Supplementary Material
1. Homogeneity of the historical time series.
First visual wave observations in the selected northern Black Sea ports were organized in
1893. However, regular routine visual observations in Yalta are available only since 1900, whereas
in Odessa and “Chersonesus lighthouse” since 1915 and 1916 respectively. Until 1938 the 12-grade
Beaufort scale had been used, and starting from 1938 it was replaced by 9-grade scale. The visual
observations had been performed until 1954 when all maritime hydrometeorological stations were
equipped by perspectometer-wavemeters. It should be emphasized that before 1913 the
observations were performed by non-professional personnel. Only in 1913 the semi-annual courses
for the observers were organized. So, in fact more or less qualified observations had begun in late
1913.
Taking into account the above-mentioned circumstances, the homogeneity of the time series
must be carefully checked. In fact, there are at least two potential problems. First of all, initial nonprofessional observations can be principally biased and contain high-amplitude random noise. That
is why the first 14 years of Yalta observations were excluded from analysis. Second, the Beaufort
scale is nonlinear and discrete, and its use may introduce some bias in comparison with actual
heights even if visual observations are performed by professional observers. Therefore we
transformed the post-1954 actual wave heights into the Beaufort scale and tested the homogeneity
of the time series for the pre- and post-1954 periods. We used 9-grade Beaufort scale for the entire
period converting early 12-grade observations into 9-grade scale.
Figure S1 and Table S1 demonstrate the time series and some statistical properties of the
stormy waves in Yalta in terms of the Beaufort scale. They clearly show that the routine
observations’ data since the mid of the second decade of XX century characterize the wave heights
and their variability quite reliably and they can be combined with the post-1954 semi-instrumental
measurements. Small negative linear trend of the stormy wind-wave heights (Beaufort numbers) is
not significant. At the same time superimposed multidecadal (~50 yrs) variability of the stormy
wind-wave heights, seen in semi-instrumental post-1954 observations, also exists in the pre-1954
period. Thus, the low-frequency variability of the wave heights in the Northern Black Sea can be
extracted using routine data since 1916, because they are quite reliable. In addition, data from all
three stations are simultaneously available only since 1916.
It is remarkable that significant secular negative trend of monthly averaged wind speed and
the associated decrease of monthly averaged wind wave heights were found on different Ukrainian
stations (see e.g. (Ilyin 2009)). Thus our own and the published by other authors results show that
the regional monthly averaged wind and Black Sea wind waves were weakening during the major
part of XX century, in contrast to stormy wind waves. The heights of stormy waves are mostly
characterized by quasi-periodical interdecadal variations because the linear trend of the maximum
wind speed is much smaller than the linear trend of the monthly average wind speed (Ilyin 2009).
Table S1
Average Beaufort number and standard deviation of winter stormy waves for three periods of
observations (showed also in Fig.S1) in Yalta in XX century
Period
1916-1946
1947-1971
1972-1995
Mean
Yalta
4.99
5.01
4.68
Sigma
0.65
0.78
0.76
2
Yalta
7
Beaufort numbers
6
5
4
3
2
1
0
1910
1930
1950
1970
1990
2010
years
a
Chersonesus lighthouse
8
Beaufort numbers
7
6
5
4
3
2
1
0
1910
1930
1950
1970
1990
2010
years
b
Odessa
7
Beaufort numbers
6
5
4
3
2
1
0
1900
1920
1940
1960
1980
2000
2020
years
c
Figure S1. Variability of Beaufort numbers in winter of 1916-2010: Yalta (a), Chersonesus
lighthouse (b) and Odessa (c). Lines show the decadal-scale trends for periods of averaging (see
Table S1)
3
2. Statistics of extreme waves and selection of the stormy wave height threshold
Extreme wave height analysis is one of the useful tools to identify the stormy conditions. It
uses the statistical functions of sampling waves including the highest waves (Hm exceeding definite
threshold). However, the absolute scale of Hm seems to be not the perfect choice (Egozcue et al.
2005; Sanchez-Arcilla et al. 2008). Wave heights of ~7.0 m are similar to wave heights of ~7.5 m;
in contrast, waves of 0.5 m and 1.0 m differ two-fold. In this case the log-transformation is a
convenient scaling procedure. So subsequent analysis will be performed with a value X=log10 Hm as
a measure of stormy wave magnitude.
