Clim. Past, 10, 1473–1487, 2014
www.clim-past.net/10/1473/2014/
doi:10.5194/cp-10-1473-2014
© Author(s) 2014. CC Attribution 3.0 License.
Testing long-term summer temperature reconstruction based on
maximum density chronologies obtained by reanalysis of tree-ring
data sets from northernmost Sweden and Finland
V. V. Matskovsky1 and S. Helama2
1Institute of Geography, Russian Academy of Sciences, Moscow, Russia
2Finnish Forest Research Institute, Northern Unit, Rovaniemi, Finland
Correspondence to: V. V. Matskovsky (matskovsky@igras.ru)
Received: 13 August 2013 – Published in Clim. Past Discuss.: 16 October 2013
Revised: 1 June 2014 – Accepted: 11 June 2014 – Published: 5 August 2014
Abstract. Here we analyse the maximum latewood density
(MXD) chronologies of two published tree-ring data sets:
one from Torneträsk region in northernmost Sweden (TORN;
Melvin et al., 2013) and one from northern Fennoscan-
dia (FENN; Esper et al., 2012). We paid particular atten-
tion to the MXD low-frequency variations to reconstruct
summer (June–August, JJA) long-term temperature history.
We used published methods of tree-ring standardization: re-
gional curve standardization (RCS) combined with signal-
free implementation. Comparisons with RCS chronologies
produced using single and multiple (non-climatic) ageing
curves (to be removed from the initial MXD series) were
also carried out. We develop a novel method of standardiza-
tion, the correction implementation of signal-free standard-
ization, tailored for detection of pure low-frequency signal
in tree-ring chronologies. In this method, the error in RCS
chronology with signal-free implementation is analytically
assessed and extracted to produce an advanced chronology.
The importance of correction becomes obvious at lower fre-
quencies as smoothed chronologies become progressively
more correlative with correction implementation. Subsam-
pling the FENN data to mimic the lower chronology sam-
ple size of TORN data shows that the chronologies bifur-
cate during the 7th, 9th, 17th and 20th centuries. We used
the two MXD data sets to reconstruct summer temperature
variations over the period 8 BC through AD 2010. Our new
reconstruction shows multi-decadal to multi-centennial vari-
ability with changes in the amplitude of the summer tempera-
ture of 2.2 ◦C on average during the Common Era. Although
the MXD data provide palaeoclimate research with a highly
reliable summer temperature proxy, the bifurcating dendro-
climatic signals identified in the two data sets imply that fu-
ture research should aim at a more advanced understanding
of MXD data on distinct issues: (1) influence of past popu-
lation density variations on MXD production, (2) potential
biases when calibrating differently produced MXD data to
produce one proxy record, (3) influence of the biological age
of MXD data when introducing young trees into the chronol-
ogy over the most recent past and (4) possible role of water-
logging in MXD production when analysing tree-ring data of
riparian trees.
1 Introduction
Dendrochronology is one of the most common methods for
obtaining the late Holocene reconstructions of past climate
variability because tree-ring chronologies are high-resolution
indicators of exactly dated paleoclimate information (Fritts,
1976). Moreover, tree-ring chronologies around the extrat-
ropical Northern Hemisphere contain clear climatic signals
of summer temperatures (Briffa et al., 2002). This dendrocli-
matic association is notably high for different tree species
growing near their polar and alpine range of distribution.
Yet the strength of this dendroclimatic response depends
on the measured tree-ring parameter. While the use of tree-
ring width chronologies as indicators of past climate vari-
ability has a long tradition in dendroclimatology, the devel-
opment of X-ray-based estimates of wood density fluctua-
tions (Schweingruber et al., 1978) has increased the value
of dendrochronology as an integral part of paleoclimatology.
Published by Copernicus Publications on behalf of the European Geosciences Union.
1474 V. V. Matskovsky and S. Helama: Testing summer temperature reconstruction
Several studies have shown that the measurements of maxi-
mum latewood densities (MXD) yield chronologies with an
improved climatic signal (Briffa et al., 2002). Consequently,
the MXD chronologies from several regions around Eurasia
and North America have been used for summer temperature
reconstructions (Hughes et al., 1984; Davi et al., 2003; Luck-
man and Wilson, 2005; Büntgen et al., 2008).
Similar studies have been conducted in northern
Fennoscandia where a few reconstructions of summer
temperatures (JJA) over the Common Era have been based
on MXD records of living and subfossil pines (Briffa et
al., 1988, 1990, 1992, 1996; Briffa and Schweingruber,
1992; Grudd, 2008; Büntgen et al., 2011; Esper et al., 2012;
McCarroll et al., 2013; Melvin et al., 2013). While the
early studies concentrated on materials from northernmost
Sweden, particularly the Torneträsk region (Schweingruber
et al., 1988; Briffa et al., 1990, 1992, 1996; Grudd, 2008),
the tree-ring materials from northern Finland (Eronen et
al., 2002) have recently been exploited for MXD analyses.
These materials have notably increased the available MXD
sample size and temporally extended the chronology for the
region to 138 BC (Büntgen et al., 2011; Esper et al., 2012).
In a recent dendroclimatic analysis Melvin et al. (2013) used
updated and adjusted subfossil (Schweingruber et al., 1988)
and living-tree (Grudd, 2008) MXD data from Torneträsk.
A brief literature review exemplifies several inconsisten-
cies in the published palaeoclimate records. While the first
of these reconstructions (Briffa et al., 1992) strove to de-
fine the degree of the 20th-century warmth in the context of
the last 1500 yr, the subsequent study (Grudd, 2008) demon-
strated a positive temperature anomaly of about 0.7–1.5◦C
(30 yr mean) for the Medieval Warm Period (MWP) rela-
tive to the 20th-century conditions (1951–1970 mean). It
also showed a cooling phase (up to 1.1 ◦C negative anomaly
for 30 yr mean) for the Little Ice Age (LIA). However,
Büntgen et al. (2011) did not find evidence for a cool pe-
riod during the LIA interval. Esper et al. (2012) stated that
the previously estimated warmth of the MWP may be no-
tably underestimated. Another detailed dendroclimatic anal-
ysis (Melvin et al., 2013) presented MWP temperatures at a
level similar to the modern ones in the region suggesting an
overestimation of the medieval warmth published by Grudd
(2008). Melvin et al. (2013) showed that the Grudd (2008)
study contained systematic bias and should not be consid-
ered suitable for use as a climate reconstruction. The results
of McCarroll et al. (2013) suggested that the 20th and 17th
centuries were warmest and coldest in the region, while sim-
ilarly warm spells were experienced during the 11th century.
While the post-1700 inconsistencies in a part of the MXD
data (Briffa et al., 1992; Grudd, 2008) have been clarified
(Melvin et al., 2013), the two data sets (Melvin et al., 2013;
Esper et al., 2012) have not been analysed collectively to re-
construct an MXD-based long-term temperature variability
over the whole of northern Fennoscandia. This situation is
even more unsatisfactory, if not worrisome, considering the
important role of the particular MXD data in several hemi-
spheric and frequently cited paleoclimate reconstructions of
the Common Era (e.g. Jones et al., 1998; Esper et al., 2002;
Osborn and Briffa, 2006; Mann et al., 2009; Ljungqvist et al.,
2012).
