Climate Change Uncertainty and Risk: from …...David N. Bresch, Reto Knutti, ETH Zürich Schedule...

Climate Change Uncertainty and Risk: from Probabilistic Forecasts to Economics of Climate AdaptationDavid N. Bresch, IED ETHReto Knutti, IAC ETHAssistants: Anina Gilgen, Martin Stolpe, Kathrin Wehrli

David N. Bresch, Reto Knutti, ETH Zürich


Introduction and logistics

About ourselves…§ https://www.usys.ethz.ch/en/people/profile.html?persid=49820§ https://www.usys.ethz.ch/en/people/profile.html?persid=146272

www.iac.ethz.ch/edu/courses/master/modules/climate-risk.html

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Introduction and logistics

§ About the course:

The first part of the course covers methods to quantify uncertainty in detecting and attributing human influence on climate change and to generate probabilistic climate change projections on global to regional scales. Model evaluation, calibration and structural error are discussed. In the second part, quantification of risks associated with local climate impacts and the economics of different baskets of climate adaptation options are assessed –leading to informed decisions to optimally allocate resources. Such pre-emptive risk management allows evaluating a mix of prevention, preparation, response, recovery, and (financial) risk transfer actions, resulting in an optimal balance of public and private contributions to risk management, aiming at a more resilient society.

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What this course aims to provide

§ Different perspectives on the problem of understanding, quantifying and communicating probability, uncertainty and risk, and how to make decisions in their presence

§ Opportunities to think about a problem, rather than providing a recipe for a solution

§ Hands on experience with simple applications§ Perspective from outside the ivory tower§ Opportunities for discussion

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Credit points

§ Credits points are given for the two Matlab exercises§ Exercises in six weeks, two hours each, highly recommended but

not mandatory § Groups of two, max. three people§ Written report§ Short presentations

§ Details, slides, presentation topics and slides:www.iac.ethz.ch/edu/courses/master/modules/climate-risk.html

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Schedule (1/2)1) 20.2.2017 Logistics, Introduction to probability, uncertainty and risk

management2) 27.2.2017 Predictability of weather and climate, seasonal prediction, seamless

prediction (Reto via skype)Exercise 1 Toy Model

3) 6.3.2017 Detection/attribution, forced changes, natural variability, signal/noise, ensembles (Reto via skype)

4) 13.3.2017 Probabilistic risk assessment model: from concept to concrete application - and some insurance basicsExercise 2 Toy Model

5) 20.3.2017 Model evaluation, multi model ensembles and structural error combined with: Model calibration, Bayesian methods for probabilistic projections (Reto via skype)

6) 27.3.2017 Toy Model presentation (8-10h, thus no lecture)7) 3.4.2017 Climate change and impacts, scenarios, use of scenarios, scenario

uncertainty vs response/impact uncertaintyExercise 3 climada – intro step-by-step, damage calculation

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Schedule (2/2)8) 10.4.2017 Basics of economic evaluation and economic decision making in the

presence of climate riskExercise 4 climada – dealing with uncertainty and effect of insurance

17.4.2017 Easter Monday and no lectures this week9) 24.4.2017 The cost of adaptation - application of economic decision making to

climate adaptation in developing and developed regionExercise 5 climada – climate scenario, economic growth and adaptation

1.5.2017 Labor day10) 8.5.2017 Shaping climate-resilient development – valuation of a basket of

adaptation optionsExercise 6 climada – preparation of presentation

11) 15.5.2017 "Reflect on what we covered so far” - open questions and issuesPresentation preparation (facultative exercise hour)

12) 22.5.2017 climada presentations - look into open questions and issues13) 29.5.2017 climada presentations - plus course wrap-up

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Climate change

§ Climate change is real and largely man made§ Now what?

Global average surface warming (o C)

Source: IPCC AR5

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Mitigation or not?

(Meinshausen et al. 2009)

§ Stabilization at two degrees above preindustrial requires emissions to be at least halved by 2050 relative to 1990

§ In many other cases there are also choices between adaptation, mitigation, or both.

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Global reasons for concerns

(Figure: IPCC AR5 WG2, 2014, Assessment Box SPM.1 Figure 1)10


What about this?

A house near the flooded village of Moorland in Somerset.

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How are those two connected?

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§ bla

Irreversible climate change§ Both adaptation and mitigation cost

money, but on different timescales and those bearing the costs may not be the same.

§ Much of the warming, once realized, is irreversible for centuries.

§ Today‘s emissions will be a legacy for many centuries.

(IPCC 2007, Plattner et al. 2008, Solomon et al. 2009)13


A1B DJF Temperature change 2080-2099 minus 1980-1999 (K)

Which model should you believe?

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Establishing confidence in a prediction§ We cannot verify our prediction, but only

test models indirectly. Which tests are most appropriate?

§ In numerical weather prediction (NWP) probability/confidence can be established by repeated verification (frequentist interpretation).

