A Review of Antarctic Surface Snow Isotopic Composition: Observations, Atmospheric
Circulation, and Isotopic Modeling*
V. MASSON-DELMOTTE,
a
S. HOU,
b
A. EKAYKIN,
c
J. JOUZEL,
a
A. ARISTARAIN,
d
R. T. BERNARDO,
e
D. BROMWICH,
f
O. CATTANI,
a
M. DELMOTTE,
a
S. FALOURD,
a
M. FREZZOTTI,
g
H. GALLÉE,
h
L. GENONI,
i
E. ISAKSSON,
j
A. LANDAIS,
a,k
M. M. HELSEN,
l
G. HOFFMANN,
a
J. LOPEZ,
m
V. MORGAN,
n
H. MOTOYAMA,
o
D. NOONE,
p
H. OERTER,
q
J. R. PETIT,
h
A. ROYER,
a
R. UEMURA,
o
G. A. SCHMIDT,
r
E. SCHLOSSER,
s
J. C. SIMÕES,
e
E. J. STEIG,
t
B. STENNI,
i
M. STIEVENARD,
a
M. R. VAN DEN BROEKE,
l
R. S. W. VAN DE WAL,
l
W. J. VAN DE BERG,
l
F. VIMEUX,
a,u
J. W. C. WHITE
v
a
Laboratoire des Sciences du Climat et de l’Environnement, IPSL/CEA-CNRS-UVSQ, Saclay, Gif-sur-Yvette, France
b
Laboratory of Cryosphere and Environment, Chinese Academy of Sciences, Lanzhou, China
c
Arctic and Antarctic Research Institute, St. Petersburg, Russia
d
Laboratorio de Estratigrafía Glaciar y Geoquímica del Agua y de la Nieve, Instituto Antártico Argentino, Mendoza, Argentina
e
Nucleo de Pesquisas Antarcticas e Climaticas, Departmento de Geografia, Instituto de Geociencias, Universidade Federal do Rio
Grande do Sul, Porto Alegre, Brazil
f
The Ohio State University, Columbus, Ohio
g
ENEA, Rome, Italy
h
Laboratoire de Glaciologie et de Géophysique de l’Environnement, CNRS-Université Joseph Fourier, Saint Martin d’Hères, France
i
Department of Geological, Environmental and Marine Sciences, University of Trieste, Trieste, Italy
j
Norwegian Polar Institute, Tromsø´, Norway
k
Earth Science Institute, Hebrew University, Jerusalem, Israel
l
Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, Netherlands
m
Departamento de Geologica y Geoquimica, Universidad Autonoma de Madrid, Madrid, Spain
n
Antarctic Climate and Ecosystems CRC, and Australian Antarctic Division, Hobart, Australia
o
National Institute of Polar Research, Research Organization of Information and Systems, Tokyo, Japan
p
Department of Atmospheric and Oceanic Sciences, and Cooperative Institute for Research in Environmental Sciences, University of
Colorado, Boulder, Colorado
q
Alfred-Wegener-Institute für Polar und Meeresforschung, Bremerhaven, Germany
r
NASA GISS, New York, New York
s
Institute of Meteorology and Geophysics, University of Innsbruck, Innsbruck, Austria
t
Department of Earth and Space Sciences, University of Washington, Seattle, Washington
u
UR Greatice, IRD, Paris, France
v
INSTAAR, Boulder, Colorado
(Manuscript received 6 July 2007, in final form 27 November 2007)
ABSTRACT
A database of surface Antarctic snow isotopic composition is constructed using available measurements,
with an estimate of data quality and local variability. Although more than 1000 locations are documented,
the spatial coverage remains uneven with a majority of sites located in specific areas of East Antarctica. The
database is used to analyze the spatial variations in snow isotopic composition with respect to geographical
characteristics (elevation, distance to the coast) and climatic features (temperature, accumulation) and with
a focus on deuterium excess. The capacity of theoretical isotopic, regional, and general circulation atmo-
spheric models (including “isotopic” models) to reproduce the observed features and assess the role of
moisture advection in spatial deuterium excess fluctuations is analyzed.
* European Project for Ice Coring in Antarctica Publication Number 188 and Laboratoire des Sciences du Climat et l’Environnement
Contribution Number 2739.
