1 ENSEMBLES GA 2007, Prague – WP6.2 Meeting 12 Nov Estimates of Windstorm induced Loss in Europe RT6, WP 6.2 Meeting ENSEMBLES GA 2007 – 12 Nov - 16 Nov.

1ENSEMBLES GA 2007, Prague – WP6.2 Meeting 12 Nov

Estimates of Windstorm induced Loss in Europe

RT6, WP 6.2 Meeting

ENSEMBLES GA 2007 – 12 Nov - 16 Nov 2006, Prague

Markus DonatGregor LeckebuschUwe Ulbrich

2ENSEMBLES GA 2007, Prague – WP6.2 Meeting 12 Nov

FUBs objective:• Estimation of loss potential due to extreme wind storms in the

ENSEMBLES climate model simulations (GCMs and RCMs)• Main topic: estimation of robustness of the scenario changes

considering the different ensemble members

Task 6.2.3: Design of sensitivity analyses based on existing climate projections and some initial performance tests

Task 6.2.5: Development of models for understanding and evaluating the impacts of extremes. These models will operate within a probabilistic framework, incorporating where possible effects of adaptation and acclimatisation to climate change.

Task 6.2.10: Preliminary evaluation of the impacts of extreme events using selected impact models for crops, human health, forest fire, forest damage, intense precipitation, drought, wind and temperature extremes from available climate projections

3ENSEMBLES GA 2007, Prague – WP6.2 Meeting 12 Nov

Data

GCMs in ENSEMBLES:

Model Resolution 20C SRES A1B # runs

MPI-ECHAM5 T63 (ca. 1,9°) 1961-2000 2071-2100 3

DMI-ECHAM5 T63 (ca. 1,9°) 1961-2000 2071-2100 1

IPSL-CM4 2,5x3,75° 1961-2000 2071-2100 1

FUB-EGMAM T30 (ca. 4°) 1961-2000 2081-2100 1 (3)

CNRM-CM3 T42 (ca. 2,2°) 1981-2000 2081-2100 1

BCCR-BCM2 T42 (ca. 2,2°) 1960-1999 2080-2099 1

HadGEM1 1,25x1,875° 1960-1999 2070-2099 1

4ENSEMBLES GA 2007, Prague – WP6.2 Meeting 12 Nov

Large-scale atmospheric circulation

Mean MSLP-Fields (winter ONDJFM)

MEAN of 9 ENSEMBLES GCM runs

5ENSEMBLES GA 2007, Prague – WP6.2 Meeting 12 Nov

Large-scale atmospheric circulation

Jones et al. (1993), Jenkinson and Collison(1977)

Classification of daily (geostrophic) circulation at 50N, 10E

Calculation of

Directional Flow (F)

and Shear Vorticity (Z)

based on MSLP data

on 2.5°x2.5° grid

[unit=hPa per 10°]

Classification into Circulation Weather Types (CWTs)

Types: directional, (anti-) cyclonal, hybrid types, undefined

Additionally: „Gale Days“ (if G=sqrt(F²+(0.5Z)²) > 30)

6ENSEMBLES GA 2007, Prague – WP6.2 Meeting 12 Nov

CWTs A1B-20C Winter (ONDJFM), signifikant 95%

-9

-6

-3

0

3

6

9

12

C AC NE E SE S SW W NW N undef . gale

Dif

fere

nce

A1B

-20C

[%

]

MPI-ECHAM5_1 MPI-ECHAM5_2 MPI-ECHAM5_3 IPSL-CM4 FUB-EGMAM DMI-ECHAM5 CNRM-CM3 BCCR-BCM2 HadGEM

CWTs 20C and ERA40 (ONDJFM)

0

10

20

30

40

50

C AC NE E SE S SW W NW N undef . gale

freq

uen

cy [

%]

MP I-ECHAM5_1 M P I-ECHAM5_2 M P I-ECHAM5_3 IP SL-CM 4 FUB-EGMAM DM I-ECHAM5 CNRM -CM3 BCCR-BCM2 HadGEM ERA40

Large-scale atmospheric circulation

Daily Circulation Weather Types (CWTs) 20C, winter ONDJFM

Changes in CWT frequencies A1B-20C, 95% significant

7ENSEMBLES GA 2007, Prague – WP6.2 Meeting 12 Nov

Gale days

CWTs on gale days in winter (ONDJFM) ERA40 and 20C

0,00

5,00

10,00

15,00

20,00

25,00

C AC NE E SE S SW W NW N undef .

day

s p

er w

inte

r

MP I-ECHAM5_1 M P I-ECHAM5_2 M P I-ECHAM5_3 IP SL-CM 4 FUB-EGMAM DM I-ECHAM5 CNRM -CM 3 BCCR-BCM 2 HadGEM ERA40

CWTs on gale days in winter (ONDJFM) A1B-20C, significant 95%

-4,00

-2,00

0,00

2,00

4,00

6,00

8,00

C AC NE E SE S SW W NW N undef .

