A new 21.3 m firn core was drilled in 2015 at a coastal Antarctic
high-accumulation site in Adélie Land (66.78∘ S; 139.56∘ E,
602 m a.s.l.), named Terre Adélie 192A (TA192A).
The mean isotopic values (-19.3‰±3.1 ‰ for
δ18O and 5.4‰±2.2 ‰ for deuterium
excess) are consistent with other coastal Antarctic values. No significant
isotope–temperature relationship can be evidenced at any timescale. This
rules out a simple interpretation in terms of local temperature. An
observed asymmetry in the δ18O seasonal cycle may be explained
by the precipitation of air masses coming from the eastern and western
sectors in autumn and winter, recorded in the d-excess signal showing
outstanding values in austral spring versus autumn. Significant positive
trends are observed in the annual d-excess record and local sea ice extent
(135–145∘ E) over the period 1998–2014.
However, process studies focusing on resulting isotopic compositions and
particularly the deuterium excess–δ18O relationship, evidenced
as a potential fingerprint of moisture origins, as well as the collection of
more isotopic measurements in Adélie Land are needed for an accurate
interpretation of our signals.
IntroductionMotivation for new coastal Antarctic firn cores
Polar ice cores are exceptional archives of past climate variations. In
Antarctica, many deep ice cores have been drilled and analysed since the
1950s. For instance, Stenni et al. (2017) compiled water stable
isotope data from 112 ice cores spanning at least part of the last
2000 years. Most deep ice cores were drilled in the central Antarctic plateau
where low accumulation rates and ice thinning give access to long climate
records. In today's context of rapid global climate change, it is of
paramount importance to also document recent past climate variability around
Antarctica. Many Antarctic regions still remain undocumented due to the lack
of accumulation and water stable isotope records from shallow ice cores or
pits (Jones et al., 2016; Masson-Delmotte et al., 2008). An accurate
knowledge of changes in coastal Antarctic surface mass balance (SMB), an
evaluation of the ability of climate models to resolve the key processes
affecting its variability, and thus an improved confidence in projections of
future changes in coastal Antarctic surface mass balance are important to
reduce uncertainties on the ice sheet mass balance and its contribution to
sea level change (Church et al., 2013).
Meteorological observations have been conducted since 1957 in manned and
automatic stations (Nicolas and Bromwich, 2014), and considerable efforts
have been deployed to compile and update the corresponding dataset
(Turner et al., 2004). This network is marked by gaps in spatio-temporal
coverage (Goursaud et al., 2017) as well as systematic
biases of instruments such as thermistors (Genthon et al., 2011).
Satellite remote-sensing data have been available since 1979 and provide
large-scale information for changes in Antarctic sea ice and temperature
(Comiso et al., 2017), but do not provide sufficient accuracy and
homogeneity to resolve trends at local scales (Bouchard et al., 2010).
Coastal shallow (20–50 m long) firn cores are thus essential to provide
continuous climate information spanning the last decades at sub-annual
resolution, at local but also regional scales. They complement stake area
observations of spatio-temporal variability in surface mass balance
(Favier et al., 2013), which also help assess the representativeness
of a single record.
Since the 1990s, efforts have been made to retrieve shallow ice cores in
coastal Antarctic areas. Most of these efforts have been focused on the
Atlantic sector, in Dronning Maud Land (Altnau et al., 2015; Graf et al.,
2002; e.g. Isaksson and Karlén, 1994) and the Weddell Sea sector
(Mulvaney et al., 2002). Fewer annually resolved water stable isotope
records have been obtained from ice cores in other regions, such as the
peninsula (Fernandoy et al., 2018), the Ross Sea sector
(Bertler et al., 2011), Law Dome (Masson-Delmotte et al.,
2003; Delmotte et al., 2000; Morgan et al., 1997), Adélie Land (Yao et
al., 1990; Ciais et al., 1995; Goursaud et al., 2017), and the Princess Elizabeth
region (Ekaykin et al., 2017). However, the recent 2000-year temperature and
SMB reconstructions for the Antarctic (Stenni et al., 2017; Thomas et al.,
2017) highlighted the need for more coastal records. In this line, new
drilling efforts have recently been initiated in the context of the ASUMA
project (Improving the Accuracy of SUrface Mass balance of Antarctica) from
the French Agence Nationale de la Recherche, which aims to assess
spatio-temporal variability and change in SMB over the transition zone from
coastal Adélie Land to the central East Antarctic Plateau (towards Dome C).
Climatic interpretation of water stable isotope records
Water stable isotope (δ18O, δD) records from central
Antarctic ice cores have classically been used to infer past temperature
changes (e.g. Jouzel et al., 1987). The isotope–temperature
relationship was nevertheless shown not to be stationary and to vary in
space (Jouzel et al., 1997), calling for site-specific calibrations
relevant for various timescales (Stenni et al., 2017). In coastal
regions, several studies showed no temporal isotope–temperature relationship
at all between water stable isotope records in firn cores covering the last
decades and near-surface air temperature measured at the closest station.
This is for instance the case in Dronning Maud Land, near the Neumayer
station (three firn cores, for which the longest covered period is
1958–2012; Vega et al., 2016), in the Ross Sea sector (one snow pit
covering the period 1964–2000; Bertler et al., 2011), and in Adélie
Land, close to Dumont d'Urville (DDU, one firn core covering the period
1946–2006;
Goursaud et al., 2017). While several three-dimensional atmospheric
modelling studies have suggested a dominant role of large-scale atmospheric
circulation in the variability of coastal Antarctic snow δ18O
(e.g. Noone, 2008; Noone and Simmonds, 2002), understanding the
drivers of coastal Antarctic δ18O variability remains
challenging (e.g. Fernandoy et al., 2018; Bertler et al., 2018; Schlosser
et al., 2004; Dittmann et al., 2016). While distillation processes are
expected theoretically to relate condensation temperature with precipitation
isotopic composition, a number of deposition processes can distort this
relationship: changes in moisture sources (Stenni et al., 2016),
intermittency or seasonality of precipitation (Sime et al., 2008),
boundary layer processes affecting the links between condensation and
surface air temperature (Krinner et al., 2008), and several
post-deposition processes, such as the effects of winds (Eisen et al.,
2008), snow–air exchanges (Casado et al., 2016; Ritter et al., 2016), and
diffusion processes in snow and ice (e.g. Johnsen, 1977).
Nevertheless, all these processes remain poorly quantified. As a result,
comparisons between firn core records with precipitation records or
simulations have to be performed carefully.
Changes in the atmospheric water cycle can also be investigated using a
second-order parameter, deuterium excess (d-excess). The definition given by
Dansgaard (1964) as d-excess =δD–8×δ18O
aims to remove the effect of equilibrium fractionation processes
to identify differences in kinetic fractionation between the isotopes of
hydrogen and oxygen. In Antarctica, spatial variations in d-excess have been
documented through data syntheses, showing an increase from the coast to the
plateau (Masson-Delmotte et al., 2008; Touzeau et al., 2016), but temporal
variations in d-excess (seasonal cycle, inter-annual variations) remain
poorly documented and understood.
Theoretical isotopic modelling studies show that d-excess depends on
evaporation conditions, mainly through the impacts of relative humidity
(RH) and sea surface temperature (SST) on kinetic fractionation at the
moisture source (Merlivat and Jouzel, 1979; Petit et al., 1991; Ciais et
al., 1995), and the preservation of the initial vapour signal during
transportation towards polar regions (e.g. Jouzel et al., 2013; Bonne et
al., 2015). The effect of wind speed on kinetic fractionation is secondary
and thus has been neglected in climatic interpretations of d-excess. Some
studies usually privileged one variable (RH or SST). For instance, glacial–interglacial d-excess has classically been interpreted to reflect past
changes in moisture source SST, neglecting RH effects or assuming
co-variations in RH and SST (Vimeux et al., 2001, 1999; Stenni et al., 2001).
Recent measurements of d-excess in water vapour
from ships have evidenced a close relationship between d-excess and oceanic
surface conditions, especially RH, at sub-monthly scales (Pfahl and
Sodemann, 2014; Uemura et al., 2008; Kurita et al., 2016). Other recent
studies have suggested that evaporation at sea ice margins may be associated
with a high d-excess value due to low RH effects, a process which may not be
well captured in atmospheric general circulation models (e.g. Kurita, GRL,
2011; Steen-Larsen et al., 2017). Several authors have thus identified the
potential to identify changes in moisture sources using d-excess
(Delmotte et al., 2000; Sodemann and Stohl, 2009; Ciais et al., 1995). The
comparison between multi-year isotopic precipitation datasets with the
identification of air mass origins using back trajectories showed, however, a
complex picture, with no trivial relationship between the latitudinal air
mass origin and d-excess (Dittmann et al., 2016; Schlosser et al.,
2008). A few studies have also explored sub-annual d-excess variations and
suggested that seasonal d-excess signals cannot be explained without
accounting for seasonal changes in moisture transport (e.g. Delmotte
et al., 2000). These features have been explored through the identification
of back-trajectory clusters and their relationship with δ18O–d-excess
relationships, including phase lags (Markle et al.,
2012; Caiazzo et al., 2016; Schlosser et al., 2017).
Most of these d-excess studies have been performed using firn records and
not precipitation samples. We stress that the impact of post-deposition
processes on d-excess remain poorly documented and understood. While
relationships between moisture origin and d-excess should in principle be
conducted on vapour measurements to circumvent the uncertainties associated
with deposition and post-deposition processes, the available vapour water
stable isotope records from Antarctica only cover 1 or 2 summer months
(Ritter et al., 2016; Casado et al., 2016) and do not yet allow exploration
of
the relationships between moisture transport and seasonal or inter-annual
isotopic variations. Also, state-of-the-art atmospheric general circulation
models equipped with water stable isotopes such as ECHAM5-wiso can capture
d-excess spatial patterns in Antarctic snow, but they fail to correctly
reproduce its seasonal variations (Goursaud et al.,
2017). Finally, the understanding of the climatic signals preserved in
d-excess is limited by the available observations. This motivates the
importance of retrieval of more highly resolved d-excess records from
coastal Antarctic firn cores.
