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NORTH SEA - CASPIAN PATTERN (NCP) and its influence on the hydroclimate of Turkey

NORTH SEA - CASPIAN PATTERN (NCP) and its influence on the hydroclimate of Turkey. OZAN MERT GÖKTÜRK İ TÜ EURASIA INSTITUTE OF EARTH SC IENCES. Contents. A re view of NCP Data sets and methodology Results: effects of NCP Problems and discussion.

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NORTH SEA - CASPIAN PATTERN (NCP) and its influence on the hydroclimate of Turkey

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  1. NORTH SEA - CASPIAN PATTERN (NCP)and its influence on the hydroclimate of Turkey OZAN MERT GÖKTÜRK İTÜ EURASIA INSTITUTE OF EARTH SCIENCES

  2. Contents • A review of NCP • Data sets and methodology • Results: effects of NCP • Problems and discussion

  3. North Sea – Caspian Pattern (NCP) (Kutiel ve Benaroch, 2002) NCPI= gpm(500hPa) (0°, 55° N ; 10° E, 55° N) –gpm(500hPa) (50° E, 45° N ; 60° E, 45° N)

  4. NCP(+) NCP(-)

  5. Progress of NCPI through the year (Kutiel ve Benaroch, 2002)

  6. NCP(-) Kutiel ve Benaroch (2002)

  7. NCP(+) Kutiel ve Benaroch (2002)

  8. Hypothesis There should be a significant relation between NCP and Turkey’s precipitation/streamflow regimes. This relation can be investigated by Pearson’s correlation coefficient and also with the Canonical Correlation Analysis.

  9. Data sets • Predictors - Large-scale 500 hPa geopotential height field - NCPI • Predictands - Monthly precipitation series of Turkey - Monthly streamflow series of Turkey

  10. Predictors • Monthly mean 500 hPa geopotential height field • 10°W - 60°E 30°N - 70°N 2.5°x2.5° grid 493 grid points • 1958 – 2003 • NCEP-NCAR Reanalysis

  11. Predictands: streamflow • Monthly, 110 stations • 1958-2003

  12. Predictands: precipitation • Monthly, 260 stations • 1958-2003

  13. Data pre-processing • “De trending” : to include only the variations. • Outlier trimming: to avoid the distortion of the analysis by the extreme values

  14. Data homogenization • Alexandersson (1986) • Based on comparison with a reference regional time series

  15. Methodology • Pearson’s correlation (well-known) Correlate NCPI with monthly streamflows and precipitations…

  16. Methodology • Canonical Correlation Analysis (CCA) Pearson korelasyonu -> univariate time series CCA -> multivariate time series

  17. CCA Space Time Any correlation between these two??? 1. Write the time series as anomalies,

  18. 2. These anomalies are composed of independent spatial patterns and their time coefficients… (von Storch ve Zwiers, 1999) Time coefficients Spatial patterns

  19. X X + X + + … =

  20. CCA 3. Find such spatial patterns that correlation between their time coefficients are the greatest. That is , maximize

  21. CCA 4. ... finally, Canonical predictor patterns Canonical predictand patterns

  22. CCA 5. -Canonical Correlation Coefficient (CCC)

  23. Variance represented ….% Corr.with NCPI = CCC = ….. Anomalies - predictors Anomalies - predictands Variance represented % ....

  24. January – streamflow Pearson’s correlations with NCPI

  25. February – streamflow Pearson’s correlations with NCPI

  26. NCP(-)

  27. r.Var. %16 NCPI cor. 0.46 January Streamflow 1. CCA pair CCC = 0.92 r.Var. %37

  28. January – precipitation Pearson’s correlations with NCPI

  29. r.Var. %23 NCPI cor. 0.72 January Precip. 1. CCA pair CCC = 0.98 r.Var. %43

  30. January vs February (precip)

  31. March – streamflow Pearson’s correlations with NCPI

  32. March – precipitation Pearson’s correlations with NCPI

  33. April – precipitation Pearson’s correlations with NCPI

  34. May – streamflow Pearson’s correlations with NCPI

  35. Streamflow... No significant relation for the rest of the year • Precipitation... No significant relation with NCP for May and June…

  36. July – precipitation Pearson’s correlations with NCPI

  37. August – precipitation Pearson’s correlations with NCPI

  38. September – precipitation Pearson’s correlations with NCPI

  39. October - precipitation Pearson’s correlations with NCPI

  40. November- precipitation Pearson’s correlations with NCPI

  41. r.Var. %26 NCPI ile 0.68 November Precip. 1. CCA pair CCC = 0.95 t.Var. %17

  42. December – precipitation Pearson’s correlations with NCPI

  43. r.Var. %17 Corr with NCPI 0.61 December Precip. 1. CCA pair CCC = 0.98 r.Var. %21

  44. Winter (DJF) – precipitation Pearson’s correlations with NCPI

  45. Winter (DJF) – streamflow Pearson’s correlations with NCPI

  46. Spring (MAM) – streamflow NCPI ile Pearson korelasyonları

  47. Summer (JJA) – precipitation Pearson’s correlations with NCPI

  48. Fall (SON) – precipitation Pearson’s correlations with NCPI

  49. Summary • NCP effective mostly in winter • NCP(+) enhances precip at Black Sea shoreline • NCP(-) enhances precip at western provinces • February, NCP(+), (subtropical jet) • Some peculiar locations (e.g. Artvin, Sinop) • NCP effective also in summer

  50. Future studies? • NCP and NAO??? A combined index? • Is NCP predictable???

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