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Eidgenössische Technische Hochschule Zürich Swiss Federal Institute of Technology European Heat Waves in a Changing Climate 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 1 WCRP / UNESCO Workshop on Extreme Climate Events, Paris, September 27-29, 2010 Schär, ETH Zürich European heatwave 2003 July 20-27 2010 temperature anomaly (NASA) (Reto Stöckli, ETH/NASA) August 2003 temperature anomaly Russian heatwave 2010 10 y 100 y 1000 y 2 10 y mean Schär, ETH Zürich Return period 100 y 1000 y (Schär et al. 2004, Nature) “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 3 Schär, ETH Zürich Prologue Climate change is much more certain than some people believe! The impacts of climate change are much more uncertain than most people believe! 4 Schär, ETH Zürich Predicting the water cycle: a key challenge Physics is complicated and rather poorly understood: Depends upon multi-scale and multiprocess interactions: • From micrometer to gigameter. • Involves dynamics, aerosols, clouds, radiation, convection, land-surface processes, etc. 5 Schär, ETH Zürich Contents Prologue Basic considerations Heatwave impacts Scenarios and observations Key uncertainties Epilogue 7 Schär, ETH Zürich Extreme events defined Mean warming Probability CTRL SCEN Temperature cold extremes Extreme events are defined in statistical terms, as events that deviate strikingly from the statistical mean. 8 Schär, ETH Zürich warm extremes 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: 1961-1990 SCN: 2071-2100 9 Schär, ETH Zürich France Iberian Peninsula Increase in variance Increase in skewness PRUDENCE, CHRM (ETH) model Fischer and Schär 2009; Clim. Dyn. Validation of daily summer temperatures OBS: 1961-1990 CTL: 1961-1990 France Iberian Peninsula Regional variations captured! 10 Schär, ETH Zürich PRUDENCE, CHRM (ETH) model Fischer and Schär 2009; Clim. Dynam, Observations from Haylock et al. 2008 Contents Prologue Basic considerations Heatwave impacts Scenarios and observations Key uncertainties Epilogue 11 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) 12 Schär, ETH Zürich Excess mortality in France Normalized mortality = mortality 2003 / longterm mean +200% +150% +100% +50% 0% -50% Note: no harvesting effect Date: August 1 - November 30, 2003 13 Schär, ETH Zürich (INSERM 2004) 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” 14 Schär, ETH Zürich Contents Prologue Basic considerations Heatwave impacts Scenarios and observations Key uncertainties Epilogue 15 Schär, ETH Zürich Regional climate scenarios ENSEMBLES Project: Transient experiments: 1950-2100 Greenhouse gas scenario: A1B Available models (period 1950-2100): 6 GCM / RCM pairs: GHG scenario GCM Coupled AOGCM (~120 km) RCM / Group CLM / ETH HadCM3Q0 RCM (25 km) HadRM / HC HadCM3Q16 RCA3 / C4I RACMO / KNMI ECHAM5 REMO / MPI RCA / SMHI 16 Schär, ETH Zürich (EU-Project ENSEMBLES, Coordinated by John Mitchel, Hadley Centre Changes in mean and variance 2071-2100 versus 1961-1990 JJA T2m change 99th percentile [K] JJA mean warming [K] Change in JJA daily variability [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 17 Schär, ETH Zürich ENSEMBLES, mean of 6 models Fischer and Schär 2010, Nature Geoscience Changes in variability 2070-2099 versus 1961-1990 (A2 scenario) Interannual var [%] Intraseasonal var [%] Seasonal var Diurnal temp range [%] [K] => Pronounced variability increases on all time-scales <= 18 Schär, ETH Zürich Schär et al. 2004; Vidale et al. 2007; Fischer and Schär 2009 (mean of 8 PRUDENCE RCMs) Observed increases in temperature variability? 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 19 Schär, ETH Zürich (Della Marta et al., 2007, JGR Observed relationship between mean and variance? Correlation between mean and variance over Europe, summer (ECA&D data set) variance mean Temporal evolution of mean and variance, Summer daily maximum temperature (La Rochelle, France) Evolution of mean and variance correlate! Warm periods have higher variability 20 Schär, ETH Zürich (Parey et al., 2010) (Yiou et al., 2009) Do peak temperature explain health impacts? JJA T2m change 99th percentile [K] Not really! Use concept of “apparent temperature” or “heat index” instead (Steadman 1979): 21 Schär, ETH Zürich 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) 22 Schär, ETH Zürich ENSEMBLES, mean of 6 models, scenario A1B Fischer and Schär 2010, Nature Geoscience Affected regions 2071-2100 Affected river basins Tejo Ebro Rhone Po Tiber Danube Geographical pattern is consistent across all model chains and health indices considered, but amplitude strongly depends upon model Affected towns Lisbon Seville Cordoba Marseille Milan Roma Napels Budapest Belgrade Bucharest Thessalonica Athens Number of days with apparent temperature ≥ 40.6°C 23 Schär, ETH Zürich (Fischer & Schär, 2010, Nature Geoscience) Contents Prologue Basic considerations Heatwave impacts Scenarios and observations Key uncertainties Epilogue 24 Schär, ETH Zürich Role of model physics Perturbed physics ensemble Testing the key physics parameters at 2xCO2 (53 simulations, 2.5x3.75 deg). Increase in July temperature extremes [factor] (99th percentile of Tmax) 25 Schär, ETH Zürich Ensemble range (80% range) (Barnett et al. 2006) Role of land-surface processes Summer 2003 Observations Control simulation Simulation with prescribed mean soil moisture evolution Number of hot days (Tmax > 90th percentile) 26 Schär, ETH Zürich (Fischer et al. 2007, see also Zaitchik et al. 2005, Seneviratne et al. 2006, Vidale et al. 2007, Fischer and Schär 2009) Role of convection … 27 Schär, ETH Zürich … and resolution 1 km 100 km (Figure: Elias Zubler) 25 km CR RCM RCM GCM [mm/h] Explicit Parameterized convection convection WET soil CTL DRY -28 Schär, ETH Zürich Soil-moisture precipitation feedback crucially depends upon representation of moist convection! [UTC] + [UTC] + [UTC] o -- IFS ECMWF + [UTC]et al, 2009; Brockhaus [UTC] [UTC] (Hohenegger et al. 2010) 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! Model biases are state dependent! Assumptions about bias changes matter! Analysis of ERA-40 driven RCM simulations (ENSEMBLES) Bayesian methodology using ENSEMBLES results (2021-2050) with TWO bias assumptions: (Christensen et al. 2008) 29 Schär, ETH Zürich PDF Observed monthly temperature [deg C] Alps PDF Temperature bias [K] constant bias | constant relation | joint estimate Scandinavia T [K] T [K] (Buser et al. 2009; Buser et al. in press) Contents Prologue Basic considerations Heatwave impacts Scenarios and observations Key uncertainties Epilogue 30 Schär, ETH Zürich Attribution of extreme events to climate change Probability of 2003-like heat waves in natural versus anthropogenic climates Associated liability issues natural anthropogenic Attributable to human influence (in a probabilistic sense). Might ultimately lead to liability claims. Easier with heat waves than other extremes. 31 Schär, ETH Zürich (Report by Munich Re, 2010) (Stott et al. 2004; see also: Schär and Jendritzky 2004; Allen and Lord 2004 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. 32 Schär, ETH Zürich