1. Modelling Short Term Impacts of Climate Change
Dr. Claire Souch

2. 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 years

3. 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
2010 AEP
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
COV
2.99

4. Anatomy of Cat Model Event Set Hazard Vulnerability Financial Module
Exposure
Define Stochastic Events
Quantify Financial Loss
Calculate Hazard for each event
Calculate Damage
Event Set
Hazard
Vulnerability
Financial Module

5. 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 ERA- Interim
“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

6. We care about all forms of “climate change”
Seasonal
Multi-decadal
Long-Term
ENSO
AMO
Global Warming
Natural cycles & trends +
Anthropogenic influences

7. The Case of Hurricanes
Hurricane activity has gone through cycles in the past
Clearest trend is for Category 3+ hurricanes:

8. The Case of Hurricanes
Correlation with Atlantic Multi-decadal oscillation
Causes cycles in sea-surface temperatures
AMO correlation to Atlantic hurricane activity, (Source: AIR)

9. 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

10. Impact of Climate Change on Extreme Events …??
IPCC SREX 2012
Reproduced from S. Seneviratne, SCOR Foundation Seminar on Climate Risks, 2015

11. 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

12. U.K. Rainfall Trends Over Time
Interdecadal variability demonstrates periods where the moving average exhibits both cycles of reduced (‘flood- poor’) and elevated (‘flood-rich’) periods
During ‘flood-rich’ periods, the natural rainfall volatility increases
U.K. rainfall variability ( ) demonstrating a greater prevalence of extreme rainfall in recent years. (Source: U.K. Metoffice)
Source: Willis, 2015

13. 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

14. 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/re- parameterisations
Large uncertainty in the science and knowledge of feedback loops
Need a focus on 5-10 year risk horizon and impact on extreme events

15. European Windstorm Variability
Cusack S. (2013): A 101 year record of windstorms in the Netherlands. Climatic Change 116,

16. 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

17. 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
non-linear increase in loss potential with increasing sea-level rise
+30% increase storm surge damage from Sandy
+20 cm sea level rise Manhattan NYC since 1900
RMS: Catastrophe Modelling and Climate Change, Lloyd’s 2014

18. More Intense Hurricanes -> Increasing Economic Damage
As SSTs increase, so does hurricane intensity (windspeeds)
Increase of 8 m/s per degree C
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

19. 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