CPClimate of the PastCPClim. Past1814-9332Copernicus PublicationsGöttingen, Germany10.5194/cp-12-15-2016Synchronizing the Greenland ice core and radiocarbon timescales over the
Holocene – Bayesian wiggle-matching of cosmogenic radionuclide recordsAdolphiF.florian.adolphi@geol.lu.sehttps://orcid.org/0000-0003-0014-8753MuschelerR.https://orcid.org/0000-0003-2772-3631Department of Geology – Quaternary Science, Lund University, Lund, SwedenF. Adolphi (florian.adolphi@geol.lu.se)15January2016121153016June20159July201511December201521December2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://cp.copernicus.org/articles/12/15/2016/cp-12-15-2016.htmlThe full text article is available as a PDF file from https://cp.copernicus.org/articles/12/15/2016/cp-12-15-2016.pdf
Investigations of past climate dynamics rely on accurate
and precise chronologies of the employed climate reconstructions. The
radiocarbon dating calibration curve (IntCal13) and the Greenland ice core
chronology (GICC05) represent two of the most widely used chronological
frameworks in paleoclimatology of the past ∼ 50 000 years.
However, comparisons of climate records anchored on these chronologies are
hampered by the precision and accuracy of both timescales. Here we use
common variations in the production rates of 14C and 10Be recorded
in tree-rings and ice cores, respectively, to assess the differences between
both timescales during the Holocene. Compared to earlier work, we employ a
novel statistical approach which leads to strongly reduced and yet, more
robust, uncertainty estimates. Furthermore, we demonstrate that the inferred
timescale differences are robust independent of (i) the applied ice core
10Be records, (ii) assumptions of the mode of 10Be deposition, as
well as (iii) carbon cycle effects on 14C, and (iv) in agreement with
independent estimates of the timescale differences. Our results imply that
the GICC05 counting error is likely underestimated during the most recent
2000 years leading to a dating bias that propagates throughout large parts
of the Holocene. Nevertheless, our analysis indicates that the GICC05
counting error is generally a robust uncertainty measurement but care has to
be taken when treating it as a nearly Gaussian error distribution. The
proposed IntCal13-GICC05 transfer function facilitates the comparison of ice
core and radiocarbon dated paleoclimate records at high chronological
precision.
Introduction
Paleoclimatology can provide significant insights into natural climate
changes and thus, improve our understanding of the climate system. Besides
the reconstruction of past climate itself, a precise chronology of each
paleoclimate record is crucial to reliably assess the dynamics of the
inferred changes. Furthermore, consistent chronologies across multiple
paleoclimate records are required to assess the spatiotemporal evolution of
climatic events and thus, to test for potential leads and lags within the
climate system and ultimately improve the understanding of the underlying
processes of past climate change. Two independent key timescales in
paleoclimatology of the past 50 000 years are the radiocarbon- (IntCal13,
Reimer et al., 2013) and the Greenland ice core timescale (GICC05,
Andersen et al., 2006; Rasmussen et al., 2006; Seierstad et al., 2014; Svensson
et al., 2008; Vinther et al., 2006). To be able to infer leads and lags
between paleoclimatic changes anchored on these chronologies at high
precision, it is crucial to test the consistency between the timescales and
establish climate-independent isochrones and thus reduce the influence of
their absolute dating uncertainties (e.g., Lane et al.,
2013). One method to compare and synchronize different timescales is the use
of cosmogenic radionuclide records, such as 10Be and 14C
(Muscheler et al., 2008, 2014a, b; Southon, 2002).
Cosmogenic radionuclides such as 10Be and 14C are produced in the
atmosphere through a nuclear cascade mainly triggered by incoming galactic
cosmic rays (GCR, Lal and Peters, 1967). The flux of GCR reaching the
atmosphere is in turn modulated by the strength of the helio- and geo-
magnetic fields resulting in varying production rates of 10Be and
14C (Masarik and Beer, 1999, 2009; Kovaltsov et al., 2012; Kovaltsov
and Usoskin, 2010). Thus, increased (decreased) intensity of the solar-
and/or geomagnetic field will result in decreased (increased) cosmogenic
radionuclide production rates. Therefore, 14C and 10Be production
rates co-vary globally due to external processes, making them a powerful
synchronization tool.
After production, 14C oxidizes to 14CO2 that enters the
global carbon cycle and gets stored in various environmental archives such
as tree rings, sediments, and speleothems. 10Be attaches to aerosols
which are deposited within 1–2 years (Raisbeck et al., 1981)
by wet and dry deposition processes and is stored in sediments including
polar ice sheets. These “system effects” (i.e., non-production influences on
10Be and 14C records such as the mixing, transport, and deposition
of 14C and 10Be) can challenge an unequivocal reconstruction of
cosmogenic radionuclide production rates from paleoarchives and thus,
synchronization efforts based on cosmogenic radionuclides.
Due to the large actively exchanging carbon reservoirs, changes in the
atmospheric 14C /12C ratio are attenuated and delayed compared to
the corresponding 14C production rate variations (Oeschger
et al., 1975). In comparison, 10Be is a more direct recorder of
production rate changes. Thus, when comparing 14C and 10Be records
directly, this difference in geochemistry has to be taken into account by
using carbon cycle models (Muscheler et al., 2004b). However, to be
fully realistic, these corrections would require prior knowledge on the
variable state of the carbon cycle, which is often difficult to quantify
(Köhler et al., 2006).
10Be records (for example from ice cores) can be affected by
non-production related processes as well. Firstly, it depends on the assumed
mode of deposition (wet vs. dry) whether the 10Be concentration (all
wet deposition) or the 10Be flux (all dry deposition) is the better
measure of atmospheric 10Be concentration changes (Alley et al.,
1995; Delaygue and Bard, 2010). In reality, both modes of deposition
contribute to the accumulation of 10Be on the ice sheet. Today, wet
deposition processes dominate over dry deposition which accounts for about
one third or less of the deposited 10Be in Greenland (Heikkilä
et al., 2011; Elsässer et al., 2015). However, this dry / wet deposition
ratio has likely been variable over time (Alley et al.,
1995). Secondly, a variety of climatic influences can leave an imprint in
ice core 10Be records. Atmospheric circulation changes and air mass
precipitation history (i.e., 10Be scavenging by precipitation prior to
the arrival of the air mass at the ice core site) may, for example, modulate
the transport path and efficiency of 10Be delivery to the ice core site
(Heikkilä and Smith, 2013; Pedro et al., 2011b, 2012). Furthermore, changes in the exchange rates between stratospheric
(high 10Be concentrations) and the tropospheric (low 10Be
concentrations) air masses can affect the tropospheric 10Be budget
(Pedro et al., 2011a). Thirdly, contrary to 14C,
10Be might not be hemispherically well mixed owing to its short
atmospheric residence time. This has led to the proposition of a so-called
“polar bias” in ice core 10Be records, stating that if polar
10Be records were dominated by 10Be produced at high latitudes,
the anisotropy of the geomagnetic shielding would lead to an enhanced solar-
and an attenuated geomagnetic modulation signal in polar 10Be records.
