Cauquoin Comparing past accumulation rate reconstructions in East Antarctic ice cores using sup10supBe water isotopes and CMIP5-PMIP3 models Climate Of The Past-2015

Clim. Past, 11, 355–367, 2015
www.clim-past.net/11/355/2015/
doi:10.5194/cp-11-355-2015
© Author(s) 2015. CC Attribution 3.0 License.
Comparing past accumulation rate reconstructions in East Antarctic
ice cores using 10Be, water isotopes and CMIP5-PMIP3 models
A. Cauquoin1 , A. Landais1 , G. M. Raisbeck2 , J. Jouzel1 , L. Bazin1 , M. Kageyama1 , J.-Y. Peterschmitt1 , M. Werner3 ,
E. Bard4 , and ASTER Team4,*
1 Laboratoire
des Sciences du Climat et de l’Environnement (LSCE/IPSL, CEA-CNRS-UVSQ), CEA Saclay Orme des
Merisiers, 91191 Gif-sur-Yvette, France
2 Centre de Sciences Nucléaires et de Sciences de la Matière (CSNSM), UMR CNRS 8609, Université Paris Sud XI, Bât 108,
91405 Orsay, France
3 Alfred Wegener Institute for Polar and Marine Research (AWI) Bussestraße 24, 27570 Bremerhaven, Germany
4 Aix-Marseille Université, CNRS-IRD-Collège de France, UM 34 CEREGE, Technopôle de l’Environnement
Arbois-Méditerranée, BP80, 13545 Aix-en-Provence, France
* M. Arnold, G. Aumaître, D. L. Bourlès and K. Keddadouche
Correspondence to: A. Cauquoin (alexandre.cauquoin@lmd.jussieu.fr)
Received: 18 July 2014 – Published in Clim. Past Discuss.: 22 August 2014
Revised: 14 January 2015 – Accepted: 2 February 2015 – Published: 5 March 2015
Abstract. Ice cores are exceptional archives which allow us
to reconstruct a wealth of climatic parameters as well as past
atmospheric composition over the last 800 kyr in Antarctica.
Inferring the variations in past accumulation rate in polar
regions is essential both for documenting past climate and
for ice core chronology. On the East Antarctic Plateau, the
accumulation rate is so small that annual layers cannot be
identified and accumulation rate is mainly deduced from the
water isotopic composition assuming constant temporal relationships between temperature, water isotopic composition
and accumulation rate. Such an assumption leads to large uncertainties on the reconstructed past accumulation rate. Here,
we use high-resolution beryllium-10 (10 Be) as an alternative
tool for inferring past accumulation rate for the EPICA Dome
C ice core, in East Antarctica. We present a high-resolution
10 Be record covering a full climatic cycle over the period 269
to 355 ka from Marine Isotope Stage (MIS) 9 to 10, including a period warmer than pre-industrial (MIS 9.3 optimum).
After correcting 10 Be for the estimated effect of the palaeomagnetic field, we deduce that the 10 Be reconstruction is in
reasonably good agreement with EDC3 values for the full
cycle except for the period warmer than present. For the latter, the accumulation is up to 13 % larger (4.46 cm ie yr−1
instead of 3.95). This result is in agreement with the studies
suggesting an underestimation of the deuterium-based accu-
mulation for the optimum of the Holocene (Parrenin et al.,
2007a). Using the relationship between accumulation rate
and surface temperature from the saturation vapour relationship, the 10 Be-based accumulation rate reconstruction suggests that the temperature increase between the MIS 9.3 optimum and present day may be 2.4 K warmer than estimated by
the water isotopes reconstruction. We compare these reconstructions to the available model results from CMIP5-PMIP3
for a glacial and an interglacial state, i.e. for the Last Glacial
Maximum and pre-industrial climates. While 3 out of 7 models show relatively good agreement with the reconstructions
of the accumulation–temperature relationships based on 10 Be
and water isotopes, the other models either underestimate or
overestimate it, resulting in a range of model results much
larger than the range of the reconstructions. Indeed, the models can encounter some difficulties in simulating precipitation changes linked with temperature or water isotope content on the East Antarctic Plateau during glacial–interglacial
transition and need to be improved in the future.
Published by Copernicus Publications on behalf of the European Geosciences Union.
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1
A. Cauquoin et al.: Accumulation rate reconstructions using 10 Be, water isotopes and CMIP5-PMIP3 models
Introduction
Polar ice cores provide reference records for past climatic
conditions over the last 130 kyr in Greenland (North Greenland Ice Core Project Members, 2004; NEEM Community
Members, 2013) and over the last 800 kyr in Antarctica
(EPICA Community Members, 2004). That ice as old as
800 kyr can be retrieved at a depth of 3200 m is due to the
very low accumulation rate encountered at this site of the
East Antarctic Plateau (2.73 cm ice equivalent (ie) per year
today; EPICA Community Members, 2004). Accumulation
rate is even smaller during glacial periods as expected from
simple thermo-dynamical considerations: cold air holds less
moisture than warm air. Still, the quantitative reconstruction
of past accumulation rate is not straightforward and large uncertainties (> 30 %) are often associated with its reconstruction in polar ice cores (Blunier et al., 2007; Guillevic et al.,
2013; van Ommen and Morgan, 1997).
Reducing the uncertainties on the reconstruction of past
accumulation rate is essential for several reasons. First and
most obviously, this will lead to an improved ice core
chronology. Second, even if the physical relationship between air moisture content and temperature holds true with
time, there is no a priori reason why the link between accumulation rate and temperature should be constant with time
in polar regions, at the very end of the water condensation
process. Because of the spatial and temporal variations in the
origin and trajectories of air masses, some decoupling can
be expected between accumulation rate and temperature or
water isotopes from which polar temperature is classically
retrieved. Finally, the simulation of polar accumulation and
of its link with temperature is a weakness for many models, such that an evaluation of the modelled relationship between temperature vs. accumulation against accumulation reconstructions is desirable. There is thus a clear need for estimates of past accumulation changes that are independent of
temperature, as well as water isotope.