Corresponding author address: Valérie Masson-Delmotte, Laboratoire des Sciences du Climat et de l’Environnement, IPSL/CEA-
CNRS-UVSQ, UMR 1572, Bat 701, L’Orme des Merisiers CEA, Saclay, 91 191 Gif-sur-Yvette CEDEX, France.
E-mail: valerie.masson@cea.fr
1J
ULY 2008 M A S SON-DELMOTTE ET AL. 3359
DOI: 10.1175/2007JCLI2139.1
© 2008 American Meteorological Society
JCLI2139
1. Introduction
Since the 1950s, it has been observed that the stable
isotopic composition of precipitation in the mid- and
high latitudes is related to air temperature (Dansgaard
1953; Epstein and Mayeda 1953; Craig 1961). In Ant-
arctica (Fig. 1), surface snow was sampled along
traverses to inland stations and firn temperature mea-
surements were used as indicators of annual mean sur-
face temperature (Epstein et al. 1963). Early studies
were conducted to determine the spatial relationship
between precipitation isotopic composition and local
temperature (Lorius et al. 1969).
Assuming that this relationship remains valid over
time, these calibrations were then used as an “isotopic
thermometer” to quantify past changes in temperature
based on the stable isotopic composition of deep ice
cores, such as the recently obtained Eupropean Project
for Ice Coring in Antarctica (EPICA) ice cores drilled
at Dome C (DC; EPICA Community Members 2004)
and in Dronning Maud Land (DML; EPICA Commu-
nity Members 2006). In Greenland, the use of the spa-
tial isotope–temperature slope has been challenged by
alternative paleothermometry methods, such as the in-
version of the borehole temperature profile (Cuffey et
al. 1992; Johnsen et al. 1995), and the thermal and
gravitational diffusion of air in the firn arising during
abrupt climate changes (Severinghaus et al. 1998; Lang
et al. 1999; Landais et al. 2004a,b,c).
In central East Antarctica, inversion of the borehole
temperature profiles is problematic because of the low
accumulation rates (Salamatin et al. 1998). Because
Antarctic climate changes are less rapid than in Green-
land, the gas fractionation method is problematic and
cannot be used easily to quantify past temperature
changes (Caillon et al. 2001; Landais et al. 2006). How-
ever, the stable isotope profiles derived from East Ant-
arctic ice cores can be directly used to estimate past
changes in accumulation through relationships between
stable isotopes, air temperature, and saturation vapor
pressure that are included in inversed glaciological dat-
ing methods (Parrenin et al. 2001). The dating of deep
ice cores itself, when constrained by age markers, can
be used to assess the stability of the isotope–tempera-
ture relationship back in time. When applied to inland
Antarctic sites, such as Vostok (Parrenin et al. 2001),
Dome Fuji (Watanabe et al. 2003), or EPICA Dome C
(EPICA Community Members 2004), inverse methods
suggest that the present-day-observed isotope–temper-
ature slopes remain valid for past periods within 20%–
30%, consistent with estimates provided by atmo-
spheric general circulation models (AGCMs) (Jouzel et
al. 2003).
Obtaining past temperature reconstructions together
with a precise estimate of their uncertainties remains
critical for the understanding of the natural pacing of
Antarctic temperature (and accumulation) changes. A
recent synthesis effort conducted over the past 200 yr
using well-dated ice cores has revealed strong interan-
nual and decadal variability, with antiphase behavior
between the Antarctic Peninsula and the inland sites,
and the impact of the Southern Hemisphere (SH) an-
FIG. 1. Map of Antarctic topography showing (left) the names of different geographical sectors mentioned in the text and (right)
the main deep ice core sites together with the main ice divide.
3360 JOURNAL OF CLIMATE VOLUME 21
nular mode on Antarctic temperature variability
(Schneider et al. 2006), together with the variability
associated with ENSO (Schneider and Noone 2007).
The Antarctic-scale coherence of temperature change
remains uncertain at lower frequencies. A stack of five
central Antarctic ice cores stable isotope records also
suggests a small magnitude (⬃0.2°C) of common cen-
tennial-scale temperature variability (Goosse et al.
