nine centuries of warm-season temperatures in west-central scandinavia

8/7/2019 Nine Centuries of Warm-Season Temperatures in West-Central Scandinavia

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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]

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Clim Dyn (2011) 36:97108

DOI 10.1007/s00382-010-0783-5


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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

B. E. Gunnarson et al.: Improving a tree-ring reconstruction from west-central Scandinavia 99

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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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