Climate Change Uncertainty and Risk: from Probabilistic … · 2016-12-19 · uncertainty vs response/impact uncertainty (RK, DB) Exercise 3 (toy model), preparation of presentation

Climate Change Uncertainty and Risk: from Probabilistic Forecasts to Economics of Climate AdaptationReto Knutti, IAC ETHDavid N. Bresch, Swiss ReAssistants: Maria Rugenstein, Martin Stolpe, Anina Gilgen

Reto Knutti / ETH Zürich | David Bresch / Swiss Re

Reto Knutti / IAC ETH Zurich | David Bresch / Swiss Re

Introduction and logistics

About ourselves… Your expectations…

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

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


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.


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


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 max. three people Written report on the exercise One short presentation

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

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


Schedule29.02.16(1) Logistics, Introduction to probability, uncertainty and risk

management, introduction of toy model (RK, DB)07.03.16 (2) Predictability of weather and climate, seasonal prediction, seamless

prediction (RK)Exercise 1 (toy model)

14.03.16 (3) Detection/attribution, forced changes, natural variability, signal/noise, ensembles (RK) Exercise 2 (toy model)

21.03.16(4) Probabilistic risk assessment model: from concept to concrete application - and some insurance basics (DB)

28.03.16Ostermontag (no course)04.04.16 (5) Model evaluation, multi model ensembles and structural error (RK)11.04.16 (6) Climate change and impacts, scenarios, use of scenarios, scenario

uncertainty vs response/impact uncertainty (RK, DB)Exercise 3 (toy model), preparation of presentation

18.04.16 (7) Model calibration, Bayesian methods for probabilistic projections (RK)25.04.16 (8) Presentations of toy model work, discussion (DB, RK)


Schedule02.05.16 (9) Basics of economic evaluation and economic decision making in the

presence of climate risk (DB)Exercise 4 (introduction to climada)

09.05.16 (10) The cost of adaptation - application of economic decision making to climate adaptation in developing and developed region (DB)Exercise 5 (impacts)

16.05.16Pfingstmontag (no course)23.05.16 (11) Shaping climate-resilient development – valuation of a basket of

adaptation options (DB)Exercises 6 (adaptation measures, preparation of presentation)

30.05.16 (12) Presentations of climada exercise, discussion (DB, RK)


Climate change

Climate change is real and largely man made Now what?

Global average surface warming (o C)

Source: IPCC AR5


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.


Global reasons for concerns

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


What about this?


How are those two connected?


Main points

Timescales and mitigation vs. adaptation Uncertainty Risk Value/purpose of models, predictions in a decision

making context Perception, framing, human dimension


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)


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

Which model should you believe?


Establishing confidence in a prediction

We cannot verify our prediction, but only test models indirectly. Which tests are most appropriate?

In 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.


Making a decision

Theory

Obser-vationsModels

42(Answer to the Ultimate Question of Life, the Universe, and Everything)


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 )


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)


Risk Risk concerns the expected value of one or more results of one

or more future events. Risk = Probability Severity

Risk is defined (e.g. ISO 31000) as the effect of uncertainty on objectives (whether positive or negative).

expected value


Risk ManagementIdentification/Awareness perception is based on a shared mental model that can be

conceptualizedQuantification From conceptual to quantitative model

Mitigation Explore/prioritize options (avoidance, reduction, prevention) through

quantificationTransfer costing of options: loss costs, cost of capital portfolio management diversificationMarket: price the option and trade etc.


Notes on perception – an illustration (static)

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


Notes on perception – an illustration (dynamic)

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

Hermann-Grid, figure by António Miguel de Campos, 2007


Notes on perception – a further illustration


Ideal model, de-constructed reality ...

photo by Roger Zenner, built by Shigeo Fukuda, 200x

Notes on perception – a further illustration


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).


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


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.


Notes on quantification and validity – reality

Reality To be more precise: Perceived reality


Notes on validity – model

Reality

Model


Notes on validity – proportions

Reality

Model

Society Environment

Legal FrameworkEconomy


Notes on validity – application

Unrealistic?

ModeledNot modeled

Reality Model: Abstraction

Described in Model

Model Reality : Interpretation (Verification/Falsification/Calibration)


Notes on validity – development

Unrealistic?

ModeledNot modeled


Described in Model


incrementalconceptional


Notes on validity – adaptation

Unrealistic?

ModeledNot modeled


Described in Model


incrementalconceptional

Cha

ngin

g re

ality

e.

g. c

limat

e ch

ange


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

Documents

Climate Change Uncertainty and Risk: from Probabilistic … · 2016-12-19 · uncertainty vs response/impact uncertainty (RK, DB) Exercise 3 (toy model), preparation of presentation