CPClimate of the PastCPClim. Past1814-9332Copernicus PublicationsGöttingen, Germany10.5194/cp-14-1755-2018Connecting the Greenland ice-core and U/Th timescales via cosmogenic
radionuclides: testing the synchroneity of Dansgaard–Oeschger eventsConnecting the Greenland ice-core and U/Th timescalesAdolphiFlorianadolphi@climate.unibe.chhttps://orcid.org/0000-0003-0014-8753Bronk RamseyChristopherErhardtTobiashttps://orcid.org/0000-0002-6683-6746EdwardsR. LawrenceChengHaiTurneyChris S. M.https://orcid.org/0000-0001-6733-0993CooperAlanSvenssonAndershttps://orcid.org/0000-0002-4364-6085RasmussenSune O.https://orcid.org/0000-0002-4177-3611FischerHubertushttps://orcid.org/0000-0002-2787-4221MuschelerRaimundhttps://orcid.org/0000-0003-2772-3631Climate and Environmental Physics, Physics Institute & Oeschger
Centre for Climate Change Research, University of Bern, Sidlerstrasse 5,
3012 Bern, SwitzerlandQuaternary Sciences, Department of Geology, Lund University,
Sölvegatan 12, 22362 Lund, SwedenResearch Laboratory for Archaeology and the History of Art, University
of Oxford, Dyson Perrins Building, South Parks Road, Oxford OX1 3QY, UKInsitute of Global Environmental Change, Xi'an Jiatong University,
Xi'an 710049, ChinaDepartment of Earth Sciences, University of Minnesota, Minneapolis,
Minnesota 55455, USAPalaeontology, Geobiology and Earth Archives Research Centre and ARC
Centre of Excellence in Australian Biodiversity and Heritage, School of
Biological, Earth and Environmental Sciences, University of New South Wales,
Sydney, NSW 2052, AustraliaAustralian Centre for Ancient DNA and ARC Centre of Excellence in
Australian Biodiversity and Heritage, School of Biological Sciences, The
University of Adelaide, Adelaide, SA 5005, AustraliaCentre for Ice and Climate, Niels Bohr Institute, University of
Copenhagen, Juliane Maries Vej 30, 2100 Copenhagen, DenmarkFlorian Adolphi (adolphi@climate.unibe.ch)20November201814111755178110July201813July20187October201831October2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://cp.copernicus.org/articles/14/1755/2018/cp-14-1755-2018.htmlThe full text article is available as a PDF file from https://cp.copernicus.org/articles/14/1755/2018/cp-14-1755-2018.pdf
During the last glacial period Northern Hemisphere climate was characterized
by extreme and abrupt climate changes, so-called Dansgaard–Oeschger (DO)
events. Most clearly observed as temperature changes in Greenland ice-core
records, their climatic imprint was geographically widespread. However, the
temporal relation between DO events in Greenland and other regions is
uncertain due to the chronological uncertainties of each archive, limiting
our ability to test hypotheses of synchronous change. In contrast, the
assumption of direct synchrony of climate changes forms the basis of many
timescales. Here, we use cosmogenic radionuclides (10Be,
36Cl, 14C) to link Greenland ice-core records to
U/Th-dated speleothems, quantify offsets between the two timescales, and
improve their absolute dating back to 45 000 years ago. This approach allows
us to test the assumption that DO events occurred synchronously between
Greenland ice-core and tropical speleothem records with unprecedented
precision. We find that the onset of DO events occurs within synchronization
uncertainties in all investigated records. Importantly, we demonstrate that
local discrepancies remain in the temporal development of rapid climate
change for specific events and speleothems. These may either be related to
the location of proxy records relative to the shifting atmospheric fronts or
to underestimated U/Th dating uncertainties. Our study thus highlights
the potential for misleading interpretations of the Earth system when
applying the common practice of climate wiggle matching.
Introduction
Precise and accurate chronologies are critical for understanding past
environmental and climatic changes. Global natural and anthropogenic archives
can only be directly compared through the development of robust chronological
frameworks, enabling studies of the spatiotemporal dynamics of past change.
These findings are crucial for understanding the nature and cause of rapid
climate changes in the past and hence characterizing the dynamics and
feedbacks of past and projected future climate change (Thomas, 2016).
However, the applicability, precision, and accuracy of the available dating
methods pose strong constraints on our ability to infer leads and lags
between climate records and, ultimately, mechanisms of change in the Earth
system. Instead, the situation is often reversed: climate changes such as
Dansgaard–Oeschger, or DO, events (Dansgaard et al., 1969, 1993) are
typically assumed to occur synchronously across the Northern
Hemisphere in different climate proxies from various regions and then used as
chronological tie points. This so-called “climate wiggle matching” forms
the chronological basis of a large part of paleoclimate records (e.g., Bard
et al., 2013; Hughen et al., 2006; Henry et al., 2016; Turney et al., 2015),
especially in the marine realm where other dating methods suffer from low
precision and poorly constrained biases such as the marine radiocarbon
reservoir age (Lougheed et al., 2013). Furthermore, it also plays a central
role for one of the most widely used dating methods in paleosciences – the
radiocarbon dating method. The current radiocarbon dating calibration curve
(IntCal13, Reimer et al., 2013) is constructed from accurately dated
tree-ring chronologies back to 13.9 ka BP (ka BP is kilo-years before
present, which is 1950 CE). Beyond this time, which encompasses all DO events, about
one-fourth of the data underlying IntCal13 obtain their absolute age from
climate wiggle matching.
Climate wiggle matching has the obvious drawback that the leads and lags
between different climate records cannot be studied once the records have
been forced to align. The approach critically rests on the assumptions that
(i) the climate change indeed occurred synchronously everywhere and that
(ii) the (sometimes fundamentally different) proxies in question record the
changes in a similar way and without intrinsic delays. These assumptions,
however, can very rarely be rigorously tested, but when they are, ample
evidence is revealed that questions their universal validity. Lane et
al. (2013) showed that rapid climate change in the North Atlantic region may
be time transgressive with regional leads and lags of the order of a century.
Nakagawa et al. (2003) argued that the onset of Greenland Interstadial 1e
(GI-1e; Rasmussen et al., 2014a) occurred multiple centuries after the
associated climate shift in Japan, and subsequent revisions of the underlying
timescales (Staff et al., 2013; Bronk Ramsey et al., 2012; Seierstad et al.,
2014) did not resolve this conundrum. Buizert et al. (2015) inferred that
the Southern Ocean response to DO events is delayed by about 200 years on
average, while the atmosphere around Antarctica reacted instantaneously
(Markle et al., 2016). Baumgartner et al. (2014) found asynchronies
between ice-core proxies for local Greenland temperature
(δ15N) and the tropical and midlatitude hydrological cycle
(CH4) during some DO events. They discussed the possibility that the climate
changes in polar and low-latitude regions may indeed be synchronous, but that
atmospheric CH4 concentrations rise with a delay during some
DO events because of compensating changes in the source strengths of the
Northern and Southern Hemisphere wetlands. Alternatively, their findings can
be explained via a real delay between Greenland climate change and the
activation of CH4 source areas during certain DO events. Fleitmann
et al. (2009) reported timing differences of DO events in Greenland ice
cores and speleothems, albeit largely within dating uncertainties. However,
they also found significant differences between speleothem records outside
their chronological uncertainties. This is complemented by a recent study
showing that the duration of a stadial–interstadial transition can differ by
up to 300 years between different East Asian speleothems (Li et al., 2017),
emphasizing the question of whether we should expect the onset, midpoint,
or end point of DO events to occur simultaneously, as this choice will lead
to different results when aligning the records.
In this paper, we attempt to provide improved constraints on the paradigm of
climate synchroneity. We employ cosmogenic radionuclides as a
climate-independent synchronization tool between the Greenland ice-core
timescale (Andersen et al., 2006; Rasmussen et al., 2006; Seierstad et al.,
2014; Svensson et al., 2006, 2008; Vinther et al., 2006) and the U/Th
timescale (Broecker, 1963; Edwards et al., 1987; Cheng et al., 2013a) and
strongly reduce the absolute dating error of the Greenland ice cores back to
45 000 years BP. This allows us to compare the timing of DO-type
variability seen in key paleoclimate records with unprecedented precision: the
Greenland ice cores and U/Th-dated (sub)tropical speleothems.
Cosmogenic radionuclides as synchronization tools
Cosmogenic radionuclides (such as 14C, 10Be, and
36Cl) are produced in a nuclear cascade that is triggered when
galactic cosmic rays (GCRs) collide with the Earth atmosphere's constituents
(Lal and Peters, 1967). While the GCR flux outside the heliosphere can be
assumed to be constant over the past million years (Vogt et al., 1990), the
flux arriving at Earth is modulated by the strength of the helio-magnetic and
geomagnetic fields (Masarik and Beer, 1999). This causes the production rates
of cosmogenic radionuclides to be inversely related to changes in solar
activity and/or the strength of the geomagnetic field. This modulation effect
leaves a globally synchronous, externally forced signal in cosmogenic
radionuclide records around the world. Hence, they can serve as a powerful
synchronization tool for climate archives from different regions. The
challenge lies in estimating potential non-production-related impacts on
radionuclide concentrations in a given archive that may result from
geochemical and meteorological processes.
After production, 14C is oxidized to 14CO2 and enters
the carbon cycle. Changing 14C production rates thus alter the
atmospheric 14C/12C ratio (expressed as per mille
Δ14C, which is 14C/12C corrected for
fractionation and decay relative to a standard, denoted Δ in Stuiver
and Polach, 1977). Due to carbon cycle effects,
these variations in Δ14C are dampened and delayed with
respect to the causal production rate changes (Siegenthaler et al., 1980;
Roth and Joos, 2013). In addition to variable production rates, changes in
the exchange rates between the different carbon pools can alter
Δ14C. The world's oceans in particular have a significantly
lower Δ14C than the contemporary atmosphere due to their
long carbon residence time (Craig, 1957). Thus, variations in the
14C exchange rates between the ocean and the atmosphere will alter
atmospheric Δ14C independent of production rate changes.