This study
In this study, we focus on the first highly resolved firn core drilled in
coastal Adélie Land, at the TA192A site (66.78∘ S, 139.56∘ E;
602 m a.s.l., hereafter named TA). Only two ice cores and
one snow pit were previously studied for water stable isotopes in this
region, without any d-excess record: the S1C1 ice core (14 km from
the TA, 279 m a.s.l.; Goursaud et al., 2017), the D47 highly resolved pit
(78 km from the TA, 1550 m a.s.l.; Ciais et al., 1995), and the
Caroline ice core (Yao et al., 1990). The climate of coastal Adélie
Land is greatly influenced by katabatic winds (resulting in a very high
spatial variability of accumulation) and by the presence of sea ice
(Périard and Pettré, 1993; König-Langlo et al., 1998),
including the episodic formation of winter polynya (Adolphs and
Wendler, 1995), which lead to nearby open water during wintertime. The
regional climate has been well documented since March 1957 at the meteorological
station of Dumont d'Urville, where multi-year atmospheric aerosol monitoring
has also been performed (e.g. Jourdain and Legrand, 2001). The
spatio-temporal variability of regional SMB has also been monitored at an
annual timescale since 2004 through stake height and snow density
measurement over a 156 km stake line (91 stakes)
(Agosta et al., 2012;
Favier et al., 2013). The TA firn core was analysed at sub-annual resolution
for water isotopes (δ18O and δD) and chemistry (Na,
SO42-, and methane sulfonate, MSA). These records were used to establish the age
scale for the firn core. Using these records, we explore (i) the links
between the TA isotopic signals, local climate, and atmospheric transport,
(ii) the possibility to extract a sub-annual signal from such a
highly resolved core, and (iii) how to interpret the d-excess signal of
coastal Antarctic ice cores.
In this paper, we first present our material and methods (Sect. 2),
then describe our results (Sect. 3) and compare them with other Antarctic
records and the outputs of the ECHAM5-wiso model in our discussion
(Sect. 4), before summarizing our key findings and formulating suggestions for
future studies (Sect. 5).
Materials and methodsField work and laboratory analyses
Here we present the results of one firn core drilled at the TA site
(66.78, 139.56∘ S; 602 m a.s.l.), located at 25 km from
the Dumont d'Urville station (DDU) and at 14 km from the S1C1 ice core
(Goursaud et al., 2017; Fig. 1). The 21.3 m long firn core was
drilled on 29 January 2015, when the daily surface air
temperature and wind speed were -8.5∘C and 3.9 m s-1
respectively at the D17 station (9 km from the drilling site).
Map showing the location of the drilling sites of the S1C1 and
TA192A firn cores (black points), the Dumont d'Urville and D17 stations
(green points), the stake points (in brown; included the three closest
stake points from the TA192A, namely the 18.3, 19.2, and
20.3 lines, and 156 km stake line), and the mean wind direction over the
period 1957–2014 (in black). Isohypses (grey lines) shown in the main
figure are simulations from adigital elevation model, large-scale resolution.
Radarsat Antarctic Mapping Project Digital Elevation Model version 2
(Liu et al., 2001). The map of Antarctica on the top left displays
the mean February and September sea ice extent over the period 1981–2010
extracted from the Nimbus-7 Scanning Multichannel Microwave Radiometer
(SMMR) and Defense Meteorological Satellite Program Special Sensor
Microwave/Imagers – Special Sensor Microwave Image/Sounder (DMSP
SSM/I-SSMIS) passive microwave data (http://nsidc.org/data/nsidc-0051,
last access: January 2018)
(Cavalieri et al., 1996) (light blue and dark blue lines
respectively), and the zoomed area (grey rectangle), while the grey
rectangle in the middle right zooms the area around the TA192A drilling site
in order to show the three closest stake locations.
The FELICS (Fast Electrochemical lightweight Ice Coring System) drill system
was used (Ginot et al., 2002; Verfaillie et al., 2012). Firn core
pieces were then sealed in polyethylene bags, stapled, and stored in clean
isothermal boxes. At the end of the field campaign, the boxes were
transported in a frozen state to the cold-room facilities of the Institute
of Environmental Geoscience (IGE, Grenoble, France). Every core piece was
weighted and its length measured in order to produce a density profile. The
cores were sampled at 4 cm resolution, leading to a total of 533 samples for
oxygen isotopic ratio and ionic concentrations following the method
described in Goursaud et al. (2017). Samples devoted to ionic
concentration measurements were stored in the cold room until concentrations
of sodium (Na+), sulfate (SO42-), and methane sulfonate (MSA)
were analysed by ion chromatography equipped with a CS12 and an AS11
separator column, for cations and anions respectively. Samples devoted to
oxygen isotopic ratio were sent to LSCE (Gif-sur-Yvette, France) and
analysed following two methods. First, δ18O was measured by the
CO2/H2O equilibration method on a Finnigan MAT 252, using two
standards calibrated to SMOW–SLAP international scales, with an accuracy of
0.05 ‰. Second, δ18O and δD were
also measured using a laser cavity ring-down spectroscopy (CRDS) Picarro
analyser, using the same standards, leading to an accuracy of
0.2 ‰ and 0.7 ‰
for δ18O and δD respectively. The resulting accuracy of d-excess,
calculated
using a quadratic approach, is 1.7 ‰.
DatasetsInstrumental data
To assess potential climate signals archived in our firn core records, we
extracted meteorological data to explore regional climate signals, and
outputs of atmospheric models to explore synoptic scale climate signals. The
regional climate is well documented since 1957 thanks to the continuous
meteorological monitoring at DDU station
(https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=90&id_rubrique=32, last
access: January 2018), with one gap between March 1959
and January 1960. We extracted near-surface temperature, humidity,
surface pressure, wind speed and wind direction data computed monthly and
annual averages over the periods 1957–2014 and 1998–2014.
The monthly average sea ice concentration was extracted from the Nimbus-7
Scanning Multichannel Microwave Radiometer (SMMR) and Defense Meteorological
Satellite Program Special Sensor Microwave/Imagers – Special Sensor
Microwave Image/Sounder (DMSP SSM/I-SSMIS) passive microwave data
(http://nsidc.org/data/nsidc-0051, last
access: January 2018), over the 50–90∘ S
latitudinal range at a 25 km × 25 km grid resolution (Cavalieri et
al., 1996). D'Urville summer sea ice extent was estimated by extracting the
number of grid points covering the area (50–90∘ S, 135–145∘ E)
where the sea ice concentration is higher than 15 %, from
December to January (included) for each year from 1998 to 2014.
SMB measurements from stake point data were obtained from the GLACIOCLIM
observatory (https://glacioclim.osug.fr/, last
access: January 2018). We extracted data
from the three stakes closest to the TA drilling site, namely 18.3
(66.77∘ S, 139.57∘ E; 1.04 km from the TA drill site),
19.2 (66.77∘ S, 139.56∘ E; 83 m from the drilling
TA site), and 20.3 (66.78∘ S, 139.55∘ E; 1.00 km
from the drilling TA site), all spanning the period 2004–2014.
Database of surface snow isotopic composition
In order to compare the d-excess record from the TA firn core with available
Antarctic values, we have updated the database of Masson-Delmotte et al. (2008),
by adding 26 new data points from precipitation and firn core
measurements provided with d-excess (Table 1). This includes data from five
ice cores from the database constituted by the Antarctica2k group
(Stenni et al., 2017). Altogether, the updated database includes 777
locations. This includes 64 coastal sites at an elevation lower than 1000 m a.s.l.,
(with 19 new datasets). These data were extracted from our updated
isotope database (Goursaud et al., 2018a) archived in the
PANGAEA data library (10.1594/PANGAEA.891279).
Site, latitude (∘), longitude (∘ E), and
reference of new d-excess data added to Masson-Delmotte et al. (2008).
Data in (a) correspond to
precipitation, (b) data correspond to ice
cores extracted from the Antarctica2k database (Stenni et al., 2017),
and (c) data are new data compared to our prior database
(Goursaud et al., 2017).
SiteLatitudeLongitudeReference(a)Frei (South Shetland Islands)*-62.20301.04Fernandoy et al. (2012)O'Higgins (north peninsula)*-63.32302.10Fernandoy et al. (2012)Halley-75.58333.50Rozanski et al. (1993)Base tte. Marsh-62.12301.44Rozanski et al. (1993)Rothera Point-67.57291.87Rozanski et al. (1993)Vernadsky-65.08296.02Rozanski et al. (1993)Vostok-78.5106.9Landais et al. (2012)DDU-66.7140.00Jean Jouzel, personal communication, June 2017Neumayer-70.7351.60Schlosser et al. (2008)Dome F-77.339.7Fujita and Abe (2006)Dome C-75.1123.4Schlosser et al. (2017)(b)EDC Dome C-75.10123.39Stenni et al. (2001)NUS 08-7-74.121.60Steig et al. (2013)NVFL-1-77.1195.07Ekaykin et al. (2017)WDC06A-79.46247.91Steig et al. (2013)IND 25B5 coastal DML-71.3411.59Rahaman et al. (2016)(c)OH-4*-63.36302.20Fernandoy et al. (2012)OH-5*-63.38302.38Fernandoy et al. (2012)OH-6*-63.45302.24Fernandoy et al. (2012)OH-9*-63.45302.24Fernandoy et al. (2012)OH-10*-63.45302.24Fernandoy et al. (2012)KC-70.522.95Vega et al. (2016)KM-70.131.12Vega et al. (2016)BI-70.403.03Vega et al. (2016)GIP-80.10159.30Markle et al. (2012)DE08-2-66.72112.81Delmotte et al. (2000)DSSA-66.77112.81Delmotte et al. (2000)D15*-66.86139.78Jean Jouzel, personal communication, June 2017TA192A-66.78139.56This study
Finally, data associated with
“*” were not provided with a dating while data in
italics have a sub-annual
resolution. Note that DE08-2 and D15 ice cores were not dated.
Atmospheric reanalyses and back trajectories
Unfortunately, because of the katabatic winds around DDU, no instrumental
method allows reliable measurements of precipitation (Grazioli et al.,
2017a). We use outputs from ERA-Interim reanalyses (Dee et al., 2011b),
which were shown to be relevant for Antarctic surface mass balance
(Bromwich et al., 2011), to provide information on DDU intra-annual
precipitation variability. We extracted these outputs from the grid point
(0.75∘× 0.75∘, ∼80 km × 80 km, point
centered at 66.75∘ S and 139.5∘ E) closest to the TA
drilling site over the period 1998–2014, at a 12 h resolution, and
calculated daily, monthly, and annual average values. We also extracted
2 m temperature (2mT), 10 m u and v wind components (u10 and v10),
and the geopotential height at 500 hPa (z500) over the whole Southern
Hemisphere (50–90∘ S), in order to investigate potential
linear relationships between our records and the large-scale climate variability.