There are few standard techniques enabling to carry out an extreme event analysis (Hawkes
et al. 2008; Lopatoukhin et al. 2000; Van Gelder and Mai 2008). Two of them, viz. Annual
Maximum Series (AMS) and peak-over-threshold (POT), will be used in extreme wave height
analysis. The AMS method is based on the use of distribution of annual maxima which, for large
samples, follows one of three asymptotic distributions: Gumbel, Weibull or Frechet. They are
conveniently unified into a single Generalized Extreme Value (GEV) distribution:
1
 


x  

FAMS x   PrX  x  exp  1   

,
 
  


where  is a shape parameter,  is a scale parameter and  denotes extremum localization.
In general case the AMS function depends on the type and parameters of the original wave
height distribution. But if this type is unknown it is recommended to apply GEV (Coles 2001). A
method of maximum likelihood (ML) is preferred to fit the distribution function. If the FAMS is
defined, a return period of extreme log-wave-height can be written as
1
 AMS x  
.
1  FAMS  x 
Parameters of GEV distribution estimated for the periods 1954-1983 and 1984-2010 are
given in Table S2. According to the results of standard t-test there is a significant difference (on
95% significance level) between the parameters μ in the positive and negative WA phases for
“Chersonesus lighthouse” and “Yalta”. Indeed, standard error for μ does not exceed 0.02. “Odessa”
station is situated on the margin of wide shallow shelf. That is why the change in values of the
statistical parameters between different phases is not so large.
Table S2
Parameters of GEV distribution function for different phases of WA
Station
Yalta
Chersonesus lighthouse
Odessa
1954-1983
1984-2010
(positive WA)
(negative WA)
ξ
ξ




0.55 0.10 -0.31 0.40 0.08 -0.87
0.67 0.08 -0.36 0.50 0.096 -0.21
0.36 0.11 -0.14 0.34 0.077 -0.17
Note that the shape parameter ξ has negative values for both subperiods. This means that the
GEV function can be reduced to one of its special cases – Weibull distribution function, which is
characterized by a notable feature linked to the existence of an upper boundary of maximum wave
height.
The AMS gives an understanding of extreme range of wave heights but is not suitable for
analysis of storm events for long-term period. The reason is that AMS technique excludes the
storms which are not the strongest ones in particular year but could be the strongest in other years
(Lopatoukhin et al. 2000). This obstacle could be overcome by using POT method (Hawkes et al.
2008). Using the recommendation of the authors of the work Egozcue et al. (2005), storm event
may be identified if observed wave heights exceed a certain critical threshold, u, for rather long
4
period (say, 12 hrs or so for the Black Sea region).
In order to analyze the number of storm events (N) for different time intervals (say, t yrs) the
statistical model of rare events (namely, the Poisson distribution) can be used (Egozcue et al. 2005;
Sanchez-Arcilla et al. 2008):

t n t
PN  n |  , t 
e ,
n!
(1)
where λ is Poisson rate and is inversely related to the return period of events τ=1/λ. The stationary
Poisson process is supposed to be formed by independent and identically distributed events. In
order to test the difference between Poisson rates 1, 2 evaluated for the two periods one should
use binomial test by conditioning on the total storm event count (Krishnamoorthy and Thomson
2004). In this case the ratio 1/2 is a binomial success probability.
Each storm can be characterized by the maximum wave height (exceeding the definite
threshold) being observed during certain period. Wave height is presented by log-transformed
variable X. As a rule X is believed to have independent statistical properties from one event to
another, but its distribution is supposed to be the same (Egozcue et al. 2005). Selection of the
distribution function can be problematic but it is strongly recommended to use Generalized Pareto
Distribution (GPD) applied to the variable X exceeded a reference threshold u or for Y=X-u
(Hawkes et al. 2008). GPD function has the following form

1
 y  
FPOT  y   PrY  y  1   , 0  y  y max ,


where ξ is a shape parameter and β is a scale parameter (Egozcue et al. 2005). GPD function is
flexible because it presents finite and infinite tailed distributions governed by two parameters.
Three attraction domains (DA) of GPD function are recognized depending on the ξ parameter. If
ξ=0 the GPD belongs to Gumbel DA and its support is infinite (ymax= +∞); if ξ>0 the GPD is in
Frechet DA with infinite support (ymax= +∞); if ξ<0 the GPD is in Weibull DA and its support is
finite with ymax=-β/ξ.
In order to specify the stormy conditions, the log-wave-heights must correspond to p values
exceeding definite level. Usually the p value between 0.01 and 0.05 is being chosen. Two criteria of
p selection are commonly applied. Firstly, the selected threshold must not be too high. Otherwise,
the number of storms will be too low. The p-parameter corresponding to Kolmogorov-Smirnov
goodness-of-fit test is widely used for these purposes (Lopatoukhin et al. 2000). Secondly, the logwave-height limit must remove the low-energy events which cannot be considered as storms.
