We assess the methodology of the so-called 14C plateau tuning (PT) technique used to date marine sediment records and determine 14C marine reservoir ages (MRAs) as recently reviewed by Sarnthein et al. (2020).
The main identified problems are linked to the assumption of constant MRA
during 14C age plateaus; the lack of consideration of foraminifera
abundance changes coupled to bioturbation that can create spurious plateaus
in marine sediments; the assumption that plateaus have the same shapes and
durations in atmospheric and oceanic records; the implication that
atmospheric 14C /12C peaked instantaneously from one plateau to the next; that the 14C plateaus represent 82 % of the total time spent between 14 000 and 29 000 cal yr BP, whereas during the remaining 18 % of
the time, the radiocarbon clock was running almost 5 times faster than the
radioactive decay; that the sparsity, combined with the level of analytical
uncertainties and additional noise, in both atmospheric and marine data do
not currently allow one to reliably or robustly identify plateaus (should
they exist) beyond 15 000 cal yr BP; and that the determination and
identification of plateaus in the deep-sea cores is reliant upon significant
changes in sedimentation rate within those marine sediments which are, a
priori, unknown and are not verified with an independent method.
The concerns we raise are supported and strengthened with carbon cycle
box model experiments and statistical simulations of pseudo-atmospheric and
pseudo-marine records, allowing us to question the ability to identify and
tune 14C age plateaus in the context of noisy and sparse data.
Introduction
Sarnthein et al. (2020) review the results of a technique based on tuning
hypothesized 14C age plateaus that they inferred in deep-sea sediment cores
with those that they have proposed as existing in atmospheric radiocarbon
archives, notably using the Lake Suigetsu record (Bronk Ramsey et al., 2012,
2020). The proposed outcomes of the so-called “plateau tuning” (PT) are to
establish accurate and precise calendar age scales of the marine sediments
and, at the same time, to determine the 14C marine reservoir ages
(MRAs) at the sea surface (for 14C measured on planktonic foraminifera)
and ventilation ages of deeper water masses (using 14C measured on
benthic foraminifera).
Sarnthein et al. (2020) review the results obtained by PT published over
the last 13 years by the Kiel group (Sarnthein et al., 2007, 2011, 2013,
2015; Balmer et al., 2016; Sarnthein and Werner, 2017; Balmer and Sarnthein,
2018; Küssner et al., 2018). By comparing the records from many
locations, the authors conclude in Sect. 2.2 that the 14C age
plateaus beyond 15 000 cal yr BP “show little coherence” with independent 10Be records based on
polar ice cores and therefore that the cause of these 14C anomalies may
not be linked to cosmogenic production changes.
The authors thus propose that extremely large and variable ventilation ages
may be the causes of these 14C age plateaus, constituting the
fingerprint of abrupt reversals of deep ocean circulation and abrupt release
or drawdown of CO2 into or from the atmosphere. Nevertheless, the
authors admit that “ocean models still poorly reproduce” their reconstruction of deep ocean circulation and
carbon cycle changes. They claim that this mismatch is due
to model deficiencies in spatial resolution and tuning with reference data.
We have strong reservations about the appropriateness of the PT technique
and consequently also the reliability of the results obtained in Sarnthein
et al. (2020). The PT technique, proposed and used by the same authors from
Kiel for more than 13 years, has not been checked and replicated
independently by other groups. Outside the Kiel group, only Umling and
Thunell (2017) have used the PT technique, but they found rather puzzling results
(see more in Sect. 2.4). PT has been presented on several occasions during
International Radiocarbon Conferences and workshops of the IntCal group, but it
has never been adopted as a viable technique to reconstruct past 14C
variations (Reimer et al., 2020; Heaton et al., 2020a; and previous IntCal
iterations by Reimer et al., 2009, 2013).
The review paper by Sarnthein et al. (2020) only compiles previous papers by
the same group. The risk is to mislead readers into thinking that the PT
technique is now firmly established. Indeed, the compiled records based on
PT lead to perplexing outcomes (no coherence with either production changes
or with ocean modeling results). This failure is linked to the inherent
pitfalls listed below, which are not treated adequately in Sarnthein et al. (2020) or in former papers by the same group.
With this extended comment, our objective is to expose and discuss openly
some of the inherent problems linked to PT. We split our discussion into two
sections. Firstly, we present our concerns from a geoscientific perspective.
Secondly, we provide our statistical concerns with the proposed PT method
and provide illustrative examples that highlight its intrinsic difficulties.
Paleoclimatic and paleoceanographic perspectivePT and 14C wiggle matching
The PT principle is reminiscent of the “14C wiggle matching
technique” used to refine the dating of large pieces of wood with multiple
14C analyses over a tree-ring sequence of at least a few decades (Bronk
Ramsey et al., 2001). However, Sarnthein et al. (2020) propose to do this
with ocean sediments, which are not annually laminated, and to obtain
calendar chronologies accurate and precise at the “decadal-to-centennial”
level mentioned in their Sect. 1.1. However, there is no independent
constraint on the sedimentation rate variations in these ocean cores
(without annual varves). Indeed, sedimentation rate changes linked to
climatic–oceanographic events (e.g., Dansgaard–Oeschger and Heinrich events)
or more local sedimentological causes could also create 14C age
plateaus.
PT is not always restricted to the tuning of a single plateau but is often used
to tune a suite of 14C age plateaus. However, one cannot reliably PT if
one cannot reliably identify and define an individual 14C plateau. A
suite of plateaus does not necessarily add strength – in fact it
potentially makes it more challenging should one miss or simply mismatch
plateaus in either the atmospheric target or the sediment record. Moreover,
the possible existence of spurious plateaus further complicates PT (see more
below).
PT and marine reservoir ages
In addition to determining the calendar chronology of ocean sediments,
PT is also used to calculate, at the same time, a variable offset with the
atmospheric 14C curve. The offset for planktonic foraminifera is often
very large (1000–2000 14C yr) and is interpreted as being due to
14C reservoir age changes at the sea surface. However, in order to
perform PT the authors are required to assume that the marine 14C
reservoir age (MRA) is strictly constant during the age plateaus, which
represent 82 % of the total time spent between 14 000 and 29 000 cal yr BP. This assumption of reservoir age stability during 14C age plateaus
is antithetical with the conclusion that these plateaus are linked to carbon
cycle changes. Such significant carbon cycle changes would have left their
imprint in 14C records (Bard, 1988), maybe even as 14C age plateaus
solely recorded in marine sediments. Hence, the 14C structures
identified by Sarnthein et al. (2020) in pelagic sediments are severely
under-constrained in 14C and calendar ages.
Indeed, changes in marine 14C reservoir age may either mask (or create
false) 14C age plateaus in the marine core, causing issues with tuning
to the atmospheric plateaus. On the one hand, a decrease in MRA coinciding
with an atmospheric 14C age plateau (i.e., a decrease in atmospheric
Δ14C) may create a set of marine 14C observations lacking
any plateau. On the other hand, an increase in MRA may make a period where the
atmospheric 14C record does not plateau (e.g., constant atmospheric
Δ14C) appear as a plateau in marine 14C observations. In
both of these instances, PT will fail. Unless MRAs only change at the boundary
times of their chosen plateau, identifying whether a potential plateau in a
set of marine observations should be tuned to an atmospheric plateau is
potentially confounded by the very changes to MRA the authors report.
Plateaus in Suigetsu and IntCal20 records
Sarnthein et al. (2020) refer to their hypothesized Lake
Suigetsu-based 14C calibration curve as a “rung ladder” that provides
the basis of PT. Actually, the series of 14C age plateaus hypothesized
by Sarnthein et al. (2020) resembles a “staircase” more than a “rung
ladder”. In Fig. 1, we have created a Lake Suigetsu-only calibration curve
using the same Bayesian statistical method used for IntCal20 (Heaton et al.,
2020b; Reimer et al., 2020) but constructed based only upon the observations
from Lake Suigetsu with its updated calendar age timescale (Bronk Ramsey et
al., 2020). Figure 1 shows the Lake Suigetsu 14C data and the resulting
Suigetsu-only radiocarbon calibration curve for the period beyond the last
14 000 cal yr BP (14 cal kyr BP), i.e., where continuous high-precision data
on tree-rings are not currently available. Superimposed horizontal lines
indicate the 15 hypothesized atmospheric 14C age plateaus of Sarnthein
et al. (2020), with their numbering as listed in their Table 1. Figure 2 is
equivalent to Fig. 1 but compares the Sarnthein 14C age plateaus
directly with the IntCal20 calibration curve (Reimer et al., 2020). In
addition to Lake Suigetsu, the IntCal20 curve uses (atmosphere-adjusted)
14C determinations from speleothems, lacustrine and marine sediments,
and corals, as well as some 14C determinations obtained from floating
tree-ring sequences. Besides the well-known plateau no. 1 corresponding to
the beginning of the Bölling period, evidence for many of the older
plateaus hypothesized by Sarnthein at al. (2020) is dubious. They are not
replicated in either our statistically robust Lake Suigetsu-only curve (Fig. 1) or the IntCal20 curve (Fig. 2). The weak evidence for many of these
hypothesized 14C age plateaus is further detailed in Sect. 3.2.
Blue dots show the Lake Suigetsu 14C data with their 1σ analytical uncertainties in both radiocarbon and calendar age (Bronk
Ramsey et al., 2020). The thin solid red line shows the pointwise posterior
mean of a radiocarbon calibration curve constructed using the same Bayesian
statistical method as IntCal20 (Heaton et al., 2020b; Reimer et al., 2020)
but based only on the Suigetsu 14C data. The accompanying shaded
interval represents the 95 % posterior predictive probability interval.
Superimposed thick green lines indicate the 15 atmospheric plateaus with
their numbering as listed in Table 1 of Sarnthein et al. (2020).
The thin solid purple line shows the pointwise posterior mean of
the IntCal20 curve, with the shaded interval representing the 95 %
posterior predictive probability interval (Reimer et al., 2020; Heaton et
al., 2020b). Superimposed thick green lines indicate the 15 atmospheric
plateaus with their numbering as listed in Table 1 of Sarnthein et al. (2020).
