Spatiotemporal Variability of Eastern Mediterranean Tree-Growth and
Climate
The eastern Mediterranean and Near East (EMNE) region is influenced by multiple local and
remote climate processes. The region is exposed to the South Asian Monsoon (Rodwell and
Hoskins, 1996; Raicich et al., 2003) and the East Atlantic Jet (Duenkeloh and Jacobeit, 2003;
Xoplaki et al., 2004; Touchan et al., 2005b) in summer. The Siberian High Pressure System (e.g.
Xoplaki et al., 2001) and North Atlantic Oscillation (Corte-Real et al., 1995; Duenkeloh and
Jacobeit, 2003; Xoplaki et al., 2004) also influence regional climate.
A semi-objective
classification of daily synoptic maps identified no fewer than 6 large synoptic groups important
to climatic variation in the eastern Mediterranean: Cyprus lows, the Persian trough, the Red Sea
trough, Sharav lows, and Siberian/subtropical highs (Alpert et al. 2004). Precipitation trends in
the last half of the 20th century appear to be related to changes in the frequency (or annual
number of days) of synoptic-types, notably the Cyprus lows and Red Sea trough.
Orography
and land-sea interactions (including distance from the sea) and smaller scale processes (Lolis et
al., 1999; Xoplaki et al., 2001, 2003a, b, 2004) are also important and can control local-scale
patterns climate heterogeneity. Mediterranean climate is further influenced by the Mediterranean
Sea itself (e.g. Trigo et al., 1999; Mariotti et al., 2002), which represents an important source of
energy and moisture for cyclone development.
Furthermore, the complex land topography
around the Mediterranean plays a crucial role in steering air flow (e.g. Bartzokas et al., 1994;
Trigo et al., 1999).
Perhaps as a consequence of these multiple superimposed influences,
hydrologic variability spans a broad range of timescales and is unlikely to be fully described by
the modern instrumental record. Meanwhile, the population of the eastern Mediterranean grows
by 3.5% annually, while irrigation practices consume at least 80% of the available water supply.
1
As a result, precipitation is a key variable affecting public health and political stability (Cullen
and deMenocal, 2000). An example of this is the drought conditions which have been a cause of
a devastating fire season across the region, resulting in the destruction of hundreds of hectares of
forests and crops (e.g., see http://www.terradaily.com/2005/050731190646.aqycjkm2.html).
Quantifying and understanding climatic changes at these regional scales are among the most
important and uncertain issues in the study of global change. For example, extreme regional
scale droughts exhibit much larger amplitudes than global averages, and affect regional societies,
economies, water supplies and agricultural ecosystems (e.g. Luterbacher et al. 2004; Xoplaki et
al. 2005). Consequently, understanding the spatiotemporal details of drought are of critical
importance in this region, where even currently the consequences can be severe, and an increased
occurrence of such events is projected.
Refined knowledge and understanding of the full range of past hydroclimatic variability in
the EMNE is critical for identifying possible causative factors and for assessing the ability of
general circulation models (GCMs) to reproduce variability at long timescales. Meteorological
data are sparse in the EMNE and are typically not long enough to effectively capture the
potential range of multidecadal to century-scale variability and the spatiotemporal response to
radiative forcing. In view of existing uncertainties, longer records of natural hydroclimatology
are necessary for assessing the causes of variability and trends in the instrumental record, and
evaluating the accuracy of the forced response in forecast GCMs. Touchan et al., (1999, 2003,
2005a, b, 2007, 2008 a, b, 2011, 2012), Akkemik et al. (2005, 2008), Köse et al,, (2011), Griggs
et al., (2007), Hughes et al., (2001), and D’Arrigo and Cullen, (2001) have demonstrated that
proxy records developed from tree rings offer a longer-term perspective on episodic drought in
the EMNE, and have identified the steps needed to improve the applicability of such records.
2
In this paper, a network of drought-sensitive tree-ring chronologies in the EMNE (33°N42°N, 21°E-43°E) is described and is analyzed for seasonal climatic signal. This analysis is a
prelimary step toward application of the network to study long-term climate variability,
associated atmosphere-ocean anomalies, and the ability of GCMs to reproduce important
drought-related features of regional climate. Correlation analysis and cluster analysis are applied
to tree-ring data and gridded climate data to assess the climate signal embedded in the network in
preparation for climate field reconstructions and formal proxy/model intercomparison
experiments. The objective of the analysis is improved design and refinement of reconstruction
protocols, such as selection appropriate comparative climate model fields, all toward the larger
goal of understanding Mediterranean drought variability and interannual to centennial scales,
providing out-of-sample assessment of GCMs, and evaluating the myriad interacting influences
on EMNE drought.