An alternate way to reconstruct past accumulation rate
is the use of beryllium-10 (10 Be), a cosmogenic isotope to
obtain an independent estimate of past accumulation. After their production in the upper atmosphere (Lal and Peters, 1967), 10 Be atoms become fixed to aerosols and fall
very quickly (within 1–2 years according to Raisbeck et al.,
1981a) on the Antarctic Plateau. A simplistic assumption,
namely that the 10 Be flux is constant through time, has been
applied to estimate accumulation changes along the Vostok
ice core, first from a limited set of measurements (Yiou et al.,
1985) and then from a more detailed but still low resolution
and discontinuous data set covering the last climatic cycle
(Jouzel et al., 1989). This assumption was suggested by the
anti-correlation observed between 10 Be concentrations in ice
and accumulation rate derived from oxygen isotopes at the
drilling site (Yiou et al., 1985). However, the assumption of
a constant 10 Be flux is limited by the heliomagnetic and geomagnetic modulations: the higher these fields are, the more
Clim. Past, 11, 355–367, 2015
primary cosmic ray particles are deflected, which leads to
a decrease in cosmogenic isotope production. For example,
this problem is important for the last glacial period, which
includes the Laschamp excursion, a dramatic short-lived decrease in the Earth’s magnetic field intensity occurring at
about 41 ka (Singer et al., 2009).
In the present study, we exploit a continuous and very detailed 10 Be time series covering a full climatic cycle over
a 86 kyr period, from Marine Isotope Stage (MIS) 9 to 10,
measured along the Dome C ice core (75◦ 060 S, 123◦ 210 E).
Two geomagnetic events are mentioned during this time
range at 290 ka (the so-called “Portuguese Margin”; Thouveny et al., 2008) and 320 ka (the so-called “Calabrian Ridge
1”; Langereis et al., 1997). This 269–355 ka record has been
prepared and measured in the framework of the PhD work of
Cauquoin (2013). It completes a high-resolution 10 Be record
between 170 and 800 ka at EDC that will be published separately. Two advantages of using the period 269–355 ka for
this study are that (1) it has the largest glacial–interglacial
range of δD and thus estimated temperature and accumulation and (2) it has relatively small geomagnetic variations as
compared, for example, to the last climatic cycle (Blake and
Laschamp excursions). Thus the sensitivity of 10 Be concentration due to accumulation variations compared to those due
to production variations should be particularly favourable.
Our manuscript is organized as follows. After reiterating
on the classical estimation of past accumulation rates from
water isotopes in ice core and a presentation of the procedure, we examine the multidecadal 10 Be record and discuss
how this ∼ 86 kyr long record allows inferences about the
associated glacial–interglacial accumulation rate change. In
the following section, we discuss the relationship between
temperature/δD and accumulation rate changes on the East
Antarctic Plateau over deglaciations using constraints both
from glaciological data and from 11 modelling outputs inferred from 8 CMIP5-PMIP3 models, plus one simulation
(ECHAM5) being equipped with stable water isotope diagnostics for the pre-industrial period (PI) and the Last Glacial
Maximum (LGM, 21 ka).
2
2.1
Methods
The classical estimation of past accumulation rates
from water isotope data on the East Antarctic
Plateau
The physical link between the moisture content of air mass
and its temperature has been systematically used to estimate past accumulation in Antarctica and establish ice core
chronologies. This approach linking accumulation rate to
temperature has been first proposed by Lorius et al. (1985)
and Ritz (1992). Using a simple unidimensional model neglecting the possible changes in circulation intensity over
the precipitation area, one infers that the precipitation rate at
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A. Cauquoin et al.: Accumulation rate reconstructions using 10 Be, water isotopes and CMIP5-PMIP3 models
a time t is given by Eq. 1, where A is the accumulation rate,
Psat the saturation pressure over ice, and t0 refers to presentday values:
A(t) = A(t0 ) ×
[∂(Psat /(T + 273))/∂T ]t
.
[∂(Psat /(T + 273))/∂T ]t0
(1)
The temperature considered in Eq. (1) is the temperature at
condensation, approximated by the inversion temperature,
Tinv , itself related to surface temperature Ts in Antarctica
over a range from −15 to −55 ◦ C (Jouzel and Merlivat, 1984)
by
Tinv = 0.67 × Ts − 1.2,
(2)
where Ts is the mean near-surface atmospheric temperature
either measured at 15 m deep in the firn or deduced from
the average of the measured temperatures at 2 m height in
weather station. This relation has been confirmed by Connolley (1996) and Ekaykin (2003) for the East Antarctic Plateau.
In Antarctica, past temperature changes are classically retrieved from water isotope records in ice cores (δD or δ 18 O)
assuming that the present-day temperature/isotope spatial
slope can be taken as a surrogate for the temporal slope at
a given site. Using alternative methods to constrain past temperature in polar regions such as combination of δ 18 O and dexcess (Vimeux et al., 2002), use of the isotopic composition
of inert gases combined with firnification models (Caillon et
al., 2001) or borehole temperature inversion (Salamatin et al.,
1998), it has been shown that errors associated with this conventional approach are estimated to be of −10 to +30 % over
glacial–interglacial transitions (Jouzel et al., 2003). However,
a recent modelling experiment has suggested that error on the
temperature reconstruction can reach up to 100 % for warmer
interglacial periods (Sime et al., 2009).
The classical method for temperature reconstruction applied to sites in East Antarctica like Vostok (Jouzel et al.,
1987; Petit et al., 1999), Dome F (Watanabe et al., 2003) and
EPICA Dome C (Jouzel et al., 2007) assumes that the temporal variations in δD, 1δD, are proportional to the temporal
variations in surface temperature, 1Tsurf :
1δD = 6.04 × 1Tsurf .
(3)
Here the temperatures are expressed in degrees Celcius.
The saturation vapour pressure over ice is linked with temperature and can be approximated in the range −70 ◦ C to
0 ◦ C by the following equation (Wagner and Pruß, 2002):
mT
Psat (T ) = A × 10 T +Tn ,
(4)
with A = 6.114742, m = 9.778707 and Tn = 273.1466.
