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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

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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)?

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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, ; Lipchenko et 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.

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

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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, , 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

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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).

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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.

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AMO index ( ) Source: Series: Mean annual values, smoothed by 5-year moving average Spectral analysis: Lomb periodogram (significant peak 66 years)

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NAO index (1824 – 2008) Source: Series: winter (Dec-Mar), smoothed by 5-year average, detrended Spectral analysis: Lomb periodogram (significant peaks on 76, 38, 22 yrs)

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NAO index paleo-reconstruction (1500 – 2001) Series: winter (Dec-Feb), smoothed by 5-year average, detrended Spectral analysis: Lomb periodogram (significant peaks on 173, 95, 67, 34, 22 yrs) 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., Extending North Atlantic Oscillation Reconstructions Back to Atmos. Sci. Lett., 2, ftp://ftp.cru.uea.ac.uk/data

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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.

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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

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Long-period variations in the Black Sea: Yalta Odessa 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

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Odessa Sevastopol 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

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Precip. River inf. Long-period variations in the Black Sea: Climatic changes of the mean annual river discharge and precipitations (km 3 ) approximated by the sum of harmonic functions with periods of 95, 67 and 34 years, revealed from spectrum of paleo-NAO

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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.

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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 Conclusion (1) :

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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

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Odessa Sevastopol Yalta Cape Khersones Feodosia Khorly Evpatoria Primorskoye Location of observation points used for the time series construction 2 kinds of time series were constructed for the each parameter: 1)Yearly mean values for 1945 – 2009 ( for S): inter-annual scale (2-year and more periods) 2)5-year mean values for ( for S): decadal scale (10-year and more periods) Hydrometeorological variables: Wind velocity (W or WV) Air temperature (TA) Water temperature (TW) Precipitations (P or Pr) Sea level (SL) Salinity (S)

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Odessa Sevastopol Yalta Cape Khersones Feodosia Evpatoria Wind velocity: yearly mean values, PC Eigenvalue % Variance Jolliffe cut-off PC-1 PC-2

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Odessa Sevastopol Yalta Cape Khersones Feodosia Evpatoria Wind velocity: 5-year mean values, PC Eigenvalue % Variance 1 3, , , , , , , , , , , ,7094 Jolliffe cut-off PC-1 PC-2

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Odessa Sevastopol Yalta Feodosia Evpatoria Air temperature: yearly mean values, PC Eigenvalue % Variance 1 4, , , , , , , , , ,64271 Jolliffe cut-off

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Odessa Sevastopol Yalta Feodosia Evpatoria PC Eigenvalue % Variance 1 4, , , , , , , , , ,32577 Jolliffe cut-off Air temperature: 5-year mean values,

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Odessa Sevastopol Yalta Feodosia Evpatoria PC Eigenvalue % Variance 14, ,219 20, , , ,364 40, , , ,85785 Jolliffe cut-off Water temperature: yearly mean values,

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Odessa Sevastopol Yalta Feodosia Evpatoria PC Eigenvalue % Variance 14, ,918 20, , , , , , , ,5089 Jolliffe cut-off Water temperature: 5-year mean values,

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Odessa Sevastopol Yalta Feodosia PC Eigenvalue % Variance Jolliffe cut-off Precipitations: yearly mean values, PC-1 PC-2 Cape Khersones

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Odessa Sevastopol Yalta Feodosia Cape Khersones PC Eigenvalue % Variance 13, ,239 20, ,668 30, , , , , ,4974 Jolliffe cut-off Precipitations: 5-year mean values, PC-1 PC-2

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Sevastopol Yalta Feodosia Khorly Evpatoria Chernomorsk Sea level: yearly mean values, PC Eigenvalue % Variance Jolliffe cut-off

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Sevastopol Yalta Feodosia Khorly Evpatoria Chernomorsk Sea level: 5-year mean values, PC Eigenvalue % Variance 1 5, ,65 2 0, , , , , , , , , , Jolliffe cut-off

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Odessa Yalta Cape Khersones Feodosia Primorskoye Salinity: yearly mean values, PC Eigenvalue % Variance 1 2, ,93 2 1, , , , , , , ,6984 Jolliffe cut-off PC-1 PC-2

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Odessa Yalta Cape Khersones Feodosia Primorskoye Salinity: 5-year mean values, PC Eigenvalue % Variance 1 1, ,8 2 1, , , , , , , ,8727 Jolliffe cut-off PC-1 PC-2

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Percentage of globality (PC-1) and regionality (PC-2) Variable 1-year averaged5-year averaged PC-1PC-2PC-1PC-2 Wind veloc Air temperat.92 Water temp.9291 Precipitations Sea level9598 Salinity

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Complex variables of the Ukrainian coast HM state: from 5 to 2 variables: PCA (correlation matrix) 5-year mean values: WV, TA, TW, Pr, SL PC Eigenvalue % Variance 13, ,543 20, ,252 30, ,022 40, ,829 50, ,353 Jolliffe cut-off PC-1: not windy, warm and watery PC-2: windy, warm and dry

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

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On inter-annual and decadal scales, variations of air and water temperatures as well as sea level are under global influence while changes of wind velocity, precipitations and salinity are 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. Conclusion (2) :

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Thanks for your attention!

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