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Global Climate Change and Uncertainty. David B. MacNeill Fisheries Specialist NY Sea Grant Extension SUNY Oswego [email protected] Global Climate Change and Uncertainty. Apocalypse. Public perceptions. Kyoto. Heresy. Biodiversity. Al Gore. Greenhouse gases. Conspiracy. IPCC.

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Global climate change and uncertainty

Global Climate Change and Uncertainty

David B. MacNeill

Fisheries Specialist

NY Sea Grant Extension

SUNY Oswego

[email protected]


Global climate change and uncertainty1

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


This presentation

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!!


Global climate change and uncertainty

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%)


But the card deck changes unexpectedly

But, the card deck changes unexpectedly……

Death cards

Other cards

The Risk ?


Some climate change perspectives

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

Global Climate Model


Climate change complexity

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

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 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

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


Three arguments for climate change

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

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


Seeing is believing

Seeing is Believing?

Muir Glacier Alaska, August 2004. photo by B.F. Molnia

Muir Glacier Alaska, August 1940.photo by W.O. Field


An exaggerated view

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


Global climate change and uncertainty

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

Mac’s Uncertainty Concept Model

Stochastic (Surprises)

Climate System

Epistemic (Unknowns)

Scientists

Science

Knowledge

Knowledge

communication (translation)

Non-Scientists

Human reflexive(volition)

Decisions


Mac s uncertainty concept model1

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


Mac s uncertainty concept model2

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


Different roles of science in gcc policy

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.


How does science work anyway

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


Addressing uncertainties

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?

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


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

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


What is the matter with science the debate continues

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


Global climate change and uncertainty

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


Global climate change and uncertainty

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


Perceptions of science

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.

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

Truth


The snowball effect

The Snowball Effect

“Other” Uncertainties

Climate Science Uncertainties


Cascading uncertainties in climate science

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


Scientists face important challenges in communicating science to non scientists

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

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

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

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


Communicating uncertainties of climate change

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

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

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 forecasts1

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

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 uncertainty1

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 uncertainty2

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 uncertainty3

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


Global climate change and uncertainty

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 forecasts2

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

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

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.

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:


Global climate change and uncertainty

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.


Epilogue

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

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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