day

s p

er w

inte

r

MP I-ECHAM5_1 M P I-ECHAM5_2 M P I-ECHAM5_3 IP SL-CM 4 FUB-EGMAM DM I-ECHAM5 CNRM -CM3 BCCR-BCM2 HadGEM

8ENSEMBLES GA 2007, Prague – WP6.2 Meeting 12 Nov

Cyclone Tracks

Track Density

MEAN of 9 ENSEMBLES GCM runs

ENSEMBLE MEAN 20C Winter ENSEMBLE MEAN A1B-20C Winter

9ENSEMBLES GA 2007, Prague – WP6.2 Meeting 12 Nov

Cyclone Tracks

Intensity (Lapl. P)

MEAN of 9 ENSEMBLES GCM runs

10ENSEMBLES GA 2007, Prague – WP6.2 Meeting 12 Nov

Extreme Wind Speeds:95th percentile of daily maximum wind

MEAN of 9 ENSEMBLES GCM runs

11ENSEMBLES GA 2007, Prague – WP6.2 Meeting 12 Nov

Storm loss model

Loss estimation 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.

annual loss ≈

country

region

year

days regionv

dayregionvregionpopc

3

98

max 1)(

),(*)(* for 98max vv

„normed loss velocity“

3

98

98

v

vvfor 98vv

12ENSEMBLES GA 2007, Prague – WP6.2 Meeting 12 Nov

Estimation of storm loss

Loss Ratio 20C-Period [unit: € per 1000€]

Leckebusch et al. (2007), GRL

Climate Change Signal (A2):

Loss ratio

ERA40 HadCM3 HadAM3P ECHAM4/OPYC3

ECHAM5/OM1

GER AVE 16.98 17.16 15.54 12.97 19.56

STD 13.27 12.31 14.65 9.17 14.26

UK AVE 12.77 12.79 12.33 13.55 12.62

STD 9.10 6.8 9.31 6.76 6.85

Loss ratio HadCM3 HadAM3P ECHAM4/OPYC3

ECHAM5/OM1

Ensemble average

Average with adaption

+ 22.4 - 1.9 - 9.1 + 2.8 + 3.6

Average without adaption

+ 79.9 + 8.0 + 20.5 + 38.4 + 36.7

STD with + 113 - 4.8 - 4.6 + 55.9 + 39.9

STD without + 233 + 8.9 + 42.2 + 148 + 108

13ENSEMBLES GA 2007, Prague – WP6.2 Meeting 12 Nov

1st Analysis of RCM Data

98th percentile of daily maximum wind (ONDJFM)

Data: ERA40 1961-2000; ENSEMBLES RCM-Simulations (ERA40-driven, 50km)

14ENSEMBLES GA 2007, Prague – WP6.2 Meeting 12 Nov

Estimation of storm lossAnnual Loss Ratio Germany, RCMs (ERA40-driven) wssmax

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

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

Lo

ss R

atio

[‰

]

GdV ERA40 ETHZ-CLM_wssmax CNRM-RM4.5_wssmax MPI-M-REMO_wssmax

KNMI-RACMO2_wssmax CHMI-ALADIN_wssmax DMI-HIRHAM_wssmax SMHI-RCA_wssmax

Data: GdV; ERA- 40 1970-1999; ENSEMBLES RCM-Simulations (ERA40-driven, 50km)

GdV ERA40

code 49

ERA40

MaxOf4

ETHZ-CLM

CNRM-RM4.5

MPI-M-REMO

KNMI-RACMO2

CHMI-ALADIN

DMI-HIRHAM

SMHI-RCA

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

Correlation to ERA40 code49 0,97 0,88 0,72 0,82 0,83 0,70 0,86 0,80

Correlation to ERA40 MO4 0,80 0,62 0,69 0,79 0,71 0,82 0,79

MEAN 0,15 0,15 0,15 0,15 0,14 0,15 0,16 0,14 0,16 0,15

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

(max. wind speed)

15ENSEMBLES GA 2007, Prague – WP6.2 Meeting 12 Nov

Estimation of storm loss

Data: GdV; ERA- 40 1970-1999; ENSEMBLES RCM-Simulations (ERA40-driven, 50km)

GdV ERA40

code 49

ERA40

MaxOf4

ETHZ-CLM

MPI-M-REMO

KNMI-RACMO2

SMHI-RCA

Correlation to GdV (1970-2000) 0,89 0,86 0,61 0,73 0,72 0,67

Correlation to ERA40 code49 0,97 0,77 0,82 0,85 0,82

Correlation to ERA40 MO4 0,77 0,69 0,82 0,79

MEAN 0,15 0,15 0,15 0,15 0,15 0,14 0,15

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

Annual Loss Ratio Germany, RCMs (ERA40-driven) wsgsmax

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

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

Lo

ss

Ra

tio

[‰

]

GdV ERA40_code49 ERA40_MaxOf4 ETHZ-CLM_wsgsmax MPI-M-REMO_wsgsmax KNMI-RACMO2_wsgsmax SMHI-RCA_wsgsmax

(max. gust)

16ENSEMBLES GA 2007, Prague – WP6.2 Meeting 12 Nov

Contribution to Deliverables

D6.15: Assessing the uncertainty in projected changes in climate extremes and their impacts on the following sectors: health, forestry, flood risk, property damage, agriculture. Month 48

D6.16: Joint WP 6.2 paper: Impacts of projected changes in climate extremes over Europe to 2100: a review of key sectors. Month 54