Multi-Decadal Variability of Wind Storms
over the North Atlantic and North-Western Europe,
a Perspective for
Loss-Oriented Modelling
Beispielbild
in the Insurance Industry
Uwe Ulbrich, Katrin Nissen, Gregor C. Leckebusch1,
Freie Universität Berlin
(1 present affiliation: University of Birmingham)
Research in collaboration with and funded by
Outline
•Motivation:
Towards a Decadal Prediction
•Approach:
Can a (multi-)decadal prediction of Atlantic-European wind
storm activity work?
•Results:
Evidence for a physical background
of wind storm predictability
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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Motivation: „Seamless Prediction“
Initial values
Climate
Monitoring
Weather
Predic
tion
Forcing
Season
Predic
tion
(Multi-)
Decadal
Prediction
month
today
Climate
Scenarios
decade
century
year
Modified after: German Weather Service, Paul Becker
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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Motivation: „Seamless Prediction“
Statements:
• Decadal and multi decadal prediction
is a major research task
• It is often thought that only large scale features like
heat waves or droughts can be addressed.
• We intend to explore the potential for
a decadal predictability
of Atlantic European wind storms
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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Approach
Defining a wind storm event
Daria 1990-01-26 max. wind (ERA40)
Daria 1990-01-26 loss potential
Correlation loss model – German Insurance (1970-2000): 0,89
Method: Klawa und Ulbrich, 2003, Natural Hazards and Earth System Sciences
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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Approach
•Number of wind storms per extended winter season (Oct-March)
•Detected with wind tracking algorithm (Leckebusch et al. 2008)
•Only storms affecting North Atlantic and North-Western Europe
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Approach
Potential physical mechanisms for
decadal variability and prediction
AMO
anomalies
Heat content
anomalies NA
Baroclinicity
anomalies
Ocean heat content of upper 300m in J (ref. to grid),
Long-term annual mean of simulation period 1960-1999
Storm activity
anomalies
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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Ocean Heat Content
OHC = ∫∫∫ cpρ (T − Tref )dxdydz
cp: specific heat capacity of sea water at constant pressure
ρ
: density of sea water
T= T (x,y,z), Tref= 0°C Water temperature
Calender annual values in the upper 300m
e.g. Levitus et al 2005
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Approach
Lack of adequate long-term observational time series ->
Model simulations used as first step:
•3 ensemble simulation ECHAM5 MPIOM
•3x 240 years
• Forced with observed greenhouse gas concentrations 18612000 and A1B scenario 2001-2100
Reasons for choice of ECHAM5 MPIOM:
• Present day and future climate wind storm activity close to
multi-model ensemble mean (Donat et al. 2010)
• Decadal variability of North Atlantic Ocean is especially well
simulated (Collins et al. 2006)
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Approach
STEP1
STEP2
time series storm activity
band pass storm activity 10-35 yrs
time series OHC at ocean grid point
band pass OHC 10-35 years
STEP3
Examine relationship
for each ocean grid
point
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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Results: Storm Composite difference in OHC
periods with decadal wind storm freq > 1 σ
minus periods with wind storm freq < 1 σ
shaded areas: signigficance level > 90%
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Ocean heat content Anomaly Index
OAI=OHCwarm – 0.5x(OHCcold1+OHCcold2)
Decadal Correlation OAI-Wind storm activity:
0.3 – 0.5 in individual runs
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Correlation OAI vs. storm activity
OAI vs.storm activity
correlation coefficient for:
simulation 1
simulation 2
simulation 3
1000 correlation coefficients:
OAI vs. band pass filtered
white noise time series
(same mean and standard
deviation as the original
wind storm time series)
◊ upper 5%
* upper 1%
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Physical interpretation
Eady growth rate:
• Measure of baroclinicity
• High Eady growth rates provide favourable conditions for cyclone
development
σBI=0.31(f/N)|dv/dz|
f: Coriolis parameter
N: static stability
dv/dz: vertical wind shear (700 – 850 hPa)
• Calculated for the extended winter season (October –March)
• Band pass filter 10-30 years
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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Approach
Potential physical mechanisms for
decadal variability and prediction
AMO
anomalies
Heat content
anomalies NA
Baroclinicity
anomalies
Ocean heat content of upper 300m in J (ref. to grid),
Long-term annual mean of simulation period 1960-1999
Storm activity
anomalies
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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Composite: Eady growth rate for high storm activity
AMO
anomalies
Heat content
anomalies NA
Baroclinicity
anomalies
periods with decadal wind storm freq > 1 σ
minus periods with wind storm freq < 1 σ
Enhanced wind storm activity is
associated with high NA baroclinicity
Storm activity
anomalies
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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Composite: Eady growth rate for high OAI
AMO
anomalies
Heat content
anomalies NA
Baroclinicity
anomalies
periods with OAI > 1 σ
minus periods with OAI < 1 σ
OHC anomalies associated with
increased baroclinicity close to Europe
Storm activity
anomalies
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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Composite Eady growth rate
periods with OAI and storm freq > 1 σ minus periods with
OAI and storm freq < 1 σ
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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Atlantic Multidecadal Oscillation
(a)
Regression pattern of monthly
sea surface temperature
(SST) anomalies (after
removing the global mean
SST anomaly) on the North
Atlantic SST Index, based on
HadISST 1870–2008.
