CatPerils_ClimateChange.pdf

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Swiss Re OPERA - Cat Perils

Z1

Natural Perils - An Overview

David N. Bresch, PhD, Swiss Re

The PerilsThe Assessment

Climate Change

Opera Seminar, London, 25 Sept 2001


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Z3

Loss Potential due to Natural Catastrophes (Cat perils)

Flood

Storms

Earthquake5-20 billion US$

20-50 billion US$

50-100 billion US$


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Z5

Insured Natural Catastrophe Losses 19702000

in USD billions, at 2000 prices. Source: Swiss Re sigma

0

5

10

15

20

25

30

35

70 73 76 79 82 85 88 91 94 97 2000

Low loss burden 2000 random,

but upward trend expected to continue:

higher insurance penetration

growing values

value concentration in coastal areas

hazard cycles and trends, eg.

natural & man-made climate change


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Z7

Windstorm: Extratropical vs Tropical Cyclone

Extratropical Cyclone Tropical Cyclone

Winter Storm Hurricane/Typhon

Energy source North/South tempera- water condensation

ture contrast also over land over ocean only

Occurrence Mid-latitudes Tropics

Winter Summer

Size 1000-2000 km 300-600 km

Gust speeds 20-50 m/s 33-90 m/s

Losses lots ofsmallto medium medium to total


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Z8

Largest storms since 1989

0 5 10 15 20 25 30 35

Hugo, Carib./US

Daria, Europe

Vivian, Europe

Mireille, Japan

Andrew, Florida

Georges, Carib./US

Mitch, Centr. Am.

Bart, Japan

Lothar, Europe

Insured loss Total loss (billion USD)

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9

11/98

08/92

10/91

02/90

01/90

09/89

80

9000

15

51

64

95

61

Death

26

600

09/99

09/98


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Z9




Climate Change



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Z10

Swiss Res technical approach to catastrophe perils

1970 1980 1990 2000

no quantitative first attempts tools for + world-wide cat risk assessment

methods for quantified main areas + sophisticated software

risk assessment and perils +hazard/vulnerability databases


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Z11

Quantify

Combine

Loss

xs Frequency

HazardInsuranceConditionsVulnerability

ValueDistribution

Concept of risk assessment - 4 box model


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Z12

Lothar 26 Dec 1999Storm catalog based on 166 events

1947-1999

For every major storm since 1947maps of the storms gust speed [m/s]

and duration [h]have been createdby the following procedure:

Surface pressure data (grid)

Surface fronts

Combined through physical model

(Blended with measured in situ data)gust speed [m/s]

Hazard - European Winter Storm Template


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Z13

Hazard - Probabilistic Storm Set

Anatol 3./4.12.1999 Lothar 26.12.1999 Martin 27./28.12.1999

166 detailed historical events 1947-1999

50 child storms for every single historical event (translation/intensification)

ensemble of 8300 storms, the probabilistic set

representing ~2500 years


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Z14

European Windstorm Risk Assessment: EuroWind

is a state-of-the-art probabilistic windstorm risk modeling toolto assess winter storm losses in Europe, jointly developed by Swiss Re (model)

and EQECat (implementation, 2nd opinion)

covers Germany, UK, Ireland, France, Belgium, Netherlands,

Luxembourg, Denmark, Sweden and Norway- the areas most

prone to winter storms

allows the calculation of windstorm scenario losses: all major 166

historical events from 1947 - 1999 plus some few events since 1897

provides rating of proportional, non-proportionaland stop losstreaties

based on the calculation of loss frequency curves:

probabilistic analysis with over 8000 historical and artificial events

explicitely models Europe-wide dependence(event-based approach)


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Z15

Storm over Europe - an underestimated risk

www.swissre.com publications


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Z16

Tropical Cyclone Risk Assessment - Fact sheet

Swiss Re Natural PerilsAssessment Program for Tropical Cyclones (SNAP-TC)

SNAP-TC allows to assess tropical cyclone risk world wideand has been fully

developed in-house by Swiss Re

SNAP-TC operates as well for extended portfoliosas for single risks

SNAP-TC basis its calculation upon a correlation matrix approach. Thus even

complex, locally irregular distributed exposurecan be pooled under one treaty

and

can be calculated all at once.

SNAP-TC is highly flexiblecan be easily adopted by the user to individual needs, such as risk classes, vulnerabilities e.g.