10Be attaches to aerosols and is transported from the stratosphere
to the troposphere within 1–2 years (Raisbeck et al., 1981), mainly via
midlatitude tropopause breaks (Heikkilä et al., 2011). It has no active
geochemical cycle, so its atmospheric concentration is a more direct
recorder of production rate changes compared with Δ14C.
However, 10Be transport and deposition in the troposphere are guided
by local meteorology and thus susceptible to changes thereof (Heikkilä
and Smith, 2013; Pedro et al., 2011). This can cause variations in
10Be records that are not related to production rate changes.
Furthermore, a so-called “polar bias” (i.e., an overrepresentation of polar
as opposed to global production rate changes) has been proposed for ice-core
records (Bard et al., 1997). This would lead to subdued geomagnetic and
enhanced solar modulation of ice-core radionuclide records due to the
geometry of the geomagnetic field. However, there is no consensus in
different empirical studies and modeling experiments on whether this effect
is present and the results may also vary regionally (Bard et al., 1997;
Heikkilä et al., 2009a; Pedro et al., 2012; Adolphi and Muscheler, 2016;
Muscheler and Heikkilä, 2011; Field et al., 2006).
The transport and deposition of 36Cl in its aerosol phase are
comparable to 10Be. However, in addition to an aerosol phase,
36Cl also has a gaseous phase (H36Cl), which is likely
dominant in the stratosphere (Zerle et al., 1997). In the troposphere, the
partitioning between the aerosol and gas phase is not well understood. It may
vary in space and time (Lukasczyk, 1994) and can change rapidly depending on
pH (Watson et al., 1990). The gaseous H36Cl phase can also be lost
from acidic ice in low accumulation sites after deposition, which is
less relevant for the high accumulation sites studied here (Delmas et al.,
2004). In Greenland, similar to 10Be, the dominant deposition
process of 36Cl is wet deposition (Heikkilä et al., 2009b),
which is supported by the overall similarity of 36Cl and
10Be variations recorded in ice cores (Wagner et al., 2001b;
Muscheler et al., 2005).
As a result, all three radionuclides depend on the same production mechanism,
which causes their production rates to covary globally. This signal can be
exploited for global synchronization of paleorecords from natural archives.
However, to identify these common changes, their different geochemistry needs
to be accounted for. In the case of radiocarbon this is achieved through
carbon cycle modeling to deconvolve the effects of the carbon cycle on the
relation between 14C production rates and Δ14C
(Muscheler et al., 2004). For 10Be and 36Cl, fluxes can
be calculated from ice accumulation rates. This provides a first-order
correction for changing paleoprecipitation rates on the ice sheet and their
influence on the radionuclide concentrations. In reality, aerosol transport
to the ice sheet is more complex and depends on changes in transport
velocity, pathways, and scavenging effects en route (Schüpbach et al.,
2018), which are difficult to constrain for 10Be due to
its stratospheric origin. Instead, comparisons of fluxes and concentrations
to other climate proxies can inform us about potential climate influences on
10Be and36Cl transport and deposition (Adolphi and
Muscheler, 2016). It is currently not possible to quantitatively correct
either of the radionuclides for these non-production influences since neither
past carbon cycle changes nor atmospheric circulation changes are
sufficiently well known. However, the potential amplitude of non-production
rate changes can be assessed through sensitivity experiments and added as an
uncertainty for the production rate signal (Adolphi and Muscheler, 2016;
Köhler et al., 2006).
The potential of this synchronization tool has been demonstrated multiple
times to infer differences between the tree-ring and ice-core timescales
(Adolphi and Muscheler, 2016; Muscheler et al., 2014a; Southon, 2002), test
the accuracy of the radiocarbon calibration curve (Adolphi et al., 2017;
Muscheler et al., 2008, 2014b), and synchronize ice cores from both
hemispheres (Raisbeck et al., 2007, 2017).
Methods and dataIce-core data
The ice-core 10Be and 36Cl data used in this study are
shown in Fig. 1. We focus on records that have been robustly linked to the
GICC05 timescale (Andersen et al., 2006; Rasmussen et al., 2006, 2008;
Seierstad et al., 2014; Svensson et al., 2008). Hence, the majority of the
data stems from the deep Greenland ice cores GRIP, GISP2, and NGRIP. In
addition, we use Antarctic 10Be fluxes from EDC, EDML, and Vostok
that have been anchored to GICC05 by matching the solar variability present in
all 10Be records and volcanic tie points (Raisbeck et al., 2017).
Data used in this study. (a–g) Individual ice-core records
of GRIP 10Be (Baumgartner et al., 1997b; Muscheler et al., 2004;
Wagner et al., 2001a; Yiou et al., 1997; Adolphi et al., 2014), GRIP
36Cl (Baumgartner et al., 1997a, 1998; Wagner et al., 2001b; Wagner
et al., 2000), GISP2 10Be (Finkel and Nishiizumi, 1997), and
10Be from EDC, EDML, Vostok, and NGRIP (all Raisbeck et al., 2017).
Each record represents deposition fluxes (green) and “climate-corrected”
fluxes (purple; see text). In addition, each panel contains the stack of all
ice-core records (black; see text). (h)10Be production
rates modeled from two geomagnetic field intensity reconstructions: GLOPIS
(green; Laj et al., 2004) and based on Black Sea sediments (purple; Nowaczyk
et al., 2013) using the production rate model by Herbst et al. (2016). The
ice-core radionuclide stack is shown in black. All records in (a–h)
are shown on the GICC05 timescale (Seierstad et al., 2014) and normalized to
(i.e., divided by) their mean. (i) Absolutely dated 14C
data from Lake Suigetsu (yellow; Bronk Ramsey et al., 2012), Hulu Cave (blue;
Southon et al., 2012), Bahamas speleothems (purple; Hoffmann et al., 2010),
and various tropical coral datasets (Bard et al., 1998; Cutler et al., 2004;
Durand et al., 2013; Fairbanks et al., 2005; shown in light blue, olive, red,
and green, respectively). The black lines encompass the ±1σ
uncertainties of IntCal13 (Reimer et al., 2013).
By calculating fluxes we make a first-order correction for the changing snow
accumulation rates between stadials and interstadials and their influence on
radionuclide concentrations (Wagner et al., 2001b; Johnsen et al., 1995;
Rasmussen et al., 2013; Finkel and Nishiizumi, 1997). The accumulation rates
for each ice core are based on their annual layer thickness – derived from
their individual timescales – corrected for ice thinning. For the Greenland
ice cores this thinning function is based on a 1-D ice flow model (Dansgaard
and Johnsen, 1969; Johnsen et al., 2001, 1995; Seierstad et al., 2014). For
the Antarctic ice cores we use the strain rate derived from the Bayesian
ice-core dating effort AICC12 (Veres et al., 2013). These strain rates are
inherently uncertain, and independently derived accumulation rate estimates
differ by up to 10 %–20 % in the glacial (Gkinis et al., 2014;
Rasmussen et al., 2013; Guillevic et al., 2013). However, these differences
are largely systematic and change only on multi-millennial timescales. The
shorter-term changes in accumulation rates are a more direct function of the
timescale that determines the age–depth relationship, and thus annual layer
thickness, and is very precise for increments of the core (Rasmussen et al.,
2006). This is important to note, as we mainly exploit production rate
changes on centennial-to-millennial timescales for synchronization.
To test for additional climate influences on 10Be or 36Cl
deposition in the ice cores, we followed the approach by Adolphi and
Muscheler (2016). For each ice core we calculated multiple linear regression
models using δ18O and snow accumulation rates as predictors
for 10Be (36Cl) fluxes and subtracted the obtained model
from the 10Be (36Cl) data. We denote the resulting record
as the “climate-corrected flux” (Fluxc). This approach may
correct climate effects on 10Be (36Cl) deposition
insufficiently, or it may overcorrect them, so it cannot be assumed per se
that the resulting record is more reliable than the original fluxes.
Nevertheless, it provides a first-order sensitivity test for the ice-core
records with respect to climate-related transport and depositional effects on
10Be (36Cl) fluxes.
To combine all ice-core records, we calculated their mean (denoted as
“stack”, Fig. 1) using Monte Carlo bootstrapping (Efron, 1979). Using seven
ice-core records in two versions (flux and fluxc) yields a total
number of 14 samples. In each iteration, 14 samples are randomly drawn (with
replacement; i.e., each record can be drawn multiple times), perturbed within
measurement errors, and stacked. By repeating this procedure 1000 times we
obtain an average relative standard deviation of 8 % between the derived
stacks, which is comparable to the measurement uncertainty of individual
measurements but larger than the expected error of the mean; this points to
systematic differences between the records. For the period over which we have data
from both hemispheres this standard deviation is only slightly higher
(10 %). Even though this is only a relatively short period (see Fig. 1),
it contains multiple DO events that are expressed differently in Northern
and Southern Hemisphere climate. Thus, this agreement can serve as an indication
that climate effects do not dominate the signal.