From the numerical solution of Eqs. (1) to (4) over the
range of temperature variations observed in East Antarctica
ice cores, it appears that the accumulation rate is almost
exponentially linked to temperature. Indeed, the Clausius–
Clapeyron relation determines that the water-holding capacity of the atmosphere increases by about 7 % for every 1 ◦ C
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rise in temperature. Keeping this idea of an exponential link,
Parrenin et al. (2007a) and Bazin et al. (2013) have formulated the accumulation/isotope relation as
A = A0 exp(β1δD),
(5)
where A0 is an estimate of the present-day accumulation
rate and β an adjustable parameter which is optimized during the chronology construction through chronological control points and an ice flow model (Parrenin et al., 2007b).
While the accumulation rate reconstruction should be rather
accurate for the upper part of the ice core, the uncertainties
increase with depth because fewer chronological constraints
are available and ice thinning becomes less predictable.
For the estimate of accumulation rate at Dome C with the
EDC3 timescale, Parrenin et al. (2007a, b) used an inverse
method in order to get the best fit with a series of age markers (listed in Table 3 of Parrenin et al., 2007a). They have
inferred the value of β in Eq. (5) as being equal to 0.0157,
a value about 50 % higher than the one (0.0102) corresponding to the saturation vapour assumption (e.g. as derived from
Eq. (1) and compared to the EDC3 accumulation rate in
Fig. 2a).
More recently, during the construction of the AICC2012
chronology, the imposed relationship between δD and accumulation rate was relaxed. The AICC2012 timescale (Bazin
et al., 2013; Veres et al., 2013) was developed for obtaining a coherent chronology between one Greenland ice core
(NorthGRIP) and four Antarctic ice cores (EDC, EDML,
TALDICE, Vostok) through the intensive use of relative tie
points in the ice and gas phases of the different ice cores.
In the chronological optimization process of AICC2012 performed by the DATICE Bayesian dating tool (LemieuxDudon et al., 2010), the scenarios for both the accumulation rate and the thinning function for the different ice cores
are allowed to vary freely, i.e. without an imposed relationship between accumulation rate and water isotopes as for
the EDC3 chronology. Although the background scenario for
EDC accumulation rate is the one given by Eq. (1), it is associated with a relatively large variance so that it can be easily
modified during the chronology optimization process. At the
end, the amplitude of glacial–interglacial variations in accumulation rate over Termination IV at Dome C is 5 % smaller
in AICC2012 than in EDC3 (Fig. 2b).
2.2
10 Be
measurements
The first procedure for measuring 10 Be in ice cores was described by Raisbeck et al. (1981b). Since then, efficiency has
been greatly improved, both due to improvement of chemical
procedures of the samples and AMS (Accelerator Mass Spectrometry) techniques (Raisbeck et al., 1987, 2007; Yiou et al.,
1997). The ice from the Dome C ice core available for this
study is a continuous series of “bag samples” (each measuring 55 cm) between 2384 and 2627 m deep. Each bag sample
was cut into five pieces of 11 cm (weighting ∼ 50 g) in order
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A. Cauquoin et al.: Accumulation rate reconstructions using 10 Be, water isotopes and CMIP5-PMIP3 models
to obtain a high-resolution 10 Be profile. This corresponds to
around 2200 samples. The preparation of the samples was
done at the Centre de Sciences Nucléaires et de Sciences de
la Matière (CSNSM) in Orsay.
The current chemical procedure is described by Raisbeck et al. (2007). The samples were melted in a centrifuge cone, in the presence of 0.25 mg of 9 Be carrier.
The Be(OH)2 was then directly precipitated with ammonia (NH4 OH). The precipitate was extracted by centrifugation, then dissolved with 250 µL of nitric acid and 500 µL
of highly pure water. The solution was transferred to a ceramic crucible to be dried on a hotplate and then heated
to 900 ◦ C for a period of 45 min over an electric furnace
in order to transform the precipitate to BeO. The beryllium
oxide was mixed with niobium (Nb) powder and pressed
into a copper cathode. The 10 Be / 9 Be measurements were
carried out at the ASTER (Accélérateur pour les Sciences
de la Terre, Environnement, Risques) AMS facility at the
Centre Européen de Recherche et d’Enseignement des Géosciences de l’Environnement (CEREGE) in Aix-en-Provence
(Arnold et al., 2010), relative to NIST (National Institute of
Standards and Technology) standard reference material SRM
(Standard Reference Material) 4325, using the certified ratio
of 2.68 × 10−11 10 Be / 9 Be. We are aware that many people now use the value of 2.79 × 10−11 given by Nishiizumi
et al. (2007) for this standard. We have continued to use the
value of 2.68 because it was in excellent agreement with the
original home-made standard of the Orsay group (Raisbeck
et al., 1978), and has been used for all our previous measurements. If desired, a conversion can be easily made, and
it will have no effect on relative values. The isobar 10 B is
suppressed by use of an absorber foil in the rare isotope path
(Klein et al., 2008). The counting statistics lead to an uncertainty of typically 4 % for 1σ standard deviation. The chemical blanks produced with our 9 Be carrier used for the ice
samples yielded an average process background 10 Be / 9 Be
of (3.95±2.35)×10−15 . In comparison, the 10 Be / 9 Be ratios
measured for EDC samples were on the order of 3.2×10−13 .
2.3
Models
In this study, we also want to test our experimental results
by comparing them with the latest climate simulations of
the LGM and PI climates, obtained in the framework of the
PMIP3 and CMIP5 projects (Braconnot et al., 2012). Both
the PI and LGM climate simulations are equilibrium simulations, i.e. obtained by imposing non-evolving boundary
conditions and forcings. Compared to the pre-industrial control simulations, LGM climate simulations are obtained by
imposing the LGM ice sheet reconstructions (topography,
albedo and land–sea mask differences due to sea-level lowering), the LGM atmospheric concentration of the main greenhouse gases as recorded by ice cores and orbital forcing
parameters for 21 ka (following Berger, 1978). The experimental setup is described in detail on the PMIP3 website:
Clim. Past, 11, 355–367, 2015
http://pmip3.lsce.ipsl.fr/. The simulations used in this study
are those which were available on the CMIP5 database as of
October 2012.