We assume that the climate variability within the region
is likely too uniform to have caused the reported deviations
in the published temperature reconstructions from adjacent
regions of northernmost Sweden (Torneträsk) and Finland.
Instead, the reasons for these deviations may more realisti-
cally stem from the tree-ring standardization and microden-
sitometric methods, as well as non-climatic noise inherent
to the variations in available samples and their site charac-
teristics. A problem associated with the dendroclimatic im-
provement, consisting of the development from the tradition
of simply registering the widths of the rings to the more so-
phisticated MXD, is the complexity of methods required to
produce the high-resolution wood density profiles. As a pit-
fall, even marginal changes in the microdensitometry meth-
ods may produce significantly altered MXD data (Helama
et al., 2010d, 2012). Tree-ring standardization is a necessary
process where the initial measurement series are transformed
into dimensionless indices to remove the biological (i.e. non-
climatic) growth variations prior to dendroclimatic interpre-
tations (Fritts, 1976). Different standardization methods pro-
duce chronologies with deviating characteristics, and partic-
ularly the long-term (i.e. low-frequency) estimation of den-
droclimatic signals is sensitive to the applied method (Briffa
et al., 1992; Esper et al., 2002; Helama et al., 2004). In-
deed, the recent reanalysis of the Torneträsk MXD data de-
tailed a set of estimation biases originating from changes
in the microdensitometry practices and tree-ring standard-
ization (Melvin et al., 2013). It is essential to note that all
the discussed studies used the same type of standardization
method, the regional curve standardization (RCS; Briffa et
al., 1992), to produce the MXD chronologies as a proxy
for temperature reconstructions, but selected different op-
tions which might explain differences between the result-
ing chronologies. While this particular method is capable of
producing chronologies with preserved low-frequency varia-
tions (Briffa et al., 1992; Esper et al., 2002; Helama et al.,
2004), it is also more sensitive to data heterogeneity and
therefore requires higher sample replication than other com-
monly applied standardization methods (Briffa and Melvin,
2011). Moreover, recent years have seen a diversification of
the original RCS method, with development of its signal-free
implementation (Melvin and Briffa, 2008) and its subsequent
correction (Matskovsky, 2011), as well as multiple curve ap-
proaches of RCS (Helama et al., 2005b; Nicault et al., 2010;
Melvin et al., 2013). The recent versions of the Torneträsk
and Fennoscandian chronologies have used the RCS along
with its variations (Esper et al., 2012; McCarroll et al., 2013;
Melvin et al., 2013) but so far no systematic study exists to
present the construction of the resulting MXD chronologies
using consistent tree-ring standardization.
Clim. Past, 10, 1473–1487, 2014 www.clim-past.net/10/1473/2014/
V. V. Matskovsky and S. Helama: Testing summer temperature reconstruction 1475
Figure 1. Subfossil and living-pine sites location for FENN and
TORN data sets.
Here we reanalyse and compare two MXD data sets from
Torneträsk (Melvin et al., 2013) and from a larger region of
Fennoscandia (Esper et al., 2012) using different standard-
ization methods. MXD variations in the data were identified
in detail and the same types of RCS method were applied to
both data sets. We calibrate standardized MXD data to instru-
mental JJA temperatures of the Tornedalen climate record lo-
cated in northern Sweden and covering the last 200 yr (Kling-
bjer and Moberg, 2003). Then we continue to reconstruct re-
gional summer temperature in the Common Era.
2 Materials and methods
2.1 Geographical setting
This study is based on X-ray-based microdensitometric data
of modern and subfossil Scots pine (Pinus sylvestris L.) tree
rings. We use published and online archived MXD data from
northernmost Sweden (i.e. Torneträsk region) and northern
Fennoscandia. These data sets are referred to as TORN
(Schweingruber et al., 1988; Grudd, 2008; Melvin et al.,
2013) and FENN (originally named N-Scan; Esper et al.,
2012).
Originally, the collection of the TORN material was car-
ried out in five individual sites around Lake Tornesträsk
(68.17–68.33◦ N, 19.75–20.75◦ E; 400–470 m a.s.l.; Fig. 1).
Three of these localities provided subfossil tree rings for ex-
tending the chronology back in time to AD 436 (Bartholin
and Karlén, 1983; Bartholin, 1987). The MXD chronology
for TORN was produced by Schweingruber et al. (1988)
and updated with more recent material of living trees by
Grudd (2008) and Melvin et al. (2013). The TORN data now
cover the period AD 441–2010 and can be downloaded via
http://www.cru.uea.ac.uk/cru/papers/melvin2012holocene/.
The subfossil FENN material of Eronen et al. (2002)
originates from the region of northern Finland. A portion
of this material, covering roughly the past two millennia
(since 138 BC) was later used to build a new MXD chronol-
ogy for the region (66.80–69.50◦ N, 23.00–29.00◦ E; 190–
340 m a.s.l.; Fig. 1). The subfossil material was updated
with living-tree material from north-west Finnish Lapland
and northernmost Sweden (Esper et al., 2012). In actual
fact, one of the living-tree sites included in the FENN data
set is from the Torneträsk region. As a result, the mod-
ern part of the FENN data set is weighted towards the
west, while the eastern half of region is represented only by
subfossil sites. Moreover, two of the three living-tree sites
(67.90◦ N, 20.10◦ E; 68.20◦ N, 19.80◦ E) are far outside the
network of subfossil sites. The FENN data can be down-
loaded via http://www.blogs.uni-mainz.de/fb09climatology/
files/2012/03/Data.pdf. For subsequent analyses FENN data
were transformed into “mean tree” series, i.e. multiple mea-
surements for each tree were averaged into one series. This
was done to avoid artificial reduction of error margins and to
bring the two data sets into correspondence.
2.2 Sampling sites
The tree-ring material originates from four different types of
sampling sites: modern samples of living pines representing
dry (inland) environments and lake riparian habitats, subfos-
sil samples preserved in subaerial conditions and subfossils
preserved in lacustrine sediments. It is notable that the sub-
fossil material of the TORN data set originates merely from
subaerial conditions (Bartholin and Karlén, 1983; Schwein-
gruber et al., 1988). In other words, there is no subfossil
material from lacustrine sediments included in the Swedish
MXD data (Grudd, 2008). On the other hand, the FENN
assemblage consists of subfossil samples collected at lakes
only (Eronen et al., 2002). The modern part of FENN MXD
data (from living trees) was produced by using tree-ring sam-
ples from only lake riparian sites (Esper et al., 2012).Thus,
the principal difference between the TORN and FENN data
sets stems from the studied habitat: the TORN data include
dry sites whereas the FENN data are represented by lake
shore environments.
2.3 Microdensitometry
X-ray-based microdensitometry produces radial wood den-
sity profiles for each tree-ring sample. Each ring exhibits an
annual density cycle where the wood of low density is formed
during the early growing season (i.e. earlywood), with in-
creasingly high densities towards the late summer and au-
tumn (i.e. latewood). The intra-annual point where the den-
sity reaches its highest value is taken as the maximum density
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1476 V. V. Matskovsky and S. Helama: Testing summer temperature reconstruction
(MXD) of that ring. The consecutive MXD values of each
sample represent the individual MXD series. These series
are of special interest in dendroclimatology because of their
strong correlations with growing season temperatures as ob-
served broadly across the circumpolar boreal forests today
(Briffa et al., 2002).
