Moeller2013 d13CH4NatGeo Supplements

Moeller2013 d13CH4NatGeo Supplements
DOI: 10.1038/NGEO1922
Independent variations of CH4 emissions and isotopic composition
over the past 160,000 years
Lars Möller, Todd Sowers, Michael Bock, Renato Spahni, Melanie Behrens, Jochen Schmitt,
Heinrich Miller, Hubertus Fischer*
*Correspondence and requests for materials should be addressed to H.F.
2. Supplementary Methods and technical descriptions
2.1 δ 13CH4 measurements performed at Alfred Wegener Institute (AWI)
EDML δ13CH4 measurements were performed using a continuous flow gas chromatography
combustion isotope ratio mass spectrometry system (GC/C/IRMS) with a preceeding purge and trap
extraction and pre-concentration setup. A few modifications have been made compared to the
published experimental and data processing procedure1,2. Sample sizes between 150-200 g of ice,
equivalent to 15-20 ml of air at standard temperature and pressure (STP) were used after scraping
off 1-2 mm at the surface with a microtome knife to minimize the risk of contamination e.g. by drill
fluid. Sealed with a copper gasket in an ultra-high-vacuum stainless steel to glass sample vessel
with an inner volume of 350 ml, the samples were melted after evacuation and the air stripped out
of the water and vessel head space with a helium carrier gas stream (150 mL/min) for 90 minutes.
The bulk of the entrained water vapor was removed by a cooled Nafion membrane and most of the
easily condensable gases like CO2 and N2O are trapped in a ⅛ inch steel trap at -196 °C (liquid
nitrogen, LN2). While methane from the sample air was subsequently pre-concentrated on a ⅛”
steel tubing filled with an adsorbent (Hayesep D 80/100 mesh, Supelco), bulk components of the air
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were vented at -140 °C. The retained sample methane and residual air components were focussed
on a cryofocus trap and separated on a 30 m GC column (30 °C, CarbonPLOT, Agilent, Böblingen,
Germany). The methane was then quantitatively combusted to CO2 in a 940 °C oxidation furnace
(Ni/CU/Pt wires in Al2O3 tubing, ThermoFinnigan, Bremen, Germany) and admitted to the IRMS
(Isoprime, Elementar, Germany) using an open split.
A pure CO2 working standard (ISO-TOP, Air Liquide, Germany) was admitted to the IRMS during
each acquisition. Moreover, a pure methane standard (99.995 vol.-% purity; AirLiquide, Germany)
admitted to the GC stream was used to monitor fractionation in the GC or the combustion oven.
20 ml STP synthetic atmospheric air standard injections from two different standard bottles
(“SynthAir” with 1000 ppb CH4 (99.995 vol.-% purity), 250 ppm ISOTOP CO2 and 250 ppb N2O
(99.999 vol.-% purity); “Crystal-Mix” with 1081 ± 22 ppb CH4 4.5, 249.9 ± 5.0 ppm CO2 and 259 ±
26 ppb; both AirLiquide, Germany) were used to correct for long-term systematic shifts and internal
calibration. The mean δ13CH4 value for “SynthAir” was -40.93 ± 0.10 ‰ (n=448) and -49.66 ±
0.11 ‰ (n=136) for “Crystal-Mix” after drift correction. The absolute accuracy of the system was
determined by 16 measurements of a modern air sample from Neumayer Station, Antarctica. The
target value (δ13CH4: -46.97 ± 0.04 ‰ versus V-PDB (n=7)) was determined by off-line sample
preparation and dual-inlet IRMS on a MAT252 mass spectrometer (ThermoFinnigan) at the Institut
für Umweltphysik, Heidelberg3. A calculated machine offset of 0.02 ‰ of our GC/C/IRMS
determined values after correction for Kr interference (see below) relative to the Heidelberg
reference value was used to correct the EDML δ13CH4 data of this study. Accordingly, our data are
reported relative to the V-PDB scale as defined by the Heidelberg measurements4.
The EDML δ13CH4 time series includes 129 data points and 32 replicates from different depth
intervals with a mean reproducibility of the replicates of ± 0.18 ‰. A total of 19 outliers, either
caused by machine instabilities or other experimental problems, were excluded from the dataset and
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remeasured. Processing of the chromatogram for each measurement has been performed using a self
developed, fully automated peak detection, integration and referencing script written in the Python
programming language ( This script allows uniform and comprehensible
background and peak detection, genuine automation for post-processing (like e.g. long term trend
corrections) and data archiving. Using this script we were able to reproduce published δ13CH4 data
for termination I2, which were obtained with the software provided with the MS (Isoprime,
Elementar, Germany) with an offset of 0.10 ‰ (1σ: 0.17€‰, n=34) without and 0.17 ‰
(1σ: 0.15€‰) with correction for Kr interference (see below). Note that both offsets are smaller
than the measurement uncertainty of this earlier data set (0.3 ‰, 1σ). This re-evaluated record for
termination I was used in Figure 1 and 2 in the main text, and is also shown in supplementary
Figure S1 for the comparison with the unmodified, uncorrected data of our previous publication.
2.2 δ 13CH4 measurements performed at Pennsylvania State University (PSU)
For the Vostok ice core sample measurements at Penn State University, two different extraction
methods were employed. A dry extraction system5 was used to liberate gas from ice samples where
N2O measurements were needed6,7. Trapped gases were liberated from 1-1.5 kg ice samples using a
“cheese grater” oscillator with immediate cryogenic freezing of liberated air into a 35 ml stainless
steel sample tube immersed in liquid Helium. Once the air was transferred, the sample tube was
isolated, removed from the Helium Dewar and equilibrated at room temperature before CH4 and
N2O analyses were performed using standard GC techniques. The sample tube was then attached to
the PreCon device where the isotopic composition of N2O and CH4 were determined8,9. For those
samples where we did not need N2O data, a wet extraction technique was used to liberate the
occluded air. Ice samples weighing 500-700 g were placed into a stainless steel extraction cylinder
and sealed with a copper gasket. After evacuation, the ice was allowed to melt for 40 min in 50 °C
water before being placed into a liquid nitrogen Dewar for 40 min to refreeze the meltwater. The
headspace was then flushed with He (40 ml/min) through a H2O trap (-110 °C) with the CH4
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ultimately trapped on a Hayesep D trap at -130 °C. After 40 min of flushing, the Hayesep D trap
was isolated and attached to the PreCon for CH4 isotopic analyses10. Both extraction systems were
routinely checked for contamination/fractionation using standard air samples of varying sizes to
mimic the amount of CH4 we extract from the ice core samples (1-3 nmol of CH4). The air
standards were introduced over the residual crushed ice or degassed refrozen meltwater and
processed as if they were a real ice sample. For the dry extraction runs, the average of eight separate
standard runs was -47.30 ± 0.31 ‰ (1σ). The average value is 0.17 ‰ lower than the assigned
δ13CH4 value for the air tank (-47.13 ‰ VPDB) but within the uncertainty associated with the
method itself. For the wet extraction system, we processed 15 standard air samples through
degassed water samples. The average δ13CH4 value for these runs was -47.10 ± 0.34 ‰. These
results are close to the assigned value for the standard (-47.13 ‰) and 0.2 ‰ higher than the results
from the dry extraction system. To account for the δ13CH4 difference between the two extraction
systems, we add 0.2 ‰ to all the dry extraction data to be consistent with the wet extraction data
and the assigned value for the air standard. We estimate the overall uncertainty based on the
replicate analyses of the standard air samples to be 0.3 ‰ (1σ).
2.3 Correction of δ 13CH4 data due to Kr interference during IRMS measurements
The chromatographic separation of CH4 and the noble gas krypton (Kr) imposes special demands on
a setup used to separate air components, owing to the very similar physico-chemical properties of
these compounds. Kr has previously not been accounted for in δ13CH4 studies, as none of the
multiple stable isotopes of Kr are close to the mass to charge ratio (m/z) 44, 45 and 46 considered in
CO2 based δ13CH4 measurements. Recently Schmitt et al. (2013) demonstrated11, that the doubly
charged isotope 86Kr2+ does in fact interfere with the δ13C measurement of CH4, if Kr enters the ion
source of the IRMS.