In addition to the CMIP5-PMIP3 atmosphere–ocean coupled simulations we are using pre-industrial and LGM simulation results obtained from the AGCM (Atmospheric General Circulation Model) ECHAM5 (Roeckner et al., 2006)
enhanced with stable water isotope diagnostics (Werner
et al., 2011). For the LGM climate simulation, PMIP3conform boundary conditions have been applied. Glacial sea
surface temperatures and sea ice coverage have been derived
from the GLAMAP Atlantic reconstruction data set (SchäferNeth and Paul, 2003). Both the PI and LGM simulation have
been performed with a fine T106L31 spectral model resolution (horizontal grid box size of approx. 1.1◦ × 1.1◦ , 31
vertical levels).
All CMIP5-PMIP3 simulations are summarized in Table 1.
From the different simulations we have used the following
variables: tas (near surface air temperature) and pr (precipitation rate). As the sublimation rate was only available for
3 models out of 11 and as its values over the sites of interest were negligible compared to the precipitation rate in
these models, we have not included the sublimation rate in
the calculation of the accumulation rate changes from LGM
to PI. We observe that tas is systematically higher than the
measured mean atmospheric temperature, which is a typical
bias of CMIP5-PMIP3 simulations in polar regions (see Risi
et al., 2010). To evaluate the consequences of this bias, we
have extracted the modelled inversion temperatures available
in the IPSL-CM5A-LR model. These data show that, in the
models, the slope of the relationship between tas and the
modelled inversion temperature is 15 % higher than the one
given by Jouzel and Merlivat (1984) (cf. Eq. 2). However,
when we use the modelled surface temperature Tsm (which is
on average 4 ◦ C lower than the simulated tas values), we obtain a slope between Tinv and Tsm very close to the observed
value of 0.67 (see Eq. 2). As a consequence, and to artificially
compensate for the cold bias of the CMIP5-PMIP3 simulations, we have extracted both tas and Tsm for the following
calculations.
For all the models, the values of the LGM–PI change in
near-surface air temperature and precipitation rate are obtained by averaging the values of temperature and precipitation on a box of latitudes 77.6 to 72.6◦ S and longitudes
120.85 to 125.85◦ E.
3
From 10 Be concentrations to accumulation rate
reconstruction
Figure 1a shows the high-resolution profile of 10 Be concentrations (available as a supplement). The time resolution for
the shown period varies between 20 years for MIS 9.3 and 70
years for MIS 10, the glacial period prior to 340 ka (Fig. 1f).
In this study, we will mainly focus on the transition between
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3.0
-1
-2
Be flux
10
(at.m .s )
2.0
80
c
60
40
-1
Be flux
10
80
-2
Corrected
(at.m .s )
d
60
1.4
1.2
*
*
e
1.0
0.8
40
f
60
50
40
30
20
280,000
300,000
320,000
-380
-400
-420
-440
d
4.0
10
-1
(at.g )
b
D (‰)
Other potential contributions to 10 Be concentration variations are linked to (1) variations in the geomagnetic field intensity over centennial to millennial scales or (2) variations
in the solar activity on decadal to centennial timescales. For
the influence of the relative changes of geomagnetic field, we
can make corrections by using independent estimates of the
field intensity obtained by a stacked record of marine sediments (Channell et al., 2009). We assume that our 10 Be flux
record reflects the globally averaged 10 Be production. We
have also carried out the calculations using the “polar bias”
assumption (polar 10 Be flux 20 % less sensitive to geomagnetic field intensity changes (Field et al., 2006), i.e. we multiply the relative variations in the 10 Be production according to
PISO-1500 by 0.8), with a negligible difference in the resulting accumulation reconstructions. After synchronizing the
timescale of the marine record with that of EDC (Cauquoin,
2013), we apply the theoretical estimate of Masarik and Beer
(2009) on the relationship between 10 Be production and geomagnetic intensity, as shown in Fig. 1d. Then, we divide
the 10 Be flux at EDC by the relative changes of global 10 Be
production according to PISO-1500. The main effect of this
correction is to remove the long-term decrease in the uncorrected 10 Be flux from 270 to 350 ka (Fig. 1e). We have also
looked at the theoretical estimate of Kovaltsov and Usoskin
(2010) on the relationship between 10 Be production and geomagnetic field intensity, with very similar results.
Since we have no independent estimate of the solar variability during the time period being studied, we must assume
that the average value of solar activity has been constant
during this time. In reality, part of the remaining centennial
structure in the 10 Be flux of Fig. 1e may be due to variations
in solar activity or to centennial geomagnetic variations not
50
10
(6)
a
Time resolution
(yr)
F [10 Be] = C[10 Be] × A × ρ.
3
150x10
359
Accumulation rate Relative Be production
-1
based on PISO-1500
(cm-ie.yr )
the coldest part of the MIS 10 reached just before Termination IV and MIS 9.3. The MIS 10 glacial maximum between
341.77 and 348.41 ka (light-blue area in Fig. 1b) is at the
same water isotopic level as the LGM (Jouzel et al., 2007).
Then, MIS 9.3 can be decomposed in two phases: (1) a period with a higher isotopic level than PI with an optimum
between 332.55 and 334.53 ka (light-red area in Fig. 1b) and
(2) a plateau between 325.92 and 330.92 ka at the same isotopic level than PI (light-yellow area in Fig. 1b).
