Transcript COP15 side event

Copernicus Institute
COP 15 side event “Adaptive management – merging top down and bottom up”
Holland Climate House, Bella Center, Copenhagen, 10 December 2009
Coping with uncertainty in
climate change adaptation
merging top down and bottom up approaches
Dr. Jeroen P. van der Sluijs
Copernicus Institute for Sustainable Development and Innovation
Utrecht University
Universiteit Utrecht
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Statistical uncertainty
PROBLEM:
Policy makers
seem to expect
that scientists
can calculate such
frequencies for
2050, 2100, etc.
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(Giorgi 2005)
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3 framings of uncertainty
(Van der Sluijs, 2006)
'deficit view'
• Uncertainty is provisional
• Reduce uncertainty, make ever more complex models
• Tools: quantification, Monte Carlo, Bayesian belief networks
'evidence evaluation view'
• Comparative evaluations of research results
• Tools: Scientific consensus building; multi disciplinary expert panels
• focus on robust findings
'complex systems view'
•
•
•
•
•
Uncertainty is intrinsic to complex systems: permanent
Uncertainty can be result of new ways of knowledge production
Acknowledge that not all uncertainties can be quantified
Openly deal with deeper dimensions of uncertainty
Tools: Knowledge Quality Assessment
“speaking truth to power” vs “working deliberatively within imperfections”
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Former chairman IPCC on objective to
reduce climate uncertainties:
• "We cannot be certain that this can be
achieved easily and we do know it will take
time. Since a fundamentally chaotic climate
system is predictable only to a certain degree,
our research achievements will always remain
uncertain. Exploring the significance and
characteristics of this uncertainty is a
fundamental challenge to the scientific
community." (Bolin, 1994)
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KNMI
2006
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Bron: Stern Review
NL Later: Sealevel rise till 2100
Worst case:
1,5 m/eeuw
Deltacommissie
65-130 cm
35-85 cm in 2100
Scenarios can be wrong
Statististical uncertainty precipitation
According to climateprediction.net
versus range KNMI scenarios
Winter
Summer 0.08
CP.net
G
G+
W
W+
0.12
Probability
0.1
0.08
0.06
G+
0.05
W
0.04
W+
0.03
0.02
0.02
0.01
0
-25
G
0.06
0.04
-50
CP.net
0.07
Probability
0.14
0
0
25
50
Precipitation change (%)
(Dessai & Van der Sluijs, 2007)
75
100
-100
-75
-50
-25
0
Precipitation change (% )
25
50
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Variability in a changing climate:
Small shift in mean = big change in frequency of extremes
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Adaptation under what uncertainty?
Planned adaptation
• to single scenario of anticipated climate impacts
no uncertainty
• to single scenario of anticipated climate impacts + to
variability
statistical uncertainty (without epistemic unc.)
• to range of scenario’s of anticipated climate impacts
(KNMI 2006 scenario’s)
scenario uncertainty
• to range of scenario’s of anticipated climate impacts
+ imaginable climate surprises (MNP Nederland
Later)
scenario uncertainty + recognized ignorance
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Decision-making frameworks
• Top down approaches
– Prevention Principle
– IPCC approach
– Risk approaches
• Bottom up approaches
–
–
–
–
–
–
Precautionary Principle
Engineering safety margin
Anticipating design
Resilience
Adaptive management
Human development
approaches
• Mixed approaches
– Adaptation Policy Framework
– Robust decision making
(figure: Dessai and Hulme 2004,
list: Dessai and Van der Sluijs, 2007)
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Risk approach (UK-CIP)
Eight stages decision framework:
1. Identify problem and objectives
2. Establish decision-making criteria
3. Assess risk
4. Identify options
5. Appraise options
6. Make decision
7. Implement decision
8. Monitor, evaluate and review.
“The risk assessment endpoints should
help
the decision-maker define levels of risk
(probabilities and consequences or
impacts)
that are acceptable, tolerable or
unacceptable”
Flexible characteristics:
cricular
Feedback and iteration
Stages 3, 4 and 5 are tiered. (identify, screen, prioritise and
evaluate before more detailed risk assessments and options
appraisals are required.)
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No regrets
• Favour adaptation strategies which will
yield benefits (for other, less uncertain,
policy concerns) regardless of whether
or not climate impacts will occur.
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“Flexible design”
Anticipating imaginable surprises
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Robustness exploration (Dessai, 2005)
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Problem: Dimensioning of water supply system
Additional water required (Ml/d) to maintain levels of service in
2030 under different demand scenarios as a function of regional
climate response uncertainty
AWS
-50
-25
0
Climate impacts
uncertainty (%)
25
25
20
15
10
5
0
-5
-10
-15
-20
-25
-30
25-50
50-75
-35
-40
-45
50
-50
Summer precipitation change (%)
-75--50
-50--25
-25-0
0-25
75-100
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Resilience
• If uncertainties about climate change
are large, one can still know how the
resilience of social-ecological systems
can be enhanced
• Resilience is the capacity of a system
to tolerate disturbance without
collapsing into a qualitatively different,
usually undesired, state
Principles:
•Homeostasis
•Omnivory
•High flux
•Flatness
•Buffering
•Redundancy
www.resalliance.org
Wardekker e.a. 2010 doi:10.1016/j.techfore.2009.11.005
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Wild Cards / Surprise scenarios
• not sufficientely known risks /
opportunities
• undermine current trends
• create new futures
• influence our thinking about past &
future
• give rise to new concepts / new
perceptions
www.steinmuller.de/media/pdf/WC_GFF.pdf
Examples for climate adaptation:
- Thermo Haline Circulation shut down
- Extreme low river run-off
- Long heatwaves and droughts
- Extreme storms
- Invasive species
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three types of wild cards
(1) extreme forms of expected trends,
(2) opposites of expected trends
(3) completely new issues (prepared for the
wrong impact)
Most options remain beneficial under type-1
wildcards.
Under type-2 wildcards, options that enhance
flexibility and responsiveness remain
beneficial
Few options protect against type-3 wildcards
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Synthesis
Statistical
uncertainty
decision making under uncertainty frameworks
Scenario Recognized
uncertainty ignorance
& surprises
IPCC approach
+
++
--
Risk approaches
++
+
--
Engineering safety margin
++

