Using new and previously published CO2 data from the
EPICA Dome C ice core (EDC), we reconstruct a new high-resolution record of
atmospheric CO2 during Marine Isotope Stage (MIS) 6 (190 to 135 ka) the penultimate glacial period. Similar to the last glacial cycle,
where high-resolution data already exists, our record shows that during
longer North Atlantic (NA) stadials, millennial CO2 variations during
MIS 6 are clearly coincident with the bipolar seesaw signal in the Antarctic
temperature record. However, during one short stadial in the NA, atmospheric
CO2 variation is small (∼5 ppm) and the relationship
between temperature variations in EDC and atmospheric CO2 is unclear.
The magnitude of CO2 increase during Carbon Dioxide Maxima (CDM) is
closely related to the NA stadial duration in both MIS 6 and MIS 3 (60–27 ka). This observation implies that during the last two glacials the
overall bipolar seesaw coupling of climate and atmospheric CO2 operated similarly. In addition, similar to the last glacial period, CDM during the
earliest MIS 6 show different lags with respect to the corresponding abrupt
CH4 rises, the latter reflecting rapid warming in the Northern
Hemisphere (NH). During MIS 6i at around 181.5±0.3 ka, CDM 6i
lags the abrupt warming in the NH by only 240±320 years. However,
during CDM 6iv (171.1±0.2 ka) and CDM 6iii (175.4±0.4 ka) the lag is much longer: 1290±540 years on average. We speculate
that the size of this lag may be related to a larger expansion of carbon-rich, southern-sourced waters into the Northern Hemisphere in MIS 6,
providing a larger carbon reservoir that requires more time to be depleted.
Introduction
Ice core studies allow us to considerably extend our knowledge about natural
climate–carbon cycle feedbacks by directly reconstructing atmospheric
CO2 from gas preserved in Antarctic ice sheets (Lüthi et al.,
2008; Petit et al., 1999). Comparing atmospheric CO2 records from
Antarctic ice cores with proxies of paleoclimate helps us to understand how
atmospheric CO2 was controlled by carbon exchange with the ocean and
land reservoirs on orbital to centennial timescales (Ahn and Brook,
2008, 2014; Bereiter et al., 2012; Higgins et al., 2015; Lüthi et al.,
2008; Marcott et al., 2014; Petit et al., 1999).
Previous work on polar ice core records revealed that temperature variations
in Greenland and Antarctica on millennial timescales appear to be a
pervasive feature during the last glacial period. While Antarctic
temperature varied gradually, Greenland temperature changes occurred
abruptly. A phase difference can be observed between millennial-scale
variations of temperature in the NH and SH (Northern Hemisphere and Southern
Hemisphere, respectively), which is referred to as the bipolar seesaw
phenomenon (Blunier and Brook, 2001; Pedro et al., 2018; Stocker and
Johnsen, 2003). Potential triggers for this climatic variability on the
millennial scale are fresh water perturbation in the North Atlantic (NA) or
alterations of sea ice extent, surface temperature and salinity in the NA
(Bond et al., 1992; Broecker et al., 1992; Heinrich, 1988; McManus et
al., 1998), which may reduce the strength of the Atlantic Meridional
Overturning Circulation (AMOC). This would cause a reduction in heat
transport from the SH to the NH, which leads to an abrupt cooling in the NA
region and a gradual warming in the SH (Stocker and Johnsen, 2003) and
the opposite behavior when AMOC is strengthened.
Existing CO2 records show the presence of millennial-scale oscillations
on the order of 20 ppm over the last glacial period (Ahn and Brook, 2008,
2014; Bereiter et al., 2012), which generally co-vary with the major water
isotope (δD) variations in Antarctic ice cores reflecting Antarctic
temperature variations (Ahn and Brook, 2008; Bereiter et al., 2012)
(Fig. 1). During cold periods in the NA, referred to as NA stadials,
atmospheric CO2 increased continuously and in parallel to Antarctic
temperature increase. Again, at the onset of warming in Greenland,
atmospheric CO2 started to decrease (Ahn and Brook, 2008; Bereiter
et al., 2012), generally in line with a co-occurring, slow Antarctic
cooling. However, the CO2 decrease did not always start at exactly the
same time as the onset of the abrupt warming in the NA, and the lag itself
varied. For example, during Marine Isotope Stage (MIS) 3, Carbon Dioxide
Maxima (CDM) lagged behind abrupt temperature change in Greenland by
870±90 years. During MIS 5, the lag of CDM with respect to abrupt
temperature warming in the NH was only about 250±190 years
(Bereiter et al., 2012). Atmospheric CO2 variations on
millennial scales are thought to be related to the role of the Southern
Ocean in carbon uptake and deep-ocean ventilation on millennial timescales
(Fischer et al., 2010; Marcott et al., 2014; Schmitt et al., 2012; Sigman
and Boyle, 2000; Toggweiler et al., 2006). In addition, atmospheric CO2 can be affected by changes in the AMOC, which affects the ventilation of
carbon from the deep ocean (Denton et al., 2010; Sigman et al., 2007).
However, the mechanisms responsible for these oscillations are still a
matter of debate (Bouttes et al., 2012; Bozbiyik et al., 2011; Gottschalk
et al., 2019; Menviel et al., 2014).
Comparing CO2 changes on millennial timescales during the past two
glacial periods, MIS 3 (60–27 ka) and early MIS 6 (185–160 ka), can
provide us with a better understanding of the carbon cycle, due to the
similarities but also differences of climate conditions and events during
the last two glacial periods (Fig. 1). Proxy evidence indicates that the
states of several important components of the climate–carbon cycle were not
the same between MIS 3 and MIS 6. Sea ice cover in the South Atlantic was
more extensive in MIS 6, and sea surface temperature in the South Atlantic
is thought to have been lower (Gottschalk et al., 2020). The
bipolar seesaw phenomenon has also been observed during the early MIS 6
period (Cheng et al., 2016; Jouzel et al., 2007; Margari et al., 2010).
However, the bipolar seesaw events during MIS 6 are longer than those found
during MIS 3. Events of massive iceberg discharge into the NA, which are
thought to have driven millennial-scale changes in the meridional
overturning circulation during MIS 3 (de Abreu et al., 2003;
McManus et al., 1999), appear to be much more frequent during MIS 3 than
during MIS 6. During the early MIS 6, iceberg discharge was muted, and during
the time period around 175 ka summer insolation levels in the NH
approached interglacial values (Berger, 1978). Due to the stronger NH
summer insolation, the Intertropical Convergence Zone (ITCZ) had shifted to
the north, which intensified monsoon systems in low-latitude regions, such
as in Asia, the Apennine Peninsula and the Levant (Ayalon et al., 2002;
Bard et al., 2002; Cheng et al., 2016). This may have led to a weaker
overturning circulation due to the reduction of the density of the North
Atlantic surface water, making the AMOC cell shallower during MIS 6 than
during MIS 3 (Gottschalk et al., 2020; Margari et al., 2010).