In order to identify the origin of air masses, back trajectories were
computed using the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated
Trajectory) model. It is an atmospheric transport and dispersion model
developed by the National Oceanic and Atmospheric Administration (NOAA)
Air Research Laboratory (Draxler and Hess, 1998), based on a mixing
between Lagrangian and Eulerian approaches (Stein et al., 2015). We set
the arrival point at the coordinates of the TA drilling site, at an initial
height of 1500 m a.s.l., and used the NCEP/NCAR Global Reanalysis ARL
archived data for forcing the meteorological conditions, as the ERA-Interim
reanalyses are not available in the required extension. Earlier studies
(e.g. Markle et al., 2012; Sinclair et al., 2010) highlighted good
performances of NCEP outputs when compared with Antarctic station data after
1979. For instance, previous studies showed that the mean sea level pressure
simulated at DDU and averaged on a 5-year running window was well captured
in NCEP reanalyses after 1986 (correlation coefficient >0.8,
bias <4 hPa, and RMSE<5 hPa) (Bromwich and Fogt,
2004; Bromwich et al., 2007). Also, Simmons et al. (2004) showed
quasi-equal 12-month running means of 2 m temperatures for the
Southern Hemisphere between the European Re-Analyses ERA-40, the NCEP/NCAR,
and the Climatic Research Unit CRUTEM2v products. We thus run daily 5-day back trajectories from January 1998 to December 2014. Each
back trajectory was analysed for the geographical position of the last
simulated point (the estimated start of the trajectory, 5 days prior to arrival
at DDU) and classified into one of the following four regions, represented
in Fig. 2 and defined by their longitude (long) and latitude (lat) as
follows: (i) the eastern sector: (0–66∘ S, 0–180∘ E), (ii) the
plateau: (66–90∘ S, 0–180∘ E), (iii) the
Ross Sea sector: (0–75∘ S, 180–240∘ E), and finally
(iv) the western sector: (0–75∘ S, 180–240∘ E) and
(50–90∘ S, 240–360∘ E).
Representation of the sectors used to classify the last point of
the simulated back trajectories by HYSPLIT over the period 1998–2014,
defined as follows: (i) the eastern sector (0–66∘ S,
0–180∘ E), (ii) the plateau (66–90∘ S, 0–180∘ E),
(iii) the Ross Sea sector (0–75∘ S, 180–240∘ E),
and finally (iv) the western sector (0–75∘ S, 180–240∘ E)
and (50–90∘ S, 240–360∘ E).
Atmospheric general circulation and water stable isotope modelling: ECHAM5-wiso
The potential relationships between large-scale climate variability and
regional precipitation isotopic composition was also investigated through
outputs of a nudged simulation performed with the atmospheric general
circulation model ECHAM5-wiso (Roeckner et al., 2003), equipped with
stable-water isotopes (Werner et al., 2011). We chose this model due to
demonstrated skills to reproduce spatial and temporal patterns of water
stable isotopes in Antarctica (Masson-Delmotte et al., 2008; Steen-Larsen
et al., 2017; Werner et al., 2011; Goursaud et al., 2017) and in Greenland
(Steen-Larsen et al., 2017).
In this study, we use the same simulation as Goursaud et al. (2017),
in which the large-scale circulation (winds) and air
temperature were nudged to outputs of the ERA-Interim reanalyses (Dee et
al., 2011a). The skills of the model were assessed over Antarctica for the
period 1979–2014. The model was run in a T106 resolution (i.e.
∼110 km × 110 km horizontal grid size). In the following, we
used the subscripts ECH, TA, and S1C1 to differ ECHAM5-wiso
outputs from the TA and S1C1 firn cores records respectively (e.g. δ18OTA
and δ18OECH). Note also that linear
relationships are considered significant when the p value < 0.05.
Modes of variability
We tested the main modes of variability were imprinted in our
recorded, especially
the Southern Annual Mode (SAM) using the index defined by
Marshall (2003) and archived on the National Center for Atmospheric Research website
(Marshall, Gareth & National Center for Atmospheric Research Staff (Eds); last modified 19 March 2018; “The Climate Data Guide: Marshall Southern
Annular Mode (SAM) Index (Station-based)”; retrieved from
https://climatedataguide.ucar.edu/climate-data/marshall-southern-annular-mode-sam-index,
last access: January 2018).
the El Niño–Southern Oscillation (ENSO) using the El Niño 3.4 index
defined by the Climate Prediction Center of NOAA's National Centers for
Environmental Prediction and archived on their website (Trenberth, Kevin
& National Center for Atmospheric Research Staff Eds); last modified 06
September 2018; “The Climate Data Guide: Nino SST Indices (Nino 1+2, 3, 3.4, 4;
ONI and TNI)”; retrieved from https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni,
last access: January 2018).
the Interdecadal Pacific Oscillation (IPO), using the IPO Tripole Index
(TPI) defined by Henley et al. (2015) based on filtered HadISST and
ERSSTv3b sea surface temperature data and archived on the internet
(https://www.esrl.noaa.gov/psd/data/timeseries/IPOTPI, last access: 20 September 2018).
the Amundsen Sea Low pressure center (ASL) archived one the National
Center for Atmospheric Research website (Hosking, Scott & National Center
for Atmospheric Research Staff Eds); last modified 19 March 2018; “The Climate
Data Guide: Amundsen Sea Low indices”; retrieved from
https://climatedataguide.ucar.edu/climate-data/amundsen-sea-low-indices,
last access: January 2018).
ResultsFirn core chronologyIce core dating
The firn core was dated using an annual layer counting method (Fig. 3). As
in Goursaud et al. (2017), we used concentrations in MSA and
non-sea-salt (nss) SO42- (nssSO4).
Identification of annual layers in the TA192A ice core based on
the dual identification of nssSO42- and MSA summer peaks and
comparison of estimated accumulation with annual accumulation measured at
the closest stakes (not shown). The thick vertical green lines correspond to
summer peaks identified from chemical signals, while the thin vertical green
line shows the additional identification of summer 2008 added to the counted
summers from the comparison between the estimated accumulation and the
accumulation record from the closest stake data (see Fig. 3). Vertical
dashed lines highlight equivocal summer peaks with a sometimes small signal
in only one of the chemical records. Water stable isotope records were not
used to build the timescale. Note that double peaks in both δ18O
and d-excess occur repeatedly within one counted year.
We have explored the validity of an approach using a definition of
nssSO4 based on a sulfate-to-sodium mass ratio of 0.25 inferred
from summer observations only. The multi-year study of size-segregated
aerosol composition conducted at the coast of TA (the DDU station) has
demonstrated that sea-salt aerosol is depleted in sulfate with respect to
sodium in winter, with a sulfate-to-sodium mass ratio of 0.13 from May to
October instead of 0.25 (i.e. the seawater composition) in summer
(Jourdain and Legrand, 2002). Even at the high plateau station of
Concordia, Legrand et al. (2017a) showed that sea-salt aerosol is depleted
in sulfate in winter (sulfate-to-sodium ratio of 0.13 from May to October).
We resampled the sulfate time series recorded in the TA with 12 points per
year and inferred seasonal average values from averages over the
corresponding subsets of points, as previously performed for isotopic records
(Sect. 3.2). We then calculated nssSO4 (noted as nssSO4∗)
using a sulfate-to-sodium ratio of 0.25 for points associated with months
from November to February and 0.13 for points associated with months from
March to September. Note that when ignoring the change in sulfate-to-sodium
ratio in winter (i.e. applying a sulfate-to-sodium ratio of 0.25 for all
the points of the year), the mean nssSO4 value is lower by 18.2 %,
decreasing from 36.5±12.3 ppb to 43.1±11.8 ppb for
nssSO4∗ (Fig. S1 in the Supplement). We thus
applied a calculation of nssSO4 for all points of our firn core, only
using the sulfate-to-sodium ratio obtained from summer observations, as
[nssSO42-] = [SO42-] - 0.25 [Na+].
For depths lower than 10 m w.e., summer (December–January) peaks were
identified (i) from nssSO4 values higher than 100 ppb, synchronous with
MSA peaks (with no threshold), and (ii) for nssSO4 values higher than
200 ppb (with or without a simultaneous MSA peak). Double nssSO4 peaks
were counted as one summer (e.g. 2012, 2003, and 2001). For depths higher
than 10 m w.e., summer peaks were identified for nssSO4 values higher
than 27 ppb. The outcome of layer counting allowed us to estimate annual
layer thickness, which, combined with the density profile, allowed us to
estimate annual SMB in the firn core. This estimated SMB was then compared
with stake area data. The three stake data closest to the TA firn core
(18.3, 19.2, and 20.3, not shown) depict the same inter-annual
variability (pairwise coefficient correlations, r>0.93 and
p values, p<0.05), giving confidence in the use of these
measurements to characterize the inter-annual variability of local SMB. The
comparison with the stake data shows that our initial layer-counted
chronology results in a mismatch in the measured versus estimated SMB for
the year 2008 (Fig. 4a). This mismatch can be resolved by identifying one more
summer peak in the chemical records (thin green line, Fig. 3). The revised
firn core SMB record from this revised chronology shows correlation
coefficients between the stake data and the TA firn core varying from 0.64
for 20.3 to 0.83 for 19.2 (p<0.05), with coherent
inter-annual variability (Fig. 4b).
Annual accumulation (cm w.e. yr-1) estimated from the layer
counting in the TA192A firn core (blue line), measured at the closest stake
point 19.2 (orange line), from the 156 km network stake data (green
line) and extracted from the ECHAM5-wiso model (red line), before (a) and
after (b) adding the identification of summer 2008 in a time interval of low
accumulation rates and skipped in the initial layer-counting approach due to
the lack of a signal in the MSA record (thin green line in Fig. 2).
Correlations between time series (TA192A series inferred from the second
dating) are inserted in the lower plot, with all linear relationships being
significant (p<0.05).
Peaks in δ18OTA or d-excessTA were not used in our
layer counting, so that our age scale is independent of a climatic
interpretation of water stable isotopes (e.g. assumption of synchronicity
between temperature seasonal cycles and water stable isotope records). We
note an uncertainty in layer counting of 3 years when comparing the outcome
of layer counting using chemical records with δ18OTA
peaks, which have nonetheless been excluded from our dating, as they do not
improve the correlations, either between the reconstructed SMB and the
stake data or between our records and the ECHAM5-wiso simulations (Tables S1 to S3).
As a result, we consider the “best guess” chronology results from the
annual layer counting based on nssSO4 and MSA refined with the
comparison with stake data, giving a total of 18 summer peaks (green
vertical lines, Fig. 3). In the following, we thus use the dated firn core
records covering the complete period 1998–2014. We note that our chronology
is more robust for the period 2004–2014, for which stake area SMB data are available.
Potential post-deposition effects
In order to test whether the available d-excessTA records are not
affected by post-deposition effects, one may apply calculations of diffusion
(e.g. Jones et al., 2017; Johnsen et al., 2000; van der Wel et al., 2015).
However, many records are not available as depth profiles, and annual
accumulation rate data are missing, precluding a systematic approach. We
thus applied a simple approach to quantify how the seasonal δ18OTA
and d-excessTA amplitudes vary through time in firn
records, as an indicator of potential post-diffusion effects. For this
purpose, we calculate the ratio between the mean amplitude of the most
recent three complete seasonal cycles (2011–2014 for TA) and the average
seasonal amplitude for the whole record (1998–2014 for TA). If seasonal
cycles are stable through time, and if there is no significant smoothing due
to post-deposition effects, we should obtain a ratio of 1. However, it is
expected to be above 1 in the case of large post-deposition smoothing. We
obtain a ratio of 0.5 for δ18OTA data, possibly reflecting
the inter-annual variability of the δ18O seasonal amplitude. We
repeated the same exercise with all eight other sub-annual
δ18O records from our database (Table S4).
