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8/7/2019 Nine Centuries of Warm-Season Temperatures in West-Central Scandinavia
1/12
Improving a tree-ring reconstruction from west-centralScandinavia: 900 years of warm-season temperatures
Bjorn E. Gunnarson Hans W. Linderholm
Anders Moberg
Received: 17 April 2009/ Accepted: 1 March 2010/ Published online: 16 March 2010 Springer-Verlag 2010
Abstract Dendroclimatological sampling of Scots pine
(Pinus sylvestris L.) has been made in the province ofJamtland, in the west-central Scandinavian mountains,
since the 1970s. The tree-ring width (TRW) chronology
spans several thousand years and has been used to recon-
struct JuneAugust temperatures back to 1632 BC. A
maximum latewood density (MXD) dataset, covering the
period AD 11071827 (with gap 12921315) was presented
in the 1980s by Fritz Schweingruber. Here we combine
these historical MXD data with recently collected MXD
data covering AD 12922006 into a single reconstruction of
AprilSeptember temperatures for the period AD 1107
2006. Regional curve standardization (RCS) provides more
low-frequency variability than non-RCS and strongercorrelation with local seasonal temperatures (51% variance
explained). The MXD chronology shows a stronger rela-
tionship with temperatures than the TRW data, but the two
chronologies show similar multi-decadal variations back to
AD 1500. According to the MXD chronology, the period
since AD 1930 and around AD 11501200 were the warmest
during the last 900 years. Due to large uncertainties in the
early part of the combined MXD chronology, it is not
possible to conclude which period was the warmest. More
sampling of trees growing near the tree-line is needed to
further improve the MXD chronology.
Keywords Dendroclimatology
Maximum latewood density Scots pine
Central Scandinavian Mountains Climate change
1 Introduction
The province of Jamtland, west-central Sweden, was
selected as a location for constructing a multi-millennium,
temperature-sensitive, tree-ring chronology that would
provide a link between the temperature-sensitive tree-ringchronologies in northern Fennnoscandia (Grudd 2008;
Helama et al. 2008) and those in central Europe (Buntgen
et al. 2006). The selected area, east of the main dividing
line of the Central Scandinavian Mountains, was expected
to be particularly suitable for the task. Scots pines (Pinus
sylvestris L.) of ages up to 700 years, growing in virtually
undisturbed forests, have been found there, and large
numbers of old pine trees that have been preserved for
centuries are found in small mountain lakes of the region
(Gunnarson 2001). As a consequence, significant effort has
been made to collect Scots pine tree-ring data from living
and subfossil wood at a number of sites in the area (see
Gunnarson 2008). Tree-ring width (TRW) chronologies
from Jamtland have been used to infer changes in climatic
variables over the last millennium, especially changes in
summer temperatures, lake-levels and winter precipitation
(Gunnarson 2001, 2008; Gunnarson and Linderholm 2002;
Gunnarson et al. 2003; Linderholm and Chen 2005), and
to assess the spatial and temporal variability in the
climate/tree-growth relationship (Linderholm 2001, 2002;
Linderholm et al. 2003; Linderholm and Linderholm
B. E. GunnarsonDepartment of Forest Ecology and Management, SwedishUniversity of Agricultural Sciences, Umea, Sweden
H. W. LinderholmRegional Climate Group, Department of Earth Sciences,University of Gothenburg, Gothenburg, Sweden
B. E. Gunnarson (&) A. MobergDepartment of Physical Geography and Quaternary Geology,Stockholm University, Stockholm, Swedene-mail: [email protected]
123
Clim Dyn (2011) 36:97108
DOI 10.1007/s00382-010-0783-5
8/7/2019 Nine Centuries of Warm-Season Temperatures in West-Central Scandinavia
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2004). Today, available TRW data from the Central
Scandinavian Mountains span more than 7,000 years,
albeit with some gaps (Gunnarson et al. 2003). These data
have been utilized in attempts to quantitatively reconstruct
summer (JuneAugust) temperatures back to 1632 BC
(Linderholm and Gunnarson 2005). However, in a recent
study, it was shown that TRW data from the Central
Scandinavian Mountains provide weak temperature infor-mation, especially on a regional scale, compared with e.g.
TRW data from northern Fennoscandia (Gouirand et al.
2008).