Proxy data over the last 250 ka. (a) Ice-rafted debris
(IRD) input in the Iberian Margin core MD95-2040 (de Abreu et
al., 2003). (b) The 21 June insolation for 65∘ N (Berger, 1978). (c) The
δ18Ocalcite from Sanbao cave, indicative of the strength
of the East Asian monsoon (Cheng et al., 2016). (d) Dust flux in the EDC ice
core (Lambert et al., 2012). (e) Published atmospheric CH4 in EDC (dark green dots) (Loulergue et al., 2008) and a composite atmospheric CH4 record from EDC derived in this study (light yellow dots). (f) Composite CO2 record from the EDC ice core derived in this study (orange dots) and a published composite CO2 record from Antarctic ice cores (dark blue dots) (Bereiter et al., 2015). (g) The δD in the EDC ice core, Antarctica (Jouzel et al., 2007). Vertical grey bars indicate the
timing of Heinrich events during the last glacial period. The event numbers
are indicated at the bottom. The marine isotope substage numbers are
written at the bottom (Railsback et al., 2015).
In order to investigate whether the different climate conditions between MIS 6 and MIS 3 could have impacted the relationship between atmospheric
CO2 and climate, we measured 177 new data points of atmospheric
CO2 concentrations from the EPICA Dome C (EDC) ice core (75∘06′ S, 123∘24′ E) spanning MIS 6 (190 to 135 ka). This new
CO2 data measured within the scope of this study is complemented with
two published datasets (Lourantou et al., 2010; Schneider et al., 2013)
and one unpublished CO2 dataset measured in 2003. We use all of the
available data from the EDC ice core to compile a composite dataset of
atmospheric CO2 covering MIS 6. In this work, we significantly
improve existing records previously obtained from the Vostok ice core
(Petit et al., 1999). The new composite dataset from the EDC core
provides a temporal resolution of ∼230 years during MIS 6, as
compared to 1000 years in the Vostok record. We also improved the relative age
uncertainties between ice and gas in the EDC core using δ15N-based estimates of firn column thickness to better constrain leads
and lags between peaks in δD (Antarctic Isotope Maxima, or AIM) in
the EDC ice core and atmospheric CO2 concentrations during the early
MIS 6, thus establishing a new chronology using new δ15N data
during the early MIS 6 in this study and published data from Landais et
al. (2013). Finally, we improve the temporal resolution of existing CH4 data from EDC (Loulergue et al., 2008) from 600 to
∼350 years to be able to more precisely calculate the shift of
CDM relative to the rapid climate change in the NH. To avoid the age
uncertainties between proxy data and atmospheric CO2 data, CH4 measurements are used as a time marker of rapid warming in the NH, as over the last glacial period CH4 and Greenland temperatures are assumed to be synchronous, with a lag of CH4 of less than ∼50 years on average (Baumgartner et al., 2014; Rosen et al., 2014).
MethodsCO2 measurements
In this study, we measured atmospheric CO2 from 177 depth intervals of the EDC ice core at the Institut des Géosciences de l'Environnement
(IGE), France and Climate and Environmental Physics (CEP), Physics
Institute, University of Bern, Switzerland. The majority of all atmospheric
CO2 samples (i.e. 150) were measured using the ball mill dry-extraction
system coupled to a gas chromatograph at IGE (Schaefer et al.,
2011). Each ball mill data point presented in this study corresponds to a
single 40 g ice sample, which was measured five times by gas chromatography
(five consecutive injections of the same extracted gas). Approximately 5 mm
of ice was trimmed from the ice core surfaces before extraction in order to
remove the external part that could be potentially contaminated with
drilling fluid or might have been subject to gas loss during storage in the
freezer (Bereiter et al., 2009). The CO2 measurements were
referenced to a secondary gas standard (synthetic air from Air Liquide, Alphagaz 28416000) containing 233.7±0.4 ppm of CO2 in dry
air, which was referenced to two primary standards (238.34±0.04 from
NOAA, CB09707, and 260.26±0.2 from CSIRO, CSIRO1677).
Blank tests using 40 g of artificial bubble-free ice were conducted every 10
measurements to quantify the precision of the system and to correct for the
CO2 contamination caused by the crushing process. Blank tests were
conducted in two steps: first, to validate the baseline of the system, a gas
standard with 233.7±0.4 ppm was injected over the bubble-free ice in
the cell. The gas was then left to equilibrate in the cell for 10 min.
The gas was analyzed twice by successive injections into the extraction line
and sample loop. Afterwards, the bubble-free ice was crushed and the gas was
analyzed five more times. The difference between the results before and after
crushing was considered the contamination effect caused by the crushing
process. These values were used to estimate the precision of the system.
Measured CO2 should be corrected for contamination caused by the
analytical procedure by comparing measured CO2 of the blank tests with
the standard gas value. However, it is not feasible to correct CO2 concentrations directly. The CO2 mole fraction is calculated as the
ratio between partial pressure of CO2 and total pressure in the
measurement line, which is a relative value. Thus, CO2 concentration
and the concentration of CO2 contamination are dependent on the total
pressure. To avoid this dependence, we corrected the absolute value (partial
pressure) of CO2 in the air by the expected partial pressure of
CO2 contamination, as estimated from the blank tests.
For this study, we used four different extraction chambers to hold the ice
core samples during crushing and measurement. Each chamber showed different
contamination levels. Therefore, blank tests were conducted on each chamber.
The data were corrected by the average of each chamber. For these
measurements, the precision of the system was estimated to be
∼1 ppm on average. On average, the extraction effect
correction corresponds to a reduction of the measured CO2 concentration
by 1.7±1.0 ppm (1σ).
Additionally, we measured 65 samples from 27 depth intervals using the
Centrifugal Ice Microtome (CIM) (Bereiter et al., 2013) at CEP. At each
depth interval replicates were made and analyzed (2.4 on average).
Approximately 5 mm of the ice surface were trimmed, and ∼8 g
of samples was analyzed on average. The pooled standard deviation of
replicates was ∼1 ppm. The WMOX2007 mole fraction scale
(Tans et al., 2017) was used as a reference for the CO2 measurements, via four different primary dry air standards for atmospheric
CO2, which were calibrated at the NOAA Earth System Research
Laboratory. The standard sample concentrations varied between 192.44 and
363.08 ppm (Nehrbass-Ahles et al., 2020).
Each CO2 record was corrected for gravitational fractionation, using
the δ15N isotope ratio (Craig et al., 1988). To this end,
88 new data points together with existing δ15 (Landais et
al., 2013) covering the late MIS 6 (156.4–139.2 ka) were used. δ15N data were linearly interpolated in age to each corresponding
CO2 data point. On average, the correction corresponds to removing
1.2±0.1 ppm from the measured CO2. In total, 177 individual ice
samples were measured for CO2 in the depth range from 2036.7 to 1787.5 m along the EDC ice core, corresponding to the time period from 189.4 to 135.4 ka (Bazin et al., 2013).
CH4 measurements
We measured the atmospheric CH4 content of 63 ice core samples, using
the wet extraction method at IGE as described in detail in Spahni et al.
(2005). This allowed us to improve the temporal resolution of existing
CH4 data (Loulergue et al., 2008) from ∼600 to
360 years on the AICC2012 chronology (see Fig. S1 in the Supplement). The precision of the system is ∼11 ppb on
average, which was estimated by blank tests.
The previous CH4 dataset (Loulergue et al., 2008) from EDC has been
produced at both IGE and CEP. CH4 measured at CEP used to be
systematically higher than CH4 measured at IGE by 6 ppb (Loulergue et al., 2008). The offsets are due to differences in corrections for contamination caused by the analytical procedure between the datasets.
Previously, 6 ppb had been added to the data obtained at IGE (Loulergue et al., 2008).