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Z17




Climate Change

Basics Impact Examples



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Z18

Climate Change - Greenhouse Effect


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Z19

Climate Change - Surface Temperature Trend


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Climate Change - Greenhouse Gases


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Z21

Climate Change - Selected Consequences

Rising sea level

More intense hydrological cycle

(precipitation, hail,)

Increased climate variability

(eg. Interannual)

Changes in regional weather patterns, storm tracks

(either in frequency or in severity)


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Z23

Excursion: Risk Mitigation - Current State

Probability that a risk

is hit by an event due

to a protection

measure failure

Event Distribution

eg. Intensity of Tropical Cyclones

Protectionmeasure

s

ExpectedMean


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Z24

Excursion: Risk Mitigation - Possible Future

Shift of Regime

OldProtectionmeasures

Probability that a risk

is hit by an event due

to a protection

measure failureOldMean

New

Mean


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Z25

Climate Impact Example 1: Global Climate Variability

Global Climate Simulation

(Calculated on Supercomputer, Grid approx 100 km x 100 km,20 vertical levels, timestep 20 min.)

Shown: Temperature evolution over 60 years

Besides well-known long-term trend,

note the large year-to-year variability


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Z26

Climate Variability - CO2 model prediction

change

> +3 C< - 3 C

Climate Impact Example 1: Global Climate Variability

If the movie does not work: see movie4climate.mpg


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Z27

Climate Impact Example 2: ENSO - Tropical Cyclones


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North Atlantic (US) hurricane

activity below normalduring El Nioconditions


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Z29

Effects of El Nioon local weather conditions

Torrential downpours: Pacific coast of Northern and Southern America

Extreme drought: On the Western rim of Pacific from Australia to Taiwan, in the

Indian subcontinent, in parts of Africa, in the northern part of South America

Tropical cyclones

Dramaticincreasein number of Tropical cyclones in Central South Pacific

Decline of activityin Eastern part of Australia, the Northwest Pacific basin

and in the North Atlantic



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Z30

Climate Impact Example 3: North Atlantic Storm Track

Thisor that ?


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Z31

Negative phase NAO-North Atlantic Oscillation (NAO)

Positive phase NAO+

Positive phase NAO+ more and stronger storms

crossing the North Atlantic warm and wet winters in

Northern Europe



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data source: Hurrell, J.W., 1995

NAO Index

-1.5

-1

-0.5

0

0.5

1

1.5

2

1825

1835

1845

1855

1865

1875

1885

1895

1905

1915

1925

1935

1945

1955

1965

1975

1985

1995

Temperature trend

North Atlantic Oscillation

Index



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Z33

North Atlantic Oscillation - The Index (1970-1999)

2.13

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

1970

1975

1980

1985

1990

1995

Dec99

data source: Hurrell, J.W., 1995



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Z34

Atlantic Storm Track Activity (1860-2100)

Easterling et al., 2000, science

Collins, Journal of Climate

Observed (20th century) Modeling (end 21st century)

Higher minimum temperatures Virtually certain >99% Very likely 90-99%

More intense mid-latitude storms Possible 33-66% Possible 33-66%

?



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Z35

North Atlantic Oscillation - European Winter Storms

There is a strong relationship between the frequency ofwinter storms and the North Atlantic Oscillation:Three quarters of all stormsbearing lossesoccurrduring months with positive NAO index (NAO+)80% of the total loss 1947-99 occurred during NAO+

There is no such clear relation between the size oflosses and the NAO: larger losses tendto occurr duringthe positive phase NAO+

Half (49%) of the winter months 1947-99 have beenNAO+ months

Unfortunately, there is no long-term prediction of theNAO possible (yet)

NAO+

Results based on Swiss Res

storm catalog1947-1999which forms the basis for the

EuroWindTMhazard set.

EuroWindTM

is the Europeanwindstorm risk assessmentmodel jointly developed byEQECat and Swiss Re.



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Z36

Climate Impact Examples

NAO - Atlantic Storm Track changes: difficult trenddetection

Risk Mitigation - climate related shift of distributions

Global Climate Variability - high variability

ENSO/El Nio - Tropical Cyclones activity cycle


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Z37

Climate Change - Swiss Res Commitment

Satisfy growing demand for cover

Support customers and the public in risk assessment and prevention

Participate in climate change researchand inform the public

Seek climate dialoguewith industry and government (UNEP initiative, IPCC)

Documents

CatPerils_ClimateChange.pdf