After a thorough review of the raw data for the ice core δ13CH4 time series, we were able to identify
irregularities in the raw chromatograms at the peak flanks of the CH4-derived CO2 for the
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measurements performed with the AWI instrument. The Kr peak causes anomalies in the ratio of
m/z = 45 to m/z = 44 and the m/z = 46 to m/z = 44 ratio which generates higher δ13CH4 values the
more Kr contributes to the total peak areas (hereafter referred to as Kr effect). While the symptoms
are distinct due to instrumental differences of the two setups, we also found the PSU system to be
affected by the Kr interference.
Because polar ice core samples are not easily replaced, we were unable to repeat the measurements
of the EDML and Vostok δ13CH4 time series with enhanced instrumental setups. Instead we
corrected the δ13CH4 values for this Kr effect as described in detail in Schmitt et al. (2013)11, which
we briefly summarize below.
A posteriori correction of the Kr effect
We applied the following strategies to correct PSU and AWI δ13CH4 measurements for the Kr effect
and account for relative laboratory offsets with respect to the VPDB scale. AWI EDML
measurements are corrected for Kr individually by a method applied to the respective raw data
chromatograms. The method uses the visible anomalies seen in the m/z ratios 45/44 and m/z 46/44
and subtracts the derived interference from the raw data time series11. Afterwards, the
chromatograms are reprocessed and the isotopic composition of the CH4 peak calculated. We refer
to these values as the Kr-corrected δ13CH4 values. In contrast, δ13CH4 values obtained without the
subtraction algorithm are referred to as original δ13CH4 values. The difference between original and
corrected values are termed Kr correction values or Δδ13CKr. For the EDML time series Δδ13CKr
range between 0.4 ‰ for interstadial (medium CH4 mixing ratios) to 0.8 ‰ during glacial (MIS 2)
and stadial conditions (lower CH4 levels) (compare also Supplementary Figure S1). The results are
further calibrated internally and are tied to the VPDB scale as outlined in section 1.1.
Unfortunately, an analogous direct approach could not be applied to the Vostok ice core δ13CH4
acquisitions performed at the PSU, as the raw chromatograms were not stored after processing the
δ13C data. Instead, we had to choose an indirect way to correct for the Kr effect in the Vostok
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δ13CH4 time series.
For the purpose of this correction the atmospheric krypton mixing ratio can safely be considered
constant over time11,12. If instrumental conditions are uniform, the Kr effect scales only with the
atmospheric concentration of methane, it is thus directly proportional to the Kr/CH4 ratio11. We
inferred the linear relationship between Kr effect and 1/CH4 from a series of air samples with
differing methane mixing ratios but similar Kr levels. The three ambient air samples were retrieved
at Niwot Ridge preserve, Colorado, US in 2007, and were part of the „2007 - IPY International Ice
Core Gas Intercalibration Exercise“ launched by Todd Sowers (Penn State University) and Ed
Brook (Oregon State University). The samples of recent ambient air were diluted with ultra pure air
(free of CH4) in order to simulate the full spectrum of atmospheric variability from present day, preindustrial to glacial conditions. Note, that the dilution did not affect the noble gas concentration in
the cylinders.
The raw data chromatograms of “IPY” air samples performed with the PSU instrument were treated
in the same manner as the EDML ice core samples, with the routine adapted to the specific
characteristics of the PSU setup. Based on these PSU measurements we obtained Kr correction
values of 0.12 ‰, 0.26 ‰ and 0.66 ‰ for cylinder “CA03560”, ”CC71560” and “CA01179”,
respectively. To a first approximation, Δδ13CKr scales with the inverse of the CH4 mixing ratios of
1852 ppb, 906 ppb and 365 ppb (illustrated in Figure S2), yielding a dependence of Δδ13CKr to CH4
for the PSU measurements. Supplementary table 1 provides a detailed compilation of results for the
“IPY” air samples for both the PSU and the AWI instruments.
When the linear Δδ13CKr - 1/CH4 relationship was applied to the CH4 mixing ratios13 of the Vostok
ice core, we derived Δδ13CKr values in the range of 0.4 ‰ for interglacial conditions (e.g. MIS 5.5)
to 0.7 ‰ and 0.8 ‰ for the glacial stages MIS 2 and MIS 6, respectively (see Figure S1). Note that
while these corrections on the PSU and AWI data of several tenth of a permille are significant, they
are still small compared to the atmospheric changes of several permille observed in our ice core
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data on glacial/interglacial time scales, and do not influence our interpretations.
2.4 AWI and PSU laboratory inter-calibration
In order to minimize offsets between the absolute standardization of both laboratory setups at the
AWI and the PSU, we performed inter-calibration measurements on three IPY air samples and on
ice core samples from the WAIS Divide ice core WDC05 A (79°27.70S 112°7.510W; 1.759 masl.).
We applied the CH4 dependence of Δδ13CKr to account for the Kr effect encountered in the ice core
and air sample measurements using the PSU instrument, and the raw data correction procedure
described for the EDML ice samples in section 1.3 for the IPY air and WDC05 A measurements
performed with the AWI setup. The results for both systems, presented in more detail in
Supplementary table 1 and 2, lead to an AWI-PSU laboratory offset of 0.09 ‰ with respect to
δ13CH4. As a final adjustment, the Vostok δ13CH4 time series is hence shifted by 0.09 ‰ towards
lower values to account for the differences in the absolute standardization of both laboratories. Note
that this inter-laboratory offset is significantly smaller than the measurement uncertainty of 0.3 ‰,
showing that the AWI and PSU data sets are fully compatible after correction of the Kr effect.
2.5 Firn column corrections applied to the δ 13CH4 ice core records
During the enclosure of air bubbles in the Antarctic ice sheet, the methane molecules like other air
components encounter diffusion processes in the open pore space of the firn column, which also
affect their isotopic composition archived after bubble close-off. In the diffusive firn zone14
methane is subject to gravitational settling, that enriches the heavier isotope at the bottom of the firn
column15. In addition, strong concentration gradients caused by rapid atmospheric methane
concentration changes, induce diffusive fluxes that lead to isotopic fractionation16. Finally, thermal
diffusion corrections are required when large temperature gradients exist in the firn layer17.
Temperature variations at Kohnen Station (EDML core site) and Vostok have been slow during the
last glacial cycle. Hence, the firn column down to the close-off depth was essentially in thermal
equilibrium and thermal diffusion effects are negligible for the datasets presented here.
