Download Modelling Short Term Impacts of Climate Change

Modelling Short Term
Impacts of Climate Change
Dr. Claire Souch
Cat Models Used in Re/Insurance Industry
for 20+ years
• Probabilistic distribution of extreme events –> impact on physical assets ->
economic and insured losses of the damage
• Pricing the cat component of premiums
• Accumulation and portfolio management
• Solvency calculations
• Internal Capital Model
• Rating Agency Capital Model
• Enterprise-wide Risk Management
• Emerging Risk Scanning
Timeframe of concern 1 - 10 years
2016
Output of Cat Models
• Describes the probability that various levels of loss will be exceeded
• Return period - annual probability of a loss being exceeded
• 1/250 = 0.4% = probability of losses exceeding $634bn in any given year is 0.4%
• Average Annual Loss (AAL)
• Expected loss per year, averaged over many years
Return Period
2011 AEP
25
145,742,224,004
150,469,597,283
50
249,720,510,504
258,018,076,190
100
364,549,856,003
376,458,553,934
250
613,981,697,858
634,895,307,835
500
807,635,079,164
834,609,543,707
1,000
984,178,046,528
1,015,205,319,660
AAL
29,140,651,630.99
30,094,441,096.02
SD
87,253,772,713.26
90,106,664,826.95
2.99
2.99
COV
2016
2010 AEP
Anatomy of Cat Model
Exposure
Define Stochastic
Events
Calculate Hazard
for each event
Calculate
Damage
Event Set
Hazard
Vulnerability
2016
Quantify
Financial Loss
Financial Module
Model Data Inputs
• Historical data, e.g.
• Weather charts – back to the late 1800s
• Windspeed data – back to the 1960s
• Re-analysis data
• e.g. European Center Medium Range Weather Forecasting (ECMWF) ERA40 and ERAInterim
• “best guess” snapshot of the world’s weather at six-hourly intervals since 1957
• Numerical models - mathematical models of the atmosphere
• Global Climate Models (GCMs) - simulate global weather patterns
• Numerical weather prediction (NWP) models – forecasting and simulating smaller scale
patterns
• Statistical modelling
• Based on storm parameters gathered from historical data
• Surface roughness, Land Use/Land Cover, Topography
• Insurance claims data and post-event building damage surveys
2016
We care about all forms of “climate change”
Seasonal
ENSO
Multi-decadal
AMO
Long-Term
Global Warming
Natural cycles & trends
+
Anthropogenic influences
2016
The Case of Hurricanes
• Hurricane activity has gone through cycles in the past
• Clearest trend is for Category 3+ hurricanes:
2016
The Case of Hurricanes
• Correlation with Atlantic Multi-decadal oscillation
• Causes cycles in sea-surface temperatures
AMO correlation to Atlantic hurricane activity, 1949-2013. (Source: AIR)
2016
The Case of Hurricanes
• ENSO also has an impact on hurricane activity
• El Nino increases wind shear, La Nina decreases wind shear
• 2015 = strong El Nino: slightly below long term average (4 hurricanes) despite
above average SSTs
2016
Impact of Climate Change on Extreme
Events …??
2016
Extremes
Observed
Hot days frequency and Very likely 
magnitude increasing
Projections
Virtually certain 
Cold days frequency and Very likely 
magnitude
Virtually certain 
Heavy precipitation
extremes
Likely more regions with

Likely  in many regions.
Very likely in mid-latitudes
and tropics
Droughts
Medium confidence in
some regional trends

Medium confidence in  in
some regions. Likely  in
some currently dry regions.
Storms
Low confidence
Low confidence in deailed
regional projections
Floods
Low confidence (because Low confidence in regional
of human and vegetation projections. Medium
water use)
confidence related to  in
heavy percipitation events
Tropical Cyclones
Low confidence
More likely than not  in
intensity in some basins
IPCC SREX 2012
Reproduced from S. Seneviratne, SCOR Foundation
Seminar on Climate Risks, 2015
Are we observing systematic changes: or
are we simply experiencing natural
variability?
 Detection and Attribution Studies
 “Detection of a change is the process of demonstrating that climate has changed in
some defined statistical sense, without providing a reason for that change.
Attribution of causes of the change is the process of evaluating the relative
contributions of multiple causal factors to a change or event with an assignment of
statistical confidence”
 Fischer & Knutti 2014 (Nature): 18% of the moderate daily precipitation extremes
over land are attributable to the observed temperature increase since pre-industrial
times
2016
U.K. Rainfall Trends Over Time
 Interdecadal variability demonstrates
periods where the moving average
exhibits both cycles of reduced (‘floodpoor’) and elevated (‘flood-rich’) periods
 During ‘flood-rich’ periods, the natural
rainfall volatility increases
 U.K. rainfall variability (1911-2015)
demonstrating a greater prevalence of
extreme rainfall in recent years. (Source:
U.K. Metoffice)
Source: Willis, 2015
2016
December 2015 Floods
• Climate change made the UK’s record
December rainfall 50% more likely
• Natural variability had a similar (or greater)
effect
• Results within days from
climateprediction.net
2016
Cat Models and Climate Change Scenarios
• Catastrophe models can be used to estimate future climate change impacts
• Adjust frequency assumptions – increase number of events
• Adjust severity assumptions – increase windspeeds
• Alter geographical distribution of event tracks
• Adjust input parameters e.g. increased sea level or rainfall intensity
• Climate conditioned model version vs baseline (historical average) version
• Cat model users largely unable to do this – requires the developer to provide these
options/re-parameterisations
• Opportunity for academic partnerships to provide these options/reparameterisations
• Large uncertainty in the science and knowledge of feedback loops
• Need a focus on 5-10 year risk horizon and impact on extreme events
2016
European Windstorm Variability
Cusack S. (2013): A 101 year record of windstorms in the Netherlands. Climatic Change 116, 693-704
2016
Impact on Expected Losses for Europe
Modelled Europe-wide (AAL) for different historical periods
(normalized relative to the 38-year record 1972–2009)
Source: RMS
2016
Sea Level Rise Already Increasing US
Hurricane losses
• Sandy caused $20-25 billion insured losses
• Study by RMS on impact of sea-level change on Superstorm Sandy using Hurricane
model
• +20 cm sea level rise Manhattan NYC
since 1900
• non-linear increase in loss potential with
increasing sea-level rise
• +30% increase storm surge damage from
Sandy
RMS: Catastrophe Modelling and Climate Change, Lloyd’s 2014
2016
More Intense Hurricanes ->
Increasing Economic Damage
• As SSTs increase, so does hurricane
intensity (windspeeds)
• Increase of 8 m/s per degree C
2016
• Getting stronger at a rate of 1 m/s per
decade = 5% increase in loss in ten years
(independent of any change in exposure)
Professor James B. Elsner, President of Climatek
Catastrophe Modelling and Climate Change, Lloyd’s 2014
Conclusions
• Catastrophe models start from physical first principles to simulate full range of
possible extreme events
• Generate a probabilistic or deterministic view of the risk
• Model impacts of catastrophe events on physical assets – damage
• Translate into economic or insured losses
• Can alter input parameters and assumptions to simulate future climate scenarios
in the medium term – 10 years or so
• Need more scientific research and understanding of mechanisms that we can
incorporate into model assumptions
2016