In this paper, we develop a seasonal temperature recon-
struction from Jamtland based on Scots pine maximum
latewood density (MXD) data, which provide stronger cli-
mate signals and greater spatial representation than the
earlier reconstruction based on TRW data. Latewood is the
part of an annual ring of wood (with compact, thick-walled
cells) formed during the later part of the growing season,
and MLD data have proven to be superior to TRW data for
reconstructing warm-season temperatures in Fennoscandia(Briffa et al. 1992, 2002; Gouirand et al. 2008; Grudd
2008). In the 1970s, Schweingruber et al. provided the first
MXD data from Jamtland (Schweingruber et al. 1987). To
assess the quality of these previously processed MXD data,
we have processed recently collected material. The main
goal of this paper is to combine and update the old
(Schweingruber) data into a single chronology, and then
calibrate the improved MXD measurements with regional
summer temperatures. A secondary goal is to consider the
spatiotemporal representation of this new reconstruction.
2 Data and methods
2.1 Study area
The study area, situated in the Swedish part of the Central
Scandinavian Mountains (Fig. 1), contains mountains with
rounded topography, generally reaching 8001,000 m a.s.l.,
but with some peaks reaching*1,700 m a.s.l. The Scandi-
navian Mountains were extensively glaciated during the
Pleistocene, and glacial deposits cover large parts of the
region. These deposits mainly consist of till, glacifluvial
deposits and small areas of lacustrine sediments (Lundqvist
1969; Borgstrom 1979). In the eastwest oriented valleys,
moist air from the Norwegian Sea easily advects into the
area. Hence, there is a precipitation gradient across the study
area that decreases from Storlien in the west
(857 mm year-1) to Duved in the east (628 mm year-1)
(Alexandersson et al. 1991) (Fig. 1). The annual mean
temperature of the area is approximately ?1C (Storlien
?1.1C, Duved ?1.3C) and the length of the growing
season is, on average, 122 days and 132 days in Storlien and
Duved, respectively (Alexandersson et al. 1991). The area is
part of the Northern Boreal Zone and the mean elevation of
the present pine tree-line is approximately 700 m a.s.l.
2.2 Maximum latewood density data
The new MXD data, from sites Rortjarn and Furuberget
(Fig. 1), were obtained using an ITRAX Woodscannerfrom Cox Analytic System (http://www.coxsys.se).
Henceforth, these data are referred to as ITRAX data, and
they consist of more than 50 radial measurement series
collected from both living and dead trees.
Two 10 mm cores, from *1.3 m above the ground,
were collected from each living or standing dead tree.
From dead trees, lying on the ground or submerged in
water, thick discs were cut from the trunks with a
chainsaw at about 11.5 m above the root collar. Samples
with poorly preserved lower sections were cut higher up.
Samples from living trees were prepared according to
standard dendrochronological techniques (Stokes andSmiley 1968) and the subfossil samples according to
techniques described by Gunnarson (2001). The annual
tree-ring widths of each sample were measured with a
precision of 0.01 mm. The two radii from each tree or
disc were cross-dated against each other, using CATRAS
software (Aniol 1991) to build a master chronology which
was cross-dated visually and verified using COFECHA
(Holmes et al. 1986).
The ITRAX Wood Scanner produces high-resolution
radiographic images. Thin laths (1.20 mm thick) were cut
from samples using a twin-bladed circular saw and treated
with alcohol in a Soxhlet apparatus to extract resins andother removable compounds unrelated to wood density of
the rings (Schweingruber et al. 1978). The laths, with 12%
water content (air dry), were then mounted in the Wood
Scanner and exposed to a narrow, high energy, X-ray beam
in 20 lm steps. The samples were X-rayed in the ITRAX
machine equipped with a chrome tube tuned to 30 kV and
50 mA, with 75 ms steptime. For each step, a sensor with a
slit opening of 20 lm registered the radiation that was not
absorbed by the sample. The Wood Scanner produced an 8-
bit, grayscale, digital image with a resolution of 2,540 dpi,
and the grey levels were calibrated using a calibration
wedge from Walesch Electronic. The radiographic images
were evaluated using WinDENDRO tree-ring image pro-
cessing software, which provides ring width and density
data from a scanned image (Guay et al. 1992). The
resulting profile of maximum and minimum density and the
mean densities of the earlywood, latewood and of each
whole ring were recorded.
As mentioned above, earlierMXD data from Jamtland are
available through the International Tree-Ring Data Bank
(ITRDB, http://www.ngdc.noaa.gov/paleo/treering.html),
98 B. E. Gunnarson et al.: Improving a tree-ring reconstruction from west-central Scandinavia
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and have been included in some large-scale temperature
reconstructions (Briffa et al. 2001). The material was col-
lected by Schweingruber et al. in the 1970s (Schweingruber
et al. 1987, 1991). However, the geographical origins and
nature of the material was collected from many various
historical buildings spread out over the Jamtland province.