Data and Methods
Tree-ring data and chronology development
This study represents the first large scale systematic dendroclimatic evaluation of a large
tree-ring network in the EMNE. Our analysis focuses on 79 tree-ring chronologies from 82 sites
in Turkey, Syria, Lebanon, Cyprus, and Greece (Table 1). This network is the result of fieldwork
conducted in the period 2000-2011. The dominant tree species in the study areas are shown in
Table 1. Samples were collected from species known to share a high degree of common variation
that is strongly driven by climate. Increment cores were taken at all sites and full cross sections
were taken from stumps of cedar and juniper. Samples were fine-sanded and crossdated using
3
standard dendrochronological techniques (Stokes and Smiley 1996; Swetnam 1985). The width
of each annual ring on the cores and cross-sections was measured to the nearest 0.01 mm.
Each series of tree-ring width measurements was fit with a 67% cubic smoothing spline with
a 50% cutoff frequency to remove non-climatic trends due to age, size, and the effects of stand
dynamics (Cook and Briffa 1990). The detrended series were then prewhitened with low-order
autoregressive models to remove persistence not related to climatic variations. The individual
indices were combined into single averaged chronologies for each combination of site and
species using a bi-weight robust estimate of the mean (Cook 1985). Visual crossdating and
computer-based quality control showed a strong similarity among several of the resulting 82
residual chronologies. Accordingly, prior to cluster analysis, the following sites were combined
to form single chronologies: 1) PIBR Forest-SYR combines Atera (ATEP), Bait Hamik (BAIP),
and Mafrak Bait Ablak (MBAP); and 2) ABCE Forest-SYR combines Bedayat Al Khandak
(BKTA) and Rawisat Almedeki (RWIA). .
Climate Data analysis
This analysis was done using 77 of the 79 residual chronologies analyzed by ECA. We
dropped from consideration two sites from Lebanon (HAD, Cedrus libani and KFR, Pinus
pinea) as they did not cover the full span of the instrumental data (starting only in 1919 and
1923, respectively). The remaining 77 sites were compared against local monthly gridded
climate data. We used 1.0 gridded monthly precipitation data from the Global Precipitation
Climatology Centre dataset covering 1901 through 2010 (GPCC, version 6; Schneider et al.,
2011; Becker et al., 2013; Schneider et al., 2013) and 0.5 gridded temperature data from the
Climatic Research Unit (CRU) TS3.1 dataset (Mitchell and Jones, 2005) covering the period
from 1901 to 2009.
4
We conducted a site-by-site correlation analysis of each residual ring-width chronology
against the local gridded climate data using the seasonal correlation (SEASCORR) procedure
developed by Meko et al. (2011) with exact simulation (Percival and Constantine, 2006) for
significance testing. We consider monthly values as well as seasonal values integrating 2, 3, or 4
months. We considered a 14-month window starting in the August prior to the growth year and
ending in the following September. We then performed a k-means cluster analysis on the
resulting 112 monthly and seasonal correlation and partial coefficients for precipitation and
temperature for each site. We estimated the optimal number of clusters using silhouette plots and
the gap statistic (Kaufman and Rousseeuw, 1990; Tibshirani et al., 2001)
Results and Discussions
Tree-ring chronologies
The lengths of the 79 combined chronologies (derived from the 82 site collections) range
from 89 (Hadad, Lebanon) to 990 years (Elmali, Turkey) (Table 1 and Fig. 2). Statistical
analyses of each chronology are summarized in Table 2. The mean correlation among individual
radii at each site represents the strength of their common signal and ranges from 0.17-0.59. The
highest correlation is for NESJ site in Turkey and the lowest is for the chronology developed
from JRA site in Lebanon.
The mean sample segment length (MSSL) of the 79 chronologies ranges from 68 to 408
years. Half of these chronologies have MSSL greater than 200 years in length and several have
MSSL exceeding 400 years.
Seventy-six of the total 79 chronologies are well replicated from trees growing on sites where
growth is strongly influenced by climatic variability. ARSTAN indicated that more samples are
needed from the remaining three sites, all from Lebanon (KAM, JRA, and HAD). The 76 sites
5
represent the first tree-ring network developed specifically for dendrolimatology in this region
and indicate the great potential that exists for future work. This spatial network of chronologies
contains seasonal precipitation and temperature signals for a fairly broad geographical area as
indicated by the analyses reported here. This provides further evidence of the value for continued
and intensified sampling in the eastern Mediterranean region. The chronologies show a common
high response to extreme low and high May-August precipitation. This synchronicity of strong
patterns in tree-ring growth suggests pan-regional forcing mechanisms for growth.
