The IPCC as parliament of things Dealing with uncertainty and value commitments in climate...

The IPCC as parliament of things

Dealing with uncertainty and value commitments in climate simulation

13 March 2012 | Arthur Petersen

IBM SupercomputerEuropean Centre for Medium-Range Weather Forecasts

Warning:take into account uncertainty

in climate simulation

IPCC 2001: taking into account all uncertainties (including model uncertainty):

largest part of warming is ‘likely’ due to anthropogenic greenhouse gases

IPCC 2007: taking into account all uncertainties (including model uncertainty): largest part of warming is ‘very likely’ due

to anthropogenic greenhouse gases

de Kwaadsteniet versus van Egmond

de Kwaadsteniet:“Computer simulations are seductive due to their perceived speed, clarity and consistency. However, simulation models are not rigorously compared with data.”

van Egmond:“Policy makers are confronted with incomplete knowledge; task of scientific advisers to report on the current state of knowledge, including uncertainties. Simulation models are indispensable.”


Simulation in scientific practice

Definition of computer simulation:“mathematical model that is implemented on a computer and imitates real-world processes”

Functions of simulation:

– technique (to investigate detailed dynamics)

– heuristic tool (to develop hypotheses, theories)

– substitute for an experiment

– tool for experimentalists

– pedagogical tool


Central activities in simulation practice

Formulating the mathematical model(conceptual and mathematical model: ‘ideas’)

Preparing the model inputs(model inputs: ‘marks’)

Implementing and running the model(technical model implementation: ‘things’)

Processing the data and interpreting them(processed output data: ‘marks’)


Four claims re. climate simulation

1. Different models give conflicting descriptions of the climate system.

2. There exists no unequivocal methodology for climate simulation.

3. The assumptions in climate simulations are value-laden.

4. Pluralism in climate modelling is an essential requirement both for ‘good’ science and for ‘appropriate’ science advising.


Funtowicz and Ravetz, Science for the Post Normal age, Futures, 1993


The challenge of post-normal science

Expert advisers should be reflexive

Methods for dealing with uncertainty should merely be considered as tools, not as the solutions

Fear for paralysis in policy making should not be allowed to block communication about uncertainty

Communication with a wider audience about uncertainties is crucial


Shifting notions of reliability

Statistical reliability (expressed in terms of probability)– How do you statistically assess climate predictions?

Methodological reliability (expressed qualitatively in terms of weak/strong points)– How do you determine the methodological quality of the different

elements in simulation practice, given the purpose of the model?

Public reliability (expressed in terms of public trust)– What determines public trust in modellers?


2222

Lesson learnt in uncertainty communication (I)

1. Conditional character of probabilistic projections requires being clear on assumptions and potential consequences (e.g. robustness, things left out)

2. Room for further development in probabilistic uncertainty projections: how to deal decently with model ensembles, accounting for model discrepancies

3. There is a role to be played for knowledge quality assessment, as complementary to more quantitative uncertainty assessment


2323

Lessons learnt in uncertainty communication (II)

4. Recognizing ignorance often more important than characterizing statistical uncertainty

5. Communicate uncertainty in terms of societal/political risks


A case of deep uncertainty: adaptation to changes in extreme weather in the Netherlands

Extreme weather events are predominantly associated with the risk of flooding, which is generally considered a government responsibility.

However, future projections for the Netherlands provide a picture which is somewhat more complex. The changes require awareness among society at large.

Yet, individuals and economic sectors have already dealt with the weather for ages and have developed knowledge and behavioural responses with respect to weather extremes.


Notions from the policy sciences and social psychology

Articulation(views with respect to extreme weather are not always coherent)

Information(access to information shapes articulation)

Differentiation(different perspectives lead to different assessments of risk and potentials)

Learning by interaction(stakeholders and scientists can learn from interacting, by which preferences for, possibly new, policy options evolve)


“Two cold winters don't deny global warming” Dutch winter 2009-2010

coldest since 1996

Questions one may ask:– How 'extreme' was this?– Will this happen less (or

more...) often in the future?– Does this fit in the 'Global

Warming'-picture?– How to optimally adapt to

changes in extremes?


Eleven city marathon

Marathon has been organized 15 times in the period 1901-2008, in the province of Friesland

How has the chance for holding a marathon changed over the past century?