There is contradicting evidence from data and modelling studies to whether
this is the case (Field et al., 2006; Bard et al., 1997; Pedro et al.,
2012; Muscheler and Heikkilä, 2011; Heikkilä et al., 2009; Elsässer
et al., 2015).
In summary, to be able to use 10Be and 14C as synchronization
tools, “system effects” on each radionuclide have to be assessed and
corrected for. If successful, this method has the advantage that it can
provide near-continuous estimates of timescale differences as opposed to
discrete tie-points obtained from tephrochronology (Abbott and Davies,
2012; Lane et al., 2013) or changes in atmospheric trace gases during
Dansgaard-Oeschger events (Blunier et al., 1998; Buizert et al., 2015).
Aim of this study
Recently, Muscheler et al. (2014a) assessed the differences of the
radiocarbon and ice core timescales for the past 14 000 years by comparing
GRIP 10Be (Yiou et al., 1997; Muscheler et al., 2004b; Vonmoos et al.,
2006) and IntCal13 14C data (Reimer et al., 2013). Here, we
revisit this approach using a different statistical framework (Bronk
Ramsey et al., 2001) that is computationally less expensive and provides
improved error estimates for the inferred timescale differences as compared
to the method used in Muscheler et al. (2014a). Furthermore, we
test the robustness of the obtained results with respect to the use of
different ice core 10Be records as well as potential “system effects”
on the radionuclide records. We focus our analysis on the period where
dendrochronologically dated high-quality 14C measurements on tree rings
are available. While this is theoretically the case back to
12 560 cal BP
(calibrated before present, AD 1950, Friedrich et al., 2004), the
accuracy of the oldest part of tree-ring chronology has recently been
questioned (Hogg et al., 2013) causing a gap in the 14C records
underlying IntCal13 around 12 000 cal BP (Reimer et al., 2013).
Hence, we limit our analysis to the Holocene where dendrochronological and
14C-data replication is high and most robust (Reimer et al.,
2013; Friedrich et al., 2004).
MethodsData
The key data used in this paper are shown in Fig. 1. The GRIP 10Be record
(Vonmoos et al., 2006; Muscheler et al., 2004b; Yiou et al., 1997) covers
almost the entire Holocene with a gap between 9400 and 10 800 years BP
(Before Present 1950 AD) and no data for sections younger than 300 years BP.
We use the data as presented in Vonmoos et al. (2006) that includes a
61-point binomial filter (roughly corresponding to a 20-year low-pass filter
or a decadal sampling resolution) minimizing weather-related noise in the
10Be data. The GISP2 10Be record (Finkel and Nishiizumi, 1997) has
a gap between 7980 and 9400 years BP and no data for sections younger than
3270 years BP. We used the GISP2 10Be record on the GICC05 timescale
(Seierstad et al., 2014). Its temporal resolution varies between 20 to
60 years with an average of one sample every 35 years. Hence, no smoothing
filter was applied. The GISP2 10Be concentrations have been normalized
to the same standard used for the GRIP 10Be measurements (NIST SRM 4325,
see Yiou et al., 1997; Muscheler et al., 2004b). The resulting GRIP and GISP2
10Be records differ by on average
0.12 × 104atoms g-1 of ice. To avoid inhomogeneities when
splicing the records together, we adjusted the GISP2 10Be data
accordingly by adding 0.12 × 104atoms g-1 to the GISP2
10Be record (see Fig. 1). We note that reconciling the 10Be records
through normalization instead of addition does not affect the results shown
here. The lower panel in Fig. 1 shows atmospheric Δ14C (that is
14C /12C after correction for fractionation and decay relative to a
standard) as reconstructed from dendrochronologically dated tree rings
(Friedrich et al., 2004) and presented in IntCal13 in 5-year resolution while
the underlying data has typically a resolution of 10 years for most of the
Holocene (Reimer et al., 2013).
Top: GRIP (grey, Vonmoos et al., 2006) and GISP2 (black, Finkel and
Nishiizumi, 1997) Holocene 10Be concentrations. The GRIP 10Be
record is smoothed by a 61 pt binomial filter (see Vonmoos et al., 2006). The
GISP2 10Be record has been shifted by
+0.12 × 104atoms g-1 to correct for a difference in the
mean of the GRIP and GISP2 10Be records. Bottom: atmospheric
Δ14C as reconstructed from tree rings (Reimer et al., 2013 and
references therein).
Comparison of 10Be fluxes and concentrations over the Holocene.
Solid black and grey curves denote 10Be concentrations and fluxes,
respectively. Dotted lines refer to the “climate corrected” (see text)
versions of concentrations and fluxes with similar colour coding as solid
lines. The top two panels show GRIP 10Be for variations on timescales
longer (top) than 500 years, and for wavelengths between 100 and 500 years
(below). The 100-year cut-off has been applied for clarity of the figure. The
bottom two panels show GISP2 10Be for the same wavelengths as for GRIP.
Statistical method
In the following section we will describe the statistics used for the
14C /10Be comparison. To be able to compare both radionuclides
quantitatively, we converted the ice core 10Be records into Δ14C variations using a box-diffusion carbon cycle model
(Siegenthaler et al., 1980; Muscheler et al., 2004b). The details
of this conversion and its uncertainties are addressed in more detail in
Sect. 2.4. In the following we will refer to these modelled Δ14C variations as “10Be-based Δ14C
anomalies”.
We employ a statistical approach that is commonly used in the “wiggle-match
dating” of 14C records that have an initial relative chronology, i.e.
the age differences between neighbouring samples are known, such as
tree-rings (Bronk Ramsey et al., 2001). Contrary to classical
14C-age calibration we use Δ14C anomalies, since 10Be
cannot provide information on absolute Δ14C (and hence,
14C-ages) which depends on 14C production rates and the state of
the carbon cycle long before the investigated period. Given the results
shown in Sect. 3.1 we employ centennial (< 500 year FFT high-pass
filter) Δ14C anomalies of the tree-ring and the 10Be-based
Δ14C records for this comparison as shown in Fig. 3.
The mathematical formulation remains, however, unchanged. The calibration
record, IntCal13 (Reimer et al., 2013), describes Δ14C anomalies for each point in time, R(t), with an associated
uncertainty, δR(t). This can be compared to 10Be-based Δ14C anomalies (Ri:n) for which we know the absolute age
differences (Δti) between each sample from ice core layer
counting. We can estimate the probability (Pi) for different assumed
timescale differences between the records (ts) for each sample by
using Eq. (8) in Bronk Ramsey et al. (2001):
Pits+Δti∝exp(-(Ri-Rts+Δti)22δRi2+δR2ts+Δti)δRi2+δR2(ts+Δti).
