Yuriy ILYIN Marine Branch of Ukrainian Hydro - meteorological Institute (MB UHI)

1. Global and Regional Factors of Inter-Annual and Inter-Decadal Variability of Hydro-meteorological conditions on the Black Sea Ukrainian Shores Yuriy ILYIN Marine Branch of Ukrainian Hydro-meteorological Institute (MB UHI) Soviet street, 61, 99011, Sevastopol, Ukraine mb_uhmi@stel.sebastopol.u

2. Main issues Part 1: • Scales of variability: interannual, decadal and climatic; • AMO and NAO as indices of external climatic influence on the Black Sea. Part 2: • Latent (no measured directly) exogenic and endogenic factors on inter-annual and decadal scales; • Is there direct correlation between AMO (or NAO) and complex regional hydrometeo indices of the Black Sea (Ukrainian shores)?

3. Introduction • MB-UHI is dealing a long time with studies of hydrometeorological conditions (regime) of the Azov and Black seas (last works are: Ilyin and Repetin, 2006; Ilyin, 2008-2010; Lipchenkoet al., 2006;Ilyin et al., 2009, etc…). See also poster by Ilyin and Repetin • Long-term changes of marine meteorological and hydrological parameters (such as air and water temperatures, wind velocity, atmospheric precipitations, sea level, water salinity) can be described as the sum of linear trends and quasi-periodic (inter-decadal and inter-annual) fluctuations.

4. Time-series representation: Linear (secular) trend Climatic (inter-decadal) variations Inter-annual and decadal fluctuations

5. Modern estimates of trends and climatic variability in time-series of main meteorological and hydrological parameters mean annual values were discussed in previous works (Ilyin, 2009-2011, Ilyin & Repetin, 2006, 2011). • They were obtained on the base of FSU and Ukrainian marine stations network observations which are performed since the end of 19th century till this time. • Some results are on poster by Ilyin and Repetin

6. How natural climatic periodicities are manifested in observational data? • Secular linear trends in the first approximation can be considered as evidence of unidirectional human impact on global and regional climate systems. However there are long-term fluctuations of climatic parameters with different periods on their background. • Unfortunately even long enough secular series of instrumental hydrometeorological observations on the Black Sea coast do not allow to obtain the statistically significant estimates of low-frequency periodicities using the standard methods of spectral analysis. • At the same time it is known that the regional climate in the Black Sea is under the influence of global processes that can be adequately described by the indices of Atlantic Multidecadal Oscillation (AMO) and North Atlantic Oscillation (NAO). • Characteristics of the ocean influence and the values of these indices for regional climate studies are in the monograph (Polonsky, 2008).

7. Climate change indices such as North Atlantic Oscillation (NAO) and Atlantic Multi-decadal Oscillation (AMO) were subjected to spectral analysis in order to obtain their significant low-frequency spectral peaks of variability.

8. AMO index (1856-2008) Source: http://www.cdc.noaa.gov/Timeseries/AMO/ Series: Mean annual values, smoothed by 5-year moving average Spectral analysis: Lomb periodogram (significant peak66 years)

9. NAO index (1824 – 2008) Source: http://www.cru.uea.ac.uk/~timo/datapages/naoi.htm Series: winter (Dec-Mar), smoothed by 5-year average, detrended Spectral analysis: Lomb periodogram (significant peakson 76, 38, 22yrs)

10. NAO index paleo-reconstruction (1500 – 2001) Series: winter (Dec-Feb), smoothed by 5-year average, detrended Spectral analysis: Lomb periodogram (significant peakson 173, 95, 67, 34, 22yrs) Reference: Luterbacher, J., Xoplaki, E., Dietrich, D., Jones, P.D., Davies, T.D., Portis, D., Gonzalez-Rouco, J.F., von Storch, H., Gyalistras, D., Casty, C., and Wanner, H., 2002. Extending North Atlantic Oscillation Reconstructions Back to 1500. Atmos. Sci. Lett., 2, 114-124. ftp://ftp.cru.uea.ac.uk/data

11. Revealed periods of climatic variability obtained for the NAO series practically coincide with the low-frequency oscillations in solar activity (SA) described by the series of Wolf numbers (Herman and Goldberg, 1981; Landscheidt, 1998). • As is known, except of the most expressed 11-year Schwabe cycles, changes in the SA have 22-year Hale cycles and the secular Gleissberg cycles. Additionally there is a 180-year cycle explained by the period of the Sun rotation relative to the centre of the solar system mass and an associated 35-year cycle. • In a circle of geo- and astrophysics possible mechanisms for the external (space) influences on Earth's climate are discussed (Landscheidt, 1998), but the debate about the prevalence of natural climate variability over anthropogenic factors (greenhouse gases) is far from complete. • Evidently the 70-year cycle of AMO is not related to extraterrestrial factors while NAO reflects both own low-frequency vibrations of the “ocean-atmosphere” and the variation of external influences on global climate.