Commonly, living trees are cored at breast height, and
the increment cores exposed for microdensitometric analy-
ses. Subfossil pinewood material can either be found lying
on dry ground or preserved in the sediment of small lakes.
In the field, the unearthed logs are sawn into disks. Subse-
quently, in the workshop, radial laths are sawn perpendicular
to the ring boundaries to be analysed using microdensitome-
try. It is important to note that even slight differences in the
laboratory protocol during the X-ray process may profoundly
alter the resulting MXD data (Grudd, 2008; Helama et al.,
2010d, 2012). MXD data may become altered depending on
the methods that are used to remove the wood extractives (or-
ganic, non-cell-wall components) prior to analyses (Helama
et al., 2010d), the wood moisture content during the mea-
surement (Helama et al., 2012) and the radial step size of the
density scanning (Grudd, 2008; Helama et al., 2012).
The two MXD data sets analysed here (TORN and FENN)
were produced using different X-ray methods and data analy-
sis. The TORN data set consists of data produced in different
decades and laboratories (Grudd, 2008; Melvin et al., 2013).
It has been shown for TORN data that the old MXD values
(Schweingruber et al., 1988) exhibit lower standard deviation
of density variability than the MXD values produced using
the evolved technical property of the X-ray scanner (Grudd,
2008). In order to deal with this bias, Grudd (2008) reduced
the variance in new MXD data to match with the old vari-
ance. More recently, Melvin et al. (2013) added more data to
the TORN data set. These data were produced in a different
laboratory. Comparisons with the differently produced MXD
data showed a need for additional adjustments for mean and
standard deviation of the data because of the different labora-
tory protocols (Melvin et al., 2013). In this study, we are us-
ing these adjusted TORN data (S88G1112A.mxd data from
http://www.cru.uea.ac.uk/cru/papers/melvin2012holocene/).
2.4 Standardization methods
2.4.1 Regional curve standardization
Our analyses are based on regional curve standardization
(RCS) (Briffa et al., 1992, 1996; Briffa and Melvin, 2011)
with the signal-free implementation (Melvin and Briffa,
2008; Briffa and Melvin, 2011) and RCS with a proposed
correction (Matskovsky, 2011). In the original RCS tech-
nique presented by Briffa et al. (1992), the same standard-
ization curve is used to detrend each series of tree-ring mea-
surements. The standardization curve is assumed to display
regionally representative cambial (that is, biological and thus
non-climatic) trends in the total tree-ring variability (Briffa
et al., 1992, 1996). Therefore, this trend can be derived as a
single mean curve of the data aligned according to their cam-
bial ages. The process of averaging the series reveals tree-
ring variations related to ageing and the expected age-related
component can be detected as the mean curve. As is typical
for the conventional RCS method, this curve is assumed to be
not affected by climate. Subsequent to averaging, the mean
curve is commonly smoothed to reduce the effect of ran-
dom fluctuations. Time-varying response smoothing (TVRS;
Melvin et al., 2007) with the routine of Melvin et al. (2007;
see their Appendix A) was used where SSY (spline stiffness
for each year) equalled the cambial age in years plus 15 yr.
We used a cubic spline with 50 % variance cut-off (Cook and
Peters, 1981) on the frequency period of SSY years. Using
this method, the RCS is carried out with a single regional
curve (referred hereafter as RC1) to standardize all tree-ring
series and the tree-ring indices are derived as ratios between
the observed tree-ring value and the value expected by the
RC1. This process is expected to remove a large portion of
age-related variations presented in the initial series of mea-
surements. Parts of each tree-ring series contributing to the
regional curve over the weakly replicated old cambial ages
(with replication less than 10) were removed before using
the RCS method.
2.4.2 Signal-free approach
The conventional RCS method can be combined with the
signal-free (SF) approach (thus, RC1SF) as suggested by
Briffa and Melvin (2011) and as already applied in several
studies (Helama et al. 2010b; Björklund et al., 2013; Melvin
et al., 2013; Cooper et al., 2013; Wilson et al., 2013). In con-
trast with conventional RCS, this method assumes that the
estimate of the standardization curve as just an average of all
available series is biased by climate because the initial tree-
ring series themselves contain a significant climatic compo-
nent. Thereby the mean curve may retain a large proportion
of climatic influence, which becomes effectively removed
from tree-ring indices because the growth component rep-
resented by the curve is removed from the initial series. As
an alternative, a more non-climatic mean curve could be pro-
duced from signal-free measurements whose climatic signal
is removed prior to estimation of the standardization curve.
Following Melvin and Briffa (2008), the removal of the cli-
matic variations is done through a process where the initial
tree-ring measurements, aligned by their calendar years, are
first divided by the index values of the conventional RCS
chronology. The resulting SF measurements are averaged,
aligned by their cambial years, following the typical RC1
procedure (see above). The new mean curve is then removed
from the initial tree-ring measurements to produce SF indices
and a new (RC1SF) chronology. The process is repeated un-
til no improvement in chronology estimation is achieved (see
Melvin and Briffa, 2008, 2013 for details).
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V. V. Matskovsky and S. Helama: Testing summer temperature reconstruction 1477
2.4.3 Correction procedure
An improvement of the chronology estimation, subsequent
to RCS and SF methods, was previously attained by applying
a correction (C) method (thus, RC1SFC; Matskovsky, 2011).
This method, RC1SFC, is a novel application of the RCS and
SF methods, tailored for detection of a pure low-frequency
signal in tree-ring chronologies. The error in RC1SF chronol-
ogy is assessed and subsequently extracted from the data in
order to produce an RC1SFC chronology.
For our purposes tree growth can be thought of as a
product of climatic and non-climatic components. When
averaging non-climatic components from different samples
(trees) we get non-climatic components in the chronology.
These types of chronology variations originate from the non-
uniform distribution of individual series in time and uncom-
mon individual tree growth peculiarities and depend on the
data set properties; thus the error associated with these vari-
ations is denoted as “data set error”. In practice it is difficult
to obtain an ideal data set without such inhomogeneities. SF
and “correction” are used to remove this non-climatic com-
ponent from the chronology, focusing on different frequen-
cies. SF deals only with high frequencies. “Correction” is
designed to deal with lower frequencies. The correction pro-
cedure consists of three steps: (1) building a chronology from
smoothed SF measurements (or SF curves – initial series
of measurements without climatic signal and high-frequency
variations): this chronology represents the data set error; and
(2) subtracting this error from the initial chronology, thus
correcting it. See the detailed algorithm of the correction pro-
cedure in the Supplement.
2.4.4 Multiple-RCS method
Our fourth type of standardization method utilizes multi-
ple RCS curves (instead of a single curve used in the RC1,
RC1SF and RC1SFC methods) to remove the non-climatic
variations (Briffa and Melvin, 2011; Melvin et al., 2013).