The Jaemtland historich or the Schweingruber Historical
dataset is hereafter referred to as SH. This chronology,
however, ends in 1827, making it difficult to assess the
quality of the dataset because of the lack of local MXD data
or temperature series to compare it with. The SH data were
acquired using the DENDRO2003 X-ray instrumentation
from Walesch Electronic (http://www.walesch.ch). This is
an analogue technique, in which the laths are placed on
standard X-ray film and exposed to X-rays. The grey-
level intensity in the film is converted to absolute
density values using a manually operated photo-sensor, with
the aid of a calibration wedge similar to the ITRAX
techniques.
2.3 Standardization: non-RCS versus RCS
Growth rates of individual trees in a stand usually vary
substantially, depending (inter alia) on microclimate and
nutrient availability (Fritts 1976). Furthermore, the height
growth rates of trees commonly decline exponentially with
age, following a classic biological growth curve, which isin part associated with the radial size of the trees increasing
each year.
To allow samples with large differences in growth rates
and undesired growth trends to be combined, the raw
(untreated) tree-ring density (or width) data obtained from
each tree at a site need to be standardized. The standardi-
zation process usually involves fitting a curve to the ring
density series, and then dividing each density value by the
corresponding curve value to generate a series of growth
indices. The end products are dimensionless tree-ring
density indices, which can then be averaged into a site
chronology. We used two methods to standardize the MXD
data: regional curve standardization (RCS) and the com-
monly used ARSTAN software (Cook and Holmes 1986),
henceforth called non-RCS. The standardization was
preformed according to test results described elsewhere
and recommendations for standardized methods to apply
for the specific material and region (Linderholm et al.
2010).
When standardizing tree-ring data by the non-RCS
method, the age-associated trend in the growth of each tree
is estimated and removed by fitting a negative exponential
curve, a straight regression line or, when no age trend is
present, a constant value to each tree-ring series and then
dividing the ring densities by the fitted curve. This should
allow for chronologies with interannual- to centennial-
scale properties to be constructed. However, this technique
may remove lower-frequency variability in the data, since
the maximum wavelength of recoverable climatic infor-
mation is usually related to the lengths of the individual
tree-ring series, the so-called segment length curse
(Cook et al. 1995).
The RCS method (Briffa et al. 1992) is designed to
preserve long-term variability in the tree-ring data. The
method was originally developed many decades ago by
Erlandsson (1936) and has recently been adopted by sev-eral investigators (e.g. Briffa et al. 1992, 1996; Esper et al.
2002). RCS removes variance associated with tree ageing
by fitting a single average biological growth curve defined
for a larger area to each individual ring density series
within the region. In executing this method, all individual
ring series should start at the birth year of the tree. When
the pith is absent, which is often the case for drilled cores
and subfossil wood, either the pith offset has to be esti-
mated or it is simply assumed that the first ring measured is
Fig. 1 Location of the samplesites
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the first cambial year; here we assumed the latter (Fig. 2).
This could result in an underestimation of the true age of
the tree-ring, leading to a positive bias in the standardized
ring density for young trees (Briffa et al. 1992). The RSC
method also relies on the assumption that there is a com-
mon growth trend within a region, but this may not be true
in an area with varying growth conditions, e.g., if there is a
large climate gradient. Linderholm et al. (2010) found thatuse of RCS resulted in slight differences over the last few
decades between two sites in the Scandinavian Mountains,
but that this was due to low sample numbers at each site.
Significant correlations with temperatures from April to
September were found for both RCS and non-RCS data
(except for May temperatures and non-RCS data), as
illustrated in Fig. 3, but there is clearly no significant
correlation between precipitation and the MXD data. Thus,
it is unlikely that the relatively strong precipitation gradient
in the area has any influence on MXD variability, at
interannual to interdecadal time scales.
2.4 Instrumental data
The closest meteorological station to the MXD sites is
Duved (400 m a.s.l., 63230N, 12560E, Fig. 1). Unfortu-
nately, data from this station only cover the period 1911
1979 for temperature and 18892003, with some missing
years, for precipitation. To assess the correlation between
monthly temperature and precipitation values and the
MXD series (Fig. 3), the Duved data were extended for-
ward to 2007, using linear regression on data from two
neighbouring stations: Storlien-Visjovalen (642 m a.s.l.,
63180N, 12070E) and Hoglekardalen (592 m a.s.l.,63070N, 13750E). Data from these two stations explain
on average 70% of the variance in Duved precipitation
(correlation 0.84) and 95% of the variance in Duved
temperature (correlation 0.97). The correlations indicate
that variations in temperature with time during the over-
lapping period have been very similar throughout the
region. In order to obtain a long calibration/verification
period of warm-season temperatures, the Duved record
was also extended back to 1870, using regression on a
regional temperature index for west-central Scandinavia
from Hanssen-Bauer and Nordli (1998). The correlation
between the west-central data and Duved AprilSeptem-
ber temperatures for the period 19111979 was 0.90.
