Introduction
The short and sparse instrumental record in the high latitudes of
the Southern Hemisphere means investigating long-term precipitation
variability in this region is difficult without access to appropriate
proxy records. Antarctic ice core data can provide suitable local
measurements for annual precipitation; however, in order to derive
accurate snow accumulation rates, snowfall must be high enough to resolve
annual layering where deposition noise due to surface processes exists.
Additionally, layer thinning from ice flow and snow densification must be
suitably constrained. Annual layering provides a record of the net snow
input at the site which is the sum of annual snow accumulation rate,
the transport of wind blown surface snow and losses through ablation.
Due to the lack of melt and relatively low wind speeds at the ice-coring site,
evaporation and wind losses are expected to make a minor contribution
to long-term variability and are neglected.
Law Dome (Fig. ) is a small independent icecap in East
Antarctica with a maritime climate yielding sufficiently
high snow accumulation rates to allow for annual layer resolution back to
22 BCE (including a year 0 in the
calendar). Law Dome is separated from the submarine-based Aurora Subglacial
Basin by the Vanderford Trench and associated Totten and
Vanderford glacier systems. Consequently, ice flow at Law Dome is largely
independent of the East Antarctic ice sheet and orography results in a strong
east–west snow accumulation gradient .
The principal ice core from Law Dome is the Dome Summit South (DSS) core
(66.7697∘ S, 112.8069∘ E, 1370 m elevation) which was
drilled approximately 4.7 km SSW of the dome summit
between 1987 and 1993. This site was selected for its favourable bedrock
topography and sufficiently low surface temperatures (mean annual average of
-21.8 ∘C) which precludes summer melt . The annual
snow accumulation rate at DSS is 0.68 myr-1IE where IE
stands for ice equivalent using an ice density of 917 kgm-3 to convert between kgm-2yr-1 of water and the ice
equivalent. This allows sub-annual resolution of water stable
isotopes and trace ions. The DSS record has been updated incrementally with
short cores drilled in subsequent years providing a complete 2035-year record
spanning 22 BCE to 2012 CE.
Law Dome, East Antarctica and the location of the Dome Summit South
(DSS) borehole. Also shown are 100 m surface elevation contours from
.
Coastal Antarctic ice core records with high resolution are being used
increasingly to reconstruct specific aspects of Southern Hemisphere climate.
For example, climate modes such as the Southern Annular Mode (SAM), the
El Niño–Southern Oscillation (ENSO) and the Interdecadal Pacific
Oscillation (IPO) have been reconstructed using annual layer-counted ice core
records from Law Dome and elsewhere .
Additionally, Law Dome has provided rainfall proxies for both eastern
Australia (1000 years) and south-west Western
Australia (750 years). The south-west Western Australian (SWWA)
rainfall proxy occurs via a direct precipitation oscillation between Law Dome
and SWWA, as a result of spatially coherent, meridional wind patterns that
push cool–dry or warm–moist air to either Law Dome or SWWA. Precipitation
oscillation has been related to long-term variability in the zonal
wave-number three (ZW3) pattern, injecting a meridional component to the
dominant westerly wind stream in the Southern Hemisphere
.
This study extends the length of the Law Dome annual snow accumulation rate record from
750 years to greater than 2000 years and increases the
instrumental overlap (and therefore calibration) period by 7 years (or
26 %) to span 1979–2012 CE.
Law Dome ice cores
Four ice cores from the Dome Summit South (DSS) site at Law Dome were used to
construct a composite snow accumulation rate record. The main DSS core
(DSS-main) is augmented in the upper portion by splicing three other ice
cores: DSS99, DSS97 and DSS1213, which cover the epochs 1841–1887, 1888–1988
and 1989–2012 CE, respectively. DSS97 and DSS99 replace sections of the
upper part of the DSS-main core that were subject to poor core quality due to
a combination of melt infiltration from thermal drilling in firn and drill
performance issues in the changeover to electromechanical drilling deeper
down. This composite record extends a previous compilation from a series of
short overlapping firn cores drilled in 2001, 2008 and 2009
and shows good replication of stable isotope signals
during the period of overlap with the previous record. Specifically, the root
mean squared difference between the layer thicknesses is 3 %, consistent
with previous findings of a strong correlation (r2∼0.95) between
annual ice thickness at the DSS site obtained from shallow firn cores
, due to the large snow accumulation rate relative to
surface relief at the site.