In this study, a new data correction was applied to data measured at
IGE, and it is no longer necessary to add 6 ppb to the new data. The
systematic offset between the new data and the previously published data
points (Loulergue et al., 2008) is only 1.7±2.4 ppb (n=63, the
standard error of the mean) during MIS 6, (the datasets agree with each
other within the measurement uncertainty).
Nitrogen isotopes
Isotopes of molecular nitrogen in air bubbles were measured by a melting
technique at the Laboratoire des Sciences du Climat et de l'Environnement
(LSCE), France. The gas was extracted from the ice by a wet extraction
technique, and the released air was analyzed by a dual-inlet mass
spectrometer (Delta V Plus; Thermo Scientific). The analytical method and
data correction are described in detail in Bréant et al. (2019).
In total, 151 samples from 88 depth intervals (63 duplicates) between the
depths of 2124.7 and 1875.0 m below the surface were measured, corresponding
to the time interval from 205 to 154 ka (Fig. S2 in the Supplement). The average
resolution on the AICC2012 chronology is ∼580 years.
Gas age revision by estimating Δdepth from δ15N
The water isotopic signature (δD), is, unlike CO2, measured on
the ice matrix. Air enclosed in an ice core moves through the porous firn
layers at the top of the ice sheet by molecular diffusion and becomes
gradually trapped in the ice at the so-called Lock-In Depth (LID, around 100 m below the surface in the case of the EDC ice core). An age difference,
thus, exists between the ice and the air at a given depth. For the
conditions of the EDC ice core, the age difference can reach up to 5 kyr
during glacial maxima and is associated with a large uncertainty (several
hundred years; Loulergue et al., 2007). The δ15N of
molecular nitrogen in air bubbles can be used to determine the LID and to
calculate the depth difference between synchronous events in the ice matrix
and air bubbles, called delta depth (Δdepth), thus creating a more
precise relative chronology of the gas – with respect to the ice-phase
(Parrenin et al., 2013). We use the Δdepth calculation to adjust
the gas chronology of EDC, while the ice chronology from AICC2012 remained
unchanged.
We calculate the height of the firn column, h, from δ15N of
N2 measurements (Craig et al., 1988; Dreyfus et al., 2010; Sowers et al., 1989) using the following equation:
h=hconv+(δ15N-Ω(T)ΔTdiff)Δm⋅g⋅1000RT-1.
In this equation, ΔTdiff is the temperature difference between
the top and the bottom of the diffusive zone as estimated by the
Goujon–Arnaud model, where surface temperature and accumulation are
estimated from the stable water isotope record (Loulergue et al.,
2007). Ω (T) is the thermal diffusion sensitivity, which has been
estimated from laboratory measurements by Grachev and Severinghaus
(2003). Δm is the mass difference between 14N and 15N (kg mol-1), g is the gravitational acceleration (9825 m s-2 for
Antarctica) (Parrenin et al., 2013), and R is the universal gas constant
(8314 J mol-1K-1). Finally, hconv is the height of the
convective zone at the top of the firn column, which is considered
negligible at EDC according to current observations (Landais et al.,
2006). The variation of the convective height is related to changes in wind
stress. According to Krinner et al. (2000), wind on the East
Antarctic plateau varied little during the LGM (Last Glacial Maximum), and we
assume that this is also the case for late MIS 6. Moreover, the convective
zone was confirmed to be very thin during the last deglaciation by
Parrenin et al. (2012). Thus, we assume that hconv is
negligible during MIS 6. Δdepth is calculated from the height of the
air column using a constant average firn density and a modeled vertical
thinning function suggested by Parrenin et al. (2013).
Raw δ15N data cannot be used directly to calculate Δdepth because bubbles at a given depth in the ice sheet can be trapped at
slightly different times, due to small physical variations that affect the
gas trapping process. This can lead to age scale inversions on depth
intervals up to 1 cm (Etheridge et al., 1992; Fourteau et
al., 2017). These layer inversions should not strongly affect our record, as
its resolution is larger than the scale of age inversion events (Fourteau et
al., 2017). However, the change in Δdepth between two different
depths (z) in the ice core, denoted ∂Δdepth/∂z,
deduced from the raw δ15N data, shows some values higher than
1, that could correspond to small stratigraphic inversions in the gas phase
(Fig. 2). Therefore, a three-point moving average weighted by the time
difference between a point and its two neighbors was applied to the δ15N dataset. The weights for the three points are equal if the time
difference is less than or equal to 500 years, which is close to the average
sampling resolution of the δ15N dataset. Otherwise, the
neighboring data points are weighted by 500/ΔT, where ΔT is
the time difference. This avoids assigning too much weight to neighboring
points where the resolution in the record is lower, which would lead to
local over-smoothing.
(a)∂Δdepth/∂z as a function of depth. Red dots represent raw δ15N measurements, and blue dots represent a weighted three-point running mean (see text for details). The dashed vertical grey line indicates ∂Δdepth/∂z=1. (b)Δdepth (bold line) for the EDC ice core from 1787.5 to 1870.2 m below the surface, deduced from δ15N and the thinning function calculated in this depth range. The two dashed lines correspond to the analytical uncertainties.
Δdepth as estimated using the three-point moving average weighted by the
time difference is shown in Fig. 2. The difference of the Lock-In Depth in
Ice Equivalent (LIDIE) calculated on the AICC2012 age scale (Bazin et al., 2013) and deduced from δ15N in this study is 0.5±3.0 m, or 0.7±5.6 % on average (see Fig. S3 in the Supplement). The AICC2012
LIDIE was calculated using a background scenario derived from δD,
using a linear relationship between δD and δ15N. Our
results show this relationship to be relatively unbiased but not entirely
exact (Fig. S3 in the Supplement). The Δdepth values are used to update the
EDC gas chronology from the original AICC2012 age scale, while the ice
chronology remains unchanged. For MIS 6, this method significantly reduces
the relative age uncertainty between air and ice to 900 years on average with
respect to the original AICC2012 chronology (see Fig. S4 in the Supplement).
Age scale of the MD01-2444 marine sediment record
The MD01-2444 marine sediment core from the Iberian Margin (Margari et
al., 2010) represents an important archive for the interpretation of our
CO2 record, as it is well resolved during MIS 6, and it provides
high-resolution benthic and planktonic foraminiferal records. The original
MD01-2444 age scale was built by matching the benthic δ18O
record (Shackleton et al., 2000) to the δD record from EDC on
the EDC03 ice age scale (Parrenin et al., 2007) using 11 tie points
(Margari et al., 2010). In order to make optimal use of the record, its
relative chronology with respect to EDC requires additional improvement
using the latest version of the EDC age scale. Thus, in this study, we
recalculated the age of the tie points using the AICC2012 chronology
(Bazin et al., 2013), and the age of the sediment record was
linearly interpolated between the tie points (Table S1 in the Supplement).
Definition of NA stadial duration
Due to the absence of a Greenland temperature record for MIS 6, the
durations of the six NA stadials must be identified using other proxies.
Here, we used two methods using both ice core and sediment core proxies.