We observe a strong anti-correlation between 10 Be concentration (Fig. 1a) and δD or δD-derived accumulation rate
(Fig. 1b). This is not unexpected since 10 Be reaches the
Antarctic Plateau primarily by dry deposition, and so the concentration of 10 Be in the ice is reduced for high-accumulation
periods. It has thus been proposed that 10 Be flux is a more appropriate parameter than concentration for estimating variations in 10 Be production (Yiou et al., 1985). This is illustrated in Fig. 1c, showing the 10 Be flux F [10 Be] as obtained
by multiplying the 10 Be concentration C[10 Be] by the accumulation rate A from the EDC3 timescale (Parrenin et al.,
2007a, b) and the density of ice ρ as
Be Concentration
A. Cauquoin et al.: Accumulation rate reconstructions using 10 Be, water isotopes and CMIP5-PMIP3 models
340,000
Age EDC3 (yr BP)
Figure 1. High-resolution 10 Be data between 2384 and 2627 m
deep (269–355 ka on the EDC3 age scale). (a) Raw 10 Be concentrations (at g−1 ). (b) In grey, δD profile at EDC including the interglacial period MIS 9.3 (Jouzel et al., 2007). In black, the accumulation rate of the site (cm ie yr−1 ) (Parrenin et al., 2007b). The
light-yellow and light-red areas show the plateau during MIS 9.3 at
the same isotopic level than PI and the MIS 9.3 optimum warmer
than PI. The light-blue area corresponds to the MIS 10 glacial maximum just before the Termination IV. (c) Calculated 10 Be flux using EDC3 accumulation rate. The light-blue curve corresponds to
raw data, and the bold-blue curve is the low-pass-filtered 10 Be flux
(1/2000 yr−1 ). (d) 10 Be production based on palaeointensity record
PISO-1500 (Channell et al., 2009) on the EDC3 age scale and calculated using calculations of Masarik and Beer (2009). The asterisks show the possible correlation with proposed geomagnetic
events: the Portuguese Margin (∼ 290 ka) and Calabrian Ridge 1
(∼ 320 ka). (e) Raw and 100-year resampled 10 Be flux corrected
by PISO-1500. (f) Time resolution of the 10 Be profile (difference
between the n and n + 1 sample ages).
recorded by the marine cores. We now use the geomagnetically corrected 10 Be flux curve of Fig. 1e to estimate the
ice accumulation rate of EDC during our time period using
Eq. (6). This procedure assumes that the spatial distribution
of geomagnetically corrected 10 Be deposition remains constant with time independent of climate and type of deposition. While it is difficult to give a quantitative uncertainty
of our constant flux assumption, we can note that the 1σ
standard deviation of the smooth corrected flux in Fig. 1e
is 8.8 %. Since this is significantly larger than the analytical uncertainty, it essentially must represent the sum of inadequately corrected production variations plus variability in
the 10 Be deposition. It thus does not seem unreasonable to
conclude that this represents an upper limit to the deposition
variability.
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A. Cauquoin et al.: Accumulation rate reconstructions using 10 Be, water isotopes and CMIP5-PMIP3 models
Age EDC3 (kyr BP)
290 310
330 350
3
2
-1
4
80
70
60
50
40
-1
Be flux (at.m .s )
2400 2450 2500 2550 2600
Depth (m)
-2
5
4
3
2
80
70
60
50
40
10
Accumulation rate
(cm-ie/yr)
2400 2450 2500 2550 2600
Depth (m)
(b)
-1
-2
4
3
2
80
70
60
50
40
10
-1
2400 2450 2500 2550 2600
Depth (m)
Be flux (at.m .s )
5
(cm-ie.yr )
Accumulation rate
2400 2450 2500 2550 2600
Depth (m)
(c)
-1
-2
4
3
2
80
70
60
50
40
10
-1
2400 2450 2500 2550 2600
Depth (m)
Be flux (at.m .s )
5
(cm-ie.yr )
Accumulation rate
2400 2450 2500 2550 2600
Depth (m)
(d)
Age EDC3 (kyr BP)
290 310
330 350
10
-1
270
Be flux (at.m .s )
5
(cm-ie.yr )
(a)
Accumulation rate
270
-2
360
2400 2450 2500 2550 2600
Depth (m)
2400 2450 2500 2550 2600
Depth (m)
Figure 2. Several accumulation rate reconstructions (left column) and the corresponding 10 Be flux corrected by PISO-1500 (right column)
discussed in Sect. 3 (coloured curves). The EDC3 reconstruction from Parrenin et al. (2007a, b) is shown in grey for comparison. (a) Saturation vapour pressure formulation. (b) Application of the AICC2012 chronology on the 10 Be record. (c) Optimization of the interglacial–
glacial amplitude coefficient (β) by minimization of the variance of the 10 Be flux corrected for past variations in geomagnetic field intensity
(red curves). (d) Accumulation rate assuming a constant 10 Be flux (fixed at 53.44 at m−2 s−1 over the whole period).
In a first attempt to use 10 Be for such a reconstruction, we
have chosen to keep the exponential link between accumulation and δD. Starting from the formulation proposed by Parrenin et al. (2007a), we have tried to adjust β in order to minimize the variance of the 10 Be flux signal while keeping consistency with the timescale of EDC3 (Fig. 2c). For this minimization, we have first applied a 100-year resampling to the
10 Be record. Using Eqs. (5) and (6), with A = 2.841 cm, i.e.
0
yr−1 , we have calculated the variance of the 10 Be flux (previously corrected for geomagnetic field intensity changes) using different values of β. The variance of the 10 Be flux is
minimized for a β of 0.0165 (the variance varies by less than
1 % for values of β between 0.0160 and 0.0171). This value
Clim. Past, 11, 355–367, 2015
is 5 % larger than used by Parrenin et al. (2007a), and corresponds to a larger glacial–interglacial amplitude by the same
amount. We also notice a general decrease in the variance by
a factor 0.99 which supports this revision of accumulation
rate estimate from δD over this glacial–interglacial cycle.