Other requirements are applied if GPD function is selected to estimate the extreme values. It
is known that the mean excess over a threshold should be a linear function of storm intensity
(Lopatoukhin et al. 2000) if shape parameter of GPD ξ<1,
  u
.
EX  u | X  u  
1
So the reference threshold, u, should belong to “linear” segment of mean excess depending on the
threshold values. In addition an inspection of “linear” segment allows us to check the fit of GPD
and to define an attraction domain. Negative linear trend of the mean excess reflects Weibull DA,
the positive one corresponds to Frechet DA. If the trend appears to be absent, hence the GPD is
assumed to be in Gumbel DA.
The mentioned selection method can be applied to analyze the long-term variability of
storm events in the coastal zone of the northern Black sea. In addition, one should keep in mind that
storms can be observed on different stations simultaneously. Such cases will be considered as a
single storm in the coastal zone of the northern Black Sea.
Inspection of Figure S2 using the above-listed recommendations clearly shows that the
optimal range of thresholds begins from the values u>0.2 (Hm>1.5m). It is interesting to note that
the value u=1.5 m is officially referred to as the threshold for dangerous event in
Hydrometeorological Service of Ukraine. Slopes of the mean excess function are negative. This
means that the Weibull DA is fit. The existence of the upper boundary of wave heights is physically
5
valid in our case because of the semi-enclosed nature of the Black sea basin and dissipating
processes acting in the coastal zone. In order to determine a threshold one should be guided by the
goodness of fit of GPD. We put the reference thresholds for each station such as those listed in
Table S3. These values define the number N of stormy wave events occurred during the period of
1954-2010.
Figure S2. Mean excess functions against threshold of log-wave-height to define a wave storm
event. The right axis indicates the number of extreme events in the sample which is depicted as
grey line.
Table S3
Parameters of GPD model for wave heights and the number of storm events (occurred during the
period of 1954-2010) corresponding to them
Chersonesus
Station
Yalta
Odessa
lighthouse
u
3.0
3.0
2.5
ξ
-0.305
-0.34
-0.16
β
0.122
0.145
0.07
N
115
341
62
Once the parameters of the GPD have been estimated, we could build a model of storm occurrence
in time. As long as the storm event occurrence is assumed to be Poisson distributed (1), the
probability of storm with a given intensity depends on the mean number, λ, of storms in a year. This
is the key moment for the storm event evaluation. Poisson process is stationary which rules out
account of any trends due to climatic variability in the datasets. However, wind wave conditions of
6
the coastal zone of the northern Black sea are characterized by interdecadal variability. This
means that the statistics of storm events may change drastically depending on the phases of WA
process. This is the case for the return period which is defined as (Egozcue et al. 2005):
 u 
 POT  x  
,
1  FPOT  x  u 
where τ(x)=1/ λ(x) is the return period of events with log-wave-heights X exceeding u.
7
3. Classification of large-scale atmospheric processes leading to the Black Sea storms.
One of the most effective methods to analyze macro-synoptic processes is their
classification based on selection of common circulation patterns from a big variety of synoptic
situations (Huth 2001). In this section, the classification of large-scale macro-synoptic processes for
the storm events with wave heights ≥ 3 m (2.5 m for “Odessa” station) and 12 hrs life time
threshold is discussed (for details, see section 2). It should be noted that the classification of largescale atmospheric patterns for stormy conditions in the Black Sea has already been done in
(Voskresenskaya et al. 2009), but without analysis of circulation types with respect to different WA
phases. Here, we pay attention to large-scale (macro-synoptic) processes associated with storm
activity in the Black Sea region and their occurrence in different WA phases.
The storm events with wave heights exceeding the chosen thresholds were observed most
frequently on the “Chersonesus lighthouse” station. In total 341 storms were observed on this
station in the period 1954-2008. 115 storm events were observed in that period on the “Yalta”
station and 62 on “Odessa” station. Taking into account that storms may be observed on different
stations simultaneously, the total number of storms in the coastal zone of the northern Black Sea,
coming from all directions, in 1954-2008 was counted as 442. The procedure of hierarchical
clusterization of 500 hPa geopotential topography patterns for these storm events permitted to
extract four well-distinguished classes (or atmospheric circulation patterns). The circulation types
were defined depending on localization of the mid-tropospheric ridge over the region restricted by
20ºN – 90ºN and 40ºW – 100ºE. The obtained principal spatial patterns are described below (for
more details, see also (Belskaya 1949; Chernova 1959; Guijarro et al. 2006; Lepeshko 1989;
Logvinov and Barabash 1982; Simonov and Altman 1991; Trigo et al. 2002; Voskresenskaya et al.
