Polar ice core water isotope records are commonly used to infer past changes in Antarctic
temperature, motivating an improved understanding and quantification of the
temporal relationship between δ18O and temperature. This can
be achieved using simulations performed by atmospheric general circulation
models equipped with water stable isotopes. Here, we evaluate the skills of
the high-resolution water-isotope-enabled atmospheric general circulation
model ECHAM5-wiso (the European Centre Hamburg Model) nudged to European
Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis using
simulations covering the period 1960–2013 over the Antarctic continent.
We compare model outputs with field data, first with a focus on regional
climate variables and second on water stable isotopes, using our updated
dataset of water stable isotope measurements from precipitation, snow, and
firn–ice core samples. ECHAM5-wiso simulates a large increase in temperature
from 1978 to 1979, possibly caused by a discontinuity in the European
Reanalyses (ERA) linked to the assimilation of remote sensing data starting
in 1979.
Although some model–data mismatches are observed, the (precipitation minus
evaporation) outputs are found to be realistic products for surface mass
balance. A warm model bias over central East Antarctica and a cold model bias
over coastal regions explain first-order δ18O model biases
by too-strong isotopic depletion on coastal areas and underestimated
depletion inland. At the second order, despite these biases, ECHAM5-wiso
correctly captures the observed spatial patterns of deuterium excess. The
results of model–data comparisons for the inter-annual δ18O
standard deviation differ when using precipitation or ice core data. Further
studies should explore the importance of deposition and post-deposition
processes affecting ice core signals and not resolved in the model.
These results build trust in the use of ECHAM5-wiso outputs to investigate
the spatial, seasonal, and inter-annual δ18O–temperature
relationships. We thus make the first Antarctica-wide synthesis of prior
results. First, we show that local spatial or seasonal slopes are not a
correct surrogate for inter-annual temporal slopes, leading to the conclusion
that the same isotope–temperature slope cannot be applied for the climatic
interpretation of Antarctic ice core for all timescales. Finally, we explore
the phasing between the seasonal cycles of deuterium excess and
δ18O as a source of information on changes in moisture
sources affecting the δ18O–temperature relationship. The few
available records and ECHAM5-wiso show different phase relationships in
coastal, intermediate, and central regions.
This work evaluates the use of the ECHAM5-wiso model as a tool for the
investigation of water stable isotopes in Antarctic precipitation and calls
for extended studies to improve our understanding of such proxies.
Introduction
The Antarctic climate has been monitored from sparse weather stations
providing instrumental records starting at best in 1957 (Nicolas and
Bromwich, 2014). Water stable isotopes in Antarctic ice cores are key to
expanding the documentation of spatio-temporal changes in polar climate and the
hydrologic cycle (Jouzel et al., 1997) for the recent past (PAGES
2k Consortium, 2013; Stenni et al., 2017) as well as for
glacial–interglacial variations (Jouzel et al., 2007; Schoenemann et al.,
2014). Water stable isotopes measured along ice cores were initially used to
infer Antarctic past temperatures using the spatial isotope–temperature
slope (Lorius et al., 1969). The focus on inter-annual variations is
motivated by the goal of quantifying temperature changes at the Earth's
surface, including Antarctica, during the last millennia, to place current
changes in the perspective of recent natural climate variability (Jones
et al., 2016), to understand the drivers of this variability, and to test
the ability of climate models to correctly represent it. This timescale is
relevant for the response of the Antarctic climate to e.g. volcanic forcing
and for the Antarctic climate fingerprint of large-scale modes of
variability such as ENSO and the Southern Annular Mode (Smith and
Stearns, 1993; Turner, 2004; Stammerjohn et al., 2008; Schroeter et al.,
2017). The various climate signals potentially recorded in precipitation
isotopic composition are, however, difficult to disentangle.
First, the original signal from precipitation may be altered due to
deposition and post-deposition processes (e.g. Sokratov and Golubev,
2009; Jones et al., 2017; Münch et al., 2017; Laepple et al., 2018).
Wind erosion and sublimation during or after precipitation have long been
known to affect ice core records (Eisen et al., 2008; Grazioli et al.,
2017). Other processes such as melt and diffusion can also alter
the preservation of isotopic signals in firn and ice and cause smoothing of
the initial snowfall signals (Johnsen, 1977; Whillans and Grootes, 1985;
Johnsen et al., 2000; Jones et al., 2017). So far, the mechanisms of such
post-deposition processes on the alteration of the initial precipitation
signals are not fully understood and quantified (Touzeau et al., 2017). Second, the Antarctic snowfall
isotopic composition may be affected by the origin of moisture and the
associated evaporation conditions, changes in the relationship between
condensation and surface temperature, or changes in the
intermittency of precipitation (e.g. Sime et al., 2009; Hoshina et al.,
2014; Touzeau et al., 2016). Although the surface snow isotopic composition
signal has classically been interpreted as a precipitation-weighted
deposition signal (Krinner and Werner, 2003), recent studies evidenced
isotopic exchanges between the Antarctic snow surface and the atmosphere
associated with snow metamorphism occurring at diurnal and sub-annual
scales (Steen-Larsen et al., 2014; Casado et al., 2016; Ritter et al.,
2016; Touzeau et al., 2016).
Second, the climatic interpretation of water stable isotopes in Antarctic ice
cores is still challenging. Quantitative approaches have relied on empirical
relationships and the use of theoretical and atmospheric models
including water stable isotopes. Pioneer studies evidenced a close linear
relationship between the spatial distribution of water stable isotopes and
local temperature (e.g. Lorius and Merlivat, 1975) and explained this
feature as the result of the distillation along air mass trajectories.
Thereupon, local temperature (i.e. at a specific site) was reconstructed
using δ18O measurements and based on the slope of the
aforementioned spatial empirical relationship as a surrogate for
relationships at annual to multi-annual scales. However, recent data
syntheses have shown that other effects had to be taken into account (e.g.
Masson-Delmotte et al., 2008). It was found that Antarctic snowfall
isotopic composition is also linked to the initial vapour isotopic
composition (Stenni et al., 2016), atmospheric transport pathways (Schlosser
et al., 2008; Dittmann et al., 2016), Antarctic sea ice extent (Bromwich and
Weaver, 1983; Noone and Simmonds, 2004; Holloway et al., 2016), and local
condensation temperature, which is itself related to surface temperature through
complex boundary layer processes (Krinner et al., 2007). Evaporation
conditions, transport, and boundary layer processes may vary through time
from seasonal (Fernandoy et al., 2018) to annual or multi-annual scale,
thereby potentially distorting the quantitative relationship between snow
isotopic composition and local surface air temperature estimated empirically
for present-day conditions (Jouzel et al., 1997).
Model studies have been key to quantitatively exploring the spatio-temporal
aspects of the relationships between precipitation isotopic composition and
temperature (Jouzel et al., 2000). Mixed-cloud isotopic models have been used
to propose a coherent interpretation of δ18O and δD data in terms of
changes in site and source temperatures (Uemura et al., 2012) or to simulate
isotopic variations along individual atmospheric trajectories (Dittmann et
al., 2016). However, such theoretical distillation models rely on the closure
assumption at the ocean surface to calculate the initial evaporation isotopic
composition and do not account for atmospheric dynamics and mixing of air
masses (Jouzel and Koster, 1996; Delmotte et al., 2000). Atmospheric general
circulation models equipped with water stable isotopes offer a physically
coherent, three-dimensional framework to investigate the weather and climate
drivers of Antarctic precipitation isotopic composition (Jouzel et al.,
2000). They play a key role in assessing how different boundary conditions
(e.g. changes in orbital forcing, changes in atmospheric greenhouse gas
concentration) affect the simulated relationships between precipitation
isotopic composition and climate variables. Most of these simulations support
the idea that the present-day isotope–temperature spatial relationship is a good
approximation for the relationships between glacial conditions and today
(Delaygue et al., 2000; Werner et al., 2018), with one exception (Lee et al.,
2008). One study used climate projections in response to increased
atmospheric CO2 concentration to explore isotope–temperature
relationships in a world warmer than today and suggested a changing temporal
isotope–temperature relationship due to changing covariance between
temperature and precipitation (Sime et al., 2009). Several observational and
modelling studies have also evidenced different isotope–temperature
relationships between the spatial relationship and those calculated at the
seasonal (Morgan and van Ommen, 1997) or inter-annual scale (Schmidt
et al., 2007).
Our study is motivated by the need for a synthesis over all of Antarctica
using a proper interpretation of processes that affect water stable isotopes
on the appropriate spatial and temporal scales. It aims to address the
following questions: (i) what is the performance of a state-of-the-art
atmospheric general circulation model with respect to existing Antarctic
observations of spatio-temporal variations in temperature, surface mass
balance, precipitation, and snow isotopic composition for present day? (ii) What can we learn from such a model for the regional relationships between
isotopic composition from the precipitation and temperature at the
inter-annual scale for the recent past and considering all of Antarctica?
