schär, eth zürich 1 european heat waves in a changing climate wcrp / unesco workshop on extreme...

1 Schär, ETH Zürich

European Heat Waves in a Changing Climate

WCRP / UNESCO Workshop on Extreme Climate Events, Paris, September 27-29, 2010

Christoph Schär and Erich FischerAtmospheric and Climate Science, ETH Zürich

http://www.iac.ethz.ch/people/schaer

Thanks to: Peter Brockhaus, Christoph Buser, Cathy Hohenegger, Sven Kotlarski, Hans-Ruedi Künsch, Dani Lüthi, Elias Zubler

Eidgenössische Technische Hochschule ZürichSwiss Federal Institute of Technology


July 20-27 2010 temperature anomaly

(NA

SA

)

Russian heatwave 2010 European heatwave 2003

August 2003 temperature anomaly

10 y10 y

1000 y 1000 y 100 y100 y

mean

Return period (Schär et al. 2004, Nature)

(Ret

o S

töck

li, E

TH

/NA

SA

)

“There was nothing similar to this on the territory of Russia during the last one thousand years."

Alexander Frolov Head of the Russian Meteorological Center


Contents

Prologue

Basic considerations

Heatwave impacts

Scenarios and observations

Key uncertainties

Epilogue


Prologue

Climate change is much more certain than

some people believe!

The impacts of climate changeare much more uncertain than

most people believe!


N216 (20 km)

Predicting the water cycle: a key challenge

Physics is complicated and rather poorly understood:

Depends upon multi-scale and multi-process interactions:

• From micrometer to gigameter.

• Involves dynamics, aerosols,clouds, radiation, convection, land-surface processes, etc.


Contents

Prologue


Heatwave impacts


Key uncertainties

Epilogue


warm extremescold extremes

Extreme events defined

Mean warming

Temperature

Probability

Extreme events are defined in statistical terms, as events that deviate strikingly from the statistical mean.

CTRL SCEN

Climate change implies changes in frequency of extremes.

Generally needs to account for changes of the whole statistical distribution (i.e. mean, variability, skewness, etc)


Frequency of daily summer temperatures

CTL: 1961-1990SCN: 2071-2100

France

Increase in variance

Iberian Peninsula

Increase in skewnessPRUDENCE, CHRM (ETH) model

Fischer and Schär 2009; Clim. Dyn.


Iberian Peninsula

Regional variations captured!

France

CTL: 1961-1990OBS: 1961-1990

PRUDENCE, CHRM (ETH) modelFischer and Schär 2009; Clim. Dynam, Observations from Haylock et al. 2008

Validation of daily summer temperatures


Contents

Prologue


Heatwave impacts


Key uncertainties

Epilogue


Impacts of the summer 2003 in Europe

Agricultural losses:12.3 Billion US$ (SwissRe estimate)

Shortage of electricity, peak prices on spot market (EEX, Leipzig)

Serious problems with- freshwater resources (Italy)- forest fires (Portugal)- freshwater fish (Switzerland)

Heat excess mortality: estimates 35’000 to 70’000

August 2003 temperatures relative to 2000-2002, 2004(Reto Stöckli, ETH/NASA, MODIS)


Excess mortality in France

Normalized mortality = mortality 2003 / longterm mean

(INSERM 2004)

Date: August 1 - November 30, 2003

Note: no harvesting effect

+200%

+150%

+100%

+50%

0%

-50%


Key health impact factors

Not only peak temperatures, but:

(1) Length of heat wave (accumulation effect)

=> study heat wave duration

(2) Daytime AND nighttime temperatures (sleep deprivation)

=> study number of hot days and nights

(3) Relative humidity (heat stress)

=> study “apparent temperature” or “heat index”


Contents

Prologue


Heatwave impacts


Key uncertainties

Epilogue


Regional climate scenarios

Coupled AOGCM(~120 km)

GHG scenario

RCM(25 km)

ENSEMBLES Project:

Transient experiments: 1950-2100 Greenhouse gas scenario: A1B

Available models (period 1950-2100): 6 GCM / RCM pairs:

(EU-Project ENSEMBLES, Coordinated by John Mitchel, Hadley Centre)

HadCM3Q0

HadCM3Q16

ECHAM5

CLM / ETH

HadRM / HC

RCA3 / C4I

RACMO / KNMI

REMO / MPI

RCA / SMHI

GCM RCM / Group


JJA mean warming [K]

Changes in mean and variance

JJA T2m change99th percentile [K]

Strongest 99th percentile increase to the north of strongest mean warming

Good agreement with PRUDENCE results (Kjellström et al. 2007, Fischer and Schär 2009)

Differences between mean and 99th percentile is due to variability changes

2071-2100 versus 1961-1990

ENSEMBLES, mean of 6 modelsFischer and Schär 2010, Nature Geoscience

Change in JJA daily variability [K]


Changes in variability

Interannual var Intraseasonal var Diurnal temp rangeSeasonal var

Schär et al. 2004; Vidale et al. 2007; Fischer and Schär 2009 (mean of 8 PRUDENCE RCMs)

2070-2099 versus 1961-1990 (A2 scenario)

=> Pronounced variability increases on all time-scales <=

[%] [%] [K][%]


Observed increases in temperature variability?

