Source: Hevia A, Sánchez-Salguero R, Linares JC, Olano JM, Camarero JJ, Gutiérrez E, Helle G, Gärtner H (2016): TRACE - Tree
Rings in Archaeology, Climatology and Ecology, Volume 14. Scientific Technical Report 16/04, GFZ German Research Centre
for Geosciences, p. 38-43. doi: 10.2312/GFZ.b103-16042. A comparison between Tree-Ring Width and Blue Intensity high
and low frequency signals from Pinus sylvestris L. from the Central
and Northern Scandinavian Mountains
M. Fuentes1, J. Björklund2, K. Seftigen1, R. Salo1, B.E. Gunnarson3, H.W. Linderholm1 &
J.C. Aravena4
1
Gothenburg University Laboratory for Dendrochronology, Department of Earth Sciences, University of Gothenburg,
Gothenburg, Sweden.
2
Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
3 Bert Bolin Centre for Climate Research, Department of Physical Geography and Quaternary Geology,
Stockholm University, Stockholm, Sweden
4
CEQUA, Cetre for quaternary studies Punta Arenas Chile,
E-mail: [email protected]
Introduction
During the last decades, dendroclimatological methods have been used to produce several climate
reconstructions, where chronologies based on maximum latewood density (MXD) data (e.g.,Briffa
et al. 2002, Gunnarson et al. 2011, Esper et al. 2012) have provided estimates of past temperature
variability. Despite an often superior signal strength of the MXD parameter compared to tree ring
width (RW) (e.g., Briffa et al. 2002), very few laboratories in the world use this technique, mainly
because this proxy is expensive and labour intensive to produce. As an alternative, blue intensity
(BI) utilizing reflected/absorbed blue light from scanned sample-images of tree rings, is explored as
a surrogate to radio densitometry (McCarroll et al. 2002, Campbell et al. 2011, Björklund et al.
2014, Rydval et al. 2014). However, BI seems more susceptible to biases caused by the transition
between the heartwood and the sapwood, but also by the mixing of modern wood and deadwood
(Björklund et al. 2014). This has according to Rydval et al. (2014) and Wilson et al. (2014)
restricted the application of the, to the MXD analogue, MXBI parameter (Björklund et al. 2014) to
frequencies higher than 20 years. To overcome this bias, Björklund et al. (2014, 2015) suggested
the use of a new variant of BI parameter: the adjusted Δblue intensity (ΔBIadj), which is derived by
subtracting the BI in the earlywood from the MXBI, after samples have been contrast adjusted,
based on their general level of staining (Björklund et al. 2015). Few comparisons between RW and
MXD have been made (e.g., Briffa et al. 2002, Franke et al. 2013), and even fewer comparisons
between MXBI and RW have been made (Wilson et al. 2014). The aim of this study is to assess
the similarities and differences in temperature sensitive Pinus sylvestris L. RW and ΔBIadjj
chronologies sampled across three sites in Sweden, by exploring 1) the climate correlation and
spectral characteristics of the different parameters, 2) the inter correlation and frequency
association between them.
Materials and Methods
We used chronologies from three sites along the Scandinavian Mountains in Sweden:1) Arjeplog,
2) Jämtland (1 and 2 are described in Björklundet al. 2014) and 3) Rogen Nature Reserves. The
latter is located close to the border to Norway(62°21’N, 12°26’E) (Fig. 1). The topography is a
broken plateau, with gentle slopes and round tops reaching between 1000 to 1200 m a.s.l.
(Länsstyrelsen 1993).The mean annual temperature at the sites is 1.3 ºC and the precipitation
sums 628 mm year-1 (1970-2000 average), at Duved meteorological station, located 400 m a.s.l.
and 125 km NE from the site). In 2011, 120samples were collected from both living and dead Scots
pine (Pinus sylvestris L.) trees in an area of 15square km by the lake Käringsjön in the Rogen
Nature Reserve. The samples were glued to wooden strips, and sanded with progressively finer
sandpaper (grit 40 to1200). Tree-rings were visually cross dated to their exact year of formation,
Fuentes et al. (TRACE Vol. 14)
39 and the widths of annual
increments were measured with a 1/100 mm precision using a sliding
measuring stage connected to the TSAP-Win software (Rinntech). The accuracy of the crossdating
was verified statistically in the COFECHA program (Holmes, 1983).For the BI analysis, samples
were cut to 4 mm thick laths and refluxed in ethanol for up to 72 hours, then air-dried and sanded
again (grit 400 to 1200). The samples were scanned at 1200-1600 dpi resolution (EPSON
Perfection 600) using Silver fast AI Professional(TM) calibrated with a colour card IT8 7/2. ΔBIadj was
calculated according to the methods described by Björklund et al. (2015). The ΔBIadjj and the RW
chronologies were standardized using a signal-free (Melvin et al. 2008) variant of the regional
curve standardization (Briffa et al. 1992) presented in Björklund et al. (2013), called RSFi. The data
used for calibration was temperature anomalies averaged over the 55°-70° N and 5°-25° E region
from the monthly gridded 5.0° x 5.0° HadCRUT4.3 dataset, spanning from 1850 to present (Fig. 1)
(Morice et al. 2012). The temperature response was analyzed using the DendroClim2002 program
(Biondi & Waikul, 2002). The stability of the relationship between the proxies (RW and ΔBIadj) and
within the proxies was assessed with moving window correlations (50-year window length, 1-year
lag). To calculate the coherence, i.e. the frequency dependent association between two time
series, the program Anclim (Stepánek 2008) was used.