Following the previous suggestions (Melvin et al., 2013), we
used two regional curves that were built using the SF routine
(thus, RC2SF) and RC2SF with Matskovsky’s (2011) correc-
tion (thus, RC2SFC). The process of defining the multiple
RCS curves began with calculating the mean of SF measure-
ments for the cambial ages 1–100 yr for each tree. These val-
ues were divided by the mean of the first 100 yr of a single
RC1SF curve (created using all trees) to yield relative growth
rate (sensu Briffa and Melvin, 2011) for MXD production
of each tree. All trees were arranged by this relative growth
rate and the full set of trees was accordingly divided into two
equally large groups (in practice, one group was larger by
one tree if the total number of series in that chronology was
odd). The regional curve was produced for each group for de-
trending each MXD series in the corresponding group. Here
again we truncated all the series of tree-ring measurements to
achieve replication of at least 10 samples for every regional
curve value and TVRS spline was used for smoothing of re-
gional curve. Comparisons between the chronologies were
carried out only for the parts of chronologies with replica-
tion of at least six series. Therefore, the common period was
limited by replication of TORN data set and was set to AD
542–2006.
2.5 Design of experiments
Five types of standardization methods were used for com-
parison under the assumptions that TORN and FENN data
sets are proxies for mean summer temperature (JJA) and that
temperature is spatially homogenous for the whole region.
Correlation between the JJA temperatures (AD 1959–2007)
as observed at Abisko and Sodankylä meteorological stations
is 0.83. Under these assumptions, improved correlation be-
tween any pair of differently standardized TORN and FENN
chronologies demonstrates a higher common (i.e. tempera-
ture) signal.
We compared five types of chronologies (RCS, RC1SF,
RC1SFC, RC2SF and RC2SFC) for TORN and FENN data
sets and also used low-pass filtered chronologies with N-year
smoothing splines (Cook and Peters, 1981) to focus on low-
frequency signals (N = 50,100,200 and 300 yr). Detailed
comparison of low-frequency signals between the TORN
and FENN data sets was carried out using their RC2SFC
chronologies. We also used subsampled FENN data because
of their higher replication. Subsampling was supposed to
highlight any difference arising in the FENN data set when
artificially reduced to the sample depth of the TORN data set.
For details of the subsampling algorithm see the Supplement.
2.6 Temperature reconstruction methods
While the FENN data set benefits from its greater sample
size and replication over the common period (since ca. AD
700), the benefits of TORN data lie in their homogeneity and
their stronger correlation to temperature. Both materials have
their benefits and following their high overall correlativity, it
was justifiable to combine the data into an enhanced MXD-
based paleoclimate reconstruction. The use of a combined
data set could be seen to benefit from several other circum-
stances (see McCarroll et al., 2013). Including a higher num-
ber of sites produces a mean series that is stronger and yields
an uncertainty estimate that incorporates the information of
changes in both data sets over time. This approach also re-
duces the effects of ecological or cultural (i.e. non-climatic)
disturbance events in the chronology. Combining the data
from sites over a larger area also produces a reconstruction
that is representative of the climate of the extended area (Mc-
Carroll et al., 2013). The temperature reconstruction was pro-
duced from a combination of TORN and FENN data sets
(called FULL data set). The FULL data set includes 430 se-
ries and covers the period 216 BC–AD 2010 (8 BC–AD 2010
with replication of more than five series). The combined
www.clim-past.net/10/1473/2014/ Clim. Past, 10, 1473–1487, 2014
1478 V. V. Matskovsky and S. Helama: Testing summer temperature reconstruction
data set has expressed population signal (EPS; Wigley et
al., 1984) values consistently larger than 0.85 after AD 535,
more than for the TORN and FENN data sets individually.
We used AD 1802–2010 as the common period for compar-
isons between the MXD proxy and instrumental data (Kling-
bjer and Moberg, 2003). It is notable that the instrumental
record used here was corrected for inhomogeneities (Klingb-
jer and Moberg, 2003, as cited in the text). Moreover, a recent
study (Helama et al., 2013) did not show increasing inhomo-
geneities in the 19th-century part of this record, in compari-
son to the 20th-century part. The data for the year AD 1815
(with missing instrumental data) were excluded from all the
dendroclimatic comparisons. The common period of instru-
mental and MXD data was divided into two periods (AD
1802–1905 and AD 1906–2010) to be used as calibration
and verification periods and vice versa for producing a JJA
temperature reconstruction from the FULL-RC2SFC MXD
chronology. We used the following commonly used statis-
tics to assess the quality of the reconstruction: Pearson cor-
relation coefficient (r), coefficient of determination (R2), re-
duction of error (RE), coefficient of efficiency (CE) and root
mean square error (RMSE) (Cook et al., 1994). The statistics
of R2, r and RMSE were calculated for calibration, verifica-
tion and common (1802–2010) period with instrumental data
(note that CE is actually R2 for verification period).
MXD-based JJA temperatures were reconstructed using
non-smoothed and smoothed variance adjustment and linear
regression methods (Lee et al., 2007, details in the Supple-
ment).
2.7 Uncertainty estimates for the reconstruction
The uncertainties of the reconstruction arising from three in-
dependent sources were estimated as follows:
1. The uncertainty of replication by the regional curve
(RC): this type of uncertainty arises from the RC
replication uncertainty that generally increases towards
higher cambial ages. This uncertainty will affect the un-
certainty of every MXD index, depending on its cambial
age. Since we used two RCs, this type of uncertainty is
also dependant on the RCs we used and hence on the
mean MXD for the first 100 yr.
2. The uncertainty of replication by the mean chronology:
this uncertainty provides the confidence interval of the
average of all MXD indices for each calendar year (tak-
ing into account the uncertainty 1)
3. The uncertainty from calibration: the uncertainty ac-
counts for the uncertainty of mean and variance (as we
use mean and variance adjustment reconstruction tech-
nique) in the calibration period (taking into account un-
certainties 1 and 2).
As the three types of uncertainties are assumed to be indepen-
dent, the uncertainty that arises from each source can be de-
Figure 2. Sample replication for FENN and TORN full data sets,
denser and less dense cohorts (trees with higher and lower average
MXD values for the first 100 yr) (a) and ratios between replications
of denser and less dense cohorts for TORN and FENN data sets (b).
rived by subtracting previous uncertainties on the same confi-
dence level. For this reason, the results in Fig. 7 show relative
values of all the three uncertainties. For details of uncertainty
estimation, see the Supplement.
3 Results
3.1 Sample size replication
The FENN data set contains 1.5–3 times more samples than
the TORN data set through most of the common period
(Fig. 2a). The replication of TORN chronology remains be-
low 20 samples over the subfossil period. The sample size
variation over the past 200 yr is explained by the high number
of core samples from living trees during this period. Over the
20th century, the absolute increase of young pines is higher
for FENN data in comparison to TORN data (Fig. 2a).
TORN and FENN full sample sets were split into trees
with dense (“trees with good growth”) and less dense (“trees
with poor growth”) MXD values. There seem to be almost
equal amounts of trees growing well and poorly growing
trees throughout the study period (Fig. 2). The visual inspec-
tion shows that the ratios between the number of denser and
less dense tree samples do not correlate in the FENN and
TORN data sets (r = 0.08; Fig. 2b). Since the data sets origi-
nate from nearby regions, the described asynchrony indicates
that the examined growth component (relative growth rate)
is not related to low-frequency climate variability. Therefore
we assume that this finding supports the use of multiple RCS
that will remove this growth component from the resulting
chronology.