Spatial distribution of Antarctica in seven regions: East Antarctic
Plateau, coastal Indian, Weddell sea, West Antarctic Ice Sheet, Victoria
Land, and Dronning Maud Land. The location of the selected READER
surface stations: Neumayer, Mawson, Vostok, Dome C, Casey, Dumont d'Urville
(noted as “DDU”), McMurdo, Byrd, Palmer, and Esperanza.
For this purpose, we focus on the high-resolution atmospheric general
circulation model equipped with water stable isotopes, ECHAM5-wiso (the
European Centre Hamburg model), which demonstrated remarkable skills for
Antarctica (Werner et al., 2011). We explore a simulation performed for the
period 1960–2013 in which the atmospheric model is nudged to the European
Reanalyses (ERA) ERA-40 and ERA-Interim (Uppala et al., 2005),
ensuring that the day-to-day simulated variations are coherent with the
observed day-to-day variations in synoptic weather and atmospheric
circulation (see Butzin et al., 2014, for more explanation). This framework is
crucial to performing comparisons between simulations and observations for
temporal variations. Second, we compile a database of precipitation, snow,
and firn–ice isotopic composition using data from precipitation sampling and
ice core records and considering δ18O and deuterium excess
(hereafter, d). These methods are described in Sect. 2. We then compare the
model outputs with the available datasets (Sect. 3). After evaluating the
near-surface temperature and the surface mass balance (hereafter SMB; Sect. 3.1), we focus on the water stable isotopes (Sect. 3.2). We emphasise
spatial patterns, the magnitude of inter-annual variability (Sect. 3.2.1 and
3.2.4), and the pattern and the amplitude of seasonal variations (Sect. 3.2.2 and
3.2.4). We explore the simulated and estimated isotope–temperature
relationships (Sect. 3.2.3) and the relationships between d and
δ18O (Sect. 3.2.3). Highlighting the strengths and
limitations of the model (Sect. 3.3), we use the simulation framework to
explore the δ18O–temperature relationship (Sect. 4.1) and
the phase lag between seasonal variations in d and δ18O
(Sect. 4.2). Finally, we focus on the implications of our results for the
climatic interpretation of water stable isotope records for seven Antarctic
regions (central plateau, coastal Indian, Weddell Sea coast, West Antarctic
Ice Sheet, Victoria Land, and Dronning Maud Land). The Antarctica2k
group (Stenni et al., 2017) indeed identified these seven Antarctic regions,
which are geographically and climatically consistent, to produce regional
temperature reconstructions using ice core records. The results of our study
thus contribute to the reconstruction of past Antarctic climate spanning the
last 2000 years (the Antarctica2k initiative) of the Past Global Changes
(PAGES) PAGES2K project (PAGES 2k Consortium, 2013) by providing
quantitative calibrations of the regional temperature reconstructions using
ice core water stable isotope records.
Material and methodsObservations and reanalysis productsTemperature and surface mass balance instrumental records
Station temperature records have been extracted from the READER database
(https://legacy.bas.ac.uk/met/READER, last access: August 2017; Turner et al., 2004).
We have
selected surface station data following two conditions: to cover the seven
Antarctic regions aforementioned (see Sect. 1 and Fig. 1) with at least
one station for each and to cover the period 1960–2013. As a result, we
have selected Neumayer, Mawson, Vostok, Casey, Dumont d'Urville (hereafter
DDU), McMurdo, Palmer, and Esperanza station surface data. Due to the short
duration of surface station records for the 90–180∘ W sector, we
have added data from the automatic weather station (hereafter, AWS) of Dome
C, but we have used it with caution as these records are associated with a
warm bias in thermistor measurements due to solar radiation when the wind
speed is low (Genthon et al., 2011). Finally, we extracted the
reconstruction of temperature for Byrd station by Bromwich et al. (2013) based on AWS data and infilled with observational reanalysis data.
No record meets our criteria for the Weddell Sea coast region (Fig. 1).
SMB data have been extracted from the quality-controlled GLACIOCLIM-SAMBA
(GC) database (Favier et al., 2013). We have selected data spanning
the twentieth century, corresponding to 3242 punctual values, which have
then been clustered within the corresponding ECHAM5-wiso grid cells for
the calculation of gridded annual average values. As described by Favier et al. (2013), the spatial
coverage of SMB field data is particularly poor
in the Antarctic Peninsula, in West Antarctica, and along the margins of the ice
sheet. As a result, SMB is not correctly sampled at elevations between 200
and 1000 m a.s.l., where accumulation rates are the highest. In central
Antarctica, areas characterised by wind glaze and megadunes are also
insufficiently documented.
ERA reanalyses
The ECHAM5-wiso model run for this study is nudged to ERA-40 (Uppala et al., 2005) and ERA-Interim (Dee et al., 2011) global atmospheric
reanalyses produced by the European Centre for Medium-Range Weather
Forecasts (ECMWF). ERA-40 covers the period 1957–2002 at a daily resolution,
with a spatial resolution of 125 km × 125 km. ERA-Interim covers the period
1979 to present at a 6-hourly resolution, with a spatial resolution of
0.75∘× 0.75∘.
For comparison with instrumental records and ECHAM5-wiso outputs, we have
extracted 2 m temperature outputs (hereafter 2 m-T) over the periods
1960–1978 and 1979–2013 for ERA-40 and ERA-Interim, respectively, at grid
cells closest to the stations where meteorological measurements have been
selected (see previous section). We have then calculated annual averages.
A database of Antarctic water stable isotopic composition from
precipitation, surface snow, and firn–ice core records
This database consists of water stable isotope measurements performed on
different types of samples (precipitation, surface snow, or shallow ice
cores) and at different time resolutions (sub-annual, annual, or multi-annual
average values; see Table S1 in the Supplement). Sample data consist of
δ18O and/or δD, providing d, if both δ18O
and δD have been measured. Altogether, we have gathered data from the
following.
A total of 101 high-resolution ice core records, including 79 annually resolved
records and 18 records with sub-annual resolution (including 5 records with
both δ18O and δD data). These data have been
extracted from the Antarctica2k data synthesis (Stenni et al., 2017) with a filter
for records spanning the interval 1979–2013, thus restricting the original
122 ice cores to a resulting 101 ice core data. Primary data sources,
geographical coordinates, and covered periods are reported in Table S1 in the Supplement.
Average surface snow isotopic composition data compiled by
Masson-Delmotte et al. (2008; available at
http://www.lsce.ipsl.fr/Phocea/Pisp/index.php?nom=valerie.masson, last
access: August 2017) expanded with datasets
from Fernandoy et al. (2012); in this case, the averaging period is based
on different time periods, with potential non-continuous records (see Table S1 in the Supplement).
Precipitation records extracted from the International Atomic Energy
Agency/Global Network of Isotopes in Precipitation (IAEA/GNIP) network
(IAEA/WMO, 2016), with monthly records available for four Antarctic stations,
complemented by daily records for four Antarctic stations from individual
studies. Precipitation records from Vostok are available but are excluded
from our analysis due to an insufficient number of measurements (29). See
orange part of Table S1 in the Supplement.
Each of the 1205 locations have given an individual index number. Data have
been processed to calculate time-averaged values (available at 1089 locations
for δ18O values, 879 locations for δD, and 770
locations for d). The ice core records with sub-annual resolution were
averaged at annual resolution over the period 1979–2013, resulting in 88 ice
core records for δ18O and only 5 for d. Most precipitation
records are not continuous and do not cover a full year, preventing the
calculation of annual mean values. We have also used sub-annual records from
22 highly resolved ice cores (including 18 records giving access to δ18O and 5 records giving access to d) and precipitation sampling
from eight stations to characterise the seasonal amplitude. For ice core records,
we have only calculated the yearly amplitude from available measurements, as
chronologies cannot be established at monthly scales. Note that this database
is publicly available on the PANGAEA data archive
(https://www.pangaea.de/?t=Cryosphere, last access: June 2018).
ECHAM5-wiso model and simulation
The atmospheric general circulation model (AGCM) ECHAM5-wiso (Roeckner et
al., 2003; Werner et al., 2011) captures the global pattern of precipitation
and vapour isotopic composition, including the spatial distribution of
annual mean precipitation isotopic composition over Antarctica (Masson-Delmotte et al., 2008). Several studies using ECHAM5-wiso have
been dedicated to model–data comparisons for temporal variations in other
regions (e.g. Siberia, Greenland; Butzin et al., 2014; Steen-Larsen et
al., 2016).