Figure 1: Map over Scandinavia: a, b and c reoresent samppling sites Rogen, Jämtland and Arjeplog
respectively. The quadrant area represents gridded HadCRUT 4.3 temperature data (55° to 70° N; 5° to 25°
E)
Results
In the high (inter-annual) frequency domain, both the RW and ΔBIadj chronologies exhibit strong
temperature responses (Fig. 2), where the climate response of ΔBIadj is generally exceeding that of
the RW data in both the length of the target season (March to September) as well as the
magnitude of the correlation values. On average, significant correlation (p<0.05) values for RW are
found with June-August temperatures, but the correlation between ΔBIadj and June and August
temperatures separately, is nearly twice as high as that of RW.
40 Fuentes et al. (TRACE Vol. 14)
Figure 2:Significant correlations (p < 0.05; dashed line)between delta blue intensity adjusted (ΔBIadj; white
bars) and tree-ring width (black bars) from Rogen, Jämtland, and Arjeplog and monthly HadCRUT4.3
temperatures (averaged over the 55° - 70°N and 5° - 25° E region) for the growing season for the period
1850-2010.
Table 1: Significant correlations (p<0.05) between tree ring data and theHadCRUT4.3temperature anomalies
(averaged from the 55° to 70°N and 5° to 25° E region )averaged over June-August and July.
Arjeplog
0.81
Rogen
RW
0.56
0.78
0.65
Rogen
ΔBIadj
HadCRUT
4.3 T JJA
HadCRUT
4.3 T July
Jämtland
0.83
Arjeplog
RW
0.52
0.80
Jämtland
RW
0.36
0.69
0.59
0.70
0.49
ΔBIadj
ΔBIadj
The difference in temperature sensitivity and spatial representativity between the ΔBIadj and RW
chronologies indicates a higher degree of similarity among parameters than within sites (Table 2).
In addition, the relationship between the proxies varies through time as shown in figure 3, with
greater correlations between ΔBIadj and RW from 1600 to 1780 and between 1900 and 1950
although prior 1600 the correlation are lower(for ΔBIadj, the running correlations were r >
0.6through the whole length of the chronology, while for RW correlations were>0.5 through nearly
the whole period, falling below the 0.5 level only between 1450 and 1550).The coherence between
RW and ΔBIadj varies across frequencies(Fig. 4),with better coherence at frequencies between 25and 65-year periods, and decreasing at frequencies <25 years. The coherence between ΔBIadj and
temperature data is higher than for RW at all sites (Fig. 5).
Table 2: Correlation matrix between the two tree-ring parameters and chronologies(significance p<0.05).
Rogen
RW
Rogen ΔBIadj
Rogen
ΔBIadj
1.00
ArjeplogΔBIadj
Rogen RW
0.65
1.00
ArjeplogΔBIadj
0.78
0.53
1.00
Arjeplog RW
0.50
0.67
0.60
1.00
JämtlandΔBIadj
0.90
0.53
0.79
0.41
1.00
Jämtland RW
0.56
0.80
0.49
0.63
0.48
Arjeplog
RW
JämtlandΔBIadj
Jämtland
RW
1.00
Fuentes et al. (TRACE Vol. 14)
41 Correlation value r
1
0,8
0,6
0,4
0,2
0
‐0,2
1200
1300
1400
1500
1600
1700
Year
Rogen RW vs ΔBIadj
Jämtland RW vs ΔBIadj
1800
1900
2000
Arjeplog RW vs ΔBIadj
p<0.05
b
Coherence
Coherence
1
0,5
0
0
50
0,5
0
100
Periods
c
1
0
50
Coherence
Figure 3:Running correlation (50-year window, 1-year lag) between ΔBIadj chronologies and their
corresponding RW versions.
