fallible = low expectations. Can’t be blamed for mistakes if it is trying. Two Opposing Metaphors for Science: God-like or Golem? Truth

30. The Snowball Effect “Other” Uncertainties Climate Science Uncertainties

31. Cascading Uncertainties in Climate Science Adapted from Schneider 1983 Global climate sensitivity Regional climate change scenarios Range of possible impacts Emission scenarios Carbon cycle response

32. Scientists face important challenges in communicating science to non-scientists • The nature of ‘normal’ scientific investigation and debate • logic vs. cognitive processes • adversarial, not focused on consensus development • debate primarily within disciplines • Isolationism • “too busy” to talk to non-scientists! • rift between physical and social scientists • Inadequate training in communication skills • dealing with media • addressing misinformation • understanding policy development process

33. Can complex science be understood by the public? • Yes, many successful examples ! • Knowledge from Scientific process • “Step-back”, discuss and debunk science myths • Myth 1: science as a collection of established facts • Myth 2: conflicting science presented in a balanced way • Myth 3: science jargon as chief obstacle

34. Interpretations of Global Climate Science Uncertainties • Scientists: • intrinsic part of science • too many variables to eliminate • can be reduced with more scientific information • general support of a “precautionary” approach”. • Policymakers: • science is sloppy • “burden of proof” • lack of/incomplete knowledge = bad science • must have all the facts: decision making/policy implementation • little/no support of precautionary steps

35. The Climate Uncertainty “Toolbox” Fisherian statistics Bootstrapping Likelihood-based approaches Bayesian statistics Jackknife Stochastic models Deterministic models Neural networks Permutation tests Monte Carlo Scenario analysis Climate models Resampling

36. Communicating Uncertainties of Climate Change • Increase science literacy • Outreach materials: Hypothetical scientific investigations. • Develop vivid narratives of potential harm • Address/communicate uncertainties to stakeholder communities. • Understand decision making mechanics, assess values and attitudes • Develop an integrative (social-natural science), participatory decision-making process • Psychometric paradigm: people (focus on a range of qualitatively distinctive factors that are irreducible by numbers) show a richer rationality than experts (focus on quantity), risk perception in social sciences, used to explain divergence between risk related judgments • People influenced by whether risk is catastrophic , future generations, involuntary incurred, , uncontrollable, delayed vs immediate, and particularly dreaded. Cass Sustein 2007: Columbia Law Review 107: 503-557

37. What are the likely climate changes over the next century, or so?? • Most global warming projections are for a 4-10 F increase by 2100 • Virtually certain:~ 95 to 100% • Warmer days and nights, fewer cold periods over most land areas • Very likely: ~ 67-95% • Warm spells/heat waves, frequency increasing over most land areas • Heavy and more frequent precipitation events • Likely:~ 33-67% • Area affected by drought increases • Intense tropical cyclone activity increases • Increased incidence of extreme high sea level (exclude tsunamis)

39. Communicating Uncertainty:Examples from Weather Forecasts • Numerical probabilities: • A 30 % chance of rain. • Qualitative or categorical forecasts: • Today’s weather will be “fine”. Handmer et al. 2007

40. Communicating Uncertainty:Examples from Weather Forecasts • Numerical probabilities: • high likelihood, tangible events • can be misinterpreted: where? when? how long? • example: 30% chance of rain • a 30% chance of rain in the forecast area. • a 30% chance of rain at a specific location in forecast area. • only 30% of the forecast area will be affected, if it does rain. • it will rain 30% of the day. • it will rain 3 out of 10 days when rain is forecasted • not useful when: • i.e. 0.0001% chance of as a severe event • Abstract, “invisible”, even catastrophic events • Public more concerned with issues of control, trust and equity Handmer et al. 2007

41. Decision-making Under Uncertainty Decisions: • based on likelihood of uncertain events • Uncertainties expressed • numerical form (odds) • subjective probabilistic statements • heuristics • Representativeness – degree of relationship, causality • Availability – ease of instances/consequences imagined • Adjustment/Anchoring –initial value adjusted to yield final answer (problem formulation or partial computation) Tversky, A. and D. Kahneeman. (1974). Judgment under Uncertainty: Heuristics and Biases. Science 185: 1124-1131

42. Decision-making Under Uncertainty • Task of choice • Framing • Relate decision making to similar problems • Used to determine outcome loss or gains • Evaluation • Act to reduce loss probability, maximize gains • Adopt risk averse stance • 3 subconscious processes (heuristics): • Representativeness – degree of relationship, causality • Availability – ease of instances/consequences imagined • Adjustment/Anchoring –initial value adjusted to yield final answer (problem formulation or partial computation) Patt, A. and S. Dessai. (2005). Communicating Uncertainty: lessons learned and suggestions for climate change assessment. C. R. Geoscience 337: 425-441 Tversky, A. and D. Kahneeman. (1974). Judgment under Uncertainty: Heuristics and Biases. Science 185: 1124-1131

