climate change uncertainty and risk: from …...david n. bresch, reto knutti, eth zürich schedule...
TRANSCRIPT
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
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
2
David N. Bresch, Reto Knutti, ETH Zürich
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.
3
David N. Bresch, Reto Knutti, ETH Zürich
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
4
David N. Bresch, Reto Knutti, ETH Zürich
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
5
David N. Bresch, Reto Knutti, ETH Zürich
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
6
David N. Bresch, Reto Knutti, ETH Zürich
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
7
David N. Bresch, Reto Knutti, ETH Zürich
Climate change
§ Climate change is real and largely man made§ Now what?
Global average surface warming (o C)
Source: IPCC AR5
8
David N. Bresch, Reto Knutti, ETH Zürich
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.
9
David N. Bresch, Reto Knutti, ETH Zürich
Global reasons for concerns
(Figure: IPCC AR5 WG2, 2014, Assessment Box SPM.1 Figure 1)10
David N. Bresch, Reto Knutti, ETH Zürich
What about this?
A house near the flooded village of Moorland in Somerset.
11
David N. Bresch, Reto Knutti, ETH Zürich
How are those two connected?
12
David N. Bresch, Reto Knutti, ETH Zürich
§ 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
David N. Bresch, Reto Knutti, ETH Zürich
A1B DJF Temperature change 2080-2099 minus 1980-1999 (K)
Which model should you believe?
14
David N. Bresch, Reto Knutti, ETH Zürich
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.
15
David N. Bresch, Reto Knutti, ETH Zürich
Making a decision
Theory
Obser-vationsModels 42
(Answer to the Ultimate Question of Life, the
Universe, and Everything)
16
David N. Bresch, Reto Knutti, ETH Zürich
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 )
17
David N. Bresch, Reto Knutti, ETH Zürich
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)
18
David N. Bresch, Reto Knutti, ETH Zürich
[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
19
David N. Bresch, Reto Knutti, ETH Zürich
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
20
David N. Bresch, Reto Knutti, ETH Zürich
Risk Management Cycleshared mental model
quantitative model
options:§ avoid§ reduce§ prevent§ transfer§ retain
21
David N. Bresch, Reto Knutti, ETH Zürich
Notes on perception – an illustration (static)
All lines are straight ...Figure by Bernard Ladenthin, 2008
22
David N. Bresch, Reto Knutti, ETH Zürich
Notes on perception – an illustration (dynamic)
There are no black dots (only large squares) ...
Hermann-Grid, figure by António Miguel de Campos, 200723
David N. Bresch, Reto Knutti, ETH Zürich
Notes on perception – a further illustration
24
David N. Bresch, Reto Knutti, ETH Zürich
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).
25
David N. Bresch, Reto Knutti, ETH Zürich
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
26
David N. Bresch, Reto Knutti, ETH Zürich
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.
27
David N. Bresch, Reto Knutti, ETH Zürich
Notes on quantification and validity – reality
Reality To be more precise: Perceived reality
28
David N. Bresch, Reto Knutti, ETH Zürich
Notes on validity – model
Reality Model
29
David N. Bresch, Reto Knutti, ETH Zürich
Notes on validity – proportions
Reality Model
Society Environment
Legal FrameworkEconomy
30
David N. Bresch, Reto Knutti, ETH Zürich
Notes on validity – application
Unrealistic?
ModeledNot modeled
Reality à Model: AbstractionDescribed in Model
Model à Reality : Interpretation (Verification/Falsification/Calibration)
31
David N. Bresch, Reto Knutti, ETH Zürich
Notes on validity – development
Unrealistic?
ModeledNot modeled
Reality à Model: AbstractionDescribed in Model
Model à Reality : Interpretation (Verification/Falsification/Calibration)
incrementalconceptional
32
David N. Bresch, Reto Knutti, ETH Zürich
Notes on validity – adaptation
Unrealistic?
ModeledNot modeled
Reality à Model: AbstractionDescribed in Model
Model à Reality : Interpretation (Verification/Falsification/Calibration)
incrementalconceptional
Cha
ngin
g re
ality
àe.
g. c
limat
e ch
ange
33
David N. Bresch, Reto Knutti, ETH Zürich
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
34
David N. Bresch, Reto Knutti, ETH Zürich
Brennpunkt KlimaSchweiz
goo.gl/EAUlJQ
35