Blue Reflectance (BLU), a new inexpensive shortcut towards robust treering based climate reconstructions?
Jesper Björklund, Kristina Seftigen and Hans Linderholm
Department of Earth Sciences, University of Gothenburg, Sweden
Introduction
Results and Discussion
In Paleoclimatology, tree-rings is used for climate reconstructions on annual to multi-millennial scale.
Methods to extract information from trees are for example measurements of ring-width (TRW) or density
of the wood i.e. Maximum Density (MXD). Historically, MXD has proved to be an excellent proxy for warm
season temperatures in cooler climates (Schweingruber et al. 1988). However, X-ray of tree-rings is timeconsuming and expensive. Furthermore, for tree-rings to robustly represent climate variability on longer
timescales, the number of trees required greatly increases (Briffa and Melvin 2008) and production rates
and costs can become a significant obstacle. Recently, reflected blue light from optically scanned images
has been explored to investigate if this is a viable shortcut to robust warm season temperature
reconstructions (e.g. McCarrol et al. 2002; Campbell et al. 2007). The parameter that that is measured is
the minimum value of reflected blue light in every annual increment henceforth abbreviated (BLU).
Previous studies have shown that BLU and MXD are highly correlated on annual to decadal scale. Now it
is time to test BLU on multi-centennial scale.
First differenced MXD and BLU series have a
correlation of r = 0.80 (n = 19 456) and the
chronologies have a correlation of r = 0.95 (n =
810, Fig 4). Examining the common variability
visually on longer timescales, the MXD and
BLUOriginal covary excellently from 1200 A.D. to
1700 A.D. (Fig 5). After this there is a dramatic
divergence.
Examining
the
MXD
and
BLUHeartwood/Sapwood this divergence is reduced
but there are still profound dissimilarities (Fig 5).
Examining the MXD BLUDeadwood/Heartwood/Sapwood
all centennial scale divergence is removed but
the chronologies have a slight overall difference
in trend (Fig 5).
Figure 4. Scatterplot with the
relationship
between first
differenced MXD
and BLU on
series level in
black and on
chronology level
in red.
Study Area
A new site in Northern Sweden 50 km north of the town of Arjeplog was sampled for Scots Pine (Pinus
Sylvestris L.). Coordinates: Lat. 66° 17’ 37’’ N, Lon. 18° 14’ 59’’ E. The source area for the sampled
trees was a north-facing slope on an 800 m high fore fell, Bårgå (Fig 1) where the pines form the treeline at 700 m a.m.s.l.
Figure 1. The fore fell Bårgå where dead and living trees were collected at the tree-line
Methods
For direct comparison of BLU vs. MXD, the same cores of wood were analyzed for both variables. The
samples were prepared according to standard dendrochronological techniques (Schweingruber et al.
1978). Resins and extractives were chemically removed with ethanol in a Soxhlet extraction apparatus.
The X-Ray was performed using an ITRAX multiscanner from Cox Analytical Systems (www.coxsys.se).
The BLU was produced with a flatbed Scanner Epson Perfection V600 Series calibrated with SilverFast
Ai professional scan software, standard protocol was followed according to Campbell et al. (2011). The
raw MXD and BLU was standardized to remove age related trends in the series with Regional Curve
Standardization (RCS; Briffa et al. 1992). This to enable comparison of chronologies on longer time
scales than the mean age of the trees Cook et al. (1995). The BLU chronology was named BLUOriginal.
Figure 5. MXD plotted
against different chronology
configurations of the BLU
data. Notice the increased
coherence with more
complex BLU chronology
configuration. This is not a
sustainable way of
producing BLU chronologies
in future work beacuse there
are also nuance differences
between deadwood. A
correction for nuance
differences is needed to
move forward with BLU on
species that have
heartwood/sapwood of this
kind.
The BLU method is interpreted, from these results to be substantially more sensitive to the discoloration
from waste products and resin in the heartwood and sapwood. Likely the longer waste products and resin
reside in the wood, the more permanent and pronounced the discoloration becomes (Fig 2). One way of
overcoming this vital weakness in BLU could be to quantify the background Blue reflectance caused by
resin discoloration in the early wood of neighboring rings and, assumed that the latewood have the same
background discoloration, remove this from BLU and make a new proxy. The difference in latewood and
ambient early wood blue reflectance (δBLU) would enable samples with heavy discolorations to be used
together with samples with little discoloration. And also ensure multi-centennial or even multi-millennial
scale variability. If this drawback can be resolved it is clear that the value of BLU is tremendous since the
climatic signal is equal in type, strength and field to MXD (Fig 6).
Figure 6. Spatial
correlations
between MXD on
left and BLU on
rihgt with AprilAugust
Temperatures.
The correlations
were made on
first differenced
data. The star in
each plot is the
sampling site
Bårgå, Arjeplog
Sweden.
Figure 2. Top sample: living tree not alcohol refluxed, middle sample: living tree refluxed with alcohol for
24 h, bottom sample: dead tree refluxed in alcohol. Notice the nuance difference between the living and
the dead refluxed trees.
Scots Pine has a pronounced difference in color between sapwood and heartwood (Fig 2). This color
difference can not entirely be removed in extraction with alcohol. To account for this, a second BLU
chronology was made which consisted one heartwood and one sapwood chronology, named
BLUHeartwood/Sapwood. The sapwood chronology’s mean and variance was adjusted to fit the BLU
heartwood chronology. A visual inspection of samples also revealed a distinct difference between
heartwood from living and dead trees (Fig 2), and a third chronology was produced using similar division
ans in the previous chronology and named BLUDeadwood/Heartwood/Sapwood. The mean and variance from the
living tree heart wood chronology was adjusted to the dead tree heartwood chronology and finally the
sap wood to the mean and variance of the combined “dead-living” composite chronology. During the
overlap between the different chronologies the sample depth is varying, diminishing in the ends of each
chronology hence replication weighted averages between the z-scored chronologies was made, see
figure 3 for overlap.
Conclusion
Figure 3. Expressed
population signal, a
measure of
chronology
confidence. EPS
below 0.85 is
considered to low for
reconstructions.
Sample depth
through time where
overlap of
deadwood, living tree
heartwood and
sapwood is shown.
Since flatbed scanners, for example the one used in this study costs roughly thousand times less than
the X-Ray machine used in this study, and the time of producing the BLU data was roughly half of the
time of producing MXD data, the need for tweaking this proxy for optimum performance is of great value.
References
References
Briffa et al. (1992) Clim Dyn. 7:111-119; Briffa and Melvin (2011) In: Hughes M K, Diaz H F, Swetnam T W(ed), Dendroclimatology:
Progress and Prospects, Springer Verlag, pp 378; Campbell et al. (2007) The Holocene 17:821; Campbell et al. (2011) Tree-ring Research
67:127-134; Cook et al. (1995) Holocene 5:229-237; McCarroll et al. (2002) Arctic, Antarctic and Alpine Research 34:450-453;
Schweingruber et al. (1988) Boreas 17:559-566; Schweingruber et al. (1978) Tree-ring bull. 38:61-91
Acknowledgements This work was supported by Vetenskapsrådet and Formas (grants to Hans W Linderholm) The poster
contributes to the Swedish strategic research area Biodiversity and Ecosystem services in a Changing Climate (BECC).