12. Given the fact that climatic changes are low-frequency oscillations with periods of no less than 30 years (Polonsky, 2008), it was attempted the Least Squares (LS) approximation of the hydrometeorological series by the superposition of harmonics with periods 95, 67 and 34 years. • Previously linear trends were removed from the original series

13. Long-period variations in the Black Sea: Odessa Yalta Climatic changes of the mean annual air temperature in Yalta and Odessa approximated by the sum of harmonic functions with periods of 95, 67 and 34 years, revealed from spectrum of paleo-NAO

14. Sevastopol Odessa Long-period variations in the Black Sea: Climatic changes of the mean annual wind velocity in Sevastopol and Odessa approximated by the sum of harmonic functions with periods of 95, 67 and 34 years, revealed from spectrum of paleo-NAO

15. River inf. Precip. Long-period variations in the Black Sea: Climatic changes of the mean annual river discharge and precipitations (km3) approximated by the sum of harmonic functions with periods of 95, 67 and 34 years, revealed from spectrum of paleo-NAO

16. Above approximations satisfactorily describe the long-period (decadal and secular) changes in observations series, which serve as proof of the natural global climatic oscillations impact on regional climate changes. • However, the nature of the original series and the low-frequency variations is unequal for different areas of the coast which reflect the impact of the various regional factors on local hydro-meteorological conditions. • Thus, climate changes reflect significant differences of physical-geographical conditions of the north-western Black Sea and the southern coast of the Crimea peninsula.

17. Conclusion (1) : • Main period of the last centuries inter-decadal variability is the period of about 70 years. Besides, significant spectral peaks were discovered in the NAO time-series on the scales of secular changes (95, 173 years) and more high-frequency inter-decadal oscillations (34, 22 years). Close periods exist also in the SA index time series (i.e. Wolf numbers). • Superposition of harmonic functions with periods 95, 67 and 34 years describes satisfactory the multi-annual fluctuations of the observed hydro-meteorological values for the Black Sea. • Regional differences of climatic variability are manifested for different regions of Ukrainian seashore

18. Factor analysis of data series • To study how related “global” and “regional” factors in time series of different parameters measured in different points of the shore, exploratory factor analysis was performed using the algorithm of principal components (PC) for correlation matrices; • Latent (not measured directly) factors: exogenic (“globality”) – unidirectional changes in all points of measurements and endogenic (“regionality”) – differently directed changes for different regions of the shore

19. Odessa Khorly Primorskoye Evpatoria Feodosia Sevastopol Yalta Cape Khersones Location of observation points used for the time series construction Hydrometeorological variables: Wind velocity (W or WV) Air temperature (TA) Water temperature (TW) Precipitations (P or Pr) Sea level (SL) Salinity (S) • 2 kinds of time series were constructed for the each parameter: • Yearly mean values for 1945 – 2009 (1952 -2009 for S): inter-annual scale (2-year and more periods) • 5-year mean values for 1925-2009 (1950-2009 for S): decadal scale (10-year and more periods)

20. PC Eigenvalue % Variance 1 3.78253 64.454 2 0.984687 16.779 3 0.44019 7.5007 4 0.342968 5.8441 5 0.192841 3.286 6 0.125398 2.1368 Jolliffe cut-off 0.27047 PC-1 PC-2 Wind velocity: yearly mean values, 1945-2009 Odessa Evpatoria Feodosia Sevastopol Yalta Cape Khersones

21. PC Eigenvalue % Variance 1 3,76792 69,916 2 0,898886 16,679 3 0,302493 5,613 4 0,265352 4,9238 5 0,116313 2,1583 6 0,0382312 0,7094 Jolliffe cut-off 0.62874 PC-1 PC-2 Wind velocity: 5-year mean values, 1925-2009 Odessa Evpatoria Feodosia Sevastopol Yalta Cape Khersones

22. PC Eigenvalue % Variance 1 4,54574 92,404 2 0,210356 4,276 3 0,091396 1,8579 4 0,0402877 0,81896 5 0,0316175 0,64271 Jolliffe cut-off 0.68872 Odessa Evpatoria Feodosia Sevastopol Yalta Air temperature: yearly mean values, 1945-2009