The ECHAM5-wiso outputs analysed in this study consist of daily values
simulated over the period 1960–2013. ECHAM5-wiso was nudged to atmospheric
reanalyses from ERA-40 (Uppala et al., 2005) and ERA-Interim (Dee et
al., 2011), which are shown to have good skills for Antarctic precipitation (Wang et al., 2016), surface pressure fields, and vertical profiles of
winds and temperatures. The ocean surface boundary conditions (sea ice
included) are also prescribed based on ERA-40 and ERA-Interim data. Isotope
values of ocean surface isotopic composition are based on a compilation of
observational data (Schmidt et al., 2007). The simulation was performed
at a T106 resolution (which corresponds to a mean horizontal grid resolution
of approx. 1.1∘× 1.1∘) with 31 vertical model
levels.
Methods for model–data comparisons
In the model, we have extracted specific daily variables for comparison with
available data and then averaged them. We have extracted daily 2 m temperature outputs (hereafter 2 m-T) for comparison with surface air
instrumental records, daily (precipitation minus evaporation) outputs
(hereafter P-E) for comparison with SMB data, and daily precipitation
isotopic composition outputs for comparison with measurements of isotopic
composition data in the precipitation. For ice core data, we averaged daily
precipitation isotopic composition weighted by the daily amount of
precipitation.
For each specific site, we selected the model grid cell including the
coordinates of the site. When comparing model outputs with the database of
surface data (time-averaged SMB and isotopic composition), available data
have been averaged within each model grid cell.
Time selection was dependent on the variables. The 2 m-T outputs have been
compared with temperature records for the period 1960–2013 based on annual
averages and selecting the same years as in the data (see Sect. 3.1.1). The
comparison with other datasets (SMB, snow, and water stable isotopes from
firn–ice cores) is restricted to the period 1979–2013 due to concerns about
the skills of the reanalyses used for the nudging prior to 1979 in
Antarctica (see next section). Daily (P-E) outputs were all extracted over
the whole period 1979–2013 and averaged (see Sect. 3.1.2). For comparison
with the surface isotopic database (Sect. 3.2.1), daily precipitation
isotopic composition was averaged by weighting by the daily amount of
precipitation over the whole period 1979–2013. For the inter-annual
variability (same section) or annual values (e.g. for d outputs, see Sect. 4), daily precipitation isotopic composition was averaged by weighting by
the daily amount of precipitation for each year of the period 1979–2013. For
sub-annual isotopic composition, we used precipitation isotopic compositions
(amplitude and mean seasonal cycle) and highly resolved ice cores (amplitude
only). Precipitation isotopic composition data consist of a very small
number of measurements, sometimes taken before 1979 (e.g. observations from
DDU consist of 19 measurements during 1973), and thus model precipitation
isotopic composition outputs were extracted at the very exact sampling date.
Then, monthly averages were performed and mean seasonal cycles were
calculated. The resulting mean seasonal cycles of precipitation isotopic
composition were obtained the same way in both precipitation data and the
model. For comparison with the mean seasonal amplitude of the highly
resolved ice cores, the mean seasonal amplitude was calculated from the mean
seasonal cycle based on the monthly averages (weighted by the precipitation
amount) over the period covered by the ice core record.
Finally, for the spatial linear relationships, the calculations reported for
each grid cell are based on the relationship calculated by including the 24
grid cells (±2 latitude steps, ±2 longitude points)
surrounding the considered grid cell.
Our comparisons are mainly based on linear regressions. Note that through
the paper, we consider a linear relationship to be significant for a
p value < 0.05.
Model skills
Surface air temperature (in ∘C) from station
instrumental records (points and dashed lines) and simulated by the
ECHAM5-wiso model (solid lines) over the period 1960–2013 for (a) the plateau, (b) coastal East Antarctic Ice Sheet, and (c) the West Antarctic Ice
Sheet. Note that the plots were organised by regions to make it more
readable: inland (a), coastal (b), and West Antarctic Ice Sheet plus
peninsula (c).
The 2 m temperature outputs (in ∘C) from ERA-40 (light
green), ERA-Interim (dark green), and ECHAM5-wiso outputs over the periods
1960–1979 (light purple) and 1979–2013 (dark purple) at the locations of
Neumayer, Byrd, Palmer, Vostok, Dome C, McMurdo, Casey, Dumont d'Urville
(written as DDU), Mawson, and Esperanza stations. Horizontal black lines
correspond to the mean data. Vertical black lines correspond to inter-annual
standard deviations: dotted lines are associated with data, while solid
lines are associated with model outputs (ERA or ECHAM).
In this section, we assess ECHAM5-wiso skills with the perspective of using
the model outputs for the interpretation of water stable isotope data. In
polar regions, isotopic distillation is driven by fractionation occurring
during condensation, which is itself controlled by condensation temperature (Dansgaard, 1964). We thus first compare ECHAM5-wiso outputs with
regional climate records, as this comparison may explain potential isotopic
biases. This includes a comparison with reanalyses in order to explore the
role of nudging in model–data mismatches. We then compare ECHAM5-wiso
outputs with our isotopic database.
Temperature and surface mass balanceComparison with instrumental temperatures records and ERA
outputs
Differences between observed (READER) and simulated (ECHAM5-wiso)
annual surface air temperature: observed average (noted as “observed μ”, in ∘C), average difference (noted as “μ differences”,
in ∘C), standard deviation from observations (noted as “observed
σ”, in ∘C), and standard deviation from the model (noted
as “simulated σ”, in ∘C) for the period 1979–2013.
We compare time series of instrumental temperature records (filled circles
and dashed lines, Fig. 2) with model outputs (solid lines, Fig. 2) from
1960 to 2013. This comparison first highlights local offsets between
observed and simulated mean values at each site, without a systematic
overall warm or cold bias. Table 1 reports the statistical analysis of
annual differences between observations and simulations (observed mean, mean
difference between the data and the model outputs, observed versus simulated
standard deviation). ECHAM5-wiso has a cold bias for 7 out of 10 stations.
While this bias is less than 2 ∘C for Dronning Maud Land (Mawson
and Neumayer) and over the peninsula (Palmer and Esperanza), it reaches
7 ∘C for the coastal Indian region (Casey and Dumont d'Urville)
and is very strong over the Victoria Land region (McMurdo), reaching
15 ∘C. This cold bias may be due to the model resolution and the
location of coastal stations in the ice-free region, where the small-scale
topographic features are not accounted for at the model resolution. In
contrast, ECHAM5-wiso has a warm bias for all the stations located inland
(Vostok, Dome C, and Byrd). Werner et al. (2011) also reported this warm bias
for the central Antarctic Plateau and suggested that it could be linked to
problems in correctly simulating the polar atmospheric boundary layer. Our
comparison also shows that the simulated inter-annual temperature
variability is larger than observed for seven out of nine sites and is particularly
overestimated for locations such as DDU, McMurdo, and Palmer, where the cold
bias is large.
Figure 2 depicts a sharp simulated increase in temperature from 1978 to 1979
for all stations, except for the peninsula region (Esperanza and Palmer).
Such a feature is not displayed in instrumental records, with one exception
at McMurdo (Fig. 2). As a result, the model–data correlation coefficient for
McMurdo is higher over 1960–2013 than over 1979–2013 (Table 2), possibly
because it is dominated by the sharp increase just prior to 1979. For all
other stations, the correlation coefficient is significantly higher in
1979–2013 than in 1960–2013. In order to assess whether ECHAM5-wiso
reproduces the temperature bias displayed by ERA-40 (Bromwich et al.,
2007), we compare outputs from ERA-40 and ERA-Interim (green bars, Fig. 3)
with ECHAM5-wiso outputs (purple bars, Fig. 3) nudged by these reanalyses
(i.e. over 1960–1978 and 1979–2013, respectively) and with the station
temperature data (horizontal black lines, Fig. 3).
Linear relationship between surface temperatures (in ∘C)
from station instrumental records and ECHAM5-wiso outputs (in ∘C)
over the period 1960–2013 and 1979–2013: the slope (in ∘C ∘C-1), the correlation
coefficient (noted as “r”), and the
p value. Data are not reported for 1960–2013 for stations for which records
only cover the second period (1979–2013). Numbers in brackets correspond to
standard errors.
All datasets reveal a cold bias simulated by both the reanalyses and
ECHAM5-wiso at all stations but Byrd and Vostok over the two periods (only
over 1960–1978 for Neumayer and Esperanza), but this bias is larger over the
period 1960–1978 compared to the period 1979–2013. This finding supports our
earlier suggestion for Dumont d'Urville (Goursaud et al., 2017) that the
1978–1979 shift simulated by ECHAM5-wiso arises from the nudging to ERA-40
reanalyses. We note that mean values and the amplitude of inter-annual
variations are different for ECHAM5-wiso and ERA (not shown), as expected
from different model physics despite the nudging technique. This finding
has led us to restrict, as far as possible, the subsequent analysis of the
ECHAM5-wiso outputs to the period 1979–2013.