First, we estimated the durations of the six NA stadials using δ18O of planktonic foraminifera and tree pollen in MD01-2444, which
reflect temperature variability in the NH (Margari et al., 2010) (Fig. 3). The midpoint of the stadial transitions in both δ18O of
planktonic foraminifera and tree pollen in MD01-2444 were used to identify
the NH stadial transitions. The time interval between two stadial transition
points were defined as the NA stadial duration. Using this approach the
duration of stadial at 6ii in the NH could not unambiguously be determined
due to small variations in the foraminifera and the pollen record. However,
the average age difference between the durations identified using the two
records is only 205 years, which is less than the sampling resolution of
MD01-2444 during MIS 6. The uncertainty of the timing of each stadial
transition was estimated as half of the temporal difference between maxima
and minima of δ18O of planktonic foraminifera. The mean values
for stadial onsets and terminations are shown as dashed lines in Fig. 3.
However, not all of the stadial durations during MIS 6c to 6d are entirely
unambiguous using this method.
The durations of the six NA stadials during MIS 6: (a)δD of the EDC ice core (Jouzel et al., 2007), (b) synthetic
Greenland δ18Oice record (Barker et al., 2011), (c)
tree pollen percentage in the MD01-2444 core (Margari et al., 2010) and
(d)δ18O of planktonic foraminifera in the MD01-2444 core
(Margari et al., 2010). Proxy data shown here are given on the
AICC2012 age scale. Red lines indicate the midpoints of the stadial
transition of both δ18O of planktonic foraminifera and tree
pollen in MD01-2444. Light green bars indicate the uncertainty of the
duration of each stadial transition, estimated as half the temporal
difference between maxima and minima of δ18O of planktonic
foraminifera before and after the transition. Red dots indicate minima and
maxima of δD in the EDC ice core as selected in this study. The
event numbers are indicated at the top.
Second, we recalculated the durations of the six NA stadials during MIS 6
period using a method developed by Margari et al. (2010). Figure 3 also
shows a synthetic Greenland δ18Oice record (Barker et
al., 2011) and Antarctic δD variations on the AICC2012 age scale.
The interval between the maximum and the preceding minimum of δD in
the EDC record can also be used to estimate the duration of the stadial
transitions (Gottschalk et al., 2020; Margari et al., 2010). In most
cases, the synthetic Greenland δ18Oice record and the
interval between the maximum and the preceding minimum of δD in the
EDC record confirm the definition of NA stadials selected by δ18O of planktonic foraminifera and tree pollen in MD01-2444. However,
the duration of the NA stadial in MIS 6iii is not clearly confirmed by
synthetic Greenland δ18Oice and δD in the EDC
(Fig. 3).
The interval between each maximum and the preceding minimum of δD in the EDC record was calculated to estimate the stadial durations using
the second method (Gottschalk et al., 2020; Kawamura et al., 2017;
Margari et al., 2010). Maxima and minima in the δD record were selected by
finding zero values in the second (Savitzky–Golay filtered) derivative of
the data (the same method we used to pick minima and maxima of atmospheric
CO2 and temperature; Figs. S5–S6, Tables S2–S3 in the Supplement). In one case (the
minimum before MIS 6i) two potential minima were selected by this method;
these values were then averaged.
The red dots and error bars in the EDC δD record in Fig. 3 show
the estimated minima and maxima of temperature corresponding to stadial
transitions using this method, along with their uncertainties. However,
using this tool, durations of MIS 6ii and 6i are apparently overestimated
due to ambiguities concerning the definition of the maximum in MIS 6i and
minimum in 6ii.
Neither our method nor that of Margari et al. (2010) can be considered
absolutely correct. To account for the differences between the two methods,
we took the stadial duration to be the mean of the durations estimated by
the two methods (Table S4 in the Supplement).
ResultsData compilation
We measured 177 samples of atmospheric CO2 from EDC during MIS 6 using
two extraction systems (the ball mill at IGE and CIM system at CEP). To
improve the resolution of our new dataset even further, we made a composite
dataset by aligning previous sets of CO2 measurements made over the MIS 6 period on the EDC ice core to our new data. First, we compared the two
existing CO2 datasets and the new CO2 dataset from EDC (Fig. 4 and Table S5 in the Supplement). There are two published CO2 datasets for EDC
during MIS6 – the first measured using the ball mill system at IGE
(Lourantou et al., 2010) and the second using a sublimation
extraction system at CEP (Schneider et al., 2013). We also added
unpublished data measured in 2003 using the CEO needle cracker measurement
system in 2003 (Monnin et al., 2004; Siegenthaler et al., 2005) (see Supplement for details). All records are on the AICC2012 air age scale (Bazin
et al., 2013). All datasets are corrected for the gravitational
fractionation effect using the new δ15N data in our study. In
total, the datasets contain 237 CO2 measurement points. Two
samples were excluded because of system operator error. Figure 4 shows
CO2 concentrations measured by the ball mill system, sublimation
(Schneider et al., 2013), the CIM and the needle cracker. Concentrations
from the CIM, sublimation and the needle cracker are systematically higher
than CO2 concentrations measured using the ball mill system
(Table S5 in the Supplement).
Atmospheric CO2 from EDC and Vostok ice cores,
compared to δD of the ice in the EDC core (temperature proxy) during
190–135 ka. Blue dots represent atmospheric CO2 from EDC as measured by the ball mill system (this study). The error bars of the new CO2 data using the ball mill extraction system indicate the standard deviation of five consecutive injections of the gas extracted from each sample into the gas chromatograph added to the precision of the measurement estimated by the
reproducibility of the control measurement (∼0.8 ppm) using a
quadratic sum. Yellow dots represent atmospheric CO2 from EDC measured using the ball mill system (Lourantou et al., 2010). The error bars indicate
the standard deviation of five consecutive injections of the gas extracted
from each sample into the gas chromatograph. Red triangles represent atmospheric
CO2 from EDC measured using the needle cracker (this study). The error bars of data points with replicates indicate the standard deviation of the mean of replicates from the same depth interval. Black inverted triangles represent atmospheric CO2 from EDC measured using the CIM (this study). The error bars of data points with replicates indicate the standard deviation of the mean of replicates from the same depth interval. Green diamonds represent atmospheric CO2 from EDC measured using sublimation extraction (Schneider et al., 2013). The error bars of data points with replicates indicate the standard deviation of the mean of replicates from the same depth interval (Schneider et al., 2013). Grey dots represent atmospheric CO2 from the Vostok ice core (Petit et al., 1999). The error bars of the CO2 data from Vostok (Petit et al., 1999) show the estimated overall accuracy for CO2 measurements. The grey line represents δD of ice in the EDC core (Jouzel et al., 2007).
The offsets between each additional dataset and our data were calculated and
corrected using a Monte Carlo procedure to account for uncertainty and a
Savitzky–Golay filter used to remove noise from our dataset (Supplement). The
offsets between the multiple datasets may be at least in part linked to
differences in extraction efficiency between the measurement methods. The
sublimation and CIM systems have high extraction efficiency on clathrates
and should therefore present more unbiased baseline CO2 values.
However, since these datasets are of much lower resolution, we have aligned
all datasets to the baseline value of our ball mill dataset.
Despite the systematic offsets, the other datasets confirm the
millennial-scale variations shown in the data from this study (Fig. 4 and
Fig. S7 in the Supplement). Although none of the individual additional datasets is of
high enough resolution to show millennial-scale variations in detail, when
aligned to our data the new data follow the millennial-scale variations with
very few outliers. The new composite atmospheric CO2 record from EDC is shown in Fig. 5.
