Using climate model simulations to guide
insurance decisions in a changing climate
Dr Joseph Daron
Climate System Analysis Group, University of Cape Town
Energy Efficiency Forum, August 31st 2012
Old Mutual, Pinelands, Cape Town
Informing adaptation
cip.csag.uct.ac.za
Key message
We cannot solely rely on information about the past to
inform our understanding of climate risks in the present,
let alone the future.
Pricing climate risk
In order to enable risk-based pricing, insurers typically refer to historical
loss data, hazard data and use empirical relationships linking hazards with
insured losses.
future climate
Establishing current
climaterisk:
risk:
Today’s
climate ≠≠ average
averagestatistics
statisticsof
ofapast
weather
Future climate
single
model simulation
“From a < 1 in 1000
year event to a 1 in
4 year event”
The ergodic assumption
Ergodic theory stems from the study dynamical systems and relates to
the concept of an “invariant measure”. An ergodic system has the
property that a time average of a system trajectory is equal to the space
average over the system “state space”.
J. C. Sprott. Chaos and Time-Series Analysis, Oxford University Press, 2003
The ergodic assumption
Frequency
1 in 10 year
return period
median
X
past
Present
Many world’s ensemble
Frequency
If these two frequency
distributions are equal,
then the ergodic
assumption can be
considered valid.
The past represents one of a
number of many possible
trajectories.
X
t0
t1
t2
t3
....
Present
Use of climate model simulations in insurance
Can we use climate model output to:
1.
2.
Improve loss estimates and avoid crude approximations to premium loading as a
result of ambiguity and uncertainty?
Assess the viability of insurance under different future climate change scenarios?
Answer = Perhaps...
...but
Using single model runs means that we can’t know the climates of our models exactly;
we rely on the ergodic assumption.
Acknowledging the realities of climate modelling
Uncertainty
Tradeoffs
Complexity
Resolution
Use of climate model forecasts in insurance
Case study focussing on weather index insurance:
The use of Bayesian Networks to combine multiple sources of climate
information to guide the pricing of rice crop microinsurance in Kolhapur,
India.
India core monsoon region (green) and the location
of Kolhapur (black circle) – IITM, 2011
Input
Data
Observed and model precipitation data for July
Payout profile
Payout amounts
Loss
Payout (% of total)
None
0
Tier 1
15
Tier 2
40
Tier 3
100
Bayesian Networks (BNs)
Invokes Bayes theorem of conditional probabilities
Gridded rainfall from
APHRODITE project
Expected loss (%)
±σ
BN using observations only
Combining multiple sources of climate information
Gridded rainfall from
APHRODITE project
HadRM3 regional model driven by 2 GCMs
From Highnoon Project
The Bayesian Network can be used to test
sensitivities to climate assumptions.
Illustrative data
BNs should be used as a ‘tool for
thinking’ (Cain, 2001).
Insurers’ current attitudes to
climate change risk
Summary
1.
There is a need to adapt in a changing climate. (Re)insurers have a
role in aiding adaptation but they must also adapt their business
practices to better communicate the changing risks.
2.
Past observations are a useful source of information but cannot
provide sufficient information to reliably quantify climate risks
under climate change.
3.
Climate model simulations can, and are, being used by (re)insurers
but they need to acknowledge the limitations in the current
generation of models and interpret the output appropriately.
Thanks for listening
jdaron@csag.uct.ac.za
www.csag.uct.ac.za
Daron, J. (2012) Examining the decision-relevance of climate model information
for the insurance industry. PhD thesis, London School of Economics and Political
Science, available at http://etheses.lse.ac.uk/380/
Daron, J. and D. A. Stainforth, A method to assess the viability of weather index
insurance under climate change. (In preparation - to be submitted to Risk Analysis)
Daron, J. and D. A. Stainforth, On quantifying modelled climate under climate
change. (In preparation)
How should we interpret the past
climate in the context of risk?
• The past represents one of a number of many possible trajectories.
• The presence of climate variability means that the climate risk at
any instant in time is rarely, if ever, equal to the distribution of past
weather.
• In forecasting risk for the next year, seasonal forecasts can be a
useful source of information.
• In forecasting evolving climate risk under climate change, climate
model simulations have the potential to provide useful and relevant
information. ...