By focusing only on the plateaus, Sarnthein et al. (2020) overlook the
implication that in their model 14C ages must jump, often
instantaneously, from one plateau to the next (i.e., like in a staircase as
shown in Figs. 1 and 2). This is particularly true for five steps between 10
plateaus (10b to 10a, 9 to 8, 6b to 6a, 5b to 5a, and 2b to 2a) for which the
calendar gaps correspond to 0 cal yr, but the atmospheric
14C ages drops by between 340 and 750 14C yr. Five other steps
(between 10 plateaus: 11 to 10b, 8 to 7, 7 to 6b, 6a to 5b, and 5a to 4) also
have minimal calendar durations (70 to 170 cal yr) but show large 14C
drops ranging from 660 to 1380 14C yr. Consequently, the total duration
of 14C plateaus represent 82 % of the time spent between 14 and 29 cal kyr BP, whereas during the remaining 18 % of the time, the radiocarbon
clock (i.e., the pace at which the 14C age changes compared with true
calendar time) was running almost 5 times faster than radioactive decay. The
implication of the hypothetical staircase shape of Sarnthein et al. (2020)'s
proposed atmospheric calibration curve is that radiocarbon would have never
behaved as a geochronometer driven by regular radioactive decay.
It is useful to convert Figs. 1 and 2 from 14C age into Δ14C in order to assess the implications of these vertical steps. This
is done in Fig. 3, which shows that 14C age plateaus are transformed
into triangular Δ14C wiggles. The consequence of abrupt
14C age drops between 14C age plateaus is that most of these
Δ14C wiggles exhibit instantaneous rises ranging in size from
50 ‰ to 250 ‰.
The two panels represent the same data shown in Figs. 1 and 2 but
converted into Δ14C with units in ‰. Short (or
zero) gaps between plateaus are transformed into abrupt rises. Age plateaus
correspond to the second parts of these atmospheric Δ14C
wiggles, during which the Δ14C decrease compensates the
radioactive decay.
There is no known mechanism that could be responsible for such abrupt and
large asymmetric wiggles of the atmospheric Δ14C. Instantaneous
14C production increases, which result in about 4 times the average
production in a year, were discovered recently (Miyake et al., 2012; Mekhaldi
et al., 2015). However, the size of these spikes attributed to extreme solar
particle events is an order of magnitude smaller in terms of Δ14C than that required to explain the jumps between the hypothesized
14C age plateaus of Sarnthein et al. (2020). Furthermore, there is no
evidence of huge corresponding spikes in the ice core 10Be record
(Adolphi et al., 2018). In addition, the impacts of abrupt changes of the
geomagnetic field were found to be negligible on the production of 14C
(Fournier et al., 2015). Finally, it is unlikely that abrupt changes of the
carbon cycle are responsible for such large, frequent, and very abrupt
Δ14C spikes. For example, switching down the deep ocean
circulation instantaneously in a carbon cycle box model leads to a rather
slow and limited Δ14C rise in the atmosphere over several
centuries (see, e.g., Fig. 4b by Goslar et al., 1995, or Fig. 5 by Hughen et
al., 1998; see also simulations performed with more complex models by Marchal
et al., 2001; Delaygue et al., 2003; Ritz et al., 2008; Singarayer et al.,
2008). Consequently, the assumption which is required to underpin PT, i.e., that
the radiocarbon calibration curve has the shape of a staircase, is in
conflict with our basic understanding of 14C as a tracer.
Marine datasets used for PT
Unfortunately, the Sarnthein et al. (2020) review paper fails to show a
single figure illustrating oceanic 14C records with their plateaus
compared and tuned with the proposed atmospheric calibration 14C
curve. It is thus necessary to dig into the literature to see the marine
core records: Figs. 3 and 4 in Sarnthein et al. (2007) for cores SO17940,
MD01-2416, ODP893A, and PS2644; Fig. 2 in Sarnthein et al. (2011) for core
MD01-2378; Figs. 3 to 13 in Sarnthein et al. (2015) for cores GIK23074,
PS2644, MD08-3180, ODP1002, MD01-2378, GIK17940, SO50-37, MD01-2416,
MD02-2489, ODP893A, and MD02-2503; Fig. S1a, b, c, d in Balmer et al. (2016) for cores GeoB1711-4, GeoB3910-1, KNR-159-5-36GGC, and MD07-3076; Fig. 4
in Sarnthein and Werner (2017) for cores GIK23258, MSM5/5-712, and T88-2;
Fig. 2 in Balmer and Sarnthein (2018) for core MD08-3180; and Fig. 4 in
Küssner et al. (2018) for core PS75-104-1.
Looking at these graphs is absolutely crucial to assess the poor quality of
the determination of 14C age plateaus in these ocean cores and their
tuning to the atmospheric 14C calibration curve. This is particularly
important as the PT implies sedimentation rates that vary by up to a factor
of 5 to 8 within a single core (e.g., cores PS2644 and MD08-3180 in Sarnthein
et al., 2015; PS75/104-1 in Küssner et al., 2018) and even much more, by
orders of magnitude, in other cores from the Nordic Seas (Sarnthein and
Werner, 2017).
The proposed PT criterion, which considers only the average sedimentation
rate over a complete core (e.g., > 10 cm kyr-1) is not sufficient to
ensure reliability in the PT approach. Rather it is the profile and range of
sedimentation rate within a sediment core that is most critical, as these
internal relative changes determine the identification of the plateaus in
the absence of calendar age information. This also applies to the impact of
bioturbation since the smoothing and phasing effects are directly related to
the ratio between the bioturbation depth and the sedimentation rate (this
ratio being the average residence time of foraminifera in the bioturbation
zone, e.g., Bard et al., 1987).
In their Supplement, Sarnthein et al. (2020)
provide summary figures for 18 individual deep-sea cores, showing the final
reconstructions of surface and deep reservoir ages versus time for each
core. However, these graphs are not particularly useful to assess PT tuning
because they do not show the raw 14C data versus depth compared to the
Suigetsu 14C record.
In fact, the changes of sedimentation rates implied by PT are even larger
than those mentioned above because remaining conflicts between atmospheric and
marine 14C records are resolved by introducing ad hoc discontinuities
into the core stratigraphies. These periods, forced to have a sedimentation
rate dropping down to zero, are assumed to be previously unnoticed
sedimentological hiatuses (e.g., nine hiatuses are inferred by PT in the 18
sediment cores presented in their claim
that the “plateau-based high-resolution chronology has led to the detection of numerous millennial-scale hiatuses overlooked by conventional methods of stratigraphic correlation. In turn, the hiatuses give intriguing new insights into past changes of bottom current dynamics linked to different millennial-scale geometries of overturning circulation and climate change”). No independent sedimentological evidence is presented to verify
these previously unnoticed hiatuses, which occur surprisingly at the
14C plateau boundaries for no obvious reason. These discontinuities may
just be artifacts of the PT method. Sarnthein and Grootes (2021) further claim that there should be a positive correlation
between hiatus frequency and sedimentation rate. This counterintuitive
hypothesis remains speculative in the absence of evidence independent of PT.
Surprisingly, the summary Fig. S2c for three cores (MD07/3088, SO213-76, and
PS97/137-1) has been changed between the submitted version (available in
Climate of the Past Discussions) and the final published paper by Sarnthein et al. (2020). The changes are
particularly important for the last two cores: for example, the Last Glacial Maximum surface
reservoir age around 20 cal kyr BP has now doubled for core PS97/137-1 (more
than 2000 14C yr in the final publication instead of 1000 14C yr
in the initial submitted version). Benthic reservoir ages have also changed
by more than 1000 14C yr (up or down) in the last two cores. In
addition, both records now exhibit two hiatuses in the final publication as
opposed to the single hiatus presented in the initial submission. In the
submitted and published versions of Sarnthein et al. (2020), these new
results were referred to a submitted paper by Küssner et al. (2021), which is
still unpublished. Hence, no explanation is available to the reader to
assess the reason for the drastic change of MRA reconstructions between the
two versions.
Lamy and Arz (2021), who studied cores from the same area
(e.g., Lamy et al., 2015), confirm the serious problem linked to the
application of PT to core PS97/137-1. They raise doubts about the highly
variable sedimentation rates within this core, which make the identification
of any 14C plateaus highly uncertain. They further note that the status
of the laminations in these sediments is still debated, which implies that a
rough count cannot be used to support the chronology based on PT. Michel
and Siani (2021) also express caution about PT results
presented by Sarnthein et al. (2020) on a Southeast Pacific core (MD07-3088)
studied previously by Siani et al. (2013). Their concern is about the
reconstructed variability of sedimentation rate based on PT (up to a factor
of 25 for that core), and Michel and Siani (2021) also underline that the MRA
cannot be precisely defined for the glacial part of the core. In addition to
South Pacific cores, Lamy and Arz (2021) extend their doubts to PT tuning applied
to other cores studied by Sarnthein et al. (2020) (e.g., GeoB3910 off Brazil in a
zone studied previously by Arz et al., 1999).
Outside the 18 records obtained by the Kiel group and compiled by Sarnthein
et al. (2020), there is one further paper by other authors who have used PT
for stratigraphical purposes and for reconstructing 14C reservoir ages.
Umling and Thunell (2017) used eyeball PT to derive their chronology for a
sediment core located at 2.7 km depth in the eastern equatorial Pacific.
Their tuning of the 14C record of core TRI163-23 onto the Suigetsu
14C record implies the unexpected presence of hiatuses in this core at
boundaries between 14C plateaus (i.e., gaps between plateaus 1a
and YD, between plateaus 1 and 1a, and between plateaus 2a and 1; see
Fig. 3c of Umling and Thunell, 2017). The first hiatus is particularly long
(1200 cal yr), but no independent data are presented to confirm the presence
of such a large unconformity in this core. In addition, the deglacial
14C reservoir ages reconstructed for core TRI163-23 exhibit
discrepancies with the record obtained by de la Fuente et al. (2015) on
another core from the eastern equatorial Pacific collected at a similar
depth (2.9 km). Grootes and Sarnthein (2021) even disagree on
specific aspects of the PT performed by Umling and Thunell (2017)
highlighting the subjectivity of the PT approach.