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In order to quantify the order of magnitude of the respective diffusion effects, we used a firn
diffusion model18 with a parameter set adapted to the glacial EDML core characteristics to calculate
the combined effects of diffusive fractionation due to gravitation and concentration changes at the
surface (see Figure S3). A methane pulse of 200 ppb with an initial rate of increase of 4 ppb/yr and
a respective decline of 0.25 ppb/yr (grey line, subfigure a)) was prescribed to mimic the most
vigorous natural methane rises throughout the glacial cycle (e.g. at the end of terminations or into
DO 21). The δ13CH4 signature of methane at the surface is prescribed at -45 ‰. Accordingly, all
changes to this value recorded at the model bubble close-off on the bottom of the firn column (blue
line, subfigure b)) are solely due to the two diffusion processes. To account for uncertainties
concerning the two most important physical parameters of firnification, site temperature and
accumulation rate, we illustrate the range of model results for the fractionation of δ13CH4 with a
minimal (-52.14 °C, 2.978 cm water equivalent per year (w.e./yr)), maximal (-46.64 °C, 5.075
cm w.e./yr) and a best guess scenario (-49.52 °C, 3.859 cm w.e./yr). This range of site temperatures
and snow accumulation rates is a realistic representation of possible glacial conditions at the core
The initial δ13CH4 value, after steady state is reached in the model, is -44.57 ‰. This offset of
0.43 ‰ relative to the atmospheric value is due to the gravitational settling that is established in the
firn column. It is also on very good agreement with measured δ15N2 for the EDML core for glacial
conditions19. The consecutive prescribed methane rise at the surface causes an additional shift in
δ13CH4 after bubble close-off in the range of -0.73 ‰ for coldest temperatures and lowest snow
accumulation and up to -1.05 ‰ for maximal temperature and accumulation rate, while the best
guess scenario amounts to -0.85 ‰. However, the effect is short-lived and decreases to levels below
our experimental uncertainty in less than 150 years after the initial methane increase. After about
500 years the δ13CH4 value is essentially back at its starting value before the CH4 increase. During
the slower decline of methane concentration back to the base level of 350 ppb, the highest observed
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diffusional fractionation of -0.24 ‰ does not even exceed the measurement uncertainty. In
conclusion, only those data points of our record may be affected that fall within the relatively short
time window during major methane concentration increases. Accordingly, we did not correct our
data for these diffusion effects, but have to keep in mind that individual samples that coincide with
the rapid CH4 changes may be biased by a few tenth of a permille towards lower (more negative)
δ13CH4 values.
Accordingly, all our δ13CH4 data are solely corrected for gravitational fractionation. Vostok samples
have been corrected with interpolated δ15N2 data according to published procedures14. No δ15N2
record covering the whole time interval of our δ13CH4 data is available for the EDML ice core to
date. However, δ15N2 data over the last glacial/interglacial transition vary only between 0.4 to
0.45 ‰. Thus, we shifted EDML values by a constant offset of 0.41 ‰ to higher values. This
0.41 ‰ shift does reflect expected values for glacial conditions very well19,20, and is also in line
with model studies21. The error introduced by this constant correction is 0.05 ‰ at the most, and
thus negligible compared to our overall analytical uncertainty of 0.3 ‰.
2.6 dD(CH4) measurements performed at University of Bern
δD(CH4) measurements were performed using a purge and trap extraction coupled to a gas
chromatography pyrolysis isotope ratio mass spectrometer (GC/P/IRMS) system as described in
detail in Bock et al. (2010)22 with some improvements which will be published elsewhere. External
precision of the presented data is about 2.5 ‰ (1σ) based on standard air measurements of
corresponding size. In Figure 3 of the main text the error bars represent the standard deviation of
standard air measurements (1σ: 1.8 ‰ to 2.9 ‰) used to calibrate the corresponding samples. All
δD(CH4) values are given with respect to the international Vienna Standard Mean Ocean Water
(VSMOW) scale. No corrections (e.g. gravitational enrichment) have been applied, as these
corrections would only be of minor importance regarding the analytical uncertainty, and do not
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affect our conclusions. Note, that no Kr interference occurs for our δD(CH4) system.
2.7 Age scales
Ice core records
If not stated otherwise, all gas ages in this document are reported according to a chronology based
on the new synchronization effort for EPICA and various other ice core chronologies by LemieuxDudon et al. (2010)23, hereafter denoted as “Unified” age scale. Where applicable, a direct age
calculation was performed by linear interpolation of the depth-age relationship provided by
Lemieux-Dudon et al. (2010)23. Other records required additional conversion steps according to the
procedures described below.
Vostok δ 13CH4 and CO2 records
For Vostok we adopted the published Vostok depth to EDC3 gas age relationship of the Vostok
CO2 record and interpolated corresponding EDC3 ages for the Vostok δ13CH4 data. However, we
observed an evident misalignment of the fast methane concentration rises at Dansgaard/Oeschger
(DO) event 24 compared to EDC CH4 data26. As part of the focus of our work is directed at relative
timings of δ13CH4 and CH4 rises, we performed a manual methane synchronization to account for
this offset. Therefore, we picked five tie-points in between DO event 21 and the Termination 2
methane rise at peak flanks (Supplementary table 3) and interpolated the included data points
linearly to the EDC3 age scale27. The EDC3 ages including the adjusted section between 83.6 kyr
BP and 128.9 kyr BP were then converted to the target “Unified” age model. The adjustments were
also applied to the Vostok CO2 record used in this work26,28. Note that the applied adjustments do
not affect the conclusions in this publication on the decoupling of CH4 and δ13CH4 or on the
coupling of δ13CH4 and atmospheric CO2.
Byrd atmospheric CO2
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No official age conversion to the “Unified” age scale exists for the Byrd ice core. Hence, we
performed another methane synchronization between the Byrd29 and EDML30 CH4 records in order
to obtain corresponding EDML depths. Those were then converted to “Unified” ages. We are aware
of the limitations of the methane synchronization approach31, especially if the resolution differences
of the compared CH4 datasets are large, or at times were CH4 variations are low and, thus, tie-points
are scarce. However, the very good temporal agreement of the Byrd32 and the EDML CO2 data33
show that the relative CH4 synchronization error is small and does not affect the conclusions in our
paper. The list of manual tie-points applied in the synchronization are presented in Supplementary
table 4.
Relative sea-level
No direct age conversion to the “Unified” target age scale could be applied to the relative sea-level
(rsl.) data without invoking an ad-hoc phase relationship between the sea level and the ice core
records. Therefore, we used the sea level record of Rohling et al. (2009)24 on the speleothem
synchronized age-scale provided by Grant et al. (2012)25 without any modifications.
3. Complementary information on past δ 13CH4 changes
3.1 DO methane variability and its missing responses in δ 13CH4
Figure S4 illustrates the missing imprint of the rapid atmospheric methane concentration variability
on the carbon isotopic signature of methane in conjunction with the six strongest DO warmings
throughout the last glacial. Segments of the CH430 and δ13CH4 data sets, centered around the
respective methane rise, have been aligned (Figure S4 a)) in order to study the phasing and
individual timing of the concentration changes and its counterpart in δ13CH4 (Figure S4 b)). Note
that the data points in δ13CH4 closest to the most vigorous methane increases are likely biased by
diffusional fractionation in the firn column (see discussion above). This may lead to offsets of
δ13CH4 data points close to major CH4 changes. For example, the two negative excursions in
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δ13CH4 of less than 1 ‰ at DO 8 and termination I are located so close to the corresponding
methane increase, that they can be attributed to the diffusional fractionation effect. We mark the
range of possibly affected values in Figure S4 with a box of the width of 150 yr (gray bar),
according to the maximum duration in our firnification model exercise, where the effect of the
diffusional fractionation exceeded our experimental uncertainty range of 0.3 ‰. We also show the
two major methane increases of termination I and II (right pair of panels in Figure S4). There is no
apparent imprint of rapid methane variability on its carbon isotopic signature over the respective
DO events and deglaciations.
