Extreme events and Euro-Atlantic atmospheric blocking in present and future climate simulations Jana Sillmann Max Planck Institute for Meteorology, Hamburg

Extreme events and Euro-Atlantic Extreme events and Euro-Atlantic atmospheric blocking in present and atmospheric blocking in present and

future climate simulationsfuture climate simulations

Jana Sillmann Jana Sillmann Max Planck Institute for Meteorology, Hamburg

International Max Planck Research School on Earth System Modelling

Paris, SAMA seminar, 20th January 2009

MotivationMotivation

http://www.conserveafrica.org.ukhttp://nedies.jrc.ithttp://www.srh.noaa.gov

heat waves floods droughts

Munich Re 2005: Increase of climate related catastrophes and associated material and human losses since 1950

IPCC 2007: ”Climate change may be perceived most through the impacts of extremes…”

cold wavesournewsbrooklyn.wordpress.com

OutlineOutline

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?

Model & Data Model & Data

Atmosphere Ocean

Coupled general circulation model ECHAM5/MPI-OM

T63 (1.875° x 1.875°)

31 vertical levels

1.5° horizontal resolution

40 vertical levels

Model & DataModel & Data

20C, A1B and B1 – each with 3 ensemble members

Extreme eventsExtreme events

Definition of extreme climate eventsDefinition of extreme climate events

Extreme event … very rare and very intense event with severe impacts on society and biophysical systems.


Methods for extreme value analysis

Identification of extreme events in Identification of extreme events in climate dataclimate data

Indices for climate extremes ¹

• based on daily temperature and precipitation data

• describe moderate and statistically robust extremes

• easily understandable and manageable for impact studies

Statistical modeling of extreme values

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

Indices for extremesIndices for extremes

What changes can we expect under What changes can we expect under anthropogenic climate change?anthropogenic climate change?

1971-2000 2071-2100

Changes in extremesChanges in extremes

Annual maximum temperature

Annual minimum temperature

[ ºC ]

[ ºC ]

Annual max. 5-day precipitation

Annual max. consecutive dry days

[ days ]

[ mm ]

Difference A1B scenario – present climateDifference A1B scenario – present climate

Atmospheric blockingAtmospheric blocking

Can we find associations between climate Can we find associations between climate extremes and atmospheric blocking?extremes and atmospheric blocking?

Winter climate of the Euro-Atlantic domain

Minimum Temperature

1961-2000 2160-2199


relationship between temperature and precipitation anomalies(Rex 1951, Trigo et al. 2004)

… 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 ( > -1.3 pvu )

* [10-6m2s-1K kg-1]


Potential Vorticity (PV) - based Potential Vorticity (PV) - based blocking indicatorblocking 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 (> 10 days) and spatial criteria (1.8*106km2) are tracked from their genesis to their lysis


Representation in present and future Representation in present and future climateclimate

Blocking events > 10daysDJF

ERA-40 re-analysis model

Blocking frequency in %

1961-2000

2160-2199


European blockings

(15°W-30°E,50°N-70°N)

%

Blocking frequency for DJFBlocking frequency %

1961-2000


Correlation of European blockings with Correlation of European blockings with winter (DJF) minimum temperaturewinter (DJF) minimum temperature

Significant Spearman’s rank correlation coefficient to the 5% significance level

1961-2000 2160-2199


Methods for extreme value analysis

Identification of extreme events in Identification of extreme events in climate dataclimate data

Statistical modeling of extreme values

• GEV – Generalized Extreme Value distribution

• parametric approach to characterize the distribution of extreme events

• calculation of return values

Indices for climate extremes

Stationary GEVStationary GEV

Generalized Extreme Value (GEV) Generalized Extreme Value (GEV) distributiondistribution

with parameters (location), (scale) and (shape)

