Global Climate Change and Uncertainty



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Global Climate Change and Uncertainty

  • David B. MacNeill
  • Fisheries Specialist
  • NY Sea Grant Extension
  • SUNY Oswego
  • dbm4@cornell.edu

Global Climate Change and Uncertainty

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

This Presentation:

  • Broad-brush overview of climate change uncertainties, communication etc. from literature sources, extension experience with scientific uncertainty.
  • Not an indictment of science or an admonishment of scientists, policy makers, government or the lay community!!

Understanding the concepts of risk and uncertainty with a deck of cards??

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

But, the card deck changes unexpectedly……

  • The Risk ?
  • Death cards
  • Other cards

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

Global Climate Model

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.

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?

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.

Communicating Science and Uncertainties Why 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

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

Is climate really changing?

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

Seeing is Believing?

  • Muir Glacier Alaska, August 1940. photo by W.O. Field
  • Muir Glacier Alaska, August 2004. photo by B.F. Molnia

An exaggerated view…..

  • Uncertainty
  • Scientist
  • Non-scientist
  • “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”.
  • “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.”
  • 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.

Mac’s Uncertainty Concept Model

  • Stochastic (Surprises)
  • Science
  • Climate System
  • Knowledge
  • Human reflexive (volition)
  • Epistemic (Unknowns)
  • Non-Scientists
  • Decisions
  • Knowledge
  • Scientists
  • communication (translation)

Mac’s Uncertainty Concept Model

  • Stochastic (Surprises)
  • Science
  • Climate System
  • Knowledge
  • Human reflexive (volition)
  • Epistemic (Unknowns)
  • Non-Scientists
  • Decisions
  • Knowledge
  • Scientists
  • communication (translation)
  • “To comprehend science as a responsible citizen…both content and 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
  • Content
  • Reasoning

Mac’s Uncertainty Concept Model

  • Surprises
  • Science
  • Climate System
  • Knowledge
  • Human reflexive (volition)
  • Unknowns
  • Non-Scientists
  • Decisions
  • Knowledge
  • Scientists
  • communication (translation)
  • “To comprehend science as a responsible citizen…both content and 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
  • Content
  • Reasoning

Different Roles of Science in GCC Policy

  • Scientific Knowledge
  • Politicians
  • Policy
  • Pure scientist
  • Science arbiter
  • Issue advocate
  • Honest broker
  • opinions
  • Advocacy
  • Decision making
  • Policy makers
  • interpretation
  • Stakeholders ??
  • Roger Pielke Jr.

How does science work, anyway?

  • 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
  • 1. Observe and describe something of interest
  • Susan Haack

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

Some predicted impacts of climate change?

  • 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?
  • Direct
  • In-direct
  • Technological advances
  • Longer growing seasons
  • New agriculture/tourism opportunities.
  • More snow?
  • Reduced heating costs
  • Fewer hypothermia deaths

GCC heretics, infidels, skeptics, nay-sayers, cynics, deniers??

  • 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

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

Perceptions of Science

  • God-like? Elitist? Crusading knight? Mad/evil?

Two Opposing Metaphors for Science: God-like or Golem?

  • “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.
  • Truth

The Snowball Effect

  • Climate Science Uncertainties
  • “Other” Uncertainties

Cascading Uncertainties in Climate Science

  • Emission scenarios
  • Carbon cycle response
  • Global climate sensitivity
  • Regional climate change scenarios
  • Range of possible impacts
  • Adapted from Schneider 1983

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

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

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

The Climate Uncertainty “Toolbox”

  • Permutation tests
  • Bootstrapping
  • Resampling
  • Stochastic models
  • Monte Carlo
  • Jackknife
  • Deterministic models
  • Neural networks
  • Fisherian statistics
  • Climate models
  • Likelihood-based approaches
  • Scenario analysis

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

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)

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

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

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

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

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

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
  • 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
  • *

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

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

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.

An Interesting Expert Opinion: An Essay: Divergent American Reactions to Terrorism and Climate Change

  • 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.
  • 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:
  • “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.
  • An Interesting Expert Opinion: An Essay: Divergent American Reactions to Terrorism and Climate Change
  • Cass Sustein 2007: Columbia Law Review 107: 503-557

Epilogue

  • “Any philosophy that in its quest for certainty ignores the reality of the uncertain in the ongoing processes of nature, denies the conditions out of which it arises.”
  • John Dewey, The Quest for Certainty, 1929

And now, the punch line(s)……

  • Climate change uncertainties: tremendous outreach challenges
  • Uncertainties are cumulative: science to policy
  • Climate change predictions: probabilistic context where possible.
  • Scenarios: address insurmountable uncertainties.
  • Integrative natural and social science approach to decision-making.
  • Outreach: science mechanics, sources of uncertainty, restore faith in science, assess/understand heuristics, facilitate improved decision-making, craft a responsible, informative and useful message.


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