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A.Kislov , G.Surkova , D.Gushina , P.Toropov , D.Blinov . Dpt. Of Meteorology and Climatology

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. Contents.

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A.Kislov , G.Surkova , D.Gushina , P.Toropov , D.Blinov . Dpt. Of Meteorology and Climatology

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

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

  4. Archive of station data Variables: Pressure, surface air temperature, water vapor pressure,wind speedand 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 daybefore 1936 4 times a dayat mean astronomic times during the 1936-1965 period. 8 times a dayat 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 modelledby 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 • Preparation of the meteorological observations for all working groups • Preparation of initial conditions for the start of the "sea models” • Hazards explicit modeling (bora, cyclones) in the COSMO and WRF • Identification of synoptic conditions, corresponding to different hazards • Projection of extreme atmospheric processes under the climate change

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

  8. Contents • Preparation of the meteorological observations for all working groups • Preparation of initial conditions for the start of the "sea models” • Hazardous weather explicit modeling (bora, cyclones) in the COSMO and WRF • Identification of synoptic conditions, corresponding to different hazards • 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 wave heights calculated by the model SWAN analysis of NCEP/NCAR 1 × 1 WRF-ARW

  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 24 hours ahead 48 hours ahead Forecast of water surface runoff by COSMO-RU. Runoff during previous 24 hours Model is capable to simulate the extreme runoff during the flood.

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

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

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

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

  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)

  18. Climate change and storm events frequency • time seriesof V  15 m/s don’t show obvious trends; • synoptic features for storm events: it is reveled that prevailing of 1st 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

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

  20. Change of occurrence of water flows predictorunder warmer climate in the Black sea area • 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.

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

  22. Conclusions and future plan • efficiency of prognostic methodologyfor 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

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

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

  25. HW types for data series 2 and 3 (SWAN calendar, 1948-2010) Black Sea – 137 cases of HW (wave height >4 m) Surface pressure - centroids I type 43 % II type 57 % Weather types are revealed by EOF and cluster analysis – two main statistically significant types

  26. How we can predict unmodelable processes based on climate simulation:application to the problem of predictingof 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 howwell climate model reproduce the desired features of SLP. • Third, to determinewhat changes occur in the SLP climate forecast. • Fourth, we have to make the transition to the prediction of thestudied 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

  27. 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 Wind speed over the sea for events when daily V>=15 m/s at least in one grid point Monthly frequency for events when daily V>=15 m/s

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

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