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V. V. Matskovsky and S. Helama: Testing summer temperature reconstruction 1479
Figure 3. Regional curves (a) and chronologies (b) for TORN,
FENN and FULL data sets and different types of standardization.
All the chronologies are smoothed with 100 yr splines. Variance and
mean standardized to the period AD 1802–2006, 15 yr smoothed
data are used for variance adjustment. S88G1112A (Melvin et al.,
2013) and N-SCAN (Esper et al., 2012) are original chronologies
for the TORN and FENN data sets, respectively.
3.2 MXD chronologies
Subsequently, the initial MXD values were transformed into
RC1, RC1SF, RC1SFC, RS2SF and RS2SFC indices us-
ing the corresponding regional curves (Fig. 3a) and av-
eraged into a corresponding set of mean MXD chronolo-
gies (Fig. 3b). The correlations between the differently stan-
dardized chronologies decline towards lower frequencies of
growth variability (Supplement Table S1). On average, the
non-smoothed chronologies agree with a mean correlation
coefficient of r = 0.66, whereas the 50 yr, 100 yr, 200 yr and
300 yr smoothing results in mean correlation coefficients of
r = 0.54, r = 0.49, r = 0.46 and r = 0.49, respectively. The
observed tendency of lower correlativity at lower frequencies
indicates the increasing uncertainties in estimating particu-
larly the long-term temperature variations by MXD proxy
data.
Invariably, the highest correlations between the chronolo-
gies of the same standardization method were those ob-
tained by RC1SFC procedure (Supplement Table S1), that
is, using a single regional curve both with signal-free and
Matskovsky’s (2011) correction implementations. This re-
sult becomes even more obvious for correlations calcu-
lated at lowest frequencies (Supplement Table S1). It be-
comes evident that the applied Matskovsky’s (2011) correc-
tion (C) subsequent to SF implementation improves the ac-
tual SF estimation, and in particular the estimation of the
low-frequency tree-ring variations, as already suggested in
the original methodological study (Matskovsky, 2011). Ap-
plying multiple-RCS technique (RC2SF and RC2SFC) pro-
duces an improvement in FENN chronologies, but not in
TORN chronologies. This may be, at least in part, owing to
a smaller sample size of the TORN data set that may not be
sufficient for robust estimation of multiple RCs. This may be
the reason for highest correlations obtained between TORN-
RC1SFC and FENN-RC2SFC on varying timescales (Sup-
plement Table S1).
Differently produced FENN and TORN chronologies
show consistent but also bifurcating dendroclimatic signals
(Fig. 3b). Apart from many common features with syn-
chronously ameliorated and deteriorated growth periods, it
is found that the FENN chronologies exhibit notably higher
growth prior to AD 900 and during the 17th century. Regard-
less of the standardization method, the TORN chronologies
indicate higher growth during the recent decades and a trend
increasing towards the present day (Fig. 3b).
Since the TORN data set is characterized by lower sample
replication through the common period (Fig. 2a), an exper-
iment was performed to test whether the observed growth
bifurcations (Fig. 3b) could be explained by this property.
The FENN data set was randomly subsampled to mimic the
TORN replication (Fig. 4a). We found that the difference in
sample replication between the two data sets was not likely
a reason for the deviating MXD fluctuations in FENN and
TORN for the above-mentioned periods (Fig. 4b).
The low-frequency fluctuations could be detailed in the
context of oscillatory modes dominating the multi-decadal
to centennial MXD variations of FENN and TORN data
(Fig. 5). Both chronologies showed nearly identical 54–56 yr
and 67–70 yr periodicities, as well as precisely identical 113
and 133 periodicities. However, the TORN chronology was
also characterized by the 33 yr, 244 yr and 488 yr periodici-
ties, whereas the FENN chronology exhibited an additional
sub-centennial oscillation of 86 yr periodicity (Fig. 5).
3.3 Climate-proxy comparisons
Comparisons between the chronologies and instrumental cli-
mate records demonstrate that both chronologies correlate
with summer temperatures with a range of coefficients r =
0.76−0.79 (Table S2). At low frequencies, however, the cor-
relations are higher when using TORN chronologies than
with FENN chronologies (r = 0.95 and r = 0.84, respec-
tively). This is not an issue of standardization method since
the dendroclimatic correlations are invariably higher in the
case of TORN, regardless of the method used (Table S2).
Rather, the FENN chronologies show warming during the
period 1802–1815 when the TORN and instrumental data
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1480 V. V. Matskovsky and S. Helama: Testing summer temperature reconstruction
Figure 4. Replication of TORN, FENN and subsampled FENN data
sets (a) and comparison of RC2SFC chronologies for different data
sets (b). Red shading shows the area between 2.5th and 97.5th per-
centiles for FENN subsampled data sets. Vertical red line shows
threshold for well-replicated data sets (more than five series). All
series smoothed with 100 yr splines. Variance and mean standard-
ized to the period AD 1802–2006; 15 yr smoothed data are used for
variance adjustment.
indicate cooling (Fig. 6). Moreover, the FENN chronologies
underestimate temperature around 1900 and since the 1950s.
In addition, normalized FENN chronologies show inflated
values relative to TORN and instrumental record z-scores
during the early-20th-century warming that occurred in the
region in the course of the 1920s and 1930s (Fig. 6).
The FULL chronologies’ correlations with instrumental
temperatures reach 0.8 after multiple RCs and signal-free im-
plementation (Table S2). Here, the FULL-RC2SFC chronol-
ogy was used for reconstructing the JJA temperature because
successive implementation of multiple RCS and signal-free
and Matskovsky’s (2011) correction routines leads to re-
moval of high- and low-frequency non-climatic signals from
tree-ring chronology (Sect. 2.4) and because it was indicated
(Sect. 3.2, Supplement Table S1) that correction procedure
improves proxy quality on longer timescales.
3.4 Summer temperature reconstruction
Our new temperature reconstruction is shown with three sep-
arate types of uncertainties since 216 BC (Fig. 7). EPS is usu-
ally used as a theoretical limit for extension of reconstruction
Figure 5. TORN-RC1SFC (a) and FENN-RC2SFC (b) spectrum.
Red line shows power spectrum of red noise with lag 1 autocorrela-
tion estimated from the series. Period: AD 542–2006.
into the past. For the FULL data set EPS is consistently larger
than 0.85 only after AD 535. We don’t restrict our reconstruc-
tion to this year because the uncertainty estimates provided
could be used for the same purpose more reasonably. Clearly,
the uncertainties arising from MXD data are considerably
narrower than the climate-proxy calibration uncertainty (un-
certainty 3), especially over the past thirteen centuries. Over
the earlier times, the uncertainties of the MXD data in-
crease, consistently with the decrease in the sample replica-
tion (Fig. 2a). At low frequencies, the Common Era of the re-
construction is governed by the warm climatic phases culmi-
nating in the 10th and 20th centuries. The peak temperatures
during these periods are comparable with each other, whereas
the warmth of the 1st century BC shows slightly higher tem-
peratures. Nevertheless, the temporal length of the medieval
warmth from the 8th to 11th centuries overshadows the other
warm periods. The coolest climatic phases prevailed during
the 6th and 7th, as well as the 13th through 19th centuries,
the latter overlapping with the timing of the Little Ice Age.