Centennial (< 500 years) Δ14C variations modelled
from GRIP and GISP2 10Be data. (a) and (b) show the
modelled Δ14C variations from 10Be concentrations (solid
black), fluxes (solid grey), “climate corrected” concentrations (dotted
black), and “climate corrected” fluxes (dotted grey) for the
GRIP (a) and GISP2 (b)10Be records. (d) and
(e) on the right side depict the probability density functions for
the maximum Δ14C difference between curves shown in (a)
and (b), respectively. (c) shows the mean of all GRIP
(black) and GISP2 (grey) 10Be based Δ14C anomalies shown in
(a) and (b), respectively. (f) shows the
corresponding probability density function of their maximum Δ14C
differences. For this comparison both ice core records have been band-pass
filtered (120–500 years) to minimize inconsistencies arising from their
different sampling resolution. The correlation between the GRIP and GISP2
records is given in (c) together with its p value.
Using Bayes' theorem to combine the probabilities for each individual
measurement we can obtain an overall probability (Ps) for each timescale difference between GICC05 and IntCal13 (Eq. 9 in Bronk Ramsey et
al., 2001):
Ps(ts)∝∏i=1nPi(ts+Δti).
To allow a continuous comparison, all records have been interpolated to
annual resolution. However, since the ice core sampling resolution is in
reality lower we do not obtain truly independent probability distributions
for each sample. Consequently, we correct for the reduced degrees of freedom
by scaling Ps as
Psscaledts=Ps(ts)1/r,
where r is the original sample spacing (years sample-1) of the ice core
10Be records. This scaling effectively widens the obtained probability
distribution and thus increases the derived uncertainties. For the filtered
GRIP 10Be record, we assume a decadal resolution.
This “wiggle-matching” is done for predefined windows of IntCal13 and GRIP
and hence, yields a probability distribution (Psscaled(ts)) for
their timescale difference for each window. We apply this method to 1000-year
windows of 14C /10Be data and investigate one window every 50 years
back in time. For each window we test for timescale differences
(shifts) of ±150 years without stretching or compression of the
timescale within this window. Hence, in analogy to 14C-wiggle-match
dating, each window could be seen as a single 1000-year long “tree” that
is being calibrated. We tested different window sizes between 500- and 2000-year length and the corresponding results are consistent within error. The
choice of a 1000-year window represents a trade-off between (i) an
increasing statistical robustness and hence, smaller uncertainties, and (ii)
a loss of detail (variability) in the final transfer function (see also
Sect. 2.5) with increasing window length.
It can be seen from Eq. (1), that contrary to the correlation analysis
employed by Muscheler et al. (2014a) this method favours
10Be /14C linkages with a direct 1 : 1 relationship between IntCal13
and 10Be-based Δ14C records. Hence, the 14C : 10Be
production rate ratio has to be assessed. Furthermore, the uncertainty for
the 10Be-based records and the 10Be : 14C conversion is
quantitatively included in the calculation and hence, needs to be estimated.
In the following sections we will outline how these factors can be initially
assessed.
Assessment of uncertainties due to climatic influences on
10Be
As outlined in the introduction, ice core 10Be records can be affected
by various climatic influences that can “contaminate” the production signal.
To account for these effects, we use four different versions of the GRIP and
GISP2 10Be records throughout the manuscript. We use 10Be
concentrations and fluxes (10Be concentration multiplied by snow
accumulation and ice density) as endmembers of the assumed mode of 10Be
deposition (wet vs. dry, respectively) on the ice sheet. To address the role
of climate influences on 10Be mixing and transport to the ice sheet, we
additionally generated “climate corrected” versions of the concentrations
and fluxes. For this purpose, we performed multiple linear regression
analysis between 10Be and climate proxy time series from the GRIP and
GISP2 ice cores. Using ice accumulation rates (Seierstad et al., 2014),
δ18O (Johnsen et al., 1995; Stuiver et
al., 1997), and ion data (Mayewski et al., 1997) as predictors, we
linearly detrended the 10Be concentrations and fluxes. This procedure
removes covariance between 10Be and climate proxy data and may thus,
diminish the climate influences in the 10Be record. It should be noted,
that this is a “blind” empirical approach that does not aim for a process
based understanding of the climate influences on 10Be. This method
would, for example, confound solar (10Be) variations that had an
influence on climate as climate influences on 10Be (Adolphi et
al., 2014). Hence, these “climate corrected” versions should rather be seen
as sensitivity tests for our analysis than as improved estimates of past
10Be production rates per se. In summary, we use four (concentrations,
fluxes, and “climate corrected” versions thereof) different versions of
the GRIP and GISP2 10Be data. Each version represents a plausible
endmember of the 10Be production rate history, depending on the assumed
mode of deposition and climatic impacts on 10Be and can thus be used
to assess the sensitivity of our analysis to these processes.
Assessment of uncertainties due to 10Be-14C
conversionCarbon cycle modelling
To be able to compare 10Be to 14C records, we converted the
10Be records into Δ14C anomalies using a box-diffusion
carbon cycle model (Oeschger et al., 1975; Siegenthaler et al.,
1980). The model was run under pre-industrial conditions and has been shown
to yield consistent results with more complex carbon cycle models for our
purposes (Muscheler et al., 2007). As outlined in the
introduction, the unknown state and dynamics of the carbon cycle introduce
uncertainty to the comparison of 10Be and 14C. To test for the
sensitivity to these effects, we conducted four experiments (Table 1). Each
experiment was forced with an idealized 200 year 14C production rate
cycle of ±20 % approximately corresponding to a solar de Vries
cycle. For two of the experiments we perturbed the state of the carbon cycle
by increasing (S1) or decreasing (S2) the air-sea gas exchange constant by
50 % mimicking changes in wind speed and/or sea ice extent. In the
scenarios S3 and S4 the ocean diffusivity parameter (ocean ventilation) was
increased and decreased by 50 %, respectively. Each experiment was spun
up for 50 000 years under preindustrial conditions until all 14C
reservoirs were in steady state. Subsequently the investigated parameter was
changed linearly from its preindustrial to its perturbed value within 50 years (transition 1). The perturbed state was then maintained for 25 000 years to reach equilibrium again (steady state) before linearly changing the
perturbed parameter back to preindustrial values within 50 years (transition
2). We use these different sensitivity experiments to obtain an uncertainty
estimate of the modelled (10Be-based) Δ14C records due to
carbon cycle effects.
Performed carbon cycle sensitivity experiments. All percentage
values refer to the control simulation under pre-industrial conditions.