Comparison of the GLACIOCLIM (hereafter and noted in the plots
as “GC”) SMB database averaged within the ECHAM5-wiso grid cells and the SMB
(i.e. precipitation–evaporation) simulated by the model, with the
spatial distribution of the accumulation (a) as simulated by the model (in
cm w.e. yr-1), (b) the ratio of the ECHAM5-wiso annual accumulation
(precipitation minus evaporation) to the GC averaged SMB (no unit), and
GC-averaged SMB values against SMB values simulated by the model (blue dots)
associated with the corresponding linear relationships (red solid line);
displayed at the logarithm scale for elevation ranges of 0–2200 m a.s.l.
(with the upper limit excluded) (c) and 2200–4000 m a.s.l. (d).
Maps displaying model–data comparisons for δ18O
time-averaged values (a) and inter-annual standard deviations (b).
Backgrounds correspond to ECHAM5-wiso simulations over the period 1979–2013,
while signs correspond to the model–data comparison. For the time-averaged
values, the comparison consists of calculating the model–data differences. Red
“+” symbols indicate a positive model–data difference, while blue “-”
symbols correspond to a negative model–data difference. For the inter-annual
standard deviations, the comparison consists of calculating the ratio of the
simulated value to the corresponding grid cell data. Red “+” symbols
indicate a ratio higher than 1, while blue “-” symbols correspond to a
model / data ratio lower than 1.
Maps displaying model–data comparisons for d time-averaged (in ‰, a) values and inter-annual standard deviations (in
‰, b). Backgrounds correspond to ECHAM5-wiso
simulations over the period 1979–2013, while signs correspond to the
model–data comparison. For the time-averaged values, the comparison consists
of calculating the model–data differences. Red “+” symbols indicate a
positive model–data difference, while blue “-” symbols correspond to a
negative model–data difference. For the inter-annual standard deviation, the
comparison consists of calculating the ratio of the simulated value to the
corresponding grid point data. Red “+” symbols indicate a ratio higher
than 1, while blue “-” symbols correspond to a model / data ratio lower than
1.
For this period marked by small temperature variations, we note that the
correlation coefficient between data and model outputs (Table 2) is very
small for McMurdo (r= 0.2) and rather small for Vostok (r= 0.6),
questioning the ability of our simulation to resolve the drivers of
inter-annual temperature variability at these locations. We observe that the
model reproduces the amplitude of inter-annual variations, with a tendency
to underestimate the variations as shown by model–data slopes from 0.6 to
1 ∘C per ∘C. As a result, ECHAM5-wiso underestimates
the magnitude of inter-annual temperature variability for these central
regions of the West and East Antarctic Ice Sheet. It will therefore be
important to test whether similar caveats arise for water isotopes.
Comparison with GLACIOCLIM database accumulation
For each grid cell in which at least one stake record is available, we have
calculated the ratio of the P-E values (which we use as a surrogate for
accumulation) simulated by ECHAM5-wiso to the averaged SMB estimate for that
grid region based on stake measurements (Fig. 4a). Due to the limited number
of grid cells containing SMB data points from 1979 to 2013 (100 cells)
located almost only on the East Antarctic Ice Sheet, we have decided to use
the dataset covering the entire twentieth century (521 cells) spread over
the continent.
The spatial distribution of SMB is well captured by ECHAM5-wiso, with
decreasing SMB values from the coast to the interior plateau (Fig. 4a).
However, the model quantitatively shows some discrepancies when compared with
the GC database. The area-weighted (by the model grid cells) mean GC SMB is
141.3 mm w.e. yr-1, while the simulated area-weighted mean P-E over the
same model grid is 126.6 mm w.e. yr-1. This underestimation covers
69.7 % of the compared areas. The 30.3 % remaining areas associated
with an overestimation of the model are located in sparse regions like in the
north of the plateau and over coastal areas (Fig. 4b). Note that the low P-E
rates over the plateau (75 mm w.e. yr-1, see Fig. 4a) counterbalance
the local overestimation at the coast, supporting the ability of ECHAM5-wiso
to resolve the integrated surface mass balance for the Antarctic ice sheet.
Figure 4c and d confirm the global underestimation by the model, with slopes
of simulated P-E against GC SMB lower than 1. This aspect is emphasised for
elevations higher than 2200 m a.s.l. (r= 0.74 and
rmse = 122.8 mm w.e. yr-1 for elevation lower than 2200 m a.s.l.,
and r= 0.83 and rmse = 55 mm w.e. yr-1 for elevations higher
than 2200 m a.s.l., with “r” the correlation coefficient, and “RMSE” the
root mean square error). The correlation coefficient (considering all
elevations) is 0.79, reflecting the non-homogenous bias over the whole
continent. This can be due first to a failure in the representativity of SMB
spatial variability when averaging GC data within ECHAM5-wiso grid cells
due to a too-small number of point measurements. Second, the model grid
resolution may be too coarse to reproduce coastal topography and thus the
associated amounts of precipitation. Finally, several key processes such as
blowing snow erosion and deposition are not taken into account in the
model. For instance, the lowest value from the GC database is
-164 mm w.e. yr-1, measured at the Bahia del Diablo glacier, a
small glacier covering important elevation ranges in a narrow spatial scale
between the front and the summit. It was the only one within the
corresponding model grid cell, so the resulting GC value within this grid
cell could not be representative of the model scale, and the
simulated P-E value is not representative of this small glacier-wide value.
When considering the whole Antarctic grounded ice sheet, the area-weighted
P-E simulated by the model amounts to 164.4 mm w.e. yr-1. This value
falls within the highest values of the 11 simulations displayed by Monaghan et al. (2006), varying from 84 to 188 mm w.e. yr-1.
However, the high range of values between the different simulations
illustrates the uncertainties related to the SMB model, mainly due to model
resolution, which is crucial to reproducing the impact of topography on
precipitations and to non-resolved physical processes (e.g. drifting snow
transport, including the erosion, deposition, and sublimation of drifting snow
particles, and clouds microphysics; Favier et al., 2018). Moreover,
this simulated value is very close to the best estimations of Antarctic
grounded ice sheet SMB, which range between 143.4 (Arthern et al., 2006) and 160.8 mm w.e. yr-1 (Lenaerts et
al., 2012). This simulated value is also very close to the one obtained by Agosta et al. (2013) for the LMDZ4 model over the period 1981–2000
(160 mm w.e. yr-1), but slightly lower than with the SMHiL model forced by
LMDZ4 (189 mm w.e. yr-1).
Comparison between measurements from precipitation samples and
ECHAM5-wiso simulated precipitation isotopic composition for grid cells
closest to the sampling locations over the same period as the data (at daily or
monthly scale when the name of the station is associated with an asterisk).
We report the mean value ± the standard deviation for
δ18O (in ‰) and for temperature (∘C).
To conclude, although the ECHAM5-wiso simulation presented in this study has a
relatively coarse resolution (110 km × 110 km compared to 15 km × 15 km for
the SMHiL model forced by LMDZ4) and does not resolve processes
contributing in the SMB (e.g. drifting snow processes), the P-E outputs are
realistic products when compared with SMB data.
Comparison with water stable isotope data
Limited by the availability of the data, we could only study model skills
with respect to spatio-temporal patterns, including seasonal and inter-annual
variations, and the simulated relationships between
δ18O and temperature. We have also extended the model–data
comparison to the second-order parameter, d.
δ18O time-averaged values and inter-annual
variability
The model–data difference of the time-averaged values is positive for 88 %
of all grid cells, suggesting a systematic underestimation of isotopic
depletion by ECHAM5-wiso (Fig. 5a). The few areas for which ECHAM5-wiso
overestimates the isotopic depletion are restricted to coastal regions. This
pattern is coherent with the temperature anomalies: ECHAM5-wiso produces too-low isotopic values
where ECHAM5-wiso has a cold bias, likely causing too-strong distillation towards coastal areas and too-high isotopic values
inland, where the warm bias limits the distillation strength. The statistical
distribution of model–data δ18O differences (not shown)
shows a wide range but an interquartile range (50 % of all values) of 1.4
to 3.9 ‰, which is therefore within 1.3 ‰
of the median. We conclude that, beyond the systematic offset linked to
climatic biases, ECHAM5-wiso correctly captures the spatial gradient
(continental effect) of annually averaged δ18O data. These results
also suggest that the spatial distribution of annual mean
δ18O values from shallow ice cores is driven by transport
and condensation processes well resolved by ECHAM5-wiso, probably with
secondary effects of non-resolved processes such as snow drift, wind erosion,
and snow metamorphism. The largest deviations are encountered in coastal
regions, where the model resolution is too low to correctly resolve topography,
advection, and boundary layer processes (e.g. small-scale storms, katabatic
winds). Katabatic winds also have the potential to enhance ventilation-driven
post-deposition processes (Waddington et al., 2002; Neumann and
Waddington, 2004).