2004). Regional Antarctic temperature and accumula-
tion reconstructions are essential for forcing Antarctic
ice sheet models and for understanding the Antarctic
ice sheet mass balance and dynamical reaction to
changing climate and sea level. Because stable isotope
records may be affected by changes in moisture origin,
syntheses of stable isotope records should be per-
formed over areas with similar moisture origins
(Reijmer et al. 2002). Regional Antarctic temperature
reconstructions are also essential for the comparison
between observed past climatic changes and simula-
tions performed by AGCMs, conducted only for the
inland East Antarctic plateau (Masson-Delmotte et al.
2006).
In parallel, the factors controlling the isotopic com-
position of Antarctic snowfall have been analyzed
based on a hierarchy of modeling approaches (Table 1).
Distillation models calculate the theoretical fraction-
ation that occurs along a cooling path with prescribed
initial evaporation and condensation conditions. Such
models have been used to assess the impact of equilib-
rium and kinetic fractionation processes on the snowfall
isotopic composition (Merlivat and Jouzel 1979; Jouzel
and Merlivat 1984). The second-order isotopic param-
eter deuterium excess d ⫽
␦
D ⫺ 8 ⫻
␦
18
O (Dansgaard
1964) is expected to be highly sensitive to kinetic effects
occurring either during evaporation at the ocean sur-
face or during atmospheric transport (e.g., reevapora-
tion of droplets or ice crystal formation). The observed
high deuterium excess values of inland Antarctic snow
cannot be simulated without taking into account kinetic
fractionation in supersaturation conditions over ice
crystals (Jouzel and Merlivat 1984; Salamatin et al.
2004). Sensitivity tests conducted with distillation mod-
els suggest that spatial variations of deuterium excess in
Antarctica may reflect, at least partly, different mois-
ture origins (Ciais and Jouzel 1994; Ciais et al. 1995;
Kavanaugh and Cuffey 2003; Masson-Delmotte et al.
2004).
AGCMs equipped with the explicit representation of
water-stable isotopes (Joussaume et al. 1984; Jouzel et
al. 1987b, 1991; Hoffmann et al. 2000) allow us to dis-
entangle the different factors involved in the spatial
(Brown and Simmonds 2004; Schmidt et al. 2005), sea-
sonal (Koster et al. 1992; Delmotte et al. 2000), inter-
annual (Werner et al. 2001; Werner and Heimann 2002;
Noone and Simmonds 2002a), or glacial–interglacial
changes (Delaygue et al. 2000) in Antarctic snow iso-
topic composition. They offer the advantage of a con-
sistent frame where tracers can be used to tag moisture
of different geographical origins (Koster et al. 1986;
Delaygue et al. 2000).
From these modeling efforts, it appears that the key
factors controlling the observed distribution of stable
isotopes in Antarctic snow are related to spatial
changes of the integrated condensation temperature
(including changes along the vapor trajectory, vertical
changes, and the intermittency of snowfall days), and
the origin of moisture (transported either at different
elevations or from different geographical areas). In
principle, condensation temperature during snowfall
episodes should be the relevant climatic parameter
used to analyze the spatial distribution of stable iso-
topes. Because only very few records of Antarctic con-
densation temperature are available, inversion tem-
perature has been used as a surrogate for condensation
temperature (Jouzel and Merlivat 1984). A detailed
study for Vostok (Ekaykin 2003) confirmed the validity
of this assumption for central Antarctica. However, this
study highlighted that most of the local accumulation
does not arise from cloud condensation, but from clear-
sky deposition of diamond dust. Until now, the com-
parison between AGCMs and isotopic data has not
been focused on the different types of precipitation.
Biases of the simulated spatial distribution for the
Antarctic surface air temperature range and of the
amount or origin of snowfall can induce difficulties in
the comparison of AGCM results with Antarctic isoto-
pic data. To analyze the stable isotopic composition of
snowfall in a model framework that is compatible with
the observed climatology, several methods must be
combined. For example, background fields of transport
that are more consistent with observations can be pro-
vided by nudging AGCMs with reanalyses (Yoshimura
et al. 2004; Noone 2006). Regional atmospheric models
can also be used to simulate local features of the Ant-
arctic atmospheric circulation and to analyze the factors
controlling the regional origin of Antarctic moisture.
Atmospheric reanalyses are used to calculate back
trajectories for individual Antarctic snowfall events.