Discarding an outlier (NUS 08-7), all ratios are between 1.0 and 2.9. Ratios
based on d-excess amplitudes are similar to those found for δ18O
(Table S5). For the TA firn core, we
again obtain a ratio of 1.1 for d-excessTA. We also note high ratios
for d-excess data in the NUS 08-7. Except for the ratios calculated in the
WDC06A, which notably differs for d-excess (1.1) compared to δ18O
(2.9), other ratios for d-excess data vary between 1.0 and 1.4,
with 20 % maximum difference compared to the corresponding ratio for
δ18O data.
For the TA, we also estimated the diffusion length (Küttel et al.,
2012) and found mean diffusion lengths of 1.4±0.3 months for
δ18OTA (with a maximum of 1.9 months in 2007) and 1.6±0.5
months for d-excessTA (with a maximum of 2.4 months in 2007).
These results suggest that potential post-deposition effects in the TA can
be neglected. Notwithstanding, a potential loss of seasonal amplitude in the
other average time series compared to the most recent seasonal cycles cannot
be discarded and has to be considered in the comparison of seasonal
amplitudes, from one core to the other, in the comparison with the seasonal
amplitude of precipitation δ18O time series, and with
ECHAM5-wiso outputs.
Mean valuesMean climate from instrumental data
Before reporting the mean values from TA records, we describe the available
meteorological data. A time-averaged statistical description of the
available meteorological data measured at DDU, the station closest to the
drilling site, is given in Table 2 for the whole available measurement
period prior to 2015 (1957–2014) and over the period covered by our TA
records, 1998–2014. For all the considered parameters (near-surface
temperature, wind direction, wind speed, humidity, and surface pressure),
the time-averaged values differ by less than 8 % (the maximum deviation
being for the wind direction) over the period 1998–2014 compared to the
whole available period. Standard deviations calculated over these two time
periods also differ by less than one respective standard deviation unit,
except for wind direction, which shows much less variability over the recent
period. We conclude that the local climate of the period 1998–2014 is
representative of the multi-decadal climate state since 1957. In
ERA-Interim, the average precipitation is 46.0±26.9 cm w.e. yr-1
over the period 1998–2014.
Number of points (n), time averages (µ), standard
deviation (σ), minimum (min), and maximum (max) values of
all the monthly meteorological observations at Dumont d'Urville from
Météo France over the period 1957–2014 (with a gap between March
1959 and January 1960, inclusive) and over the period 1998–2014 for
near-surface temperature (Ts, ∘C),
wind speed (ws, m s-1),
wind direction (wd, ∘E), relative humidity
(RH, %), and annual precipitation and accumulation (precipitation
minus evaporation and sublimation) from ERA-Interim reanalyses (Prec. ERA,
mm w.e. yr-1).
Finally, we compare the statistical description of the sea ice concentration
for the four aforementioned regions (Sect. 2.2) over the available period
1979–2014, with the period covered by the TA firn core 1998–2014 (Table 3).
We note that the mean difference between the two periods is maximum for the
local sea ice concentration (135–145∘ E), with 8.9 %
difference, whereas it remains below 1.5 % for the other sectors. The
extrema (minimum and maximum values) vary by 0.5 % on average (all
regions included) from one sector to another, with a maximum difference of
2.9 %. As a result, the mean sea ice concentrations of the period
1998–2014 are also representative for the last decades over large sectors
from the Amundsen Sea to Indian Ocean.
Number of points (n), time averages (µ), standard
deviation (σ), minimum (min), and maximum (max) values of
monthly meteorological sea ice concentration over the periods 1979–2014 and
1998–2014 extracted from the Nimbus-7 Scanning Multichannel Microwave
Radiometer (SMMR) and Defense Meteorological Satellite Program Special
Sensor Microwave/Imagers – Special Sensor Microwave Image/Sounder (DMSP
SSM/I-SSMIS) passive microwave data (http://nsidc.org/data/nsidc-0051,
last access: January 2018)
(Cavalieri et al., 1996), and for the four regions defined
as (i) “local” (135–145∘ E), (ii) “Indian” (100–145∘ E),
(iii) “Amundsen” (160–205∘ E), and (iv) “regional” (100–205∘ E).
1979–2014 1998–2014 IndianLocalAmundsenRegionalIndianLocalAmundsenRegionaln432432432432204204204204µ41.147.056.642.941.651.257.443.4σ6.87.711.58.76.68.011.68.8Min31.038.236.328.731.138.436.628.9Max55.466.074.258.853.865.273.957.7Mean values recorded in the TA firn core
Time-averaged values calculated from TA records are reported in Table 4. The
average SMBTA is 75.2±15.0 cm w.e. yr-1. Stake data points
from GLACIOCLIM show that this site of high accumulation is located in an
area of large spatial variability. This feature is confirmed by the values
given by (i) the stake data closest to the TA site (19.2, 100 m for the
TA site and associated with a 76.6±25.8 cm w.e. yr-1 mean
accumulation rate) compared to further stake data (18.2, 1.04 km from
the TA site and associated with a 47.7±15.7 cm w.e. yr-1 mean
accumulation rate), (ii) our mean SMB reconstruction from the S1C1 ice core
almost 4 times lower than for the TA (21.8±6.9 cm w.e. yr-1;
Goursaud et al., 2017), and (iii) mesoscale fingerprints such as
the SMB estimated for coastal Adélie Land by Pettré et al. (1986),
based on measurements at stakes located from 500 m to 5 km from the
coast (Table 2).
Time averages (µ) and standard deviation (σ)
of the reconstructed accumulation (cm w.e.) and of the signals recorded in
the TA192A ice core obtained from the resampling of the isotopic and
chemical variables for δ18O (‰), d-excess
(‰), Na, MSA, and nssSO4 (ppb), over the 17
annual values.
The average δ18OTA value is -19.3‰±3.1 ‰,
close to the average δ18OS1C1 of
-18.9‰±1.7 ‰, and the average d-excessTA is
5.4‰±2.2 ‰. Compared to the 64 points located at
an elevation lower than 1000 m a.s.l. from our database, the δ18OTA
and d-excessTA average values are slightly higher than
the average low-elevation records (-22.7‰±8.8 ‰
and 4.8‰±2.3 ‰ for δ18O and d-excess
respectively, Fig. 5). Finally, the average TA concentrations in Na+,
MSA, and nssSO4 are 126.0±276.5, 4.5±5.6, and
36.5±44.2 ppb respectively. Note that the Na+ average
concentration value is affected by strong peaks in 2003 and 2004 with annual
values of 369.4 and 388.5 ppb. Excluding these two peaks, the average
Na+ concentration is reduced to 93.2 ppb (with a standard deviation of
38.6 ppb). These concentrations will be discussed later in Sect. 4.4.
Spatial distribution of δ18O (a, ‰)
and d-excess (b, ‰) in surface Antarctic snow based
on our updated database combining data from precipitation, surface snow,
pits, and shallow ice cores. Bigger points with a black edge correspond to
new data compared to Masson-Delmotte et al. (2008).
To summarize our findings, the TA records encompass a period (1998–2014)
representative of multi-decadal mean climatic conditions. The isotopic mean
values appear lower than the average of other Antarctic low-elevation
records (as shown in Fig. 4). The local SMB is remarkably high for East
Antarctica, consistent with stake measurements performed close to the TA site.
Inter-annual variations
In the following, we refer to seasons as follows: summer (December to
February), autumn (March to May), winter (June to September), and spring
(October and November). This cutting was defined based on the mean seasonal
cycle of temperature, showing the highest values from December to February,
and a plateau of low values from May to September (Fig. 8a). In the TA
records, resampled with 12 points per year, we identified seasonal average
values by calculating averages over the corresponding subsets of points
(e.g. for autumn, we select from the third to the fifth points out of
the 12 resampled points within the year). We are fully aware that this is a
simplistic approach, assuming a regular distribution of precipitation year
round, and that our chronology is more accurate for summer than for other
seasons, due to the layer-counting method. We nevertheless checked that the
distribution of precipitation simulated by ERA-Interim within each year is
rather homogeneous (Table S6).
Trends in time series
Here we report the analysis of potential trends from 1998 to 2014 and the
identification of remarkable years. Figures 6 and 7 display the time series
of meteorological variables, Dumont d'Urville summer sea ice extent and TA records
over 1998–2014. In Figs. 6 and 7, we chose not to display standard
deviations for readability, but they are reported in the Supplement (Figs. S2 and S3).
Significant increasing trends are detected in the annual values of
d-excessTA (0.11 ‰ yr-1, r=0.61 and
p<0.05) as well as of Dumont d'Urville summer sea ice extent (r=0.77,
p<0.05).
Meteorological time series over the period 1998–2014 averaged at
the inter-annual scale. Near-surface temperature (∘C), relative
humidity (%), sea level pressure (hPa), wind speed (m s-1), and
direction (∘E) were provided by Météo France. The local sea ice
concentration (%) is extracted in the 135–145∘ E sector (with
a latitudinal range of 50–90∘ S) from the Nimbus-7 Scanning
Multichannel Microwave Radiometer (SMMR) and Defense Meteorological
Satellite Program Special Sensor Microwave/Imagers – Special Sensor
Microwave Image/Sounder (DMSP SSM/I-SSMIS) passive microwave data
(Cavalieri et al., 1996). Horizontal dashed lines correspond to the
climatological averages over 1998–2014 for each parameter. Remarkable years,
i.e. associated with values deviating by at least 2 standard deviations from
the climatological mean value, are highlighted with red shading (positive
anomalies) or blue shading (negative anomalies). The same figure with
standard deviations is available in the Supplement (Fig. S2).
Dated TA192A ice core annually averaged records over the period
1998–2014: accumulation, cm w.e. yr-1; concentrations of Na+
(ppb), nssSO4 (ppb), MSA (ppb), δ18O (‰),
and d-excess (‰). Horizontal
dashed lines correspond to 1998–2014 average values. Remarkable years
(i.e. associated with values deviating by at least 2 standard deviations from the
climatological mean value) are highlighted with red shading (positive
anomalies) or blue shading (negative anomalies). The same figure with
standard deviations is available in the Supplement (Fig. S3).
Pairwise linear regressions between variables
We performed pairwise linear regressions for all records (meteorological and
firn core records), using, on the one side, annual averages and, on the other
side, monthly or seasonal values. As previously observed by Comiso et al. (2017),
we report a significant anti-correlation between annual regional
sea ice concentration (i.e. 100–205∘ E, but not with other
sectors) and DDU near-surface air temperature (r=-0.56 and p<0.05).