Plateau boundaries and MRA changes
The PT technique is focused on the detection and use of 14C age
plateaus. This implies that a large part of the 14C record is left
unused in the matching process. This is actually surprising because plateaus
are used in the frame of PT to define the absolute chronology of the marine
sediment core, while plateaus in the 14C calibration curve are
generally viewed as poor periods for obtaining precise calibrated ages in
contrast to “high-slope” parts of the calibration curve (e.g., Svetlik et
al., 2019). The only potential justification we can identify for such an
approach would be if one believed that changes to MRA could only occur at
plateau boundaries (and remained otherwise constant). However, there is no
special significance in the plateaus of the 14C calibration curve.
After transformation to atmospheric Δ14C (Fig. 3), a
14C age plateau is only the second part of a wiggle, during which the
Δ14C decrease compensates for the radioactive decay. If anything,
the entire Δ14C wiggle is the feature that should be matched
(equivalent to a high-slope–low-slope sequence in the plot of 14C age versus calendar age).
Without a clear justification for why plateau boundaries would coincide with
all MRA changes, there is thus no reason to match the 14C age plateaus
only and discard the other parts of the 14C sequence. Matching the
entire 14C record with the target curve would provide a stronger
analogy with the wiggle matching technique used for tree sequences (but
without resolving the remaining pitfalls of having no independent calendar
dating for most ocean sediments and that no matching could be done unless
one already knew the MRA). Matching the entire 14C sequence is also the
method used to synchronize floating tree-ring sequences to a master
chronology (e.g., Capano et al., 2020, used for the IntCal20 curve by Reimer
et al., 2020).
Indeed, even if MRA changes were to occur only at plateau boundaries, the
marine and atmospheric 14C age records should show entirely the same
shape, just with piecewise constant offsets during and between each plateau.
This would provide an even stronger argument that the whole 14C record
should be matched. Plateau tuning would then reduce to finding the change
points in the piecewise constant MRA offset between atmospheric and marine
14C ages.
Spurious plateaus in marine sediments
In contrast to the atmospheric 14C calibration curve, there is
indeed a special significance in a 14C age plateau observed in ocean
sediments. As mentioned above, within a sediment core a 14C age
plateau could be a simple consequence of an abrupt sedimentation rate
increase or even a slump that mixes sediments of the same age. Another
potential source of 14C age plateaus has been completely overlooked by
Sarnthein et al. (2020): the coupling of continuous bioturbation with
changes in the abundance of the 14C signal carrier. Indeed, the
assemblages of foraminifera used for 14C analyses often varied in the
past due to global or regional paleoceanographic conditions (these large and
systematic faunal changes are the basis for the use of planktonic
foraminifera in paleothermometry, e.g., Imbrie and Kipp, 1971; Kucera et al.,
2005).
In particular, an abrupt decrease or an abrupt increase of the foraminifera
abundance will inevitably create a 14C age plateau, as theorized by
Broecker et al. (1984). The first demonstration was provided by Bard et al. (1987), who showed that 14C age plateaus and ∂18O phase
lags measured on two planktonic species in a deep-sea core from the North
Atlantic could be explained quantitatively by bioturbation modeling forced
with the abundance records of both species. Since 1987 many other groups
have made similar observations of 14C age plateaus and discrepancies
explained by bioturbation coupled with foraminifera abundance changes (e.g.,
Costa et al., 2018; Ausin et al., 2019).
In order to interpret 14C age plateaus in ocean sediments, it is thus
indisputably necessary to show the absolute abundance records of the
different foraminifera used for 14C dating (counts expressed in number
per gram of sediment). This has never been the case for any of the PT papers
by the Kiel group used for this new compilation (Sarnthein et al., 2007,
2011, 2013, 2015; Balmer et al., 2016; Sarnthein and Werner, 2017; Balmer
and Sarnthein, 2018; Küssner et al., 2018; Sarnthein et al., 2020). It is
possible that several marine 14C age plateaus could be mere sedimentary
artifacts.
Sarnthein and Grootes (2021) cite another paper by Ausin et al. (2021) published after the submission of our paper.
This recent work is based on a sediment core from the Iberian margin for
which Sarnthein and Grootes (2021) provide a graph representing the foraminifera
abundance counts, showing no obvious correlation between 14C plateaus
and drops in abundances. This information is indeed useful, and it is
unfortunate that these data are neither shown nor provided in the paper by
Ausin et al. (2021).
Nevertheless, caution should be taken with the work by Ausin et al. (2021),
who obtain low MRA and benthic 14C values in this Iberian Margin core,
notably during Heinrich stadial 1 (HS1) and the Last Glacial Maximum (LGM),
in stark contrast with records obtained on nearby cores (Skinner et al.,
2014, 2021). The MRA drop (down to 300 years) during HS1 based on PT is also in
conflict with modeling results (Delaygue et al., 2003; Ritz et al., 2008;
Franke et al., 2008; Butzin et al., 2017). Although Sarnthein and Grootes (2021)
present the new paper by Ausin et al. (2021) as a “nice test case” of PT,
the strong disagreement with the literature is not reassuring. Furthermore,
it also remains to be seen if the bioturbation–abundance coupling could not be
an adequate explanation for some 14C age plateaus in the 20 other
published records based on PT and for which the foraminifera counts are not
available.
Attenuation and phase lag in the ocean
Let us now assume that 14C age plateaus in marine records do match
those identified in the atmospheric record (i.e., the basic assumption of
the PT technique). What remains problematic, if we are intending to use PT
to create a precise calendar age time scale for the marine record, is that
marine 14C age plateaus are assumed to be as sharp and as long as their
corresponding atmospheric plateaus, even in the case of very large surface
reservoir ages reconstructed by the method. The sections below show that
this conflicts with our understanding of the carbon cycle.
For the past 14 kyr, high-resolution 14C data on tree-ring and
10Be on polar ice cores have shown that most centennial Δ14C wiggles in the atmosphere are due to cosmogenic production changes
(Beer et al., 1988; Adolphi and Muscheler, 2016; Adolphi et al., 2017, 2018), mainly linked to the
solar variability as illustrated by studies covering the last millennium
(e.g., Bard et al., 1997; Muscheler et al., 2007; Delaygue and Bard, 2011).
This 14C production signal is transferred from the atmosphere to the
ocean surface before being slowly transported to the deep ocean. The
atmospheric 14C wiggle inevitably gets damped in the other reservoirs
of the carbon cycle, notably the surface ocean. In addition, the oceanic
wiggle is not strictly in phase with the atmospheric one. Both damping and
phasing effects conflict with the main assumption of the PT technique,
namely that marine 14C age plateaus can be matched directly to
atmospheric ones.
(a) Amplitude ratio of Δ14C wiggles in the atmosphere and the surface ocean created by sinusoidal changes of the 14C production as an input to the 12-box model shown in the upper insert (Bard et al., 1997). This normalized attenuation factor is plotted versus the
signal period of production variations (on a log scale). The greater the
attenuation, the more the amplitude of the sinusoid in the surface ocean is
reduced. The factor would be equal to 1 if atmospheric and oceanic
amplitudes were the same. The dashed light blue curve shows the calculation
results for the Indo-Pacific surface box, while the dotted dark blue line
stands for the Southern Ocean surface box. These two boxes differ by their
surface 14C reservoir age at steady state (320 and 890 years for the
Indo-Pacific and Southern Ocean surface boxes, respectively). The vertical
dash-dotted black line underlines the 500-year period wiggle used to construct
panel (b). The inset graph shows the geometry of the model by Bard et al. (1997) with numbers on boxes indicating their steady-state Δ14C (‰), numbers in parenthesis stand for halving the
meridional overturning circulation (MOC). Note that the main goal of the
simulations in Fig. 4 is to demonstrate that age plateaus almost disappear
and are delayed in the surface ocean due to the smoothing effect of the
carbon cycle, even if it stays strictly constant. These simulations are not
intended to simulate 14C and MRA changes due to variations of the ocean
circulation. On this subject, it should be noted that part of the
simulations presented by Goslar et al. (1995) and mentioned in Sect. 2.3
and 2.8 were obtained with the very same box model used for our
simulations but with a variable MOC. (b)14C age versus calendar age plot computed for sinusoidal 14C
production wiggles with a period of 500 years and an amplitude change of
±15 % around the mean value (in order to produce oscillations
around the 1 : 1 line, 14C ages are calculated with the true half-life of 5730 years). The solid red curve shows the evolution for the tropospheric box, exhibiting 14C plateaus (marked with black arrows) when the slope of the relationship goes down to zero. The age plateau corresponds to the
second part of the atmospheric Δ14C wiggle, during which the
Δ14C decrease compensates the radioactive decay. The
dashed light blue curve shows the calculation results for the Indo-Pacific surface box,
while the dotted dark blue line stands for the Southern Ocean surface box.
The blue curves are offset with respect to the atmospheric curve by their
respective marine reservoir ages. In both cases, the slope of the
relationship does not decrease to zero, implying the absence of true
14C age plateaus in the surface ocean.
Using numerical models, it is possible to quantify the damping and phasing
effects, which depend directly on the duration of the Δ14C
wiggle and on the carbon residence time in the considered carbon cycle
reservoir (or chain of reservoirs). A convenient way is to consider a
sinusoidal 14C production leading to attenuated and shifted 14C
signals in the atmosphere and the ocean surface (see Fig. 4). The
Indo-Pacific low-latitude surface box of the 12-box model by Bard et al. (1997) has a 14C reservoir age of 320 14C yr at steady state. The
relative attenuation (i.e., the amplitude in the troposphere divided by the
amplitude in the surface ocean) is a factor of 1.8 for 500 cal yr long
14C wiggles and a factor of 3.1 for 200 cal yr long wiggles. The phase
lag between atmospheric and oceanic response also varies with the duration
of the 14C production signal: about 60 and 45 cal yr for 500 and
200 cal yr long wiggles, respectively.
