1. Atmospherically – induced hazards in the coastal zone and the possibility of their decadal and centennial prediction A.Kislov, G.Surkova, D.Gushina, P.Toropov, D.Blinov. Dpt. Of Meteorology and Climatology NRAL, 14.12.2012

2. Contents 1. Preparation of the meteorological observations for all working groups 2. Preparation of initial conditions for the start of the "sea models” 3. Hazardous weather explicit modeling (bora, cyclones) in the COSMO and WRF 4. Identification of synoptic conditions, corresponding to different hazards 5. Projection of extreme atmospheric processes under the climate change

3. Contents 1. Preparation of the meteorological observations for all working groups 2. Preparation of initial conditions for the start of the "sea models” 3. Hazardous weather explicit modeling (bora, cyclones) in the COSMO and WRF 4. Identification of synoptic conditions, corresponding to different hazards 5. Projection of extreme atmospheric processes under the climate change

4. Archive of station data Archive of station data Variables: Pressure, surface air temperature, water vapor pressure, wind speed and direction, characteristics of cloudiness, current weather OBSERVATION PERIOD: maximum period of the data span is 01.01.1871 to 01.01.2001 most stations (more 60%) operated during the period 1936-1990 The measurements were made: 3times a day before 1936 4 times a day at mean astronomic times during the 1936-1965 period. 8 times a day at United Time Coordinate (UTC) since 01.01. 1966 structure of the data archive: Archive contains three types of data files (ID – index of station): STN_ID.dat (2095 files, 1 per station), data in ASCII STN_ID.flg (2095 files, 1 per station), quality marks of data ussr_hly_stn.list.txt (1 file with essential station metadata such as station identifier, coordinates, elevation, date of the first and the last records, and station name) Density measuring network

5. Calendars of hazardous weather Hazardous weather (HW) cases selection – three calendars 1) Calendar for extreme observed wave storms and surges (1948- 2012) 2) Calendar for wave storms with significant wave height 4 m modelled by wave model SWAN (1948-2012) 3) Calendar of hazardous weather (HW) in the future (2046-2065) – detected by the statistical methods, data of numerical climate simulations (CMIP3)

6. Contents 1. Preparation of the meteorological observations for all working groups 2. Preparation of initial conditions for the start of the "sea models” 3. Hazards explicit modeling (bora, cyclones) in the COSMO and WRF 4. Identification of synoptic conditions, corresponding to different hazards 5. Projection of extreme atmospheric processes under the climate change

7. Initial and boundary conditions for wave modelling Reanalysis NCEP/NCAR (1948-2012) 1,9x1,9 degree Mesoscale non- hydrostatic atmospheric model COSMO-RU ERA-40 (1958-2002) 2,5x2,5 degree Surface WIND DATA ERA-Interim (1979- 2012), 1x1 degree Every 6 hours Downloading data => decoding => preparing for selected area

8. Contents 1. Preparation of the meteorological observations for all working groups 2. Preparation of initial conditions for the start of the "sea models” 3. Hazardous weather explicit modeling (bora, cyclones) in the COSMO and WRF 4. Identification of synoptic conditions, corresponding to different hazards 5. Projection of extreme atmospheric processes under the climate change

9. Some results of the statistical evaluation of forecast accuracy a) the average forecast (blue line) and observed (red line) wind speed; b) the empirical pdf of forecast (red bars) and actual (blue bars) wind speeds over the “test area” (along the horizontal axis – the intervals in m/s), c) modal values of wind speed: forecast (red line) and the actual value (blue line) (along the horizontal axis – hours forecast).

10. Wind speed (a,c) and wave heights (b,d) at the time of Novorosiysk bora averaged over 26. 01.2012 analysis of NCEP/NCAR 1 × 1 WRF-ARW wave heights calculated by the model SWAN

11. On the way to the forecast of water flood in Sochi-Tuapse in October 2010 by COSMO-RU Precipitation during last 12 hours. Forecast for 10:00 MSK 16.10.2010 66 hours ahead 48 hours ahead24 hours ahead Model is capable to simulate the extreme runoff during the flood. Forecast of water surface runoff by COSMO-RU. Runoff during previous 24 hours

12. Contents 1. Preparation of the meteorological observations for all working groups 2. Preparation of initial conditions for the start of the "sea models” 3. Hazardous weather explicit modeling (bora, cyclones) in the COSMO and WRF 4. Identification of synoptic conditions, corresponding to different hazards 5. Projection of extreme atmospheric processes under the climate change

13. Predictors: large zonal frontal zone expanding in north-south direction, temperature jumps precipitations fall conditions, wind direction in the mouth of river etc Predictors: the main factor is abundant precipitation. No unified scheme of synoptic situation, but the intensive frontal zone is always presented Synoptic situations associated to the various types of flood Storm surges Water-flow Predictors: trajectories of depressions, wind speed and wind direction, duration of wind forcing Ice-jam Neva River 28.10.2006 12 UTCDon River 28.02.2005 12 UTC Mzymta 26.10.1997 00 UTC Pechora 2.06.2008 12 UTC