3 The Jamtland MXD chronology
3.1 Comparing old and new MXD data
The two MXD datasets, the old SH data and the new
ITRAX data, differ in several ways. For example, simple
data descriptors, such as the mean and standard deviation,
are not the same, and the geographical distribution of
sampled trees also differs. The SH data were obtained from
more samples and stretch further back in time, but the exact
geographical origins of the samples are unclear. The IT-
RAX data were obtained from trees growing close to the
tree-limit, where growth is primarily controlled by climate.Grudd (2008) showed that the averaged MXD mean for
data from the more northerly Tornetrask region, when
measured with both the ITRAX and Walesch method, was
virtually identical. However, measurement of samples with
both techniques, comparing SH and ITRAX data revealed
that the ITRAX measurements yielded slightly higher
variance than the Walesch measurements and had to be
adjusted accordingly. Since it was not possible to re-mea-
sure the SH data from Jamtland, the raw SH and ITRAX
chronologies were analyzed for their mean and variance.
Our results showed a significantly higher standard
deviation in the ITRAX data than in the SH data, andwhen the smoothed Hugershoff function (Warren 1980)
was fitted to the RCS data, the SH data gave density values
that were approximately 0.1 g/cm3 higher than the ITRAX
data (Fig. 2). When applying the RCS method, it is
essential that there is no systematic difference in mean and
variance caused by choice of methods or sampling sites.
Therefore, the ITRAX and SH data were standardized
separately.
Fig. 2 Smoothed regionalgrowth curves used for regional
curve standardization (RCS) ofthe Schweingruber historical(SH) and ITRAX data (blue andred, respectively), plotted withchronology averages of annualgrowth values (blue and red)
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Following the standard dendroclimatological approach,
the signal strength and confidence intervals of the chro-
nologies were estimated by calculating the R-Bar and
expressed population signal (EPS) statistics for 50-yearwindows moving in 25-year time steps (Wigley et al.
1984). The EPS represents the percentage of the variance
in the hypothetical population signal in the region that is
accounted for by the chronology. It is determined by the
number of series and the average correlation between all
pairs of series (R-Bar). EPS values greater than 0.85 are
generally regarded as adequate (Wigley et al. 1984). The
EPS values are generally higher for the SH data than for the
ITRAX measurements in the period of data overlap,
reflecting the higher replication in SH data (Fig. 4). The
correlation between SH and ITRAX data is calculated
using a 50-year running window for the overlapping per-iod. The average correlation is r= 0.5 and it reaches
around 0.9 in some periods. However, it is much lower at
the beginning and the end of the overlapping period, when
the correlation is approximately 0.3 (Fig. 5). Despite this
sometimes weak correlation, which is dominated by the
year-to-year variations, the lower-frequency variations
show notable similarities. In particular, before around
1600, the time course of the two records is similar. How-
ever, there is a discernable difference from around 1600 to
the end of the SH record (Fig. 5a, b). Within this period,
the SH values are on average higher than the ITRAX
values before around 1730, and then lower for the period
after 1730. However, the correlation for the period 1600
1780 is 0.7 and exceeds 0.8 in the mid seventeenth century
(Fig. 5). The reasons for these discrepancies and temporal
instability of the correlation between the two series are
unclear, but it may possibly be partly due to changes in the
material used in the SH collection. For both records, the
sample depth (Fig. 4) is adequate, as reflected in the often
rather high EPS values. The SH data, however, have gen-
erally lower R-Bar values than ITRAX (Fig. 4), which is
probably a result of a wider geographical spread of the
collected samples. The ITRAX sampling sites are more
homogeneous and only ca. 15 km apart. Moreover, they are
both close to the present tree-line, whereas the SH sites
possibly cover the entire province of Jamtland, or a large
part of it. This means that the SH sample area may be
approximately 200 km wide and at elevations well below
the tree-line (Fig. 1).