This relationship is strongest in autumn (r=-0.75 and p<0.05),
where it holds for sea ice in all sectors, and disappears in spring
or summer. Confirming earlier studies (Minikin et al., 1998), we
observe a close correlation between annual concentrations of MSA and
nssSO4 (r=0.76, p<0.05). Statistically significant linear
relationships appear between the isotopic signals (δ18OTA
and d-excessTA) and nssSO4 in spring (r=0.65 and r=0.55 for
δ18OTA and d-excessTA respectively, p<0.05)
and only between d-excessTA and nssSO4 in autumn (r=0.65 and
p<0.05). We find no relationship between δ18OTA
and the DDU near-surface temperature. Our record depicts a significant
anti-correlation between annual values of SMBTA and d-excessTA
(r=-0.59 and p<0.05), as well as a significant correlation
between d-excessTA and Dumont d'Urville summer sea ice extent (r=0.65 and
p<0.05). Finally, a systematic positive significant correlation is
identified between d-excessTA and δ18OTA, except in
summer. It is the strongest in austral spring, with a correlation
coefficient of 0.75 (with a slope of 0.61 ‰ ‰-1).
In order to understand the specificities of the TA record, we explored the
temporal correlation between δ18O and d-excess from all
available Antarctic records (Table 5, cells in bold for significant
relationships), using all data points (in order to be able to exploit
non-dated depth profiles) as well as inter-annual variations, when
available. When focusing on significant results, we note that most
precipitation datasets depict an anti-correlation between d-excess and
δ18O (three out of nine precipitation records). Although we are
cautious with the short DDU precipitation time series (with only 19 points,
and p values = 0.08, cell in italics in Table 5), it shows a positive
relationship, similar to the one identified in the TA record. We conclude
that the positive correlation observed in the TA records is specific to the
coastal Adélie Land region, which is unusual in an Antarctic context.
The d-excess versus δ18O linear relationship of data from
our database provided with d-excess over the whole time series (left side of
the table) and over annual averages (inter-annual scale, right side of the
table): slope (‰ ‰-1),
correlation coefficient (r), p value (p val), and standard error of the
slope (SE, ‰ ‰-1). Cells
in bold show a significant relationship (p<0.05) and the cell in
italic is to be taken with caution (see DDU line, p<0.10).
Inter-annual relationships could not be computed for undated data (D15 and
OH ice cores) or for precipitation data monitored for too short of a time and thus appear as empty cells.
Whole time series Inter-annual scale NumberSlopeNumberSlopeof points(‰ ‰-1)Rp valSEof points(‰ ‰-1)rp valSEEDC Dome C140-0.41-0.310.000.11623-0.41-0.310.000.11NUS 08-72413-0.36-0.280.000.08626-0.36-0.280.000.08NUS 07-12990.170.250.150.122990.170.250.150.12NVFL-12330.470.400.000.022330.470.400.000.02WDC06A41 1200.250.250.000.0020560.050.060.010.02IND 25B512970.170.080.450.221400.170.080.450.22BI4040.010.020.700.0317-0.57-0.570.010.20KC3430.220.230.000.05480.050.060.700.12KM4250.040.060.240.0418-0.86-0.570.010.29DSSA161-0.03-0.040.580.056-0.41-0.740.040.15DE08-258-0.11-0.080.570.1958-0.11-0.080.570.19D15-1126-0.03-0.030.720.07D15-2191-0.06-0.100.190.04OH4318-0.11-0.110.130.07OH5213-0.05-0.040.450.07OH61240.010.010.860.08OH9232-0.05-0.040.570.08OH10190-0.10-0.040.580.17DDU190.480.410.080.26Dome C501-1.48-0.840.000.044-2.75-0.980.020.42Dome F351-1.60-0.890.000.05Halley532-0.20-0.180.000.0549-0.23-0.120.410.28Marsh19-0.86-0.510.030.35Neumayer336-0.06-0.070.210.05190.280.310.200.21Rothera194-1.00-0.580.000.1018-1.21-0.860.000.18Vernadsky372-1.33-0.570.000.0835-1.89-0.690.000.29Vostok27-0.73-0.630.000.18Remarkable years
Using only annual SMB, water stable isotope, and chemistry TA records, we
finally searched for remarkable years, defined here as deviating from the
1998–2014 mean value by at least 2 standard deviations. We highlight three
remarkable years (red-shaded for high values and blue-shaded for low values,
Figs. 6 and 7):
2007, with very low SMBTA;
2009, with remarkably high δ18OTA;
2011, with high MSA, Dumont d'Urville summer sea ice extent, and wind speed values.
We had previously noted that the years 2003 and 2004 are associated with very
high Na+ values and add that the year 1999 experienced low nssSO4 values.
In summary, we identify increasing trends in d-excessTA and sea ice
concentration, no significant correlation between δ18OTA
and DDU near-surface temperature, and an anti-correlation between
d-excessTA and SMBTA. We also note two remarkable years in
SMBTA (dry 2007) and δ18OTA (high 2009).
Finally, no systematic relationships are identified between chemistry and
water stable isotope signals (e.g. parallel trends, inter-annual
correlation, and remarkable years).
Intra-annual scaleMean cycles
The high resolution of the TA record allows us to describe the mean seasonal
cycles (Fig. 8), as well as to explore the inter-annual variability of the
seasonal cycle.
Mean seasonal cycles over the period 1998–2014. Meteorological
observations are averaged from daily data, for near-surface temperature (a,
∘C), relative humidity (b, i %), surface pressure (c, hPa),
wind speed (d, m s-1), wind direction (e, ∘E), and local sea ice
concentration, i.e. averaged over a 135–145∘ E ridge (f, %).
Seasonal cycles from ice core records are averaged from the resampled time
series for Na (g, ppb), nssSO4 (h, ppb), MSA (i, ppb),
δ18O (j, ‰), and d-excess (k, ‰).
The inter-annual standard deviation is highlighted with the grey shading.
Among the meteorological variables, only near-surface temperature, relative
humidity, and sea level pressure show a clear seasonal cycle. Temperature
(Fig. 8a) is minimum in July and maximum in January, while relative humidity
and pressure (Fig. 8b and c respectively) are minimum in spring (in
November and October respectively) and maximum in winter (in August and
June respectively), as reported in earlier studies (Pettré and
Périard, 1996). The average seasonal cycles of wind speed and wind
direction are flat but marked by large inter-annual variations (Fig. 8d and e).
Finally, the local sea ice concentration shows a rapid advance from
March to June, a plateauing from June to October, and a rapid retreat from
October to November, with a minimum in February (Fig. 8c), as previously
reported by Massom et al. (2013).
In the TA firn core, Na, nssSO4, and MSA show symmetric cycles with
minima in winter and maxima in summer (by construction of our timescale)
(Fig. 8g–i), consistent with previous studies of aerosols and ice core
signals (e.g. Preunkert et al., 2008). The δ18OTA seasonal cycle is surprisingly asymmetric (Fig. 8j), with a
maximum in December and a minimum in April, thus not in phase with the
seasonal cycles of local sea ice concentration (Fig. 8f) or DDU temperature
(Fig. 8a). The mean d-excessTA seasonal cycle (Fig. 8k) is marked by a
maximum in February, 2 months after the δ18OTA maximum,
and a minimum in October, 6 months after the δ18OTA
minimum. We then calculated the mean of the isotopic seasonal amplitudes,
preferentially to the amplitude of the mean seasonal cycle, due to the
different timing of peaks from one year to another. The mean δ18OTA
seasonal amplitude is 8.6‰±2.1 ‰, more than 3 times higher than found in the S1C1 ice core, and close to
the DSSA mean δ18O seasonal cycle of 8.0‰±2.8 ‰.
The mean d-excessTA seasonal amplitude is 6.5‰±2.8 ‰,
close to the DSSA value of 5.3‰±1.0 ‰. Compared with other precipitation and firn/ice
core isotopic data from other regions of Antarctica (Table S7), the average seasonal amplitude obtained from TA
δ18O is closest to the one obtained at KM, BI sites in Dronning
Maud Land, and Vernadsky or Rothera on the peninsula but is much larger than
identified from NUS 08-7 or WDC06A and significantly smaller than at Halley
(by a factor of almost 2), Neumayer (a factor of 2.3), Dome C, or Dome F (a
factor of ∼4). In addition to DSSA, the average seasonal
amplitude obtained from TA d-excess is also comparable to the one obtained
in the KM, BI, and IND25B5 firn cores in Dronning Maud Land but is
systematically higher (by a factor higher than 3) than in precipitation
datasets. This calls for systematic comparisons of d-excess seasonal
amplitudes in precipitation and snow data.
Due to their common symmetric aspect, significant positive linear
relationships emerge from the mean seasonal cycles of (i) temperature,
nssSO4, and MSA (r>0.93 and p<0.05), (ii) nssSO4
and MSA (r=0.97 and p<0.05), (iii) nssSO4 and
Na+ (r=0.93 and p<0.05), and finally (iv) δ18O
with nssSO4 (r=0.75 and p<0.05). Due to the asymmetry of
water stable isotope seasonal cycles, no linear relationship is detected
between the seasonal cycles of DDU near-surface temperature,
δ18OTA, and d-excessTA. Finally, the seasonal cycle of
d-excessTA is clearly anti-correlated with all sea ice concentration
indices (local, Indian, Amundsen, and regional), with correlation
coefficients varying between -0.83 and -0.80.
Inter-annual variability of peaks
Over the whole period covered by the TA firn core (1998–2014), the seasonal
cycle of δ18OTA shows a large inter-annual variability
(Table 6). δ18OTA maximum values occur primarily in summer
(41 % of the time) and winter (41 %) and more rarely in spring (12 %)
and in autumn (6 %). The same feature is observable for
d-excessTA, which most of the time has its maximum in summer (38 %)
and winter (43 %) and more scarcely in spring (6 %) and in autumn (13 %).
Fraction of annual maxima (%) of δ18O and
d-excess identified during each season: austral summer (DJF, i.e. from
December to February), austral autumn (MAM, i.e. from March to May),
austral winter (JJAS, i.e. from June to September), and austral spring
(ON, i.e. from October to November) in the data over the period
1998–2014 and in the ECHAM5-wiso simulation (model) over the period
1998–2013. The analysis is based on resampled data (ice core) and monthly
values (model).
In summary, the TA water stable isotope seasonal cycles display an
asymmetry, with higher isotopic values in austral spring than in austral
autumn. The d-excessTA seasonal cycle is anti-correlated with the
reconstructed SMB. Finally, the TA isotopic seasonal cycles show a high
inter-annual variability from one seasonal cycle to another one, with no
recurrent pattern between those of δ18OTA and
d-excessTA.
Influence of synoptic weather on TA records: insights from ECHAM5-wiso
simulation, ERA-Interim reanalyses, back trajectories, and modes of
variability
In order to explore the influence of the synoptic-scale weather on TA
records, we explore outputs of ECHAM5-wiso and back-trajectory calculations,
driven by atmospheric reanalyses. None of the associated atmospheric
simulations resolve local processes such as katabatic winds or sea
breeze. We used the ECHAM5-wiso model outputs to explore the following
questions: (i) do ECHAM5-wiso outputs show similarities with the corresponding
observed variables for their inter-annual variability, trends, and remarkable
years? (ii) What are the simulated seasonal cycles for δ18O and
d-excess? (iii) What are the simulated relationships between local
near-surface air temperature and δ18O and between δ18O
and d-excess at the seasonal and inter-annual scales? (iv) Are there
significant relationships between our isotopic records and the large-scale
climatic variability?