Radiocarbon data
For the purpose of this study we have to focus on radiocarbon records that
are absolutely dated. Furthermore, the length and sampling resolution of the
records need to be sufficient to resolve centennial-to-millennial production
rate changes. The records that fulfill these criteria are shown in Fig. 1 and
comprise 14C data from various U/Th-dated coral records
(Bard et al., 1998; Durand et al., 2013; Cutler et al., 2004; Fairbanks et
al., 2005), as well as 14C measured in two speleothems (Southon et
al., 2012; Hoffmann et al., 2010). In addition, we use the 14C
record from Lake Suigetsu (Bronk Ramsey et al., 2012) since the U/Th-dated
records do not directly reflect atmospheric 14C, only the ocean
mixed layer (corals), and in the case of speleothems a mixture of
atmospheric and soil CO2 and carbonate bedrock from above the
cave. The timescale of the Lake Suigetsu record is based on varve counting,
corrected for long-term systematic errors by matching its 14C
record to the 14C variations in speleothems (Bronk Ramsey et al.,
2012). Hence, it is not truly independently dated. However, similar to
ice-core layer counting, this varve count adds constraints, especially on
centennial timescales, so Δ14C variations on these
timescales should be relatively unaffected by this tuning to the speleothem
14C data. Thus, even though the timescale may not be independent,
this record can still be used to verify the existence of
Δ14C variations in the atmosphere seen in the mixed layer
records.
In addition, we use the available tree-ring records back to 14 000 cal BP
(calibrated before present, 1950 CE) in the revised version by Hogg et
al. (2016) (not shown in Fig. 1 for clarity).
Carbon cycle modeling
To be able to compare ice-core and radiocarbon records directly we have to
account for the effects of the carbon cycle. Following earlier studies
(Muscheler et al., 2004, 2008), we use a box-diffusion carbon cycle model
(Siegenthaler et al., 1980) to model Δ14C from the ice-core
radionuclide records. We assume that ice-core 10Be (36Cl)
variations are proportional to 14C production rate changes (see
also the following section) and model Δ14C anomalies from each
realization of the ice-core stack, as well as the single ice-core records
(Fig. 2). It can be seen that the modeled Δ14C records from
the individual ice-core records differ in their long-term trends since the
carbon cycle integrates over time so that relatively small but systematic
differences in the radionuclide fluxes (possibly arising from uncertainties
in the strain rates) have a significant effect on longer timescales.
However, all records show the same overall evolution of
Δ14C. Furthermore, especially when subtracting the long-term
trend and isolating variations on timescales shorter than 5000 years, the
agreement is very high (on average within 15 ‰ at 1σ;
Fig. 2b), which is the part of the signal that we will be exploiting in our
synchronization effort.
Production rate ratio
Modeling Δ14C values from 10Be measurements is
based on the assumption that 10Be and 14C production rate
changes are proportional to each other. However, different production rate
models differ in their sensitivity of 14C and 10Be
production rate changes to variations in the geomagnetic field (Cauquoin et
al., 2014). For a given geomagnetic field change, the production rate model
by Masarik and Beer (1999, 2009) yields 30 %–50 % lower
10Be production rate changes than the calculations by Poluianov et
al. (2016) and Herbst et al. (2016). For 14C, on the other hand, all
models yield roughly similar amplitudes. This leads to differences in the
14C/10Be production rate ratio for a given change in the
geomagnetic field. If Masarik and Beer (1999) are correct, the variations in
ice-core 10Be records have to be upscaled by 30 %–50 % to
be proportional to 14C production rate changes, while no such
scaling is necessary when the other production rate models are used. In
addition, the amplitudes in 14C and 10Be may differ due
to the presence of polar bias (see Sect. 2). If this effect was present, then
geomagnetic field changes should cause bigger variations in 14C
than 10Be.
Since the presence of a polar bias is debated and the physical reason for
the differences between the production rate models is unresolved, we chose
an empirical approach to scale the ice-core record appropriately.
Modeled Δ14C anomalies from individual ice-core
records (see legend; solid lines are based on radionuclide fluxes, while
dashed lines are inferred from fluxc) and the realizations of the
ice-core stack (black line shows the mean of all realizations, dark and light
grey shading encompass 68.2 % and 95.4 % probability ranges).
(a) The unfiltered model output. (b) The records after
variations with frequencies < 1 / 5000 a-1 have been
subtracted (FFT-based filter).
We use three geomagnetic field intensity reconstructions around the Laschamp
geomagnetic field minimum (Laj et al., 2000, 2004; Nowaczyk et al., 2013) and
calculate the resulting 10Be production rate changes using the
production rate models by Masarik and Beer (1999) and Herbst et al. (2016)
(Fig. 3a–c). Subsequently, we scale the ice-core 10Be record to
minimize the root mean square error (RMSE) between ice-core and geomagnetic-field-based records (Fig. 3d). It can be seen that the RMSE reaches a minimum
for a 10Be scaling factor of ∼1 (for Masarik and Beer, 1999)
and ∼1.3 (for Herbst et al., 2016). This represents a fortunate
coincidence; irrespective of which production rate model is used, the
amplitude of the ice-core 10Be variations has to be increased by
approximately 30 % to match 14C. If the production rate model
by Masarik and Beer is used, then the amplitude of the ice-core
10Be record is in agreement with geomagnetic field data, but due to
the higher production sensitivity of 14C (see above),
10Be variations have to be increased by ∼30 %. Similarly,
if the production rate model by Herbst et al. is used, then the amplitude of
the ice-core 10Be record is 30 % smaller than implied by
geomagnetic field data (possibly due to a polar bias), while the sensitivity
of 14C and 10Be is the same. Again, the net effect is that the
10Be variations have to be scaled up by 30 % for the comparison
to 14C.
Comparison of ice-core-based and geomagnetic-field-based
reconstructions of 10Be production rates. (a–c) The
ice-core stack (black) in comparison to 10Be production rates based
on geomagnetic field reconstructions and two different production rate models
(Herbst et al., 2016, in pink and Masarik and Beer, 1999, in green).
(a) The Black Sea geomagnetic field record (Nowaczyk et al., 2013),
(b) the NAPIS geomagnetic field stack (Laj et al., 2000), and
(c) the GLOPIS geomagnetic field stack (Laj et al., 2004).
(d) The RMSE between the ice-core data and the
geomagnetic-field-based records when variations in the ice-core record are
scaled by different factors (x axis). The colors correspond to the
production rate models. The line styles indicate the geomagnetic field
records (see legend) and the symbols denote the RMSE minima.
The state of the carbon cycle
As mentioned in Sect. 2, a quantification of transient carbon cycle changes
and their influence on Δ14C is challenged by insufficient
knowledge of inventories and processes. The contribution of single processes
to Δ14C changes over the last glacial cycle is likely within
30 ‰ and, due to compensating effects, their combination is
likely not bigger than 40 ‰ (Köhler et al., 2006). Here we use
the Laschamp event to estimate the state of the ocean ventilation around
40 ka BP.
The datasets underlying IntCal13 all show an increase of about 320 ‰
in Δ14C into the Laschamp event (Fig. 4), albeit at
different absolute levels (see Fig. 1). This is ∼100 ‰ more
than the compiled IntCal13 curve itself implies. This disagreement can be
explained by differences in timing and absolute Δ14C between
the different datasets leading to smoothing and dampening of
Δ14C variations during the construction of IntCal13. Also,
geomagnetic field changes yield a Δ14C change more in line
with the individual 14C datasets than with IntCal13, even when
assuming a preindustrial carbon cycle.
To estimate the mean state of the carbon cycle during this period, we run our
carbon cycle model with different (constant) values of ocean diffusivity. We
find that modeled and measured Δ14C around the Laschamp
event match best in amplitude when we run the model under conditions in which
ocean ventilation is reduced to ∼75 % of its preindustrial value
(Fig. 4). This is in broad agreement with previous modeling experiments
(Köhler et al., 2006; Roth and Joos, 2013) and proxy data (Henry et al.,
2016).
In the following, we will use this estimate for the parameterization of our
model. As mentioned above, a transient adjustment of carbon cycle parameters
is uncertain and will hence not be attempted. Instead, we ascribe an
associated uncertainty to the modeled Δ14C based on the
carbon cycle sensitivity experiments by Köhler et al. (2006).
Furthermore, it should be noted that by only using (filtered)
Δ14C anomalies as synchronization targets, we (i) avoid
systematic carbon cycle influences on Δ14C levels and
(ii) minimize transient carbon-cycle-related changes in Δ14C
(Adolphi and Muscheler, 2016).
The Laschamp event in measured and modeled Δ14C.
(a–f)Δ14C anomalies in
macrofossils from Lake Suigetsu (yellow; Bronk Ramsey et al., 2012), tropical
corals (blue; Fairbanks et al., 2005), foraminifera from Cariaco Basin
sediments (red; Hughen et al., 2006), foraminifera from Iberian Margin
sediments (light blue; Bard et al., 2013), Bahamas speleothems (green;
Hoffmann et al., 2010), and IntCal13 (black; Reimer et al., 2013). All data
are shown as anomalies to their error-weighted mean prior to the Laschamp
event, i.e., the Δ14C increase. The dashed boxes encompass
the time periods and Δ14C uncertainties (error of the error-weighted mean)
used for the definition of the pre- and post-Laschamp event
levels. (g, h) Modeled Δ14C using
the GLOPIS (Laj et al., 2004) geomagnetic field record and the
ice-core stack as production rate inputs. The different colored lines
reflect different carbon cycle scenarios (see legend; PI denotes
preindustrial). The conversion of geomagnetic field intensity to
14C production rate is based on the production rate model by Herbst
et al. (2016). Note that the amplitude of the 10Be variations has
been increased by 30 % as discussed in Sect. 3.3.1.