3.2 The composite MXD chronology
The two MXD chronologies, SH (11071827, with a gapbetween 1292 and 1315) and ITRAX (12922006), were
combined into a single chronology (11072006). This
combined chronology consists solely of the SH data before
1292, ITRAX alone for 12921315, the average of the two
during the period 13161827, and ITRAX data alone after
1827. The variance in each chronology was stabilized
according to the Briffa RBAR-weighted method imple-
mented in ARSTAN software (Cook and Krusic 2005), in
order to compensate for variations in sample depth in the
Fig. 3 Correlations betweenMXD data, standardized withRCS and non-RCS methods(see text for explanation), andboth monthly mean temperature(grey bars) and monthly totalprecipitation (white bars) overthe common period 1912 to2007. Correlations are givenfrom the month of October theyear before tree-growth (t- 1)to September of the growth year(t). Crosses indicate significantcorrelation at P = 0.05 level
Fig. 4 Upper panel R-Bar and EPS (see text for explanation) plottedfor 50-year windows with 25-year overlap for the two RCSstandardized chronologies, ITRAX (brown) and Schweingruberhistorical (lilac). Lower panel sample depth (number of replicateseries for each year) through time for the two chronologies
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chronologies. Two procedures were applied to eliminate
further artificial differences in the statistical properties of
the different parts of the combined chronology. First, the
SH chronology was adjusted for mean level and variance toagree with the ITRAX chronology for the period of over-
lap. Then, since the number of chronologies varies through
time (one in the first part, two in the middle, and one in the
last part), an adjustment (Osborn et al. 1997) was also
made to the average time-series to avoid spurious changes
in variance. For each separate chronology (i.e. SH and
ITRAX), the software ARSTAN 40 (Cook and Krusic
2005) was used to estimate 95% confidence intervals (CI)
for the mean MXD values in each year, using a bootstrap
technique. For the period 13161827, when we used the
average of the two series, it was necessary to combine two
95% confidence intervals into a single, representativeinterval for the series. To do this, we used the following
relation:
CIcombined 0:5
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiCI2SH CI
2ITRAX
q
where CISH and CIITRAX are the 95% confidence intervals
for the respective chronologies. However, CISH was first
adjusted by multiplying the output from the software by the
variance adjustment factor that was applied to the entire SH
record, in order to match the variance in ITRAX data. The
underlying assumption here is that the random errors in the
two chronologies are independent. The CI-values (not
shown) reflect how the uncertainty in the final mean
chronology varies with time. This information is subse-
quently used to modify the standard errors of the calibrated
temperature reconstruction, so that they reflect the tempo-
ral changes of the uncertainty in the final composite MXD
chronology.
The calibration statistics in Table 1 over the full (1870
2007) period show that the RCS and non-RCS MXD
data explain 51 and 43% of the variance in observed April
to September mean temperatures (TAS), respectively. This
suggests that the MXD data may be used to more suc-
cessfully reconstruct TAS. The final model used to recon-
struct TAS back to 1107 was derived from linear regression
of the instrumental data on the proxy data, over the full
period of overlap. However, the poor RE and CE statistics
(e.g. CE close to zero in 19392007) indicate that the
calibrated reconstruction should be treated with care
(Table 1).Reconstructions that have poor validation statistics (i.e.,
low CE) will have correspondingly wide uncertainty
bounds, and thus can be seen to be unreliable. A CE statistic
close to zero or negative suggests that the reconstruction is
no better than the mean, so the accuracy for time averages
shorter than the validation period will be low. The two
calibrated MXD reconstructions plotted together with Du-
ved TAS (Fig. 6) indicate that the lower correlations
obtained in the calibration period (19392007) are mainly
due to discrepancies between observed temperatures and
MXD from the 1940s to the 1970s. From the end of the
1970s until 2007, there is a better visual agreement betweenthe records, similar to that in the early calibration period
(18701938). Thus, the so-called divergence problem,
which has been observed in many chronologies based on
tree-ring data acquired from material from various geo-
graphical locations (Wilson et al. 2007; DArrigo et al.
2008), does not seem to be the reason behind the observed
drop in TAS/MXD correspondence in the later calibration/
verification period. The agreement between the smoothed
data (here corresponding to decadal timescales) in Fig. 6
suggests that there is a relatively good agreement for longer
than interannual timescales. From Table 2, which shows
correlations at timescales longer than decadal and multi-decadal, we also see a strong association between Duved
TAS and RCS MXD, reaching 0.82 for timescales of 30-
years and longer, while correlations between Duved TASand non-RCS MXD are weaker. However, these estimated
correlations are only indicative, due to the low number of
degrees of freedom in the smoothed data.
To evaluate the accuracy of TAS values predicted from
the MXD series, we correlated our reconstructions with the
CRUTS3.0 gridded temperature dataset (Mitchell et al.
2004) for all grid cells available for a northern European
region centered on our field sites, at 0.5 longitude by 0.5
latitude spatial resolution, using the KNMI climate
explorer tool (Royal Netherlands Meteorological Institute;
http://climexp.knmi.nl; van Oldenborgh et al. 2009) for the
period 19012006. The results suggest that our recon-
structions provide some information on TAS variability for
much of central Scandinavia (Fig. 7). In particular, the
RCS MXD reconstruction has good spatial representation,
showing correlations with other TAS of[0.6 for a large
area of central Sweden and Finland, and correlations[0.7
for the west-central areas.