To illustrate the relative attenuation increase with a larger reservoir age
it is useful to consider the simulation for the Southern Ocean surface box
which has a reservoir age of 890 14C yr in the 12-box model. For the
same 500 and 200 cal yr long Δ14C wiggles, the relative
attenuation factors are 3.0 and 3.4, respectively. Figure 4a shows the results
of these calculations for signal periods ranging from 2000 to 100 calendar
years. The period is equivalent to the duration of the Δ14C
wiggle and to twice the duration of the 14C age plateau, which
corresponds to the descending part of the Δ14C wiggle.
As illustrated in Fig. 4b, a 500 cal yr long wiggle of cosmogenic production
varying by ±15 % around its mean value is sufficient to produce an
atmospheric 14C age plateau. However, in the surface ocean boxes, the
slope of the 14C age versus calendar age curves does not drop to zero, i.e.,
there is an absence of a 14C age plateau in the modeled surface ocean
environment despite the atmospheric plateau. This is a direct consequence of
the damping of 14C signals by the carbon cycle.
The 14C bomb spike of the early 1960s provides further evidence of the
smoothing and phasing effects. The main 14C injection lasted only a few
years and the bomb spike can be viewed as the impulse response function of
the carbon cycle. In detail, the bomb pulse is complicated because the
Δ14C decrease observed in the atmosphere over the last 50 years
is also partially due to the Suess effect linked to the input of
anthropogenic CO2 devoid of 14C (Levin and Hesshaimer, 2000). In
any case, the ocean surface Δ14C increased in the 1970s by 150 ‰–200 ‰ above pre-bomb values, which is about 5 times
less than the maximum anomaly in the atmospheric pool. This large damping
effect remains true even for the shallowest lagoons (Grottoli and Eakin,
2007), which illustrates the efficacy of ocean mixing to counterbalance
air–sea gas exchange.
Today the ocean surface Δ14C is still above these pre-bomb
values (e.g., Andrews et al., 2016) and this anomaly will remain with us for
many decades to come as the present level in the ocean surface is only about
halfway through its long-term asymptotic decrease. The calculation of an
average phase lag between atmosphere and ocean is difficult because the
impulse response functions are completely asymmetric (i.e., the delay for
the signal rise is totally different to that observed for the signal
decrease). To sum up, the bomb spike in the surface ocean is also a century-scale event, with an attenuation compatible with that calculated by
considering sinusoidal signals (Fig. 4a), which remains the traditional way
used in signal analysis (i.e., so-called Bode plots).
Plateaus and carbon cycle changes
The considerations above apply to Δ14C wiggles linked to
14C production changes, but it has likewise been suggested that
14C age plateaus may also correspond to carbon cycle changes, notably
at the end of the Younger Dryas (YD) climatic event (Oeschger et al., 1980; Goslar
et al., 1995; Hughen et al., 1998). This YD 14C age plateau may have been
caused by an abrupt resumption of the meridional overturning circulation as
simulated by numerical models (Goslar et al., 1995; Stocker and Wright,
1996, 1998; Hughen et al., 1998; see Fig. 17 by Bard, 1998, comparing the
14C age plateau simulated by the 12-box model and the Bern 2.5D
physical model).
In such a case, the 14C perturbation originates from the ocean, and the
relative attenuation and phase relationships with the atmospheric pool are
more complex, exhibiting regional differences. The surface ocean regions
responsible for uptake or outgassing of CO2 exhibit large effects with
no delay, whereas other regions are only affected in a passive way through
the atmosphere. For those widespread passive regions, we must refer back to
the calculations shown in Fig. 4a and b, which lead to large attenuations
in the ocean.
The study of atmosphere and surface ocean 14C wiggles linked to
spatially variable ocean–atmosphere exchange is inherently more complex as
it requires spatial resolution with 2D and 3D models and consideration of
regional 14C data to identify active and passive regions. However,
regional gradient changes of surface 14C simulated by models are
generally on the order of a few centuries (Stocker and Wright, 1998;
Delaygue et al., 2003; Butzin et al., 2005; Franke et al., 2008; Singarayer et
al., 2008; Ritz et al., 2008), rather than the millennia advocated by
Sarnthein et al. (2020).
The tephra method to reconstruct MRA
In Sect. 1.2 of their paper, Sarnthein et al. (2020)
mention a completely different method to reconstruct MRA based on 14C
datings of the same volcanic ash layer (tephra) in land and marine
sediments. This is indeed a precise and accurate method used in many studies,
including those cited by Sarnthein et al. (2020). However, they should have
also cited the two seminal papers on the subject: Bard (1988) was the first
to specifically propose the use of volcanic ash layers to reconstruct past
MRA variations, and Bard et al. (1994) were the first to reconstruct past MRA
changes with this powerful method.
The further advantage of the MRA method based on tephra is that the downcore
profile of volcanic shard counts provides a useful constraint on the
bioturbation depth and intensity, which also affect 14C ages measured
on foraminifera. Indeed, this shard profile is the impulse response function
of the bioturbation filter and hence provides information that can be used
to correct 14C ages used for the MRA calculation (Bard et al., 1994).
Another route to detect and constrain bioturbation is to study the total
scatter of 14C ages measured on single specimens of foraminifera and to
compare it with the smaller dispersion linked to analytical errors only
(Fagault et al., 2019; Dolman et al., 2021).
Fundamental to the accuracy, robustness, and reliability in the
estimates of the timescales and MRAs obtained by PT, is whether true
atmospheric 14C age plateaus can be consistently identified and also
matched between sparsely sampled and noisy records. These plateaus must be
reliably identified both in the atmospheric master target record, Lake Suigetsu in the
case of Sarnthein et al. (2020), and in the marine sediment cores for which
one wishes to infer a calendar chronology. In the case of marine sediment
cores, identification of these atmospheric 14C age plateaus is further
confounded by potential MRA changes, bioturbation, and the lack of an
independent timescale. One needs to have confidence that the plateaus
identified in both the marine and atmospheric 14C records are not only
genuine atmospheric features but have also been correctly paired together.
Sarnthein et al. (2020) do not appear to address this issue – instead
concentrating on an argument as to whether the true underlying atmospheric
14C record contains plateaus. The presence of 14C plateau periods in the underlying atmosphere is a
necessary condition for PT (discussed further in Sect. 3.8). However, the presence of such periods (in the unknown true atmosphere) is certainly
not sufficient to ensure the reliability of PT. Rather, the main statistical
concern for PT is as to whether any such genuine atmospheric 14C age
plateaus can be identified in both the sparse and uncertain Lake Suigetsu
14C record and the marine sediment 14C record being
plateau-tuned. If one either mislocates a true 14C age plateau in one
or other of the records or incorrectly pairs plateaus between the records,
then the subsequent timescales of the sediment core and MRA estimation will
be unreliable. We raise specific statistical concerns regarding the reliable
identification and pairing of potential plateaus.
Identification of atmospheric plateaus
The first statistical requirement for the PT technique is the ability
to precisely identify hypothesized 14C age plateaus based upon limited
sequences of noisy and sparsely sampled 14C observations. The
limitations in the amount of data available in the underlying records used
for PT and their observational noise lead to rather ambiguous estimations
of the locations and durations of the proposed Sarnthein et al. (2020)
14C age plateaus. The challenge of plateau identification will be most
significant in the ocean sediment cores to be tuned, since they are
typically the most sparsely sampled and lack a timescale, as we discuss
later. However, such ambiguity is also present in the atmospheric records.
This raises questions regarding the reliability of the atmospheric
14C age plateaus used as a target.
In the case of Sarnthein et al. (2020), the hypothesized atmospheric
14C age plateaus are selected based on the Lake Suigetsu and Hulu
14C records (see Sarnthein et al., 2020's Fig. S1, showing 14C error
bars at 1σ). Sarnthein et al. (2020) invoke ad hoc changes of the
dead carbon fraction (DCF) of the Hulu Cave speleothems (Sect. 2.2) and
an argument that filtering has removed the plateaus within the speleothem
14C record to explain the lack of correspondence between the
14C age plateaus they outline in the Lake Suigetsu target curves
compared with the Hulu Cave target (Sect. 1.2). There is no obvious reason
that would explain systematic changes in the speleothem DCF occurring only
during the atmospheric 14C age plateaus in such a way as
to both mask these atmospheric plateaus in the speleothem 14C record
and to ensure that the speleothem record does not generate additional
spurious, DCF-driven, 14C age plateaus between the genuine atmospheric
ones. As already underlined in our Sect. 2.2, the opposite hypothesis is
made by Sarnthein et al. (2020) for MRA, which is assumed to remain constant
during the atmospheric 14C age plateaus, changing only at boundary
times between plateaus.
Based on the comparison with the precise IntCal13 record over the past 14 cal kyr BP, the DCF for Hulu is on the order of 450 ± 70 14C yr
(1σ, Cheng at al., 2018). This mean value and standard deviation
have been further tested and refined in the frame of IntCal20 by comparing
the Hulu data for individual speleothems with the tree-ring 14C record
over the past 14 cal kyr BP. This IntCal20 testing gives an estimate of 480 ± 55 14C yr (Reimer et al., 2020; Heaton et al., 2020b). The low
value and stability of the Hulu DCF are attributed to the characteristics of
the Hulu cave with its sandstone ceiling and open system conditions with the
soil above it. In a similar way to the ocean modeling in Sect. 2.7, the
carbon transport and mixing processes leading to the DCF should have
somewhat smoothed the atmospheric 14C variations. Although a specific kind of
modeling should be performed for a particular cave system, the DCF value for
Hulu cave (ca. 480 14C yr) is equivalent to the average MRA of
low-latitude to midlatitude surface oceans (Heaton et al., 2020a). Consequently, the
14C age plateaus should be smoothed and delayed at a similar level as
in surface ocean records, for which Sarnthein et al. (2020) assume that
plateaus are of the same duration and timing as in the atmospheric record.