ECHAM5-wiso similarities with the corresponding observed variables
For inter-annual variations, the annual means of DDU near-surface
temperature and the simulated 2 m temperature
(2mTECH) are
significantly correlated (slope of 0.50±0.14, r=0.67, and
p<0.05). This relationship is valid for all seasons. It is the
strongest in winter (slope of 1.1±0.1∘C ∘C-1,
r=0.93 and p<0.05) and the weakest in summer (slope of
0.98±0.3∘C ∘C-1, r=0.69, and p<0.05).
There is no significant correlation between water stable isotope
records from the TA and simulated by the ECHAM5-wiso, for either δ18O or d-excess. Finally, we found no significant trend in any
model output over 1998–2014.
In terms of remarkable years, ECHAM5-wiso shows a low δ18OECH
mean value in 1998 and a high d-excessECH mean value
in 2007 (Fig. S4). Only the year 2007 is
remarkable in both the data (low reconstructed SMB) and the model. We thus
explored the model more deeply. The highest d-excess value was simulated
7 May (Table S8). When comparing
from 6 to 8 May 2007, with daily averages over
the period 1979–2014, the model simulates similar near-surface temperature
but particularly low precipitation and wind components (zonal and
meridional). Despite the small precipitation amount, the daily isotopic
anomaly is sufficiently large to drive the annual anomaly (Fig. S5). The d-excess values higher than 30 ‰ (threshold chosen as it corresponds to the maximum
d-excess mean + standard deviation simulated by ECHAM5-wiso over
Antarctica at the monthly scale; see Fig. S6)
occur only four other times over 1998–2014. We nevertheless remain cautious
with these values, which could be due to a numerical artefact.
t2mECH–δ18OECH, SMBECH–d-excessECH, and
δ18OECH–d-excessECH
relationships are not identified in ECHAM5-wiso seasonal or annual outputs.
Likewise, no significant relationship could be identified between
d-excessTA and SMB simulated by ERA using both annual and seasonal values.
Simulated seasonal cycles for δ18O and d-excess
We now explore the seasonal variations in δ18OECH and
d-excessECH over the period of simulation (1998–2014, Table 6). The
peaks in tδ18OECH predominantly occur in spring and
summer (25 % and 63 % respectively), while they only happen 12 % of
the time in winter and never in autumn. The d-excessECH peaks most
often in autumn (69 %) and secondarily in winter (31 %), but never
during the other seasons. As a result, the model simulates more regular
isotopic seasonal cycles with δ18O maxima during spring to
summer seasons and d-excess maxima during autumn to winter seasons, than
identified in the TA record.
Relationships with the large-scale climatic variability
The ERA-Interim outputs allow us to investigate whether the large-scale
climatic variability influences the isotopic composition of Adélie Land
precipitation recorded in the TA firn core. We looked at the simulated
linear relationships between the TA isotopic records (δ18OTA
and d-excessTA) with the ERA-Interim outputs (2mT,
u10, v10, and z500, Sect. 2.2). Here we report only significant
relationships with absolute correlation coefficients higher than 0.6. For
δ18OTA, we found a correlation with 2mT over the
Antarctic plateau (Fig. 12a), as well as a correlation with v10 (Fig. 12b)
along the westerly wind belt, at ∼ 55∘ S, 100∘ E
and ∼ 55∘ S, 130∘ E in
the Indian Ocean and at ∼ 55∘ S, 10–50∘ E
in the Atlantic Ocean, and a very little area on coastal
Dronning Maud Land at ∼ 60∘ S, 30–40∘ E.
For d-excessTA, we found a correlation with 2mT (Fig. 12c) toward
the Lambert Glacier at ∼ 70–80∘ S, 30–40∘ E
and an anti-correlation in the south of the peninsula at
∼ 55–65∘ S, 250–300∘ E. Finally, we
noted a correlation between d-excessTA and u10 (Fig. 12d) at a very
narrow area of Dronning Maud Land at ∼ 80∘ S,
10–20∘ E and an anti-correlation on the westerly wind belt in
the Atlantic Ocean at ∼ 55∘ S, 40–50∘ E. No correlation
is found with z500, δ18OTA, or
d-excessTA.
Note that no significant relationship is obtained between the TA records and
any mode of variability.
Origin of air masses
Finally, we used the HYSPLIT back-trajectory model to count the proportion
(percentage) of air mass back trajectories, based on daily calculations
over the period 1998–2014, and averaged at the annual and seasonal scale,
from four different regions (Sect. 2.3): the plateau, the eastern Atlantic
Ocean and the Indian Ocean (eastern sector), the Ross Sea sector (RSS), and
the West Antarctic Ice Sheet with the Pacific Ocean and the western Atlantic
Ocean (western sector), as displayed in Fig. 2. On average, the highest
annual proportion of air masses comes from the eastern sector (54.1±8.3 %)
and the East Antarctic plateau (32.5±4.9), while a small
proportion of air masses come from the western sector (9.7±3.7 %)
and from the RSS (3.6±1.3 %). A k-mean clustering over the
last points of the whole back trajectories indicate two main origins, in the
Indian Ocean (62.4∘ S, 131.7∘ E) and in the coastal
West Antarctic Ice Sheet (73.4∘ S, 227.5∘ E).
Inter-annual variations in back trajectories (Fig. 9b) reveal a positive
trend for the fraction of air masses coming from the western sector (slope of
0.41±0.16 % yr-1, r=0.55, and p<0.05) and
remarkable years: 1999, which was identified as having a remarkable high δ18O
value and low nssSO4 value in our TA records, is here associated
with a minimum of back trajectories from the plateau, and the year 2011, which
was associated with particularly high MSA in our TA record, shows a
particularly
low proportion of air masses coming from the eastern sector and a
particularly
high proportion of air masses coming from the Ross and western sectors.
Results of daily back-trajectory calculations over the period
1998–2014 with (i) the percentage (%) of the sum of back trajectories passing
over each defined region: (i) the eastern sector (0–66∘ S, 0–180∘ E),
(ii) the plateau (66–90∘ S, 0–180∘ E), (iii) the Ross
Sea sector (0–75∘ S, 180–240∘ E), and finally
(iv) the western sector (0–75∘ S,
180–240∘ E) and (50–90∘ S, 240–360∘ E)
(see Sect. 2.3), and with (ii) averages at the annual scale
(b) and at the mean seasonal scale (c).
In (b) and (c)) horizontal dashed
lines correspond to the mean value and vertical solid lines to standard
deviations. Remarkable years, i.e. associated with values deviating by at
least 2 standard deviations from the climatological mean value, are
highlighted with red shading (positive anomalies) or blue shading
(negative anomalies).
The seasonal cycles of back trajectories per region are shown in Fig. 9c.
The percentage of back trajectories coming from the plateau displays peaks in
autumn and spring (March and November), those from the Ross Sea sector in
winter and summer (January and June), those from the eastern sector in
autumn and winter (May and August), and finally those from the western
sector in spring (November). We note a significant linear correlation
between the seasonal cycles of the percentage of δ18O and
back trajectories coming from the Ross Sea sector (r=0.68 and p<0.05)
and from the western sector (r=0.59 and p<0.05) and
between the seasonal cycles of d-excess and the percentage of
back trajectories coming from the western sector (r=-0.67 and p<0.05).
Finally, we associated each daily back trajectory with daily precipitation
δ18O and d-excess values simulated by ECHAM5-wiso in the
precipitation and classified the time series for each variable by
back-trajectory sectors. We then computed the corresponding seasonal
cycles (Fig. 10). The mean δ18OECH value is slightly
as high for air masses coming from the eastern sector (-20.6 ‰
compared to -21.9±0.2 for the other sectors).
The asymmetry in δ18OECH is particularly well marked for
air masses coming from the Ross Sea and western sectors, with peaks in
August and September respectively (resulting in a winter amplitude more than
twice higher compared to the eastern and plateau sectors) and corresponding to
higher precipitation amounts during these months during the winter season.
The d-excess mean seasonal cycles substantially differ by their amplitude:
for air masses coming from the western sector, it is 11.8 ‰, with outstanding values in March and October
(minima) and in May (higher than the mean plus 2 standard deviations),
whereas it varies between 3.2 ‰ and 3.6 ‰ for the other sectors.
Seasonal cycles of precipitation (mm month-1). (a)δ18O
(‰, b), and d-excess (‰, c) simulated by
ECHAM5-wiso by back-trajectory regions: (i) the eastern
sector (0–66∘ S, 0–180∘ E), (ii) the plateau (66–90∘ S,
0–180∘ E), (iii) the Ross Sea sector (0–75∘ S, 180–240∘ E),
and finally (iv) the western sector
(0–75∘ S, 180–240∘ E) and (50–90∘ S, 240–360∘ E) (see Sect. 2.3).
The back trajectory of 7 May 2007 (shown to be remarkable
for simulated d-excess by ECHAM5-wiso) was identified as coming from the
western sector, but those associated with the four other remarkable simulated
d-excess (i.e. >30 ‰) indicate air masses
coming from the three other regions, and sometimes varying with the hour of the
day (Fig. S6).
In summary, we found a mismatch between ECHAM5-wiso outputs and the TA data
for d-excess variations. There are no similarities for trends, for seasonal
cycles, or for inter-annual isotopic variations. Similar to in the TA
firn core, ECHAM5-wiso simulates no δ18OECH – t2mECH
correlation but no d-excessECH – δ18OECH. Both TA
records and ECHAM5-wiso depict an unusual feature in 2007, with dry
conditions and high d-excess values. The comparison between TA records and
air mass back trajectories suggests that the asymmetry in the δ18O
seasonal cycle is due to the precipitation of air masses coming
from the western sector and that an increased occurrence of (rare) air
masses coming from the western sector is associated with high d-excess values.
DiscussionSMB
The estimated SMB of East Antarctica does not show a clear trend since 1900
(Favier et al., 2017). Recent studies (Altnau et al., 2015; Ekaykin et
al., 2017; Vega et al., 2016) report negative SMB trends in coastal areas
contrary to positive trends for the plateau. In particular, Thomas et al. (2017)
report an unprecedented negative trend observed in Victoria Land for
the last 50 years (1961–2010). For our study period (17 years for the TA
record and the ECHAM5-wiso simulation), we observe no significant trend.
