1.
Uncertainty from above and encounters in the
middle
Workshop - Climate Change and Uncertainty from Above and Below
27-28 January, 2016
Conference Room 2, India International Centre, New Delhi
Suraje Dessai
University of Leeds
Centre for
Climate Change
Economics and Policy

2.
Climate change uncertainty from
Above and Below
Climate
adaptation
policy
World development
Global greenhouse gases
Global climate models
Regionalisation
Impacts
Vulnerability
(physical)
Vulnerability
(social)
Adaptive capacity
Indicators base on:
Technology
Economic resources
Information & skills
Infrastructure
Equity
Institutions
Past Present Future
Bottom-up approach
Top-down approach
Global
Local
Dessai, S. and M. Hulme (2004) Does climate adaptation policy need probabilities? Climate
Policy, 4, 107-128.

3.
Climate change uncertainty from
Above and Below
Climate
adaptation
policy
World development
Global greenhouse gases
Global climate models
Regionalisation
Impacts
Vulnerability
(physical)
Vulnerability
(social)
Adaptive capacity
Indicators base on:
Technology
Economic resources
Information & skills
Infrastructure
Equity
Institutions
Past Present Next season, year, decade and beyond
Bottom-up
approach
Top-down approach
Global
Local

4.
Advancing Knowledge Systems to
Inform Climate Adaptation Decisions
(2012-2017)
Research Domain 2
The social status of techno-scientific
knowledge in adaptation to climate change
Research Domain 1
Understanding climate
information needs across society
Methods:
• Documentary analysis of
official sources
• In-depth interviews (n=95)
with climate experts,
government officials, and
consultants
Joint work with Dr
James Porter

5.
UK Adaptation Context: Legislation

6.
UK Adaptation Context: Science
Met Office Hadley Centre – unified
model, Numerical Weather
Prediction and Climate Change
World-leading status with
international collaborations and
research substantially contributing
to the IPCC assessment reports
Small, centralised, network of UK
climate science (e.g. NERC)
Met Office Hadley Centre has a
strong commitment to serve policy
priorities
Climate Prediction Programme
(CPP), funded by Defra and DECC

7.
A chronology of UK climate scenarios
CCIRG91 CCIRG96
UKCIP98 UKCIP02
UKCP09
Hulme, M. and S. Dessai (2008) Negotiating future climates for public policy: a critical assessment of the
development of climate scenarios for the UK. Environmental Science & Policy, 11, 54-70

8.
UKCP09 projections
• First projections designed to
treat uncertainties explicitly
(Murphy et al. 2009)
• More informative but also more
complex than previous
scenarios (Murphy et al. 2009)
• Designed to inform adaptation
decisions – “usable science”
• Cost £11 million
• User Interface
• Reviewed by Steering and User
group and 5 experts

9.
Change in temperature
(c) UKCP09 outlines the
probability of different
amounts of change in
temperature
Probabilityofchange
Change in temperature
(b) Using many models gives a
range of different changes
in temperature but no
information on which to use
Change in temperature
(a) UKCIP02 gave a single
estimate of change in
temperature
(a) (b) (c)
UKCP09
On interpreting multi-model ensemble outputs:
‘They’re very useful, but they’re ad hoc in construction… They provide no basis to advise
users on whether a response “near the middle” should be considered more likely than one
“at the edge”, or if the actual response lies outside the multi-model range altogether’
(MOHC Climate Scientist 6 – Interview).

10.
(a) 10% probability that
change in temperature
is very likely to be
greater than this
(c) 90% probability
that temperature
change is very
likely to be less
than this
(b) 50% probability that
change in temperature,
also known as the
“central estimate”, will
likely be in this range
UKCP09 provided probabilities measuring how strongly different outcomes for climate
change were supported by evidence available at the time (models, observations,
understanding). Rise in temperature.
(a) (b) (c)

11.
Bayesian framework to handle
uncertainty
'from a methods point of view the goal just seemed right and it was something
that should be done.[What] really gives me confidence is the Bayesian
framework... we've put our own interpretation on it... but it's all written down in
the maths, it's there to debate... you can see it in black-and-white.It's just
good science' (Met Office Scientist 3, Interview).

12.
Change in temperature
(c) UKCP09 outlines the
probability of different
amounts of change in
temperature
Probabilityofchange
Change in temperature
(b) Using many models gives a
range of different changes
in temperature but no
information on which to use
Change in temperature
(a) UKCIP02 gave a single
estimate of change in
temperature
(a) (b) (c)
UKCP09
Changing relationship between climate
scientists and users (roles and
responsibilities)
Listening and responding to user
demands (higher spatial resolution
and quantification of certainty)
Complexity of the method has
restricteduptake and shifted
responsibility onto consultancies
Was the science stretched too far
(e.g. cascade of uncertainty,
user-demand)?

