PowerPointPrsentation

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Extreme events and Euro-Atlantic atmospheric
blocking in present and future climate
simulations Jana Sillmann Max Planck
Institute for Meteorology, Hamburg International
Max Planck Research School on Earth System
Modelling Paris, SAMA seminar, 20th January
2009
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Motivation
http//www.conserveafrica.org.uk
http//nedies.jrc.it
http//www.srh.noaa.gov
ournewsbrooklyn.wordpress.com
heat waves
floods
droughts
cold waves
IPCC 2007 Climate change may be perceived most
through the impacts of extremes
Munich Re 2005 Increase of climate related
catastrophes and associated material and human
losses since 1950
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Outline

• Theory
• Climate model and data
• Defining extreme climate events and atmospheric
  blocking

• Questions
• Is the model able to capture observed patterns of
  climate extremes?
• What changes in extremes can we expect under
  anthropogenic climate change?
• Can we find associations between climate extremes
  and atmospheric blocking?
• Can we use these associations in the statistical
  modeling of extreme events?

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Model Data
Model Data
Coupled general circulation model ECHAM5/MPI-OM
Atmosphere
Ocean
T63 (1.875 x 1.875) 31 vertical levels
1.5 horizontal resolution 40 vertical levels
20C, A1B and B1 each with 3 ensemble members
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Extreme events
Definition of extreme climate events
Extreme event very rare and very intense event
with severe impacts on society and biophysical
systems.
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Extreme events
Identification of extreme events in climate data
Methods for extreme value analysis

• based on daily temperature and
• precipitation data
• describe moderate and statistically
• robust extremes
• easily understandable and
• manageable for impact studies

• Yearly/monthly indices
• Minimum of daily minimum temperature
• Maximum of daily maximum temperature
• Maximum 5 day precipitation
• Maximum number of consecutive dry days

¹ Expert Team on Climate Change Detection
Monitoring and Indices
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Indices for extremes
What changes can we expect under anthropogenic
climate change?
1971-2000
2071-2100
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Changes in extremes
Difference A1B scenario present climate
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Atmospheric blocking
Can we find associations between climate extremes
and atmospheric blocking?
Winter climate of the Euro-Atlantic
domain Minimum Temperature
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Atmospheric blocking
sustained, quasi-stationary, high-pressure
systems that disrupt the prevailing westerly
circumpolar flow
Height of tropopause (2 pvu )

• elevated tropopause associated with strong
  negative potential vorticity anomalies ( gt
  -1.3 pvu )

? relationship between temperature and
precipitation anomalies (Rex 1951, Trigo et al.
2004)
10-6m2s-1K kg-1
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Atmospheric blocking
Potential Vorticity (PV) - based blocking
indicator

• Blocking detection method (Schwierz et al. 2004)
• Identification of regions with strong negative
  PV anomalies between 500-150hPa
• PV anomalies which meet time persistence (gt 10
  days) and spatial criteria (1.8106km2) are
  tracked from their genesis to their lysis

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Atmospheric blocking
Representation in present and future
climate Blocking events gt 10days DJF
1961-2000
model
ERA-40 re-analysis
Blocking frequency in
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Atmospheric blocking
1961-2000
European blockings (15W-30E,50N-70N)
Blocking frequency
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Atmospheric blocking
Correlation of European blockings with winter
(DJF) minimum temperature
1961-2000
2160-2199
Significant Spearmans rank correlation
coefficient to the 5 significance level
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Extreme events
Identification of extreme events in climate data
Methods for extreme value analysis
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Stationary GEV
Generalized Extreme Value (GEV) distribution
with parameters ? (location)?, ? (scale) and ?
(shape)
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Stationary GEV
Parameters for DJF minimum temperature
location
scale
shape
ERA-40
20C
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Non-stationary GEV
Can we use the association between extreme events
and atmospheric blocking in the statistical
modeling of extreme events?
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Covariate atmospheric blocking
Euro-Atlantic domain
Blocking frequency
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Statistical modeling
Model selection

degrees of freedom
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Non-stationary GEV
Model selection for minimum temperature extremes
in winter
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Non-stationary GEV
Slope of the location parameter
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Non-stationary GEV
Grid-point example at 9ºE, 53ºW
GEV distribution for the stationary and
non-stationary model 1
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Non-stationary GEV
Return values at grid point 9ºE, 53ºW
T-year return value is the (1-1/T)th quantile
of the GEV distribution
median
90 confidence interval
20-year return value
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Non-stationary GEV
20-yr return values for minimum temperature
extremes in winter
Significant differences between RV20 of
stationary and non-stationary GEV distribution
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Summary

• Is the model able to capture observed patterns
  of climate extremes?
• What changes in extremes can we expect under
  anthropogenic climate change?
• increase of temperature and precipitation
  extremes as well as dry periods
• regional and seasonal distinguished changes of
  extremes in future climate

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Summary

• Can we find associations between climate
  extremes and atmospheric blocking?
• atmospheric blocking favors extreme cold
  nighttime temperatures in Europe
• association remains robust in future climate, but
  influence of blocking events diminishes due to
  decreasing blocking frequency
• Can we use these associations in the statistical
  modeling of extreme events?
• atmospheric blocking implemented as covariate in
  the GEV can explain more of the variability in
  the underlying data
• modeling of colder return values possible

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Outlook

• Improvement of the statistical modeling
• longer climate simulations (500-year control
  run) to further test the statistical robustness
  of the results
• apply Generalized Pareto distribution
• use other or more covariates

• Usage of this methodology for statistical
  downscaling
• limit region of interest, e.g. to northern,
  southern Europe
• find appropriate covariate for that region
• test method with observations

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Thank you very much!
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Indices for extremes
Is the model able to capture observed patterns of
climate extremes?
HadEX dataset indices for extreme events
calculated on the basis of a worldwide weather
observational dataset from the Hadley Centre
(3.75 x 2.5 horizontal resolution) (Alexander
et al. 2006) Time coverage 1951-2001
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Present climate
Temperature indices - global
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Present climate
Precipitation indices - global
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Present climate
Temperature indices - regional
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Present climate
Precipitation indices - regional
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Indices for extremes
Temperature indices - global
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Atmospheric Blocking
Pot. Vorticity (PV)-based Blocking indicator
captures the block at the core PV-anomaly at
tropopause level (Croci-Maspoli 2007)
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Atmospheric Blocking
PV-based Blocking identification
averaged PV-anomaly between 500 and 150hPa
(Schwierz et al. 2004, GRL)?
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Atmospheric Blocking
PV-based Blocking identification
filled contours indicate vertically-averaged PV
anomalies (0.7pvu steps)? red APV blocking
location
(Schwierz et al. 2004, GRL)?
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Atmospheric blocking
Composite maps
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Modeling Diagnostic
Testing the method for El Nino and its impact on
precipitation for 1961-2000 winter (ONDJFM)

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Model Diagnostic
Model Diagnostic at Grid Point 9E, 53N for
min.Tmin (ONDFM)?
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Statistical modeling
Generalized Extreme Value (GEV) distribution

• Block maxima approach
• Daily minimum temperature data are blocked into
  sequences of length n, generating a sequence of
  block minima to which the GEV distribution can be
  fitted
• select block size (e.g., 1 season, 1 month)?
• choose smallest event in each block
  (month or season)
• fit GEV distribution to selected
  extreme events
• estimation of GEV parameters for each global
  grid point via
• Maximum-Likelihood