One of the major objectives of dendroclimatogists is to obtain the longest possible tree-ring
records from living and dead trees to investigate climate variability over several centuries or
longer. In most cases we were able to collect samples that were several hundred years in length.
This was accomplished primarily by applying experience gained in the semi-arid American
Southwest in the selection of species, sites, and individual trees for analysis. Furthermore, the
MSSL of the chronologies is adequate to investigate centennial climate variability when
combined, as here, with conservative de-trending of the individual measurement series.
Climate Data analysis
Analysis of monthly correlations and partial correlations between gridded climate data and
our tree-ring width chronologies reveals a pervasive positive association with May, June, and
sometimes July precipitation, positive correlations with winter and spring (December through
April) temperatures, and negative relationships with May through July temperature, although
there are site-to-site exceptions to these general patterns (Figure 3).
Our cluster analysis suggests three groups of sites based on their association with climate
(Figure 4). Iterative testing of the gap statistic suggested the data could reasonably be considered
to be represented by 2, 3, or 5 clusters. Using 2 clusters, however, failed to fully capture
6
differences in the climate response seen in Figure 3 and silhouette plots using 5 clusters revealed
the spurious development of small clusters consisting of only 1 or 2 sites and whose climate
response could not be differentiated from other clusters. We therefore proceed with our analysis
for the results using three clusters.
The average climate response of the three clusters is shown in Figures 5, 6, and 7. These
indicate (1) a cluster characterized by a May-June positive precipitation response and a positive
seasonal temperature response throughout most of the late prior and current growing season, (2)
a cluster with a positive precipitation response in May-June with a negative temperature response
during the summer and a negligible temperature response during the spring and winter, and (3) a
cluster with a positive May-June precipitation response, a positive winter-spring temperature
response, and a subsequent and abrupt negative temperature response during summer.
A map of the distribution of the site cluster assignments (Figure 8) shows that Clusters 2 and
3 are intermingled across the domain, although Cluster 2 is broadly coastal and predominantly
found in the Levant and Greece. Cluster 3 is the largest and is composed of the majority of the
sites throughout Turkey, Cyprus, and Crete. The majority of the sites within Cluster 1 are found
in northeastern Turkey.
We can examine the distribution of species across our cluster (Figure 9). Cluster 1 contains
all the Pinus sylvestris (PISL) sites, but also one Pinus nigra (PINI) chronology, several
Juniperus excelsa (JUEX) sites, and a Cedrus libani chronology. For Clusters 2 and 3, the
remaining Pinus nigra and Juniperus excelsa are distributed between them. The majority of the
Cedrus libani chronologies are in Cluster 2 as are all the Abies, while Cluster 3 contains all the
Pinus brutia (PIBR) chronologies and the Cedrus brevifolia (CEBR) site.
7
An elevational dependence is evident in the clusters. Sites in Cluster 1 are at a significantly
higher elevation (Figure 10, c.f. McGill et al. ( 1978)), consistent with the location of the Pinus
sylvestris that comprise this cluster, while Cluster 2 and Cluster 3 are composed of sites at lower
elevations but are not distinguishable from one another. The elevational dependence may reflect
a tendency for species to be stratified by elevation, and may also suggest elevationally related
climate regimes (e.g., high mountain) that can impose similar tree-growth variations in
chronologies from widely separate locations.
Although individual sites can reflect a diversity of monthly or seasonal climate responses,
considered across the entire network of 77 sites a number of consistent features emerge from our
cluster analysis: Most of the network has an early summer (May-June) positive response to
precipitation. Temperature responses are more diverse, but generally fall into one of three
categories: a positive response to winter-spring temperature, a positive response to response in
nearly all seasons, and a negative response to temperature during the summer. The clearest
difference amongst the clusters we have identified here is between Cluster 1, characterized by
higher elevation Pinus sylvestris in northeastern Turkey with a positive temperature response
through most of the year, and lower elevation sites throughout Turkey, Greece, and the Levant in
Cluster 2 and 3 with May-June precipitation responses dominated by Pinus nigra. Cluster 2 tends
to be found near the Mediterranean and has no positive response to temperature, while Cluster 3
is widespread throughout Anatolia and switches from a positive response to winter-spring
temperature to a negative response during the summer and coincident with a positive correlation
with precipitation.