Further, one would expect that due to the time-directional nature of any
speleothem filtering (i.e., that it averages over past atmospheric 14C
concentrations), 14C age plateaus in the Hulu record should either be
seen with a time lag compared with the atmosphere or at least towards the
more recent end of the atmospheric plateau. This is not the case in
Sarnthein et al. (2020), where Fig. S1 predominantly proposes Hulu Cave
14C age plateaus that occur at the beginning (i.e., older end) of their
hypothesized atmospheric 14C age plateaus.
When one considers the uncertainty in the 14C determinations, which are
used to identify the hypothesized atmospheric 14C age plateaus (see
Fig. S1 of Sarnthein et al., 2020, showing error bars plotted at 1σ), it is unclear how strong the evidence is for several of the plateaus in the
atmospheric target suite. When measurement uncertainties are large, one
would expect (simply due to the randomness of these uncertainties) to
observe sequences of 14C determinations that are non-monotonic even
when the underlying atmospheric 14C age to calendar age is
monotonically increasing. Based upon the Lake Suigetsu observations and
uncertainties, it is therefore hard to assess whether some of the
hypothesized atmospheric 14C age plateaus really exist or are instead
just random artifacts due to measurement uncertainty. This is particularly
true for the upper panel of their Fig. S1, which is focused on the period between 21
and 27 cal kyr BP.
To illustrate this concern about the reliability of the hypothesized
atmospheric target plateaus more clearly, as mentioned in Sect. 2.3, we
created a Lake Suigetsu-only calibration curve. This used the same Bayesian
statistical method as implemented for IntCal20 (Heaton et al., 2020b; Reimer
et al., 2020) but was constructed based only upon the 14C observations
from Lake Suigetsu (using the updated Lake Suigetsu calendar age timescale
provided by Bronk Ramsey et al., 2020). Figure 1 shows the Lake Suigetsu
14C data with their 1σ analytical uncertainties (both in
radiocarbon and calendar age) and the resulting Suigetsu-only radiocarbon
calibration curve with its 95 % posterior predictive probability interval.
Superimposed horizontal lines indicate the 15 hypothesized atmospheric
plateaus with their numbering as listed in Table 1 of Sarnthein et al. (2020). Besides the well-known plateau no. 1, corresponding to the beginning
of the Bölling period, it is dubious as to whether many of the older
plateaus in particular are supported by the Lake Suigetsu data based on our
statistical assessment.
Furthermore, most of the Sarnthein et al. (2020) hypothesized 14C age
plateaus have calendar durations exceeding the 95 % posterior predictive
probability interval around the Lake Suigetsu data (notably plateaus nos.
2a, 4, 8, and 10b). The two plateaus (nos. 2b and 6b) with the shortest
duration (410 cal yr) are compatible with the probability interval around
the Lake Suigetsu data. However, these sections of the Suigetsu-only
calibration curve are also compatible with straight oblique lines with no
plateau at all. Such a conclusion is supported by Fig. 2, which compares the
plateaus and the IntCal20 curve. Only a few plateaus could correspond to
particular structures of the IntCal20 curve, notably plateau no. 1, which is
already known, and maybe plateaus nos. 7, 10a, and 11, although their
identified structures are much shorter, and thus smaller in Δ14C (Fig. 3), than the hypothesized plateaus would imply.
Matching plateaus
Current 14C sediment-based records do not have the resolution or
precision in 14C measurement one might ideally desire – it is
for this reason the community aims for the use of tree-rings to construct
the internationally ratified IntCal calibration curve. For example, the Lake
Suigetsu record (Bronk Ramsey et al., 2012) upon which PT is based contains
only 76 observations from 12–13.9 cal kyr BP with 14C age
uncertainties varying between 39 and 145 14C yr (1σ). The
Cariaco marine record (Hughen et al., 2006) is one of the more
highly resolved collections of foraminifera but contains only 24 observations
from 14–15.9 cal kyr BP with 14C age measurement uncertainties of
around 40 14C yr (1σ).
In light of such sparse sampling and measurement uncertainty, it is unclear
how much confidence one can have that the identified 14C age plateaus, in either
the target Lake Suigetsu record or the marine record one intends to tune,
are genuine atmospheric phenomena and whether they are correctly paired
between the two records. One would need to be confident both that the Lake
Suigetsu record had identified all genuine atmospheric 14C age plateaus
and further that one can then pair each identified marine 14C age
plateau correctly with its corresponding atmospheric 14C age plateau.
This latter step needs to take into account that the marine 14C age
plateau is offset by an unknown and potentially varying surface marine
reservoir age.
If one fails to identify a genuine atmospheric 14C age plateau using
the Lake Suigetsu record, then one cannot presumably be sure one is matching
the same 14C age plateaus between the records with the consequential
risk of severe misalignment of the marine core. As a hypothetical example,
suppose there were five true atmospheric 14C age plateaus, but only
three of these five plateaus were identifiable in the noisy Lake Suigetsu
observations. The other two genuine plateaus would therefore remain unknown
in our target. Further, suppose one then identified three plateaus in the
marine core to be tuned. With there being five true underlying 14C age
plateaus, these three marine plateaus could correspond to different plateaus
from the three identified in the Lake Suigetsu target. Despite this, PT
would confidently align the three plateaus in both records. The resulting
alignment could however be entirely incorrect, leading to errors in the
marine core chronology and MRA reconstruction.
Identification of marine plateaus
Further statistical difficulties arise in determining 14C age
plateaus in the marine core since before PT is performed one does not have
a calendar timescale on which to provide a gradient (in terms of 14C yr (cal yr)-1). Without the ability to work out the 14C gradient per calendar year,
identifying a 14C age plateau is considerably more challenging. A
natural option might be to use the depth scale within the core. This would
be equivalent to assuming a constant sedimentation rate. However, in then
tying or matching subsequent plateau to the atmospheric 14C age plateau,
this assumption of constant sedimentation would be overridden and
potentially significantly violated. This introduces significant circularity
and potential for contradiction into the PT approach. In fact, in previous
work, the PT method appears to provide sedimentation rates that vary by up
to a factor of 5 to 8 within a single core (e.g., cores PS2644 and MD08-3180
in Sarnthein et al., 2015; PS75/104-1 in Küssner et al., 2018),
considerably more (by orders of magnitude) in other cores from the Nordic
Seas (Sarnthein and Werner, 2017), and even more in ad hoc hiatuses
mentioned above in Sect. 2.4. The question of how one identifies a marine
14C age plateau in the context of a changing and unknown sedimentation
rate and calendar age scale – for which estimates only become available after
one has already been required to select plateaus and perform the tuning –
does not have a straightforward solution and is prone to confirmatory bias.
Objective plateau identification
The original PT method (Sarnthein et al., 2007, 2011) was based only on
visual inspection of the observed 14C ages versus depth to determine the
14C age plateaus (both the duration of the plateaus and their constant
14C ages including an unknown marine reservoir age). In an attempt to
reduce the subjectivity of eyeball evaluation, Sarnthein et al. (2015)
calculated the first derivative of a locally fitted 14C age versus depth
curve to identify the 14C age plateaus (i.e., times when the slope
drops to near zero). This refinement goes in the right direction, but the
authors admit that there is room for subjectivity when choosing the level of
smoothing and the threshold for defining a plateau. In addition, their
derivation technique does not explicitly consider the different analytical
uncertainties of the individual 14C measurements on atmospheric and
marine samples that are quite variable, a notable example being the Lake Suigetsu
14C data measured on small plant macrofossils of varying carbon masses
(Bronk Ramsey et al., 2012). Such analytical uncertainties should be taken
into account because a kink or a plateau in a 14C versus depth
relationship within a record could be a structure within the analytical
errors rather than an atmospheric feature. PT that includes such random analytical
error would be useless.
In any case, Sarnthein et al. (2015) chose to keep the visual inspection as
their main tool: “we continued to base our calculations of reservoir ages, our tuned calendar ages of plateau boundaries, and sedimentation rates on the boundary ages defined by visual inspection.”. In Sarnthein et al. (2020), it seems that eyeball
inspection has been preferred again: indeed Table 1 provides 14C age
plateaus obtained “by means of visual inspection” in the target records (Lake Suigetsu varved sediments and
Hulu cave stalagmites).
Weninger (2021) advocates for the use of an alternative
automated technique (a “summed probability distribution”) proposed by
Weninger and Edinborough (2020) for detecting plateaus in the 14C
record. Unfortunately, this paper does not provide the necessary
mathematical details to reproduce and test the proposed technique. It should
also be noted that Weninger and Edinborough (2020) only claim the detection
of 4 plateaus in the 24 to 14 kyr BP time window of IntCal20 (see their Fig. 1), in contrast with the 11 plateaus named by Sarnthein et al. (2020) over
the same period.
Testing PT
To objectively assess the ability to reliably identify and tune
14C age plateaus in the context of the noisy and sparse 14C data
currently available to us, we performed a simulation study. For this study,
we aimed to investigate two aspects: firstly, can we reliably and robustly
identify atmospheric 14C age plateaus in data that are of comparable
density and precision to those from Lake Suigetsu (Bronk Ramsey et al.,
2020). Secondly, having simulated paired 14C age and depth data
from a hypothetical marine core with comparable precision and density to the
Cariaco Basin (Hughen et al., 2006), can we use PT to accurately reconstruct
the marine record's underlying calendar age scale?
Readers should note that our simulated pseudo-Cariaco marine core is densely
sampled compared with the marine cores studied by Sarnthein et al. (2020).
Moreover, in our study we have simplified the tuning problem by setting a
constant MRA for our simulated marine record. The additional complexities
introduced to PT should MRAs change at any time are not considered. Consequently, our simulation results should
be considered a best-case scenario for the PT method.