The 1195.6 m DSS-main ice core record is dated by identifying annual
layers in the seasonally varying water stable isotope ratios
(δ18O and δD). Annual boundaries are defined
by the peak of the summer isotope maximum, which has been identified as
occurring on average around 10 January . The isotope
layer counting is augmented by seasonally varying trace ions to a depth of
796.138 m, which corresponds to 22 BCE and shows excellent
agreement with the dating of major volcanic events .
Dating error is small; ±1 year prior to 894 CE and reaches a maximum
uncertainty of +4/-7 years at 22 BCE, which reflects ambiguities in the
interpretation of the record . The uncertainty estimate
allows 22 BCE to be a maximum of 7 years older or 4 years younger than dated.
All depths (except Fig. ) and annual layer thicknesses
are reported as ice equivalent depth (z), measured downward from the
ice surface and calculated from the physical depth (z′) using
z=∫0z′ρηdη,
where the density profile, ρη, is based on an
empirical fit to the DSS-main core measurements .
Time independence of the density profile Sorge's Law, is confirmed by the close correspondence between
the density profiles from DSS-main and DSS1213 (separated in time by 15
years), as shown in Fig. .
Firn density from the DSS-main and DSS1213 ice cores as a function
of physical depth.
Accumulation history
As firn is advected deeper into the ice sheet due to burial by subsequent
snowfall and the bulk downward and outward flow of the ice sheet, the
annual layers thin as a result of the vertical velocity gradient (the
vertical strain rate). This thinning can be mathematically modelled and
a correction factor applied if the vertical strain rate profile is known
or can be estimated. A correction was applied to the composite record
using coefficients modelled from the DSS-main core, which provided more
robust estimates than the shorter records as the extra length damps the
influence of short-duration fluctuations.
Ice thinning is modelled by applying a calculated vertical strain rate
profile to a layer of initial thickness equal to the long-term average
annual snow accumulation rate. The actual snow accumulation rate for
any year (and corresponding depth) is then estimated as the ratio of the
actual annual layer thickness relative to the modelled layer thickness
multiplied by the long-term average annual snow accumulation rate,
a(t)=a×λ(t)Λ(t),
where a(t) is the annual snow accumulation rate for year t, a is the
long-term annual snow accumulation rate, λ(t) is the observed annual
layer thickness for year t and Λ(t) is the corresponding
modelled annual layer thickness based on the long-term annual snow
accumulation rate and the vertical strain rate profile. This method
is unable to distinguish between a constant vertical strain rate or a
linear trend in snow accumulation. Therefore, it is assumed that
any change in layer thickness is due only to the vertical strain and
that there is no long-term trend in snow accumulation rate.
Two accumulation histories, each corrected using a different vertical strain
profile are compared. The vertical strain rate models used are (i)
a piece-wise linear model and (ii) a power-law
model .
(a) Piece-wise linear fit to the annual layer thickness
data and (b) power-law fit to the annual layer thickness data.
Annual (grey) and smoothed (coloured) snow accumulation rate history
for 22 BCE–2012 CE based on (a) piece-wise linear vertical strain
rate profile, with the long-term average snow accumulation rate of
0.680 myr-1IE shown (dashed horizontal), (b) power
law vertical strain rate profile, with the long-term average snow
accumulation rate of 0.686 myr-1IE shown (dashed horizontal)
and (c) difference in the smoothed
accumulation histories (piece-wise linear-power-law vertical strain rate) normalised by
the power-law vertical strain rate (black) and accumulation excess (Eq. ) for positive (blue) and negative (red) periods.