Composite atmospheric CO2 (left axis) from the EDC ice core (this study) compared to the EDC water isotopic record (right axis) (Jouzel et al., 2007). The blue line indicates a Savitzky–Golay filter using a 150-year cut-off period (dotted blue line). Vertical dotted blue lines indicate the six CDM events that we identify during the early MIS 6. The numbers of the CDM events are indicated at the top of the figure.
Comparison with Vostok CO2 record for MIS 6
The new composite atmospheric CO2 record from EDC was compared to the existing CO2 data from the Vostok ice core, measured using the ball mill system (Fig. S7 in the Supplement), where the CO2 record from Vostok was
aligned to the AICC2012 gas age scale (Bazin et al., 2013).
Atmospheric CO2 data from Vostok are corrected for the gravitational
fractionation effect using the existing δ15N data (Bender,
2002).
Atmospheric CO2 variability in the EDC ice core shows similar general
patterns as those retrieved from the Vostok ice core, although previously
unidentified features are resolved due to the improved temporal resolution.
CO2 concentrations from Vostok appear systematically higher than those from EDC by 4.6±3.0 ppm on average and the difference increases to
more than 10 ppm at the beginning of Termination 2 at around 135 ka.
Three main mechanisms may explain the offsets between the EDC and Vostok
measurements: firstly, contamination due to the extraction system was
assumed to be negligible at the time of the Vostok sample measurements at IGE
(Petit et al., 1999). Based on recent ball mill measurements conducted
within the scope of this study, we presume that the neglected correction
concerning the data published by Petit et al. (1999) amounts to
approximately 1.7 ppm, reducing the average apparent offset to about 3 ppm.
Secondly, part of this remaining offset between the new EDC and previously
published Vostok record may be related to age scale uncertainties due to a
limited number of stratigraphic tie points between the two cores
(Bazin et al., 2013). In particular, this effect may explain part of
the larger offset during the glacial termination in air younger than 140 ka (Fig. S7 in the Supplement). Thirdly, the ball mill system has a different
extraction efficiency depending on the presence of bubbles and/or clathrates
in the ice sample, which may affect the accuracy of the reconstructed
absolute mean CO2 level. When the air is extracted from an ice core
sample where bubbles and clathrates co-exist, different proportions of
bubbly and clathrate ice may lead to biased CO2 concentrations
(Lüthi et al., 2010; Schaefer et al., 2011). The Vostok measurements
were made on recently drilled ice, in which clathrates had less time to
transform into secondary bubbles (Vladimir Lipenkov, 2015 personal communication). Accordingly, at least part of
the approximately 3 ppm offset may be due to these systematic extraction
differences (see Supplement).
In summary, the individual datasets used for our data compilation and the
Vostok ice core record all show the same millennial CO2 variability
over MIS 6. Analytical and ice-relaxation-related offsets could be removed
to synthesize all records. This allows us to compile a precise and
high-resolution record; however, the true absolute mean level of atmospheric
CO2 is only known to be better than 5 ppm, due to this offset correction.
However, this has no impact on the conclusions drawn from the millennial
CO2 variability, which is the focus of this study.
Atmospheric CO2 variability on millennial timescale
Margari et al. (2010) suggested that MIS 6 can be divided into
three sections depending on the degree of climatic variability observed in
δD (indicative of Antarctic climate variability) and CH4
(reflecting NA climate variability) in EDC: early (185.2–157.7 ka),
transition (157.7–151 ka) and late MIS 6 (151–135 ka) (Figs. 5 and 6). Climatic oscillations on millennial timescales are pervasive during the early MIS 6 period (185–160 ka) (Barker et al., 2011;
Cheng et al., 2016; Jouzel et al., 2007; Margari et al., 2010, 2014),
which is similar to MIS 3 (Figs. 1 and 6).
However, during the late MIS 6 period, i.e., the Penultimate Glacial Maximum (PGM),
millennial variability is subdued and more resembles the climate
variability on millennial timescales during MIS 2 (Figs. 1 and 6).
During the transitional period from 157 to 151 ka, δD in EDC slowly
increased (Jouzel et al., 2007). Like δD in EDC, CO2 variations on millennial timescales are pervasive during the early MIS 6
period (185–157.7 ka). During the transitional period from 157 to 151 ka, atmospheric CO2 increased slowly, while during the late MIS 6 period CO2 variation is subdued (Fig. 5).
Comparison of climate proxies with atmospheric CO2 during MIS 6 period. Vertical dotted blue lines indicate the six CDM events during the early MIS 6: (a) 21 June insolation at 65∘ N (Berger, 1978), (b)
ice-rafted debris (IRD) input in the Iberian Margin core MD95-2040
(de Abreu et al., 2003), (c) atmospheric CH4 in the EDC ice core
(Loulergue et al., 2008; this study), (d)δ18O of planktonic
foraminifera in the Iberian Margin marine core MD01-2444
(Margari et al., 2010), (e)δ18O of benthic foraminifera in the
Iberian Margin marine core MD01-2444 (Margari et al., 2010), (f)δD
of the EDC ice core (Jouzel et al., 2007) and (g) our new composite CO2 record during the MIS 6 period. The numbers of CDM events are indicated at the
top.
While there was an indication of millennial CO2 variability in the
Vostok record, the resolution of that record did not allow us to constrain
this variability. We now present clear evidence of millennial variability of
CO2 concentrations during MIS 6 that are associated with Antarctic
Isotope Maxima (AIM) events (EPICA Community Members, 2006), thanks to the
improved time resolution and precision of the obtained CO2 data (Fig. 5). To better discuss millennial variability in MIS 6, a Savitzky–Golay filter with a 150-year cut-off period was used to filter out
centennial-scale variability and noise (see Fig. 5 and Fig. S5 in the Supplement).
Five prominent and one subdued CO2 variations were detected in
atmospheric CO2 during early MIS 6 (Fig. 5 and Fig. S5 and Table S2
in the Supplement), where we name CDM according to the numbering by Margari et al. (2010) and Gottschalk et al. (2020) (Fig. 5). The five prominent peaks are
observed at 160.7±0.3 (CDM 6vi), 164.2±0.3 (CDM 6v),
169.6±0.2 (CDM 6iv), 174.3±0.2 (CDM 6iii) and 181.3±0.1 ka
(CDM 6i). Note that the provided uncertainty was calculated with
respect to the position of each maximum and does not include the absolute
age uncertainty of the ice core (in each case around 3.0 kyr, 1σ)
(Bazin et al., 2013). We can also identify one low-amplitude
CO2 peak at around 150 ka, representing another potential candidate
for a CDM (Fig. 5). This atmospheric CO2 variation is of triangular
shape and follows the δD pattern. The change of direction is also
associated with a CH4 peak. This variation has analogues in MIS 4 and MIS 10 (Jouzel et al., 2007; Nehrbass-Ahles et al., 2020).
Each CDM is associated with an AIM event. The short AIM 6ii event
corresponds to CDM 6ii at around 178 ka. CDM 6ii has an amplitude of
only ∼4 ppm, and the CO2 variation becomes even less
pronounced after filtering. Due to the smoothing of the CO2 variation at CDM 6ii, atmospheric CO2 and δD composition in EDC appear decoupled. This observation seems confirmed when considering the
relationship between atmospheric CO2 change and the duration of NA
stadials calculated using tree pollen and the δ18O composition
of planktic foraminifera in an Iberian Margin core (Margari et al.,
2010) for MIS 6 (Fig. 3 and Table S4 in the Supplement), or using isotopic records from Greenland ice cores (Rasmussen et al., 2014) for MIS 3 as shown
in Fig. 7. Here, the magnitude of atmospheric CO2 change is
generally correlated with the NA stadial duration (r=0.87, n=6) during
the early MIS 6 period, and CDM 6ii is by far the shortest of all six events
detected in MIS6.
