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The IPCC as
parliament of things
Dealing with uncertainty
and value commitments
in climate simulation
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
IBM Supercomputer
European Centre for Medium-Range Weather Forecasts
IPCC 2001: taking into account all
uncertainties (including model uncertainty):
largest part of warming is ‘likely’ due to
anthropogenic greenhouse gases
Warning:
take into account uncertainty
in climate simulation
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.”
13 March 2012 | Arthur Petersen
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
13 March 2012 | Arthur Petersen
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’)
13 March 2012 | Arthur Petersen
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.
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
Funtowicz and Ravetz, Science
for the Post Normal age,
Futures, 1993
13 March 2012 | Arthur Petersen
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
13 March 2012 | Arthur Petersen
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?
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
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
22
13 March 2012 | Arthur Petersen
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
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13 March 2012 | Arthur Petersen
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.
13 March 2012 | Arthur Petersen
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)
13 March 2012 | Arthur Petersen
“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?
13 March 2012 | Arthur Petersen
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?
13 March 2012 | Arthur Petersen
Eleven city marathon
Annual chance for an 'Elfstedentocht'
95% confidence limits (approx.)
0.4
2.5
0.3
3.3
4.0
5.0
0.2
6.7
10
0.1
Projections for 2050 for four scenarios:
once every 18, 29, 55 or 183 years
0.0
1900
1910
1920
1930
1940
1950
1960
1970
1980
20
1990
Year
13 March 2012 | Arthur Petersen
2000
2010
Average return period (years)
Chance for a marathon Et
0.5
What is the impact of weather extremes, how
can we adapt?
13 March 2012 | Arthur Petersen
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 governance
and for methodology to assess
policy options with different,
even conflicting, outcomes
 Need for indicators of
outcomes for evaluating
policy options relevant for
stakeholders and reliable
for scientists
13 March 2012 | Arthur Petersen
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
13 March 2012 | Arthur Petersen
Global climate models and regional embedded models
Global model
Regional model
13 March 2012 | Arthur Petersen
Different sources of uncertainty - Global
Source: E. Hawkins & R. Sutton, Bull. of Amer. Meteor. Soc., aug. 2009, 1097-1107
13 March 2012 | Arthur Petersen
Different sources of uncertainty - Regional
Source: E. Hawkins & R. Sutton, Bull. of Amer. Meteor. Soc., aug. 2009, 1097-1107
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
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)
Consideration of remaining uncertainty is based
on current methodologies.
12
13 March 2012 | Arthur Petersen
Example from the IPCC WG I 2007 (continued)
“Very likely” means a chance >90%. But what kind
of probability are we dealing with here?
likelihood using expert judgement, of an
“assessed likelihood,
of an outcome
or a result”
outcome
or a result”
Draft SPM
Final
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
13 March 2012 | Arthur Petersen
Importance of identifying high-confidence findings
13 March 2012 | Arthur Petersen
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
13 March 2012 | Arthur Petersen
Quite some risk for losing uncertainty information
13 March 2012 | Arthur Petersen
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
C1
C2
C3
C4
C5
C6
C7
Inaccurate referencing (3)
Insufficiently substantiated attribution (1)
Insufficiently founded generalization (2)
Insufficiently transparent expert judgment (10)
Inconsistency of messages (2)
Untraceable reference (3)
Unnecessary reliance on grey referencing (2)
Statement unavailable for review (1)
13 March 2012 | Arthur Petersen
Errors and shortcomings in AR4 WG II (8 chapt.)
Table SPM.2
Africa
Asia
Additional
Major
Minor
#
S
Major
Minor
#S
C3,C5,C7
E1b,C3
3
E1a,C4
E1b
3
1
C3,C6
E2
2
E2,C3
1
C1
C4,E1a
3
C3
1
E1a,C3(3),C4
5
1
E1a(2),E2,C5(2),
C6
6
E2,C3
2
C2,C3
Aust & NZ
Europe
L America
C2
N America
C3
1
Poles
Islands
Total
#E
Total
#C
C2(2),C3(2)
C5, C7
6
E1b, E2
2
2
E1a
1
E1a(4),E1b,
E2(3)
8
8
C3(4)
4
6
C3,C4,C6,C1
4
C3(3),C4,
C5(2),C6
8
13
13 March 2012 | Arthur Petersen
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
13 March 2012 | Arthur Petersen
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
 The IPCC acts as a Latourian “Parliament of Things” –
if only the actors would admit...
13 March 2012 | Arthur Petersen