Along the back trajectories, distillation models can be
implemented to estimate the snowfall isotopic compo-
sition (Helsen et al. 2007). Because the backward tra-
jectories generally do not capture the evaporation pro-
cess in the moisture source areas, monthly mean fields
of atmospheric water vapor isotopic composition simu-
lated by general circulation models have been used to
initialize vapor isotopic composition for trajectory cal-
1JULY 2008 M A S SON-DELMOTTE ET AL. 3361
TABLE 1. Hierarchy of modeling approaches used to analyze the processes responsible for the isotopic composition of Antarctic
snowfall.
Method References Advantages Limitations
Rayleigh or mixed phase
distillation model
Jouzel and Merlivat (1984),
Jouzel (1986),
Fisher (1990), Ciais and
Jouzel (1994), Kavanaugh
and Cuffey (2003), and
Salamatin et al. (2004)
Key microphysical processes
represented
Assumption on initial evaporation
conditions (closure equation or
iso-AGCM water vapor fields)
Ability to perform sensitivity tests
on different aspects of the
fractionation along the water
cycle path (evaporation,
supersaturation, etc.)
Assumption on the relationship
between condensation and
surface temperature
Poor representation of convection
processes (not a key limitation
for inland Antarctica)
Atmospheric general
circulation models
equipped with the
explicit modeling of
water-stable isotopes
Joussaume et al. (1984),
Jouzel et al. (1987b),
Hoffmann et al. (1998,
2000), Werner and
Heimann (2002), Noone
and Simmonds (1998), and
Schmidt et al. (2005)
Intrinsic model coherency Potential biases in model
climatologies, especially in
Antarctica
Full coupling between
meteorological conditions and
distillation
Limitations resulting from model
resolution and adaptation of
parameterizations for
Antarctica (katabatic winds,
boundary layer processes,
stratospheric processes, cloud
microphysics)
Possibility to explore the temporal
stability of spatial relationships
in response to various climate
forcings
Difficult to isolate the relative role
of different processes (moisture
origin, trajectory, condensation,
etc.)
Identification of synoptic
weather characteristics
and back trajectories for
snowfall events using
atmospheric reanalyses
Reijmer et al. (2002) and
Schlosser et al. (2004)
Realistic synoptic framework Difficult to isolate the relative role
of different processes (moisture
origin, trajectory, condensation,
etc.) on final precipitation
isotopic composition
Possibility to relate clusters of
snowfall events to synoptic
weather systems using their
isotopic composition
Difficult to follow moisture
transport in the back trajectories
Simple isotopic model
calculations along back
trajectories using
AGCM water vapor
climatological
distribution
Helsen et al. (2007) Quantify the impact of airmass
origins on final moisture
isotopic composition
Possible incoherencies between
iso-AGCM water vapor isotopic
composition fields and back
trajectories from reanalyses
No hypothesis on condensation
temperature (derived from
reanalyses)
Limited representation of
convective processes
Nudging of iso-AGCMs
with reanalyses
Noone (2006) Analysis of model results in a
dynamical framework coherent
with observations
See section on iso-AGCM
regarding AGCM limitations
Regional atmospheric
models nudged with
reanalyses
Gallee et al. (2001),
Bromwich et al. (2004),
and van den Broeke and
van Lipzig (2005)
Good representation of key
processes relevant for Antarctic
precipitation (cloud
microphysics, boundary layer,
postdepositional effects)
Difficult to follow moisture
transport
Difficult to isolate the relative role
of different processes (moisture
origin, trajectory, condensation,
etc.) on final precipitation
isotopic composition
Regional atmospheric
models with the explicit
modeling of stable
isotopes
Sturm et al. (2005) Advantages of mesoscale models
with the coherency between
atmospheric dynamics and
isotopes of water
Not yet achieved for Antarctica
3362 JOURNAL OF CLIMATE VOLUME 21
culations (Helsen et al. 2006). Backward air trajectories
calculated for different Antarctic areas (Reijmer et al.
2002; Helsen et al. 2006) showed different moisture
transport paths for coastal areas, where seasonal con-
vection and cyclonic activity play a large role, and in-
land sites, where clear-sky precipitation may be a dom-
inant contribution to local snowfall (Ekaykin 2003).