2009)).
In order to describe the large-scale (macro-synoptic) processes leading to stormy regional
conditions, NCEP/NCAR re-analysis data on geopotential of 500hPa surface for the period 19482010 were used. The time step of the re-analysis data is 6 hrs (06, 12, 18 and 24 GMT).
Atmospheric circulation index (ACI, see (Beamish et al. 1999; Sidorenkov and Orlov 2008)) was
also used to characterize the atmospheric circulation regime in the Northern Hemisphere. This
index was derived depending on the type of atmospheric circulation, and is defined using the
classification system developed by Russian meteorologists (e.g., Girs 1971). They extracted three
principal types of general circulation in extratropical regions of the Northern Hemisphere, viz. the
western (W) type, representing the zonal circulation pattern; the meridional (C) type, which is
characterized by predominant northwest to southeast pattern (associated with weak Subpolar Low
and strong Siberian High); the easterly (E) type, which is similar to type C but features opposite
polarity of pressure system. Each ACI value corresponds to an anomaly of the number of days in a
year with fixed type of circulation compared to long-term averaged number of days. The ACI curve
is commonly represented as a cumulative sum of definite type of anomalies as shown in Fig.6 (see
main text) for type C.
To describe typical atmospheric circulation patterns different classifications are commonly
applied. The goal of classifications is to obtain a set of typical circulation patterns representing
some general features of pressure (or geopotential) fields (Batyreva et al. 1993; Esteban et al. 2006;
Fereday et al. 2008; Gruza and Ran’kova 1970; Huth 2001; Jiang et al. 2005; Ped’ and Popov
1980). As a rule, separation of atmospheric patterns is performed by means of various multivariate
techniques, namely, correlation method, principal component analysis (PCA), cluster analysis (both
hierarchical and non-hierarchical), etc. Details of these methods and their applications can be found
in (Holt 1999; Huth 2001; Wilks 2006).
In the present study the patterns of geopotential of 500hPa surface corresponding to the
most significant wind-wave anomalies (intense storms in the northern Black Sea region) were
extracted from the NCEP-NCAR re-analysis data. We defined the intense storms using the
following two criteria. First of all, the wave height and storm duration must exceed the thresholds
for all the stations analyzed. Then, we took into account the sample size which is very important for
significant statistical processing. The sampling of the intense storm events was performed using the
chosen thresholds (if wave heights reached at least 3 m on “Chersonesus lighthouse” or “Yalta”
8
maritime stations, and 2.5 m on “Odessa” station during the period exceeding 12 hrs). Such
approach guarantees reliable identification of stormy and quite large ensemble size. Besides, the
most severe wind-wave anomalies in the Black Sea coastal zone of Ukraine were divided into
groups by the value of maximum wave heights: over 5m, 4.0 – 4.9m, 3.5–3.9m and 3.0–3.4 m, and
the associated meteorological conditions were selected and analyzed, too.
For the purpose of classification of atmospheric processes it is reasonable to use hierarchical
cluster analysis (Wilks 2006) because of the absence of information about an exact number of
selected groups. The main idea of analysis to be applied is that the group must consist of points
separated by small distances compared to the distance between clusters. In the present work
Euclidean distance was used as a distance measure. Several agglomerative methods were applied:
single-linkage, complete-linkage, average-linkage, centroid-linkage, median-linkage and Ward’s
methods. Cluster analysis was accomplished using standard software Statistica 6.0. As an input data
we used the patterns of geopotential height (500hPa) deviations from the averaged pattern
normalized by its standard deviation (s.d.) and represented in a T-mode (Huth 2001).
Search of the pressure fields belonging to certain cluster was performed using similarity
criterion suggested by the authors of the paper (Batyreva et al. 1993). In fact, it implies a triple
similarity of certain pressure fields and etalon (center of cluster), namely the similarity of the
pressure fields themselves and coincidence of sign of zonal and meridional pressure gradients over
the majority of the analyzed area. In the present study, the fields were considered being similar to
etalon if the sign of pressure anomalies, zonal and meridional gradients coincides for at least 80%
of total area. As a result, four well-separated clusters (or certain space patterns of the pressure
fields) have been extracted. All of them correspond to meridional types of atmospheric circulation.
The circulation type 1 implies the synoptic processes characterized by anticyclonic ridge (or
anticyclone) located over the British Isles or/and Western Europe, while altitude depression is
located to the East of this region. However, two different subtypes were classified within the type 1
(1a and 1b). They differ by predominant direction of meridional processes and their manifestations
in the Black Sea region.