Stationary GEVStationary GEV

Parameters for DJF minimum temperatureParameters for DJF minimum temperature

ERA-40

20C

A1B –20C

location scale shape

Non-stationary GEVNon-stationary GEV

Can we use the association between Can we use the association between extreme events and atmospheric blocking in extreme events and atmospheric blocking in the statistical modeling of extreme events?the statistical modeling of extreme events?

stationary GEV non-stationary GEV

COV – time dependent covariate

Atmospheric blocking as covariate derived from the PV-based blocking indicator (CAB)

Covariate atmospheric blockingCovariate atmospheric blocking

Euro-Atlantic domainEuro-Atlantic domain

European blockings

Euro-Atlantic blockings

Blocking frequency %

Statistical modelingStatistical modeling

Model selectionModel selection

Model choiceModel choice

Deviance Statistic:

where nllh0(M0) is the neg. log-likelihood of simple model

nllh1(M1) is the neg. log-likelihood of more complex model

nllh

353349348

example

*

* degrees of freedom


Model selection for Model selection for minimum temperature extremes in winterminimum temperature extremes in winter

model model model


Slope of the location parameterSlope of the location parameter

ºC/blocking freq. %


Grid-point example at 9ºE, 53ºWGrid-point example at 9ºE, 53ºW

GEV distribution for the stationary and non-stationary model 1


Return values at grid point 9ºE, 53ºWReturn values at grid point 9ºE, 53ºW

T-year return value … is the (1-1/T)th quantile of the GEV distribution

median

20-year return value

90% confidence interval


20-yr return values for minimum 20-yr return values for minimum temperature extremes in wintertemperature extremes in winter

20C A1B

Significant differences between RV20 of stationary and non-stationary GEV distribution

ERA40

SummarySummary

• 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

SummarySummary

• 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

OutlookOutlook

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

Thank you very much!Thank you very much!


Is the model able to capture observed Is the model able to capture observed patterns of climate extremes?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

Present climatePresent climate

Temperature indices - global


Precipitation indices - global


Temperature indices - regional


Precipitation indices - regional


Temperature indices - global

Pot. Vorticity (PV)-based Blocking indicatorPot. Vorticity (PV)-based Blocking indicator

Atmospheric BlockingAtmospheric Blocking

… captures the block at the core PV-anomaly at tropopause level (Croci-Maspoli 2007)

latitude [°N]

climatological tropopause

instantaneous tropopause

PV

anom

aly

[pvu]

25 35 45 55 65 75

PV anomaly < -1.3 pvu

tropopause = 2pvu [10-6 m2 s-1 K kg-1]

[hPa]

150

500

800

PV-based Blocking identification

(Schwierz et al. 2004, GRL)


averaged PV-anomaly between 500 and 150hPa

filled contours indicate vertically-averaged PV anomalies (0.7pvu steps)red = APV* blocking location

(Schwierz et al. 2004, GRL)


PV-based Blocking identification


Composite mapsComposite maps

Testing the method for Testing the method for El Nino El Nino and its impact on and its impact on precipitationprecipitation for for 1961-2000 winter (ONDJFM) winter (ONDJFM)

Modeling DiagnosticModeling Diagnostic

Best model

0 1 2 3 4 5

model #

xCOV(t)

(mm

)

5 4 3 2 1 0 -1 -2 -3 -4

Model Diagnostic at Grid Point [9E, 53N] for min.Tmin (ONDFM)

Model DiagnosticModel Diagnostic

0.0 0.2 0.4 0.6 0.8 1.0empirical

mo

de

l

0.0

0

.2

0.4

0

.6

0.8

1

.0

Probability Plot

Quantile Plot

-1 0 1 2 3 empirical

mod

el

0

2

4

Statistical modelingStatistical modeling

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

Generalized Extreme Value (GEV) Generalized Extreme Value (GEV) distributiondistribution

Documents

Extreme events and Euro-Atlantic atmospheric blocking in present and future climate simulations Jana Sillmann Max Planck Institute for Meteorology, Hamburg