3.2 Proxy evidence for C4 plant expansion
Methane production under natural conditions involves the decomposition of organic precursor
material that has previously been accumulated by plants through photosynthetic sequestration of
CO2. Owing to fundamental physiological differences of the two major photosynthetic pathways,
which characteristically discriminate the heavy isotope ¹³C during carbon assimilation, the typical
ranges of isotopic signatures imposed on the plant material differ considerably34,35. 85–90 % of
terrestrial plant species today, covering the whole spectrum of vegetation from grasses and herbs to
shrubs and trees, follow the C3 photosynthetic pathway36. C3 plant biomass is characterized by a
depleted δ13C signature (-32 ‰ - -22 ‰), caused by the lower reactivity of 13CO2 with the primary
carboxylating enzyme RUBISCO37. C4 vegetation on the other hand, mostly grasses and sedges, are
able to pre-concentrate CO2 internally at the cost of reduced quantum yield38. As a consequence, C4
plant carbon fixation fractionates less against ¹³C (~-16 ‰ - -9 ‰). The isotopic composition of the
terrestrial biosphere, i.a. the pre-cursor biomass for methanogenesis, is controlled by the primary
productivity of an assemblage of plants under local growth conditions, the individual adaptation of
its members to this conditions, as well as its tolerance against limitation factors. Plants of both
photosynthetic pathways are unequally tolerant to limitations in CO2, light intensity, local
temperature, and to moisture and nutrient availability35,39,40. Seasonality of precipitation has an
equally significant impact on the local balance between C3 and C4 vegetation40. However, the
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relative importance of any of the limitations, especially in terms of a competitive advantage of plant
families against others in the struggle for habitats, remains an unresolved and vitally discussed
Accordingly, it is neither physiologically well constrained how strong a C3 to C4 plant shift might
have been in tropical regions under generally colder, drier conditions, and low CO2 levels that were
characteristic for the glacial period, nor is it extensively documented by the scarce terrestrial proxy
evidence from these areas. Such a shift, however, is one of the relevant processes of our hypothesis
to explain the observed, very pronounced δ13CH4 changes. Therefore, we will now discuss some of
the ecosystem evidence that is available and relevant for our hypothesis.
In temperate regions in Northern- and Meso-America46 and the Chinese loess plateau47-49, growing
season temperature and the local climatic constellation seems to out-compete the physiological
effect of low CO2 level as predominant control upon the C3/C4 ratio. With warm growing seasons
in the tropics, however, water insufficiency and low CO2 possess an increased influence as plantgrowth limitation factors and pose high adaptive pressure on prevalent ecosystems50-53.
Recent vegetation model experiments indicate high vegetation sensitivity to low atmospheric CO2
levels during glacial periods44,45,54,55. Globally, simulations for glacial climate conditions and
typical CO2 concentrations lead to significant retractions of closed canopy forest habitats in favor of
open vegetation types43,56,57. Tropical rainforests seem especially affected by the combined effects
of increased aridity and low CO2, and large arboreal areas are replaced by open savanna- and shrublike vegetation.
Analogue findings are well documented by terrestrial proxy data from tropical regions in Africa58-61,
Meso- and South America61-64. These studies also report large proportions of C4 vegetation
contributing to the widespread grasslands in equatorial Africa and South America in the LGM.
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Glacial-Interglacial differences in δ13C of vascular plant waxes from sediment cores off the East
Atlantic coast close to the river mouths of the Congo and Angola basin, indicate 3-4 ‰ shifts
towards higher δ13C values and thus, relative increases of C4 abundance in the range of 20-40 %59.
Increased C4 contribution has also been inferred from another marine core retrieved at the Guinea
Plateau margin recording Sahara/Sahel vegetation60. It also indicates raised aridity, falling
temperatures and exceptionally high C4 predominance for the period between 71 and 65 kyr BP, i.e.
the MIS5/4 transition, which is also characterized by a strong increase (+4 ‰) in our δ13CH4 record.
A comparable study from the Cariaco Basin in the tropical west Atlantic, reported a 4-5 ‰ δ13C
decrease in leaf waxes from the LGM to the Preboreal Holocene62. This probably documents a
reoccupation of forest vegetation in the peripheral Amazonian lowlands, that potentially retracted
under glacial conditions65-69. Moreover, huge land masses the size of Europe from South Thailand
to Sumatra, Borneo and Java became exposed in South-East Asia when sea-level fell in glacial
periods. This territory, known as Sundaland, was also vastly covered by savanna type vegetation
with considerable C4 contribution70,71.
A global shift of C3 to C4 plants may not be representative for the conditions encountered in
permanent (tropical) wetlands. Intuitionally, one may expect that the missing water limitation in
such a wetland would reduce the adaptive pressure on plants in that ecosystem and hence level
competitive advantages of one species against another. C3 plants, for example, are not forced into
the natural trade-off between necessary stomatal opening for carbon sequestration and excessive
water loss. In this light it is yet not fully understood, why large modern wetland ecosystems in
tropical East Africa72, South Africa73,74 or areas of the Amazon floodplain75 exhibit clear C4 plant
predominance (mostly papyrus). Moreover, there is evidence that C4 dominance in east-equatorial
Africa (at least near lake Challa) persisted during both wet and dry phases under glacial
conditions76. For the δ13CH4 change observed in our record we speculate that seasonally inundated
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wetlands played an increasing role for CH4 emissions during cold climate conditions. These tropical
non-permanent wetlands should foster the shift to open grasslands with high C4 contribution, as the
higher water use efficiency and productivity of the C4 plants under low CO2 levels in glacial
periods should proof advantageous in this environment of seasonally contrasting very dry to very
wet conditions.
3.3 Impact of location and habitat on the isotopic signature of C3 plants
Environmental factors as humidity, light availability, CO2 concentration and use of recycled CO2
differ largely between C3 plant habitats like rainforest and savanna77. Dense closed canopy
rainforest habitats provide a higher degree of natural protection from wind movement and hence
reduced water loss and air mass exchange, but on the other hand increase the competition for light
intensity. The low photon flux caused by the light deficit for example diminishes carbon fixation
rates, especially in the undergrowth vegetation. Lower vegetation layers furthermore assimilate
more respired CO2 that already underwent fractionation78,79. Rainforest habitats have much higher
water supply and air moisture levels compared to open vegetation types. Reduced evapotranspirative water loss in closed canopies allows longer periods of opened leaf stomata and, thus,
increased CO2 levels in the leafs. Light deficit and high intracellular CO2 levels enhance the
discrimination of the heavier 13C isotope during photosynthesis80. In contrast, open shrub, herb and
grassy C3 vegetation is, especially in low latitudes, exposed to high levels of direct sunlight and
high leaf temperatures. To avoid extensive water loss, the stomatal conductance of these plants is
usually highly restricted37, and carbon dioxide limitation in the leaf cells reduces the relative
discrimination of the heavier isotope by the enzymes involved in photosynthesis. As a consequence
of these effects, C3 rainforest plant material is found to be 3-4 ‰ more depleted than C3 plant
material from open savanna77.
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3.4 Role of water table changes
Hydrological changes in the past have direct implications on water table heights in wetlands. The
soil oxidative layer thickness directly affects the net/gross primary productivity and thus CH4
emission strength of a given wetland system. But an increased/decreased oxidative layer thickness
would also induce an enrichment/depletion in the mean δ13CH4 signature of emitted methane as a
higher/lower proportion of methane is oxidized on its way to the surface. This parameter is highly
dependent on the local hydrology, topography and soil characteristics and poorly constrained
spatially and over time. Our record suggests that it is of minor importance for the millenial scale
variability of methane over the DO cycles as there is no remarkable imprint on δ13CH4. A general
contribution to the glacial-interglacial difference in δ13CH4 due to enhanced global aridity in the
course of the glaciation, however, cannot be ruled out.
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1. Behrens, M.; Schmitt, J.; Richter, K.-U.; Bock, M.; Richter, U. C.; Levin, I. & Fischer, H. A
gas chromatography/combustion/isotope ratio mass spectrometry system for high-precision
δ13C measurements of atmospheric methane extracted from ice core samples. Rapid Commun
Mass Spectrom, 22, 3261-3269 (2008).
2. Fischer, H.; Behrens, M.; Bock, M.; Richter, U.; Schmitt, J.; Loulergue, L.; Chappellaz, J.;
Spahni, R.; Blunier, T.; Leuenberger, M. & Stocker, T. F. Changing boreal methane sources
and constant biomass burning during the last termination. Nature, 452, 864-867 (2008).
3. Poß, C. Analysis of the variability of the atmospheric methane budget in high polar regions
with a regional trajectory model and using measurements of stable isotopes. PhD thesis,
University of Heidelberg, Germany, (2003).