Synchronization – effects of the carbon cycle and the archive
The synchronization method follows Adolphi and Muscheler (2016) and is
outlined and tested in detail therein. In brief, sections of modeled
(ice-core-based) Δ14C anomalies are compared to the measured
Δ14C. For our analysis we employ high-frequency changes in
Δ14C since carbon cycle changes have only limited effects on
atmospheric Δ14C on shorter timescales (Adolphi and
Muscheler, 2016). Similarly, as shown in Fig. 2, the agreement of the
different ice-core records is better on shorter timescales. In this study, we
employ two types of high-pass filtering: an FFT-based high-pass filter and
simple linear detrending. The choice of filter is based on the data sampling
resolution. For the highly resolved tree-ring data we use a 1000-year
high-pass FFT filter, while the lower-resolved and more unevenly sampled
coral, speleothem, and/or macrofossil data are filtered by linear detrending to avoid the
interpolation to equidistant resolution required for FFT analysis. Similarly,
the high sampling resolution of the tree-ring data allows us to compare the
data in 2000-year windows, while we increase the window length to 4000 and
5000 years for the lower-resolved data prior to 14 ka BP. The exact
frequencies and window lengths are also given in the Results section. Using
the same statistics as for radiocarbon wiggle-match dating (Bronk Ramsey et
al., 2001), we then infer a probability density function (PDF) for the
timescale difference between the modeled and measured Δ14C
records. For details on the statistics of this methodology we refer the
reader to Adolphi and Muscheler (2016). Here we focus instead on additional
uncertainties that arise when comparing modeled atmospheric
Δ14C to 14C records from the ocean mixed layer
(corals) or speleothems.
Δ14C variations in the atmosphere are dampened and delayed
compared to the causal production rate changes. Both factors, attenuation and
delay, depend on the frequency of the production rate change (Roth and Joos,
2013; Siegenthaler et al., 1980). The dampening is largest at high
frequencies and decreases with longer periods. On the other hand, the
apparent peak-to-peak delay between sinusoidal production rate changes and
the resulting Δ14C change increases with increasing
wavelengths. Similar effects occur when comparing atmospheric and oceanic
Δ14C changes to each other: the ocean reacts to atmospheric
Δ14C changes with a delayed and dampened response that is
wavelength dependent. Hence, we need to take these factors into account when
comparing a modeled atmospheric Δ14C record to mixed layer
marine records. However, the frequency dependence of the attenuation and
delay makes it difficult to explicitly correct for this since atmospheric
Δ14C changes vary on different timescales simultaneously.
Furthermore, the coral records vary in their sampling frequency and often it
is not precisely known over how much time an individual 14C sample
integrates.
The delay between Δ14C in the atmosphere and ocean
mixed layer. (a) Modeled Δ14C from the ice-core
stack around the Laschamp event. The modeled atmospheric
Δ14C is shown in black, while the ocean mixed layer is shown
in grey. (b) The inferred delay from our synchronization method when
comparing the atmospheric to the mixed layer signal for different low-pass
filters of the mixed layer signal (x axis).
Figure 5 shows a sensitivity test regarding these effects. We modeled
Δ14C from the ice-core stack around the Laschamp event and
compared the atmospheric Δ14C to the mixed layer
Δ14C in the model. To simulate the effect of varying
averaging effects of the coral samples, we low-pass filtered the mixed layer
signal with increasing cutoff wavelengths. For each filter, we then inferred
the apparent delay between the mixed layer (i.e., the “coral”) and the
atmosphere. In doing so we infer that even though the signal is dominated by
a long-lasting Δ14C increase, the inferred delay is small
(∼30 years) as long as the coral samples do not integrate over long
times. Only when assuming that each coral sample averages over more than
1000 years do we infer delays of about 120 years. Nevertheless, this experiment
also shows that within reasonable bounds of averaging, the delay of the mixed
layer to atmospheric signal is limited.
The speleothem Δ14C reacts differently than the ocean mixed
layer. The so-called dead carbon fraction (DCF) of a speleothem consists of
two main contributors: (i) respired soil organic matter that is older (in
14C years) than the atmospheric 14C signal and
(ii) carbonate bedrock that contains no 14C. Applying the model of
Genty and Massault (1999), we model speleothem Δ14C using
different assumptions on the age of the respired soil organic matter and
fraction of carbonate bedrock in drip-water CO2. We do this for two
examples: (i) a speleothem with an apparent DCF (i.e., offset from the
atmosphere) of 5.8 % (resembling the Hulu Cave speleothem record by
Southon et al., 2012) and (ii) a speleothem with an apparent DCF of
25.7 % (resembling the Bahamas speleothem by Hoffmann et al., 2010). By
assuming different ages of the soil respired carbon (τ=10–400 years;
see Fig. 6), we adjust the fraction of 14C-free CO2 so
that the apparent DCF for each speleothem is matched. The age of the soil
respired carbon is defined following Genty and Massault (1999): if, for
example, τ=100 years, then the activity of the soil respired CO2
is the mean of the atmospheric activity over the past 100 years prior to
sampling (also accounting for decay within these 100 years). For simplicity
we assume a uniform age distribution for the soil respired carbon.
Subsequently, we compare the modeled speleothem Δ14C to the
original atmospheric input using our synchronization method and plot the
inferred delay (Fig. 6b). From this experiment it can be seen
that the controlling factor on the inferred delay is the age of the soil
respired matter that acts as an integrator (low-pass filter) of the
atmospheric 14C signal. The fraction of 14C-free
carbonate has no influence on the lag between Δ14C changes
in the atmosphere and the speleothem, but only dampens the amplitude of the
corresponding change. Realistic ages of soil respired carbon differ from
region to region, but even though some slow-cycling fractions of soil organic
matter may be up to several thousand years old (Trumbore, 2000), the major
contributors to soil CO2 are considerably younger and of the order
of decades (Genty et al., 2001; Fohlmeister et al., 2011).
From these experiments we conclude that our systematic matching uncertainties
to coral and speleothem records are probably below 100 years. We note that
this uncertainty is asymmetric since the ocean–speleothem signal cannot lead
the atmosphere, so the offset is unidirectional.
Effect of varying ages of soil respired CO2 and fractions
of CO2 from 14C dead carbonate on the
Δ14C in speleothems. (a) Atmospheric
modeled Δ14C from the 10Be stack (black) and two
modeled speleothem scenarios with a net DCF of 5.8 % (warm colors) and
25.7 % (cold colors). For each speleothem, a number of different ages
for the respired soil organic matter have been assumed (see legend) and the
input of 14C-free CO2 from carbonate has been adjusted to
obtain the correct apparent DCF value between 39 and 40.5 ka BP. (b) The
inferred delay when we apply our synchronization method
to match the atmospheric Δ14C to the speleothem record.
Change-point detection window for each record. For each investigated
climate event and record, the change-point detection algorithm has been
applied between t1 and t2. The windows have been defined visually, ensuring a
sufficient amount of data prior to and after the transition. For each record,
only events that are well expressed in the climate proxy records at high
resolution have been investigated. For the ice-core record, t1 and t2
typically encompass 500 years prior to and after the nominal transition ages
by Rasmussen et al. (2014a). The exact values have been adjusted to exclude
overlap with other transitions where necessary (Erhardt et al., 2018).
Synchronization of GICC05 to tree-ring and Hulu Cave records during
the last deglaciation. (a) Ice-core-based modeled
Δ14C anomalies on the original GICC05 timescale (thin black
line, light grey shading encompasses the ±10 ‰ uncertainty
(±1σ) of the modeled Δ14C based on the
carbon cycle sensitivity experiments by Adolphi and Muscheler, 2016) and
synchronized timescale (bold grey line). Tree-ring data underlying IntCal13
are shown in pink. Revised Northern Hemisphere tree-ring data according to
Hogg et al. (2016) are shown in orange (preboreal pines), dark blue
(late glacial pine), and light blue (Younger Dryas B chronology). New kauri
Δ14C data by Hogg et al. (2016) are shown in purple
(FIN11) and green (Towai). Hulu Cave H82 Δ14C data are shown
as white squares. All symbols are shown with ±1σ error bars.
All data are FFT filtered to isolate Δ14C variations on
timescales < 1000 years. (b–d) Inferred
probability distributions of timescale differences between GICC05 and
tree rings (orange) and Hulu Cave (black). The symbols and error bars denote
means and 68.2 % and 95.4 % confidence intervals of the inferred
timescale difference. Each of the lower panels refers to a 2000-year
subsection of the data indicated at the top of each panel.
Change-point detection in climate records
To test the synchroneity of rapid climate changes, we compare the timing of
DO events seen in Greenland ice cores (Andersen et al., 2004) to a number of
well-known U/Th-dated speleothems that show DO-type variability from
Hulu Cave (Cheng et al., 2016), Sofular Cave (Fleitmann et al., 2009), El
Condor, and Cueva del Diamante (both Cheng et al., 2013b).
We use a probabilistic model to detect the onset, midpoint, and end of the
rapid climate transitions in each individual record. The employed model
describes the abrupt changes as a linear transition between two constant
states. Any variability due to the long-term fluctuations of the climate
records around the transition model is described by an AR(1) process that is
estimated in conjunction with the transition model. The model is
independently fitted to windows of data on their individual timescales
(Table 1 and Fig. 13) around the rapid transitions. Inference was performed
using Markov chain Monte Carl sampling (MCMC) to obtain PDFs of the timing of
the onset, the length, and the amplitude of each transition in each record.
Using these PDFs we can calculate delays of the onset, midpoint, and end of
the climate transitions between different records, propagating the respective
uncertainties of the parameters. For each record, only events that are well
expressed and measured in high resolution have been fitted. The approach and
inference procedure are described in more detail in Erhardt et al. (2018).
Synchronization results between 18 000 and 25 000 years BP.