The warmest and coolest 30 yr periods occurred AD 27–56
and AD 536–565, respectively, when JJA temperatures aver-
aged 15 and 12.8 ◦C, translating into maximal multi-decadal
temperature amplitude of 2.2 ◦C. The reconstruction exhibits
periodic oscillations on distinctly multi-decadal (55 to 66 yr),
centennial (86 to 128 yr), multi-centennial (171 to 513 yr)
and millennial (1027 yr) timescales (Fig. 8).
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V. V. Matskovsky and S. Helama: Testing summer temperature reconstruction 1481
Figure 6. TORN, FENN and FULL chronologies with Tornedalen
JJA temperatures (a) and residuals of the chronologies from
Tornedalen JJA temperatures (b). Variance and mean adjusted to
AD 1802–2006 using 15 yr smoothed data. All the data smoothed
with 50 yr splines. S88G1112A (Melvin et al., 2013) and N-SCAN
(Esper et al., 2012) are original chronologies for the TORN and
FENN data sets, respectively.
4 Discussion
4.1 Comparison of data sets
Focusing on low-frequency fluctuations, the TORN (Schwe-
ingruber et al., 1988; Grudd, 2008; Melvin et al., 2013) and
FENN (Esper et al., 2012) data sets show several common
anomalies of synchronously high and low MXD values, as
well as several common periodicities. Despite high correla-
tion with summer temperatures and overlapping provenance
of the original tree-ring material, the MXD data do not show
fully consistent growth variations throughout the study pe-
riod. The most obvious periods with the bifurcating dendro-
climatic fluctuations occurred in the 7th, 9th, 17th and 20th
centuries (Fig. 3b). Yet, the results from spectral analysis in-
dicate disparate dendroclimatic signals in the form of sig-
nificant oscillatory modes (Fig. 5). It is reasonable to argue
that these dissociations likely represent MXD variations not
closely related to the regional climatic signal. Therefore, be-
fore interpreting the obtained paleoclimate information on
the summer temperatures, it may be useful to discuss a.
Samples for the two chronologies originate from adjacent
and partially overlapping geographical settings in northern
Fennoscandia and we suggest that results showing diverging
MXD fluctuations between the chronologies are not driven
by spatial anomalies in regional climate. Indeed, one con-
flicting result was identified for the late 20th century when
both data sets contain tree-ring materials from living trees
of the same (Torneträsk) region and thus for the period of
supposedly enhanced homogeneity of provenance. Instead of
climate, there are six likely sources of heterogeneity: (i) data
quantity, (ii) biogeographical differences in source materials,
(iii) varying microdensitometric and (iv) tree-ring standard-
ization methods, (v) cambial age structure of the chronology
and (vi) habitat of riparian and inland pines.
4.1.1 Data quantity
Data quantity is a factor that distinctly divides the origi-
nal tree-ring materials according to chronologies with high
(FENN) and low (TORN) replications (Fig. 2a). The sample
replication and the law of large numbers should not be over-
looked in the course of any dendrochronological procedure
(Fritts and Swetnam, 1989). Moreover, it may be generally
comfortable to rely on massive sample replication in den-
drochronology (Büntgen et al., 2012). Even so, an increasing
sample replication may not necessarily remove the system-
atic effects if present in the data. Subsampling of the more
deeply replicated data set (FENN) revealed, however, that the
diverging MXD fluctuations could not be reproduced by ran-
domly reducing the quantity of that data set (Fig. 4b). Thus,
we argue that the data quantity may not be a primary fac-
tor producing the differences as observed between the TORN
and FENN chronologies.
4.1.2 Biogeographical aspects
The primary difference between the TORN and FENN
data sets stems from their spatial characteristics (Fig. 1).
While the TORN was collected from a restricted locality
of Torneträsk in northernmost Sweden (Bartholin and Kar-
lén, 1983; Schweingruber et al., 1988; Grudd, 2008), the
subfossil sampling sites of FENN are spread over north-
ern Finnish Lapland (Eronen et al., 2002). Two of the three
modern FENN sites are located in northern Sweden, in-
cluding Torneträsk (Esper et al., 2012). Despite this prox-
imity of the modern sampling sites in the two collections,
the two chronologies in fact diverged intriguingly after AD
1950 with notably higher growth observed for the TORN
chronologies (Fig. 6). Since the tree-ring material of FENN
and TORN for this period comes from neighbouring sites,
it cannot be the biogeographical aspects which play a cru-
cial role in the observed divergence. It is interesting, how-
ever, that the preceding periods of divergence (during the
7th, 9th and 17th centuries) showed an opposed mismatch (in
respect of the 20th century) where the FENN chronologies
come with higher growth. In a biogeographical context, the
MXD may not be insensitive to stand density variations, as
shown by thinning experiments. These studies indicate that
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1482 V. V. Matskovsky and S. Helama: Testing summer temperature reconstruction
Figure 7. Reconstruction of JJA temperatures with 95 % uncer-
tainty intervals. See text for details. Values are smoothed with 100 yr
splines.
the P. sylvestris latewood density (pertaining to MXD) in-
creases substantially following stand thinning (Peltola et al.,
2007). Likewise, it is known that the studied sites have ex-
perienced major changes in their natural stand densities over
the past millennia with highest overall stand density during
the MWP (Helama et al., 2005a, 2010a). Thus, past fluctua-
tions in stand density may at least hypothetically explain the
diverging MXD chronologies.
4.1.3 Microdensitometry
Varying microdensitometric methods have previously been
shown to produce significantly altered MXD data (Helama
et al., 2010d, 2012). Indeed, the initial materials of the two
data sets were produced in different laboratories and over
the course of a quarter of a century. Grudd (2008) described
this development in the context of the X-ray microdensito-
metry sensor width. In particular, the sophistication of the
device results in data with higher MXD standard deviations
(Grudd, 2008). The variance of the new data was adjusted
to expected variance by the old data (Grudd, 2008) and here
we have used similarly rescaled (Melvin et al., 2013) MXD
data only. Although such adjustment of the initial data is a
logical approach to solving the problem of differently pro-
duced MXD data, the issue may be too complex to be sur-
mounted by linear scaling of the initial data. A change in
laboratory measurement protocol may change the amplitude
(as measured by standard deviation) and the autocorrelation
of the MXD series (Helama et al., 2010d, 2012). Rescaling
of tree-ring series with dissimilar autocorrelation structures
may distort their low-frequency band of variations in partic-
ular (Helama et al., 2009a). The calibration of the differently
produced MXD data may introduce, at least hypothetically,
additional levels of uncertainty for the MXD values.
4.1.4 Tree-ring standardization
Tree-ring standardization is sometimes, mistakenly, taken as
an obstacle for deriving low-frequency climate information
Figure 8. Fourier analysis for the JJA temperature reconstruction.