In a second attempt, we have performed a test with the
assumption of a strictly constant 10 Be flux, after a geomagnetic field intensity correction on the 10 Be concentration in
the ice. We have deduced the resulting accumulation by dividing the 10 Be flux F [10 Be] (53.44 at m−2 s−1 here) by
the 10 Be concentration (corrected for radioactive decay and
geomagnetic modulation) C[10 Be] times the ice density ρ
according to Eq. 6. The inferred accumulation is reported
www.clim-past.net/11/355/2015/
A. Cauquoin et al.: Accumulation rate reconstructions using 10 Be, water isotopes and CMIP5-PMIP3 models
0.0
(a)
-0.4
EDC3 accumulation rate MIS9-MIS10
EDC3 accumulation rate Holocene-LGM
Saturation vapour pressure formulation
10
β-optimized Be accumulation rate
AICC2012 accumulation rate
10
Constant Be flux
hypothesis
-0.6
-0.8
D
ln(accumulation rate)
-0.2
CCSM4_r1
CCSM4_r2
CNRM-CM5
FGOALS-g2
GISS-E2-R_p150
GISS-E2-R_p151
IPSL-CM5A-LR
MIROC-ESM
MPI-ESM-P_p1
MPI-ESM-P_p2
MRI-GCM3
ECHAM5
-1.0
-1.2
-12 -11 -10 -9
0.0
(b)
-7
-6
-5
DTS (K)
-4
-3
-2
-1
0
EDC3 Holocene-LGM
EDC3 MIS9-MIS10
Saturation vapour pressure formulation
β-optimized
AICC2012
Constant
ECHAM5
10
10
Be accumulation rate
Be flux hypothesis
-0.4
-0.6
-0.8
D
ln(accumulation rate)
-0.2
-8
-1.0
-1.2
-60
-50
-40
-30
DdD (‰)
-20
-10
0
Figure 3. (a) Accumulation rate vs. temperature change between
the LGM and PI for 12 different simulations and comparison with
the relationships from EDC3 (last deglaciation and MIS 9.3) and
our reconstructions (average during the glacial and the interglacial
period). (b) Accumulation rate vs. δD change for both ice core data
and ECHAM5 simulation results.
in Fig. 2d. The general shape of the accumulation rate reconstruction follows the evolution of the EDC3 accumulation rate. However, while there is no significant difference in
the accumulation reconstruction between the MIS 10 glacial
maximum and the plateau of the MIS 9.3 (increase of 1.548
for the 10 Be-based accumulation rate reconstruction and of
1.624 cm ie yr−1 for EDC3), there is a clear difference for accumulation rate increase between the plateau and the MIS 9.3
optimum (1.364 cm ie yr−1 for the 10 Be-based reconstruction
against 0.920 cm ie yr−1 for EDC3). The 10 Be-based accumulation rate for the latter is up to 13 % larger than the EDC3
reconstruction (4.46 cm ie yr−1 instead of 3.95).
www.clim-past.net/11/355/2015/
361
Even if the assumption of a strictly constant 10 Be flux is
not realistic, we have tested whether the inferred accumulation rate is consistent with chronological constraints (Table 2). For this aim, we have imposed this accumulation
rate as a background accumulation rate scenario for EDC in
the DATICE tool for chronology optimization with a very
small associated variance. The other background scenarios
for the four other ice cores (NorthGRIP, EDML, Taldice,
Vostok) are kept identical as those of AICC2012 (Bazin et al.,
2013; Veres et al., 2013). In this DATICE experiment, we
still use the same five ice cores as in AICC2012 for facilitating the comparison with previous chronological studies but
for our period of interest, only Vostok goes back until MIS
9/10 and can influence the chronology of EDC. With such
a background accumulation rate for EDC, the minimization
of DATICE is easily reached with very small modifications
of the thinning function, well within the imposed variance,
compared to the AICC2012 chronology. We find the same
trends on the resulting accumulation rate as for the 10 Bebased one.
We conclude that both methods show an underestimation
of accumulation deduced from water isotopes for the optimum of MIS 9.3. This is in agreement with the study of
Parrenin et al. (2007a), which suggested that the deuteriumbased reconstruction underestimates accumulation for the
optimum of the Holocene. However the existence of a strong
link between past changes in accumulation and temperature
is confirmed to first order by our 10 Be approach and we next
examine how palaeoclimate simulations performed with different GCMs might reveal further insight on this link between accumulation and temperature.
4
Accumulation vs. temperature/δD relationship in
East Antarctica
We compare the outputs from the models described in
Sect. 2.3, with the accumulation rate reconstruction presented in the previous section.
Figure 3a shows a compilation of accumulation rate and
temperature change for the 11 different simulations included
in the CMIP5-PMIP3 coupled models plus ECHAM5, between the LGM and the PI. We have chosen to focus only
on the relationship between the change in accumulation rate
and the change in temperature between the LGM and PI. Indeed, we can hardly discuss absolute levels of temperature
and accumulation rate for two reasons. First, the CMIP5PMIP3 models are known to overestimate temperature on
the East Antarctic Plateau. Second, our 10 Be data do not
cover the last deglaciation as the model simulations do but
instead the transition occurring between MIS 10 and the optimum of MIS 9.3, which has larger associated temperature
and accumulation rate increases than the last deglaciation
(Fig. 3a). Still, the accumulation rate vs. temperature slope
reconstructed from water isotopes in the ice core (Eqs. 5 and
Clim. Past, 11, 355–367, 2015
362
A. Cauquoin et al.: Accumulation rate reconstructions using 10 Be, water isotopes and CMIP5-PMIP3 models
Table 1. List of all simulations used in this study (see Fig. 3). The ensemble member (r hN i i hMi p hLi) formatted as shown below (e.g.
“r3i1p21” with r for “realization”, i for “initialization method indicator” and p for “perturbed physics”) distinguishes among closely related
simulations by a single model (Taylor et al., 2012). The reference given in brackets next to the ensemble number in columns 4 and 5 is the
name of the simulation used in Fig. 3.