13.
‘it ends up pushing people towards complete rejection or more dangerously
complete acceptance. Imagine if we had a large number of intelligent numerate
users who embraced the probabilities, who learnt how to use them, and then
realised five years down the line that these are immature probabilities, that the
Andes are 1km too short, and we knew this back in 2009. Why would they trust us
again?’ (Academic Climate Scientist 7 – Interview).
'There was a feeling that you shouldn't be seen arguing about what we can or can't
do on climate change because that'll undermine the need for action. I was
sympathetic with that view when UKCP09 started but I'm much less so now. I think
the public needs to hear scientific disagreement, especially for things as serious as
climate change' (Academic Climate Scientist 5 - Interview).
Will users take responsibility?
Atmosphere for criticism?
Is scientific disagreement in public
necessarily bad? Especially with the
danger of UEA leaked emails used by
the anti-science lobby to cast doubt
Next wave of scepticism will come from
within the academy

14.
Is co-producing climate science and decision-
making a risk worth taking (for scientists)?
• UK government is committed to creating usable science for
adaptation decision-making
• But scientists have competing priorities
• If scientists respond too strongly to user demands they can
risk pushing science farther than it’s ready to go (displeasing
their peers)
• If scientists fail to respond strongly enough they can risk users
being unable to apply complex climate information.
• Creating usable science is not a neutral activity (Turnhout et
al 2016). Rather it’s the contested outcome of intense
political struggles over its meaning and application, where
new frictions, antagonism, and power concerns are often
introduced (Klenk & Meeham 2015).

15.
Encounters in the middle: robust
decision-making and the management of
deep uncertainty in climate change
adaptation

16.
Why is there uncertainty about
future climate?
Future
society
GHG
emissions
Climate
model
Regional
scenario
Impact
model
Local
impacts
Adaptation
responses
The envelope of uncertainty
Thecascadeofuncertainty
Wilby and Dessai (2010)

17.
Uncertain knowledge
Future
society
GHG
emissions
Climate
model
Regional
scenario
Impact
model
Local
impacts
Adaptation
responses
Envelope of uncertainty
Thecascadeofuncertainty
Adapted from Wilby and Dessai (2010)

18.
Robust decision-makingand
deep uncertainty
Robust Decision Making (RDM) is a family of decision
analytic methods developed specifically for decisions with
long-term consequences and deep uncertainty (Lempert et al.
2006)
Deep uncertainty is a situation in which analysts do not know
or cannot agree on (1) models that relate key forces that
shape the future, (2) probability distributions of key variables
and parameters in these models, and/or (3) the value of
alternative outcomes (Hallegatte et al. 2012)

19.
Vulnerability
(now)
Adaptation
options
A,B, C....
Preferred
measures
B,H, S, W
Vulnerability
(future)
Robust
measures
B,W
Adaptation
pathways
W then B
Observed climate
variability and
change
Observed
non-climatic
pressures
Climate change
narratives
Narratives of
non-climatic
pressures
Social acceptability
Technical feasibility
Economic appraisal
Regulatory context
Adaptation principles Sensitivity analysis
Performance appraisal New evidence
Monitoring
A Framework for
Robust Adaptation
Wilby, R. L. and S. Dessai
(2010). "Robust adaptation to
climate change." Weather
65(7): 180-185.
Dessai, S. and R. Wilby. “How
Can Developing Country
Decision Makers Incorporate
Uncertainty about Climate
Risks into Existing Planning
and Policymaking Processes?”
World Resources Report,
Washington DC.

20.
“Top-down” and“bottom-up”
Top-down scenario,impacts-first approach (left panel) and bottom-up vulnerability,
thresholds-first approach (right panel) – comparison of stages involved in identifying and
evaluating adaptation options under changing climate conditions (IPCC SREX, 2012).

21.
An example: Thames
Estuary 2100
Ranger et al. 2013

22.
Adaptation pathways and tipping
points
Haasnoot et al. 2013 Exploring pathways for sustainable water management in river
deltas in a changing environment. Climatic Change

23.
Applying RDM in the Cauvery River
Basin in Karnataka
90°0'0"E
90°0'0"E
80°0'0"E
80°0'0"E
70°0'0"E
70°0'0"E
30°0'0"N 30°0'0"N
20°0'0"N 20°0'0"N
10°0'0"N 10°0'0"N
80°0'0"E
80°0'0"E
78°0'0"E
78°0'0"E
76°0'0"E
76°0'0"E
14°0'0"N 14°0'0"N
12°0'0"N 12°0'0"N
10°0'0"N 10°0'0"N
8°0'0"N 8°0'0"N
• CRB-K (area: ~35960 sq.km) has a
unique combination of characteristics:
high groundwater extraction, rapidly
expanding cities (Bangalore, Mysore etc),
increasing costs for pumping water to
urban areas, falling water quality,
irrigation expansion and conflict with
riparian states
• Uncertain future socio-economic changes
– Urban expansion and increasing water
use
– Trade-off between increasing irrigation
efficiency and irrigation expansion
• Uncertain future climatic conditions
• What water management strategies are
robust to wide ranges of uncertainty by
the 2030s and 2050s?

24.
Initial reflections of applying RDM in
the middle/hybrid space
• RDM approaches require specific expertise from
analysts and a small number of stakeholders, thus
leaning towards the above perspective (perhaps
characterised as technocratic)
• Tensions between the above and below
perspectives, namely:
– expertise (scientists and elites versus the public)
– temporality (long term versus the now)
– representation (the powerful few versus the
disempowered many)