Fig. 5 Comparison ofa the interannual and b multi-decadal (30-yearspline) variabilities in the Schweingruber historical (SH; in lilac) andITRAX RCS MXD (in brown) chronologies. c The correlation values(50-year window) between the two overlapping interannual data sets
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4 900 years of AprilSeptember mean temperatures
The new reconstructions of AprilSeptember temperatures
over the AD 11072006 period are shown in Fig. 8, with
their full annual resolution and as smoothed time-series
that highlight variations at multi-decadal timescales, toge-
ther with confidence intervals of1 standard errors (SE)
from the calibration. The SE-series have first been
smoothed with the same filter as the reconstruction, for
enhanced visibility. Moreover, the SE-values have been
inflated for all periods where the bootstrap estimates of the
uncertainty in the chronology (see Sect. 3.2) indicate larger
uncertainty than in the calibration period. The changing
widths of the resulting SE-bands thus visually reflect the
combined uncertainty in the calibration relationship and in
the chronology itself, before the calibration period.
A comparison of the two reconstructions clearly shows
that low-frequency variability is weaker in the non-RCS
Table 1 Results of calibrating and verifying maximum latewood density (MXD) data for Scots pine tree-ring growth over the indicated periodsbetween 1870 and 2007
MXD RCS MXD non-RCS
Calibration period 18701938 19392007 18702007 18701938 19392007 18702007
Correlation, R 0.80 0.56 0.71 0.80 0.45 0.66
Explained variance, R2 0.65 0.31 0.51 0.64 0.20 0.43
Observations 69 69 138 69 69 138Verification period 19392007 18701938 19392007 18701938
Explained variance, R2 0.31 0.65 0.20 0.64
Reduction of error, RE 0.22 0.62 0.12 0.44
Coefficient of efficiency, CE 0.03 0.56 -0.10 0.38
Fig. 6 Reconstructed AprilSeptember temperatures (red)compared with observed Duvedtemperatures (black) for the18702007 period. Thin linesshow interannual variability;thick lines show decadalvariability (Gaussian filteredwith sigma = 3, approximatelycorresponding to 10-yearmoving averages)
Table 2 Correlation between smoothed Duved TAS and recon-structed TAS from MXD data
RCS MXD TAS non-RCS MXD TAS
Gauss filter r = 3 r = 9 r = 3 r = 9Duved TAS 0.73 0.82 0.57 0.62
Smoothing was done with Gaussian filters where the r values 3 and 9correspond to 10- and 30-year timescales, respectively
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MXD reconstruction. Since this reconstruction is less
strongly associated with observed temperatures (probably
partly because of the weak low-frequency variability), we
focus on the RCS MXD reconstruction. The main features
of this reconstruction are as follows. There is a steep
increase in inferred temperatures at the beginning of the
twelfth century, followed by a century of warm tempera-
tures (ca. 11201220). After a sharp temperature drop in
the 1220s, the following 150 years have colder tempera-
tures. There is a partial recovery to warmer temperatures
from ca. 1370 to 1570, although not to as high temperatures
as in the twelfth century. After 1570, the data indicate a
rather marked cooling, lasting until around 1600.
Thereafter, there are three centuries of relatively low
average temperatures (potentially a manifestation of the
Little Ice Age in this region), until around 1910. How-
ever, as in the previous periods, inferred temperatures
during the cold interval are highly variable, with some
individual years being quite warm, most notably in the
1820s. The most pronounced cold period occurs near 1600,
but a period near 1700 also appears to have been cold. Therecord ends with a sharp increase in temperatures from
around 1910 to the 1940s, followed by decreasing tem-
peratures for a few decades. Finally, another sharp increase
in TAS commenced in the late 1990s, and estimated tem-
peratures in 2003 and 2006 reached values higher than
previously encountered in the series. Considering the long-
term changes during the entire 900-year TAS record, the
two warmest periods are the mid to late twentieth century
and the period from AD 1150 to 1250. Although the highest
individual values occur near the end of the series, it is not
possible to conclude whether the present and relatively
recent past are warmer than the 11501250 period. This isbecause the uncertainty in the inferred temperatures is
larger than the difference between the two periods. More-
over, the reconstruction before around 1300 AD is based on
very little data, which further complicates a direct com-
parison of the relative warmth in the two periods. Never-
theless, the data suggest that the two periods were the
warmest of the last nine centuries, and of comparable
warmth during the AprilSeptember season.