Conclusion
This study represents the first large-scale systematic dendroclimatic sampling focused on
developing chronologies from Turkey, Greece, Cyprus, Syria, and Lebanon. This spatial network
8
of chronologies contains seasonal precipitation and temperature signals for a fairly broad
geographical area as indicated by the analyses reported here. This provides further evidence of
the value for continued and intensified sampling in the eastern Mediterranean region. The
chronologies show a common high response to extreme low and high May-June precipitation.
This synchronicity of strong patterns in tree-ring growth suggests pan-regional forcing
mechanisms for growth.
Collectively, these findings suggest that it may be possible to reconstruct three fields from
these data. May-June precipitation is an important control on nearly the entire network, while the
negative association particularly in Cluster 2 with summer temperatures suggest the Palmer
Drought Severity Index (PDSI; Palmer, 1965) is an additional and reasonable reconstruction
target. Finally, the existence of a winter-spring and even in some cases summer temperature
signal, particularly in Pinus sylvestris from northeastern Turkey, suggest the possibility of
performing temperature reconstructions over at least part of the eastern Mediterranean domain.
9
Table 1. Site information for the eastern Mediterranean region.
10
Table 1. Continued
11
Table 2 Summary statistics for the 79 chronologies for the ARSTAN program
Country
Site
Total Chronology
Common Interval
Code
MSSLa
Stdb
SKc
KUd
1st Year
Time Span
MCARf
Evg PC1 (%)
EPSe>0.85
Greece
Turkey
MOL
203
0.15
-0.13
0.53
1613
1890-2008
0.35
37
VKA
355
0.20
0.22
0.39
1279
1709-1992
0.38
40
PEF
217
0.15
-0.99
5.98
1687
1873-2008
0.35
37
KAS
112
0.15
-0.07
-0.36
1885
1924-2007
0.42
45
POT
97
0.20
0.21
0.83
1909
1932-2008
0.41
44
ANAL
70
0.23
0.09
0.43
1921
1962-2008
0.52
55
ANAH
87
0.22
0.21
2.62
1910
1939-2004
0.45
47
SUBP
235
0.18
0.25
0.57
1728
1792-2000
0.40
42
GUZ
235
0.19
0.35
0.97
1781
1807-2004
0.38
40
PIG
263
0.19
-0.22
0.67
1709
1772-2008
0.37
40
ALM
233
0.13
0.70
3.80
1770
1822-1996
0.31
34
YATS
186
0.13
0.21
0.79
1803
1853-2001
0.32
35
ESE
239
0.14
0.48
1.07
1682
1837-2001
0.34
36
GAV
170
0.16
0.22
0.10
1804
1812-2001
0.48
49
AYA
175
0.12
0.02
0.19
1802
1857-2001
0.33
35
ALD
248
0.24
0.66
1.25
1700
1850-2009
0.43
45
DEL
207
0.20
0.09
0.28
1757
1828-1999
0.43
46
IST
144
0.11
-0.13
0.57
1846
1905-2001
0.26
29
TOZ
166
0.15
0.65
0.55
1799
1885-2000
0.47
50
AKY
200
0.12
-0.07
1.00
1757
1855-2001
0.35
35
CAL
150
0.17
0.28
1.18
1836
1890-1997
0.32
35
DEM
167
0.24
0.51
1.44
1866
1894-2010
0.32
35
KAY
212
0.17
-0.13
0.35
1775
1830-2009
0.37
40
KAV
185
0.22
-0.04
0.75
1806
1871-2010
0.36
38
UDT
108
0.15
0.46
0.46
1910
1925-2007
0.27
30
KOC
139
0.17
0.37
0.73
1883
1895-2012
0.28
31
KIR
175
0.20
0.70
1.99
1823
1851-2002
0.49
50
ALI