The relationship between NA stadial duration and magnitude of
CO2 increase. Green dots indicate non-Heinrich CDM events during MIS 3, green circles indicate classic Heinrich CDM events during MIS 3 and red-filled green circles indicate non-classic Heinrich CDM events during the MIS 3 period. Blue dots indicate CDM events during MIS 6. Non-classic Heinrich events are defined as ice discharge events with different IRD source signatures from Heinrich events, occurring at transitions in NH ice volume.
We note a similar correlation between the NA stadial duration and
atmospheric CO2 change during MIS 3 (r=0.85, n=14). When the NA
stadial duration was shorter than 1500 years, atmospheric CO2 varied
less than 5 ppm (Ahn and Brook, 2014; Bereiter et al., 2012) as is the
case for CDM 6ii. Margari et al. (2010) note one exception, AIM 14, during
which ice discharge may have led to a stronger perturbation to AMOC.
Both Bereiter et al. (2012) and Ahn and Brook (2014) observed that during
short NA stadials which last less than 1300 years, the CO2 maxima do
not appear to have a consistent phase relationship with AIM and CO2 and δD anomalies are not correlated (Ahn and Brook, 2014).
We observe the same to be the case when MIS 6 is included (r=0.07,
n=11), however, we cannot exclude that the firn column-induced smoothing
at EDC obliterated small atmospheric responses with magnitudes smaller than
5 ppm for such short stadials. In both MIS 3 and 6, CO2 is highly
correlated with δD anomalies during the longer stadials (r=0.78,
n=9) with a clear increase in CO2 (Ahn and Brook, 2014;
Bereiter et al., 2012).
We observe that during the last two glacial periods, the amplitude of
CO2 is strongly related to the NA stadial duration (r=0.88, n=20).
This observation implies that despite the different climate boundary
conditions during the last two glacials, the behavior of atmospheric
CO2 was similar (see Fig. 1) and the overall bipolar seesaw coupling of climate and atmospheric CO2 acted the same way.
Leads and lags between CO2 and the abrupt warming in NH
To better understand the link between the bipolar seesaw mechanism and
atmospheric CO2 variability on millennial timescales, we calculated
the varying time lag for each CDM following abrupt warming events in the NH
(see Figs. S8–S11 in the Supplement) (Bereiter et al., 2012). Due to the
lack of an MIS 6 temperature proxy in Greenland, and due to the difficulty
of placing marine temperature proxies (Shackleton et al., 2000) on a
precise common chronology with the EDC ice core, in this work CH4
measurements performed on the EDC ice core were used as a time marker of
rapid warming in the NH (Baumgartner et al., 2014; Brook et al., 1996;
Huber et al., 2006). Because CH4 and CO2 signals are both
imprinted in the air bubbles, there is no chronological uncertainty when
comparing the timing of changes of those two signals. The only remaining
uncertainty is related to analytical uncertainties and to the temporal
resolution of the two records. We pick intervals when CH4 increases
rapidly by at least 50 ppb over a time period of less than 1 kyr that
correspond to AIM (Buizert et al., 2015; Loulergue et al., 2008). The
timing of abrupt CH4 increases was defined as the midpoint between the beginning of the increase of CH4 and its peak (see Supplement for details). The
age uncertainty of the midpoint is defined by half the time difference
between the two endpoints.
Figure 8 shows the shifts of CDM with respect to the onset of the abrupt
warming in the NH. During the MIS 6 period, three abrupt NH warmings (as
inferred from the CH4 signal) at 181.5±0.3, 175.4±0.40
and 171.1±0.2 ka (2σ) were found. These events correspond
to CDM 6i, CDM 6iii and CDM 6iv, respectively. CDM 6vi, CDM 6v and CDM 6ii
do not have corresponding rapid changes in the methane record that fulfill
our detection criterion of a 50 ppb increase; this may be due to slow gas
trapping as compared to interglacial periods, which could smooth out smaller
changes. A synthetic Greenland temperature record (Barker et al., 2011)
shows abrupt temperature jumps at CDM 6vi, CDM 6v and CDM 6ii as well.
However, this record is calculated using EDC δD, and the large
relative chronological uncertainty (∼900 years on average)
between ice and gas phases does not allow us to make any conclusions about
leads and lags using this record. We therefore exclude these events from our
lag analysis.
CDM lags relative to abrupt temperature increases in the NH. Dotted grey
lines indicate when climate changes abruptly in the NH as indicated
by the CH4 jumps. During the last glacial period, the AIM number corresponds to the DO number for corresponding DO and AIM events, and for MIS6 numbers correspond to event numbers. (a) Atmospheric CO2 as measured using the TALDICE ice core during MIS 3, (b) atmospheric CO2 as measured using the Byrd ice core during MIS 5, (c) atmospheric CO2 as measured using EDML ice core during MIS 5, (d) new CO2 composite as derived in this study for MIS 6. For MIS 6, we selected the 3 CDM that correspond to an abrupt methane increase; the other CDM do not correspond to an abrupt change that fulfill our 50 ppb detection criterion (see main text for details). Note that the scale of the y axis is not the same for the four panels.
From MIS 6i to MIS 6iv, the lag of CO2 with respect to abrupt warmings
in the NH, which were identified from this chronological comparison between
EDC CH4 and CO2, becomes significantly larger. During the
earliest MIS 6, atmospheric CO2 increases rapidly (by ∼4.2 ppm in 240±320 years) following the abrupt CH4 increase at
181.5±0.3 ka. The peak of CDM 6i is nearly synchronous with the
onset of the NH abrupt warming (nonsignificant lag of 240±320 years,
Fig. 8). During CDM 6iv and 6iii, CO2 concentrations show a much
slower increase over a duration of ∼3.3 kyr. Here, the
CO2 maximum lags significantly behind the onset of the NH abrupt
warming by 1460±270 years and 1110±460 years, respectively
(1290±540 years on average, with the error calculated by propagation
of the uncertainties in the individual events). Interestingly, these two CDM
events occurred during MIS 6d (Fig. 1), when iceberg discharge was muted
and the ITCZ is thought to have shifted northward, intensifying monsoon
systems in low-latitude Northern Hemisphere regions, such as in Asia, the
Apennine Peninsula and the Levant (Ayalon et al., 2002; Bard et al.,
2002; Cheng et al., 2016). This may have led to a weaker overturning
circulation due to the reduction of the density of the NA surface water,
making the AMOC cell shallower with a smaller threshold in NA during MIS 6
than during MIS 3 (Margari et al., 2010). Therefore, the two different
CO2 lag timescales with respect to abrupt warming in NH during MIS 6 might be explained by this difference in background climate conditions.
Indeed, these features during MIS 6 appear fully compatible with those
observed during the last glacial period (Bereiter et al., 2012). To compare
CO2 variations with respect to abrupt warming in the NH during the last two glacial periods, in this study we also re-estimated the abrupt CH4 rise during the last glacial period with the same methodologies. Figure 8 also
shows the CO2 evolution during the onset of abrupt warming in the NH during MIS 3 and MIS 5 (Ahn and Brook, 2014; Bereiter et al., 2012).