.... but interpretation of climate model output warrants caution.
Combining multiple sources of climate information
The Bayesian Network can be used to test sensitivities to climate
assumptions.
Time period
Information Source
Past
Observations
11.7
27
Seasonal Forecast
9.5
18
HadCM3 driven Hindcast
10.4
22
ECHAM5 driven Hindcast
88.5
29
HadCM3 driven forecast
7.76
20
ECHAM5 driven forecast
87.7
31
2020 to 2049
Expected Loss
(%)
Std Dev (%)
BNs should be used as a ‘tool for thinking’ (Cain, 2001).
Kolhapur district has a population of 3.5
million.
�� ������ ��� ������
• The district has a total land area of 768,500
hectares, of which 104,000 (13.5%) are devoted
to rice production.
•
•
Rice is a Kharif crop so the main sowing season
is in the summer monsoon (June to
September).��������
�������
Data correct for 2001, available from the Collector Office, Kolhapur (2009)
Insurance Planning Time Horizons
Strategic general insurance issues sensitive to climate risk
(Figure 6.1: PhD Thesis, Daron 2011)
What is the role for climate models?
• In the absence of reliable model projections, perhaps we should
continue to calculate risk and estimate insurance losses using
observations only.
• In the absence of reliable and/or useful observations, perhaps we
could improve loss estimates and assessment of climate risk by
working to develop more reliable and more informative model
projections.
Model projections can (and perhaps should) be used to inform
insurance decisions and/or help determine the level of capital and
reinsurance required to support insurance products.
Combining multiple sources of climate information
Weights can be
adjusted to test
BN sensitivities
Expected loss increases to 18.4%
Are BNs the way forward?
Cain (2001)
“A BN should be used as a ‘tool for thinking’, not as an automatic
answer provider.”
Daron and Stainforth (to be submitted)
“BNs can be tailored to the needs and interests of index insurers,
excluding irrelevant and superfluous information.”
“The absence of reliable quantitative model projection data means
that choices considered optimal based on the BN output may lead to
maladaptation and risk significant losses to insurers.”
...finally, a word of caution
Are these commonly held assumptions circulating in the
index insurance community valid?
1.
“Any climate change-induced increase in weather variability is
expected to occur gradually, so concerns over climate change
are not sufficient to rationalize dramatic year-to-year changes
in index insurance premiums”.
Skees et al. (2011) [emphasis added]
2. “Contracts are typically drawn up for a single season or a few
years at most, so they can be adapted as climate change takes
hold.”
Hellmuth et al. (2009)
Current generation of climate
model output
http://www.earth.columbia.edu/news/2003/story10-09-03.html
Primary research questions
• What are the climate model information needs and
desires to guide strategic decision-making within the
insurance industry?
• Is the current generation of climate model experiments capable
of providing such information?
• How can we improve the design of climate model
experiments to provide more relevant information to
guide insurance and adaptation decisions?
Climate uncertainty...is different
We have inherent irreducible uncertainty, so even armed with a perfect
“Climate
is what
youis
“Given
perfect
model,
future
climate
what
you expect
to expect”
model,
weahave
to consider
probabilities.
Inexpect”
the imperfect
model,
the
probability distribution is conditional on the model assumptions.
Probability
μ future
μ
Model uncertainty
Temperature
probability
Natural internal variability
probability
10th
percentile
+
possible future climate
μ change
90th
percentile
90th percentile change
A “useful” definition of
climate under climate change
Temporal averages of meteorological variables do not provide sufficient
information to guide insurance decisions (and many adaptation decisions).
Rather, to aid insurance decisions in general and index insurance decisions in
particular, climate can be more suitably defined as:
“The distribution of states consistent with the system’s forcings, conditioned on
the uncertainty in the initial state of the system.”
Main message from PhD research:
In order to provide robust and relevant data to inform adaptation decisions in
general and insurance decisions in particular, climate model experiments ought
to be designed and run using large initial condition ensembles.
Daron, J. and D. A. Stainforth, On modelled climate under climate change. (In preparation)
Tidal gauge date for
Simon’s Town
Available from Permanent
Service for Mean Sea Level:
www.psmsl.org