To maximize objectivity, we aimed to implement the automated 14C age
plateau identification approach presented in Sarnthein et al. (2015). The
description of their automated approach lacks precise detail to be
completely reproducible; however, we hope that our method follows the
principles sufficiently closely. Having simulated our cores, to estimate the
local 14C yr (cal yr)-1 gradient at any depth, we fit a kernel-weighted
linear model using a N(0,502 cal yr2) kernel (or an
analogue in the case of the marine core for which we have no known calendar
age scale) and based upon a fixed number of samples in the neighborhood of
the depth under consideration. We also show the results using a N(0,1002 cal yr2) kernel to illustrate the effects on gradient
estimation and plateau identification in our simulated pseudo-atmospheric
cores. This wider kernel will extend the effective width of the window in
which we calculate the local gradient, using more neighboring observations
and performing more smoothing. Further details are given in Sect. 3.7 and 3.8.
Sarnthein et al. (2015) do not specify definite rules as to either the
choice of bandwidth (the standard deviation of the weighting kernel) to use
when estimating the local gradient within a record or the subsequent
gradient threshold that defines a 14C plateau. The subjectivity in
these selections, which as we show makes a considerable difference to the
number and locations of plateaus, will always reduce the ability of PT to
provide an objective approach. Indeed, as explained in Sect. 3.5, Sarnthein et al. (2015) admit to a final selection that is based upon agreement with their
eye-balled choices.
Sarnthein et al. (2015) trialed two thresholds for the local gradient to
determine a 14C plateau. They state that a value of 0 14C yr (cal yr)-1
generated too many short potential plateau periods. In increasing the
threshold value, these disconnected short time periods will tend to merge
with one another to create longer time periods. However, other disconnected
time periods may simultaneously be introduced. Sarnthein et al. (2015)
therefore also trialed increasing the threshold to 1 14C yr (cal yr)-1
which they suggest agreed better with their visual preferences. However, a
time period with a gradient of 1 14C yr (cal yr)-1 is quite a distance
from what most would describe as a plateau. Indeed, a gradient of 1 is what
the slope (14C yr (cal yr)-1) should be without any perturbation of the
radioactive decay. Nevertheless, in order to test this issue, we now show
our results alongside three potential gradient thresholds of 0, 0.5, and 1 14C yr (cal yr)-1 that one might use to identify a plateau.
Atmospheric baseline
To further increase objectivity, we aim to have an underlying
“ground-truth” atmospheric 14C baseline for the simulated cores that accurately
reflects the true size and scale of atmospheric 14C age variation and
potential plateaus. Consequently, our study considers the period from 12–13.9 cal kyr BP, and we use the IntCal20 curve (Reimer et al., 2020) as our
ground-truth atmospheric 14C baseline, shown as a black line in Fig. 5a–c. Since this period of IntCal20 is based upon densely sampled 14C
determinations from highly resolved tree-rings, we can be confident it
represents genuine atmospheric 14C age variation and plateaus. Even
here though, exactly what would be considered to constitute a 14C age
plateau, and how many are present, is ill-defined.
Simulation study to identify the ability of a Suigetsu-style
record to reliably identify atmospheric 14C age plateaus. In panels (a)–(c) we present three simulated atmospheric records generated by sampling, subject to noise, from the high-precision tree-ring-based section of IntCal20 between 12–13.9 cal kyr BP (shown as a black line) with a sampling
density matching that of Lake Suigetsu (Bronk Ramsey et al., 2020). The
level of noise added to create the simulated 14C observations (blue,
red, and green dots) is of an equivalent level to that present in Lake
Suigetsu 14C. For each simulated set of observations, we present an
estimate of the local gradient (shown as blue, red, and green curves with
their 95 % confidence intervals) according to a locally weighted linear
model as proposed by Sarnthein et al. (2015). These local gradient estimates
are obtained using a N(0,502 cal yr2) kernel to provide the
weightings. We overlay three gradient thresholds (0, 0.5, and 1 14C yr (cal yr)-1) that might be used to identify a 14C age plateau. Shown as
a rug at the bottom of each plot are the time periods in each core that
correspond to a local gradient below each threshold (color coded by
threshold). In panel (d) we overlay the gradient estimates to assess
consistency (or lack of) between the three simulated cores in terms of the
number and location of 14C age plateaus one might identify. As a rug,
we plot the time periods in each core (color coded by core) that correspond
to a local gradient below the threshold of 1 14C yr (cal yr)-1 proposed by Sarnthein et al. (2015).
To this tree-ring-based plateau baseline, we create a pseudo-Suigetsu
atmospheric 14C record by randomly sampling (fairly evenly spaced)
calendar ages between 12–13.9 cal kyr BP and adding noise comparable to
that seen in the 14C determinations of Lake Suigetsu (Bronk Ramsey et
al., 2020). For this pseudo-atmospheric record, we assume the calendar ages
of the 14C determinations are known precisely, which aids plateau
identification.
We also create a pseudo-Cariaco marine 14C record by again sampling
(fairly evenly spaced) calendar ages and adding noise comparable to the
14C determinations in the Cariaco Basin record (Hughen et al., 2006). To
make this analogous to a genuine marine record PT, the calendar age
information within the pseudo-marine record was then dropped. We assumed the
14C measurements were evenly spaced in depth along the core. We sought
to identify if we could then reconstruct this underlying calendar age
information using PT.
Simulated atmospheric 14C records
We initially simulated three hypothetical cores recording atmospheric
14C. These aimed to represent data similar to Lake Suigetsu, both in
terms of sampling density and 14C age measurement uncertainties. There
are 76 14C observations from Lake Suigetsu between 12 and 13.9 kcal BP.
For each of our atmosphere-recording pseudo-cores, we sampled N= 76
observations as follows.
Simulate calendar ages θi through the following steps:
sample the Uk∼Unif[12,13.9] for k=1,…,2N;
order sampled values to obtain U(1)<U(2)<…<U(2N);
set θi=U(2i) for i=1,…,N (i.e., every
second ordered value).
This provides a set of random (but relatively evenly sampled) calendar ages.
Simulate 14C ages Xi for i=1,…,N as Xi∼N(μ(θi),σi2), where
μ(θi) is the mean of the IntCal20 curve at
calendar age θi and σi2 is the variance of the
14C age measurements reported in the true Lake Suigetsu record (Bronk
Ramsey et al., 2020).
To estimate the gradient (14C yr (cal yr)-1) at any given calendar
age t, following the approach described in Sarnthein et
al. (2015) as best we could, we fitted a linear model to the nearest 60 observations, in terms of calendar
age, weighted according to their reported uncertainties
σi and a N(0,502 cal yr2) kernel centered on t
– this gives a weighted moving window, where those observations more than
100–200 cal yr from t have little weight. In our idealized scenario, we
assumed the calendar ages of the observations were known exactly for
weighting and gradient estimation – in the genuine Lake Suigetsu record
these calendar ages are themselves uncertain, making gradient estimates
somewhat harder.
Figure 5a–c show, for each of the three atmosphere-recording pseudo-cores,
the simulated data and estimated gradient (with 95 % intervals for the
estimate obtained by the weighted linear model). Gradient estimates at the
calendar age extremes should be treated with caution and are thus not
considered below. In Fig. 5a–c we overlay the three gradient
thresholds (0, 0.5 and 1 14C yr (cal yr)-1) that might be considered
as potential plateau indicators as discussed in Sect. 3.6. Shown as a rug
at the bottom of each plot are the time periods (color coded according to
threshold) for which the gradient falls below each threshold. Figure 5d shows
the gradient estimates for all three cores overlain with the recommended
Sarnthein et al. (2015) gradient threshold of 1 14C yr (cal yr)-1. Here the
rug shows the time periods in all the cores (color coded by core) where the
locally estimated gradient falls below 1 14C yr (cal yr)-1. For the
automated PT approach to be reliable it is necessary, yet not sufficient,
for those periods and plateaus in the atmosphere-recording pseudo-cores that one
intends to use as an atmospheric tuning target for the marine record to
align.
The same as Fig. 5 but using a wider N(0,1002 cal yr2)
kernel to provide the weightings for the local gradient estimate. The same
three simulated atmospheric cores are used.
We also repeated our gradient estimation technique using a wider N(0,1002 cal yr2) kernel. This creates a wider moving window and
gives weight to a greater number of neighboring observations when estimating
the local gradient. The results for the same three simulated
pseudo-atmospheric cores with this alternative wider kernel (which applies
greater smoothing) can be seen in Fig. 6.
We observe that while the gradients obtained in each simulated core show
some of the same main features, differences remain that are critical to PT.
In particular, we see that the choice of kernel and the gradient cutoff for
identifying a plateau is key and that selection is non-trivial if one wishes to
maintain consistency in creating a robust atmospheric target between
records. Varying these subjective choices, even within the same core, can
make a significant difference to the number and location of atmospheric
plateaus which one would estimate. This has significant consequences upon
the reliability of any subsequent PT of a marine core.
While we can seem to identify elements of the long YD 14C plateau from
12–12.4 cal kyr BP in all our simulated “Suigetsu-quality” atmospheric cores, beyond
this it is hard to consistently and reliably identify what constitutes a
plateau. Whatever the choice of threshold and kernel, the number and timing
of plateau periods are not consistent between the three simulated records.
Using a threshold of 1 14C yr (cal yr)-1 and a N(0,502 cal yr2) kernel, we may estimate either 5 or 6 plateau periods (Fig. 5d),
depending upon the simulated core we consider. Further, other than the YD
plateau, these do not accurately align between the three cores. Even if one
sought to merge the plateaus, it is unclear as to how one would know which
to merge with others.
If one has different numbers of 14C age plateaus in the atmospheric
target, then one will tune the marine core very differently. A PT using the
blue simulated record as an atmospheric target (Fig. 5a) would likely lead
to very different results than tuning to the red simulated record (Fig. 5b).
We see in Fig. 5d the lack of consistency in the alignment of the identified
14C age plateau periods from 13–14 cal kyr BP, making it very hard to
know what plateaus one should aim to tune. In the time period we consider
for our study, Sarnthein et al. (2020) identify two plateaus they aim to use
as tuning targets (at 11.9–12.48 kcal BP and 13.66–14.04 cal kyr BP),
plus a potential third plateau at
12.78–13.08 cal kyr BP, but whether they always use this third plateau is unclear. However, in our simulation study we find just as
strong support for a plateau around 13.4 cal kyr BP in some of our simulated
cores. Further, looking at the underlying IntCal20 estimate which, as
explained above, is tree-ring-based and highly resolved in this period, the
level of visual evidence for a plateau from 12.78–13.08 cal kyr BP is
unclear.
