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Dr. C. Giannakopoulos Institute of Environmental Research & Sustainable Development, National Observatory of Athens, Greece. WP6.2 Progress and Plans Linking impact models to probabilistic scenarios of climate ENSEMBLES General Assembly Prague, 12 – 16 November 2007.

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Forest fire impact study Completion of journal paper: P. Good, Moriondo M., Giannakopoulos C. and Bindi M., The meteorological conditions associated with extreme fιre risk in Italy and Greece: relevance to climate model studies, Int. J. Wildland Fire, in press, Collaborative study between National Observatory of Athens and University of Florence Conference proceedings following ENSEMBLES Deliverable D6.9 1Giannakopoulos, C., P.LeSager, E. Kostopoulou, A. Vajda, and A. Venlainen, Intercomparison study of modelled forest fire risk in the Mediterranean for present day conditions, 6th International Workshop on Advances in Remote Sensing and GIS Applications in Forest Fire Management, Thessaloniki, Greece, September Collaborative study between National Observatory of Athens and Finnish Meteorological Insitute. Plan to convert to journal paper and involve also University of Florence.

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Dr. C. Giannakopoulos Dr. B. E. Psiloglou Heat Stress and Mortality in Athens: Impact Model Construction Impact Model Construction and Validation ENSEMBLES General Assembly Prague, 12 – 16 November 2007.

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Introduction Global climate change will have direct impacts on human health, including increased mortality due to heat stress and heat waves. An empirical-statistical model for heat stress is constructed for the city of Athens, using the JUNE-AUGUST or JUNE-SEPTEMBER months of the observational period The ultimate aim will be to use ENSEMBLES multiple regional climate model output to estimate daily mortality under a climate change world.

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Data Availability All-cause daily mortality data for the district of Athens, Greece, covering the period , were acquired. Source: Greek National Institute of Statistics, Greece. Daily climate data (Air Temperature & Rel. Humidity, Maximum & Minimum Air Temperature, Wind, Solar rad. Intensity), for the same period were provided Source: National Observatory of Athens, Greece.

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All-cause daily mortality data for Athens, Greece, and for the period Light-blue line is a smoothed 30-days running mean.

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Mean number of all-cause deaths per month, for ATHENS MONTH

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Calculation of EXCESS DEATHS for summer months, for ATHENS and for the period Two approaches were taken inorder to calculate EXCESS DEATHS, i.e. deaths beyond those expected for a specific period in that population: A. Use of a FIXED MEAN of daily mortality for each summer month, for the period (78.9 deaths for June, 81.2 in July and 79.1 in August). B. Use of a 30-days running mean, which smoothes the fluctuations in the death data, but selecting only the summer months. In each case, daily excess deaths were calculated by subtracting the expected form the observed daily death values. Example: for the fixed mean approach, that meant that subtracting 78.9 from every observed daily death for June, 81.2 from every observed daily death of July, and 79.1 from every observed daily death of August. In the 30-days running-mean method, from each observed daily death its corresponding expected value was subtracted.

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Example for EXCESS Deaths calculation DATE MAXIM. AIR TEMP OBSERVED NUMBER OF DEATHS EXCESS DEATHS (FIXED MEAN FOR JULY=81.2) 30-DAYS RUNNING MEAN EXCESS DEATHS USING 30-DAYS RUNNING MEAN 01/07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ /07/ Calculated

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EXCESS Deaths: Description of Method 1. Heat-related deaths were defined as the number of deaths occurring in excess of the number that would have been expected for that population in the absence of stressful weather ( McMichael et al., WHO, 1996 ). 2. Each number of excess deaths was then grouped into the corresponding 1 o C interval of maximum air temperature for simplification purposes (common in an ecological study design) Example: if on the 16 th of July the maximum temperature was 39.3 o C and there were 10 excess deaths, 10 would be put in the o C interval. 3. All excess deaths in each 1 o C interval for the entire period were added in order to find out where heat-related deaths were no longer detectable. In this way only temperatures over a certain threshold were regressed. Example: if the maximum temperature on the interval o C was observed 5 times, and the calculated excess deaths were: +20, -15, +12, -7 and +10, then SUM=(+20)+(-15)+(+12)+(-7)+(+10)=20.