Piece-wise linear vertical strain rate model
The effects of layer thinning due to vertical strain rate can be accounted
for by linear least squares fitting of a model to the
ice equivalent annual layer thickness data as a function of ice equivalent
depth. This model has two distinct regions: a lower region with a linear
increase in vertical strain rate from zero at the base of the ice sheet and
an overlying region of constant vertical strain rate. Integration of such a
strain rate profile yields a vertical velocity profile with corresponding
quadratic and linear segments. Previous modelling of the layer thinning for
the DSS-main core , using a
relation, places the transition between these regions at a depth of
839 m (ice equivalent), somewhat below the 774 mIE maximum
depth of the composite record in this study. Consequently, the annual layer
thickness model requires just two free parameters: the long-term annual snow
accumulation rate and the constant vertical strain rate. These are estimated
from the intercept and slope, respectively, of a least squares fit to the ice
equivalent layer thickness as a function of ice equivalent depth.
The long-term average snow accumulation rate calculated from the layer
thickness model applied to the DSS-main core is
6.80×10-1±4.0×10-3 (1 standard
error) myr-1IE and the vertical strain rate is
6.32×10-4±7.8×10-6 yr-1. The fit to the annual
layer thickness data is shown in Fig. a and the snow
accumulation rate time series in Fig. a. A smoothed
snow accumulation rate time series using a Gaussian low-pass filter with
width σ=2.99 years (equivalent half power width 10 years) is also shown.
Power-law vertical strain rate model
The piece-wise linear model assumes that in areas near ice divides and dome
summits the vertical strain rate depth profile is proportional to the
horizontal velocity profile. This can be approximated by a linear lower
segment and a constant upper segment . The
depth profile of horizontal ice velocity at the DSS site was determined by
repeated measurements of the borehole inclination following ice core drilling
. Fitting a power-law distribution to
these horizontal velocities provides the basis for an improved vertical
strain rate profile. As the temperature at the base of the borehole is below
the in situ freezing point , we ignore any terms
representing melt or slip at the base of the ice sheet. The free parameters
of the model are estimated by least squares fitting to the borehole
displacements of . Data near the surface (depths less than
75 m) were excluded as the upper section of the borehole was
thermally drilled, and the corresponding large borehole diameter creates
unreliable inclination data in this zone.
At depths below 800 m, which is beyond the zone of interest for the
present study, the flow regime at DSS becomes more complex due to the
influence of the surrounding bedrock topography. Additionally, deformation
rates, particularly the simple shear strain rate, are increasingly influenced
by the development of large-scale polycrystalline anisotropy below
800 m . In situations where ice flows over rough
bedrock topography, the undisturbed flow in the upper portion of the ice
sheet – that is relevant to the 2 kyr accumulation record – can be
approximated by assuming flow over an offset smoothed surface above the true
base . The (virtual) origin of the velocity profile
corresponding to the smoothed bed is also offset from the physical origin of
the velocity profile at the bedrock.
The borehole horizontal displacement (D) is approximated using the shape function approach of , namely a power-law profile with
parameters determined from the model fit:
D=C1-zH-z0p,
where z is the ice equivalent depth (m),
C = 4.35 ± 3.8×10-3 m is an arbitrary scaling
factor, the exponent of the power law is p=4.19 ± 0.025 and
z0= 74.66 ± 0.83 m represents the (positive) vertical
displacement of the virtual velocity profile origin. The constant
H=1178.22 m represents the approximate ice equivalent ice sheet
thickness from the displacement data. The horizontal displacement and the
derived power law are shown in Fig. .
Power-law velocity profile fit (line) to the horizontal displacement
data (dots) of .