In conclusion, we see that using different Suigetsu-quality atmospheric 14C records to
initially identify 14C age plateaus could lead to quite different
target matchings for the marine records. Not only might the atmospheric
target 14C age plateaus identified not align with those one would have
obtained using a different initial atmospheric record, but one might also be
aiming to identify different number of 14C age plateaus in the marine
core. Further, if there are in fact more genuine atmospheric 14C age
plateaus than the atmospheric-recording core indicated, this could lead to
erroneous pairings between the atmospheric and marine records.
Simulated marine 14C records
We also created two pseudo-marine 14C records to span 12–13.9 cal kyr BP, again using the tree-ring-based section of IntCal20 as our
ground-truth atmospheric 14C baseline. For these pseudo-marine records,
we aimed to represent the relatively high density and precision of the
Cariaco Basin un-varved 14C record (Hughen et al., 2006). These simulated
pseudo-marine cores are created similarly to the pseudo-atmospheric
simulated records above, but after creation we remove the calendar age
information that aids 14C yr (cal yr)-1 gradient calculation. This makes
them analogous to the marine cores used by Sarnthein et al. (2020). Instead
14C age plateaus in the pseudo-marine must be identified using only
their ordering (or simulated depth) within the core – a considerably more
challenging and less robust task. We aim to compare our simulated marine
14C records against our pseudo-atmospheric cores of Sect. 3.8 to
assess the ability to identify and match shared 14C age plateaus.
For this element of the study, we make the very strong simplifying
assumption that there are no MRA changes in the marine records. This
simplifying assumption will aid us considerably in identifying the
atmospheric component of the 14C age signal in our simulated marine
records. As such, this part of our study only considers the effect on
identifying plateaus in light of the increased sparsity in marine records
and the lack of timescale on which to reliably infer the 14C to
calendar age gradient. MRA changes will add a very considerable further
layer of confounding and difficulty.
Simulation study to identify the ability to identify 14C age
plateaus in marine records for which calendar age scales are not initially
known. In panel (a), we simulate two marine records between 12–13.9 cal kyr BP, again using the tree-ring-based IntCal20 mean as the ground truth. These
simulated marine cores are based upon the sampling density of the Cariaco
Basin un-varved record (Hughen et al., 2006) with a similar level of
observational 14C noise and have been created with a constant MRA of
400 14C yr. To identify plateaus, we first estimate the gradient on
the basis of an unknown calendar age scale. We create a pseudo-calendar age
scale by rescaling the observations so that they are equally spaced along the cores
before applying our locally weighted linear model approach, using an
equivalent N(0,502 cal yr2) kernel, to estimate the
gradients on this (equally spaced) pseudo-scale. Estimates of pseudo-gradients
and implied relative sedimentation rates on this equally spaced
pseudo-timescale are provided in panel (b). At the top, we plot (color coded by core) the section of core for which we would obtain a local
pseudo-gradient below 1 14C yr (pseudo-cal yr)-1 and which could be identified as a 14C age plateau. Scaling back to the true underlying calendar age timescale, panel (c) indicates where, in terms of the actual calendar age scale, one might classify plateaus. In panel (d), these are overlain against the first of our simulated atmospheric records in Fig. 5 to assess synchroneity. As a rug we show the time periods, on the true underlying calendar age timescale, corresponding to local gradient threshold estimates below 1 14C yr (cal yr)-1 for the simulated atmospheric record and 1 14C yr (pseudo-cal yr)-1 for the two simulated marine records.
For IntCal13, in the 14–15.9 cal kyr time period, the Cariaco Basin
un-varved 14C record contained 24 observations (Hughen et al., 2006).
Note that this 14–15.9 cal kyr period is used as representative of
sampling density since the Cariaco un-varved record does not extend to 12 cal kyr. For each pseudo-marine core, mirroring the approach given in our
pseudo-atmospheric simulated records, we simulated N= 24 random
observations with underlying calendar ages again sampled according to every
other ordered value of a uniform distribution to create relatively
equally spaced ages ranging from 12–13.9 cal kyr BP. However, for these marine
cores, we selected 14C age measurement uncertainties that matched those
in the Cariaco Basin (1σ of approximately 40 14C yr) and
applied an adjustment of a constant MRA of 400 14C yr. The resultant
simulations are shown in Fig. 7 – with the simulated data shown in Fig. 7a.
Since the Cariaco Basin 14C record is quite densely sampled compared to
marine records studied by Sarnthein et al. (2020) and we have applied a
constant MRA, this set-up provides a best-case scenario for PT.
As discussed above and in Sect. 3.4, estimating the gradient (14C yr (cal yr)-1) and identifying 14C age plateaus is made more complex here
since, in a marine core, the calendar ages are unknown before PT. One is
therefore required to select a prior pseudo-calendar scale on which to
estimate a gradient and hence identify plateaus. We chose to estimate the
gradient by assuming our 14C observations are equally spaced in depth (and
hence equally age-spaced along our pseudo-calendar scale) along the core as
shown in Fig. 7b. Since by construction our true calendar ages, θi, are relatively evenly spaced, this pseudo-calendar scale should
equate to a sedimentation rate that is not unrealistically variable
compared to the reconstructed estimates inferred by the PT in Sarnthein et
al. (2015), Sarnthein and Werner (2017), and Küssner et al. (2018). The
inferred sedimentation rate that would generate this equal spacing in depth
can be seen at the foot of Fig. 7b. The maximum variation in sedimentation
rate within the simulated cores is on the order of 10. Such equal spacing
can equivalently be interpreted as using the ordering information only to
determine the gradient, i.e., the change we observe moving from one 14C
observation to the next. This is a natural approach as it requires no a
priori assumptions regarding the unknown true calendar ages.
The same linear model approach as used for the pseudo-atmospheric cores was
then applied with weightings determined according to our even-depth
observational spacing (i.e., using the observational order only). We used
the nearest 20 marine 14C observations and a kernel on the depth scale
analogous to the N(0,502 cal yr2) used for the
pseudo-atmospheric observations. This depth kernel was applied so that 50 cal yr corresponded to 50/1900 (2.6 %) of the entire pseudo-marine core's depth (i.e., so
the 1.9 kyr period between 12–13.9 cal kyr period covered the full
pseudo-marine core). The obtained pseudo-gradient estimates for the cores
are shown in Fig. 7b on this even-spacing (or observational-order) basis.
We would select 14C age plateaus and the observations belonging to
them based upon Fig. 7b with its even-depth-spaced, pseudo-calendar
timescale. We have overlain thresholds of 0, 0.5, and 1 14C yr (cal yr)-1 on
this pseudo-gradient scale. Shown at the top of this plot are the sections
of each simulated core (color coded according to the core) for which the
local pseudo-gradient lies below a threshold of 1 14C yr (pseudo-cal yr)-1.
For example, in the simulated marine core A (shown in purple) we might
identify an order-based plateau ending around the sixth observation
(perhaps covering the first to the sixth) since in this neighborhood
the 14C gradient on the pseudo-calendar scale is below 1.
We can infer where any order-based 14C age plateaus we identify
correspond to on the true calendar age timescale by transforming back from
the ordered or even-depth spacing to the underlying calendar ages of our
observations. Figure 7c shows the order-based gradient estimates (and the
observations in each simulated marine core) when plotted against the true
“unknown” calendar age timescale. In our example, the 14C age plateau
identified between the first and sixth observation in simulated marine
core A of Fig. 7b corresponds to a 14C age plateau covering the
interval 12–12.5 cal kyr BP. Shown as a rug are the underlying time periods
for the sections of each core (color coded) we would assess as corresponding
to a 14C age plateau when applying a threshold of 1 14C yr (pseudo-cal yr)-1.
In Fig. 7d, we overlay the order-based marine core 14C gradients, after
they have been transformed back to their true underlying timescales, against
the 14C gradient obtained from our first simulated Suigetsu-quality atmospheric record.
The rug indicates the plateaus one would identify based on a threshold of 1
14C yr (cal yr)-1 in the simulated (blue) atmospheric record and 1 14C yr (pseudo-cal yr)-1 in the two simulated (purple and orange) marine records.
For PT tuning to provide reliable MRA estimates, the identified plateaus (or
dips in gradient) in the simulated marine cores and the simulated
atmospheric 14C record should align in Fig. 7d. This does not reliably
occur.
The large amount of noise in the marine records, their sparsity, and
critically their unknown true timescale make identification of any 14C
plateaus very difficult with the potential to be unreliable and
inconsistent. Highly different numbers and locations of 14C plateaus
would be indicated when applying the method to the two simulated marine
records and for all gradient thresholds. Furthermore, the marine
14C age plateaus may not correspond to the 14C age plateaus in the
atmospheric record. Should one attempt to align the indicated
14C age plateaus of either of the simulated marine cores to the
atmospheric target by PT, one would obtain significantly incorrect chronologies
for the marine cores. Further, this misalignment would then result in
significant inferred MRA changes; such changes in MRA would be incorrect
since the underlying marine records were created with a constant MRA.
While simple, this simulation study illustrates some of the potential
difficulties of identifying 14C age plateaus in the presence of
observational noise and a lack of sampling density. Were the simulated data
to have much higher precision and be sampled much more densely, we would
expect the 14C age plateaus to align more consistently. Such a
preliminary study with an exact and reproducible approach is needed to
assess the robustness of the method. The fundamental questions of how to
remove the potentially confounding effects of MRA changes, the lack of a
timescale for the marine records on which to calculate the 14C age
gradient combined with significant sedimentation rate changes, and other
potential geoscientific factors in identifying 14C age plateaus will
however still remain.