Institute
Model
CNRM-CERFACS
CMIP5 experiment
Model ensemble member used (rhNiihMiphLi)
21k
lgm
Model institution
0k
piControl
CNRM-CM5
Centre
National
de
Recherches
Météorologiques/Centre Européen de
Recherche et Formation Avancée en Calcul
Scientifique, France
r1i1p1 (CNRM-CM5)
r1i1p1 (CNRM-CM5)
NASA-GISS
GISS-E2-R
NASA Goddard Institute for Space Studies,
USA
r1i1p142 (GISS-E2-R_p150)
r1i1p142 (GISS-E2-R_p151)
r1i1p150 (GISS-E2-R_p150)
r1i1p151 (GISS-E2-R_p151)
IPSL
IPSL-CM5A-LR
Institut Pierre-Simon Laplace, France
r1i1p1 (IPSL-CM5A-LR)
r1i1p1 (IPSL-CM5A-LR)
LASC-CESS
FGOALS-g2
LASG, Institute of Atmospheric Physics,
Chinese Academy of Sciences and CESS,
Tsinghua University, China
r1i1p1 (FGOALS-g2)
r1i1p1 (FGOALS-g2)
MIROC
MIROC-ESM
Japan Agency for Marine-Earth Science and Technology, Atmosphere and
Ocean Research Institute (the University
of Tokyo), and National Institute for
Environmental Studies, Japan
r1i1p1 (MIROC-ESM)
r1i1p1 (MIROC-ESM)
MPI-M
MPI-ESM-P
Max Planck Institute for Meteorology,
Hamburg, Germany
r1i1p1 (MPI-ESM-P_p1)
r1i1p1 (MPI-ESM-P_p2)
r1i1p1 (MPI-ESM-P_p1)
r1i1p2 (MPI-ESM-P_p1)
MRI
MRI-CGCM3
Meteorological
Tsukuba, Japan
r1i1p1 (MRI-CGCM3)
r1i1p1 (MRI-CGCM3)
NCAR
CCSM4
r1i1p1 (CCSM4_r1)
r2i1p1 (CCSM4_r2)
r1i1p1 (CCSM4_r1)
r2i1p1 (CCSM4_2)
Research
Institute,
National Center for Atmospheric Research/Dept. of Energy/NSF, USA
3, respectively) is almost the same for the transition between
MIS 10 and MIS 9 and the last deglaciation as shown in
Fig. 3a. We have also checked that the model results shown in
Fig. 3a do not change if we replace the 1Ts calculation based
on the near-surface air temperature (tas) with one based on
surface temperature (Tsm ). Finally, using the IPSL-CM5ALR model, we have tested the influence of the topography
changes on the temperature vs. accumulation rate slope by
keeping an identical Antarctic ice cap for the LGM and PI
conditions and verifying that the relationship remains the
same.
We observe a relatively good agreement between the slope
of accumulation vs. temperature over a glacial–interglacial
transition of several models (MPI-ESM-P, CCSM4,
FGOALS-g2), with an average slope of 0.23 cm ie yr−1 ◦ C−1
(bold black line in Fig. 3a). Other models are clearly outside
the range of the reconstructed accumulation–temperature
relationship, either because they overestimate (MRIGCM3, GISS-E2-R) or underestimate it (IPSL-CM5-LR,
MIROC-ESM) or because they simulate very weak changes
(CNRM-CM5). Indeed, one can notice a large spread
between the different model outputs. The difference in the
accumulation rate vs. temperature relationship between
different GCM simulations is much larger (100 %) than
for the different reconstructions based on 10 Be flux and/or
Clim. Past, 11, 355–367, 2015
chronological constraints. The slope based on the relationship between accumulation rate and saturation pressure over
ice is 28 % lower (brown line in Fig. 3a). We conclude that
generally the CMIP5-PMIP3 models have more or less difficulties to accurately simulate the temperature–accumulation
relationship on the Antarctic Plateau between glacial and
interglacial conditions and need to be improved in the future.
To avoid any assumption on the relationship between water isotopes and temperature, we have directly compared the
accumulation rate with water isotope variations for both ice
core data and model outputs (Fig. 3b). In our study, only one
model (ECHAM5) is equipped with water isotopes diagnostics. As it was also observed for the temperature change,
the δD increase during the deglaciation is smaller in the
ECHAM5 simulations than in ice core records. However the
slope of accumulation rate vs. δD given by ECHAM5 compares very well with our different accumulation rate vs. δD
slope inferred from both water isotopes and 10 Be. Only the
slope deduced from the saturation vapour pressure formulation is lower by ∼ 30 % compared to EDC3. We observe,
however, that the slope of accumulation rate vs. temperature
changes of ECHAM5 is smaller than the one reconstructed
from water isotopes or 10 Be in ice core, and so the modelled
Antarctic δD–temperature gradient in ECHAM5 for LGM–
PI climate changes at EDC is much lower than the local
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A. Cauquoin et al.: Accumulation rate reconstructions using 10 Be, water isotopes and CMIP5-PMIP3 models
363
Table 2. List of markers used to constrain EDC and Vostok ice cores between 269 and 355 ka for the AICC2012 chronology. References
(“Ref.”): (1) Suwa and Bender (2008), (2) Lipenkov et al. (2011), (3) Raynaud et al. (2007) and (4) Bazin et al. (2013).
Vostok age markers
Depth
Age
Uncertainty
Type of marker
Ref.
Ice age markers
2882.1
2883.02
2912.1
2962.7
3005.6
3011
3040.7
3043.04
3080.5
3130.6
3145.95
3157.1
275 200
275 950
286 300
296 800
307 700
307 950
319 200
318 950
330 000
339 700
346 950
349 200
4000
6343
4000
4000
4000
6424
4000
6308
4000
4000
6527
4000
δO2 /N2
air content
δO2 /N2
δO2 /N2
δO2 /N2
air content
δO2 /N2
air content
δO2 /N2
δO2 /N2
air content
δO2 /N2
1
2
1
1
1
2
1
2
1
1
2
1
Gas age markers
2887
2947.6
2990.3
3026.9
3062.5
3101.4
3146.2
3173.8
272 900
285 900
297 500
308 300
318 300
329 000
340 300
351 000
6000
6000
6000
6000
6000
6000
6000
6000
δ 18 Oatm
δ 18 Oatm
δ 18 Oatm
δ 18 Oatm
δ 18 Oatm
δ 18 Oatm
δ 18 Oatm
δ 18 Oatm
1
1
1
1
1
1
1
1
2500.25
2510.75
2610.8
306 950
318 950
346 950
6652
6242
7120
air content
air content
air content
3
3
3
Depth EDC
Depth Vostok
Uncertainty
Type of marker
2419.34
2451.33
2501.4
2521.2
2544.83
2583.9
2613
2911.06
2954.61
3018.01
3051.5
3079.41
3123.5
3162.8
1500
1500
1500
1500
1500
1500
1500
CH4
CH4
CH4
CH4
CH4
CH4
CH4
EDC age markers
Ice age markers
Stratigraphic links
between EDC and Vostok
geographical gradient as already shown in previous studies
(Schmidt et al., 2007; Lee et al., 2008; Sime et al., 2008,
2009). This could imply a problem in the estimation of the
surface temperature with measured δD or even indicate that
the δD–temperature slope is under-evaluated in the model
compared to the hypothesis of the spatial relationship between precipitation isotopic composition and local temperature (Lorius et al., 1969). But given the uncertainties and
the lack of models equipped with water isotope diagnostics,
it is difficult to conclude on this point. Finally, this implies
that the models matching the accumulation vs. temperature
relationship of EDC3 for the last glacial–interglacial change
www.clim-past.net/11/355/2015/
Ref.