ControlS1S2S3S4Air/Sea exchange100 %150 %50 %100 %100 %Ocean ventilation100 %100 %100 %150 %50 %10Be /14C production rate ratio
To compare tree ring and ice core radionuclide records we used the
normalized 10Be records as 14C production rate input for the
carbon cycle model. This yields a 10Be-based Δ14C anomaly
record that can be directly compared to the tree-ring data. Hence, we have
to assume a ratio between the production rates of 14C and 10Be.
This ratio depends on the radionuclide production cross sections and the
energy spectrum of the incoming GCR. Model estimates of relative
14C : 10Be production rate increases for a change in the solar
modulation parameter from 700 to 0 MeV at modern geomagnetic field strength
differ between 1.34 (Masarik and Beer, 2009) and 1.04
(Kovaltsov et al., 2012; Kovaltsov and Usoskin, 2010).
Similarly, the predicted 14C : 10Be production rate ratios for
changes in the geomagnetic field strength are model-dependent for unresolved
reasons (Cauquoin, 2014).
Furthermore, the 14C : 10Be production rate ratio depends on the
presence of a potential “polar bias” (see introduction). If a “polar bias”
was present (Bard et al., 1997; Field et al., 2006) the ratio between
14C and ice core 10Be variations could be biased towards lower
values. Bard et al. (1997) report a value of 0.65 for the South Pole
10Be record. For Greenland, however, high-resolution 10Be records
do not support such a strong polar bias but would instead be consistent with
a well-mixed atmosphere (Pedro et al., 2012; Muscheler and Heikkilä,
2011). Simply comparing the standard deviations of centennial variations of
IntCal13 and 10Be-based Δ14C anomalies leads to ratios
between 0.95 and 1.05 (σ14CIntCal/σ14C10Be) depending on which ice core (GRIP/GISP2) and which
version of the 10Be records (concentration, flux, climate corrections)
is used. Thus, we start with a 14C : 10Be production rate ratio of
1 : 1 and test the sensitivity of our results to this assumption by repeating
the calculations outlined in Sect. 2.2 using 14C : 10Be ratios of
1.5 : 1 and 0.5 : 1.
Timescale transfer function
The methodology outlined in Sect. 2.2 yields a probability estimate of the
IntCal13-GICC05 timescale difference every 50 years. These probability
distributions are, however, not fully independent since neighbouring 1000-year windows overlap and are, hence, largely based on the same data. To
create a timescale transfer function we employed a Monte-Carlo procedure
that creates 20 000 possible transfer functions based on independent, i.e.
non-overlapping, windows. Each iteration, (i) randomly selects one of the
youngest (most recent) 20 windows and (ii) randomly samples from the
probability distribution Psscaled(ts) of this window as well as
the older non-overlapping windows (i.e. one window every 1000 years so that
the selected windows are fully independent with respect to the data points
they contain). The resulting transfer functions are then interpolated to
annual resolution and converted into probability distributions for the
timescale difference at each point in time. For each transfer function we
assume that both timescales are correct at 0 BP (i.e. AD 1950).
Iterative structure of the synchronization method
The separate aspects of our synchronization method outlined above are
applied in an iterative manner to obtain robust and self consistent error
estimates for our results. The different steps involved are carried out in
the following order.
We create four versions of both ice core 10Be records as endmembers of
plausible 10Be production rate histories (see Sect. 2.3).
We convert these 10Be records into Δ14C using a
box-diffusion carbon cycle model (Sect. 2.4.1) assuming a
14C : 10Be production rate ratio of 1 (see Sect. 2.4.2).
The difference between the different 10Be-based Δ14C
records, and results from the carbon cycle sensitivity experiments (see
Sect. 2.4.1) serve as initial uncertainty estimates for the
10Be-based Δ14C records.
We then compare the tree ring and 10Be-based Δ14C records
with respect to their timescale differences using the statistics outlined in
Sect. 2.2. We test for the robustness of these results by using all four
different 10Be versions of GRIP and GISP2 separately as well as
10Be-14C conversion factors of 0.5 and 1.5 (see Sect. 2.4.2).
Calculating an initial timescale transfer function (see Sect. 2.5) we then
synchronize IntCal13 and GICC05. This enables us to directly compare tree
ring and 10Be-based Δ14C records and estimate the optimal
14C : 10Be production rate ratio, as well as uncertainties for the
10Be-based Δ14C record.
Based on these posterior estimates of the 14C : 10Be ratio and the
uncertainty of the 10Be records, we repeat the calculations outlined in
Sects. 2.2 and 2.5 yielding our final estimates of the IntCal13-GICC05
timescale differences over the Holocene.
ResultsClimate and carbon cycle related uncertainties in the GRIP and GISP2
10Be records
Figure 2 displays the different 10Be production rate scenarios from
GRIP (top two panels) and GISP2 (lower two panels) 10Be concentrations
(Conc), fluxes (Flux) and their climate corrected versions (Concclim
and Fluxclim, respectively). Dividing the 10Be records into
centennial (< 500 years) and millennial (> 500 years)
variations indicates that the different 10Be versions mainly differ in
the low-frequency range. These millennial differences can systematically
affect the modelling of Δ14C since the carbon cycle acts as an
integrator over 14C production rate variations. The centennial changes
in the GRIP 10Be versions, however, are highly coherent and indicate a
limited climate influence on 10Be on these timescales and the same
holds true for the GISP2 10Be versions. This is in agreement with
Adolphi et al. (2014) who showed that centennial GRIP 10Be
variations are dominated by solar activity changes and indicate only little
sensitivity to the assumed mode of 10Be deposition even over large
deglacial climatic transitions. It should be noted that this statement
solely refers to the filtered centennial 10Be variations investigated
here. Other potential climatic influences on 10Be such as changes in
the stratosphere-troposphere exchange rates are, however, difficult to
assess from climate proxy data and will thus not be removed by our
detrending technique. Thus, in the following sections we will focus on centennial
(< 500 years) changes in 10Be and 14C production rates to
avoid systematic errors originating from uncertainties in the millennial
10Be production rate history.
The left-hand panels in Fig. 3 show the corresponding modelled Δ14C anomalies from the centennial 10Be variations indicated in
Fig. 2 assuming a 14C : 10Be production rate ratio of 1 : 1. As
expected, similar to the 10Be records these variations are highly
coherent. The right panels in Fig. 3 display histograms of the maximal
Δ14C difference between the different production rate histories
(i.e. the absolute Δ14C difference between the highest and the
lowest modelled Δ14C version at each point in time). It can be
seen that the different 10Be versions translate into a modelled Δ14C uncertainty of about ±3 ‰ (1σ)
for GRIP (Fig. 3a, d) and GISP2 (Fig. 3b, e). Similarly, the Δ14C anomalies modelled from GRIP and GISP2 10Be agree within
±2.5 ‰ (1σ, Fig. 3c, f).