(a) The thin black line shows the modeled Δ14C
curve based on the ice-core stack on its original timescale. The bold black
line and grey shading show the synchronized ice-core record including assumed
±1σ uncertainties of ±30 ‰. The different colored
symbols indicate various 14C datasets underlying IntCal13, which is
shown as the green envelope. Lower panels: each panel shows PDFs of the
inferred timescale difference between the ice-core stack and IntCal13
(green), a combination of all U/Th-dated records (speleothems–corals,
pink), the H82 speleothem (blue), and Lake Suigetsu (yellow). Symbols of
similar color show the inferred mean and 68.2 % and 95.4 % confidence
intervals. Color-coded text indicates χ2 probabilities for the
goodness of fit between modeled and measured Δ14C curves
after synchronization. Small (e.g., < 0.1) values would indicate
significant disagreement. Note that all χ2 probabilities are
relatively high, indicating that our uncertainty estimate for the modeled
Δ14C is very conservative. Each of the lower panels refers
to a specific subsection of the data indicated at the top of each panel.
Timescale differences between GICC05 and the U/Th timescale
In the following sections we will show the synchronization results for
different time windows. We focus our analysis on three distinct windows:
10–14, 18–25, and 39–45 ka BP. The youngest window is
defined by the presence of high-resolution tree-ring data for 14C
back to 14 ka BP. Going further back in time it becomes increasingly
challenging to unequivocally identify common structures in the various
Δ14C records that are suitable for synchronization because
the resolution of the individual records decreases back in time, while their
differences to each other grow steadily (see Fig. 1i). Hence, we focus
on the well-known Laschamp event around 41 ka BP and the period between
18 and 25 ka BP, i.e., preceding the major carbon cycle changes associated with the
deglaciation. We omit the period between 25 and 39 ka BP. As discussed in
Reimer et al. (2013) and seen in Fig. 1i there is substantial
disagreement between the datasets underlying IntCal13 at that time that are
impossible to reconcile within their respective age and/or 14C
uncertainties. Hence, any structure in the Δ14C records
may also be unreliable and thus lead to erroneous synchronization results.
10 000–14 000 years BP
In the 10–14 ka BP interval, we synchronize the ice-core stack to
high-resolution tree-ring and speleothem Δ14C data (Fig. 7).
The high sampling resolution of the 14C records allows us to focus
on centennial-to-millennial Δ14C changes (< 1000 years)
for which carbon cycle influences on Δ14C can be expected
to be small (Adolphi and Muscheler, 2016). In concordance with
earlier studies (Muscheler et al., 2014a) we find that GICC05 is
∼65 years older than the tree-ring timescale at the onset of the
Holocene, but that this offset vanishes over the course of the Younger Dryas
interval.
While Muscheler et al. (2014a) argued that this changing offset may
be in part due to errors in the timescale of the floating late glacial pines,
we can now support this change in the timescale difference through the
U/Th-dated speleothems: the synchronization of the ice-core stack to
the H82 speleothem from Hulu Cave (Southon et al., 2012) leads to
fully consistent results as inferred from the tree rings. This indicates that
the most likely explanation is an ice-core layer counting bias, i.e., that the
GICC05 timescale suggests too-old ages at the onset of the Holocene, but is
accurate within a few decades during GI-1.
Interestingly, we do not observe any significant differences between the
results stemming from tree rings and the speleothem records. As shown in
Sect. 3.4, we could expect a delay in the speleothem Δ14C
compared to the atmosphere if the respired soil organic carbon contribution
to the soil CO2 was very old. This would result in GICC05 appearing
older in comparison to the speleothem than relative to the tree rings. The
lack of this delay implies that the majority of the respired soil organic
carbon at Hulu Cave must be younger than ∼100 years (see Fig. 6). This
is supported by the fact that the centennial Δ14C variations
in the tree-ring and speleothem data have the same amplitude (Fig. 7). If
old organic carbon significantly contributed to the soil CO2, we
would instead expect to see a stronger smoothing of short-term
Δ14C variations.
18 000–25 000 years BP
Due to the irregular and lower sampling resolution of the 14C
records beyond 15 000 cal BP, we chose to linearly detrend each dataset
(instead of band-pass filtering) to remove offsets between the different
14C datasets (see Fig. 1i) and highlight common variability.
Furthermore, we have to increase the length of the comparison data windows to
4000 years to ensure sufficient structure in the 14C sequences
entering the comparison. Each window is detrended separately in the analysis
to isolate short-term Δ14C variability. We note, however,
that detrending each 14C dataset over the entire timeframe
(18–25 ka BP) instead does not alter the results significantly. Compared to
the high-frequency Δ14C changes studied between
10 and 14 ka BP, the longer-term variations used for synchronization here may
have been increasingly affected by carbon cycle changes. To account for this,
we increase the uncertainty estimate of the modeled Δ14C
changes to ±30 ‰ (±1σ), which is sufficiently
large to account for estimated carbon-cycle-driven Δ14C
changes from modeling experiments during the entire glacial
(Köhler et al., 2006). We note that this is a conservative
estimate, given that during this period neither modeling (Köhler et
al., 2006; Muscheler et al., 2004) nor data (Eggleston et al.,
2016) suggest large carbon cycle changes.
It can be seen in Fig. 8 that it is challenging to infer robust
covariability in multiple 14C records. However, the millennial
evolution of Δ14C does show common changes in the
18–25 ka BP interval. Synchronizing the ice-core stack to data from (i) Hulu
Cave H82 speleothem, (ii) Lake Suigetsu macrofossils, (iii) the IntCal13
stack,
or (iv) a combination of all U/Th-dated records (speleothems–corals)
leads to consistent results within uncertainties for each choice of time
windows: all records imply that GICC05 shows younger ages compared to the
14C records around this time.
Close-up of measured and modeled Δ14C anomalies
between 21 and 23 ka BP. The thin grey line shows modeled atmospheric
Δ14C from the ice-core stack on the GICC05 timescale. The
bold black and dashed red lines show the modeled atmospheric and ocean mixed
layer Δ14C curves after synchronization to the 14C
records (yellow: Lake Suigetsu; blue: Hulu Cave; purple and white: corals.
The inset panel shows the PDF of the inferred timescale difference between
GICC05 and the combination of all 14C records. The black line is
based on using only the modeled atmospheric Δ14C. The red
dashed line is based on comparing coral and speleothem data to the modeled
mixed layer Δ14C and Lake Suigetsu data to modeled
atmospheric Δ14C. The green line shows modeled
Δ14C based on geomagnetic field changes.
The most significant structure that is present in all measured and modeled
14C records during this time is the centennial
Δ14C increase around 22.1 ka BP (see Fig. 9). Comparing the
ice-core stack to Δ14C between 21 and 23 ka BP indicates an offset
of ∼550 years between GICC05 and the U/Th timescale around this
time (GICC05 being younger). To account for the potential delay of coral and
speleothem Δ14C compared to the atmosphere, we also modeled
the mixed layer Δ14C signal from the ice-core stack and
synchronized this signal to the measured 14C data (Fig. 9). As
discussed in Sect. 3.4, we find very little difference in the inferred
timing since the Δ14C variation is relatively rapid
(centuries). Comparing the Δ14C anomalies to geomagnetic
field data shows that a small part of the longer-term development of this
structure is probably driven by geomagnetic field changes. The amplitude
(∼50 ‰) and short duration (centuries) of the
Δ14C increase, however, suggest that this change is mainly
driven by a series of strong solar minima comparable to the grand solar
minimum period around the onset of the Younger Dryas (Muscheler
et al., 2008). We used this tie point (Fig. 9) in the final synchronization
as it is the best-defined feature in this time interval and consistent
within error with the estimates shown in Fig. 8.
39 000–45 000 years BP
Our oldest tie point is the previously discussed Laschamp event around
41 ka BP. The only independently and absolutely dated 14C record
around this time that has a sufficient sampling resolution is the Bahamas
speleothem by Hoffmann et al. (2010). While offset in absolute
Δ14C (see Fig. 1), the U/Th-dated coral data support
the amplitude and timing of the Δ14C increase seen in the
speleothem even though precise synchronization is hampered by the low
sampling resolution of the corals. The Lake Suigetsu record is characterized
by large uncertainties and scatter around this time. As discussed in Sect. 3.3.2,
IntCal13 is smoothed around Laschamp, having a smaller amplitude and a
less sharp rise in Δ14C. For this tie point, we merely
remove the error-weighted mean between 39 and 45 ka BP from each dataset, since
detrending would remove the largest part of the signal. Hence, there are
large Δ14C modeling uncertainties associated with unknown
carbon cycle changes, and we assume a Gaussian ±1σ error of
50 ‰, which we consider conservative since sensitivity experiments
imply that the impact of carbon cycle changes on Δ14C was
likely below 40 ‰ (Köhler et al., 2006).
Synchronization of 10Be and 14C around the
Laschamp event. The black lines encompass the modeled Δ14C
anomalies (±1σ) from the ice-core data shifted by +252 years
(68.2 % confidence interval =-103 to 477 years) according to their best
fit to the speleothem 14C data. The green patch shows the ±1σ envelope of IntCal13. The blue and purple symbols show
Δ14C from Bahamas speleothem and corals, respectively. The
yellow symbols show Δ14C anomalies based on Lake Suigetsu
macrofossils. All datasets have been centered to 0 ‰ by subtracting
the error-weighted mean of each dataset. The inset shows the PDF of the
inferred age differences between the ice-core data and IntCal13 (green), Lake
Suigetsu (yellow), and the Bahamas speleothem (blue). The dashed blue line
corresponds to age differences from the modeled mixed layer
Δ14C and the Bahamas speleothem.