4
4
4
4
4
4
4
would not necessarily accurately reproduce the associated
accumulation rate vs. δD slope.
Finally, an important result highlighted in our study is a
possible underestimation of accumulation rate during periods warmer than today, as already suggested by Parrenin
et al. (2007a) for the optimum of the Holocene. This cannot be tested with the compilation of model outputs displayed here that were only run on colder conditions than
the pre-industrial period. Using the relationship between accumulation rate and surface temperature from the saturation
vapour formulation, the 10 Be-based accumulation rate reconstruction suggests that the temperature increase between the
plateau and the MIS 9.3 optimum is underestimated by 2.4 K
Clim. Past, 11, 355–367, 2015
364
A. Cauquoin et al.: Accumulation rate reconstructions using 10 Be, water isotopes and CMIP5-PMIP3 models
with respect to water isotopes reconstruction (5.7 vs. 3.2 K).
This is in line with the 3 K underestimation for the peak of the
last Antarctic interglacial temperature from the relationship
between temperature and surface snow isotopic composition
as shown in Sime et al. (2009), suggesting an underestimation of about a factor of 2 for warm interglacials compared
to a level similar to present day. Instead, the temporal slope
is less affected, for example, by about 30% when comparing
the plateau (similar to present day) to MIS 10 glacial maximum (similar to the LGM). Indeed, using the 10 Be-based
accumulation rate reconstruction and the saturation vapour
relationship between accumulation rate and surface temperature leads to an underestimation of the temperature difference between the MIS 10 glacial maximum and the plateau
by the water isotopes reconstruction by 25 % only (10.2 vs.
8.2 K). This value is, however, in the upper range of the interval −10 to +30 % estimated from different approaches by
Jouzel et al. (2003). We should keep in mind that these estimates rely on a close relationship between the derivative
of the saturation vapour pressure and accumulation change,
which is subject to large uncertainties.
5
Conclusions
We have produced the first record of 10 Be concentration
at high resolution in an ice core over a whole climatic
cycle (355 to 269 ka), including a period warmer than
pre-industrial. After correction for geomagnetic intensity
changes, it is generally assumed that the variations in 10 Be
concentration are mainly linked to variations in the accumulation rate of snow. We have used this property to reconstruct
the past accumulation rate at EDC and to compare it with the
deuterium-based accumulation rate reconstruction. We have
deduced that the 10 Be reconstruction is in reasonably good
agreement with EDC3 values for the full cycle except the period warmer than present. For the latter, the accumulation is
up to 13 % larger (4.46 cm ie yr−1 instead of 3.95). This is
in agreement with the study of Parrenin et al. (2007a), who
suggested that accumulation rate reconstruction from water
isotopes underestimates accumulation for the optimum of the
Holocene. Using the relationship between accumulation rate
and surface temperature from the saturation vapour formulation, the 10 Be-based accumulation rate reconstruction suggests that the temperature increase between the MIS 9.3 optimum and present day may be underestimated by 2.4 K with
respect to the water isotopes reconstruction. Finally, the relationship between temperature and accumulation rate is comparable when using the different reconstructions and 4 out of
12 (3 out of 7 models) CMIP5-PMIP3 simulations for LGM–
PI climate changes. However, we have noticed a large spread
in the model outputs. We conclude that the CMIP5-PMIP3
models can encounter some difficulties in simulating precipitation changes linked with temperature or water isotope conClim. Past, 11, 355–367, 2015
tent on the Antarctic Plateau during large climatic shifts and
need to be improved in the future.
The Supplement related to this article is available online
at doi:10.5194/cp-11-355-2015-supplement.
Acknowledgements. We acknowledge F. Parrenin, L. Sime and
one anonymous referee for their useful comments, which helped
to improve this manuscript. This work is a contribution to the
European Project for Ice Coring in Antarctica (EPICA), a joint
European Science Foundation/European Commission (EC) scientific programme, funded by the EC and by national contributions
from Belgium, Denmark, France, Germany, Italy, the Netherlands,
Norway, Sweden, Switzerland and the UK. The main logistic
support was provided by IPEV and PNRA. We acknowledge
the World Climate Research Programme’s Working Group on
Coupled Modelling, which is responsible for CMIP and the
Paleoclimate Modelling Intercomparison Project (PMIP). We thank
the climate modelling groups (listed in Table 1 of this paper) for
producing and making available their model output. For CMIP,
the US Department of Energy’s Program for Climate Model
Diagnosis and Intercomparison provides coordinating support and
led development of software infrastructure in partnership with
the Global Organization for Earth System Science Portals. This
work was funded by the French ANR project Dome A and through
ERC grant COMBINISO (project no. 306045). The ASTER AMS
national facility (CEREGE, Aix en Provence) is supported by
the INSU/CNRS, and the ANR through the “Projets thématiques
d’excellence” programme for the “Equipements d’Excellence”
ASTER-CEREGE action, IRD, and CEA.
Edited by: E. Wolff
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