1. Schär, ETH Zürich 1 European Heat Waves in a Changing Climate WCRP / UNESCO Workshop on Extreme Climate Events, Paris, September 27-29, 2010 Christoph Schär and Erich Fischer Atmospheric 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 Zürich Swiss Federal Institute of Technology

2. Schär, ETH Zürich 2 July 20-27 2010 temperature anomaly (NASA) Russian heatwave 2010European heatwave 2003 August 2003 temperature anomaly 10 y 1000 y 100 y mean Return period (Schär et al. 2004, Nature) (Reto Stöckli, ETH/NASA) “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 3 Contents Prologue Basic considerations Heatwave impacts Scenarios and observations Key uncertainties Epilogue

4. Schär, ETH Zürich 4 Prologue Climate change is much more certain than some people believe! The impacts of climate change are much more uncertain than most people believe!

5. Schär, ETH Zürich 5 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.

6. Schär, ETH Zürich 6 Precipitation scenarios for Switzerland Precipitation change [%] Western SwitzerlandNortheastern Switzerland Southern Switzerland DJFMAMJJASONDJFMAMJJASON DJFMAMJJASON (C2SM, CH2011 Report, to be published spring 2011) 2070-2099 versus 1980-2009, Scenario SRES A1B Uncertainties are unacceptably large (e.g. for adaptation purposes) Scenario yields consistent signs in different seasons

7. Schär, ETH Zürich 7 Contents Prologue Basic considerations Heatwave impacts Scenarios and observations Key uncertainties Epilogue

8. Schär, ETH Zürich 8 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. CTRLSCEN 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 9 Frequency of daily summer temperatures CTL: 1961-1990 SCN: 2071-2100 France Increase in variance Iberian Peninsula Increase in skewness PRUDENCE, CHRM (ETH) model Fischer and Schär 2009; Clim. Dyn.

10. Schär, ETH Zürich 10 Iberian Peninsula Regional variations captured! France CTL: 1961-1990 OBS: 1961-1990 PRUDENCE, CHRM (ETH) model Fischer and Schär 2009; Clim. Dynam, Observations from Haylock et al. 2008 Validation of daily summer temperatures

11. Schär, ETH Zürich 11 Contents Prologue Basic considerations Heatwave impacts Scenarios and observations Key uncertainties Epilogue

12. Schär, ETH Zürich 12 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 13 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 14 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 15 Contents Prologue Basic considerations Heatwave impacts Scenarios and observations Key uncertainties Epilogue

16. Schär, ETH Zürich 16 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 17 JJA mean warming [K] Changes in mean and variance JJA T2m change 99th 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 models Fischer and Schär 2010, Nature Geoscience Change in JJA daily variability [K]

18. Schär, ETH Zürich 18 Changes in variability Interannual varIntraseasonal var Diurnal temp range Seasonal 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 19 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 Europewarming has maximum in Iberian Peninsula

20. Schär, ETH Zürich 20 Observed relationship between mean and variance? Correlation between mean and variance over 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) mean variance

21. Schär, ETH Zürich 21 JJA T2m change 99th 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 22 Changes in apparent temperature 1961-19902021-20502071-2100 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 A1B Fischer and Schär 2010, Nature Geoscience

23. Schär, ETH Zürich 23 Number of days with apparent temperature ≥ 40.6°C Affected regions 2071-2100 Affected river basins Tejo Ebro Rhone Po Tiber Danube Affected towns Lisbon Seville Cordoba Marseille Milan Roma Napels Budapest Belgrade Bucharest Thessalonica Athens (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 24 Contents Prologue Basic considerations Heatwave impacts Scenarios and observations Key uncertainties Epilogue

25. Schär, ETH Zürich 25 Role of model physics (Barnett et al. 2006) Increase in July temperature extremes [factor] (99th percentile of Tmax) Ensemble range (80% range) Perturbed physics ensemble Testing the key physics parameters at 2xCO 2 (53 simulations, 2.5x3.75 deg).

26. Schär, ETH Zürich 26 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 simulationObservations Simulation with prescribed mean soil moisture evolution Number of hot days (T max > 90 th percentile) Summer 2003

27. Schär, ETH Zürich 27 Role of convection …

28. Schär, ETH Zürich 28 GCM 100 km … and resolution CR RCM 1 km RCM 25 km (Figure: Elias Zubler) IFS ECMWF [mm/h] [UTC] +--++ o Explicit convection Parameterized convection WET soil CTL DRY [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 29 Role of model biases Special 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! Temperature bias [K] Observed monthly temperature [deg C] (Christensen et al. 2008) Model biases are state dependent! Analysis of ERA-40 driven RCM simulations (ENSEMBLES) Assumptions about bias changes matter! Alps Scandi- navia  T [K] PDF (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 30 Contents Prologue Basic considerations Heatwave impacts Scenarios and observations Key uncertainties Epilogue

31. Schär, ETH Zürich 31 Probability of 2003-like heat waves in 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

32. Schär, ETH Zürich 32 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.