Leckebusch et al.: European property damage potentials RT6.2 Meeting Helsinki, 26.-27.4.2007 European property damage potentials: development and application

Leckebusch et al.: European property damage potentials

RT6.2 Meeting Helsinki, 26.-27.4.2007

European property damage potentials: development and application of a simple

storm regression model to global and regional simulations

G.C. LeckebuschM. DonatU. Ulbrich FU Berlin

MSC Napoli



Economic loss Insured loss

Economic and insured loss: Germany 1970 - 1998

Introduction: Storm damages in the past



Economic and insured losses

Source: Münchener Rück, Jahresrückblick Naturkatastrophen 2004



1000 hPa Stormtrack (Winter)

Stormtrack is originally defined as bandpass (2.5-8 days) filtered standard deviation of the geopotential height in 500 hPa

Thus, the stormtrack reflects the variability caused by travelling extra-tropical cyclones and high-pressure systems in the mid-latitudes

In this study we used the 1000 hPa level, due to data availability, for winter.

Data used: On IPCC AR4 Model Data Portal available 20 contributing models



1000 hPa Stormtrack (Winter)

Ulbrich et al., submitted to J. Clim.

20 IPCC GCMs



NCEP-Re

ECHAM4

HadAM3P ECHAM5

HadCM3

NCEP

Validation: Cyclone Track density; All systems

Units: Cyclone systems per winter

Winter: Oct.-Mar.



HadCM3 ECHAM4

HadAM3P ECHAM5

Validation: Cyclone Track density; 5% strongest systems

NCEP-Re

Units: Cyclone systems per winter

NCEP-Re

Strong: exceedance of the 95th percentile of the Laplacian of MSLP



A2 - Climate Change Signal: Cyclone Track density; All systems

ECHAM4

ECHAM5HadAM3P

HadCM3Coloured:

90/95/99th Significance Level

Dashed lines:negative changes

Solid lines:positivechanges



A2 - Climate Change: Track density; 5% strongest systems

HadCM3

HadAM3P ECHAM5

ECHAM4 Coloured:

90/95/99th Significance Level

Dashed lines:negative changes

Solid lines:positivechanges

Leckebusch & Ulbrich (2004) ; Leckebusch et al. (2006)



A2 - Climate Change: Number of systems: all vs. intense systems

Lambert & Fyfe (2006)



CWTs - Climate change signal A1B-20C winter (ONDJFM)

-10-8-6-4-20

246810

Z AZ NE E SE S SW W NW N undef. gale

Diffe

ren

ce

A1

B-2

0C

[%

]

CWTs ERA40 and 20C 1961-2000 winter (ONDJFM)

051015202530354045


fre

qu

en

cy [%

]

Circulation Weather Types during winter (ONDJFM) in an ensemble of GCMs

recent climate:

climate change:(A1B)



05

10152025

Z AZ NE E SE S SW W NW N undef .

[day

s/ye

ar]

-15

-10

-5

0

5

10

Z AZ NE E SE S SW W NW N undef.

[day

s/ye

ar]

Circulation Weather Types on gale days(ONDJFM)

recent climate (20C):

climate change (A1B-20C):



98th percentile of daily max. wind speed (ONDJFM)

ERA40ECHAM5-OM1run1 (MPI-M)

ECHAM5-OM1(DMI)

EGMAM

20C

A1B-20C



Storm (loss) days per winter

• 98th percentile typical threshold for loss damages

• Storm (loss) day if 98th percentile is exceeded in at least 20% of the investigation area (red box)

0

1

2

3

4

5

6


[da

ys

/ye

ar]

20C

-2

02

46

810

12

Z NE SE SW NW undef.

[day

s/ye

ar]

A1B-20C (98th percentile not adapted)

-2

02

46

810

12

Z NE SE SW NW undef.

[da

ys/y

ea

r]

A1B-20C (98th percentile adapted)



Model theory

• Loss depends on - local gust wind speed- insured property or amount of forest in the area

• insured property values can roughly be estimated from population density

• Loss increases with wind speed above a threshold.Different storm-loss functions have beenproposed, a frequent one is: loss ~ v3.

Estimation of future changes in climate extremes and their relation to property damage

Following the “multi model approach”direct use of GCM/RCM output in the impact model



This wind speed is approx. equal to the 98th percentile of wind speeds at regular (non-coastal, no mountain) stations in Germany

• Germany: Insurance companies pay when wind speeds exceed Bft 8 = 17.2 – 20.7 m/s

For property damages:



Loss ≈

regions

year

days regionv

dayregionvregionpopc

3

98

max 1)(

),(*)(* for 98max vv

„normalized cubic wind“

3

98

98

v

vvfor 98vv

Approach based on:

Klawa, M. und U. Ulbrich, 2003:

A model for the estimation of storm losses and the identification of severe winter storms in Germany.

Natural Hazards and Earth System Sciences, Vol. 3, 725-732.



2: GIS (ArcGIS) - including global population distribution data on 1x1 degree grid- including interpolation of forestry data to model grid via GIS

(at present: nearest neighbour)- Calculation of accumulated damage potential for different time

slices and/or regions

1: Calculation of „normalized cubic wind“ from input data (e.g. ERA40) per year

Model structure

3: Fitting the calculated values per year and region to observed losses

year

days regionv

dayregionv3

98

max 1)(

),(

What have we achieved so far?