δ18O inter-annual standard deviation is underestimated by
the model for 92 % of the 179 grid cells in which this comparison can be
performed (Fig. 5b).The interquartile range of the ratio between the
simulated and observed standard deviation varies from 0.4 to 0.6 (not shown),
with an underestimation by a factor of 2 for about 50 % of the grid cells.
No such underestimation of inter-annual standard deviation was identified for
the simulated temperature.
We now focus on our model–data comparison of precipitation data. Both
precipitation isotopic composition and temperature measurements are available
for only eight locations and for short time periods (Table 3). These data
evidence the altitude and continental effect with increased isotopic
depletion from Vernadsky (averaged δ18O of -9.9 ‰) to Dome F (averaged δ18O
of -61.3 ‰). For five out of the eight records, the isotopic depletion
is stronger in ECHAM5-wiso than observed (Dome C included). The observations
depict an enhanced inter-daily δ18O standard deviation for
inland sites, from 3.1 at Vernadsky to
10.8‰ at Dome F. The simulated δ18O
inter-daily standard deviation is 1.1 to 3.8 times larger than observed,
ranging from 5.1 to 19.2 ‰. For the exact same time
period corresponding to the short precipitation isotopic records, ECHAM5-wiso
simulates colder than observed temperatures at all stations but Dome F and
Dome C, i.e. over the plateau. This finding is consistent with results from
ice core records reported previously and consistent with the isotopic
systematic biases. From this limited precipitation dataset, there is no
systematic relationship between model biases for temperature (mean value or
standard deviation) or for δ18O in contrast with the
outcomes of the model–data comparison using the whole dataset, including
surface snow. At Dome C, ECHAM5-wiso underestimates the standard deviation of
temperature, but strongly overestimates the standard deviation of δ18O.
As a conclusion, while δ18O time-averaged model–data biases
are consistent with temperature biases using the whole dataset, no systematic
relationships emerge between model biases for temperature and δ18O
measured in precipitation.
δ18O seasonal amplitude
High-resolution δ18O data allow us to explore seasonal
variations. This includes 18 ice core records with sub-annual resolution,
four
IAEA/GNIP monthly precipitation datasets, and four daily precipitation
monitoring records.
In order to quantify post-deposition effects in ice cores, we calculated the
ratio of the first three seasonal amplitudes by using the mean seasonal amplitude
in sub-annual ice cores (See Table S2 in the Supplement). We find a mean
ratio of 1.40 ± 0.47. We explored whether this ratio was related to
annual accumulation rates (see Fig. S3 in the Supplement), without any
straightforward conclusion. We also observe that five ice cores depict a
ratio lower than 1, including one with a mean yearly accumulation of 15 cm w.e. yr-1, a feature which may arise from inter-annual variability in
the precipitation seasonal amplitude or in post-deposition processes. This
empirical analysis shows that a loss of seasonal amplitude due to
post-deposition processes is likely in most cases, with an average loss of
the seasonal amplitude of approximately 70 % compared to the amplitude
recorded in the upper part of the firn cores (first 3 years).
We have calculated the mean of the δ18O annual amplitude
(i.e. maximum–minimum values within each year) in ice core records
(triangles in Fig. 7a) and the mean seasonal amplitude of precipitation time
series (circles in Fig. 7a) for comparison with ECHAM5-wiso outputs (Fig. 7,
Table 4). Unfortunately, a too-small number of measurements (19 daily
measurements) were monitored at DDU, preventing the representation of
the full seasonal cycle. The data depict the largest seasonal amplitude in
the central Antarctic Plateau, reaching up to 25.9 ‰ at
Dome F. ECHAM5-wiso underestimates the seasonal amplitude (by 14 to 69 %)
when compared to precipitation data, but overestimates the seasonal amplitude
when compared to ice core data (from 11 to 71 %). The overestimation when
comparing with ice core data is consistent with the attenuation of signal by
post-deposition effects (as previously mentioned) rather than a model bias.
Average seasonal amplitude of precipitation δ18O(a) and d (b) (in ‰) simulated by ECHAM5-wiso (colour
shading) over the period 1979–2013 and calculated from precipitation data
(circles) and ice core records (triangles) over their respective available
periods.
δ18O mean seasonal amplitude (in
‰) calculated for precipitation and sub-annual ice core
data, as well as simulated by ECHAM5-wiso for the same time period as the
data. The time resolution used in the model corresponds to the time
resolution of the precipitation data and to the annual scale for the ice
core data (i.e. yearly averages based on daily precipitation isotopic
composition weighted by the amount of daily precipitation). The data type is
identified as 1 for precipitation samples and 2 for ice core data.
Average seasonal cycles from precipitation data over the available
period (dashed lines with points) and simulated by the ECHAM5-wiso model over
the period 1979–2013 (solid lines) of the temperature (in ∘C) (a), the precipitation (in mm w.e. yr-1), the precipitation
δ18O (in ‰) (c) and the deuterium excess
(in ‰) (d). Data are shown for different durations depending on
sampling, while model results are shown for the period 1979–2013. The
number of points used for the observations is given in Table 3.
The simulated mean seasonal δ18O amplitude increases
gradually from coastal regions to central Antarctica (more than 15 and up to 25 ‰ for some areas;
Figs. 7a and 8c, solid lines). The model–data comparison suggests that
this pattern is correct and that the model may underestimate the inland
seasonal amplitude. As previously reported for annual mean values, systematic
offsets are also identified for seasonal variations, with a systematic
overestimation of monthly isotopic levels inland (e.g. for Dome C and Dome
F) and a systematic underestimation on the coast (e.g. for Vernadsky and
Halley). The model–data mismatch is largest during local winter months.
Minima are observed and simulated in winter (May–September) at most
locations, except for Rothera and Vernadsky where the data show a minimum in
July but the model produces a minimum in late autumn (April). Maximum values
are observed and simulated in local summer (December–January); a secondary
maximum is also sometimes observed and simulated in late winter
(August–September). Data from Marsh station show maxima in January, April,
and August, whereas the model only produces a single summer maximum value.
In summary, we report no systematic bias of the seasonal temperature
amplitude (Fig. 8a). The seasonal pattern for the temperature is similar
compared to δ18O, with minima in winter and the largest
model–data mismatch in winter. Secondary minima or maxima cannot be discussed
with confidence, as they have low amplitudes. We also highlight that
model–data offsets are larger in winter. Note that precipitation and d
seasonal cycles are described in Sect. 3.2.4.
δ18O–T relationships
Table 5 reports the temporal δ18O–T relationships
established from precipitation and temperature observations and those
simulated by ECHAM5-wiso. This calculation is based on daily or monthly
values (depending on the sampling resolution) and includes seasonal
variations. The data display significant linear relationships for all sites
but Marsh (p value = 0.07), with an increased strength of the correlation
coefficient from the coast (e.g. r= 0.38 at Rothera) to the East Antarctic
Plateau (e.g. r= 0.88 at Dome F). The lowest slopes are identified in the
peninsula region, with a mean slope of 0.32 ‰ ∘C-1 for Rothera and Vernadsky, while the highest slopes occur over the
East Antarctic Plateau, with a mean slope of 0.68 ‰ ∘C-1 for Dome C and Dome F. These temporal slopes appear
mostly lower than the spatial slopes and those expected from a Rayleigh distillation
with a single moisture source (typically 0.8 ‰ ∘C-1).
Slope (in ‰ ∘C-1), correlation
coefficient, and p value of the δ18O–temperature linear
relationship from precipitation measurements over the available period at
daily or monthly (when the name of the station is associated with an
asterisk) scale, depending on the time resolution of the data, and from the
ECHAM5-wiso model over the observed period at the time resolution of the
data. Numbers in brackets correspond to the standard errors.