Atmospheric CO2 during MIS 3, as shown in Fig. 8, was reconstructed
from the Talos Dome ice core (TALDICE). For MIS 5 it was obtained from Byrd
and the EPICA Dronning Maud Land (EDML) ice core (Ahn and Brook, 2008;
Bereiter et al., 2012). In Bereiter et al. (2012), both TALDICE and EDML
records were used during MIS 3 and compared to the onset of abrupt warming
in the NH as indicated by the co-occurring CH4 rise. However, here we only use data from TALDICE, which are more accurate due to the narrower age
distribution of the gas trapped in the LID (Bereiter et al., 2012).
Using the same method as above, the average value of CDM lag with respect to
the abrupt warming in NH was calculated. The average CDM lags with respect
to the abrupt warming in the NH for the MIS 3 and 5 periods are 770±180 and 280±240 years. Thus, over the course of the last glaciation, the
lag of CO2 maxima with respect to the abrupt NH warming events
significantly increased. We observe the same trend through the millennial
events depicted during MIS 6, albeit with different absolute lags.
DiscussionAtmospheric CO2 variability on millennial timescales
Similar to the AIM amplitude (Capron et al., 2010; EPICA Community
Members, 2006), we found that the amplitude of atmospheric CO2 variations is well correlated to the NA stadial duration during MIS 6 and
MIS 3, which implies that the amplitude of CO2 variations might also be
affected by the duration of AMOC disruption during the early MIS 6
period (Margari et al., 2010). This hypothesis is also supported by
a recent study using oceanic sediment cores from the Southern Ocean
(Gottschalk et al., 2020). The authors report that respired
carbon levels in the deep South Atlantic decrease when AMOC is weakened
during both glacial periods, and the amount of carbon loss in the deep South
Atlantic is highly correlated with the duration of NA stadials.
As mentioned above, atmospheric CO2 on millennial timescales can be
controlled by CO2 exchange between the ocean and the atmosphere, as
well as changes of terrestrial carbon stocks. Coupled climate carbon cycle
models reported that the variations of atmospheric CO2 concentration on
millennial timescales are mainly dominated by the deep ocean inventory,
requiring a few millennia to re-equilibrate to climate change
(Schmittner and Galbraith, 2008). On the other hand, the response of the terrestrial biosphere is usually fast (decadal to centennial timescale) (Bouttes et al., 2012; Menviel et al., 2014; Schmittner and Galbraith, 2008). Although different models differ significantly in the CO2 response to AMOC changes, the initial CO2 evolution of the terrestrial biosphere and deep ocean to AMOC perturbations are opposite in model simulations
(Gottschalk et al., 2019). Thus, due to the opposite direction
of CO2 change of ocean and terrestrial reservoirs, atmospheric CO2 variations might be muted if the NH duration is short
(Bouttes et al., 2012; Menviel et al., 2014; Schmittner and Galbraith, 2008), while during
long stadials the carbon release from the ocean dominates. There is, on the
other hand, evidence that not all of the processes of CO2 exchange
follow these general trends. For example, atmospheric CO2 might be
changed on centennial timescales by carbon exchange between the deep and
surface ocean (Rae et al., 2018) or atmospheric CO2 might be
influenced slowly by soil decomposition (Köhler et al., 2005),
and it is important to note that modeling studies do not agree in the
amplitude or even the direction of the modeled net CO2 exchange.
The more prominent CO2 changes during stadials involved with the
Heinrich events may be related to a stronger reduction of the North Atlantic
Deep Water (NADW) formation during Heinrich events (Henry et al.,
2016; Margari et al., 2010), which would cause a stronger upwelling of deep
water in the Southern Ocean (Menviel et al., 2008; Schmittner et al.,
2007). These events may reduce stratification in the Southern Ocean due to
an increase in salinity of the surface waters and a relative freshening of
the deep water (Schmittner et al., 2007). As a result, atmospheric
CO2 can be increased due to upwelling and outgassing of CO2 in
the Southern Ocean (Schmittner et al., 2007; Schmitt et al., 2012). The
co-occurring upwelling in the SO during AIM for the last termination has
been examined (Anderson et al., 2009), but due to the lack of proxy data
with precise age scale for upwelling in the Southern Ocean, this hypothesis
cannot be confirmed during MIS 6.
According to the results of Margari et al. (2010), the six AIM
events of MIS 6 were likely affected by AMOC perturbations of similar
strength. During these events, there is no clear evidence for freshwater
perturbation in the NA (Fig. 1), and the strength of the associated AMOC
perturbations is estimated to be similar to that during non Heinrich
stadials or non-classic Heinrich (different ice-rafted detritus source
signatures from Heinrich events, occurring at transitions in ice volume) AIM
events of MIS 3. The durations of NA stadials during early MIS 6 (except for
AIM 6ii) appear to be longer than those during non-Heinrich AIM events of
MIS 3, which might be caused by the different climate boundary conditions
during MIS 3 and the increase of the hydrological cycle during the early MIS 6. A longer duration of the AMOC disruption apparently impacts the
amplitude of CO2 variations (Bouttes et al., 2012;
Menviel et al., 2008). Considering that the duration of the AMOC disruption may be related with climate background conditions (Bouttes et al., 2012;
Menviel et al., 2008), this observation suggests how different climate
background conditions may impact atmospheric CO2. The strength of AMOC perturbations also appears to be an important factor in determining the
amplitude of CO2 variations. For example, the duration of the Heinrich events in MIS 3 (AIM 8, 12 and 14) is shorter than any of the MIS 6 events except for 6ii, but atmospheric CO2 varied significantly in all three.
The relationship between the amplitude of atmospheric CO2 variations and the NA stadial duration is explained by the duration of AMOC disruption during the early MIS 6 period. However, the temporal resolution of δ18O composition of planktonic foraminifera in MD01-2444 and the
precision of the age scale are too low to precisely define the duration of
stadials during MIS 6. Additional proxy data providing information about
climate change in the NH are needed to confirm the relationship between
atmospheric CO2 variations and the NA stadial duration (Fig. 3).
The limited available proxy data permit only to formulate a hypothesis for
the mechanisms responsible for CO2 variability during MIS 6 but not
to rigorously test it. To compare the behavior of the bipolar seesaw with
atmospheric CO2 variations, additional investigations about AMOC
disturbances and their associated climate responses are needed.
Why did CO2 lag the abrupt warming in the NH during MIS 6d?
Two different lags of the CO2 maxima with respect to NH warming are
present in the MIS 6 period (Fig. 8). CDM 6i is nearly synchronous with
the abrupt warming in the NH (no significant lag of 240±320 years),
while the lags for CDM 6iii (1110±460 years) and CDM 6iv (1460±270 years) are much longer. Two modes of CO2 variations are also observed during the last glacial period. As the last glaciation progressed from MIS 5 to MIS 3 (Figs. 1 and 8), the lag of CO2 maxima with respect to NH millennial-scale warming significantly increased. This observation may be explained by the different AMOC settings in MIS 5 and MIS 3 (Bereiter et al., 2012). We speculate that, as observed during the last glacial period, the configuration of oceanic circulation during MIS 6d might also be the cause of the change in the time lags between NH abrupt warming events and CO2 variations during the early MIS 6.