Application of loss model on climate simulations

Loss Ratio in ECHAM5/OM1, 20C, run 1

0

0,2

0,4

0,6

0,8

1

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

Lo

ss R

atio

[‰

]

jährl. Werte Mittelw ert

Loss Ratio in ECHAM5/OM1, A1B, run 1

0

0,2

0,4

0,6

0,8

1

20

71

20

72

20

73

20

74

20

75

20

76

20

77

20

78

20

79

20

80

20

81

20

82

20

83

20

84

20

85

20

86

20

87

20

88

20

89

20

90

20

91

20

92

20

93

20

94

20

95

20

96

20

97

20

98

20

99

21

00

Loss

Rat

io [

‰]

jährl. Werte Mittelwert

ERA 40

(1971-2000)

EH5/OM1, 20C

(1971-2000)

EH5/OM1, A1B

(2071-2100)

Mean value 0,1395 0,1284 0,1507

Std. deviation 0,0894 0,0707 0,1494

+ 17 % + ~110 %



Loss ratio: Control Climate

Leckebusch et al., 2007, GRL

ACC signal (A2):



Model improvements in ENSEMBLES

c165/166 c49

1970-2000 0,78 0,83

Correlation with insurance data (GdV):

Loss Ratio based on momentary wind values vs. daily maximum gust from ERA40

0,000

0,100

0,200

0,300

0,400

0,500

0,600

0,700

los

s r

ati

on

[‰

]

GdV c165/166 c49

Input parameter: Wind gusts (Forecasts!)

Overestimation in 1993Underestimation in 1990

Further investigation with respect to the kind

of exceedance



• 1993 more weak events than 1990• 1990 more extreme exceedances of 98th Percentile than 1993

Approach 2 („dynamic“):Loss limit individually adjusted after loss events

Approach 1 („static“):Loss limit consistently increased

GERMANY: Exceedance of 98th Percentile (1971-2000) in ERA40



„dynamic approach“Schadensatz [0,01€ pro 1000€] auf Basis von P98

0

10

20

30

40

50

60

70

80

1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

GdV P98_c49_jahr P98_c49_winter P98_0.1v_d500 P98_0.1v_d500_ES P98_0.1v_d1000 P98_0.1v_d1500

P98_jahr P98_winter 0.1v_d500 0.1v_d1000 0.1v_d1500

1970-1999 0,83 0,90 0,877 0,883 0,878

Correlation with real damage data (GdV)



98th percentile of maximum wind speed (ONDJFM)ERA40

Regional Climate Model analysis:

Max of 4 gust forecast



98th percentile of maximum wind speed (ONDJFM)RCMs forced by ERA40



GdV ERA40

code 49

ERA40

MaxOf4

ETHZ-CLM

CNRM-RM4.5

MPI-M-REMO

KNMI-RACMO2

CHMI-ALADIN

DMI-HIRHAM

SMHI-RCA

Korrelation mit GdV (1970-2000) 0,89 0,86 0,82 0,79 0,73 0,76 0,75 0,78 0,64

Korrelation mit ERA40 code49 0,97 0,88 0,72 0,82 0,83 0,70 0,86 0,80

Korrelation mit ERA40 MO4 0,80 0,62 0,69 0,79 0,71 0,82 0,79

Mittelwert 0,15 0,15 0,15 0,15 0,14 0,15 0,16 0,14 0,16 0,15

Standardabweichung 0,12 0,12 0,11 0,07 0,09 0,10 0,10 0,09 0,10 0,08

Loss Ratios, RCMs (ERA40-driven) wssmax

0,000

0,100

0,200

0,300

0,400

0,500

0,600

0,700

0,800

1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

GdV ERA40_code49 ERA40_MaxOf4 ETHZ-CLM_wssm ax CNRM-RM4.5_wssm ax MPI-M-REMO_wssm ax KNMI-RACMO2_wssm ax

CHMI-ALADIN_wssm ax DMI-HIRHAM_wssm ax SMHI-RCA_wssm ax



Availability of data from RT2A: Milestones M2A.2.2/M2A.2.3 “Provision of Stream One simulations [...] on servers or on request” (due in August 2006) seems NOT fulfilled adequately yet and leads to a delay in further analysis, as even on request data availability seems poor (see table).Availability of GCM data:

HadGem1 (METO-HC)

Requested, but not made available yet

IPSL-CM4 (IPSL) Available at PCMDI (IPCC AR4)*

ECHAM5/MPI-OM (MPIMET) Available via CERA / on request

ECHAM5/MPI-OM (DMI) Available on request

EGMAM (FUB) Available on request

CNRM-CM3 Available at PCMDI (IPCC AR4)*

ARPEGE-MICOM-OASIS (NERSC) Requested, but not made available yet

* Output of IPCC AR4 runs at PCMDI is onlay available in a daily resolution (daily mean values). For some analysis we plan to do, 6-hourly values are required. Thus, model output from PCMDI archive is only partly suitable for our analysis in ENSEMBLES.

Data problem: (March 2007)

Documents

Leckebusch et al.: European property damage potentials RT6.2 Meeting Helsinki, 26.-27.4.2007 European property damage potentials: development and application