NumberData ECHAM5-wiso over of pointsthe observed period slopeslope(‰ ∘C-1)rp value(‰ ∘C-1)rp valueRothera*1940.31 (0.06)0.38<0.0010.01 (0.03)0.23<0.001Vernadsky*3720.32 (0.04)0.39<0.0010.09 (0.02)0.25<0.001Halley*5520.47 (0.02)0.76<0.0010.48 (0.02)0.68<0.001Marsh*190.61 (0.31)0.440.070.47 (0.23)0.430.06Dome F3510.76 (0.02)0.88<0.0010.700.62<0.001Dome C5010.59 (0.02)0.64<0.0010.94 (0.07)0.55<0.001Neumayer3360.57 (0.03)0.69<0.0010.29 (0.06)0.29<0.001
In the ECHAM5-wiso model, as for the data, the simulated isotope–temperature
relationship is statistically significant for all sites but Marsh
(p value = 0.06). However, correlation coefficients are very small for
Rothera and Vernadsky, which are thus excluded from further analyses. In the
simulation, correlation coefficients are the highest for Halley, Dome C, and
Dome F (up to 0.55) and the lowest for Neumayer (as low as 0.29). The slope
is the lowest at Neumayer, with a value of
0.29 ‰ ∘C-1, increases at Halley with a value of
0.48‰ ∘C-1, and is the highest over the plateau with
values of 0.70 ‰ ∘C-1 at Dome C and up to
0.94 ‰ ∘C-1 at Dome F.
To summarise, ECHAM5-wiso tends to underestimate the strength of the
isotope–temperature relationship, but correctly simulates a larger strength
of the correlation in the central Antarctic Plateau compared to coastal
regions. There are significant differences in the isotope–temperature slopes
for both coastal and central plateau locations. While there is some
agreement (e.g. for Dome F and Halley), the model also produces
non-realistic slopes, with a much larger slope than observed
at Dome C, for instance.
The δD–δ18O relationship and d patterns
The δ18O–δD linear relationship is expected to be
affected by different kinetic fractionation processes, for instance
those associated with changes in evaporation conditions. We first compare the
δ18O–δD linear relationship in the available
precipitation and ice core data and simulated by ECHAM5-wiso (Table 6).
Significant correlation is observed for all observational datasets but Marsh,
as expected from meteoric samples, assuming correct preservation of samples
and accurate isotopic measurements. We stress that the smallest correlation
coefficient is identified at Vernadsky (r= 0.96), suggesting potential
artefacts for this record. In the observations, the
δD–δ18O slope varies across regions. While slopes
higher than for the global meteoric waterline (i.e.
> 8 ‰ ‰-1) are identified at DDU and in
Dronning Maud Land, lower slopes are identified in the Antarctic Peninsula
(6.6 to 7.0 ‰ ‰ -1) and in the central East Antarctic
Plateau (6.5 and 6.4 ‰ ‰-1 at Dome C and Dome F,
respectively). In the model, outputs also display significant linear
relationships. They show higher values of the slope than observed in the
Antarctic Peninsula, at DDU, and at Dome F and lower than observed for the
other regions, including Dome C. These results appear coherent with
associated coastal versus inland temperature and isotopic distillation
biases.
Slope (in ‰ ‰-1),
correlation coefficient, and p value of the δ18O–δD
linear relationship from precipitation measurements (top of the table) and
ice core data (bottom of the table) over the available period at daily or
monthly scale (identified with an asterisk) and from the ECHAM5-wiso model
over the observed period at the time resolution of the data for the
precipitation and at the annual scale for the ice core data. Numbers in
brackets correspond to the standard errors.
Figure 6 compares the spatial patterns of the d time-averaged model–data
difference (characterised at 293 grid cells in our database; see Fig. 6a),
and the situation is contrasted with 50 % of positive and negative
differences. We can identify systematic trends, with an underestimation of
the mean d levels in ECHAM5-wiso for the central East Antarctic Plateau and
the peninsula and an overestimation above Victoria Land (Fig. 6a). Due to
the temperature dependency of equilibrium fractionation coefficients leading
to a gradual deviation from the meteoric waterline (calculated at the
global scale, at which a coefficient of 8 results from the average
equilibrium fractionation coefficients), d increases when temperature
decreases (Masson-Delmotte et al., 2008; Touzeau et al., 2016). For
central Antarctica, the d bias is thus consistent with the warm bias and the
lack of isotopic depletion. The upper and lower quartiles of the model–data
differences range within ±1.5 ± 0.1 ‰, suggesting that the model outputs remain close to those observed.
The d pattern is similar to that of δ18O: ECHAM5-wiso
underestimates the d standard deviation for 90 % of grid cells, with an
interquartile range comparable to the one for the ratio of standard
deviations for δ18O (Fig. 6b). Table 7 displays the
comparison of the statistics between d in the observations and in
ECHAM5-wiso. In the observations, the time-averaged d is particularly low in
the peninsula (-3.6 to 8.6 ‰), intermediate in
the coastal regions of Dronning Maud Land, Victoria Land, and Adélie Land (4.4
to 8.6 ‰), and very high in the central Antarctic
Plateau (up to 17.5 ‰ for Dome C). Lower coastal values
and higher inland values are captured by ECHAM5-wiso, albeit with large
offsets for each site reaching several per mille. These findings are
consistent with the map showing the time-averaged precipitation d simulated
by ECHAM5-wiso over the period 1979–2013 (Fig. 6a), with very low coastal
values (close to zero) and increasing values towards the interior of
Antarctica, reaching values higher than 16 ‰ on the
plateau. ECHAM5-wiso mainly underestimates the d intra-annual standard
deviation for 10 sites out of 15 (Table 7 and Fig. 6b).
Mean value ± standard deviation (in ‰) of sub-annual d
in observational time series at daily or monthly scale (identified with an
asterisk) for the precipitation and for the ice core data and simulated d by
ECHAM5-wiso for the same time period as the observations for precipitation
and at the annual scale for the ice core. Mean values which are overestimated
by ECHAM5-wiso are written in italic.
Figure 8d depicts the mean d seasonal patterns of the precipitation data and
corresponding model outputs. The data show different patterns from one
location to another. While d measured at Neumayer, Halley, and Rothera
displays a maximum in autumn (March–April), it appears in late autumn (May)
at Marsh and in winter (June–August) at Vernadsky. Maxima for central
stations are observed later, in May–July for Dome C and July–September for
Dome F. In short, most coastal areas are associated with a maximum d in
autumn, while central areas are associated with a later maximum d, i.e. in
winter or late winter, that is thus in anti-phase with δ18O and
temperature. The seasonal amplitude increases from the coast to the plateau.
In the model, for central areas, a first d maximum is simulated earlier than
observed (February–March for Dome F and May–June for Dome C), followed by a
second maximum in late winter (August for Dome F and September for Dome C).
For coastal areas, the amplitude of the simulated d signal is too small to
unequivocally estimate the timing of the maximum. Note the very low value
simulated at DDU in July, which appears to be an outlier when comparing this
value with the average modelled d value for all days in August 1973 (+5.9 ‰). No link emerges between the modelled seasonal
patterns in d and in temperature (Fig. 8a), accumulation (Fig. 8b), or
δ18O (Fig. 8c).
The d mean seasonal amplitude (in ‰) calculated
for precipitation at daily or monthly scale (identified with an asterisk)
and sub-annual ice core data, as well as simulated by ECHAM5-wiso for the
same time period as each record. The data type is identified as 1 for
precipitation samples and 2 for ice core records. Amplitude values that are
overestimated by ECHAM5-wiso are written in italic.
StationTypeObservedECHAM5-wisoamplitudeoutputs for theobserved period(‰)(‰)Rothera*110.73.1Vernadsky*111.82.1Halley*16.73.8Marsh*125.84.5Neumayer17.35.3Dome F140.112.2Dome C141.025.4NUS 08-723.514.0NUS 07-121.915.8WDC06A21.016.5IND2522.311.7GIP217.86.9
Finally, Table 8 reports the d mean seasonal amplitude values for the
precipitation data and ice core records, as well as for the model outputs
covering the observation. They clearly show an increase in d seasonal
amplitude from the coast to the plateau (see also Fig. 7b), with values
varying from 6.7 at Halley to 41 ‰ at Dome C. ECHAM5-wiso systematically underestimates the d mean seasonal
amplitude when compared with precipitation data, while it systematically
overestimates it when compared with ice core data (from 9.4 to 15.5 ‰), with the exception of the GIP ice core. Again, we
cannot rule out a loss of amplitude in ice core data compared to the initial
precipitation signal due to the temporal resolution and post-deposition
effects.
Strength and limitations of the ECHAM5-wiso model outputs
The isotopic model–data time-averaged biases appear coherent with
temperature. A warm bias over central East Antarctica and a cold bias over
coastal regions lead to a too-low and too-strong isotopic depletion,
respectively. Temperature and distillation biases also explain the
underestimation of d above the central East Antarctic Plateau.