As outlined in the introduction, the state and the dynamics of the carbon
cycle impose an uncertainty on the 10Be-14C comparison that is
difficult to quantify from the data itself (Köhler et al.,
2006; Muscheler et al., 2004b). Figure 4 shows the results from the performed
carbon cycle sensitivity experiments (see Sect. 2.4.1, Table 1). It can be
seen that the millennial Δ14C variations are substantially
altered by carbon cycle perturbations (Fig. 4b). Changes in ocean
ventilation (experiments S3 and S4) as well as air-sea gas exchange
(experiments S1 and S2) can cause Δ14C anomalies larger than
the amplitude of Δ14C anomalies induced by 14C production
rate changes only (control). However, as before, the centennial Δ14C variations are considerably less affected by these perturbations
(Fig. 4c). The increase (decrease) of air-sea gas exchange or ocean
ventilation does lead to a decrease (increase) in the amplitude of the
modelled centennial Δ14C variations. However, these changes in
amplitude are largely limited to about ±3 ‰
(Fig. 4d) except for about 200–300 years around the timing of the
carbon cycle perturbation itself (Fig. 4, transitions 1 and 2).
Importantly, the phase of the centennial Δ14C variations is not
affected by the imposed carbon cycle changes. Since the applied carbon cycle
changes in our sensitivity experiments are likely unrealistically large for
Holocene conditions (Köhler et al., 2006; Roth and Joos, 2013), we
conservatively assume a 1σ uncertainty of ±3 ‰
(see Fig. 4d, “steady state”) for the
modelled Δ14C records due to carbon cycle effects.
Carbon cycle sensitivity experiments. (a) Normalized
14C production rate input to the model. (b) Modelled
Δ14C anomaly. (c) centennial (< 500 year)
anomalies of modelled Δ14C shown in (b). (d)
differences in the centennial Δ14C variations (c) from the
control run. All model runs and panels are shown for the transition from
preindustrial to perturbed conditions (transition 1, right), steady state of
the perturbed conditions (steady state, middle), and the transition back to
preindustrial carbon cycle conditions (transition 2, left). See also
Sect. 2.4.1.
Adding the uncertainties due to climate impacts on 10Be (±3 ‰)
and the carbon cycle (±3 ‰) in quadrature we thus obtain an initial uncertainty
estimate of about ±4.5 ‰ for the modelled Δ14C records.
Sensitivity of the synchronization method to uncertainties in the
10Be-14C conversion
In the following we will compare the centennial Δ14C (i.e.,
< 500 years, separated by an FFT-based high-pass filter) anomalies
reconstructed from tree rings (IntCal13) and ice cores (GRIP/GISP2
10Be-based) with respect to their timescale differences. The choice of
a 500-year high-pass filter results from the climate and carbon cycle
related uncertainties shown in Sect. 3.1 which increase on longer
timescales. We use the statistical framework outlined in Sect. 2.2 and
assign an initial uncertainty of ±4.5 ‰ to the
10Be-based Δ14C records. The uncertainties for the
tree-ring based Δ14C anomalies are taken from IntCal13
(Reimer et al., 2013). For this purpose we spliced the GISP2
10Be versions into the corresponding GRIP 10Be versions to fill
the gap in the GRIP record between 9400 and 10 800 years BP and create a
continuous record for the entire Holocene. Hence, in the following “GRIP”
refers to this combination of GRIP and GISP2 data, while results for the
GISP2 data are only shown for periods where they have not been used to fill
the gap in the GRIP record.
Figure 5 displays the obtained probability distributions
Psscaled(ts) for each sliding window, centred on
its mean age. The results are shown for all four GRIP 10Be versions
(panel a), in comparison to results based on GISP2 data only (panel b), as
well as for different assumed 14C : 10Be production rate ratios
(panel c). The different GRIP 10Be versions yield consistent estimates
of the IntCal13-GICC05 timescale differences throughout the Holocene. The
only marked difference occurs around the 8.2 ka BP event (Blockley et al.,
2012). During this period the 10Be flux indicates a more rapid increase
in the IntCal13-GICC05 timescale difference as compared to all other
10Be versions. As noted by Muscheler et al. (2004a) the accumulation
rate anomaly associated to the climate oscillation around 8200 years ago
appears to lead to an “over correction” of the 10Be deposition during
flux calculation. This leads to a worse agreement between 14C and
10Be fluxes as compared to 14C and 10Be concentrations (see
Fig. 3 in Muscheler et al., 2004a). This is corroborated by the fact that
results based on the “climate corrected” 10Be flux follow the
probability estimates of 10Be concentrations (Fig. 5a).
Probability distributions for IntCal13-GICC05 timescale differences
(Psscaled(ts), see Sect. 2.1) for each 1000-year
window based on the mean of GRIP 10Be concentrations, fluxes, and their
climate corrected versions (grey-scale patches in all panels). The gap in the
GRIP 10Be record between 9400 and 10 800 BP has been filled with data
from the GISP2 ice core. Each probability distribution is centred on the mean
age of the investigated window. (a) Comparison to 95 % probability intervals
based on GRIP 10Be concentrations (solid orange), fluxes (solid blue)
and their “climate corrected versions (dashed pink and green lines).
(b) Comparison to 95 % confidence intervals based on the mean of GISP2
10Be concentrations, fluxes, and their climate corrected versions.
Results for GISP2 are only shown for periods where it has not been used to
fill the gap in the GRIP record. (c) Comparison to results based on a
different scaling (factors of 0.5 and 1.5 shown as blue and green lines,
respectively) of the GRIP 10Be record.
Comparing GRIP based results to GISP2 based estimates indicates consistent
estimates of the timescale differences. The larger uncertainties of the
GISP2 based results are due to the lower sampling resolution of the GISP2
10Be record (see Eq. 3).
Figure 5c shows the sensitivity of our results to the assumed
14C : 10Be production rate ratio. It can be seen that the inferred
timescale differences are relatively insensitive to the assumed
14C : 10Be ratio. However, the derived uncertainty of
Psscaled(ts) does increase with lower 14C : 10Be ratios.
This can easily be understood by imagining a scaling of zero for the
10Be-based record which would result in an infinitely wide probability
distribution.
In summary, our method of estimating the IntCal13-GICC05 timescale
difference is (i) largely robust for all versions of the GRIP 10Be
record, (ii) consistent for GRIP and GISP2 10Be data, and (iii)
independent of the assumed 14C : 10Be production rate ratio.
However, this analysis also shows that it is important to compare 10Be
concentrations and fluxes to identify potential caveats as seen around the
8.2 ka BP event. Furthermore, while the estimate of the most likely
timescale difference (i.e. the location of the maximum of
Psscaled(ts)) may not be affected by the assumed
14C : 10Be ratio, the uncertainty of this estimate is. Hence, in the
following section we will derive a posterior estimate of the
14C : 10Be ratio, as well as a refined uncertainty estimate of the
10Be-based Δ14C records.
