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.
2Swiss 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: mauricio.fuentes@gu.se
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,
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.
70'N
65'N
60'N
SS'N
10'E 20°E 30°E
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.
Rogen ΔBIadj
Rogen RW
Arjeplog ΔBIadj
Arjeplog RW
Jämtland ΔBIadj
Jämtland RW HadCRUT
4.3 T JJA
0.81 0.56 0.83 0.52 0.80 0.36
HadCRUT 4.3 T July
0.78 0.65 0.69 0.59 0.70 0.49
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 25- and 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 ΔBIadj
Rogen RW
ArjeplogΔBIadj Arjeplog RW
JämtlandΔBIadj Jämtland RW Rogen ΔBIadj 1.00
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 1.00
1.0 Rogen
0.9 - 0.8
.!:::.. 0.7
5
0.6 :; 0.5~ 0.4
0 0.3 u 0.2 0.1
0.0 '--'-'--'-'--~ .___,---'--'""---'-'""--'-'-- MAR APR MAY JUN JUL AUG SEP
Months
1.0 Jamtland
0.9 - 0.8
.!:::.. 0.7
5
0.6 :; 0.5~ 0.4
0 0.3 u 0.2 0.1
0. 0 '--'-'- --'-'---'-"---'---'--'""----'-'""--'-'-- MAR APR MAY JUN JUL AUG SEP
Months
1.0 Arjeplog
0.9 - 0.8
.!:::.. 0.7
5
0.6 :; 0.5~ 0.4
0 0.3 u 0.2 0.1
0. 0 '-...-' _ _.__L_- .___,__,__----'--'""---'-'""-- ' - ' - -
MAR APR MAY JUN JUL AUG SEP Months
b c
0 0,5 1
0 20 40
Coherence
Periods
0 0,5 1
0 20 40
Coherence
Periods
0 0,5 1
0 20 40
Coherence
Periods
b c
0 0,5 1
0 50 100
Coherence
Periods
0 0,5 1
0 50 100
Coherence
Periods
0 0,5 1
0 50 100
Coherence
Periods
Figure 3:Running correlation (50-year window, 1-year lag) between ΔBIadj chronologies and their corresponding RW versions.
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.
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
‐0,2 0 0,2 0,4 0,6 0,8 1
1200 1300 1400 1500 1600 1700 1800 1900 2000
Correlation value r
Rogen RW vs ΔBIadj Year Arjeplog RW vs ΔBIadj Jämtland RW vs ΔBIadj p<0.05
,.,
--·---
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 19- yearcycles, 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).
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