In Adélie Land, a quality controlled SMB dataset has been developed
(Favier et al., 2013), but the drivers of SMB spatio-temporal
variability remain unexplored (Favier et al., 2017). This is related to
the challenges in monitoring (1) precipitation in windy areas, (2) sublimation
of precipitating snowflakes (Grazioli et al., 2017b) in the katabatic
flow, (3) and the amounts of surface erosion or deposition according to
surface wind convergence or divergence, of drifting snow fluxes, and of
sublimation of the drifting snow particles (Gallée et al., 2013; Amory
et al., 2017, 2016). The low correlation (over 1998–2006)
between TA192A annual accumulation and the first shallow ice core
(S1C1; Goursaud et al., 2017), collected 14 km from TA192A site,
demonstrates this complexity, even though this mismatch may be explained by
age scale uncertainties. The S1C1 reconstructed accumulation was also weakly
correlated with stake data and model outputs, reflecting the random snow
accumulation amounts due to the presence or absence of sastrugi and the
potential occurrence of annual erosion at the S1C1 site (Fig. S7). Here, the TA accumulation record is highly
correlated not only with the closest stake data, but also with the
ECHAM5-wiso model output over the period 2004–2014, showing the robustness of
our reconstruction for this period. The fact that the TA record, the
ECHAM5-wiso output for the corresponding grid point, and the 156 km
stake area data are pairwise correlated (0.79≤r≤0.90 and
p<0.05) indicates that the TA firn core captures a 100 km scale
regional signal. The differences between the local and regional SMB signal
are (Fig. 3b) (i) a higher local SMB average compared to the regional SMB
and (ii) the shift of the minimum peak of 2007 in the local signal (i.e. the TA
firn core and the 19.2 stake data) to 2008 in the regional signal (see
the 2007–2008 plateau in the 156 km network and the 2008 minimum value
simulated by the ECHAM5-wiso model in Fig. 3b).
The anti-correlation between the d-excessTA and SMBTA shows the
possibility of using water isotope firn core records from Adélie Land to
complete the document of the SMB spatio-temporal variability. Dry air masses
from the western sector may be associated with particularly high d-excess
values. The remaining uncertainty in the dating and the extraction of a pure
signal limited our investigation.
As a conclusion, the absence of similarity between the TA and the S1C1
accumulation reflects the uncertainty in the S1C1 dating resulting from the
large spatial variability and from more frequent erosion processes occurring
at the S1C1 site. More ice core records within a 100 km area will allow the
reduction of uncertainties in the interpretation of ice core signals, in
particular in the link with the atmospheric variability.
The δ18O–temperature relationship in coastal Antarctic
regions
Several studies have shown that the annual δ18O–temperature
relationship is weak in coastal regions. As an example, over Dronning Maud
Land, Isaksson and Karlén (1994) found a weaker correlation between
δ18O records and Halley temperature for coastal ice cores,
i.e. for sites below 1000 m a.s.l., with a correlation coefficient of 0.56
compared to a correlation coefficient of 0.91 for sites above 1000 a.s.l.
More recently, Abram et al. (2013) reported a coefficient correlation
of 0.52 for the relationship between δ18O recorded in the James
Ross Island ice core (at a high of 1524 m a.s.l., with a mean reconstructed
SMB of 63 cm w.e. yr-1) and the near-surface temperature measured at
Esperanza station (n=56 and p<0.05). In coastal West Antarctica,
Thomas et al. (2013) also reported a significant but weak correlation
between the δ18O recorded in an ice core drilled on the
Bryan coast and the near-surface temperature simulated by ERA-Interim, over
the period 1979–2009. Closer to Adélie Land, in Victoria Land,
Bertler et al. (2011) found a correlation coefficient of 0.35 between
the δ18O recorded in the Victoria Lower Glacier ice core (at a
high of 626 m a.s.l.) and the summer near-surface temperature measured at
Scott Base station (n=30 and p<0.0005). In this study, we find no
relationship between the DDU near-surface temperature and the δ18OTA,
based on annual averages. Similarly, no relationship had
been identified in the S1C1 core (Goursaud et al., 2017) and was here
not simulated by the ECHAM5-wiso model.
Our study shows that changes in air mass trajectories (dynamics) may
dominate over thermodynamical controls (condensation temperature) on the coastal
Adélie Land δ18O signal, as shown by the asymmetry of the
δ18O seasonal cycle recorded in the TA firn core (Sect. 3.4
and Fig. 8). The coupling of calculations of air mass back trajectories and
ECHAM5-wiso outputs suggests that δ18O significant high values
occurring during wintertime would be brought by air masses coming from the
western sector (Sect. 3.5 and Fig. 10).
The δ18O measured in the ice of coastal Adélie Land may
thus not allow the reconstruction of surface temperatures of this region. However,
correlations between δ18OTA and the 2 m temperature by
ERA-Interim over each grid point of Antarctica (Fig. 12a) show significant
relationships over the plateau, confirmed by a significant correlation
between annual δ18OTA and the near-surface temperature
measured at Dome C over the period 1998–2014 (slope of
0.70‰±0.29 ‰ ∘C-1,
r=0.53, p<0.05).
These results support previous studies suggesting warm intrusions offshore of
Dumont d'Urville towards Dome C (Naithani et al., 2002).
Finally, the significant linear relationships with the u10 wind component
above the westerly wind belt and at some coastal Antarctic area (Fig. 12b)
stress the influence of processes other than thermodynamic driving the
isotopic composition of Adélie Land precipitations.
Water stable isotope, a fingerprint of changes in air mass
origins
The mean d-excessTA is 5.4‰±1.0 ‰, close to
the 4.7‰±0.4 ‰ value simulated by the ECHAM5-wiso
model for the coastal Indian region defined in Goursaud et
al. (2017, different definition than in this
study), and the 5.2‰±0.6 ‰ value for the grid
point corresponding to the TA drilling site. Inter-annual variations in
d-excessTA (Fig. 7) are anti-correlated with TA reconstructed SMB, a
feature not depicted by ECHAM5-wiso.
Main trajectories imprinted in the TA isotopic records
We suggest that air masses associated with small or large precipitation amounts
are associated with different trajectories and moisture sources: main air
mass origins from the Indian Ocean. These maritime air masses, isotopically
enriched, may transport water vapour to the plateau, as shown by the
significant relationship between δ18OTA and the
near-surface temperature of the plateau, especially in winter. Rare dry air
masses may also come from the western sector, with a signal preserved in
d-excess. The following is evidence of the last point:
the positive trends both for the TA d-excess and the percentage of air
masses coming from the western sector;
an anti-correlation between the seasonal cycles of the TA d-excess and
percentages of air masses coming from the western sector;
the high simulated d-excess amplitude simulated by ECHAM5-wiso for air
masses coming from the western sector, reflecting outstanding values
occurring in autumn and winter.
The particular case of 7 May 2007 with very high d-excess
values simulated by ECHAM5-wiso corresponds to an air mass trajectory
from the western sector.
As discussed earlier, the last item should be considered with caution as the
four other remarkable d-excess values (i.e. higher than 30 ‰)
simulated by ECHAM5-wiso are associated with air
masses coming from other regions (Fig. S6) and
also with the fact that such high values could be due to potential numerical
artefacts.
Linear relationships between d-excessTA and ERA-Interim outputs
strengthen the link between the climate variability of western Antarctic and
associated southern oceans, as we note an anti-correlation between
d-excessTA and the 2mT in the south of the peninsula, the Ellsworth
region, and the Bellingshausen Sea (r>0.6). We also note an
anti-correlation between d-excessTA and the u10 wind component over the
coastal Ross Sea sector, consistent with air mass trajectories coming from
western Antarctica towards Adélie Land via the Ross Sea sector. These
dry air masses might originate from the Amundsen and Bellingshausen seas
(Emanuelsson et al., 2018; Winstrup et al., 2017) but cannot be
directly linked to the Amundsen Sea cyclonic, as we obtain no significant
relationship with the ASL center pressure indices.
Potential interaction with sea ice
Noone and Simmonds (2004) have shown, thanks to climate modelling, that
water stable isotopes were conditioned by changes in sea ice extent (a
contraction in sea ice increases the local latent heat and temperature due
to open water) but confirmed that a thorough understanding of the main
mechanisms controlling the d-excess was still needed. Also, earlier studies
have suggested the use of d-excess ice core records to reconstruct past sea
ice extent (e.g. Sinclair et al., 2014). Although we find a
significant correlation between the d-excessTA and the Dumont d'Urville summer
sea ice extent (Sect. 3.3), a correlation map between the annual
d-excessTA and the summer sea ice concentration
(Fig. S8)
shows significant correlations with further sea ice
areas (e.g. an anti-correlation in the Amundsen Sea and correlations in the
Bellingshausen, Scotia, and Lazarev seas). We also noted a coincidence between
the sign of the correlation of the relationship between the d-excessTA
and the sea ice concentration and the sea ice extent trend over the period
1998–2014 (Fig. S9), especially positive
correlations (negative) associated with positive sea ice concentration trends.
These findings call for mechanistic studies to understand the different
processes behind d-excess associated with each air mass origin.
As we suggested a particular d-excess signature in the TA firn core,
associated with air masses coming from the western sector, we tested the
possibility for the d-excessTA to imprint changes in the Ross polynya.
We thus estimated it, by counting the annual sea ice concentration over the
polygon (60–70∘ S; 150–210∘ E), to be lower than 15 %.
But we find no significant correlation between this estimated Ross
polynya and the d-excessTA over the period 1998–2014.
The δ18O–δD relationship
Earlier studies showed empirically that the relationship between d-excess
and δ18O, and mainly the phase lag between signals within the
seasonal cycle, may indicate variations in the origin of the moisture source.
This phage lag was shown to be of ∼3–4 months over coastal
regions such as Law Dome (Masson-Delmotte et al., 2003), Dronning Maud
Land (Vega et al., 2016) and the Ross Sea sector (Sinclair et
al., 2014). By contrast, most studies identified an anti-phase over the East
Antarctic plateau (e.g. Landais et al., 2012; Ciais et al., 1995),
and at D47, situated close to the TA drilling site (Ciais et al., 1995).
We thus focus on the outcome of the running linear regression between
d-excessTA and δ18OTA over 10 points all along the
core (Fig. 11). We focus on the periods (53.3 %) when a significant linear
relationship is identified (i.e. p<0.05). The time-averaged
correlation coefficient is 0.71±0.45, which is consistent with the
results obtained from the annual averages (varying from 0.51 in autumn to
0.75 in spring, Sect. 3.3). The time-averaged slope is
0.83‰‰-1±0.83 ‰ ‰-1.
These positive values
prevail for 91.5 % of the significant linear regressions. However, we
observe remarkable deviations from this overall relationship. In particular,
linear regressions within the year 2007 show slopes lower than the time averaged
minus 2 standard deviations (with a minimum value of
-1.46 ‰ ‰-1), and others within the
year 2011 show surprisingly very high slopes up to 6.9 ‰ ‰-1.
The years 2007 and 2011 were also previously
noticed: the mean d-excessECH simulated by ECHAM5-wiso for the year
2007 was shown to be driven by the high value occurring on 7 May,
associated with air masses coming from the western sector. The year 2011 is
associated with a minimum of annual back-trajectory percentages from the
eastern sector and maxima of back trajectories from the Ross Sea and the western
sector. As a result, the δ18O–d-excess relationship may be a
fingerprint of changes in air mass origins, and particularly of the
occurrence of precipitation of air masses coming from the western sector.