Posterior estimate of the 14C : 10Be production rate ratios and
uncertainties
As shown in the previous section, our estimates of the most likely timescale
difference between IntCal13 and GICC05 are largely independent of which
10Be record (GRIP/GISP2) and which version thereof (concentration,
flux, climate corrections) is used, as well as which 14C : 10Be
ratio is assumed. Hence, we calculated an initial GICC05-IntCal13 transfer
function (Sect. 2.5) and synchronized the tree ring based and
10Be-based Δ14C record. This enables us to compare the
records with respect to the most likely 14C : 10Be ratio. In
addition, we can derive a posterior estimate of the modelled 10Be-based
Δ14C uncertainty.
After synchronization we can compare tree ring and 10Be-based Δ14C sample pairs assuming different 10Be scaling factors (i.e.
14C : 10Be ratios) between zero and two. The difference between tree
ring and 10Be-based Δ14C sample pairs (δ(t)) is a
function of the uncertainty of IntCal13 (δIC(t)) and the
uncertainty of the 10Be-based records (δBe(t)) in the form
that
δ(t)=δ(t)IC2+δ(t)Be2.
Hence, we can rearrange Eq. (4) and use the quoted uncertainties of
IntCal13 to derive δ(t)Be:
∂(t)Be=∂(t)2-∂(t)IC2;∂t>∂(t)IC∂(t)Be=0;∂t≤∂(t)IC.
These uncertainties can be summarized to the rooted mean square error
(RMSE10Be). This way we can obtain the optimal 10Be
scaling factor (where the RMSE10Be minimizes) and the
associated uncertainty of the 10Be-based Δ14C records (the
minimum of the RMSE10Be). Figure 6 displays the results of
this analysis indicating an optimal 10Be scaling factor of around 0.7.
Assuming that the centennial 10Be and 14C production rate changes
are mainly modulated through solar activity this low scaling factor would
point to a strong polar bias of the GRIP and GISP2 10Be records (see
Sects. 1 and 2.4.2). However, when investigating the Δ14C time
series it becomes apparent that this low scaling leads to an underestimation
of the amplitude of virtually all grand solar maxima and minima (i.e. large
Δ14C anomalies) in the 10Be-based Δ14C record
(Fig. 7, top). This bias is induced by the fact that the Δ14C
anomalies are normally distributed around 0 ‰ leading to a majority
of the Δ14C values lying close to zero dominating the
RMSE10Be. Hence, for these values a low scaling of the
10Be-based Δ14C records will simply act to reduce noise from
the record and thus reduce the RMSE10Be.
Rooted mean square error (RMSE10Be, see text) of synchronized
centennial IntCal13 and 10Be-based Δ14C variations as a
function of different 10Be-scaling factors (14C : 10Be ratios).
Results for the different versions of the GRIP10Be record are shown on
the left, while GISP2 10Be-based results are shown on the right.
Comparison of synchronized tree-ring (black) and ice core (grey)
based Δ14C anomalies for 14C : 10Be ratios of 0.7 (top)
and 1.1 (bottom).
To avoid this bias, we performed a binned regression analysis. We divided
the tree ring and 10Be-based Δ14C sample pairs into bins
of 2.5 ‰ (defined based on the tree ring Δ14C anomalies) and calculated the RMSE10Be for each bin
(RMSE10Be_bin). These uncertainties for each bin can
then be summarized to an overall RMSE10Be as
RMSE10Be=RMSE10Be_bin2‾.
This binning leads to an equal weighting of small and large Δ14C anomalies in the comparison of the Δ14C records. It
can be seen that this method indicates a larger 14C : 10Be ratio of
about 1.1 (Fig. 8) and avoids the systematic underestimation of large
amplitude Δ14C anomalies (Fig. 7, bottom). Depending on the
production rate model used, this scaling indicates a weak (Masarik and
Beer, 2009, 1999) or no (Kovaltsov et al., 2012; Kovaltsov
and Usoskin, 2010) polar bias in the Greenland 10Be records. In
addition, it can be seen that the minimum of the RMSE10Be becomes
larger than without binning, indicating an uncertainty of about 4 ‰
for the 10Be-based Δ14C records.
This is due to the above-described effect, that the noise is not
artificially supressed and can be seen by comparing the decadal scale peaks
in the top and bottom panels of Fig. 7. The larger 10Be scaling
factor makes the 10Be record appear noisier. However, firstly, this
noise may represent remaining influences of “system effects” on ice core
10Be records and, hence, represent an uncertainty that has to be taken
into account. Secondly, it should be kept in mind that IntCal13 is a stack
of multiple 14C data sets which will inevitably result in smoothing.
This smoothing may also reduce the amplitude of “real” Δ14C
variations instead of merely reducing noise, since the differences between
the underlying raw data sets of IntCal13 are potentially in part systematic
(Stuiver et al., 1998; Adolphi et al., 2013).
Rooted mean square error (RMSE10Be) of IntCal13 Δ14C and 10Be based Δ14C records from GRIP (left) and
GISP2 (right) for different scalings of the 10Be based data after
synchronization. The RMSE10Be has been calculated for binned data (bin
size = 2.5 ‰, see text) taking IntCal Δ14C errors into account.
In conclusion we use a 14C : 10Be ratio of 1.1 : 1 and an uncertainty
of 4 ‰ for the modelled Δ14C record to
derive a final IntCal13-GICC05 transfer function in the next section. It
should be noted that this uncertainty estimate is only valid for the
centennial (< 500 year) variations studied here.
IntCal13-GICC05 transfer function
Using the estimated 14C : 10Be ratio of 1.1 and a 10Be-based
Δ14C error of ±4 ‰ (±1σ) (see previous
section), we recalculated the “wiggle-match” probability
distributions (Psscaled(ts), Eq. 3) for the IntCal13-GICC05
timescale difference (Fig. 9, grey shading). For these calculations we
used the mean of all GRIP10Be-based Δ14C versions
(concentration, flux, climate corrections) and filled the gap between 9400
and 10 800 years BP using the GISP2 data. Based on these probability
distributions we modelled the IntCal13-GICC05 transfer function as described
in Sect. 2.5. The resulting transfer function (Fig. 9 solid lines)
averages out some short-term fluctuations in the timescale difference
compared to the initial “wiggle-match” probability distributions. As
described in Sect. 2.5 this is due to the used window length of 1000 years to determine Psscaled(ts) at each point in time, preventing
an independent assessment of faster changes in the timescale difference.
Nevertheless, the estimated uncertainties of the timescale transfer function
(thin black lines in Fig. 9) encompass the uncertainties of the
“wiggle-match” probability distribution at each point in time.