How will it change in the future?


Eleven city marathon

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

0.0

0.1

0.2

0.3

0.4

0.5

Cha

nce

for

a m

arat

hon

Et

4.0

6.7

3.3

Ave

rage

ret

urn

peri

od (

year

s)

Annual chance for an 'Elfstedentocht'95% confidence limits (approx.)

20

10

5.0

2.5

Projections for 2050 for four scenarios: once every 18, 29, 55 or 183 years


What is the impact of weather extremes, how can we adapt?


Bridging the gap between science and policy Uncertainties with respect to climate change and extreme

weather events; knowledge about future is based on models

Need for adaptive governanceand for methodology to assesspolicy options with different,even conflicting, outcomes

Need for indicators ofoutcomes for evaluatingpolicy options relevant forstakeholders and reliablefor scientists


Bridging the gap: selected approach

Two postdoctoral studies: Social scientist: engaging with the stakeholders; analysing

the process; co-developing adaptation options Statistician/climate scientist: studying the uncertainty range

of climate projections and decadal predictions of weather extremes; co-developing indicators

Team of political scientists,statisticians, climate modellers,social scientists


Global climate models and regional embedded models

Regional model

Global model


Different sources of uncertainty - Global

Source: E. Hawkins & R. Sutton, Bull. of Amer. Meteor. Soc., aug. 2009, 1097-1107


Different sources of uncertainty - Regional

Source: E. Hawkins & R. Sutton, Bull. of Amer. Meteor. Soc., aug. 2009, 1097-1107


Example from the Intergovernmental Panel on Climate Change WG I (2007)

“Most of the observed increase in globally averaged temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations12.” (SPM)

12 Consideration of remaining uncertainty is based on current methodologies.


Example from the IPCC WG I 2007 (continued)

“Very likely” means a chance >90%. But what kind of probability are we dealing with here?

“assessed likelihoodof an outcome or a result”

Draft SPMFinal SPM

“assessed likelihood, using expert judgement, of an outcome or a result”


Importance of identifying high-confidence findings


Process: Openness, peer review, supervision

Openness: PBL registration website for possible errors– 40 reactions in total; 3 of which relevant for our investigation

Draw on IPCC authors to give feedback

Internal and external peer review

Independent supervision by KNAW Royal Netherlands Academy of Arts and Sciences


Quite some risk for losing uncertainty information


What can go wrong? E1 Inaccurate statement

– E1a Errors that can be corrected by an erratum (5)– E1b Errors that require a redoing of the assessment of the issue

at hand (2) E2 Inaccurate referencing (3) C1 Insufficiently substantiated attribution (1) C2 Insufficiently founded generalization (2) C3 Insufficiently transparent expert judgment (10) C4 Inconsistency of messages (2) C5 Untraceable reference (3) C6 Unnecessary reliance on grey referencing (2) C7 Statement unavailable for review (1)


Errors and shortcomings in AR4 WG II (8 chapt.)Table SPM.2 Additional

Major Minor#S Major Minor #S

Africa C3,C5,C7 E1b,C3 3 E1a,C4 E1b 3

Asia C2,C3 1 C3,C6 E2 2

Aust & NZ E2,C3 1 C1 C4,E1a 3

Europe C3 1 E1a,C3(3),C4 5

L America C2 1E1a(2),E2,C5(2),C

6 6

N America C3 1

Poles

Islands E2,C3 2

Total #E E1b, E2 2 2 E1a 1 E1a(4),E1b,E2(3)

8 8

Total #C C2(2),C3(2) C5, C7

6 C3(4) 4 6 C3,C4,C6,C1 4 C3(3),C4,C5(2),C6

8 13


The IPCC: science or politics?

Assessments are social constructs that contain both scientific and political elements

Successful? Depends on ability to connect to climate science and policy

Generally voiced criticism: IPCC not open enough to skeptics


The IPCC: science or politics? (II)

Practice: procedures ensure inclusivity; skeptics do have influence; reflexivity on dissensus is moderate (neither low nor high)

Not: “scientific consensus”. But: “policy-relevant assessment acknowledging uncertainty”

Still, the communication of uncertainty can be further improved

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