§ Probability in climate change is a degree of belief in a Bayesian sense and is inherently subjective.

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Making a decision

Theory

Obser-vationsModels 42

(Answer to the Ultimate Question of Life, the

Universe, and Everything)

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Do we trust a model?§ “There is considerable confidence that climate models provide

credible quantitative estimates of future climate change, particularly at continental scales and above. This confidence comes from the foundation of the models in accepted physical principles and from their ability to reproduce observed features of current climate and past climate changes.” (IPCC AR4 FAQ 8.1)

§ “A vigorous Climate Prediction Project [ ] would ensure that the goal of accurate climate predictions at the regional scale could begin to aid the global society in coping with the consequences of climate change.” (http://wcrp.wmo.int/documents/WCRP_WorldModellingSummit_Jan2009.pdf )

§ “New models that exploit extreme scale computing could determine the future frequency, duration, intensity, and spatial distribution of droughts, deluges, heat waves, and tropical cyclones.” (http://www.sc.doe.gov/ober/ClimateReport.pdf )

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Do we trust a model?§ “All models are wrong, but some are useful.” (Box 1979).§ “Verification and validation of numerical models of natural systems is

impossible. This is because natural systems are never closed and because model results are always nonunique.” (Oreskes et al. 1994)

§ “…what these instances of fit [between their output and observational data] might confirm are not climate models themselves, but rather hypotheses about the adequacy of climate models for particularpurposes.“ (Parker 2009)

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[Weather and climate] RiskRisk is the combination of the probability [or likelihood] of a consequence and its magnitude:risk = probability x severity

or, to be more specific:risk = hazard x exposure x vulnerability

= (probability x intensity) x exposure x vulnerability

severityIllustration: IPCC, AR5

Hazard:

Exposure

Vulnerability

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Risk1 ManagementRisk identification: Shared mental model, the prerequisite for awareness§ perception is based on a shared mental model

à wider sharing builds awarenessRisk analysis: Quantification, the basis for decision-making§ Risk model: the quantitative expression of a shared mental model

à allows to assess risk mitigation optionsRisk mitigation: Prioritization based on metrics, options are to § Avoid§ reduce§ prevent§ transfer : Insurance puts a rice tag on risks à incentive for prevention§ or retain the risk 1 risk = probability severity

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Risk Management Cycleshared mental model

quantitative model

options:§ avoid§ reduce§ prevent§ transfer§ retain

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Notes on perception – an illustration (static)

All lines are straight ...Figure by Bernard Ladenthin, 2008

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Notes on perception – an illustration (dynamic)

There are no black dots (only large squares) ...

Hermann-Grid, figure by António Miguel de Campos, 200723


Notes on perception – a further illustration

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Notes on perception – probabilitiesRange of numerical probabilities that respondents attached to qualitative probability words in the absence of any specific context. Figure redrawn from Wallsten et al. (1986)

IPCC Word Probability range

Virtually certain >0.99

Very likely 0.9-0.99

Likely 0.66-0.9

Medium likelihood 0.33-0.66

Unlikely 0.1-0.33

Very unlikely 0.01-0.1

Exceptionally unlikely <0.01Mapping of probability words into quantitative subjective probability judgments, used by WG I and II of the Intergovernmental Panel on Climate Change Third Assessment (IPCC 2001a, b) based on recommendations developed by Moss and Schneider (2000).

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Notes on perception – heuristic of availability

If respondents made perfect estimates, the results would lie along the diagonal.

Figure redrawn from Lichtenstein et al. (1978)

or: cognitive bias

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Notes on perception – framing

Time series of reported experimental values for the speed of light over the period from the mid-1800’s to the present (black points). Recommended values are shown in gray.

For details, see Henrion and Fischhoff (1986) from which this figure has been redrawn.

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Notes on quantification and validity – reality

Reality To be more precise: Perceived reality

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Notes on validity – model

Reality Model

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Notes on validity – proportions

Reality Model

Society Environment

Legal FrameworkEconomy

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Notes on validity – application

Unrealistic?

ModeledNot modeled

Reality à Model: AbstractionDescribed in Model

Model à Reality : Interpretation (Verification/Falsification/Calibration)

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Notes on validity – development

Unrealistic?

ModeledNot modeled



incrementalconceptional

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Notes on validity – adaptation

Unrealistic?

ModeledNot modeled



incrementalconceptional

Cha

ngin

g re

ality

àe.

g. c

limat

e ch

ange

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Note on decision strategies

In the face of high levels of uncertainty, which may not be readily resolved through research, decision makers are best advised to not adopt a decision strategy in which (a) nothing is done until research resolves all key uncertainties, but rather (b) to adopt an iterative and adaptive strategy.

(a) (b)

Source: UKCIP

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Brennpunkt KlimaSchweiz

goo.gl/EAUlJQ

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Climate Change Uncertainty and Risk: from …...David N. Bresch, Reto Knutti, ETH Zürich Schedule...

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