1
0,5
0
100
Periods
0
50
100
Periods
Figure 4: Coherence between the ΔBIadj and RW parameters (black lines) from a) Rogen, b) Arjeplog c)
Jämtland sites. Dashed lines show the p<0.05 confidence intervals.
b
0,5
0
0
0
20
Periods
40
1
Coherence
Coherence
0,5
c
1
Coherence
1
0,5
0
0
20
Periods
40
0
20
Periods
40
Figure 5: Coherence between the studied proxies (ΔBIadj = black lines, RW = grey lines)from a) Rogen b)
Arjeplog and c) Jämtland and HadCRUT4.3 JJA temperature data Dashed lines show the p<0.05 confidence
intervals.
Discussion
The comparisons between ΔBIadj and RW in terms of its climate sensitivity gave similar results to
those previously reported in comparative studies of MXD and RW (e.g., Briffaet al.1992, Wilson et
al. 2014, Esper et al. 2015).We showed that the ΔBIadj parameter possesses a stronger and
seasonally longer correlation window with regional temperatures than itsRW counterpart. We
further revealed that while the ΔBIadj temperature signal and the intercorrelation between the ΔBIadj
chronologies are consistent across time and space, the correlations between RW and ΔBIadj are
not, suggesting that the two proxies may not record entirely the same information (Fig. 3). Possibly
additional influences on tree growth, such as precipitation, or local site conditions causes the
periodical decoupling between the two proxies, and most likely changes in temperature sensitivity
affect RW more than BI. Several studies have suggested that there isa memory effect in the RW
proxy, where information from one year can be carried over for one or more years (Franke et al.
2013, Esper et al. 2015, Bunde et al. 2013, Schneider et al. 2015), which could cause a decoupling
from ΔBIadj. This is suggested by the changes in coherence through frequencies between the
proxies (Figs. 3 and 4) and the lower temperature responses. But we cannot conclusively find
evidence of this in either of the analyses.
42 Fuentes et al. (TRACE Vol. 14)
The coherence analysis, however, reveals that at the lower frequencies, both proxies follow each
other relatively well with correlations of 0.73, 0.23 and 0.65 (Rogen, Jämtland and Arjeplog,
respectively). The coherences between RW and ΔBIadj indicates that they contain similar
information at periods of about 20 and 60 years, implying that these proxies may be combined to
investigate climate variability on those frequencies, while care should be taken when looking at
other frequencies if they are combined. Also, the fact that the relationship between RW and ΔBIadj
changes through time, adds difficulties to interpretations of reconstructions made using a
combination of these parameters derived from Scots pine growing across the sites investigated in
this study. For example, exploratory testing showed that composite chronologies of ΔBIadj and RW
from each site in our network were able to explain 15-20 % less of the variance in the regional
temperature history, compared to what was derived from the ΔBIadj parameter alone (results not
shown here). While the coherence between ΔBIadj and temperature revealed clear patters across
our three sites, the coherency between combinations of RW-ΔBIadj and the temperature showed
less consistency across sites (i.e. Rogen decreased in all frequencies, Arjeplog maintained the
level of coherency at the maximum peak but narrowed from 16 to 24-year cycles to 16 to 19yearcycles, and for frequencies lower than that range the coherence dropped below significance,
and Jämtland improved at 30-year cycles). Another approach could be to combine band pass
filtered chronologies from RW and ΔBIadj, where the best frequencies from both parameters are
used (cf. Wilson et al. 2014), but then little information from RW would anyway be used. Although
not explicitly tested, the differences between the sites and methodologies are also a result from for
example, sampling design, sample depth and will also affect the characteristics and relationships
of these proxies at different frequencies.
Conclusions
Here we present similarities and differences between thetree-ring width (RW) and adjusted Δblue
intensity (ΔBIadj) parameters derived from three Scots pine chronologies in central and northern
Sweden. Our results suggest that the ΔBIadj parameter has better skill to portray temperature
variability than RW at all frequency ranges. We also show that although RW and ΔBIadj have
significant coherence between 20 to 60-year cycles, the relationship between these proxies is
unstable through time, implying differences in climate sensitivity.
Aknowledges
This work was supported by KVA, the Swedish Geographical Society, Adlerbertskastiftelsen,
Filosofiskafakultetetsdonationsfond, the Swedish Research Council VR (grant to H. Linderholm)
and FORMAS mobility starting grant for young researchers (grant # 2014-723 to K. Seftigen). This
research contributes to the Swedish strategic research areas Modeling the Regional and Global
Earth system (MERGE), and Biodiversity and Ecosystem services in a Changing Climate (BECC).
References
Biondi F. and Waikul K. (2004): Dendroclim 2002: a C++ for statistical calibration of climate signal
on tree-ring chronologies. Computers and geosciences 30: 303-311
Bunde A, Büntgen U, Ludescher J, et al. (2013): Is there memory in precipitation? Nature Climate
Change 3: 174–175.
