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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
2 Schär, ETH Zürich
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
3 Schär, ETH Zürich
Contents
Prologue
Basic considerations
Heatwave impacts
Scenarios and observations
Key uncertainties
Epilogue
4 Schär, ETH Zürich
Prologue
Climate change is much more certain than
some people believe!
The impacts of climate changeare much more uncertain than
most people believe!
5 Schär, ETH Zürich
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.
7 Schär, ETH Zürich
Contents
Prologue
Basic considerations
Heatwave impacts
Scenarios and observations
Key uncertainties
Epilogue
8 Schär, ETH Zürich
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)
9 Schär, ETH Zürich
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.
10 Schär, ETH Zürich
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
11 Schär, ETH Zürich
Contents
Prologue
Basic considerations
Heatwave impacts
Scenarios and observations
Key uncertainties
Epilogue
12 Schär, ETH Zürich
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)
13 Schär, ETH Zürich
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%
14 Schär, ETH Zürich
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”
15 Schär, ETH Zürich
Contents
Prologue
Basic considerations
Heatwave impacts
Scenarios and observations
Key uncertainties
Epilogue
16 Schär, ETH Zürich
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
17 Schär, ETH Zürich
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]
18 Schär, ETH Zürich
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][%]
19 Schär, ETH Zürich
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
20 Schär, ETH Zürich
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
21 Schär, ETH Zürich
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.
22 Schär, ETH Zürich
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
23 Schär, ETH Zürich
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
24 Schär, ETH Zürich
Contents
Prologue
Basic considerations
Heatwave impacts
Scenarios and observations
Key uncertainties
Epilogue
25 Schär, ETH Zürich
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).
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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
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Role of convection …
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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!
29 Schär, ETH Zürich
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
30 Schär, ETH Zürich
Contents
Prologue
Basic considerations
Heatwave impacts
Scenarios and observations
Key uncertainties
Epilogue
31 Schär, ETH Zürich
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
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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.