162
0.19
0.06
-0.11
1850
1861-2001
0.40
45
ATA
190
0.18
0.17
-0.04
1809
1844-2001
0.41
45
DED
238
0.11
0.24
0.30
1789
1836-2010
0.24
27
KIZ
245
0.20
0.37
0.19
1727
1779-2010
0.39
41
DEY
154
0.14
-0.45
1.55
1844
1880-2009
0.35
37
GOK
107
0.16
0.19
0.58
1898
1912-2011
0.36
39
HOD
169
0.15
0.06
0.38
1863
1883-2010
0.28
31
CIRP
390
0.20
0.40
3.82
1577
1689-2001
0.37
41
SUBJ
299
0.18
0.12
0.14
1338
1672-1950
0.44
50
AZY
204
0.13
0.08
0.00
1789
1861-2000
0.31
34
DUD
146
0.20
0.23
0.53
1831
1893-1999
0.48
51
KATC
157
0.18
0.17
0.20
1815
1878-2000
0.47
49
KAR
238
0.11
0.24
0.30
1789
1836-2010
0.24
27
SEN
245
0.20
0.37
0.19
1727
1779-2010
0.39
41
12
Cyprus
Syria
Lebanon
ANA
231
0.17
0.38
0.18
1736
1797-2001
0.40
46
SILJ
218
0.22
0.24
0.14
1727
1890-2000
0.52
56
UCK
183
0.18
0.29
0.39
1741
1845-2010
0.49
53
BOZC
91
0.20
0.01
1.08
1940
1959-2011
0.36
39
BOZP
172
0.21
0.27
0.27
1811
1872-2009
0.46
48
BOLC
206
0.22
0.10
1.24
1523
1851-2010
0.48
50
NESJ
408
0.25
0.12
-0.01
1296
1579-1976
0.59
60
GOLP
169
0.17
0.32
0.94
1798
1885-1999
0.48
50
KAG
367
0.13
0.12
0.30
1557
1582-2010
0.26
31
GOLJ
341
0.16
0.14
0.49
1594
1759-2000
0.32
36
BAB
171
0.19
-0.50
0.25
1751
1797-1996
0.59
65
ARAJ
357
0.18
0.70
2.02
1616
1719-1976
0.33
37
KOP
295
0.15
0.24
0.91
1682
1744-2000
0.31
35
YEB
200
0.19
0.18
0.26
1743
1818-2000
0.36
41
ELMJ
316
0.20
0.41
0.17
1102
1703-1960
0.42
44
ELMC
330
0.17
-0.40
1.21
1505
1759-1997
0.48
50
AMF
169
0.16
-0.03
0.64
1779
1844-2010
0.37
40
STP
213
0.16
-0.02
1.15
1765
1829-2002
0.37
39
TRI
241
0.19
0.66
2.71
1719
1813-2002
0.37
38
AMB
200
0.17
0.34
1.08
1734
1842-2002
0.40
42
TUL
315
0.18
0.52
2.76
1605
1788-2010
0.40
42
ART
332
0.18
0.41
0.16
1719
1731-1998
0.37
40
CHI
332
0.19
0.62
0.81
1596
1727-2002
0.41
44
HAM
275
0.21
0.07
5.26
1629
1786-2005
0.46
48
AMIN
325
0.14
0.16
0.61
1591
1640-1934
0.30
33
PIBR¹
89
0.13
-0.39
0.08
1909
1937-2000
0.20
24
KNM
99
0.20
0.16
0.04
1883
1921-2001
0.36
41
ABCE²
110
0.25
0.77
2.86
1872
1913-2000
0.26
31
KAM
120
0.20
0.40
2.67
*
1921-2009
0.20
23
WAB
137
0.14
-0.22
0.44
1876
1913-2001
0.22
28
JRA
86
0.13
0.14
0.93
*
1928-2009
0.17
24
EHD
128
0.13
-0.29
0.55
1866
1891-2001
0.37
40
HAD
75
0.09
0.15
0.66
*
1950-2011
0.20
24
BSH
278
0.19
-0.32
0.93
1547
1807-1993
0.42
44
KFR
68
0.15
0.80
1.26
1949
1959-2011
0.26
29
TAN
128
0.19
-0.01
0.15
1857
1910-2010
0.41
44
ARJ
113
0.14
0.10
0.48
1875
1925-2001
0.31
34
MAA
151
0.19
-0.40
0.90
1757
1881-2002
0.44
46
a
MSSL is Mean Sample Segment Length; bStd is Standard Deviation; cSK is skewness
d
KU is Kurtosis; eEPS is Expressed Population Statistic (Wigely et al., 1984); fMCAR is Mean
Correlation Among Radii; gEV is Explained Variance; *More samples are needed; ¹ Brutia pine
forest combines ATEP, BAIP, and MBAP; ²Abies Celicica forest combines BKTA and RWIA
13
Fig 1
14
Fig 2.
15
Fig. 3
16
Fig. 4
17
Fig 5
18
Fig 6
19
Fig 7
20
Fig 8
21
Fig 9
22
Fig. 10
23
24