Subtype 1a (Fig.S3) is characterized by developed anticyclone ridge in the mid-latitudes of
the North Atlantic and over British Isles, intense cyclonic activity over the northern North Atlantic
and associated north-western transfer of warm air masses. Planetary altitudinal frontal zone is
characterized by high-magnitude meander over the Western Europe (Fig. S3a). This creates the
favorable conditions for generation of so-called “diving” cyclones (which are characterized by
southward-southeastward propagation) to the central European Russia and Ukraine. As a result,
cold air masses penetrate to the southern regions and this promotes generation of southern cyclones
(Belskaya 1949; Guijarro et al. 2006; Lepeshko 1989; Trigo 2002).
6
18
Chersonesus lighthouse
Yalta
Odessa
16
Chersonesus lighthouse
Yalta
Odessa
5
Number of cases
Number of cases
14
12
10
8
6
4
3
2
4
1
2
0
2010
2005
2000
1995
1990
1985
1980
1975
1970
1965
1960
1955
II III IV V VI VII VIII IX X XI XII
1950
0
I
a
b
c
Figure S3. Composite map of synoptic processes of subtype 1a leading to storms in coastal
zone of the northern Black sea (a), seasonal variation of storms’ number associated with the
subtype 1a (b), variation of yearly averaged storms’ number per month in 1954-2010 associated
with subtype 1a (c). Red bars mark “Yalta”, blue – “Chersonesus lighthouse”, green – “Odessa”.
Dashed blue curve shows the location of planetary frontal zone.
9
Subtype 1a is the most active in the cold season with maximum frequency in January (21
events during the studied period), while in the warm season such events occur very rarely (1-3
events per month, but never in June) (Fig.S3b). The stormy waves associated with synoptic
conditions of subtype 1a are registered over the entire Black Sea. Majority of storms associated
with this kind of synoptic processes was observed in 1950 until early 1980’s (Fig. S3c). Maximum
wave height was observed on 17 September 1981. This storm was a result of penetration of
southern cyclone coming from Balkan region to the N-W Black Sea. Maximum wave height (4.9
m) was registered at “Chersonesus lighthouse”. This storm occurs after more than two days of SWW stormy wind when the wind speed reached 25-26 m/s (Evstigneev et al. 2011; Naumova et al.
2010).
Subtype 1b (Fig.S4) is characterized by warm and developed ridge of subtropical
anticyclone oriented from Azores Islands to Scandinavia and Western Barents Sea. Extended
cyclonic system above Taimyr peninsula and Kara Sea creates deep depression propagating to the
southern regions of Eastern Europe and Mediterranean region. Arctic air masses propagate from
Taimyr peninsula to the Western Mediterranean. Planetary frontal zone is meridionaly oriented
(Fig. S4a). This promotes the penetration of cold air masses from the Arctic basin to S-SW and
creates favorable conditions for generation of Mediterranean cyclones and their propagation on the
Black Sea region.
Like in the case 1a, storms generated by synoptic processes of subtype 1b are most active in
the cold season with maximum storm number in January-February (22 events per month in the
studied period). In the warm season they occur very rarely, viz. 1-2 events per month and never in
June (Fig. S4b). Under macro-synoptic pattern of subtype 1b high stormy waves were registered
over the entire Black Sea-Azov Sea coastal region.
7
14
Chersonesus lighthouse
Yalta
Odessa
10
8
6
4
5
4
3
2
1
2
b
2010
2005
2000
1995
1990
1985
1980
1975
1970
1965
1960
II III IV V VI VII VIII IX X XI XII
1955
I
1950
0
0
a
Chersonesus lighthouse
Yalta
Odessa
6
Number of cases
Number of cases
12
c
Figure S4. The same as in Fig.S3, but for the subtype 1b
Dominated number of storm events of subtype 1b occurred in 1956-1982 (Fig.S4c). The
maximum storms’ number associated with this subtype (9 events) was registered in 1960. Besides,
the absolute maximum of stormy wave height (7.3m) was observed on 10 November 1981. This
storm was a result of penetration of southern cyclone from Aegean Sea to the NW Black Sea.
Westerly wind speed reached 25-28 m/s. The other intense storms were observed on 7 January 1965
and 16 November 1981, when the wave heights exceeding 3 m occurred in Odessa. The first storm
was a result of simultaneous penetration of southern cyclone from Balkan Peninsula and North
Atlantic cyclone into the Black Sea region. The second situation was associated with cyclone
penetration from Asia Minor (Simonov and Altman 1991; Voskresenskaya et al. 2009).