5 Discussion
It has previously been shown that, at high latitudes, MXD
data from Scots pine can provide a stronger temperature
proxy for an extended seasonal window than TRW data
(e.g. Briffa et al. 1990, 2002). TRW data from suitable trees
in the Central Scandinavian Mountain region are predomi-
nately correlated with July temperature (Linderholm and
Gunnarson 2005), while MXD data have proven to have a
wider response window, including AprilJune and August
September (Linderholm et al. 2010). We have here dem-
onstrated that a reconstruction based on MXD data can
explain 51% of the variance in observed AprilSeptember
temperatures in the Central Scandinavian Mountain region.
The Central Scandinavian Mountains MXD chronology can
thus be used as a warm-season temperature proxy for cen-
tral-western Scandinavia, as anticipated when sampling
started in the 1990s. This is an important addition to
Tornetrask data, which provide a strong northern Scandi-
navian temperature signal (Gouirand et al. 2008).
One of the main aims in this study was to combine
previously collected and processed MXD material devel-
oped by Fritz Schweingruber (the SH data), with our more
Fig. 7 Spatial correlation of AprilSeptember reconstruction fromJamtland MXD a non-RCS and b RCS with AprilSeptemberaveraged CRU TS3 temperature 19012006. Analysis using KNMIClimate explorer (http://climexp.knmi.nl; van Oldenborgh et al. 2009)
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recently collected and processed data (the ITRAX data).
Here, we discuss some of the problems connected with the
data and with the combination of the two chronologies into
a composite.
As mentioned in the data section, the MXD chronology
from Schweingruber is based on historical samples that end
in 1827, from historical buildings. The present tree-line for
pine in Jamtland is at 700 m a.s.l., but the average mean
elevation of the sampled SH material is only 500 m a.s.l.
(according to ITRDB). This is a difference of 200 m,
implying that at least some of the SH data came from trees
growing well below the tree-line. The ITRAX chronology,
on the other hand, is based on data from trees that grew at
or close to the tree-line. This growth environment should
provide a higher degree of temperature-limiting effects on
tree-growth, which in turn should theoretically provide
greater accuracy in the reconstruction. However, MXD
data tends to have a climate response that depends less on
site specific characteristics than ring width data. It is
unfortunately not possible to test the climate validity of SH
data directly against instrumental data, since the SH data
do not overlap the local instrumental record, but the lower
elevation of the SH sites may well influence the validity of
the SH chronology as a temperature proxy. The main
advantage of the SH data is that it reaches further back in
time, thus extending the more recently collected data. The
obvious disadvantage of having tree-ring material from a
wide range of different sites and elevations is that the trees
might reflect both temperature and precipitation signals,
and possibly other environmental influences. This would
influence the correlation between trees and give relatively
low R-Bar values. Indeed, although the ITRAX chronology
is based on fewer samples, the ITRAX dataset mostly
shows slightly higher R-Bar values than the SH data and
EPS values for ITRAX are above 0.85 after c. AD 1630,
suggesting that the sampled material was more strongly
temperature-limited (Fig. 4). Disagreements between the
SH and ITRAX MXD series are apparent in the period
when they overlap (Fig. 5). The Schweingruber series
contains missing values, as well as a gap between AD 1292
and 1315. This period is overlapped by the ITRAX data,
but unfortunately this section of the reconstruction has
rather low sample depth. The possible mixture of temper-
ature and precipitation climate signals in the SH data may
also influence the combined reconstruction, especially in its
oldest part where it is solely based on SH data. As a result,
there is considerable uncertainty when comparing the
inferred early warm period around AD 1150-1250, with the
present warm period.
As a final point of discussion, we compare our new
MXD reconstruction of AprilSeptember temperatures for
the Central Scandinavian Mountains with that of Fenno-
scandian JuneAugust summer temperatures (Gouirand
et al. 2008) which incorporates inferences from Tornetrask
TRW data (Grudd et al. 2002). There are clear similarities
between the reconstruction presented by Gouirand et al.
(2008) and our new reconstruction, in particular between AD
1300 and 1900 (Fig. 9a). However, the two reconstructions
Fig. 8 Reconstructed AprilSeptember temperature with two stan-
dardization methods; non-RCS and RCS. The sample depths(overlapping number of trees) are shown at the bottom. Thin linesshow the interannual variability and thick lines the decadal variability(30-year spline function). Error bands of the standard error (1 SE)
are indicated by the sand-coloured shading and are based on
unfiltered data. The width of these bands has been inflated beforethe calibration period to represent the time-varying uncertainty in thechronology average. To enhance visual performance, the error bandshave been filtered (30-year spline function)
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are, essentially, in opposite phases between AD 1100 and
1300. The Gouirand et al. (2008) reconstruction (which in
this part solely relies on data from northernmost Fenno-
scandia) indicates that this was a cool period, but this is not
seen in our record. The low sample depth of the Schwe-
ingruber data in this period suggests that the new Ja mtland
temperature reconstruction is not sufficiently robust during
its first two centuries. Therefore, the discrepancy betweenthe reconstruction of Gouirand et al. and ours for this
period may be at least partly due to less reliable data in the
new reconstruction. On the other hand, regional differences
in the temporal evolution of warm-season temperatures in
central and northern Scandinavia cannot be entirely
excluded, as a tentative explanation for the differences
between the two records. Previously, Gunnarson and Lin-
derholm (2002) suggested that the Medieval Warm Period
(MWP) was of shorter duration and more pronounced in the
Central Scandinavian Mountains than in Northern Scandi-
navia. However, to investigate whether such regional dif-
ferences really existed, it would seem necessary to undertakefurther studies of a set of tree-ring chronologies sampled
from a transect along the Scandinavian mountain chain.
