1. March 2015.

2. Outline: Introduction What is the Heat Wave? Objectives Identifying and comparing the current and future status of heat wave events over Iran Intensity, Frequency, Duration Data and Materials Observational data (1981-2010) / irimo Daily atmospheric large scale variables (1981-2099): NCEP predicates in observation periods GCM modeling outputs in observation periods and future periods/ CCCSN Methods Types of Downscaling Methods: Dynamical, Statistical Statistical DownScaling Model (SDSM) Generation of Climatic Scenarios by SDSM Uncertainty Analysis Analyzing and Extraction of Heat Waves Mapping and Drawing: Spatial Analysis by GIS Results changes in the occurrence of heat wave: Intensity, Frequency, Duration Conclusion What’s happening?

3. What is the Heat Wave? absolute and numerical thresholds(different numerical thresholds in each countries ) relative and percentile thresholds(with a comprehensive vision ) In the field of heat waves, Barros began his studies in the late 19th century. Barros considered heat wave as a continued period of three consecutive days or more, that the average daily temperature is reached 32.2 ° C (Robinson 2001). There is no comprehensive and universal definition of a heat wave (Tamrazian et al. 2008). in this regard, any temperature above a certain threshold as single or one-day, is not a heat wave but the heat waves is different in duration characteristics of its, from extreme indices of hot days. Any time a person is exposed to heat, they are in danger of suffering from heat exhaustion. These heat-related illnesses have killed thousands over the last 100+ years. For example, in 1980, a heat wave in the United States killed more than 1,250 people. That is nothing compared to the 50,000+ that died in the European heat wave of 2003. In this study, if the daily maximum temperature of the studied station for a period with at least three consecutive days and more is higher than the percentile thresholds of 90, 95 and 98, it is considered as a period of heat wave.

4. Topography map of the study area: Iran Objectives Identifying and comparing the current and future status of heat wave events during the warm period of year in Iran Intensity of Heat Waves Frequency of Heat Waves Duration of Heat Waves Why Iran? In summary reports of extreme climatic events of Iran in 2012 (IRIMO 2012), an increase in extreme climatic events particularly heat waves over Iran and the necessity of knowing to climatic extremes phenomena in the national arena for mitigation and adaptation planning is considered. 44 synoptic stations of Iran warm period of year: April, May, June, July, August, September Observation period (1981-2010), near future (2041-2070) and far future (2071-2099) periods.

5. Data: Observational data (1981-2010) /IRIMO daily maximum temperatures, as predicated that prepared by Iran Meteorological Organization in 44 synoptic stations (via http://www.irimo.ir) Modeling data inputs, as predictors includes: Daily atmospheric large scale variables (1981-2010)/NCEP GCM modeling outputs during (1981-2010) and (2041-70; 2071-99) periods/ CCCSN (Canadian Climate Change Scenarios Network, via http://www.cccma.ec.gc.ca) The large-scale predictors compared to the average and standard deviations of the observation period (1981-2010) was normalized.

6. The grid box of Hadcm3 model

7. Methodology: Downscaling Methods: Dynamical, Statistical Using of Statistical DownScaling Model (SDSM) Preparing of data base(observation and modeling data) Monitoring and Screen Variables (observational Variable as dependent, large scale variables as independent) Calibration and Validation of Modeling Results (T-test,SE, R2, RMSE, MBE) Climatic Scenarios Generation for future decades Uncertainty Analysis Analyzing of results, includes: Extraction of Heat Waves from Daily Maximum Temperature Time Series, Mapping and Drawing of Heat Wave features by GIS

8. The concept of spatial downscaling: Dynamical: additional data and physical processes in models similar to GCMs but much higher resolution, covering only select portions of the globe Statistical: Empirical relationships : Stochastic Weather Generator (SWG) and Multiple Linear Regression (MLR)

9. SDSM Model : SDSM model has been developed by Wilby et al. (2002) as an appropriate tool for statistical downscaling and making meteorological data using a combination of Stochastic Weather Generator (SWG) and Multiple Linear Regression (MLR).

10. Analyzing of the outputs:

13. Uncertainty Analysis In assessing climate change and theirs projection, there are two main groups of uncertainty: The first case is related to dynamic structure of General Circulation models and their process numerical discrete each other which the contribution of this type of uncertainty gradually decreases with the passage of time due to increasing the quality and accuracy of the models. The second case is related to the amount of output and greenhouse gas emissions. Generally in this study, to evaluate the uncertainty arising from the use of various model-scenarios, the Mean Observed Temperature method (Equation 4) is used. In this equation, is the weight given to each model-scenarios (j), is the difference among the average of simulated maximum temperature in the future periods from the average of observed maximum temperature for each day (i) and n: is the number of model-scenarios.

14. Analyzing of the outputs: Extraction of Heat Waves: Intensity, Frequency, Duration Intensity of Heat Waves

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16. Frequency of Heat Waves

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18. Duration of Heat Waves

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20. What’s happening? * The southern areas that are adjacent to the coasts will have the lowest rising changes in the occurrence of heat waves but in this regard, what seems important is these are due to existence more humidity and high capacity of humidity in the atmosphere will severely faced to risk of human health in coming decades. ** the mountainous regions where are located in the northern latitudes will have the greatest increasing changes in heat waves events.. *** Overall, comparative analysis indicates that the heat waves in future decades will be more sudden, more severe and shorter lasting with lower frequencies than base period.

21. Acknowledgement The authors would like to acknowledge the Iran Meteorological Organization for providing the data. Thanks to the Builders of SDSM Software that it is related to the preparing and sending some parts of the daily large-scale atmospheric variables in the extent of Iran. Thanks also to the climatology department of University of Sistan and Baluchestan (USB) and meteorology department of Istanbul Technical University (ITU) in the field of scientific collaborations.