4. Calibration of the Heidelberg δ13CH4 measurements was performed using pure CO2 IAEA
Standard Reference Materials (RM 8562: δ13C = -3.72 ‰, RM 8564: δ13C = -10.45 ‰, RM
8563: δ13C = -41.59 ‰5). The absolute agreement of the Heidelberg δ13CH4 measurements on
the VPDB CO2 scale is estimated to be better than ±0.1 ‰. An intercomparison with three air
samples from the southern hemisphere between Heidelberg and NIWA yielded a difference
NIWA-Heidelberg δ13CH4 = 0.04 ± 0.04 ‰3.
5. Sowers, T. & Jubenville, J. A modified extraction technique for liberating occluded gases
from ice cores. J. Geophys. Res., 105, 29155-29164 (2000).
6. Sowers, T.; Alley, R. B. & Jubenville, J. Ice Core Records of Atmospheric N2O Covering the
Last 106,000 Years. Science, 301, 945-948 (2003).
7. Miteva, V.; Sowers, T. & Brenchley, J. Production of N2O by Ammonia Oxidizing Bacteria at
Subfreezing Temperatures as a Model for Assessing the N2O Anomalies in the Vostok Ice
Core. Geomicrobiology Journal, 24, 451-459 (2007).
8. Sowers, T.; Rodebaugh, A.; Yoshida, N. & Toyoda, S. Extending records of the isotopic
composition of atmospheric N2O back to 1800 A.D. from air trapped in snow at the South
Pole and the Greenland Ice Sheet Project II ice core. Global Biogeochem. Cycles, 16, 1129-
9. Sowers, T.; Bernard, S.; Aballain, O.; Chappellaz, J.; Barnola, J.-M. & Marik, T. Records of
the δ 13C of atmospheric CH4 over the last 2 centuries as recorded in Antarctic snow and ice.
Global Biogeochem. Cycles, 19, (2005).
10. Sowers, T. Atmospheric methane isotope records covering the Holocene period. Quaternary
© 2013 Macmillan Publishers Limited. All rights reserved.
Science Reviews, 29, 213-221 (2010).
11. Schmitt, J.; Seth, B.; Bock, M.; Fischer, H.; Möller, L.; Sapart, C.; Prokopiou, M.;
Röckmann, T. & Sowers, T. On the interference of Kr during carbon isotope analysis of
methane using continuous-flow combustion–isotope ratio mass spectrometry. Atmos. Meas.
Tech., 6, 1425-1445 (2013).
12. Headly, M. A. & Severinghaus, J. P. A method to measure Kr/N2 ratios in air bubbles
trapped in ice cores and its application in reconstructing past mean ocean temperature. J.
Geophys. Res., 112, D19105- (2007).
13. Petit, J. R.; Basile, I.; Leruyuet, A.; Raynaud, D.; Lorius, C.; Jouzel, J.; Stievenard, M.;
Lipenkov, V. Y.; Barkov, N. I.; Kudryashov, B. B.; Davis, M.; Saltzman, E. & Kotlyakov, V.
Four climate cycles in Vostok ice core. Nature, 387, 359-360 (1997).
14. Sowers, T.; Bender, M.; Raynaud, D. & Korotkevich, Y. S. δ 15N of N2 in Air Trapped in
Polar Ice: a Tracer of Gas Transport in the Firn and a Possible Constraint on Ice Age-Gas Age
Differences. J. Geophys. Res., 97, 15683-15697 (1992).
15. Schwander, J.; Stauffer, B. & Sigg, A. Air mixing in firn and the age of the air at pore close-
off. Annals of Glaciology, 10, 141-145 (1988).
16. Trudinger, C. M.; Enting, L. G.; Etheridge, D. M.; Francey, R. J.; Levchenko, V. A.; Steele,
L. P.; Raynaud, D. & Arnaud, L. Modeling air movement and bubble trapping in firn. J.
Geophys. Res., 102, (1997).
17. Severinghaus, J. P.; Grachev, A. & Battle, M. Thermal fractionation of air in polar firn by
seasonal temperature gradients. Geochem. Geophys. Geosyst., 2, (2001).
18. Schwander, J.; Sowers, T.; Barnola, J.-M.; Blunier, T.; Fuchs, A. & Malaize, B. Age scale of
the air in the summit ice: Implication for glacial-interglacial temperature change. J. Geophys.
Res., 102, 19483–19493 (1997).
19. Landais, A.; Barnola, J.; Kawamura, K.; Caillon, N.; Delmotte, M.; Van Ommen, T.;
Dreyfus, G.; Jouzel, J.; Masson-Delmotte, V.; Minster, B.; Freitag, J.; Leuenberger, M.;
Schwander, J.; Huber, C.; Etheridge, D. & Morgan, V. Firn-air δ15N in modern polar sites and
glacial-interglacial ice: a model-data mismatch during glacial periods in Antarctica?.
Quaternary Science Reviews, 25, 49-62 (2006).
20. Landais, A.; Dreyfus, G.; Capron, E.; Masson-Delmotte, V.; Sanchez-Goñi, M.; Desprat, S.;
Hoffmann, G.; Jouzel, J.; Leuenberger, M. & Johnsen, S. What drives the millennial and
orbital variations of δ18Oatm? Quaternary Science Reviews, 29, 235-246 (2010).
21. EPICA community members, One-to-one coupling of glacial climate variability in
Greenland and Antarctica. Nature, 444, 195-198 (2006).
22. Bock, M.; Schmitt, J.; Behrens, M.; Möller, L.; Schneider, R.; Sapart, C. & Fischer, H. A
© 2013 Macmillan Publishers Limited. All rights reserved.
gas chromatography/pyrolysis/isotope ratio mass spectrometry system for high-precision δD
measurements of atmospheric methane extracted from ice cores. Rapid Commun. Mass
Spectrom., 24, 621-633 (2010).
23. Lemieux-Dudon, B.; Blayo, E.; Petit, J.-R.; Waelbroeck, C.; Svensson, A.; Ritz, C.; Barnola,
J.-M.; Narcisi, B. M. & Parrenin, F. Consistent dating for Antarctic and Greenland ice cores.
Quaternary Science Reviews, 29, 8-20 (2010).
24. Rohling, E. J.; Grant, K.; Bolshaw, M.; Roberts, A. P.; Siddall, M.; Hemleben, C. & Kucera,
M. Antarctic temperature and global sea level closely coupled over the past five glacial
cycles. Nature Geosci, 2, 500-504 (2009).
25. Grant, K. M.; Rohling, E. J.; Bar-Matthews, M.; Ayalon, A.; Medina-Elizalde, M.; Ramsey,
C. B.; Satow, C. & Roberts, A. P. Rapid coupling between ice volume and polar temperature
over the past 150,000 years. Nature, 491, 744-747 (2012).
26. Loulergue, L.; Schilt, A.; Spahni, R.; Masson-Delmotte, V.; Blunier, T.; Lemieux, B.;
Barnola, J.-M.; Raynaud, D.; Stocker, T. F. & Chappellaz, J. Orbital and millennial-scale
features of atmospheric CH4 over the past 800,000 years.. Nature, 453, 383-386 (2008).
27. Parrenin, F.; Barnola, J.-M.; Beer, J.; Blunier, T.; Castellano, E.; Chappellaz, J.; Dreyfus,
G.; Fischer, H.; Fujita, S.; Jouzel, J.; Kawamura, K.; Lemieux-Dudon, B.; Loulergue, L.;
Masson-Delmotte, V.; Narcisi, B.; Petit, J.-R.; Raisbeck, G.; Raynaud, D.; Ruth, U.;
Schwander, J.; Severi, M.; Spahni, R.; Steffensen, J. P.; Svensson, A.; Udisti, R.; Waelbroeck,
C. & Wolff, E. The EDC3 chronology for the EPICA Dome C ice core. Clim. Past, 3, 485-
497 (2007).