Björklund, J., (2014): Tree rings and Climate: standardization, proxy development and
Fennoscandia summer temperature. Ph.D. Thesis. Department of Earth, Sciences University of
Gothenburg ISBN 978-91-628-9062-9
Fuentes et al. (TRACE Vol. 14)
43 Björklund, J., Gunnarson, BE.,Seftigen, K., Zhang, P., and Linderholm, HW., (2015):Using
Adjusted Blue Intensity data to attain high-quality summer temperature information: A case
study from Central Scandinavia.The Holocene 25(3): 547– 556.
Briffa KR, Jones PD, Bartholin TS, Eckstein, D., Schweingruber, FH.,Karlén, W., Zetterberg, P.
Eronen, M. (1992): Fennoscandian summers from AD 500: Temperature changes on short and
long timescales. Climate Dynamics 7: 111–119.
Briffa, KR., Osborn, TJ.,Schweingruber, FH., Jones, PD., Shiyatov SD. Vaganov EA. (2002): Treering width and density data around the Northern Hemisphere: Part 1, local and regional climate
signalsThe Holocene 12: 737-757.
Campbell R, McCarroll D, Loader NJ, Grudd, H., Robertson, I., Jalkanen, R. (2007): Blue
reflectance in Pinussylvestris: Application, validation and climatic sensitivity of a new palaeoclimate proxy for tree ring research. The Holocene 17: 821–828.
Esper J., Frank D., Timonen M., Zorita E., Wilson RJS., Luterbacher J., Holzkämper S., Fischer
N., Wagner S., Nievergelt D., Verstege A., Büntgen U. (2012): Nature Climate Change 2: 862866.
Esper, J. (2015): Memory effects in tree-ring width and maximum latewood density in response to
volcanic eruptions: evidence from northern Fennoscandia. TRACE, tree rings in Archaeology,
Climatology and Ecology. 13: 34-41. Scientific Technical Report STR GFZ German Research
Centre for Geosciences.
Franke, J., Frank, D., Raible, CC., Esper, J., Brönnimann, S. (2013): Spectral biases in tree-ring
climate proxies, Nature Climate Change 3(4), 360–364.
Gunnarson, BE.,Linderholm, HW., and Moberg, A., (2011): Improving a tree-ring Reconstruction
from west-central Scandinavia: 900 years of warm-season temperatures Climate Dynamics
36:97–108
Holmes, R.L., 1983. Computer assisted quality control in tree-ring dating and measurement. TreeRing Bulletin 43, 69–78.
Länsstyrelsen
(1993):
Rogens
Natur
Reservat,
Skötselplan
dnr
231-8091-91,
(http://www.lansstyrelsen.se/jamtland/SiteCollectionDocuments/Sv/djur-och-natur/skyddadnatur/naturreservat/harjedalen/rogen/Beslut-och-skotselplan-Rogen.pdf)
McCarroll, D., Pettigrew, E., Luckman, A., Guibal, F. Edouard, JL. (2002): Blue Reflectance
provides a surrogate for latewood density of high-latitude pine tree-rings. Arctic, Antarctic and
Alpine Research 34: 450–53.
Melvin, TM.,Briffa, KR., A ‘‘signal-free’’ approach to dendroclimaticstandardisation. (2008):
Dendrochronologia 26 (2): 71-86
Morice, CP., Kennedy, JJ., Rayner, NA., Jones, PD. (2012): Quantifying uncertainties in global and
regional temperature change using an ensemble of observational estimates: The HadCRUT4
dataset. Journal of Geophysical Research 117: 1-22.
Rydval, M., Larsson, LÅ.,McGlynn, L., Gunnarson, BE., Loader, NJ., Young GHF., Wilson RJS.,
(2014): Blue intensity for dendroclimatology: Should we have the blues? Experiments from
Scotland. Dendrochronologia 32: 191–204
Schneider, L., Smerdon, J. E. Büntgen, U., Wilson, RJS.,Myglan, VS., Kirdyanov, AV., Esper, J.
(2015): Revising midlatitude summer temperatures back to A.D. 600 based on a wood density
network, Geophysical Research Letters, 42, 4556–4562.
Stepánek, P. (2008): AnClim software for time series analysis. Dept of geography, fac of sciences
Masaryk university Brno 1.6 MB http://www.climahom.eu
Von Storch H and Zwiers FW (2004): Statistical Analysis in Climate Research. Cambridge:
Cambridge University Press. 484 pp.
Wilson, RJS., Rao, R., Rydval, M., Wood, C., Larsson, LÅ., Luckman, BH. (2014): Blue Intensity
for dendroclimatology. The BC blues: A Case study from British Columbia, Canada The
Holocene 24 (11): 1428– 1438.