Red line shows power spectrum of red noise with lag 1 autocorrela-
tion estimated from the series. Period: 8 BC–AD 2010.
from tree-ring proxies. In reality, however, the standardiza-
tion is fully necessary for the isolation of low-frequency cli-
mate information in this proxy. Many standardization meth-
ods are indeed incapable of retaining the long-term and -
period growth variations (Cook et al., 1995). As a conse-
quence, tree rings have even become notoriously poor indica-
tors of low-frequency climate variability for a wider reader-
ship (e.g. Broecker, 2001), particularly for those not familiar
with background and techniques. In fact, tree-ring chronolo-
gies are one of the few proxies for which sensible estimates
of their skill at indicating “long timescale” variance can be
calculated. In recent decades the standardization has however
undergone a methodological development. Several methods
(Helama et al., 2005b; Nicault et al., 2010; Briffa and Melvin,
2011; Matskovsky, 2011; Melvin et al., 2013) have evolved
from the simple RCS method (Briffa et al., 1992) for im-
proved low-frequency preservation. Interestingly, the RCS
method was applied in the study region for P. sylvestris tree-
ring data as early as the 1930s (Erlandsson, 1936) whereas
the subsequent studies have used data of the same species and
region for systematic comparisons between RCS and other
methods of standardization (Briffa et al., 1992, 1996; Cook
and Briffa, 1990; Helama et al., 2004). In the same context,
Esper et al. (2012) showed that if such growth properties
underlie the initial data, the RCS may preserve even multi-
millennial long-term trends in the resulting MXD chronolo-
gies. Similarly, our results indicate that the preservation of
the low-frequency variations is not an issue that could have
caused the observed differences between TORN and FENN
chronologies. This statement is in accordance with the re-
sults showing evidence for similar divergences between the
chronologies regardless of the varying (RCS) standardiza-
tion method (Fig. 3b). That is, the 7th-, 9th-, 17th- and 20th-
century bifurcations remain as the most notable differences
between the TORN and FENN chronologies.
In the context of standardization, our results provided sev-
eral other new implications. Regarding the correction pro-
cedure, it was found that the dendroclimatic correlations
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V. V. Matskovsky and S. Helama: Testing summer temperature reconstruction 1483
of FENN-RC1SFC were notably higher in comparison to
FENN-RC1SF. That is, the correction procedure improved
the value of that MXD data set as a summer temperature
proxy. This improvement likely arises from the very non-
uniform age structure of the same data set over the last
two centuries, with an increase of young trees (younger
than 100 yr). It is likely that the correction procedure may
have significantly reduced the “data set error” due to an un-
even age distribution over the period of AD 1950–1990 (see
Fig. 6). Nevertheless, the correction procedure may not al-
ways improve the proxy on short timescales, as could be
implied for the instrumental period (Table S2). In fact, the
correction procedure was designed for amplification of the
low-frequency climate signal in tree-ring proxies, and its per-
formance may not be evident in the short term. Indeed, the
correction showed its advantages in improving the quality
of chronologies on long timescales (Supplement Table S1).
Nevertheless, its further investigation with regard to extended
data sets from different forest types is recommended.
The benefits of using multiple RCs may be limited by the
size of data set, as outlined above (Sect. 3.2). This deduction
is supported as the use of multiple RCS provides improved
correlations with the more densely replicated FENN data
set but not with smaller TORN data set (Supplement Table
S1). Interestingly, when TORN replication is enhanced by
living trees, the multiple-RCS (TORN-RC2SF) chronology
does show higher correlations with the instrumental record
(Table S2). Consistently, the dendroclimatic correlations im-
prove with multiple RCS in the case of FENN and FULL
data sets. Moreover, the correction procedure failed to im-
prove the TORN-RC2SF (see Supplement Table S1 for 100,
200 and 300 yr smoothing). We assume this to be a conse-
quence of reducing the quality of TORN-RC2SF chronology
via the use of multiple RCs in the case of the less well repli-
cated TORN data set.
4.1.5 Cambial age structure
As previously alluded to, the TORN and FENN chronologies
deviate notably over the last half of the 20th century when
the age structure diverges with a more pronounced addition
of MXD series of young pines to the FENN data set. Ob-
viously, the cambial age structure of the chronology could
serve as a specific factor explaining the observed MXD bi-
furcation. However, an outline of the coherence of long-term
MXD variant between different age classes was provided by
Esper et al. (2012). Yet, the analysis of Melvin et al. (2013)
showed that their young pines (TORN) come with higher
MXD values, particularly in the late 20th century. These lines
of evidence allow us to assume that the increase of young
pines in the FENN data set over this period could not result
in lowered MXD values, in comparison to TORN data. A
more detailed investigation of age-dependent growth varia-
tions is recommended to clarify the influence of young pine
MXD series for estimating the mean chronology.
4.1.6 Riparian and inland habitats
It is essential to note that the initial tree-ring materials orig-
inate from predominantly dry inland (TORN) and lake ri-
parian (FENN) habitats. Moreover, the subfossil period of
the chronologies is covered with snags from subaerial con-
ditions (TORN) and sub-aquatic stems recovered from lake
sedimentary archives (FENN) (Bartholin and Karlén, 1983;
Schweingruber et al., 1988; Eronen et al., 2002; Grudd,
2008; Esper et al., 2012). In the study region, the riparian
P. sylvestris are expected to experience less drought stress
and, in fact, do exhibit a stronger dendroclimatic response
in their tree-ring widths to summer temperatures (Hund-
hausen, 2004). These findings are contrasted by our MXD
results showing that, actually, the TORN chronologies of
inland habitats demonstrate appreciably higher correlations
with summer temperature than the FENN chronologies of
riparian habitats (Table S2). Secondly, a previous compar-
ison using tree-ring material from living pines of riparian
and inland habitats showed notably higher MXD indices
from riparian trees, in comparison to pines growing in inland
conditions (Esper et al., 2012). However, the inland TORN
chronologies showed higher MXD values in the 20th century,
in better accordance with instrumental temperature data than
the riparian FENN chronologies (Fig. 6). Parallel observa-
tion was previously derived for P. sylvestris tree-ring widths
in the central Scandinavian Mountains where their wet-site
chronology explained considerably less (24 %) instrumental
climate variance than the dry-site (43 %) chronology (Linder-
holm, 2001). Waterlogging, which has been seen as probably
the primary restriction on the radial growth of P. sylvestris at
wet sites (Moir et al., 2011), may also be a factor leading to
noisier MXD variations in the FENN data (Fig. 6).
Also the spectra of the TORN and FENN chronologies dif-
fered (Fig. 5). While the 56, 67, 113 and 133 yr periodicities
in TORN agreed reasonably with the 54, 70, 113 and 133 yr
periodicities in FENN data, only the TORN chronology ex-
hibited a 33 yr periodicity whereas the FENN chronology
showed a solitary periodicity of 86 yr. Similarly, a dispar-
ity of periodicities in P. sylvestris tree-ring widths was de-
tected in the central Scandinavian Mountains where the dry-
site and wet-site chronologies displayed periodicities of 66 yr
and 19 yr, respectively (Linderholm, 2001). Because of this,
the spectral analysis would in fact imply differentiated low-
frequency variations in the studied inland and riparian MXD
data.