After 1900, there are also some notable differences
between the new Jamtland reconstruction and the one by
Gouirand et al. (2008). The Jamtland reconstruction shows
stronger warming in the first half of the twentieth century
than the latter, and is thus more similar to the TRW-based
reconstruction of Grudd et al. (2002). However, the new
improved Tornetrask MXD reconstruction (Grudd 2008)
suggests that twentieth century warm-season temperatures
were not particularly warm in a 1500-year context. Grudd
(2008) stated that this discrepancy between MXD and
TRW data was most likely an effect of major changes in
the density of the pine population at the northern tree-line.
However, early logging in Jamtland decreased the fre-
quencies of large and old pine trees in the tree-line zone
during the late nineteenth century (Lars Ostlund, personalcommunication 2007). Regardless of this extensive change
in the density of the pine population, there is no substantial
discrepancy between TRW and MXD data in Jamtland
(Fig. 9b). The MXD and TRW reconstructions for Jamt-
land show similar multi-decadal variations, at least back to
AD 1500. Prior to AD 1500, the two records are occasionally
out of phase, e.g. the Jamtland MXD shows relatively
warmer temperatures around AD 11001250 and cooler
temperatures around AD 12501350. The asynchronous
changes around AD 11001250 are probably related to the
uncertainty in the early SH data. Despite rather weak
correlation between Jamtland TRW and observed temper-atures in the calibration period, as reported by Gouirand
et al. (2008), the TRW and MXD data covary between
1300 and 2000 at timescales longer than the annual
(Fig. 9a, b). Due to the ambiguities associated with the
provenance of the tree-ring data from the early part of the
MXD chronology, efforts should be made to significantly
improve this part of the Jamtland MXD record with data
from tree-line sites. If this can be done, the Jamtland
MXD chronology can provide an important complement to
Fig. 9 Comparisons betweenthe new MXD RCSreconstruction of the west-central Scandinavian (Jamtland)AprilSeptember temperature(black) and a the FennoscandianJuneAugust temperaturereconstruction (red) fromGouirand et al. (2008) and b aJuneAugust temperaturereconstruction for Jamtlandbased on TRW data(Linderholm and Gunnarson2005). Thick lines represent
smoothing with a 30-year splinefunction
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high-resolution proxies from other climate-sensitive
archives in Fennoscandia and enhance our understanding of
past temperature variability in this region.
6 Conclusion
The Scots pine MXD chronology from the province ofJamtland in the Central Scandinavian Mountains has been
updated to AD 2007. By combining previously processed
data from Schweingruber (historical MXD) with new data
acquired using an ITRAX scanner, we have developed a
continuous chronology and a temperature reconstruction
for the AprilSeptember season back to 1107 AD. The aim
of this study was to assess the possibility to improve the
previous temperature reconstruction for this region, which
was based on TRW data. We conclude that the new MXD
reconstruction provides better estimates of local tempera-
tures than the previously developed TRW-based recon-
struction. The new MXD reconstruction also has a widerseasonal response window than the previous JuneAugust
reconstruction, and wider spatial representation, centered
on central Scandinavia. The RCS standardization method
resulted in a stronger calibrated temperature signal and
stronger reconstructed low-frequency variability compared
with the non-RCS method for this region. From the
reconstructed AprilSeptember temperature record, it may
be concluded that the late twentieth century and the period
around 11501200 were the two warmest periods during
the last 900 years. However, it is not possible to conclude
which of these intervals was the warmest, due to large
uncertainties in the early part of the tree-ring MXD data.
Acknowledgments This research was undertaken as part of the EUproject Millennium (Contract No. 017008 GOCE), with additionalfunding from the Swedish Research Council (VR, grants to H. Lin-derholm and A. Moberg, respectively). The careful reviews by twoanonymous reviewers have helped to significantly improve the qualityof this paper.
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