Synchronizing the ice-core stack to the speleothem, Lake Suigetsu, and
IntCal13 data yields significantly different results. We infer that GICC05
produces ages about 250 years younger than the U/Th-dated speleothem
data (Fig. 10). The IntCal13 record, however, implies a larger difference of
∼1000 years. Using Lake Suigetsu data, on the other hand, leads to
multiple probability peaks, two of which are in agreement with the speleothem
and one with the IntCal13 record. The large scatter of the Lake Suigetsu
data,
however, leads to poor statistics (low χ2 probabilities).
Furthermore, the Lake Suigetsu timescale is only constrained by varve
counting back to 39 ka BP and based on extrapolation for older sections
(Bronk Ramsey et al., 2012); it hence provides less precise constraints
on the timing of the Δ14C increase.
Comparison of the ice-core stack (blue) to Ar–Ar dates of the
Laschamp excursion (yellow: Singer et al., 2009; pink: Laj et al., 2014) and
relative geomagnetic field intensity (black; NRM/ARM; reversed y axis) from
a U/Th-dated speleothem (Lascu et al., 2016). The individual
speleothem U/Th dates are shown on the bottom of the figure with their
±2σ uncertainties. Each panel shows a different shift of GICC05
according to the results from Fig. 10.
To test which of these links is the most likely we turn to independent
radiometric ages of the Laschamp excursion. Pooled Ar–Ar, K–Ar, and
U/Th ages on lava flows place the period of (nearly) reversed field
direction at 40700±950 years BP (Singer et al., 2009)
or 41300±600 years BP (Laj et al., 2014). In addition, a
North American speleothem provides a U/Th-dated transient evolution of
the geomagnetic field (Lascu et al., 2016), with the lowest
intensities occurring at 41100±350 years BP. Comparing the ice-core
10Be stack to these data clearly shows that all of these records
rule out the +1000 year time shift implied by IntCal13, as it would induce
a significant disagreement between radiometrically dated magnetic field
records and the dating of the 10Be peak in the ice cores (Fig. 11).
We hence argue that the 252-year offset inferred from the comparison to the
Bahamas speleothem is the most likely estimate of the timescale difference
between GICC05 and the U/Th timescale around this time. Similar to
before, assuming that the speleothem represents a mixed layer signal instead
of direct atmospheric Δ14C does not significantly affect the
inferred timescale differences (see Fig. 10 inset, blue dashed line).
Transfer function between the U/Th timescale and GICC05. The
transfer function is shown in black with dark and light grey shading
encompassing its 68.2 % and 95.4 % confidence intervals. The black
dots with error bars show the match points used between 14C and
10Be. The red star shows the difference between ages of a glacial
kauri tree 14C sequence on Lake Suigetsu 14C and GRIP
10Be
(Turney et al., 2016). The blue open square
shows the age difference between the 40Ar/39Ar age of the
Campanian Ignimbrite (Giaccio et al., 2017) and a tentatively associated
spike in the GISP2 SO4 record (Fedele et al., 2007) on the GICC05
timescale (Seierstad et al., 2014).
Transfer function
To construct a continuous transfer function between GICC05 and the
U/Th timescale we apply a Monte Carlo approach. Each iteration
consists of (i) randomly sampling the PDFs at each tie point and (ii) interpolating
between the tie points using an AR process that is
constrained by the GICC05 maximum counting error (MCE). We use the tie points
shown in Figs. 7, 9, and 10, i.e., three tie points between ice cores and
tree rings during the deglaciation, one tie point between ice cores and the
combination of corals, speleothems, and Lake Suigetsu during the LGM, and one
tie point between ice cores and the Bahamas speleothem around the Laschamp
event. For the interpolation, we use the time derivative of the MCE (i.e.,
its growth rate) as an incremental error estimate. During periods when the
growth rate is > 0, GICC05 may be stretched (compressed), while a
growth rate of 0 does not allow this, independent of what the absolute MCE is
at that time. By multiplying this growth rate with a random realization of an
AR process (Φ=0.9, σ=1), we simulate how much of that
uncertainty has been realized in each iteration of the Monte Carlo
simulation. Subsequently integrating over the resulting time series of
simulated miscounts, we again obtain an absolute error estimate, i.e., one
possible realization of the MCE. The parameters for the AR process were
chosen so that the simulated realization of the MCE explores the whole
absolute counting error space, without frequently exceeding the permitted
growth rate of the MCE. A larger Φ would increase interpolation uncertainty,
but also frequently violate the constraints of the layer count. A smaller
Φ, on the other hand, would decrease the uncertainty due to shorter
decorrelation length (see also the discussion in
Rasmussen et al., 2006). In each iteration,
this realization is then anchored at the sampled tie points (step i) by
linearly correcting the offset between the sampled tie points and the
simulated counting error. Hence, this procedure provides us with a correlated
interpolation uncertainty over time, taking into account some of the
constraints provided by the ice-core timescale itself, but giving priority to
our inferred tie points. We note that this treatment of the MCE as an
AR process leads to larger interpolation errors compared to assuming a white
noise model, which would lead to very small uncertainties that average out
over a long time (see also discussion in
Rasmussen et al., 2006). Furthermore, we treat the MCE as ±1σ
instead of ±2σ as proposed by Andersen et
al. (2006), which additionally increases our interpolation error. We stress
that this procedure does not aim to provide a realistic model of the ice-core
layer counting process and its uncertainty, which is clearly more complex
(see Andersen et al., 2006; Rasmussen et al., 2006), nor should it be
interpreted such that the MCE was a 1σ uncertainty. However, our
approach allows us to infer a conservative estimate of the interpolation
uncertainty, while at the same time it takes advantage of the fact that GICC05
is a layer-counted timescale and hence cannot be stretched or compressed
outside realistic bounds. This procedure was repeated 300 000 times, which was
found sufficient to obtain a stationary solution leading to 300 000 possible
timescale transfer functions.
Figure 12 shows the resulting mean transfer function along with its
confidence intervals. First, it can be seen that all tie points fall into
the uncertainty envelope of GICC05. The implied change in the timescale
difference between the youngest two tie points (i.e., over the course of
GS-1) and between 13 000 and 22 000 years BP is slightly larger than
allowed by the MCE, although the latter is consistent within the uncertainties
of the tie point at 22 000 years BP. We can see that the use of the MCE to
determine the interpolation error leads to small uncertainties wherever the
change in the timescale difference is large (e.g., over the 13 000–22 000 years BP
interval): stretching GICC05 by as much as the counting error
allows, requires that every uncertain layer has in fact been a real annual
layer, leaving little room for additional error. Between 22 000 and 42 000 years BP,
the interpolation uncertainties are determined by the MCE and
thus grow or shrink at a rate determined by the MCE.
Our results are in very good agreement with the results by
Turney et al. (2016) around Heinrich 3. In this
study, a kauri-tree 14C sequence was calibrated onto Lake Suigetsu
14C and also matched on GICC05 via 10Be. The difference
of the inferred ages (i.e., kauri on Suigetsu vs. kauri on GICC05) matches
with our proposed transfer function (red star in Fig. 12).
Figure 12 also shows the inferred offset between the 40Ar/39Ar age
of the Campanian Ignimbrite (Giaccio et al., 2017) and a
tentatively attributed SO4 spike in the GISP2 ice core (Fedele et
al., 2007). Even though it obviously requires a well-characterized tephra
find in the ice cores to ensure that the SO4 peak is indeed associated
with the Campanian Ignimbrite, at least from a chronological point of view,
our transfer function does not preclude this link. However, no matching
shards were identified in this period (Bourne et al.,
2013).
Timing of abrupt climate changes in different climate records.
The climate archive and proxy are indicated in each panel. The black lines
show the mean of the fitted ramps and their 95 % confidence intervals
(dashed). The dots mark the midpoint of the mean transition. The U/Th dates
and their ±1σ uncertainties of each climate record are shown
at the bottom of the figure in color coding corresponding to the respective
climate record. Each time series is shown on its original timescale not
applying any synchronization.
The timing of DO events
To investigate the synchroneity of climate changes recorded in different
parts of the globe, we compare ice-core data to a selection of well-dated
speleothem records. The well-known Hulu–Dongge Cave records have become
iconic blueprints for intensity changes in the East Asian summer monsoon
(EASM) anchored on a precise U/Th timescale (Cheng et al., 2016;
Dykoski et al., 2005; Wang et al., 2001). The speleothem records from Cueva
del Diamante and El Condor reflect changes in precipitation amount over
eastern Amazonia associated with the South American monsoon
(Cheng et al., 2013b). The speleothem records from Sofular
Cave, Turkey, are not straightforward in their mechanistic interpretation, but
likely reflect a mix of temperature and seasonality of precipitation
(δ18O), type and density of vegetation, soil microbial
activity (δ13C), and hence effective moisture and temperature
(Fleitmann et al., 2009). While this list of
speleothem data can certainly be expanded in future studies, we chose these
four speleothem records from three different regions that are all well dated and
sensitive to the position of the ITCZ and compare it to the ice-core records.
We used the NGRIP Ca record (Bigler, 2004), which shows the largest signal-to-noise ratio across DO
events (compared to, e.g., δ18O), making
their identification more precise. In addition, the Ca aerosols originate
from Asian dust sources (Svensson et al., 2000) and are thus more
directly related to Asian hydroclimate (Schüpbach et al., 2018), making
them potentially more comparable to, for example, the Hulu Cave record.
Potential phasing differences between different climate proxies in the ice
core are small compared to our synchronization uncertainties
(Steffensen et al., 2008).
Timing differences of the onset (a, b), midpoint (c, d), and end
(e, f) of rapid climate changes in NGRIP and speleothems (colored
PDFs;
see legend) and the timescale transfer function inferred from radionuclide
matching (black line and grey shadings as in Fig. 12). (a, c, e) The PDFs of timing
differences including only uncertainties from the
determination of the change points in the climate records; panels (b, d, f)
also include the speleothem dating uncertainties.