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4. Finally, the sum of the excess deaths in each interval was divided by the frequency of occurrence of that temperature interval in the period, to give the number of deaths per day for a particular temperature interval. Example: if there were 681 deaths (the sum of all excess deaths negative & positive that occurred) in the 39 o C interval (i.e o C), and the number of times this temperature interval was observed in the period is 27, then the number of excess deaths per day is equal to 681/27=25.2 EXCESS Deaths: Description of Method

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Example for EXCESS Deaths per DAY calculation LOW LIMIT UPPER LIMIT SUM OF ALL EXCESS DEATHS (POS. or NEG.) NUMBER OF TIMES OBSERVED EXCESS DEATHS PER DAY FREQUENCY OF APPEARENCE Period: , Months: JUNE-JULY-AUGUST, using FIXED MEAN values Total number of data=1260

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Summer Deaths per Day for each temperature interval for Athens, during the period A V-shape relationship between mortality and temperature has been observed. The observed/expected analysis, under both approaches, showed that hotter days were associated with greater mortality risk. It becomes clear that the 30-days running mean approach gives a more conservative estimate of excess deaths than the fixed-summer months mean. Both approaches were consistent in showing that heat-related deaths were not discernible below 34 o C. Substantial heat-related deaths, occurred at very high temperatures. The temperature interval/frequency curve shows that very high maximum temperatures rarely occurred in the period.

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Summer Deaths per Day: Changing the month period

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Climate-Mortality relationship The non-linear regression of excess deaths per day above 34 o C (in 1 o C intervals) by its maximum temperature interval led to exponential equations. Using Summer Fixed Means Y= exp( X) R 2 =0.615 Using 30-days running mean Y= exp( X) R 2 =0.725

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Climate-Mortality relationship: Model Validation The testing of predictive models is the most critical stage of an impact assessment ( Parry & Carter 1998 ) : 1. In order to validate the model, available all-causes mortality data for Athens ( ) were split into two samples, hereafter referred to as 1990s ( ) and 2000s ( ). 2. For both periods, associations between mortality and climate were established for maximum air temperatures over 34 o C (the observed threshold for both periods), using the same observed/expected analysis and the regression methods described/used above. This test was performed both for summer fixed-mean values and 30-days running means. (see the following diagrams) 3. Finally, each function was applied to the daily maximum temperature series of the other time period, and observed and modeled data were compared by using the correlation between predicted vs. observed values and residual analysis approach.

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Climate-Mortality EXP-relationships for 1990s and 2000s 1990s 2000s Using Summer Fixed Means Y= exp( X) R 2 =0.722 Using 30-days run. Mean Y= exp( X) R 2 =0.769 Using Summer Fixed Means Y= exp( X) R 2 =0.619 Using 30-days run. Mean Y= exp( X) R 2 =0.862

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Climate-Mortality relationship: Summer FIXED means Model Validation: Residual analysis

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Climate-Mortality relationship: 30-days running means Model Validation: Residual analysis

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Climate-Mortality relationship: Summer FIXED means Model Validation: Correlation between obs. – pred. Corr. coef.= p>99% N=10 Corr. coef.= p>95% N=8

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Climate-Mortality relationship: 30-days running means Model Validation: Correlation between obs. – pred. Corr. coef.= p>99% N=10 Corr. coef.= p>99% N=8

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Calculation of EXCESS DEATHS for summer months, for ATHENS and for the period , using the HEAT INDEX value The same methodology was used in order to calculate EXCESS DEATHS based on the values of HEAT INDEX. Two approaches for the Heat-Index estimation: A. MAX-Heat-Index: was calculated using the Maximum air temperature and the corresponding Relative Humidity between 13:00 – 17:00 LST time period. B. MIN-Heat-Index: was calculated using the Minimum air temperature and the corresponding Relative Humidity between 5:00 – 7:00 LST time period. Heat-Index equation Heat-Index equation (C.SCHOEN, J.Appl.Meteor., 2005): HEAT-INDEX = T EXP( T) (1.0 - EXP( (T d -14.0))) where T is the air temperature and T d is the dew point temperature: T d = (237.3 * AB)/( AB), where AB = ((17.27 * T)/( T)) + LOG(RH/100.0), RH=rel.humidity

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Heat Index MAX and MIN

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Summer Deaths per Day for each Heat Index interval for Athens, during the period

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Heat-Index-Mortality relationship The non-linear regression of excess deaths per day above 35 o C (in 1 o C intervals) by its MAX-Heat-Index interval, and above 24 o C (in 1 o C intervals) by its MIN-Heat-Index interval led to exponential equations.

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Conclusions Our study has so far shown that there have been considerable heat-related deaths in the city of Athens, both from moderate and extreme heat, during the summer months of 1992–2006. The empirical-statistical model constructed is shown to reproduce well the observed heat-related deaths. This makes the model more reliable for the quantification of the potential impacts of climate change on health to be studied using ENSEMBLES regional model output.

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Acknowledgement : This work was supported by EU project ENSEMBLES under contract number GOCE-CT THANK YOU FOR YOUR ATTENTION

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