A list of at least four conceivable factors having caused
the described MXD divergences could include (1) the past
changes in stand density that could have altered the MXD
growth disproportionately near the timberline (i.e. TORN
habitats) and in more southern sites (i.e. FENN), (2) the in-
clusion of young pines and their MXD values for chronology
estimation, (3) the calibration of differently produced MXD
data into a combined record, and (4) the potential role of wa-
terlogging which may have occasionally restricted the MXD
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1484 V. V. Matskovsky and S. Helama: Testing summer temperature reconstruction
production on wet sites. Detailed investigations on these is-
sues are too far beyond the scope of this study. Instead, we
urge future research to delve into the roles of the four afore-
mentioned factors in producing the MXD variations in rele-
vant biogeographical settings.
4.2 Low-frequency temperature variations
For the Common Era, the reconstruction (Fig. 7) exhibits
consistency with the climatic phases through the MWP and
LIA (Lamb, 1977). Thus, the reconstruction is similar to pre-
vious investigations using P. sylvestris tree-ring widths and
MXD as proxies of Fennoscandian past temperature vari-
ability (Briffa et al., 1992, 1996; Grudd, 2008; Helama et
al., 2009a, 2009c; Esper et al., 2012; McCarroll et al., 2013;
Melvin et al., 2013). Moreover, the reconstruction agrees
with the previous studies showing evidence of comparatively
similar warmth during the MWP and the most recent decades
(Briffa et al., 1992; Grudd et al., 2002; Helama et al., 2009a,
2009c; McCarroll et al., 2013; Melvin et al., 2013). Even
warmer times were experienced during the earliest centuries
of the reconstruction (Esper et al., 2012). These are also the
times when the uncertainty envelope of the reconstruction
becomes wider. The widening (see Fig. 7) stems especially
from the decreased sample replication over the earlier period
(Fig. 2a). Nevertheless, the long duration of the MWP makes
it incomparable to the 20th-century warmth. As an interest-
ing feature of the reconstruction, the start of the MWP ap-
pears as early as the 8th century, lasting until the end of the
11th century, whereas the widespread positive temperature
anomalies in the Northern Hemisphere are observed from the
9th to 11th centuries (Ljungqvist et al., 2012).
Apart from these millennial temperature variations, the re-
construction displays periodicities on multi-decadal, centen-
nial and multi-centennial timescales (Fig. 8). In Fennoscan-
dia, the tree-ring variations of similar multi-decadal (53 to
66 yr) scales have previously been linked (Linderholm, 2001;
Helama et al., 2009c) to variations in sea surface temper-
atures of the North Atlantic Ocean (Schlesinger and Ra-
mankutty, 1994). Likewise, the century-scale variations (80
to 120 yr) in the regional tree-ring-based temperature recon-
structions have been attributed to the Gleissberg (de Jager,
2005) cycle of solar activity (Briffa and Schweingruber,
1992; Ogurtsov et al., 2001). Spectral densities between
250 yr and 350 yr are common to temperature histories from
the Greenland ice cores, the North Atlantic and the Tor-
neträsk tree-ring widths (Sejrup et al., 2011) but their pos-
sible origins may remain uncertain. Longer-scale variations
(500 to 1000 yr) may be indicative of North Atlantic circula-
tion patterns (Chapman and Shackleton, 2000).
Actually, the North Atlantic influence could be expected
to drive a notable part of the natural climate variability in the
study sites. The region is directly downwind of the Atlantic
Ocean where the air pressure patterns, especially the North
Atlantic Oscillation (NAO), impact the regional temperature
as well as precipitation variations on synoptic scales (Hurrell,
1995). Apart from the instrumentally observed variations, the
NAO has been shown to exhibit centennial fluctuations with
pervasively positive and negative phases during the MWP
and LIA (Trouet et al., 2009; Mann et al., 2009). In fact, the
NAO sensitivity of the climate has been described near the
study region using several multi-proxy data sets for the MWP
and LIA, showing seasonal variations in winter (warmer and
moister) and summer (cooler and less rainy) climate, in ac-
cordance with the expected NAO influence (Helama et al.,
2009b; Luoto and Helama, 2010; Helama and Holopainen,
2012; Luoto et al., 2013; Nevalainen et al., 2013). Moreover,
the temperature variations through the MWP and LIA could
be linked to the periods of more and less intensive phases in
the formation of North Atlantic deep water (Helama et al.,
2009c).
5 Conclusions
We have taken advantage of two well-known tree-ring data
sets, the Torneträsk MXD chronology in northern Sweden
and the newly constructed Fennoscandian MXD chronol-
ogy, to develop a new MXD-based temperature reconstruc-
tion that preserves the low-frequency variability. We applied
RCS-based novel standardization techniques and also, for the
first time for these data sets, represented uncertainties emerg-
ing from different sources and discussed the potential sources
of noise in these data sets. The reconstruction provides a
valuable proxy for exploring the past climate variability in
the high latitudes of Europe. Existence of some noise does
become obvious as the two chronologies deviate in the in-
strumental period as well as in their late Holocene common
period. Although the MXD records have been used regularly
for palaeoclimate reconstruction both in the study region and
elsewhere and despite the past efforts to understand MXD be-
haviour over time and space, our results suggest that the low-
frequency band of MXD variations in particular may contain
a proportion of tree growth variations unrelated to actual tem-
perature history. Based on these findings, we have identified
several additional issues that dendroclimatic research could
focus on in more detail to obtain a more advanced under-
standing of potential pitfalls when using high-latitude MXD
data for palaeoclimate temperature reconstructions: (1) in-
fluence of past population density variations on MXD data,
(2) potential biases when calibrating differently produced
MXD data to produce one proxy record, (3) influence of
young pine MXD data on the most recent past and (4) possi-
ble role of waterlogging on MXD production when analysing
tree-ring data of riparian trees. Our experiments have shown
that application of novel standardization techniques can re-
duce unwanted biases connected to these pitfalls. Thus, our
new reconstruction can be used as the source of information
about year-to-year as well as centennial and longer varia-
tions of summer temperature in northern Fennoscandia for
Clim. Past, 10, 1473–1487, 2014 www.clim-past.net/10/1473/2014/
V. V. Matskovsky and S. Helama: Testing summer temperature reconstruction 1485
the Common Era. Described differences between the sub-
regional data sets added an uncertainty to the MXD-derived
temperature history. It is likely that associated discrepancies
can be explored by use of other proxies that can reproduce
low-frequency past temperature variations, in combination
with tree-ring data. Such complementary data can be derived
from other tree growth (McCarroll et al., 2013) or pollen
proxies (Helama et al., 2010c).
The Supplement related to this article is available online
at doi:10.5194/cp-10-1473-2014-supplement.
Acknowledgements. We acknowledge the corresponding authors
(Esper et al., 2012; Melvin et al., 2013) for making the data
available. We are thankful to A.-L. Daniau and A. Reyes who
helped to improve the quality of the manuscript. Comments by two
referees, T. M. Melvin and J. Björklund, on the earlier version of
the manuscript are acknowledged. This study was supported by the
Russian Foundation for Basic Research (grant no. 12-05-31126)
and the Academy of Finland (grant no. 251441). V. V. Matskovsky
thanks PAGES and the Asia-Pacific Network for Global Change
Research for their financial support to attend the PAGES 2nd Young
Scientists Meeting and 4th Open Science Meeting.
Edited by: A.-L.Daniau
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