28. Lüthi, D.; Floch, M. L.; Bereiter, B.; Blunier, T.; Barnola, J.-M.; Siegenthaler, U.; Raynaud,
D.; Jouzel, J.; Fischer, H.; Kawamura, K. & Stocker, T. F. High-resolution carbon dioxide
concentration record 650,000-800,000 years before present.. Nature, 453, 379-382 (2008).
29. Blunier, T. & Brook, E. J. Timing of Millennial-Scale Climate Change in Antarctica and
Greenland During the Last Glacial Period. Science, 291, 109-112 (2001).
30. Schilt, A.; Baumgartner, M.; Schwander, J.; Buiron, D.; Capron, E.; Chappellaz, J.;
Loulergue, L.; Schüpbach, S.; Spahni, R.; Fischer, H. & Stocker, T. F. Atmospheric nitrous
oxide during the last 140,000 years. Earth and Planetary Science Letters, 300, 33-43 (2010).
31. Blunier, T.; Spahni, R.; Barnola, J.-M.; Chappellaz, J.; Loulergue, L. & Schwander, J.
Synchronization of ice core records via atmospheric gases. Clim. Past, 3, 325-330 (2007).
32. Ahn, J. & Brook, E. J. Atmospheric CO2 and Climate on Millennial Time Scales During the
Last Glacial Period. Science, 322, 83-85 (2008).
33. Bereiter, B.; Lüthi, D.; Siegrist, M.; Schüpbach, S.; Stocker, T. F. & Fischer, H. Mode
change of millennial CO2 variability during the last glacial cycle associated with a bipolar
© 2013 Macmillan Publishers Limited. All rights reserved.
marine carbon seesaw. Proceedings of the National Academy of Sciences, 109, 9755-9760
34. O'Leary, M. H. Carbon Isotope Fractionation In Plants. Phytochemistry, 20, 553-567 (1981).
35. Farquhar, G. D.; Ehleringer, J. R. & Hubick, K. T. Carbon Isotope Discrimination and
Photosynthesis Rid A-3722-2008. Annual Review of Plant Physiology and Plant Molecular
Biology, 40, 503-537 (1989).
36. Gerhart, L. M. & Ward, J. K. Plant responses to low [CO2] of the past. New Phytologist,
188, 674-695 (2010).
37. Farquhar, G. D.; Oleary, M. H. & Berry, J. A. On the Relationship Between Carbon Isotope
Discrimination and the Inter-cellular Carbon-dioxide Concentration In Leaves Rid A-3722-
2008 Rid B-8211-2009. Australian Journal of Plant Physiology, 9, 121-137 (1982).
38. Ehleringer, J. R. Implications of quantum yield differences on the distributions of C3 and C4
grasses. Oecologia, 31, 255-267 (1978).
39. Cowling, S. A. & Sykes, M. T. Physiological Significance of Low Atmospheric CO2 for
Plant/Climate Interactions. Quaternary Research, 52, 237-242 (1999).
40. Ehleringer, J. (2005). The Influence of Atmospheric CO2, Temperature, and Water on the
Abundance of C3/C4 Taxa. In: Baldwin, I.; Caldwell, M.; Heldmaier, G.; Jackson, R.; Lange,
O.; Mooney, H.; Schulze, E.-D.; Sommer, U.; Ehleringer, J.; Denise Dearing, M. & Cerling,
T. (Ed.), Ecological Studies, Springer New York.
41. Sankaran, M.; Hanan, N. P.; Scholes, R. J.; Ratnam, J.; Augustine, D. J.; Cade, B. S.;
Gignoux, J.; Higgins, S. I.; Le Roux, X.; Ludwig, F.; Ardo, J.; Banyikwa, F.; Bronn, A.;
Bucini, G.; Caylor, K. K.; Coughenour, M. B.; Diouf, A.; Ekaya, W.; Feral, C. J.; February,
E. C.; Frost, P. G. H.; Hiernaux, P.; Hrabar, H.; Metzger, K. L.; Prins, H. H. T.; Ringrose, S.;
Sea, W.; Tews, J.; Worden, J. & Zambatis, N. Determinants of woody cover in African
savannas. Nature, 438, 846-849 (2005).
42. Edwards, E. J.; Osborne, C. P.; Strömberg, C. A. E.; Smith, S. A. & Consortium, C. 4. G.
The Origins of C4 Grasslands: Integrating Evolutionary and Ecosystem Science. Science, 328,
587-591 (2010).
43. Prentice, I. C.; Harrison, S. P. & Bartlein, P. J. Global vegetation and terrestrial carbon cycle
changes after the last ice age. New Phytologist, 189, 988-998 (2011).
44. Bragg, F. J.; Prentice, I. C.; Harrison, S. P.; Eglinton, G.; Foster, P. N.; Rommerskirchen, F.
& Rullkötter, J. Stable isotope and modelling evidence for CO2 as a driver of glacial–
interglacial vegetation shifts in southern Africa. Biogeosciences, 10, 2001-2010 (2013).
45. Claussen, M.; Selent, K.; Brovkin, V.; Raddatz, T. & Gayler, V. Impact of CO2 and climate
on Last Glacial Maximum vegetation - a factor separation. Biogeosciences Discuss., 9, 15823-
© 2013 Macmillan Publishers Limited. All rights reserved.
15852 (2012).
46. Huang, Y.; Street-Perrott, F. A.; Metcalfe, S. E.; Brenner, M.; Moreland, M. & Freeman, K.
H. Climate Change as the Dominant Control on Glacial-Interglacial Variations in C3 and C4
Plant Abundance. Science, 293, 1647-1651 (2001).
47. Zhang, Z.; Zhao, M.; Lu, H. & Faiia, A. M. Lower temperature as the main cause of C4
plant declines during the glacial periods on the Chinese Loess Plateau. Earth and Planetary
Science Letters, 214, 467-481 (2003).
48. Vidic, N. J. & Montañez, I. P. Climatically driven glacial-interglacial variations in C3 and
C4 plant proportions on the Chinese Loess Plateau. Geology, 32, 337-340 (2004).
49. Wang, G.; Feng, X.; Han, J.; Zhou, L.; Tan, W. & Su, F. Paleovegetation reconstruction
using δ13C of Soil Organic Matter. Biogeosciences, 5, 1325-1337 (2008).
50. Ehleringer, J. R.; Cerling, T. E. & Helliker, B. R. C4 photosynthesis, atmospheric CO2, and
climate. Oecologia, 112, 285-299 (1997).
51. Street-Perrott, F. A.; Huang, Y.; Perrott, R. A.; Eglinton, G.; Barker, P.; Khelifa, L. B.;
Harkness, D. D. & Olago, D. O. Impact of Lower Atmospheric Carbon Dioxide on Tropical
Mountain Ecosystems. Science, 278, 1422-1426 (1997).
52. Sage, R. F.; Wedin, D. A. & Li, M. (1999). 10 - The Biogeography of C4 Photosynthesis:
Patterns and Controlling Factors. In: Sage, R. F. & Monson, R. K. (Ed.), C4 Plant Biology,
Academic Press.
53. Sage, R. F. The evolution of C4 photosynthesis. New Phytologist, 161, 341-370 (2004).
54. Harrison, S. P. & Prentice, C. I. Climate and CO2 controls on global vegetation distribution
at the last glacial maximum: analysis based on palaeovegetation data, biome modelling and
palaeoclimate simulations. Global Change Biology, 9, 983-1004 (2003).
55. Prentice, I. C. & Harrison, S. P. Ecosystem effects of CO2 concentration: evidence from past
climates. Clim. Past, 5, 297-307 (2009).
56. Jolly, D. & Haxeltine, A. Effect of Low Glacial Atmospheric CO2 on Tropical African
Montane Vegetation. Science, 276, 786-788 (1997).
57. Woillez, M.-N.; Kageyama, M.; Krinner, G.; de Noblet-Ducoudré, N.; Viovy, N. & Mancip,
M. Impact of CO2 and climate on the Last Glacial Maximum vegetation: results from the
ORCHIDEE/IPSL models. Clim. Past, 7, 557-577 (2011).