14. Contents 1. Preparation of the meteorological observations for all working groups 2. Preparation of initial conditions for the start of the "sea models” 3. Hazardous weather explicit modeling (bora, cyclones) in the COSMO and WRF 4. Identification of synoptic conditions, corresponding to different hazards 5. Projection of extreme atmospheric processes under the climate change

15. Interannual, decadal and centennial projections. How they can be predicted? Interannual, decadal and centennial hazardous weather projections. How they can be predicted? NAO ЕNSO Sahel PDO CMIP5 (Coupled Model Intercomparison Project) G.Meehl

16. Method of hazardous weather projection for a long time: step by step

17. Wind speed more than 15 m/s: observation, hindcasting, projecting The Black Sea costal wind observations (1948-2011)

20. Climate change and storm events frequency -time series of V  15 m/s don’t show obvious trends; -synoptic features for storm events: it is reveled that prevailing of 1 st type of SLP fields for storms took place for the last 60 years and it is expected to continue in the 21 century; -climate projection based on ECHAM5 simulation shows slight redistribution of monthly frequency of strong winds and conservation of the ration of storms SLP fields types

21. The same projecting extreme wind speed for the Caspian Sea MPI-ECHAM5 I type 65 % II type 35 % Caspian Sea – 137 cases of HW (wave height >4 m)

22. Number of cases 1961-19801981-20002046-2065 Winter 303381217grad T> 18 o C/1000 km 131713 grad T>18 o C/1000 km precipitation>20 mm/day Summer 203875grad T> 12 o C/1000 km 112 grad T>12 o C/1000 km precipitation>20 mm/day The probability of occurrence of predictors for water flows in the Black sea region was estimated for modern climate and global warming conditions using the outputs of ECHAM5/MPI-OM model. It is shown that the occurrence of intensive frontal zone in the South of Russia will increase (decrease) in summer (winter) under warmer climate conditions which may contribute to the increase of water flow risks in summer. Change of occurrence of water flows predictor under warmer climate in the Black sea area

23. Number of cases with intensive frontal zone Winter Number of cases Years Number of cases with intensive frontal zone Summer Number of cases Years Change of intensive frontal zone occurrence in the Black sea area

24. Conclusions and future plan -efficiency of prognostic methodology for changes of hazardous weather have been demonstrated -model MPI-ECHAM5 shows good agreement with assessment of frequency of storms SLP fields and used for projection of storms frequency in 21 century; it allows to hope that this technique can give relevant practice information -All CMIP5 models will be used for realization of this task, based both on different RCPs and different decadal forecasting

25. Thank you! G.Meehl. 2012.Hamburg

27. 1948-2010 reanalysis MPI-ECHAM5 Frequency of each of 19 HW and climate projection Black Sea

28. EOF HW type III 10,440,45 20,200,21 30,10 40,07 50,04 60,03 7 0,02 8 HW types for data series 2 and 3 (SWAN calendar, 1948-2010) Black Sea – 137 cases of HW (wave height >4 m) Weather types are revealed by EOF and cluster analysis – two main statistically significant types I type 43 % II type 57 % Surface pressure - centroids

29. How we can predict unmodelable processes based on climate simulation: application to the problem of predicting of hazardous storm wind speeds on the shores of the Black Sea First, we have to establish a relationship in quantitative terms (based on observations) between storm wind speed and sea-level pressure (SLP) field Second, we will test how well climate model reproduce the desired features of SLP. Third, to determine what changes occur in the SLP climate forecast. Fourth, we have to make the transition to the prediction of the studied hazard storm. /First & Second/ Storms over the Black Sea area have been studied for the last 60 years based on reanalysis data and coastal observations. A wind speed of 15 m/s is chosen as a threshold to detect the storm situation. EOF analyses of SLP are applied to identify the main types of atmospheric circulations causing severe winds and storm waves. The first three EOFs cover more than 70% of the total dispersion in all cases. This fact allows to create a ‘data bank’ of filtered SLP pattern for previous storms and to compare any single case with the data base. wind

30. Important changes of the storm activity on the shores of the Black Sea will not be expected under the future global warming scenario /Third &Fourth / Data source: CMIP3 Data type: daily sea level pressure (SLP) Climate model: MPI-ECHAM5 (Max Planck Institute for Meteorology,Hamburg, Germany) Numerical experiments ID: - 20C3M (1961-2000); -A2(SRES scenario) – 2046-2065 Purpose: -to verify model ability to simulate relative frequency and number of storm - events (similarity of SLP fields with corr>=0.85); - to check possible changes of storm events frequency in 21 century; MPI-ECHAM5 : Relative frequency of storm events (left) and number of days (right) Wind direction frequency for events when daily V>=15 m/s Monthly frequency for events when daily V>=15 m/s Wind speed over the sea for events when daily V>=15 m/s at least in one grid point

31. Спасибо за внимание! А.В.Кислов МГУ, географический факультет, кафедра метеорологии и климатологии avkislov@mail.ru