23. PC Eigenvalue % Variance 1 4,12873 91,872 2 0,209957 4,672 3 0,110598 2,461 4 0,0300555 0,66879 5 0,0146398 0,32577 Jolliffe cut-off 0.62916 Odessa Evpatoria Feodosia Sevastopol Yalta Air temperature: 5-year mean values, 1925-2009

24. PC Eigenvalue % Variance 1 4,5332 92,219 2 0,153216 3,1169 3 0,116208 2,364 4 0,0709133 1,4426 5 0,0421697 0,85785 Jolliffe cut-off 0.6882 Odessa Evpatoria Feodosia Sevastopol Yalta Water temperature: yearly mean values, 1945-2009

25. PC Eigenvalue % Variance 1 4,10097 90,918 2 0,278897 6,1831 3 0,0726385 1,6104 4 0,0351542 0,77937 5 0,0229547 0,5089 Jolliffe cut-off 0.63149 Odessa Evpatoria Feodosia Sevastopol Yalta Water temperature: 5-year mean values, 1925-2009

26. PC Eigenvalue % Variance 1 3.21358 64.279 2 0.7646 15.294 3 0.465429 9.3097 4 0.333887 6.6785 5 0.221895 4.4384 Jolliffe cut-off 0.6986 Odessa Feodosia Sevastopol Yalta PC-1 PC-2 Precipitations: yearly mean values, 1945-2009 Cape Khersones

27. PC Eigenvalue % Variance 1 3,35931 67,239 2 0,682865 13,668 3 0,456327 9,1337 4 0,322828 6,4616 5 0,174731 3,4974 Jolliffe cut-off 0.69945 Odessa Feodosia Sevastopol Yalta Cape Khersones PC-1 PC-2 Precipitations: 5-year mean values, 1925-2009

28. PC Eigenvalue % Variance 1 5.69787 95.154 2 0.161114 2.6906 3 0.0533152 0.89036 4 0.043241 0.72212 5 0.0224371 0.3747 6 0.0100571 0.16795 Jolliffe cut-off 0.69992 Khorly Chernomorsk Evpatoria Feodosia Sevastopol Yalta Sea level: yearly mean values, 1945-2009

29. PC Eigenvalue % Variance 1 5,80404 97,65 2 0,0741789 1,248 3 0,0307556 0,51745 4 0,0246876 0,41536 5 0,00772474 0,12996 6 0,00234684 0,039484 Jolliffe cut-off 0.69344 Khorly Chernomorsk Evpatoria Feodosia Sevastopol Yalta Sea level: 5-year mean values, 1925-2009

30. PC Eigenvalue % Variance 1 2,09728 44,93 2 1,0009 21,442 3 0,78665 16,853 4 0,51704 11,077 5 0,265998 5,6984 Jolliffe cut-off 0.65351 Odessa Primorskoye Cape Khersones Feodosia Yalta PC-1 PC-2 Salinity: yearly mean values, 1952-2009

31. PC Eigenvalue % Variance 1 1,93269 55,8 2 1,14691 33,113 3 0,264239 7,629 4 0,089541 2,5852 5 0,0302281 0,8727 Jolliffe cut-off 0.48491 Odessa Primorskoye Cape Khersones Feodosia Yalta PC-1 PC-2 Salinity: 5-year mean values, 1950-2009

32. Percentage of “globality” (PC-1) and “regionality” (PC-2)

33. PCA (correlation matrix) 5-year mean values: WV, TA, TW, Pr, SL PC Eigenvalue % Variance 1 3,24748 69,543 2 0,9457 20,252 3 0,37465 8,022 4 0,0854076 1,829 5 0,0165286 0,353 Jolliffe cut-off 0.65377 Complex variables of the Ukrainian coast HM state: from 5 to 2 variables: PC-1: not windy, warm and watery PC-2: windy, warm and dry

34. r = 0.54 (p=0.03) Significant (but not too close) correlation was obtained only between AMO and PC-2 on decadal scale

35. Conclusion (2) : • On inter-annual and decadal scales, variations of air and water temperatures as well as sea levelare under global influence while changes of wind velocity, precipitations and salinityare subjected also by substantial regional impact (more or less evident result, except for water temperature) • To date, no practically significant linear correlations were obtained between global indices (AMO and NAO) and some measured or latent parameters used for the description of HM conditions within the Ukrainian Black Sea shore on inter-annual and decadal scale of variability.

36. Thanks for your attention!