IntCal13-GICC05 age transfer function (thick black line) and its
2σ confidence intervals (thin black lines) based on the probability
distributions (Psscaled(ts), grey shading) obtained from
comparing the GRIP 10Be-based Δ14C (mean of concentration,
flux and climate corrections) and IntCal13 Δ14C records.
Figure 10 shows three examples of GRIP 10Be based Δ14C
anomalies before (grey) and after (black) synchronization to IntCal13 (red).
The examples encompass (i) a period of relatively low Δ14C
variability (±5–7 ‰) but good agreement between
GRIP and IntCal13 (Fig. 10a), (ii) a period of large Δ14C
variability (±10 ‰) but less good agreement
between GRIP and IntCal13 (Fig. 10b), and (iii) a section of large
Δ14C (±10 ‰) variability and
excellent agreement between GRIP and IntCal13 (Fig. 10c). It can be
seen that in all cases the fit between GRIP and IntCal13 is improved when
applying the proposed GICC05-IntCal13 transfer function. However, Fig. 10b
also shows that short periods of disagreement (i.e., around 7250–7500 years BP) may remain, as they cannot be reliably resolved by our
method which matches 1000-year long sections. It should, however, be noted
that matching these short sections would (i) represent a serious violation
of the GICC05 counting error which is minimal over these short periods of
time (±6 years at 2σ between 7250–7500 years BP), and
(ii) not account for the possibility that 10Be and 14C may simply
not agree due to the caveats outlined in the introduction. Furthermore, the
applied shift of GICC05 in Fig. 10b) leads to an improved agreement
between 14C and 10Be after and prior to 7250 and 7500,
respectively. Hence, we consider it unlikely that for this short period of
time the timescale difference deviates significantly from the estimate for
the entire window.
GRIP/GISP2 10Be based Δ14C before (grey) and
after (black) synchronization to IntCal13 (red) for the sections (a) 3500–4500 years BP,
(b) 7000–8000 years BP, (c) 10 000–11 000 years BP.
Comparison of the derived IntCal13-GICC05 timescale transfer
function (black lines, this study) to the results by Muscheler et al. (2014a,
grey lines), and independent age markers that have been linked independently
to the IntCal13 and GICC05 timescales at high precision (symbols). The
results of this study and Muscheler et al. (2014a) are shown with their respective
95 % confidence intervals (dashed lines). The independent age markers are
plotted as the difference between their estimated ages based on radiocarbon
dating (Saksunarvatn Ash, Santorini), historical documents (Vesuvius) and
dendrochronology (775 and 994 AD events), and their respective GICC05-ages.
The plotted 1σ error bars largely reflect uncertainties in the
radiocarbon-dating and calibration of the Saksunarvatn Ash
(Lohne et al., 2013) and the Santorini eruption
(Friedrich et al., 2006). Note that the identification of the
Santorini tephra in ice cores has been challenged based on its geochemistry
(Pearce et al., 2004).
Top: comparison of the derived IntCal13-GICC05 transfer function
(thin grey lines and shading, dashed lines denote the 95 % confidence
interval) to the GICC05 maximum counting error (bold grey lines). Bottom:
same as above but expressed as the rate of change (year year-1) of the GICC05
maximum counting error and the derived timescale transfer function.
Discussion
Figure 11 shows the obtained estimate of the IntCal13-GICC05 timescale
difference in comparison to the results obtained by using the method of
Muscheler et al. (2014a, re-run with a 1000 year window length)
and age markers that have been independently anchored on both timescales.
Our results are fully consistent with the results obtained by Muscheler et
al. (2014a). While this is expected to some extent, as our study
and the work by Muscheler et al. (2014a) are based on the same
data, it shows that the statistical approach used here leads to similar
results as the Monte-Carlo lag-correlation analysis but is computationally
much less expensive. Furthermore, as shown in Fig. 5, we obtain similar
results when using the GISP2 10Be instead of the GRIP 10Be record
lending additional support to the robustness of our results. The additional
modelling of the transfer function employed here (Sects. 2.5 and 3.4)
leads to a smoother development of the timescale difference which is more
realistically reflecting limitations of the method imposed by the window
size of the 14C-10Be comparison. The difference between the
timescale transfer functions around 8200 years BP is induced by the fact
that Muscheler et al. (2014a) based their calculations on 10Be
fluxes which are influenced by accumulation rate changes around this time as
discussed in Sect. 3.2 and in Muscheler et al. (2004a).
The largest difference between the results presented here and by those of
Muscheler et al. (2014a) is seen in the derived error estimates. We
obtain strongly reduced uncertainties for the estimated timescale
differences. This is likely due to the fact that Muscheler at al. (2014a)
used a comparably ad hoc and highly conservative method to
derive their uncertainties. By taking the distribution of the mean
r2 values of all iterations, Muscheler et al. (2014a) do not
include the results of the Monte-Carlo analysis of the “Best Fits” in
their error estimate. Thus, 14C-10Be matches that may not be the
most likely solution in any of the iterations become included in the
uncertainty envelope. In comparison, the statistics employed here allow a
direct analytical assessment of the synchronization uncertainties. Hence,
while our uncertainty estimates are significantly smaller, we consider them
more robust. Theoretically, systematic errors from undetected biases in the
10Be record could lead to erroneous results. However, the results shown
in Sect. 3.2 demonstrate the consistency of GRIP and GISP210Be-based
calculations as well as for different climate corrections and thus do not
indicate such biases (see Fig. 5). In conclusion, while largely
consistent, we regard the method employed here as a significant improvement to
the approach by Muscheler et al. (2014a).
Comparing our results to independent estimates of IntCal13-GICC05 timescale
differences further supports our analyses (Fig. 11, symbols). Two major
solar proton events (“775 and 994 AD events”) leaving well-defined spikes
in the 14C content of dendrochronologically dated trees (Miyake et
al., 2012, 2013; Güttler et al., 2015) as well as in
Greenland ice core 10Be records (Mekhaldi et al., 2015; Sigl et al.,
2015) indicate an IntCal13-GICC05 timescale difference of -7 ± 2
(2σ) years for both events (Sigl et al., 2015). Consistent
with these findings, we obtain IntCal13-GICC05 differences of -4 ± 4
and -6 ± 5 years (2σ) for the 994 and 775 AD event,
respectively. It should be noted that these annual radionuclide excursions
are not present in the data used here, which is of decadal resolution, and are
hence, independent estimates of the timescale difference.