However, some characteristics are not explained by model skills for
temperature. At sub-annual timescales, ECHAM5-wiso always overestimates the
standard deviation of δ18O in precipitation (Table 3), but
results for d are mixed (Table 7). ECHAM5-wiso always underestimates the seasonal
amplitude of δ18O and d in precipitation but always
overestimates the seasonal amplitude of δ18O and d in firn–ice
cores (Tables 4 and 8). Differences between the model and firn–core data are
at least partially due to diffusion processes, but no clear reason can be
given for the other isotopic biases.
We do not find any clear link between other model biases for d and those for
temperature or δ18O.
Sampling Antarctic snowfall remains challenging (Fujita and Abe, 2006;
Landais et al., 2012; Schlosser et al., 2016; Stenni et al., 2016). Sampling
is likely to fail to capture small events and may also collect surface snow
transported by winds or hoar. Snow samples may undergo sublimation before
collection. The fact that ECHAM5-wiso appears to overestimate the
variability of precipitation isotopic composition may be related to an
improper characterisation of the full day-to-day variability of real-world
precipitation from daily precipitation sampling. Alternatively, this feature
may also arise from a lack of representation of small-scale processes
(boundary layer processes, wind characteristics, snow–atmosphere interplays)
in ECHAM5-wiso. These processes may contribute to a local source of
Antarctic moisture (through local recycling), reducing the influence of
large-scale moisture transport (resolved by ECHAM5-wiso nudged to
reanalyses) on the isotopic composition of precipitation and its day-to-day
variability.
Caveats also limit the interpretation of the comparison of ECHAM5-wiso
precipitation outputs with surface snow or shallow ice core data. Such
records are potentially affected by post-deposition processes, such as wind
scoring, erosion, snow metamorphism between precipitation events, and
diffusion.
Our apparently contradictory findings for model–data comparisons with respect
to inter-annual variations (from ice cores) and inter-daily variations (from
precipitation data) call for more systematic comparisons between
δ18O records of precipitation and ice cores at the same
locations over several years.
Use of ECHAM5-wiso outputs for the interpretation of ice core
records
Linear analysis of annual ECHAM5-wiso outputs from 1979–2013 for
the temporal δ18O–temperature relationship (using the
2 m temperature and the precipitation-weighted δ18O).
Maps show the slope of the linear regression (‰ ∘C-1)
at the right side (a, c, e) and the correlation coefficient at the
left side (b, d, f). The upper plots use outputs at the spatial
scale (a, b), the middle plots at the inter-annual scale (c, d), and the lower plots at the seasonal scale (e, f). Areas where
the results of the linear analysis are not significant are hatched (p value > 0.05).
In this section, we use the model outputs to help in the interpretation of
ice core data: we quantify the inter-annual isotope–temperature relationships
(Sect. 4.2) and characterise the spatial distribution of seasonal
δ18O–d phase lag. Based on the confidence we can have in the
model for each of the seven aforementioned regions (see Sect. 1 and Fig.1),
we formulate recommendations for the future use of ECHAM5-wiso outputs
(Sect. 4.3).
Spatial and temporal isotope–temperature relationships
First, we use ECHAM5-wiso to investigate spatial δ18O–temperature relationships (Fig. 9a and b) and then
inter-annual (Fig. 9c and d) and seasonal relationships (Fig. 9e and f). For
spatial relationships, the strength of the linear correlation coefficient is
higher than 0.8. The spatial slope shows regional differences. It is
generally smaller near the coasts (less than
0.8 ‰ ∘C-1), with the exception of Dronning Maud Land, and increases at elevations higher than 2500 m a.s.l., with values above
1.2 ‰ ∘C-1 in large areas. Furthermore, ECHAM5-wiso
simulates the spatial heterogeneity of the gradient in the central East Antarctic
Plateau around Dome C, Dome A, and Dome F. Such variability may arise from
the simulated intermittency of precipitation and from differences in
condensation versus surface temperature.
At the inter-annual scale (Fig. 9c and d), results are not significant for
large areas encompassing the Dronning Maud Land region, the Antarctic
Peninsula, the Transantarctic Mountain region, the Ronne and Filchner ice
shelf regions, part of Victoria Land, and along the Wilkes Land coast. For
the whole continent, the correlation coefficient varies between 0.5 and 0.6
(with few values reaching 0.6 at the upper limit and 0.3 at the lower
limit). Where correlations are significant, the inter-annual δ18O–temperature slope increases from the coasts
(0.3 to 0.6 ‰ ∘C-1) to the inland regions, where it can exceed
1 ‰ ∘C-1 for some high-elevation
locations. The low correlation may be due to the small range of mean annual
temperature over the period 1979–2013 and is not necessarily indicative of a
weak sensitivity to temperature change.
Finally, at the seasonal scale, results are significant almost over the
whole continent (with the exception of two little areas in the peninsula and
East Antarctica) and the correlation coefficients are equal to 1
everywhere but along the coastal regions in the Indian Ocean sector, where
the correlation coefficient can decrease down to 0.75. Slopes are lower than
for spatial and inter-annual relationships, with values from 0.0 to 0.3 ‰ ∘C-1 along the coast (higher over
Dronning Maud Land and the Ross Ice Shelf region), around 0.5 ‰ ∘C-1 inland for altitudes lower than
2500 m a.s.l. (with the exception of lower values above the Transantarctic
Mountains), and up to 0.8 ‰ ∘C-1 over
the East Antarctic Plateau.
To conclude, the coherent framework provided by the ECHAM5-wiso simulation
covering the period 1979–2013 shows that annual δ18O and
surface temperatures are only weakly linearly related in several areas. This
suggests that the inter-annual variability of δ18O is
controlled by other processes, for instance those associated with synoptic
variability and changes in moisture source characteristics (Sturm et
al., 2010; Steiger et al., 2017). Moreover, our results rule out the
application of a single isotope–temperature slope for all Antarctic ice core
records on the inter-annual timescale, and the seasonal
isotope–temperature slope is not a surrogate for scaling inter-annual
δ18O to temperature.
We have also used the simulation to explore linear relationships between d
and surface air temperature, without any significant results (not shown).
δ18O–d phase lag
Deuterium excess (d) has originally been interpreted as a proxy for relative humidity at the
moisture source (Jouzel et al., 2013; Pfahl and Sodemann, 2014; Kurita et
al., 2016). However, recent studies of Antarctic precipitation data combined
with back-trajectory analyses did not support this interpretation (e.g.
Dittmann et al., 2016; Schlosser et al., 2017), calling for further work to
understand the drivers of seasonal d variations. The phase lag between d and
δ18O was initially explored to identify changes in
evaporation conditions (Ciais et al., 1995). In ECHAM5-wiso, this phase
lag is calculated as the lag that gives the highest correlation coefficient
between d and δ18O (Fig. 10) using the mean seasonal cycle
from monthly averaged values. If there were no seasonal change in moisture
origin and climatic conditions during the initial evaporation process, one
would expect d to be in anti-phase with δ18O due to the
impact of condensation temperature on equilibrium fractionation. For regions
with small seasonal amplitude in condensation temperature, a constant initial
isotopic composition at the moisture source would imply a stable d year-round. In such regions, the simulated phase lag likely therefore reflects
seasonal changes in the d of the initial moisture source. The comparison with
precipitation data (Sect. 3.2.4) showed that ECHAM5-wiso had low seasonal
amplitude in coastal regions (Fig. 8d), making the discussion of seasonal
maxima difficult. These comparisons are also limited by the duration of the
precipitation records. Here, we use the full simulation (1979–2013) to
investigate the phase lag between the mean seasonal cycle of d and
δ18O. Clear spatial patterns are identified for the
distribution of this phase lag (see Fig. 10). At intermediate elevations
(between 1000 and 3000 m a.s.l.), d seasonal variations occur in phase
(within 2 months) with the seasonal cycle of δ18O (and
surface air temperature). By contrast, a phase lag of several months is
identified over coastal areas and over the central East Antarctic Plateau.
Along the Wilkes Land coast and the Dronning Maud Land region, the time lag
is between 2 and 4 months below 1000 and 500 m a.s.l.,
respectively. Over the West Antarctic Ice Sheet, the phase lag is higher than
2 months below 500 m a.s.l. and can even reach 6 months (indicating an
anti-phase between d and δ18O). Over the central East
Antarctic Plateau (above 3000 m of elevation), the phase lag reaches several
months again, especially near Dome C. Obtaining longer precipitation records
and comparing the phase lag identified in precipitation and surface snow
records would be helpful to understand whether post-deposition processes,
which are not included in ECHAM5-wiso, affect this phase lag. The different
characteristics of seasonal d changes suggest different seasonal changes in
moisture origin at coastal, intermediate, and central plateau regions,
supporting the identification of specific coastal versus inland regions to
assess the isotope–temperature relationships. Note that the few available
datasets are in line with the simulation.