58. Wooller, M. J.; Swain, D. L.; Ficken, K. J.; Agnew, A. D. Q.; Street-Perrott, F. A. &
Eglinton, G. Late Quaternary vegetation changes around Lake Rutundu, Mount Kenya, East
Africa: evidence from grass cuticles, pollen and stable carbon isotopes. J. Quaternary Sci., 18,
3-15 (2003).
59. Rommerskirchen, F.; Eglinton, G.; Dupont, L. & Rullkötter, J. Glacial/interglacial changes
© 2013 Macmillan Publishers Limited. All rights reserved.
in southern Africa: Compound-specific δ 13C land plant biomarker and pollen records from
southeast Atlantic continental margin sediments. Geochem. Geophys. Geosyst., 7, (2006).
60. Castañeda, I. S.; Mulitza, S.; Schefuß, E.; Lopes dos Santos, R. A.; Sinninghe Damsté, J. S.
& Schouten, S. Wet phases in the Sahara/Sahel region and human migration patterns in North
Africa. Proceedings of the National Academy of Sciences, 106, 20159-20163 (2009).
61. Hessler, I.; Dupont, L.; Bonnefille, R.; Behling, H.; González, C.; Helmens, K. F.;
Hooghiemstra, H.; Lebamba, J.; Ledru, M.-P.; Lézine, A.-M.; Maley, J.; Marret, F. &
Vincens, A. Millennial-scale changes in vegetation records from tropical Africa and South
America during the last glacial. Quaternary Science Reviews, 29, 2882-2899 (2010).
62. Hughen, K. A.; Eglinton, T. I.; Xu, L. & Makou, M. Abrupt Tropical Vegetation Response
to Rapid Climate Changes. Science, 304, 1955-1959 (2004).
63. Punyasena, S. W.; Mayle, F. E. & McElwain, J. C. Quantitative estimates of glacial and
Holocene temperature and precipitation change in lowland Amazonian Bolivia. Geology, 36,
667-670 (2008).
64. Lane, C.; Horn, S.; Mora, C.; Orvis, K. & Finkelstein, D. Sedimentary stable carbon isotope
evidence of late Quaternary vegetation and climate change in highland Costa Rica. Journal of
Paleolimnology, 45, 323-338 (2011).
65. Mayle, F. E.; Burbridge, R. & Killeen, T. J. Millennial-scale dynamics of southern
Amazonian rain forests. Science, 290, 2291-2294 (2000).
66. Turcq, B.; Cordeiro, R. C.; Sifeddine, A.; Simões Filho, F. F.; Albuquerque, A. L. S. &
Abrão, J. J. Carbon storage in Amazonia during the Last Glacial Maximum: secondary data
and uncertainties. Chemosphere, 49, 821-835 (2002).
67. Mayle, F. E. & Beerling, D. J. Late Quaternary changes in Amazonian ecosystems and their
implications for global carbon cycling. Palaeogeography, Palaeoclimatology, Palaeoecology,
214, 11-25 (2004).
68. Mayle, F. E.; Beerling, D. J.; Gosling, W. D. & Bush, M. B. Responses of Amazonian
ecosystems to climatic and atmospheric carbon dioxide changes since the last glacial
maximum. Philosophical Transactions of the Royal Society of London. Series B: Biological
Sciences, 359, 499-514 (2004).
69. Anhuf, D.; Ledru, M.-P.; Behling, H.; Da Cruz Jr., F.; Cordeiro, R.; Van der Hammen, T.;
Karmann, I.; Marengo, J.; De Oliveira, P.; Pessenda, L.; Siffedine, A.; Albuquerque, A. & Da
Silva Dias, P. Paleo-environmental change in Amazonian and African rainforest during the
LGM. Palaeogeography, Palaeoclimatology, Palaeoecology, 239, 510-527 (2006).
70. Bird, M. I.; Taylor, D. & Hunt, C. Palaeoenvironments of insular Southeast Asia during the
Last Glacial Period: a savanna corridor in Sundaland? Quaternary Science Reviews, 24, 2228-
© 2013 Macmillan Publishers Limited. All rights reserved.
2242 (2005).
71. Wurster, C. M.; Bird, M. I.; Bull, I. D.; Creed, F.; Bryant, C.; Dungait, J. A. J. & Paz, V.
Forest contraction in north equatorial Southeast Asia during the Last Glacial Period.
Proceedings of the National Academy of Sciences, 107, 15508-15511 (2010).
72. Jones, M. B. & Muthuri, F. M. Standing biomass and carbon distribution in a papyrus
(Cyperus papyrus L.) swamp on Lake Naivasha, Kenya. Journal of Tropical Ecology, 13,
347-356 (1997).
73. Kotze, D. C. & O'Connor, T. G. Vegetation variation within and among palustrine wetlands
along an altitudinal gradient in KwaZulu-Natal, South Africa. Plant Ecology, 146, 77-96
74. Saunders, M.; Jones, M. & Kansiime, F. Carbon and water cycles in tropical papyrus
wetlands. Wetlands Ecology and Management, 15, 489-498 (2007).
75. Piedade, M. T. F.; Junk, W. J. & Long, S. P. The Productivity of the C4 Grass Echinochloapolystachya On the Amazon Floodplain Rid A-2488-2008. Ecology, 72, 1456-1463 (1991).
76. Sinninghe Damsté, J. S.; Verschuren, D.; Ossebaar, J.; Blokker, J.; van Houten, R.; van der
Meer, M. T.; Plessen, B. & Schouten, S. A 25,000-year record of climate-induced changes in
lowland vegetation of eastern equatorial Africa revealed by the stable carbon-isotopic
composition of fossil plant leaf waxes. Earth and Planetary Science Letters, 302, 236-246
77. Vogts, A.; Moossen, H.; Rommerskirchen, F. & Rullkötter, J. Distribution patterns and
stable carbon isotopic composition of alkanes and alkan-1-ols from plant waxes of African
rain forest and savanna C3 species. Organic Geochemistry, 40, 1037-1054 (2009).
78. Medina, E. & Minchin, P. Stratification of δ 13C values of leaves in Amazonian rain forests.
Oecologia, 45, 377-378 (1980).
79. Buchmann, N.; Guehl, J.-M.; Barigah, T. S. & Ehleringer, J. R. Interseasonal comparison of
CO2 concentrations, isotopic composition, and carbon dynamics in an Amazonian rainforest
(French Guiana). Oecologia, 110, 120-131 (1997).
80. Ehleringer, J. R.; Field, C. B.; Lin, Z.-F. & Kuo, C.-Y. Leaf carbon isotope and mineral
composition in subtropical plants along an irradiance cline. Oecologia, 70, 520-526 (1986).
81. Mitchell, L. E.; Brook, E. J.; Sowers, T.; McConnell, J. R. & Taylor, K. Multidecadal
variability of atmospheric methane, 1000-1800 C.E.. J. Geophys. Res., 116, (2011).
82. Mischler, J. A.; Sowers, T. A.; Alley, R. B.; Battle, M.; McConnell, J. R.; Mitchell, L.;
Popp, T.; Sofen, E. & Spencer, M. K. Carbon and hydrogen isotopic composition of methane
over the last 1000 years. Global Biogeochem. Cycles, 23, (2009).
83. Chappellaz, J.; Barnola, J. M.; Raynaud, D.; Korotkevich, Y. S. & Lorius, C. Ice-core record
© 2013 Macmillan Publishers Limited. All rights reserved.
of atmospheric methane over the past 160,000 years. Nature, 345, 127-131 (1990).
© 2013 Macmillan Publishers Limited. All rights reserved.