Parallel with these improvements on the modeling
side, intensive field campaigns have been carried out in
various sectors of Antarctica, including coordinated in-
ternational traverses and presite surveys in search of
optimal deep-drilling sites (Table 2). In this work, we
have compiled a database of the available measure-
ments of snowfall, surface snow, or firn core isotopic
composition, taking into account their local variability.
This database predominantly includes published mea-
surements of Antarctic snow isotopic composition, and
some unpublished data (Table 2) prior to the new field
campaigns planned during the fourth International Po-
lar Year.
The second section describes the various datasets,
the quality control methodology, and the assessment of
uncertainties, as well as the resulting spatial distribu-
tion of Antarctic snow-stable isotopic composition. The
third section is focused on the comparison between
these observations and a variety of model results based
on simple isotopic models, regional atmospheric mod-
els, and isotopic AGCMs. The main outcomes of the
paper and suggested ways forward are presented in the
conclusions.
2. A database of Antarctic snow isotopic
composition
a. Sampling sites and related documentation
Table 2 presents the list and references of the various
sources of information compiled to produce the full
Antarctic database (available as an Excel file online at
http://www.lsce.ipsl.fr/Pisp/24/valerie.masson-delmotte.
html). When available, we have included geographical
information on the sampling location, such as annual
mean temperature (estimated either from firn tempera-
ture measurements or automatic weather station sur-
face air monitoring), latitude, longitude, and elevation
(Fig. 2). In some cases, estimates of local accumulation
rates are available, based on the identification of an-
nual layers and reference horizons (e.g., Frezzotti et al.
2005). To quantify the continentality of the sites, we
have estimated the horizontal distance to the nearest
coast using the Antarctic coastline (this measurement
may not reflect the distance along moisture trajectories,
which is important for isotope physics). We have not
included error bars on the estimates of annual mean
temperature and accumulation rate because of a lack of
consistent methodologies to evaluate these uncertain-
ties.
The surface snow isotopic composition has been
measured from direct precipitation sampling at a few
sites [Global Network for Isotopes in Precipitation
(GNIP) stations from the following International
Atomic Energy Agency (IAEA) stations: Neumayer,
Dumont d’Urville, Vostok, and Dome Fuji] over vary-
ing durations, sometimes over 1 yr (Motoyama et al.
2005; Fujita and Abe 2006) or, for Neumayer, continu-
ously since 1981 (Schlosser et al. 2004). Most surface
snow samples have been collected along traverses con-
ducted by individual groups or coordinated within the
International Trans-Antarctic Scientific Expeditions
(ITASE; Mayewski et al. 2005).
Surface snow-sampling procedures differ signifi-
cantly from one site to another. In some cases, shallow
snow cores or pits, typically 1 m deep, were sampled
and one or several measurements were performed. In
other cases, longer firn or ice cores have been analyzed
with a subannual resolution. The database includes a
description of the depth range and, if available, the
temporal range (dating). In places where deep firn or
ice cores have been drilled, meteorological data and
detailed snow measurements are available. In some
cases, there is no seasonal resolution either because of
the low accumulation rates or the crude measurement
resolution.
Finally, for each location we have reported the snow
isotopic composition (
␦
D,
␦
18
O, and deuterium excess
d), in per mille (‰) with respect to the Vienna Standard
Mean Ocean Water (V-SMOW). When more than five
measurements were performed at the same location
(corresponding either to detailed measurements on a
firn profile or to seasonal samples of snowfall), we have
also included basic statistics (number, mean, maximum,
minimum values, and standard deviation of the mea-
surements) in the table. The country where the mea-
surements were performed is also indicated.
The final database includes 1279 locations, out of
which 938 have
␦
D measurements, 1125 have
␦
18
O
measurements, and 794 have both isotopes, making it
possible to calculate the deuterium excess (Fig. 3). For
each site, deuterium excess values were calculated from
individual measurements of
␦
D and
␦
18
O conducted on
the same samples. Note that for each site we have cal-
culated an average of all
␦
D data available, an average
of all
␦
18
O data available, and an average of deuterium
excess data (which may be on a restricted subset of data
for which both isotopes have been measured). There-
fore, the reported deuterium excess is not systemati-
cally identical to
␦
D ⫺ 8
␦
18
O calculated from the mean
1JULY 2008 M A S SON-DELMOTTE ET AL. 3363