(Della Marta et al., 2007, JGR)

Analysis of 54 high-quality homogenized temperature records from 1880-2005.

Finds a statistically significant increase in peak temperatures.

Finds a statistically significant variability signal.

Geographical pattern of trends is consistent with climate change scenarios:• variability increases has

maximum in Central Europe• warming has maximum in

Iberian Peninsula


Observed relationship between mean and variance?

Correlation between mean and varianceover Europe, summer

(ECA&D data set)

(Yiou et al., 2009)

Temporal evolution of mean and variance,Summer daily maximum temperature

(La Rochelle, France)

Evolution of mean and variance correlate!

Warm periods have higher variability

(Parey et al., 2010)

mea

nva

rian

ce


JJA T2m change99th percentile [K]

Do peak temperature explain health impacts?

Not really!Use concept of “apparent temperature” or “heat index” instead (Steadman 1979):

Accounts for effects of temperature AND humidity.

Measure of the effort needed to maintain human body temperature under shaded and ventilated conditions.

Based on a biophysical model that accounts for the effects of radiation, clothing, heat-transfer, sweating, etc.


Changes in apparent temperature

1961-1990 2021-2050 2071-2100

Number of days with apparent temperature ≥ 40.6°C(large heat stroke risk with extended exposure)

Dramatic increases in low-altitude Mediterranean (river basins and coasts)

ENSEMBLES, mean of 6 models, scenario A1BFischer and Schär 2010, Nature Geoscience


Number of days with apparent temperature ≥ 40.6°C

Affected regions2071-2100

Affected river basinsTejoEbro

RhonePo

TiberDanube

Affected townsLisbonSevilleCordobaMarseilleMilanRomaNapels BudapestBelgradeBucharestThessalonicaAthens

(Fischer & Schär, 2010, Nature Geoscience)

Geographical pattern is consistent across all model chains and health indices considered, but amplitude strongly depends upon model


Contents

Prologue


Heatwave impacts


Key uncertainties

Epilogue


Role of model physics

(Barnett et al. 2006)

Increase in July temperature extremes [factor](99th percentile of Tmax)

Ensemble range(80% range)

Perturbed physics ensembleTesting the key physics parameters at 2xCO2

(53 simulations, 2.5x3.75 deg).


Role of land-surface processes

(Fischer et al. 2007, see also Zaitchik et al. 2005, Seneviratne et al. 2006, Vidale et al. 2007, Fischer and Schär 2009)

Control simulationObservationsSimulation with prescribed

mean soil moisture evolution

Number of hot days (Tmax > 90th percentile)

Summer 2003


Role of convection …


GCM

100 km

… and resolution

CR RCM

1 km

RCM

25 km

(Fig

ure:

Elia

s Z

uble

r)

IFS ECMWF

[UTC] [UTC] [UTC]

+-- + +--o

Explicitconvection

Parameterizedconvection

WET soilCTLDRY

[UTC][UTC]

[mm

/h]

[UTC] (Hohenegger et al, 2009; Brockhaus et al. 2010)

Soil-moisture precipitation feedback crucially depends upon representation of moist convection!


Role of model biasesSpecial role of biases for analysis of extremes:

• biases in statistical distribution beyond biases in mean (i.e. in variability)!

• impacts often depend on absolute thresholds => biases particularly crucial!

Tem

per

atu

re b

ias

[K

]

Observed monthly temperature [deg C]

(Christensen et al. 2008)

Model biases are state dependent!

Analysis of ERA-40 driven RCMsimulations (ENSEMBLES)

Assumptions about bias changes matter!

Alps

Scandi-navia

T [K] T [K]

PD

FP

DF

(Buser et al. 2009; Buser et al. in press)

Bayesian methodology using ENSEMBLES results (2021-2050) with TWO bias assumptions:

constant bias | constant relation | joint estimate


Contents

Prologue


Heatwave impacts


Key uncertainties

Epilogue


Probability of 2003-like heat wavesin natural versus anthropogenic climates

(Stott et al. 2004; see also: Schär and Jendritzky 2004; Allen and Lord 2004

natural

anthropogenic

Attributable to human influence (in a probabilistic sense).

Might ultimately lead to liability claims.

Easier with heat waves than other extremes.

Attribution of extreme events to climate change

(Report by Munich Re, 2010)

Associated liability issues


Summary

Observations and models show relevance of warming AND changes in variance.

Peak temperatures: - changes not co-located with changes in mean,- but affected by variance changes.

Health impacts: - changes not co-located with changes in peak temperature,- most significant impact in low-altitude river basins and along coasts,- Pattern robust but amplitude uncertain.

Major uncertainties related to - soil-moisture precipitation feedback (land-surfaces and convection),- bias assumptions.

schär, eth zürich 1 european heat waves in a changing climate wcrp / unesco workshop on extreme...

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