Figure legends
Figure S1 | Magnitude of the Kr effect Δδ 13CKr derived from two distinct methods for the
AWI and PSU instrument
a) the new δ¹³CH4 time series of Vostok (light blue) and EDML (red), the Holocene GISP II
record11 (dark blue, on its original time scale), and the published EDML record2 over termination I,
that was processed anew according to the procedures in this publication (purple). The corrected
time series are illustrated with thicker lines and large circle markers. The original records before the
correction are shown as thin lines with small white markers.
b) The range of Δδ13CKr values applied as individual correction to the respective datasets to account
for the Kr-bias on δ¹³CH4. The color coding is the same as in a). Note that Δδ13CKr of both, Vostok
and GISP II data measured at the PSU, were inferred indirectly from CH4 mixing ratios, while for
the two EDML records the correction is based on the Kr-induced anomaly derived from the ioncurrent ratios (see section 1.3 for a detailed description of both approaches).
Figure S2 | Linear scaling of the size of interference on carbon isotope measurements caused
by Kr (Δδ 13CKr) relative to variations in CH4 mixing ratios for the PSU instrument
Atmospheric krypton mixing ratios are considered constant over time13.The size of the interference
(Kr effect or Δδ13CKr) thus scales with CH4 concentrations. Measurements of three ambient air
samples were used to infer a linear relationship between Δδ13CKr and the inverted CH4 mixing ratio
for the PSU instrument, that is used to account for the Kr effect of ice core (Vostok, WDC05) and
laboratory inter-calibration measurements (see section 1.3 for further details). Results for the IPY
cylinder with a mixing ratio representative for glacial conditions (“glacial”, 365 ppb) is shown as a
light blue diamond, the one with “preindustrial” values (906 ppb) in orange, and the one
representative for modern concentrations (“present-day”, 1852 ppb) in dark red, all illustrated with
its corresponding 1σ error range. The linear relationship in red allowed a first order estimate of
© 2013 Macmillan Publishers Limited. All rights reserved.
Δδ13CKr for ice core samples measured at the PSU lab, based on interpolated CH4 mixing ratios of
the respective samples.
Figure S3 | Model results to quantify fractionation processes in the firn column due to
atmospheric CH4 concentration changes
a) Artificial atmospheric methane pulse at the model firn “surface” (grey line), and its
corresponding concentration after the “bubble close-off” at the bottom of the firn column at three
different accumulation rate and temperature regimens (“maximum” temperature: purple line;
“minimum” temperature: red line and “best guess” temperature: light blue line). b) Shifts of the
carbon isotope signature of methane caused by the gravitational and diffusional fractionation in the
firn column from the constant value at the “surface” (gray dashed line) to the signals enclosed in the
model “bubbles” (colored lines) at the firn bottom, according to their respective scenario (color
coding similar to a))
Figure S4 | Representation of major Dansgaard-Oeschger methane rises during the last
glacial period, and the respective imprint on its carbon isotope signature
a) Six strong methane rises (color coding in the figure legends) coeval to major
Dansgaard/Oeschger (DO) events (left and middle figure column) and the two ultimate terminations
(right column) in a 6,000 year time window aligned and centered around the major methane rise
(illustrated by the vertical gray line). All CH4 data from the EDML ice core30 b) Corresponding
time windows of the respective δ¹³CH4 data sections from Vostok and EDML (this study, color
coding and alignment according to a)). The shaded bar represents the time window, in which data
points might be biased by diffusional fractionation beyond our measurement uncertainty.
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Supplementary Table 1 | Comparison of δ¹³CH4 results for the measurements of air samples
performed at the AWI and PSU laboratories, that were part of the „2007 - IPY International
Ice Core Gas Intercalibration Exercise“.
Ambient air was diluted to resemble atmospheric methane mixing ratios typical for present day
(1852 ppb), pre-industrial (906 ppb) and glacial (365 ppb) conditions. δ¹³CH4 values are reported
with respect to VPDB. The first two columns report original measurements, the following two
columns the respective values after the correction for Kr interference. Deviations in the carbon
isotopic signature caused by the ionized Kr (Δδ13CKr) and its dependency on CH4 levels are
summarized in the final column. The results are further used to infer the absolute standardization
offset between both laboratories.
S a m p le I D | e p o c h
δ 1 3 C H 4 (‰ )
1 σ (‰ )
O rig in a l
A W I a n a ly s e s ( 5 /2 0 1 0 )
C A03560 | pre s e nt da y
C C 71560 | pre indus tria l
C A01179 | g la c ia l
P S U a n a ly s e s ( 7 /2 0 0 7 )
C A03560 | pre s e nt da y
C C 71560 | pre indus tria l
C A01179 | g la c ia l
D if f e re n c e ( A W I -­‐ P S U )
C A03560 | pre s e nt da y
C C 71560 | pre indus tria l
C A01179 | g la c ia l
A v g . la b o f f s e t
S t . d e v . ( 1 σ)
δ 1 3 C H 4 (‰ )
(‰ )
Δδ 1 3 C K r (‰ )
C o rre c t e d f o r K r
0 .2 1
0 .3 5
0 .1 4
0 .3 6
Supplementary Table 2 | δ¹³CH4 values of WDC05 A ice core material measured with the
AWI and PSU instruments to further test the alignment of both systems.
No adjustments have been applied to correct for gravitational settling. Values denoted as “original”
© 2013 Macmillan Publishers Limited. All rights reserved.
are inferred according to the standard routines in the respective laboratories, before the correction
for Krypton interference. Acquisitions with the AWI instrument were treated similar to EDML ice
and IPY air samples correcting the chromatograms for the Kr interference, PSU measurements
using the CH4 dependent Δδ13CKr to account for Kr. WDC05A [CH4] values and approximate age of
the samples (black diamond) were interpolated from data of Mitchell et al.,201181. Note, that the
two PSU measurements (marked by asterisks) are not from the same depth as the AWI sample, but
δ¹³CH4 variability (1σ) is less than 0.3 ‰ over the depth interval 161.5 m (1593 AD) to 173.4 m
(1540 AD) and thus in the the order of our analytical uncertainty82.
S a m p le Lab
WD C 05A*
WD C 05A
WD C 05A*
D e p t h A g e ♦ (m )
(AD )
δ 1 3 C H 4 1 σ (‰ )
(‰ )
δ 1 3 C H 4 1 σ (‰ )
(‰ )
Δδ 1 3 C K r (‰ )
O rig in a l
C o rre c te d f o r K r
Supplementary Table 3 | Manual CH4 tie-points used for the adjustment of the misaligned
Vostok section in between 129-84 kyr BP28.
The synchronization was performed with EDC26 and Vostok83 atmospheric methane records. The
EDC3 ages of the five listed Vostok sample depths were adjusted according to the corresponding
EDC tie points. All Vostok records presented in this study are dated according to this adjustments.
t ie -­‐p o in t
E D C d e p t h
(m )
E D C 3 a g e
( y e a r s B P )
E D C C H ₄
V o s t o k d e p t h
(m )
V o s t o k C H ₄
1 24 1 .67
1 36 7 .89
1 47 3 .40
1 54 3 .59
1 72 3 .46
8 36 2 7
9 58 6 6
1 06 7 81
1 15 0 81
1 28 8 71
5 00 .7
4 70 .2
5 10 .1
4 80 .2
5 59 .9
1 26 6 .83
1 44 0 .34
1 53 6 .00
1 63 5 .97
1 88 1 .99
5 00 .1
4 70 .0
5 10 .8
4 80 .4
5 60 .1
Supplementary Table 4 | Methane synchronization tie-points used for dating the Byrd ice core
© 2013 Macmillan Publishers Limited. All rights reserved.
records shown in this work. The age conversion to the “Unified” age scale23 is based on
EDML30 and Byrd29 atmospheric methane records.
t ie -­‐p o in t
E D ML d e p t h
“ U n if ie d ” a g e
E D ML C H ₄
B y rd d e p t h
B y rd C H ₄
(m )
( y e a rs B P )
(m )
© 2013 Macmillan Publishers Limited. All rights reserved.
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