(b) The North Atlantic SST Index,
defined as the average
monthly SST anomaly over
the North Atlantic (0◦–70◦N)
minus the global mean
monthly SST anomaly (red
and blue bars).
Deser et al. 2010
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Relationship between OAI and AMO
Lag-correlation OAI vs. SST-Index (AMO)
-> OHC anomalies develop during the transition of the AMO from
its positive to its negative phase
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Conclusions
•
•
•
Significant relationship between decadal storm activity
and North Atlantic OHC anomaly pattern
High OAI develops during transition of AMO from positive to
negative phase
Can the AMO as a natural mode of the
Meridional Overturning Circulation be predicted?
Our results based on an Atmosphere-Ocean GCM
demonstrated potential predictability of storm climate
Future Research Initiatives:
Research on decadal predictions
initialized with the present day state of the climate system.
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Conclusions
• Significant relationship between decadal storm activity and OHC
• High storm activity is associated with positive OHC anomaly around
45°N 35°W and negative anomalies to its North, South and East
• OHC anomaly can be described by index (OAI)
• High OAI leads to enhanced baroclinicity consistent with increased
storm activity in the region
• High OAI develops during transition of AMO from positive to negative
phase
• AMO is a natural mode of the Meridional Overturning Circulation,
which is potentially predictable (Collins et al. 2006, Griffies and Bryan,
1997)
Decadal wind storm variability in the North Atlantic and North
Western European region might be to some extend predictable
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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Thank you for your attention !
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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EXTRA FOLIEN
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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Ocean Heat Content
Ocean heat content of upper 300m
Long-term annual mean of simulation period 1960-1999
Unit: Joule
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North Atlantic ocean heat content
Time series of North Atlantic
heat content anomaly
of upper 300m (0°N-70°N)
simulations 20C_1 and A1B_1
approx observed trend
Levitus at al. 2005
(heat content
anomaly upper
300m in 1022 J)
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North Atlantic ocean heat content
RUN1
RUN2
RUN3
Time series of North Atlantic
heat content anomaly
of upper 300m (0°N-70°N)
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Climate signal OHC
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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Properties band pass filter
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OHC patterns related to storm activity
RUN1
pos
neg
pos
neg
pos
neg
Correlation between number of
storms affecting Atlantic Box
and annual 300m OHC; bandpass
10-35 years
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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OHC patterns related to storm activity
RUN2
Correlation between number of
storms affecting Atlantic Box
and annual 300m OHC; bandpass
10-35 years
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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OHC patterns related to storm activity
RUN3
Correlation between number of
storms affecting Atlantic Box
and annual 300m OHC; bandpass
10-35 years
Ulbrich et al., Multi-Decadal Variability of Wind Storms. Lloyds Old Library, 12 July 2011
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Composites OHC
RUN1
RUN2
RUN3
Difference bandpass-filtered OHC
(years bp-filtered storm count > 1sigma)
-(years bp-filtered storm count <1sigma)
approx. 30 years in each group
OHC upper 300m annual mean
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