Macro-synoptic processes of type 2 are accompanied by two pronounced anticyclonic ridges
over the Western Europe (oriented from Mediterranean basin to Scandinavia) and Western Siberia.
As a result, cold Arctic air masses are being transferred from Barents Sea and Kara Sea to the SW.
This provides intensification of North Atlantic and Mediterranean cyclones reaching the Black Sea
10
region. This is the most frequent type of meridional circulation leading to the storms in the Black
Sea (see, Fig. S5).
Depending on the localization of ridges with respect to the Black Sea basin two subtypes of
the type 2 were extracted. The subtype 2a corresponds to air masses transfer to the Black Sea from
the south, whereas the other one (2b) is due to transfer from the north and north-west (Figs.S6a,
S7a). In contrast to both subtypes of type 1, only the subtype 2a associated with the Black Sea
storms occurs quite often in a warm season, viz. 3-5 events in summer months and 8 events in
September (Fig. S6b). This subtype accounts for ~ 25% of storm events on the SW Crimean coast.
40
13.1%
21.4%
12.9%
19.8%
type 1 (subtype a)
type 1 (subtype b)
type 2 (subtype a)
type 2 (subtype b)
type 3
type 4
Repeatidness (%)
30
19.0%
20
10
13.8%
0
type 1 (subtype b)
type 2 (subtype a)
type 2 (subtype b)
type 3
a
b
Figure S5. Reoccurrence (%) of types of synoptic processes leading to stormy waves in the northern
Black Sea for the waves’ heights exceeding 3m (a) and 5m (b).
16
14
12
10
8
6
4
Chersonesus lighthouse
Yalta
Odessa
5
Number of cases
4
3
2
1
2
a
b
Figure S6. The same as in Fig.S3, but for the subtype 2a.
c
The storms’ frequency associated with the subtype 2a was at the maximum in 1954-1982, as
well as in the case of subtype 1a. In general the numbers of storms associated with the subtype 2a
and subtype 1a are similar to each other (cf. Fig.S3c and Fig.S6c). The total yearly number of
storms of such kinds does not exceed 5-7 cases. Maximum wave high (7.0 m) for this type of
synoptic processes was observed on 21 November 1960 as a result of southern (Genoese) cyclone
penetration into the Black Sea region. Another such storm, reaching the criterion of severe event
(6.0 m), was registered on 3 March 1988 when the southern cyclone came from Tyrrhenian Sea.
Besides, the dangerous storms of 14 November 1992 (4.0 m) and 11 November 2007 (5.0 m) were
generated by the synoptic conditions of subtype 2a. In the first case it was a result of Genoese
cyclone penetration into the Black Sea region, in the second case – by a cyclone from Adriatic
region (Voskresenskaya et al. 2009).
2010
2005
2000
1995
1990
1985
1980
1975
1970
1965
II III IV V VI VII VIII IX X XI XII
1960
I
1955
0
0
1950
Number of cases
6
Chersonesus lighthouse
Yalta
Odessa
11
6
25
Chersonesus lighthouse
Yalta
Chersonesus lighthouse
Yalta
5
Number of cases
15
10
5
4
3
2
1
a
b
Figure S7. The same as in Fig.S3, but for the subtype 2b.
2010
2005
2000
1995
1990
1985
1980
1975
1970
1965
II III IV V VI VII VIII IX X XI XII
1960
I
1955
0
0
1950
Number of cases
20
c
Subtype 2b is accompanied by North Atlantic cyclones moving from Norwegian Sea or
southern part of Scandinavia to the southeast as a result of favorable orientation of planetary frontal
zone (Fig.S7a). About 6.3% of such cyclones reach the Black sea predominantly between
September and April (Chernova 1959). Synoptic subtype 2b is more active in January (up to 22
events in the studied period), whereas in warm season these events are very rare (1-2 events and
never in May, June and August) (Fig. S7b). Like in the other cases, the maximum number of storms
associated with subtype 2b was registered in the period from 1954 to 1982 (Fig.S7c). About 24% of
the storms observed on the SW coast of the Crimea were associated with the synoptic processes of
subtype 2b. Thus, about 50% of all storms in this region were associated with synoptic processes of
type 2.
The third type of meridional circulation form is characterized by extended high pressure
area (anticyclonic blocking) over the Eastern and Central Europe (Fig.S8a). Typically, North
Atlantic cyclones intensify and move from Iceland to the British Isles and Barents Sea along the
northern periphery of this area. This leads to intense transfer of warm air masses to the Northern
regions. Simultaneously, anticyclonic blocking over East and Central Europe is accompanied by
southern cyclones’ penetration into the Black Sea-Azov Sea region along its southern periphery.