Based on tephra findings in the GRIP ice core
(Barbante et al., 2013), the historically dated AD 79
eruption of Vesuvius has been used as a reference point in the GICC05
chronology (Vinther et al., 2006). However, our
results indicate a timescale offset of -11 ± 6 (2σ) years at
AD 79 (1871 years BP, see Fig. 11). Assuming that the tree-ring
chronologies are correct at this time, this would imply an age of AD 90
±6 for the GRIP tephra layer – incompatible with an attribution to
the age of the Vesuvius eruption within 2σ. This result is in
agreement with the analysis by Sigl et al. (2015) who recently counted
annual layers in the NEEM and NEEM-2011-S1 ice cores and dated this marker
horizon to AD 87 and 89, respectively.
The age of the Minoan eruption of Santorini has long been debated and the
presence of an unequivocally attributable signal in the ice core records has
been questioned (Pearce et al., 2004; Hammer et al., 1987, 2003;
Friedrich et al., 2006). The GICC05 age of 3591 ± 5 BP of an
identified tephra horizon is incompatible with the radiocarbon-based age of
3563 ± 14 cal BP of the Santorini eruption (Δ=-28 ± 15 years).
Our results indicate a chronology difference of -20 ± 5 years
around this time, reconciling the two aforementioned ages (see Fig. 11,
open diamond). Hence, at least from a chronological point of view, it cannot
be ruled out that the ice core tephra may be ascribable to the Santorini
eruption (Muscheler, 2009).
Volcanic glass shards from the Saksunarvatn ash have been found in the GRIP
ice core (Grönvold et al., 1995), as well as in multiple
marine, lacustrine and terrestrial sites, of which the Lake Kråkenes
record provides the highest resolution radiocarbon-based age for the deposit
(Lohne et al., 2013). The dating difference of -86 ± 35 years
between the radiocarbon-based age (10210 ± 35 cal BP,
±1σ, Lohne et al. 2013) and the GICC05 age (10 296 BP,
Abbott and Davies, 2012) of the Saksunarvatn ash is consistent with our
estimated timescale difference of -66 ± 10 years during this time
interval.
In summary, our results are consistent within uncertainties with all
independent age markers that link the GICC05 and IntCal13 timescales over
the Holocene.
Figure 12 displays the inferred IntCal13-GICC05 timescale differences in
comparison to the GICC05 maximum counting error (Rasmussen et al.,
2006; Vinther et al., 2006). Assuming that the tree-ring chronologies
underlying IntCal13 are accurate throughout the Holocene our results imply
an underestimation of the absolute dating uncertainty of GICC05 for large
parts of the Holocene. Furthermore, it can be seen that the counting error
appears to be systematic, in that most uncertain years
(counted as 0.5 ± 0.5 years,
Rasmussen et al., 2006) have indeed not been true calendar years during the
Holocene (i.e., a systematic over-counting of years). Nevertheless, when
comparing the rate of change of the inferred IntCal13-GICC05 timescale
difference to the rate of change of the maximum counting error (i.e. the
relative maximum counting error) it can be seen that – even though
systematic – the identification of uncertain years in the ice core records
is accurate. Except for the most recent 2000 years where (potentially
erroneous) fix-points like the Vesuvius eruption are used to constrain
GICC05 the relative layer counting uncertainty appears to be an accurate
uncertainty estimate. This can be seen in Fig. 12 (lower panel) which
indicates that the rate of change of the GICC05 maximum counting error is
consistent within error with the rate of change of the IntCal13-GICC05
timescale difference prior to 2000 years BP. This is important to note as
it generally supports the GICC05 layer counting methodology and uncertainty
which forms the basis of GICC05 back to 60 000 years BP
(Svensson et al., 2008), even though the
systematic nature of the derived timescale differences challenges the use of
the maximum counting error as a nearly Gaussian distributed 2σ
uncertainty during the Holocene (Andersen et al., 2006). It
can, however, not be assumed that the counting error continues to be
systematic beyond this period, since the parameters used for layer
identification as well as the sources of uncertainty (e.g. melt layers)
differ back in time under changed climatic conditions (Rasmussen et al., 2006).
Alternatively, uncertainties in the dendrochronologies underlying IntCal13
could contribute to the growing discrepancy between IntCal13 and GICC05 over
the Holocene. This appears, however, unlikely since the tree-ring
chronologies have been cross-dated back to 7272 cal BP to the Irish Oak
Chronology (Pilcher et al., 1984) and back to 9741 cal BP
using independently constructed German Oak Chronologies (Friedrich et
al., 2004; Spurk et al., 2002). Furthermore, the gradual development of the
timescale difference appears consistent with a counting uncertainty, while a
dendrochronological mismatch could be expected to cause sudden “jumps” in
the timescale difference. However, consistently missing tree rings in both
German oak chronologies for the period older than 7272 cal BP could
theoretically contribute to the growing timescale difference.
Conclusions
We employed a novel approach to infer timescale differences between two of
the most widely used chronologies in Holocene paleoclimatology, the
radiocarbon (IntCal13, Reimer et al., 2013) and Greenland ice core
(GICC05, Svensson et al., 2008) timescales. Our
results are largely consistent with the results of Muscheler et al. (2014a)
but yield significantly smaller and more robust uncertainty
estimates. The inferred timescale differences are consistent with
independent tie-points obtained from volcanic tephras and solar proton
events. However, in agreement with Sigl et al. (2015) our analyses indicate
that the attribution of an ice core tephra to the AD 79 eruption of Vesuvius
(Barbante et al., 2013) may be erroneous which leads
to a propagating ice core dating bias that affects large parts of the
Holocene. Nevertheless, the identification of uncertain years in the ice
core during the Holocene is otherwise generally accurate as expressed in the
relative counting error (Fig. 12 lower panel). This is important to note
as it, in principle, supports the layer counting method and uncertainty
estimates also beyond the period investigated here. Furthermore, it should
be noted that these conclusions are based on the assumption that the
tree-ring timescale is accurate.
Independent of the accuracy of either of the two chronologies we provided a
high-precision transfer function between the radiocarbon and Greenland ice
core timescales. This allows radiocarbon dated and ice core paleoclimate
records to be compared at high chronological precision which will improve
studies of leads and lags within the climate system throughout the Holocene
(Bronk Ramsey et al., 2014). Furthermore, the methodology
outlined here can be applied to link high-resolution 14C records such
as floating tree-ring chronologies to ice core timescales and thus, aid in
testing and improving the glacial radiocarbon dating calibration curve.
Information about the supplement
The proposed GICC05-IntCal13 transfer function shown in Figs. 9, 11 and 12
is available as a Supplement to this paper and on NOAA.
The Supplement related to this article is available online at doi:10.5194/cp-12-15-2016-supplement.
Acknowledgements
The study was supported by the Swedish Research Council (VR) through a
Linnaeus grant to Lund University (LUCCI). This work was supported by a
grant from the Swedish Research Council (Dnr: 2013-8421). We thank
Anders Svensson for providing GICC05 snow accumulation rates.
Edited by: L. Skinner
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