Variability in atmospheric 14C
Finally, implicit in the Sarnthein et al. (2020) paper is a suggestion
that using Lake Suigetsu alone provides a more precise reconstruction of
atmospheric 14C levels from 55–13.9 cal kyr BP than the IntCal20
synthesis (Reimer et al., 2020). Specifically, that by combining 14C
records from a diverse range of archives, the IntCal curves lose genuine
atmospheric structure that can be extracted from Suigetsu. This perspective
is suggested by Sarnthein et al. (2020) through an argument that the
IntCal20 curve has lesser variation and fewer wiggles from 55–13.9 cal kyr BP, where it is based upon a range of archives, than from 13.9–0 cal kyr BP, where it is based upon highly resolved tree-ring determinations.
Indeed, Reimer et al. (2020) recognize the limitations of the current
archives upon which they base the IntCal20 curves. The ideal is for a truly
atmospheric 14C record extending back to 55 000 cal yr BP that also
provides sufficient detail to reconstruct the high-frequency component of
the 14C signal reliably and precisely. However, this characterization
of IntCal20, as overly smooth and hence unreliable from 55–13.9 cal kyr BP, is to misunderstand what the IntCal curve represents.
The values published as the IntCal curves aim to provide pointwise summaries
of the 14C age, in terms of the mean and uncertainty, at any chosen
calendar age. This is not the same as trying to represent the level of
14C variation from a single year to the next. Critically, a smooth pointwise
mean does not necessarily imply no variation in atmospheric 14C levels.
More likely, it represents that we do not yet know when and with what
magnitude any such 14C variations occur.
Construction of the IntCal20 curve uses Bayesian splines (Heaton et al., 2020b).
The outputs of this Bayesian approach are a large set of posterior curve
realizations that aim to find a trade-off between passing near the observed
14C determinations on which the curve is based, while not being so
variable as to overfit the data and introduce spurious features that are
simply artifacts of the analytical sampling errors within the 14C
observations. The pointwise summary is then based upon averaging over 2500
of these curve realizations.
Plot of five individual spline realizations randomly selected from
the Bayesian posterior of the Markov Chain–Monte Carlo approach used to
create IntCal20. The published IntCal20 consists of pointwise means (shown
as a solid purple line) and 95 % pointwise predictive intervals (dotted purple line) that are obtained by averaging over 2500 of these individual
curve realizations (Heaton et al., 2020b). Superimposed thick green lines
indicate the 15 atmospheric plateaus with their numbering as listed in Table 1 of Sarnthein et al. (2020).
The IntCal approach does, in fact, assume there are similar levels of
short-term atmospheric variability from 55–13.9 cal kyr as in the
tree-ring-based section from 13.9–0 cal kyr BP. This can be seen by
looking at the 5 individual posterior realizations, randomly selected from the
2500 used to create the final IntCal20 pointwise summary, shown in Fig. 8.
All these individual realizations exhibit significant short-term 14C
variability, although none are on the scale of the hypothesized Sarnthein et al. (2020) plateaus as they must still agree with the pointwise IntCal
uncertainties (shown at the 95 % level as the dotted purple envelope).
However, as we do not yet have sufficiently detailed 14C measurements,
the precise timing and magnitude of the fluctuations in the realizations is
unknown. Consequently, when averaged to provide the pointwise estimates, the
realizations generate an IntCal pointwise mean (shown as solid purple) that
is smoother than any individual realization.
Conclusion and outlook
Creating reliable calendar chronologies for deep-sea cores and
understanding the scale and timings of MRA changes (14C depletion), as
well as their variation across the globe, are key for the understanding of past Earth systems and carbon cycles. If the MRA is known, then 14C
calibration can be used to create the calendar chronology. Conversely, if
the calendar chronology is known, 14C can be used to infer the MRA.
When neither the MRA nor the calendar chronology are known in advance, this
creates a challenging problem. However, in such circumstances when neither
MRA nor calendar chronology is known, we have strong reservations regarding
the ability of PT to robustly provide such chronologies or accurately infer
MRA changes.
The 14C changes in marine sediment cores do not accurately mirror
atmospheric variability due to attenuation; phase shift of the atmospheric
signal; and variability in sedimentation rate, MRA, and foraminifera
assemblages. Further, low-resolution and noisy 14C datasets,
particularly hard to avoid in marine sediment cores, severely limit the
ability to reconstruct the fine-scale structures needed to tune to atmospheric
14C signal. Consequently, the identification and tuning of hypothesized
14C age plateaus between sparse marine sediment and atmospheric records
may lead to significant misalignment. The resultant calendar chronology for
the marine sediment and inferred MRA changes may therefore be equally
incorrect.
Techniques do exist to provide calendar chronologies for deep-sea cores
independently from 14C (and which can then be used to infer MRA
changes). These utilize alternative age links between proxies in combination
with age–depth modeling. One approach is to use climate proxies with the
sediment cores, for example these have been tied to the oxygen isotopic
(δ18O) profile of Hulu Cave stalagmites, which have been
accurately dated by U-Th (Bard et al., 2013; Heaton et al., 2013; Hughen and
Heaton, 2020). Potential age links are also provided by dated tephra layers
(e.g., Bard et al., 1994; Siani et al., 2013). Some of these approaches have
been developed by the INTIMATE group and are discussed and implemented in
Austin et al. (2011) and Waelbroeck et al. (2019). The latter includes some
of the sediment cores in Sarnthein et al. (2020).
Even amongst these alternative approaches to create calendar chronologies
for deep-sea cores, those based upon climate proxies (i.e., all except the
use of tephra) require significant assumptions about the global
synchronicity of the climate changes used to tie the records together.
Although evidence does support an assumption of globally synchronous timing
for certain rapid paleoclimatic changes (Corrick et al., 2020), it is
important to consider the specific climate changes used for tying and to
recognize the uncertainty that potentially non-exact synchronicity
introduces into the resulting marine core calendar chronology (Heaton et al.,
2013). The use of these climate proxy approaches to tuning will also mask
possible non-synchronous climate changes and precludes studying the climate
dynamics on the centennial to pluri-decennial timescale (Mekhaldi et al.,
2020).
Regarding the identification of atmospheric 14C variation, we recognize
that a precise, highly resolved, and directly atmospheric set of 14C
archives extending back to 55 kcal BP would provide a step change in our
ability to understand higher-frequency variations in past 14C levels.
While the Lake Suigetsu 14C record provides a first step towards this
goal, it is not of sufficient resolution or density to provide this – and,
as explained in Sarnthein et al. (2020) and Bronk Ramsey et al. (2020), its
varve-counted timescale needs adjustment using the Hulu Cave 14C
record. To accurately identify the higher-frequency components of 14C
variation from 55–14 cal kyr BP we must await recovery of new archives,
notably floating tree-ring sequences.
IntCal20, compared to previous versions of the 14C calibration curve,
profits from a multitude of improvements in all 14C archives beyond 14
cal kyr. These include new high-resolution 14C and U-Th data from Hulu
Cave (Cheng et al., 2018), reanalysis of the Lake Suigetsu timeline (Bronk
Ramsey et al., 2020), updates in the calendar scale and MRA modeling of the
Cariaco basin 14C dataset (Hughen and Heaton, 2020), floating tree-ring 14C sequences (Adolphi et al., 2017; Turney et al., 2010, 2016;
Capano et al., 2018, 2020), and modeling MRA time variations to correct the
marine-based records (Butzin et al., 2020). These have then been combined
using a more robust Bayesian spline statistical approach (Heaton et al.,
2020b) that aims to identify shared features seen in multiple records
without being overly influenced by individual outlying 14C measurements
that may not provide an accurate representation of atmospheric 14C
levels. However, as accepted by the IntCal20 authors, the sampling
resolution and nature of the diverse records (including their uncertain
calendar timescales as well as the indirect nature of 14C records taken
from both stalagmites and marine sediment cores) mean that centennial
14C variations are not able to be reliably resolved at present beyond
14 cal kyr BP. The discovery of further floating 14C tree-ring
sequences (e.g., Cooper et al., 2021) hold promise in addressing this.
The extreme variations of the PT-inferred sedimentation, including frequent
hiatuses, should be tested with independent techniques in order to prove
that PT is reliable and that sedimentation rate variations and hiatuses are
not artifacts of PT. For these crucial tests, PT should be performed
completely independently from the results obtained with other techniques
(e.g., tuning with tephra or with climate proxy records). In other words,
these other time markers should not be combined to PT if one wants to test
the validity of this method.
In order to further understand the possible existence of long 14C
plateaus, it may also be useful to plan new model simulations taking into
account carbon cycle and oceanographic changes during Heinrich and
Dansgaard–Oeschger events. Numerical experiments performed with box models
and 3D Earth system models with relevant changes (e.g., of the Meridional
Overturning Circulation of the ocean) may be compared and would provide a
stringent test in a wiggle-matching exercise.
Code availability
Code to reproduce our simulation study and recreate Figs. 5, 6 and 7 is available at 10.5281/zenodo.5176137 (Heaton and Bard, 2021).
Data availability
The data used and represented in figures are taken from the literature with relevant citations of published papers.
Author contributions
The authors worked jointly on the various aspects of the paper, with an emphasis on radiocarbon data, cosmogenic production and carbon cycle changes for EB and on the detection of radiocarbon variations and statistical modelling for TJH.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank the editor, André Paul, the referees, Paula
Reimer and an anonymous referee, and the commenters, Michael Sarnthein, Pieter Grootes, Frank Lamy, Helge Arz, Elisabeth Michel, Giuseppe Siani, and Bernhard Weninger, for their comments which helped us in revising and strengthening our paper. Timothy J. Heaton was funded by a Leverhulme Trust Fellowship
RF-2019-140/9. Edouard Bard is supported by EQUIPEX ASTER-CEREGE and ANR
CARBOTRYDH.
Financial support
This research has been supported by the Leverhulme Trust (grant no. RF-2019-140/9), EQUIPEX ASTER-CEREGE and ANR CARBOTRYDH.
Review statement
This paper was edited by André Paul and reviewed by Paula Reimer and one anonymous referee.
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