Best correlated phase lag between the mean seasonal cycle of
deuterium excess and that of δ18O simulated by ECHAM5-wiso
over the period 1979–2013 (colour shading) and calculated from precipitation
data (circles). The sign provides information on the sign of the correlation between
δ18O and d (e.g. positive numbers correspond to a correlation,
while negative numbers correspond to an anti-correlation). The absolute value
corresponds to the lag (in months) between δ18O and
deuterium excess corresponding to the highest correlation of monthly averaged
values. This figure also displays the Antarctic topography, with isohypses
(in m a.s.l.).
Recommendations for the different regions of Antarctica
In this section, we summarise our findings based on the model–data
comparisons and the analysis of model outputs for the seven Antarctic regions
selected by the Antarctica2k program, as shown in Fig. 1 (Stenni et al.,
2017). The regions depend on geographical and climatic characteristics.
Results from Sect. 3 were averaged over each region and are given in Table 9.
Evaluation of the ECHAM5-wiso model for seven Antarctic regions: East
Antarctic Plateau, coastal Indian, Weddell Sea, peninsula, West Antarctic Ice
Sheet, Victoria Land, and Dronning Maud Land (7). We regionally averaged the
time-averaged δ18O mean (model–data) differences (in
‰), the inter-annual δ18O standard
deviation (model / data) ratio, the time-averaged d mean (model–data)
differences (in ‰), and the inter-annual d (model–data)
standard deviation ratio using only precipitation data. Italic cells
correspond to parameters for which we support the validity of the use of
ECHAM5-wiso for the considered region, underlined cells to parameters for which we suggest
some caution, and bold cells to parameters for which we suggest not using ECHAM5-wiso
outputs for the considered region. The numbers in brackets correspond to the
number of data points.
We first discuss the systematic model biases. The maximum time-averaged
model–data differences (3.8 and 2.6 ‰ for δ18O and d, respectively) are
identified in the Weddell Sea area. Minimum time-averaged model–data
differences occur in different regions for δ18O and d
(Victoria Land and Dronning Maud Land, respectively).
For inter-annual standard deviation, the model–data mismatch is smallest for
Victoria Land (ratio of 1.1 and 1.0 for δ18O and d,
respectively). Results for δ18O show that the simulated
inter-annual variability can be considered close to reality (model–data ratio
higher than 0.7) only for Victoria Land and the plateau, acceptable
(model–data ratio higher than 0.5) for the Weddell Sea area and the West
Antarctic Ice Sheet, but significantly different from observations in the
other three regions. The model–data mismatch is larger for d inter-annual
variability, with acceptable inter-annual variability only for Victoria Land
and the plateau. However, these results are clearly limited by the low number
of observational records for some regions.
Table 10 provides a brief overview of ECHAM5-wiso outputs for our seven regions
of interest in terms of mean climate and isotopic variables, their
standard deviation, seasonal amplitude, and the calculated regional δ18O–T relationship. The main findings are again the highest slope
simulated for the central Antarctic Plateau, followed by the Dronning Maud
Land and West Antarctic Ice Sheet regions, and weak correlations in some
regions (Weddell Sea, Antarctic Peninsula) where water stable isotope
outputs are not good predictors of inter-annual temperature change within
ECHAM5-wiso, together with low correlations and slopes for the other coastal
regions (Indian Ocean sector, Victoria Land).
Exploration of the ECHAM5-wiso model outputs (1979–2013) for seven
Antarctic regions: east plateau, coastal Indian, Weddell Sea, peninsula, West
Antarctic Ice Sheet, Victoria Land, and Dronning Maud Land (7). For each of
the variables precipitation (in mm w.e. yr-1), temperature
(in ∘C), δ18O (in ‰), and d (in
‰), we regionally averaged the annual mean values (lines
1 to 4), the inter-annual standard deviation (lines 5 to 8), and the mean
seasonal amplitude (lines 9 to 12). Finally, we calculated the statistics of
the inter-annual δ18O–temperature linear relationship: the
slope (noted as “a”, in ‰ ∘C-1), the
correlation coefficient (noted as “r”), and the p value for each region.
RegionsPlateauCoastalWeddellPeninsulaWestVictoriaDronningIndianSeaAntarcticLandMaudIce SheetLandTime-Precipitation6.740.79.068.825.914.124.3averaged(in cm w.e. yr-1)valuesTemperature-39.8-20.1-29.3-14.2-24.2-27.7-19.7(in ∘C)δ18O (in ‰)-42.3-24.3-30.6-18.9-26.6-28.8-25.2d (in ‰)6.94.73.63.13.33.33.8Inter-Precipitation0.64.21.69.02.52.43.3annual(in cm w.e. y-1)standardTemperature0.50.70.80.90.70.70.5deviation(in ∘C)δ18O (in ‰)0.60.40.80.40.70.90.4d (in ‰)0.40.40.40.20.40.50.5MeanPrecipitation5.228.47.245.019.212.219.3seasonal(in cm w.e. yr-1)amplitudeTemperature23.916.524.217.821.724.418.1(in ∘C)δ18O (in ‰ )10.94.412.24.18.810.77.3d (in ‰)7.34.34.72.54.15.14.2Inter-a0.70.20.30.10.60.60.4annualr0.60.40.30.30.60.50.4δ18O–temperature5.9 ×10-58.5 ×10-34.5 ×10-26.5 ×10-21.9 ×10-42.7 ×10-32.2 ×10-2relationshipp valueConclusions and perspectives
This study presents a systematic evaluation of a present-day Antarctic
climate simulation using the ECHAM5-wiso atmospheric circulation model
equipped with water stable isotopes. For this simulation covering the
period 1960–2013, the model has been nudged to ERA atmospheric reanalyses.
In particular, we tested its ability to correctly capture time-averaged
values, inter-annual variations, and seasonal cycles in surface mass
balance, temperature, and precipitation isotopic composition in Antarctica.
As fare as possible, we discarded model results prior to 1979, as model–data
differences prior to 1979 may arise from uncertainties in the reanalyses
prior to the period for which satellite data were assimilated.
Despite some divergences, simulated P-E values are found to be a good surrogate for
SMB. Most artefacts in modelled δ18O are coherent with those
for temperature, with systematic biases in different regions. Some of these
artefacts may be linked to the nudging method and the reanalyses. Model–data
comparisons are limited by data availability and by the fact that deposition
and post-deposition processes are not considered in the simulation. This is
particularly true for precipitation amounts, for which there is a lack of direct
measurements, and isotopic analysis for many regions at a multi-annual timescale. A
systematic comparison between water isotope measurements from
precipitation and surface snow or ice core samples is needed for further
in-depth studies of this topic. We note a lower quantitative performance from
ECHAM5-wiso for d (time-averaged values and inter-annual standard deviations)
than for δ18O, beyond its remarkable ability to resolve the
spatial distribution of time-averaged d values. Our findings confirm several
other studies conducted in other regions highlighting the fact that
atmospheric models including ECHAM5-wiso tend to underestimate the
variability of d in surface vapour (e.g. Steen-Larsen et al., 2016). Expanding
earlier site-specific studies, we show that the strength and slope of the
δ18O–temperature linear relationship is dependent on the
timescale in Antarctica over the last 4 decades. This finding has
implications for past temperature reconstructions using ice core records.
Finally, interesting results emerge for regional differences in the phase lag
between the mean seasonal cycle in δ18O and d, calling for
further studies to better characterise this feature in precipitation and ice
core records and better understand the implications of these lags for the
representation of seasonal changes in moisture source effects.
Our study deserves to be expanded to other atmospheric models equipped
with water stable isotopes and other nudged simulations using different
reanalyses datasets to assess the robustness of our findings. Furthermore,
obtaining more high-resolution ice core records is crucial to
better assessing model skills for inter-annual variations. More measurements of
precipitation, surface snow, and vapour monitoring for water isotopes would
also help to better characterise deposition and post-deposition processes,
their implication for model–data evaluation studies, and for an improved
climatic interpretation of ice core records.
Data availability
All data used in this paper are publicly available. Table
S1 in the Supplement resumes the type, location, covered
period, and data citation of each record. The isotopic time-averaged values
and standard deviations from precipitation, snow and firn–ice cores, and
seasonal precipitation data (accumulation, temperature, and isotopic
composition) were archived on the PANGAEA data library at
https://doi.pangaea.de/10.1594/PANGAEA.891279 (Goursaud, 2018). Ice
core data extracted from the Antarctica2k working group are available on the
NOAA World Data Center for paleoclimatology
(https://www.ncdc.noaa.gov/paleo-search/study/22589, Stenni et al., 2018).
The supplement related to this article is available online at: https://doi.org/10.5194/cp-14-923-2018-supplement.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
This study was 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.
Edited by: Ed Brook
Reviewed by: two anonymous referees
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