43. Decision-making Under Uncertainty • Stochastic uncertainties (unpredictability/surprises) • Framing: (usually) in frequentist terms • Uncertainty: probability expressed relative frequencies • Heuristic: Availability = analogy • Evaluation: Less risk averse, under-estimate risk, less prone to illogical choice • Epistemic uncertainties(structural/ignorance) • Framing (often) in Bayesian terms • Uncertainties: ambiguous probability estimates, numerical ranges confidence, expert opinion • Heuristic: Representativeness= common, familiarity • Evaluation: More risk averse, over-estimate risk, more prone to logic errors Patt, A. and S. Dessai. (2005). Communicating Uncertainty: lessons learned and suggestions for climate change assessment. C. R. Geoscience 337: 425-441 Tversky, A. and D. Kahneeman. (1974). Judgment under Uncertainty: Heuristics and Biases. Science 185: 1124-1131

44. Decision-making Under Uncertainty Decisions: • based on likelihood of uncertain events • Uncertainties expressed • numerical form (odds) • subjective probabilistic statements • heuristics • Representativeness – degree of relationship, causality • Availability – ease of instances/consequences imagined • Adjustment/Anchoring –initial value adjusted to yield final answer (problem formulation or partial computation) Tversky, A. and D. Kahneeman. (1974). Judgment under Uncertainty: Heuristics and Biases. Science 185: 1124-1131

45. Graphical Communication of Uncertain Quantities to Non-Technical People Risk Analysis 7 (4)Ibrekk et al. 1987 * * 9 graphical representations of the same snow fall predictions

46. Communicating Uncertainty:Examples from Weather Forecasts • Qualitative or categorical forecasts: • “Fine” • Also misinterpreted: does it mean • No rain? • Sunny/sunshine? • Not too hot/moderate temperature? • Clear day/ not cloudy or overcast? • Lovely weather/a nice day? • No wind/light winds? • Some cloud/may be overcast? Handmer et al. 2007

47. Communicating Uncertainty:When Uncertainties are Insurmountable • Scenarios • Coherent, plausible, alternative representations of future climate • Projections/modeled responses (not forecasts) from climate “drivers”. • Descriptions: current states, drivers, step-wise changes, future images. • Assessments future climate conditions (very high uncertainties). • Assist in designing adaptation/mitigation strategies • Provide better understanding of interactions/dynamics

48. Outreach: Uncertainties of Climate Change • Increase science literacy • vivid narratives of potential harm/benefits • Communicate uncertainties to stakeholder communities. • Assess values and attitudes • Develop an integrative (social-natural science) decision-making process • Psychometric paradigm: people (focus on a range of qualitatively distinctive factors that are irreducible by numbers) show a richer rationality than experts (focus on quantity), risk perception in social sciences, used to explain divergence between risk related judgments • People influenced by whether risk is catastrophic , future generations, involuntary incurred, , uncontrollable, delayed vs immediate, and particularly dreaded.

49. Terrorism: low probability, palpable, catastrophic risks are immediate, short term Concern to US, Britain an allies. Perceived high risk recurrence neglect probability visual anger, fear, Huge costs justified to protect national security benefits unimportant 2005-2006: $255 $318 billion committed to war on terror vs $312 billion for entire Kyoto protocol. Public opinion 2004 48% Britons: top global priority 2006 80% Americans top global priority Climate change: high probability, impalpable, catastrophic Long-term risk, affect future generations. Concern to other nations only serious mitigative/adaptive action unlikley climate change causes obscure (uncertainties) people lack experience make risks apparent, real or impending, cost benefits, Public opinion 2000 CC: ranked environment as 16th most important issue and 12th out of 13 top environmental problems 2004: 63% Britons: top global environmental issue. An Interesting Expert Opinion: An Essay: Divergent American Reactions to Terrorism and Climate Change Cass Sustein 2007: Columbia Law Review 107: 503-557 Similarities:potentially catastrophic outcomes, difficulty assigning probabilities to risks Divergence: simple facts and political responses to each risk:

50. An Interesting Expert Opinion: An Essay: Divergent American Reactions to Terrorism and Climate Change Cass Sustein 2007: Columbia Law Review 107: 503-557 “We have to deal with this new type of threat [terrorism] in a new way we haven’t yet defined.. With a low-probability, high impact event like this.. if there is a 1% chance that Pakistani nuclear scientists are helping Al Qaeda build or develop a nuclear weapon, we have to treat it as a certainty in terms of our response”-- Dick Cheney, Former Vice-President “Climate change is the most severe problem we are facing today - more serious than the threat from terrorism” – Sir David King Director, Smith School of Environment, Oxford; Research Director, Dept. of Physical Chemistry, Cambridge; Former Chief Scientific Advisor to Blair Administration.