-
Anticipating design
++
+
+
Resilience

+
++
Adaptive management
++
-
--
Prevention Principle
++

--
Precautionary Principle
+
++
++
Human development approaches

+
+
Adaptation Policy Framework
+
+
+
Robust decision making
+
++
+
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Synthesis
Uncertainty assessment methods
Statistical
uncertainty
Scenario
uncertainty
Recognized
ignorance &
surprises
Scenario analysis ("surprise-free")

++
-
Expert elicitation
+
+
+
Sensitivity analysis
+


Monte Carlo
++
-
-
Probabilistic multi model ensemble
++

+
Bayesian methods
++
-

NUSAP / Pedigree analysis
+
+
++
Fuzzy sets / imprecise probabilities
+

+
Stakeholder involvement

+
+
Quality Assurance / Quality Checklists
+
+
++
Extended peer review (review by stakeholders)

+
++
Wild cards / surprise scenarios
-
+
++
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Synthesis
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Concluding remarks
• No ‘silver bullet’ for adaptation under
uncertainty
• Very context dependent
• Assess relative importance of:
– Statistical uncertainty: predict-then-act
– Scenario uncertainty: robustness
– Ignorance: resilience & flexibility
• Synthesis matrix provides preliminary
guidance for analysts
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Download 2007 rapport:
www.nusap.net/adaptation
Case studies 2008-2009:
- Delta committee
(water safety)
- Nature / Waddensea
- Health impacts
Team
- Arjan Wardekker MSc
- Arie de Jong MSc
- Petra Westerlaan
- Dr Pita Verweij
- Dr. Jeroen van der Sluijs