Figure 13 shows the ice-core and speleothem climate records on their original
individual timescales, along with the fitted ramps to the rapid climate
changes. Note that we could not fit each climate event for every record,
since the method requires a minimum number of data points defining the levels
before and after each transition to produce reliable estimates. Already
visually, a lag of climate changes in Greenland compared to the speleothem
records can be consistently identified between 20 and 35 ka BP when all
records are on their original timescales. Combining the PDFs of the detected
change points in Greenland and the speleothems allows us to infer a
probability estimate of the timing difference between climate events in
Greenland and speleothems. These differences are shown in Fig. 14 along
with our transfer function based on the matching of radionuclides from Fig. 12.
This comparison shows that the differences in the timing of starting point, midpoint,
and end point of DO events in speleothems and ice cores largely fall within
the uncertainties of our radionuclide-based timescale transfer function.
Thus, rapid climate changes occur synchronously in Greenland and the (sub)tropics. Notable exceptions are (i) the transition from GS-1 to the Holocene
around 11.6 ka BP, (ii) Heinrich event 2 at 24 ka BP, and (iii) DO 11
around 43 ka BP. However, there is large scatter among the different
speleothem-based estimates at these events, indicating that these events are
asynchronous in the different speleothem records on their respective
timescales. Consequently, some of these records also imply asynchronous
climate shifts with Greenland ice cores. This may either be interpreted as an
indication of time-transgressive climate changes or as a bias in individual
speleothems either in how climate is recorded in the speleothem or their
dating (for example, through detrital thorium).
Average lead–lag between the onset of DO events in the speleothems
and NGRIP. Each panel (color) shows the PDF for the lead–lag of the onset in
the speleothem compared to NGRIP, averaged over all investigated DO events
(i.e., excluding the GS-3 dust peak, H2). Panel (f) shows the PDF of
the average of all DO events and speleothems. The dark and light shading of
the PDF in each panel indicates 68.2 % and 95.4 % intervals.
Averaging over all DO events, we can estimate an overall probability of
leads and lags. By using the individual realizations of the
radionuclide-based transfer function (see Sect. 4.4) we take into account
that the uncertainties of the transfer function are strongly autocorrelated.
For each realization, we randomly sample the PDFs for the onset of the
DO events for the ice-core and speleothem records (see Sect. 3.5), perturb
the speleothem-based estimates within their U/Th dating errors, determine
the lead or lag between the DO onset in ice-core and speleothem records, and
correct it for the expected lag from the realization of our transfer
function. By averaging over all DO events we thus obtain a mean lag for each
realization and speleothem. In addition, we combine the different
speleothem-based estimates of each realization by averaging over their mean
lags to obtain an overall (speleothem and DO event) mean lag. Converting
the obtained lags from each realization into histograms we estimate the PDFs
of average lags between ice-core and speleothem records.
Our lag estimates critically depend on our ability to fill the gaps between
the widely spaced tie points, and thus on our assumptions about the ice-core
layer counting uncertainty, and how well our AR(1) process model can capture
these (Sect. 4). However, we note that by treating the MCE as a highly
correlated 1σ (instead of 2σ) uncertainty, our error
estimate can be regarded as very conservative since it allows for large
systematic drifts in each realization of the transfer function that will
result in large errors of the mean.
The resulting PDFs of the lag between speleothems and ice cores are shown in
Fig. 15. The uncertainties are mainly determined by our synchronization
uncertainty. Thus, the uncertainty is only marginally reduced when averaging
over all speleothems (Fig. 15, bottom). Because each realization of the
transfer function varies smoothly, the offset between speleothem and ice-core
records will be systematic for all speleothems in each realization and is
thus only marginally reduced by averaging.
We find that all speleothem records except Cueva del Diamante
(Cheng et al., 2013b) indicate synchroneity with NGRIP
within 1σ and that the delay obtained for Cueva del Diamante falls
within 2σ. We note that the speleothem data from El Condor
(Cheng et al., 2013b) from the same region as Cueva del
Diamante do not indicate a significant lag to Greenland. Overall, our
analysis cannot reject the null hypothesis of synchronous DO events in
Greenland ice cores and (sub)tropical speleothems (lag: μ±1σ=26±189 years).
Climate changes around H2. (a–h) NGRIP Ca
(Bigler, 2004) on the synchronized timescale (Fig. 14), Hulu Cave
δ18O (Cheng et al., 2016), El Condor δ18O
(Cheng et al., 2013b), Cueva del Diamante δ18O (Cheng et
al., 2013b), (e) Pacupahuain
δ18O (Kanner et al., 2012), Paixao δ18O
(Stríkis et al., 2018), Lapa Sem Fim δ18O (Stríkis
et al., 2018), and Jaragua Cave δ18O (Novello et al., 2017).
The arrows on the
right-hand side of each axis point in the direction of the signature of increased
precipitation on δ18O through the amount effect (Dansgaard, 1964).
The light grey box marks H2. The dark grey box highlights the preceding δ18O anomaly
in El Condor and Diamante caves. (i, j) Precipitation (color) and wind (arrows) response to
freshwater forcing in the CCSM3 climate model (freshwater-only experiment of TraCE21k,
all other forcings are held at 19 ka BP conditions; He, 2011). The red (blue) line depicts the
latitude of the precipitation maximum during strong (weak) AMOC states. Only wind anomalies
> 1 m s-1 are plotted. The cave sites are indicated as dots. Panel (i) shows
the winter (December–February) response, while panel (j) shows the summer
(June–August) response. Anomalies are plotted as weak–strong AMOC mode.
Discussion
Our proposed transfer function quantifies the long-term differences between
the Greenland ice-core and U/Th timescale and allows for their
synchronization. Even though based on only a few tie points, this can be used
to evaluate the absolute dating accuracy of Greenland ice-core records during
the past 45 ka BP, while maintaining the strength of their precise relative
dating. In combination with similar work done for the Holocene (Adolphi
and Muscheler, 2016; Muscheler et al., 2014a), the picture emerges that the
GICC05 counting error may be systematic: when accumulation and data
resolution are high (e.g., in parts of the Holocene), too many annual layers
have been counted, whereas during periods of low accumulation (e.g., GS-1 and
GS-2) there is a tendency to identify too few annual layers. In principle,
this is well captured by the GICC05 uncertainty estimate as the derivative of
our transfer function is (within error) consistent with the increase in the
counting error. However, our results caution against the use of the GICC05
counting error as a 2σ uncertainty as is often done in the
literature. Originally, Andersen et al. (2006) pointed out that
the MCE is not a true σ uncertainty but proposed that a Gaussian
distribution with 2σ= MCE could serve as a pragmatic
approximation. In combination with results from the Holocene
(Adolphi and Muscheler, 2016) our study implies that the counting
error can be strongly correlated over extended periods of time. This is in
line with the discussion in Rasmussen et
al. (2006), who point out that the main contribution to a potential bias in
the layer count is the definition of how an annual layer is manifested in the
proxy data. The data resolution and the manifestation of annual layers
change between different climate states (Rasmussen et al., 2006), likely due to
changes in aerosol transport and deposition resulting from variations in the
atmospheric circulation and seasonality of precipitation
(Merz et al., 2013; Werner et al., 2001). According to our
analysis, the largest relative (i.e., year year-1) change in the difference
between GICC05 and the U/Th and tree-ring timescale occurs over GS-1
(11 653–12 846 years BP) and GS-2 (14 652–23 290 years BP). Both of these
periods have likely been characterized by an increased relative contribution
of summer precipitation to the annual ice layer (Werner et
al., 2000; Denton et al., 2005), and the annual layers in the ice core have
been identified in a similar way in both intervals
(Rasmussen et al., 2006). In the 11–13 ka BP
interval, the offset between GICC05 and the tree-ring timescale changes from
-60 (95.4 % range: -77 to -42) years to zero (95.4 % range: -12 to +21)
years. During the same interval, the GICC05 maximum counting error grows by
46 years. Although consistent within the absolute error margins, this stretch
of GICC05 over GS-1 thus slightly exceeds the range allowed by the GICC05
counting error. Muscheler et al. (2014a) discussed the fact that this
stretch may be partly explained by errors in the placement of the oldest part
of the tree-ring chronologies. However, here, we use a revised late glacial
tree-ring dataset in which the different chronologies are connected much more
robustly (Hogg et al., 2016). Furthermore, our analysis of the fully
independent Hulu Cave 14C data yields similar results (Fig. 7).
Hence, our analyses clearly show that the GS-1 interval is about 60 years too
short in the GICC05 timescale.
Between 15 and 22 ka BP, our analysis yields a change in the GICC05 offset
from +118 (95.4 % range: 2–220) years to +549 (95.4 % range: 207–670)
years, while the GICC05 counting error grows by 335 years. Thus, again, our
transfer function changes a little faster than the maximum counting error
allows during this interval. We note that our 14C–10Be
match point around 22 000 years BP has a relatively low signal-to-noise ratio
in the 14C data (see Figs. 8–9) and should thus be regarded as
tentative. However, as shown in Fig. 8 our results are generally robust
against different choices of subsets of the 14C data and time
windows. Nevertheless, it can also be seen that the estimates of the most
likely age difference (i.e., the peak of the PDFs) differ slightly for
different choices of 14C data. Hulu Cave yields a most likely
offset of ∼325 years, while Suigetsu implies a bigger age difference of
∼550 years that coincides with a secondary probability peak in the Hulu
Cave PDF. We note that assuming increased amounts of old soil organic carbon
contributing to speleothem formation would lead to an even stronger
difference between these estimates (see Sect. 3.4). Hence, we propose an
age difference of +549 (95.4 % range: 207–670) years based on the
combination of all data (Fig. 9) that is consistent within error with the
estimates based on the single datasets shown in Fig. 8, but stress that
this tie point should be reevaluated as new suitable 14C data
become available in the future.