The 10-point running slope (‰ ‰-1) and running correlation coefficient
calculated between d-excess versus δ18O calculated from the raw
data of TA192A. Significant results are indicated by thick lines (p<0.05).
The date associated with the results corresponds to the first point of
the regression calculation (applied to 10 points).
Coefficient correlation between the TA isotopic records and
ERA-Interim reanalyses, (a) between δ18OTA and the 2 m
temperature, (b) between δ18OTA and the 10 m v wind component,
(c) between d-excessTA and the 2 m temperature, and finally (d) between
d-excessTA and the 10 m u wind component. The blue point locates the
TA192A drilling site, and the blue lines contour the significant
correlations.
We undertook the same exercise with outputs of the ECHAM5-wiso model (Fig. S10),
where only 19.2 % of the simulated
linear regressions are significant (i.e. p<0.05). All significant
relationships have negative correlation coefficients and slopes of
time-averaged values -0.72‰‰-1±0.25 ‰ ‰-1 and -0.39‰‰-1±0.23 ‰ ‰-1
respectively (this is consistent with the
annual means, Sect. 3.4). Moreover, these significant relationships do not
occur during the remarkable years 2007 and 2011 identified in the TA firn core.
As a result, we propose that remarkable anomalies in
d-excess / δ18O running linear relationships provide an isotopic fingerprint
associated with changes in dominant air mass trajectories. But a more
comprehensive mechanistic study would be necessary to quantify the
fractionation processes associated with different moisture source and
transport characteristics.
Limits associated with model–data isotopic comparisons
We note a mismatch between ECHAM5-wiso outputs and the data (Sect. 3.5 and
Fig. S4). This could be related to (i) post-deposition processes associated with wind scoring or snow metamorphism
not resolved in ECHAM5-wiso, (ii) the key role of very local atmospheric
circulation effects related to katabatic wind processes, not resolved in
large-scale atmospheric reanalyses and simulations, (iii) or the
difficulties of ECHAM5-wiso to resolve the processes associated with the
ocean boundary vapour d-excess, a mismatch already identified in the Arctic
(Steen-Larsen et al., 2017).
The first issue is related with the robustness of records from a single
coastal firn core. Several studies have evidenced signal-to-noise limits
(e.g. Mulvaney et al., 2002; Graf et al., 2002). Given the high SMB
estimated from TA, diffusion effects can be ignored (Frezzotti et al.,
2007), and the estimated inter-annual variations in TA SMB are closely
correlated not only with stake data closest to the drilling site, but also
with the 156 km network stake data and to precipitation from the
corresponding grid point of ECHAM5-wiso within a 100km×100 km area. This
finding supports an interpretation of the TA record being representative of
a regional SBM signal (100 km scale). However, we cannot draw any conclusion
of the signal-to-noise aspects of the water stable isotope records, given
the lack of coherency between the inter-annual variability in the TA and
S1C1 δ18O records for the few years of overlap (unfortunately,
there are no striking features during the common period records, which makes
it challenging to match the isotope records) and the lack of any other
d-excess record within hundreds of kilometers.
The second source of uncertainty lies in the mismatch between inter-annual
variations from coastal Adélie Land meteorological observations and the
TA records, with ECHAM5-wiso outputs. For instance, we only see high
correlation for the surface air temperature inter-annual variations for
winter and weak correlation for wind speed in spring and summer. These
findings suggest limitations in the skills of either atmospheric reanalyses
or the ECHAM5-wiso model to correctly capture the processes responsible for
local climate variability. We had previously reported the capability of
ECHAM5-wiso to correctly simulate observed large-scale features of water
stable isotopes and SMB across Antarctica, for spatio-temporal patterns
identified from datasets spanning the last decades such as mean values,
amplitudes and phases of mean seasonal cycles, amplitude of inter-annual
variance, strength of isotope–temperature relationships, and d-excess versus
δ18O relationships) (Goursaud et al.,
2017). We thus highlight here specific challenges related to the Antarctic
coastline, where local processes associated with katabatic winds, open water
(e.g. polynya), and local boundary layer processes (e.g. snow drift) may
affect isotopic records without being resolved at the resolution of
reanalyses and ECHAM5-wiso simulation.
Our study therefore depicts limited understanding of the drivers of seasonal
and inter-annual variability in the coastal Adélie Land hydrological cycle
and thus calls for more isotopic measurements (from ice cores, snow
precipitation, and water vapour) in Adélie Land to reduce uncertainties.
Chemistry
We compare the chemical concentrations recorded in the TA firn core with
the S1C1 core (Goursaud et al., 2017), for their common period (1998–2006).
The mean concentrations are slightly lower for the TA than the S1C1 firn
core from 30 % for Na+ to 50 % for MSA (Table S9). This decrease with the increasing distance from the
coast (or elevation above sea level) is consistent with atmospheric studies
showing a decrease in levels from the coast to the plateau for sea salt
(Legrand et al., 2017b) and sulfur aerosols (Legrand et al.,
2017a). No significant linear regression emerges from any chemical species,
highlighting a high spatial variability and/or the uncertainty in the dating
of the S1C1 firn core.
Finally, we initially processed chemical measurements in our firn core to
support the isotopic records not only for dating, but also to identify air
mass origins, making the hypothesis of three possible cases. (i) Air masses
formed near the sea ice margin may be associated with relatively high
d-excess and δ18O values, due to respectively a high kinetic
fractionation due to evaporation under low humidity levels and limited
distillation effects. Such a configuration should be associated with low
sea-salt concentrations due to the presence of the sea ice, as shown by
atmospheric studies (Legrand et al., 2016). (ii) In contrast, vapour
formed over the ocean in the absence of sea ice may be associated with high
δ18O values, low d-excess, and high sea-salt concentrations.
(iii) Finally, air masses from central Antarctica may be associated with
depleted δ18O values and high d-excess, while air masses from
ocean regions may lead to intermediate δ18O and d-excess
values, due to distillation effects, and evaporation under relatively humid
conditions, but with low sea-salt concentrations.
The period from December 2003 to February 2004, associated with Na+
values higher than the mean plus 5 standard deviations, probably caused
by marine advections, is not distinguishable in the isotopic records. None
of the three aforementioned cases were systematically observed.
To make it short, taking into account the definition of only summer observations
does not alter our results. The sea-salt and sulfur concentrations
measured along the TA records are slightly lower compared to the S1C1 firn
core, consistent with the coast-to-plateau depletion previously observed
in atmospheric measurement. Unfortunately, we could not use the sea-salt
measurements to support our hypotheses regarding the air mass origins
associated with isotopic compositions.
Conclusions and perspectives
In this study, we report the analysis of the first highly resolved firn core
drilled in Adélie Land covering the very recent period 1998–2014, with a
sub-annual resolution. The chronology was based on chemical tracers (Na,
nssSO4, and MSA) and adjusted by 1 year based on stake area
information. Three δ18O peaks found no counterparts in the
chemical records. The high estimated SMB rate of 74.1±14.1 cm w.e. yr-1
limits the effects of diffusion and ensures that records with
sub-annual resolution are preserved (e.g. Johnsen, 1977). The good
consistency of the estimated annual SMB variations with observations on
stakes reflects that high accumulation amounts are needed to ensure that
small-scale SMB random variability caused by presence of sastrugi, dunes, and
barchans is negligible when compared to the mean accumulation value. This
condition allows us to avoid the erosion of seasonal or annual layers, which
would lead to removal of the annual cycle of the recorded signal. For this
reason, obtaining long-term observations on distributed stake networks around
a drilling site or ground-penetrating radar data is crucial to accurately
select a drilling site, by retrieving the location of mesoscale accumulation
maxima and by rejecting zones with potential erosion.
Using an updated database of Antarctic surface snow isotopes, we showed that
not only δ18OTA but also d-excessTA mean values are
in line with the range of coastal values in other locations.
Neither in the TA–DDU dataset nor in the ECHAM5-wiso output do we see
any significant relationship between inter-annual variations in δ18O
and local surface air temperature. The anti-correlation between
annual reconstructed SMBTA and d-excessTA leads us to suggest that
changes in large-scale atmospheric transport could lead to an explanation
for this feature. In particular back-trajectory simulations from HYSPLIT and
atmospheric outputs from ECHAM5-wiso at the seasonal cycle show the
occurrence of air masses coming from the western sector during autumn and
winter, corresponding to high simulated d-excess values. Also, the
identification of remarkable years in both back-trajectory percentages and the relationships between d-excess and δ18O also leads us to
evidence a potential in the d-excess–δ18O to identify
remarkable features in moisture transport.
We cannot explain at this stage the observed positive trends in the
d-excessTA. We suggest that an improved understanding of the drivers of
moisture transport towards coastal Adélie Land can benefit from the
interpretation of water stable isotope tracers, especially d-excess, through
mechanistic studies and the exploration of global atmospheric models. Ways
forward include a better documentation of the spatio-temporal variability in
SMB and water stable isotopes using a matrix of coastal firn core records
spanning longer periods over the last decades (17 points being small to
assess linear relationships and record climate shifts, e.g. the IPO shift
occurring in 1998; Turner et al., 2016); a better documentation of the
relationships between precipitation and ice core records through the
monitoring of the isotopic composition of surface vapour and precipitation
snow and firn (Casado et al., 2016; Ritter et al., 2016); and the
implementation of water stable isotopes in regional models resolving the key
missing processes linked for instance with katabatic winds, boundary layer
processes, and wind drift (Gallée et al., 2013).
Data availability
The TA192A isotope and chemical firn core records were archived in the
PANGAEA data library at 10.1594/PANGAEA.896623 (Goursaud et al., 2018b).
The supplement related to this article is available online at: https://doi.org/10.5194/tc-13-1297-2019-supplement.
Author contributions
ML and SP sampled the ice core
and monitored the chemical measurements. BM monitored the water
stable measurements. VF provided the accumulation data from the
stake measurements. MW performed the ECHAM5-wiso simulations.
SG dated the firn core, analysed the data, and wrote the
paper with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
This study has been supported by the ASUMA project supported by the ANR
(Agence Nationale de la Recherche, project no. ANR-14-CE01-0001),
which funded the PhD grant of Sentia Goursaud and the publication costs of
this paper. The authors also acknowledge the support from Institut
Paul-Emile Victor (IPEV) for the surface mass balance observatory in
Antarctica (GLACIOCLIM-SAMBA). This project also received support from the
Investissements d'Avenir project EquipEX “Equipement d'Excellence” CLIMCOR
(ANR-11-EQPX-0009-CLIMCOR).
Review statement
This paper was edited by Becky Alexander and reviewed by Elizabeth Thomas, Daniel Emanuelsson, and one anonymous referee.
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