Uncertainty, climate
scenarios and adaptation
Suraje Dessai
Tyndall Centre for Climate Change Research, UK and
School of Environmental Sciences
University of East Anglia, Norwich, UK
CLIMATE CHANGE KIOSK EVENT CALENDAR
GLOBAL CLIMATE SCIENCE
Uncertainty in climate change
SOCIETY/
ECONOMY
EMISSIONS
CONCEN- RADIATIVE
TRATIONS FORCING
CLIMATE
SEA-LEVEL
IMPACTS
[Source: Carter, 2000]
Why might we need probabilities of climate change?
• To assess the seriousness of impacts we need to know how
likely they are to occur (Schneider, 2001, 2002).
• Probabilities represent uncertainty explicitly and thus better
fit a risk assessment framework: “the reason for
quantifying risk it to make coherent risk management
decisions under uncertainties and within resource
constrains” (Pate-Cornell 1996); this allows decisionmakers to hedge the risk of climate change
• Several communities (water resource managers and
engineers) demand it!
• The central role played by prediction in guiding
decision-making.
What are the problems in estimating probabilities?
Knowledge
about
outcomes
• Probabilities only one method to
represent uncertainties
• Confusion about probabilities, risk and
uncertainty (various definitions and
typologies) Incomplete vs unknowable knowledge
Outcomes
poorly
defined
Stirling (1998)
no
ra
Un
ce
rt a
Set of
discrete
outcomes
Continuum
of
outcomes
Ig
Fuzzy
logic
in
Subjective ty
probabilities
Ri
sk
Frequentist
probabilities
Firm basis
for
probabilities
Shaky basis
for
probabilities
nc
e
Scenario
analysis
No basis for
probabilities
Epistemic vs stochastic uncertainty
Subjective vs aleatory uncertainty
Type B vs type A uncertainty
Reducible vs irreducible uncertainty
…
Knowledge about
likelihoods
Different types of uncertainty in the context of climate change
Type of knowledge
Type
uncertainty
of
Possible to
probabilities
represent
with
Incomplete
Epistemic
Yes, but limited by knowledge
IncompleteUnknowable
Natural stochastic
Yes, but with limits
Unknowable
Human reflexive
No, scenarios required
Cumulative distribution function
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
1
2
3
4
5
6
7
8
Climate sensitivity (K)
Uniform Forest et al. (2002)
Gregory et al. (2002)
Expert Wigley & Raper (2001)
Knutti et al. (2002)
IPCC range (1990-2001)
Expert Forest et al. (2002)
Andronova & Schlesinger (2001)
IPCC TAR GCMs (2001)
Tol & de Vos (1998)
9
10
Emission scenarios
Global climate sensitivity
Regional climate change
Local climate change
0.25
0.06
SRES A1
SRES A1
2030
SRES A2
0.05
SRES A2
0.2
SRES B1
SRES B1
SRES B2
0.04
SRES B2
IPCC TAR (2001)
0.15
p 0.03
p
0.1
2070
0.02
2100
0.05
0.01
0
0
1
2
3
4
5
6
Global mean temperature change (ºC)
7
8
0
0
1
2
3
4
5
Global mean temperature change (ºC)
6
7
8
Mean precipitation change for Southeast Asia in 2100 under SRESA2-ASF
1
0.9
0.8
0.7
0.6
PREC_djf
0.5
PREC_jja
0.4
0.3
0.2
0.1
0
-50
-40
-30
-20
-10
0
10
20
30
40
50
Sensitivity of adaptation
decision-making
Adaptation decisions
Probability
A – Alternative supply required
B – Build new storage
C – Operations management changes
D – No changes required
E – Operations management changes
F – Develop small infrastructure
G – Develop big infrastructure
Climate variable
A B
C
D
Decisions
E
F
G
Sensitivity of adaptation
decision-making
Adaptation decisions
Probability
A – Alternative supply required
B – Build new storage
C – Operations management changes
D – No changes required
E – Operations management changes
F – Develop small infrastructure
G – Develop big infrastructure
Climate variable
A B
C
D
Decisions
E
F
G
Further reading
Dessai and Hulme (2003) Does climate policy
need probabilities? Tyndall Working Paper
34
http://www.tyndall.ac.uk/publications/workin
g_papers/wp34.pdf