Such synoptic situation leads to quite long-term (5-6 days) stormy E and NE winds in the Black Sea
region (Logvinov and Barabash 1982).
The synoptic processes of type 3 are most active in the cold season with maximum in
January (20 events). In the warm season they are very rare: 1-2 events per month, and never in May
through July (Fig.S8b). Most vulnerable regions for storms associated with the third synoptic type
are the N-W Black Sea and Southern Crimean coast, where recurrence of such storms is about 39%.
5
16
14
12
4
Number of cases
Number of cases
Chersonesus lighthouse
Yalta
Odessa
Chersonesus lighthouse
Yalta
Odessa
10
8
6
4
3
2
1
2
a
b
Figure S8. The same as in Fig.S3, but for the type 3.
c
2010
2005
2000
1995
1990
1985
1980
1975
1970
1965
1960
II III IV V VI VII VIII IX X XI XII
1955
I
1950
0
0
12
Wave heights on the Southern Crimea coast during such events reach extreme values. For
example, the strong storms of 6 January 1969 and 10 March 1970 were accompanied by the
described synoptic processes and southern cyclones’ penetration from Eastern Mediterranean. In
that time, wind speed reached 28-34 m/s and 16-20 m/s on the Southern Crimea coast and N-W
Black Sea coast respectively. The strong stormy waves were observed also on the Western Crimean
coast (wave height reached 5 m) and Kerch strait (4 m) (Naumova et al. 2010). Decadal-scale
variability of type 3 recurrence is the same as for the previous types. Its maximum number was
observed in 1954-1983 (Fig.S8c).
Synoptic processes of type 4 are characterized by eastern state of anticyclonic ridge. In the
cold season its axis lies on the line Tbilisi-Samara (Fig.S9a). The secondary altitude ridge is located
over the North Atlantic. In this case, ultra-polar intrusion on the Europe is absent (Logvinov and
Barabash 1982). Quite intense cyclonic activity is observed over the Western Europe and
Mediterranean region. Then, the Mediterranean cyclones are going to the Black Sea region.
Synoptic processes of type 4 are registered throughout a year. However, the maximum
number of storms of this type (12 events per month) was observed in December (Fig.S9b). This
type is a main trigger of the N-W Black Sea storms. Its recurrence is about 32% here, whereas for
the other regions of the Black Sea the contribution of this type is 11-13%.
12
7
Chersonesus lighthouse
Yalta
Odessa
8
6
4
2
5
4
3
2
1
b
2010
2005
2000
1995
1990
1985
1980
1975
1970
1965
1960
II III IV V VI VII VIII IX X XI XII
1955
I
1950
0
0
a
6
Number of cases
Number of cases
10
Chersonesus lighthouse
Yalta
Odessa
c
Figure S9. The same as in Fig.S3, but for the type 4.
Interdecadal variations of regional storms’ number associated with the type 4 are similar to
the previous cases. The storms of such kind are more frequent in 1954-1981 (Fig.S9c). The
maximum storms’ number of this type was observed in 1965 (8 events). The maximum wave height
(4.9 m) was registered on 4 April 1979 on the S-W Crimean coast.
The frequency of different types of synoptic processes can be systematized as follows. The
type 2 processes are the most frequent; the type 4 is the rarest. The analysis of seasonal distribution
of the types of synoptic processes shows the predominance of subtype 2b in the cold season. It is
interesting to note that the most severe storms were not induced by the full set of types and
subtypes occurring simultaneously. For instance, storms with wave heights more than 5m were
conditioned by four types and subtypes namely 1b, 2a, 2b and 3. The highest wave (7.3 m) was
marked on 10 November 1981 when the process of subtype 1b was observed.
It should be emphasized once more that all defined types correspond to meridional forms of
atmospheric circulation. The most frequent macro-synoptic situation favoring the stormy
conditions in the Black Sea region is the following. Persistent meridional ridge (or anticyclonic
blocking) is located over the Eastern Europe. The North Atlantic cyclone propagating from the
North-West or/and southern cyclone spreading from Mediterranean region move along its
periphery. This creates an increased pressure gradient in the studied region and stormy conditions
over the northern Black Sea.