Bereiter et al. (2012) explained that during MIS 3 the
oceanic circulation in the Atlantic was in a more “glacial” state, with
shallower NADW and carbon-rich Antarctic Bottom Water (AABW) extended to
the north, while during MIS 5 ocean circulation was similar to the present, in
what can be referred to as a “modern-like” state. At the onset of abrupt
warming in the NH, AMOC is thought to accelerate rapidly, delivering heat to
the north and resuming the formation of NADW. When the NADW cell expands,
AABW is withdrawn and the upwelling of carbon-rich deep water in the
Southern Ocean is enhanced. Essentially, over the timescale of ocean
overturn, part of the previously expanded carbon-rich southern-sourced water
is converted to carbon-poor northern-sourced water.
Thus, during MIS 3 CO2 continues to be released into the atmosphere for another 500 to 1000 years after the NADW resumption (Bereiter et al.,
2012). However, during MIS 5 where AABW was not expanded in the NA, less
CO2 can be released from the ocean to the atmosphere after NADW
resumption.
The temporal resolution of proxy data related to oceanic circulation during
MIS 3 and 5 is unfortunately not sufficient to validate from the marine
realm itself whether the two different modes of CO2 variations reflect the hypothesized mechanism described above. Modeling studies of the carbon stock in AABW and NADW during MIS 5 and 3 have been attempted. However, dependent on the chosen model, the modes of atmospheric CO2 variation are different (Gottschalk et al., 2019). Some studies (for example, Menviel et al., 2008) mention an atmospheric CO2 decrease when the AMOC is resumed. Others, for example Bouttes et al. (2012),
confirm an atmospheric CO2 release from the ocean when AMOC resumed.
In spite of the inconclusive modeling studies (Gottschalk et
al., 2019), limited proxy evidence does not exclude the possibility that the
configuration of AMOC and its changes over MIS 6 may explain the presence of
two different CDM lags. We find this hypothesis to be worth at least a
speculative discussion. According to the benthic δ13C record in
the MD01-2444 core (Margari et al., 2010), the value of benthic
δ13C during 180–168 ka was lower than during MIS3, which
indicates that the NA overturning cell during MIS 6 was likely even
shallower than that during MIS 3 (Margari et al., 2010). This
implies southern-sourced water masses were more expanded to the north, and
the density difference between the northern-sourced water masses and
southern-sourced water masses increased. This shallower oceanic circulation
during MIS 6 (Margari et al., 2010) could have caused the
millennial-scale delays with respect to abrupt NH warming events. It could
also explain the longer lag between the abrupt warming in NH and CDM during
MIS 6d (1290±540 years on average) when compared to the lags of CDM
(770±180 years on average) during MIS 3. However, because of the low
accumulation at EDC and its wider age distribution, the estimation of the
exact timing of CDM from the EDC ice core might be less accurate compared to
that from the TALDICE ice core with a narrower gas age distribution
(Bereiter et al., 2012). The remaining uncertainty is related
to analytical uncertainties and to the temporal resolution of the two
records. To further investigate the exact relationship between CDM and
abrupt warming in the NH, additional CO2 measurements from a higher
accumulation site would be helpful.
Conclusions
Using new and existing CO2 data from the EPICA Dome C ice, we
reconstruct a high temporal resolution record of atmospheric CO2 during
the MIS 6 period (189–135 ka). In this study, we investigate how
different climate background conditions during the last two glacial periods
may have impacted atmospheric CO2. Millennial-scale atmospheric
CO2 changes are revealed during the last two glacial periods, with
amplitudes ranging between 15 to 25 ppm, mimicking similar patterns in
Antarctic δD variations (Ahn and Brook, 2014; Bereiter et al.,
2012). On the other hand, during short NA stadials which last less than
1300 years, atmospheric CO2 variations are negligible and decoupled from
δD in EDC. This finding suggests that during the last two glacial
periods the amplitude of millennial CO2 variations is strongly
influenced by the NA stadial duration (r=0.88, n=20).
In the earliest MIS 6 (MIS 6i and 6iv, corresponding to 189 to 169 ka),
a change of CO2 lags with respect to the onset of rapid NH – warming as deduced from atmospheric CH4 changes – is revealed. CDM 6i (at ∼182 ka) is nearly synchronous with the abrupt warming in
the NH (nonsignificant lag of 240±320 years), while the lags during MIS 6d corresponding to CDM 6iv and 6iii (at ∼171 and
∼175 ka, respectively) are much longer, 1290±540 years on average. Similar observations are drawn for the time period covered
by our study as for previous studies on MIS 3 and MIS 5 periods, although
the lag of CO2 with respect to NH warming reaches even larger values during MIS 6d. We tentatively attribute this to a generally weaker and
shallower AMOC during MIS 6 compared to MIS 3 as suggested by the results
from Margari et al. (2010). However, the limited available proxy data from
the marine realm only permits an exploratory discussion of the mechanisms
responsible for CO2 variability during MIS 6. Because the boundary
conditions of the last glacial period cannot be applied to MIS 6, additional
proxy data and multiple modeling studies conducted during the MIS 6 period
are needed.
Data availability
Data available in the Supplement. All data will be available on the NOAA (National Oceanic and Atmospheric Administration) and PANGAEA (Paleoclimatology database websites) shortly.
The supplement related to this article is available online at: https://doi.org/10.5194/cp-16-2203-2020-supplement.
Author contributions
The research was designed by JS, RG, FP and JC. The CO2 measurements were performed by JS with contributions from GT, LS and BB. The data analyses were led by JS with contributions from JCB, RG, FP and JC and HF. The methane data was provided by GT and JC. The nitrogen isotopes data was provided by AL. JS wrote the manuscript with inputs from all authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work is a contribution to the “European Project for Ice Coring in
Antarctica” (EPICA), a joint European Science Foundation/European
Commission scientific program, funded by the European Union and by national
contributions from Belgium, Denmark, France, Germany, Italy, the
Netherlands, Norway, Sweden, Switzerland and the United Kingdom. The main
logistical support was provided by IPEV and PNRA. This is EPICA publication
no. 316. It also received funding from the European Community's Seventh
Framework Programmes ERC-2011-AdG under grant agreement no. 291062 (ERC
ICE&LASERS). As it is part of the PhD work of Jinhwa Shin, it was also supported by
the LabEX OSUGat2020 project of the Grenoble Observatory of Sciences of the
Universe (OSUG). The Swiss authors also acknowledge long-term financial support
for ice core research at the University of Bern by the Swiss National
Science Foundation under grants 200020_159563,
200020_172745, 200020_172506 and
20FI21_189533. The authors would like to thank Grégoire Aufresne
for providing assistance with the additional methane measurements,
Xavier Faïn and Kévin Fourteau for their help with the CH4 analytical system, and Dominique Raynaud
for the valuable discussions. We thank Eric Monnin and Urs Siegenthaler for
providing additional CO2 data. We would like to thank
Julia Gottschalk for the
discussions about CO2 variability and climate change during the last two glacial periods.
Financial support
This research has been supported by the ERC-2011-AdG (grant no. 291062, ERC ICE&LASERS), the Swiss National Science Foundation (grant no. 200020_159563), the Swiss National Science Foundation (grant no. 200020_172506), the Grenoble Observatory of Sciences of the Universe (LabEX OSUGat2020 project grant), the Swiss National Science Foundation (grant no. 200020_172745), and the Swiss National Science Foundation (grant no. 20FI21_189533).
Review statement
This paper was edited by Denis-Didier Rousseau and reviewed by four anonymous referees.
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