Assuming that the U/Th dates are absolute, our transfer function can
be used to account for the bias in the GICC05 timescale and thus facilitate
comparisons of ice-core records to other absolutely dated archives. However,
we note that our synchronization does not necessarily lead to consistent
timescales with radiocarbon-dated records. As discussed in Sect. 3.3.2
(Fig. 4) and Sect. 4.3 (Figs. 10 and 11), discrepancies in the datasets
underlying IntCal13 can lead to erroneous structures in the calibration
curve. The reduced amplitude of the Δ14C change around the
Laschamp geomagnetic field minimum in IntCal13 compared to its underlying
data implies that IntCal Δ14C must be offset prior to and/or
after the Laschamp event. This underlines the challenges in radiocarbon
calibration around this time pointed out by Muscheler et
al. (2014b). More recently, Giaccio et al. (2017) also pointed
out that paired 40Ar/39Ar and 14C dating of the Campanian
Ignimbrite around 40 ka BP yields inconsistent ages when the 14C
age is calibrated with IntCal13. Since IntCal13 in principle should be tied
to the U/Th age scale for sections older than 13.9 ka BP, this
implies either an inconsistency between Ar/Ar and U/Th dating or in
the reconstructed 14C levels of the calibration curve. The latter
would be congruent with the conclusions by Muscheler et al. (2014b).
If the problem was indeed the IntCal 14C reconstruction, a
synchronization of ice-core 10Be to IntCal 14C would not
resolve this bias, since the problem would not be one of chronology, but of
14C measurement and/or archive.
Our analysis provides the first rigorous test of whether DO events recorded
in speleothems and ice cores occur synchronously. We reject the hypothesis of
leads and lags larger than 189 years at the one sigma level, consistent with
the findings of Baumgartner et al. (2014). Since we
compare to speleothem records from different regions, this also highlights
that the ITCZ likely migrated synchronously (within uncertainties) over the
different ocean basins and continents during the onset of DO events
(Schneider et al., 2014). However, there are also differences
between the different speleothem records, which could be due to limitations
in their dating or related to how directly individual archives record the
rapid climate changes. The most notable examples are the onset of the
Holocene and GI-11, which appear to occur asynchronously in the different
speleothems (see Figs. 13 and 14). Another example is the younger GS-3 dust
peak in the Greenland ice cores that appears to coincide with the East Asian
summer monsoon decline seen in Hulu Cave, but postdates the precipitation
increase seen in El Condor and Diamante. This change in the speleothems is
typically attributed to the southward shift of the ITCZ as a response to
Heinrich event 2.
Figure 16 shows the period around H2. First, we note that the younger of
the two GS-3 dust peaks in the Greenland ice cores (Rasmussen et al.,
2014a) occurs coevally (within chronological uncertainty) with the ITCZ
movement recorded by the speleothems. At this time, the East Asian summer
monsoon is strongly reduced as implied by decreased Hulu Cave δ18O (Cheng et al., 2016). Coevally, precipitation increases in the
South American summer monsoon region (Novello et al., 2017; Stríkis
et al., 2018). The records thus exhibit more pronounced stadial conditions
than normally seen during (non-Heinrich) DO events. However, taken at face
value, the precipitation increase at El Condor and Cueva del Diamante, the
two northernmost sites shown in Fig. 16 (Cheng et al.,
2013b), significantly predates the event seen in Greenland and Hulu Cave. It
also predates the more southern South American sites Lapa Sem Fim
(Stríkis et al., 2018) and Jaragua
(Novello et al., 2017) by more than 500 years. This could
either point to errors in the dating of the El Condor and Diamante
speleothems or be related to their latitudinal position. A freshwater-only
experiment (all other boundary conditions held constant at 19 ka BP levels)
with the Community Climate System Model 3 (TraCE-MWF; He, 2011) shows
that during a weak AMOC state, reduced advection of moisture from the
tropical Atlantic leads to lower precipitation north of the ITCZ, while the
ITCZ position over South America itself changes very little (Fig. 16). El
Condor and Cueva del Diamante are both located very close to the LGM position
of the ITCZ. It is hence possible that when Northern Hemisphere summer
insolation reached its lowest values over the past 50 ka BP around H2, the
ITCZ migrated to a position south of El Condor and Cueva del Diamante and
during its transition caused the reconstructed precipitation change. As a
result, the precipitation response to freshwater forcing would change sign at
these cave sites. The sites located slightly further south only show a weak
(Pacupahuain) or no (Paixao) response during this period, but are both
characterized by increased variability. The two southernmost sites, on the
other hand (Jaragua and Lapa Sem Fim), remain south of the ITCZ throughout
and hence show a clear increase in precipitation coeval with the signal in
Greenland and Hulu Cave. In this context, the precipitation increase in El
Condor and Cueva del Diamante around 25 ka BP (i.e., prior to H2) may signify
when the ITCZ transitions over the sites. The subsequent reduction in AMOC
strength during H2 then leads to a decrease in precipitation in northwest
South America, an increase further south, and little change in between.
Tentative support for this can be drawn from the response of the El Condor
and Cueva del Diamante speleothems to GI-2.2 and GI-2.1 for which, albeit weakly,
the δ18O records imply an increase in precipitation during
GI-2 opposite to their response to DO events during MIS-3 (Figs. 13,
16). Thus, this analysis indicates that the seemingly asynchronous response
to climate change in different proxy records may indeed only reflect site-specific changes in the proxy response. Alternatively, we cannot rule out
undetected issues with the U/Th ages of these speleothems. A detailed
analysis of this observation feature is beyond the scope of this paper, but
in the context of a timescale perspective, which is the focus of this work,
it highlights the caveats of climate wiggle matching between single records,
even if the mechanistic link between regional climate changes may be
relatively well understood.
Conclusion
We present the first radionuclide-based comparison between the Greenland Ice
Core Chronology 2005 (GICC05) and the U/Th timescale. We find that
GICC05 is accurate within its stated absolute uncertainties, but also that
the maximum counting error of the GICC05 may be at the limit to capture the
total uncertainty accumulated within certain climatic periods. Our analysis
indicates that the relationship between GICC05 and the U/Th timescale
over the last 45 ka drifts over time and reaches its maximum offset around
22 ka BP. We propose a transfer function that quantifies this drift and
facilitates analysis of ice-core and U/Th records, such as
speleothems, on a common timescale. Thus, this transfer function allows
for the further integration of key timescales in paleosciences and contributes to the
INTIMATE (INTegration of Ice-core, MArine, and TEerrestrial records)
initiative (Bjorck et al., 1996; Rasmussen et al., 2014b; Bronk Ramsey et
al., 2014). Provided that U/Th ages are regarded as accurate, the
transfer function strongly reduces the absolute dating uncertainty of
Greenland ice cores back to 45 ka BP. We reject the hypothesis of leads or
lags larger than 189 years between Greenland, East Asia, and South America at
the one sigma level. We show that the southward ITCZ shift around
24.5 ka BP seen in speleothems, typically associated with H2, coincides
with the younger GS-3 dust peak recorded in Greenland ice cores. However, we
also highlight inconsistencies between speleothem records around the onset of
the Holocene, late GS-3, and GI-11 and thus caveats to the commonly applied
practice of climate wiggle matching.
By comparing various 14C records underlying IntCal13 as well as
ice-core 10Be data and geomagnetic field records, we infer that the
current radiocarbon calibration curve underestimates the amplitude and
rapidity of the Δ14C change around the Laschamp event at
41 ka BP. This adds to previous studies (Giaccio et al., 2017; Muscheler
et al., 2014b) highlighting the fact that there are likely systematic errors in
IntCal13 that will directly translate into errors of radiocarbon-based
chronologies around that time. The combination of several internally
inconsistent datasets in IntCal13 can lead to erroneous timing and amplitude
of Δ14C changes. Hence, great care has to be taken when
attempting to use sections older than 30 ka BP of IntCal13 directly for
studies of 14C production rates and/or carbon cycle changes.
The transfer function shown in Fig. 12 will be made
available as a Supplement to this paper and on NOAA.
The supplement related to this article is available online at: https://doi.org/10.5194/cp-14-1755-2018-supplement.
FA designed and carried out analyses and wrote the paper in
correspondence with CBR and RM. TE designed and applied break-point detection
analysis and wrote the corresponding “Methods and data” section (Sect. 3.5). FA, RM, CBR, SOR, CT,
and AC initiated the project. RLE and HC provided speleothem data. AS and SOR
provided insights into the ice-core chronology. HF and TE gave insights into
aerosol transport and deposition. All authors discussed and commented on the
paper.
The authors declare that they have no conflict of
interest.
Acknowledgements
FA was supported through a grant by the Swedish Research Council
(Vetenskapsrådet no. 2016-00218). CBR was partially supported through
the UK Natural Environment Research Council (NERC) Radiocarbon Facility
(NRCF010002). TE and HF acknowledge the long-term support of ice-core
research at the University of Bern by the Swiss National Science Foundation
(SNSF) and the Oeschger Center for Climate Change Research. SOR gratefully
acknowledges support from the Carlsberg Foundation to the project
ChronoClimate. This work was partially supported by the Swedish Research
Council (grant DNR2013-8421 to RM), the NSF 1702816 to RLE, and the
Australian Research Council DP170104665 to CT and AC. We gratefully
acknowledge the financial support of the University of Adelaide Environment
Institute for the initial Marble Hill meeting that initiated this work.
Edited by: Denis-Didier Rousseau
Reviewed by: Paula Reimer, Niklas Boers, Frédéric Parrenin, and Jeff Severinghaus
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