13
Interdecadal variability of macro-synoptic patterns is likely generated by the large-scale
processes in the coupled ocean-atmosphere system. Extended spatial character of the multidecadal
variability of wind-wave parameters in the entire Atlantic – European region confirms that. Indeed,
different authors showed that there is ~50 yrs variability of storms’ activity in Irish and Northern
Seas (Bacon and Carter 1993; Weisse and Gunther 2006). Wave height tendencies in the North
Atlantic found by Bacon and Carter (1993) are consistent with the results of Kushnir et al. (1997).
The period 1960-1980 was also notable for the European part of Russia as it was accompanied by
increased frequency of cyclones (Technical summary 2008). Several studies found the same
tendencies of cyclonic activity in the Black Sea basin (e.g., Polonskii et al. 2007). As shown above,
our results well agree with the investigation based on analysis of voluntary ship observations of
wind and waves in the western Black Sea (Valchev et al. 2009). McCabe et al. (2001) noted that the
tendency of intensification of severe storm events in 60-70’s and their weakening in 90’s was also
manifested in variability of the frequency of extratropical cyclones in the entire Northern
Hemisphere. Figure 6 (see paragraph 3.3) clearly demonstrates the mutual conformity of SST
variability in the North Atlantic/Pacific and type of atmospheric circulation in the Northern
Hemisphere governing the macro-synoptic pattern in the region of interest.
14
4. Statistical significance of the detection of multidecadal oscillations
Since the typical period of the discussed multidecadal oscillation is about 50 yrs, the
possibility of the statistically significant detection of this oscillation from majority of available
instrumental data (their typical length is ~100 yrs) is problematic. Schlesinger and Ramankutty
(1994) used the parametric method of spectra assessment for analysis of century-scale temperature
time series all over the globe. They were the first who showed that strong multi-decadal signal has
North Atlantic origin. However, Elsner and Tsonis (1994) pointed that significant detection of
signal of such kind is impossible. Their comment provoked a long discussion of this problem in the
literature (e.g., see Elsner and Tsonis (1994); Polonskii (2008); Polonskii et al. (2004); Polonsky et
al. (2012); Schlesinger and Ramankutty (1994)). From the formal viewpoint, the authors of the
comment Elsner and Tsonis (1994) are certainly right. For the statistically significant detection of
multidecadal oscillation, it is necessary to have substantially longer series of observations than the
majority of available data of routine hydrometeorological observations can provide.
There are just a few instrumental time series in the Eastern European region with lengths
exceeding 200 years. Besides, few suitable regional reconstructions are also available. One of the
co-authors of the present study (Polonskii 2008) considered in that context 220 yrs time series of
routine meteorological observations in Warsaw and one of the recent reconstructions of the time
variation of temperature in the Alps for a 2000-yr period (from 90 BC to 1935 AD) (Mangini et al.
2005). The annual average air temperature was reconstructed in Mangini et al. (2005) from isotopic
composition of oxygen in the stalagmites of one of the Alpine caves. Time series in Warsaw was
processed using wavelet decomposition. Reconstructed time series were analyzed using the Fourier
spectrum technique. Both time series demonstrated that there are high-magnitude ~50-60 yrs
oscillations. Reconstructed (since 17th century) index of North Atlantic Oscillation is characterized
by the same multidecadal variations (Luterbacher et al. 1999). Similar oscillations in the
characteristics of the Black Sea cyclones have been found by the authors of Polonskii et al. (2007)
and Polonsky et al. (2012). Results published in Polonskii et al. (2004) clearly show that this is
mostly a result of North Atlantic variability. In addition, the outputs of the best general circulation
models reveal the significant European multidecadal oscillations and they confirm the North
Atlantic origin of these oscillations (Delworth et al. 1996; Knight et al. 2006). The results and
discussion published in Elsner and Tsonis (1994), Knight et al. (2006), Msadek et al. (2011), Wyatt
et al. (2011) clearly showed that Atlantic-European multidecadal climate variability is mostly
determined by AMO.
Secondary (but significant) impact of PDO on the Black Sea cyclonic activity in winter was
emphasized in Polonsky et al. (2012). It was shown that joint AMO+PDO impact on the cyclonic
activity in the Black Sea region is robust. Principal conclusion of this study was as follows.
Regional cyclonic activity is at a maximum when negative phases of AMO and PDO coincide. If
long-term SST anomalies in the North Atlantic and North Pacific are negative the Atlantic cyclones
reaching the vicinity of the Black Sea region are more numerous. At the same time, there are more
frequent Mediterranean cyclones generated as a result of regional atmosphere instability.
So, the result on regional multidecadal variability of different meteorological and marine
parameters associated with AMO and PDO is robust. It is confirmed by different kind of
observations, paleoreconstructions and output of the long-term numerical simulations in the
framework of the state-of-the-art global models.