Integrating Eq. () with respect to depth yields the vertical
velocity profile v(z)
v(z)=a-sCz-zp+1zH-z0p,
where a is the long-term annual snow accumulation rate (in
myr-1IE) and s is a scaling factor linearly related to the
modelled vertical strain rate at the surface.
A least squares optimisation of the resulting vertical velocity profile to
the observed annual layer thicknesses for the DSS-main core yields a
long-term average annual snow accumulation rate of 6.86×10-1±4.1×10-3 myr-1IE and a surface vertical strain rate
of 6.57 × 10-4± 8.1 × 10-6, with the fit shown
in Fig. b. The resulting snow accumulation
time series, including a 10-year low-pass Gaussian filtered version, is shown
in Fig. b.
Discussion
Strain rate model
The piece-wise linear vertical strain rate model assumes that flow near an
ice divide is two-dimensional, so the vertical strain rate profile with depth
is proportional to the longitudinal derivative of the horizontal velocity
profile. As described previously, the horizontal velocity profile is
approximated by a linear lower segment and a constant upper segment. While
this may be a good approximation for sites where the flow is two-dimensional,
the DSS site is located ∼4 ice thicknesses (4.7 km) from the Law Dome
summit, where the flow is three-dimensional and slightly divergent
. Here, a power-law approximation to the horizontal
velocity profile is more realistic. A comparison of the modelled vertical
strain rate profiles at DSS (Fig. ) show that the piece-wise
linear model generates higher vertical strain rates at depth. This leads to
an underestimation of annual layer thicknesses at depth, and correspondingly
higher snow accumulation rates.
Vertical strain rates from the piece-wise linear and power-law models which were calculated using the annual layer thickness data.
Furthermore, the nature of the least squares fit required to calibrate
the piece-wise linear model compensates for the excessive vertical strain
rate at depth by lowering the constant strain rate in the upper region of
the depth profile, producing a lower and potentially unrealistic estimate
of layer thinning in this region. The result is a small negative trend in
snow accumulation with depth in the upper part of the core, switching to
a positive trend in the deeper parts. Overall, this produces a shallow
concave bias in the snow accumulation rate estimates, with lower values
at middle depths (Fig. a). Furthermore, there is more
low-frequency power in the spectrum of the piece-wise linear snow accumulation
rate time series compared to the equivalent spectrum for the power-law model,
which is consistent with removal of the concave bias
in vertical strain rates from the piece-wise linear model.
Although we cannot exclude the possibility that this concave shape
reflects a real environmental signal, we consider the power-law
vertical strain rate profile to provide a more realistic snow accumulation
rate reconstruction at DSS through its connection with the observed
horizontal displacement data.
The vertical strain rate magnitude at DSS is not solely related to the
horizontal displacement profile, rather, it is dependent on the shape of the
profile, due to the three-dimensional divergent nature of the DSS flow
regime. The scaling parameter s (Eq. ) accounts for
the effects of three-dimensional flow in the power-law strain rate model.
Accordingly, we base the subsequent analyses on this model. Note, however,
that the differences between the snow accumulation rate histories derived
using the two vertical strain rate models are small
(< 0.04 myr-1IE for any year, see
Fig. c).
The shortcomings of the piece-wise linear vertical strain rate model,
particularly for capturing variability on multi-centennial and shorter
timescales, are illustrated by comparing estimates of the vertical strain
rate at the surface calculated using running 100 m subsets of the
annual layer thickness data for each of the vertical strain rate models
(Fig. ). The vertical strain rates at the surface are
more constant, and hence more internally consistent, for the power-law model. It
should be noted that the results for both models are noisier in the upper
portion of the ice-sheet (above ∼400 m) and are not shown.
This is driven by the significantly fewer annual data points in 100 m
intervals from this zone due to the reduced effects of layer thinning near
the surface and imperfections in the density model.
The estimated vertical strain rate at the surface for the two models are in
reasonable agreement (6.32×10-4±7.8×10-6 yr-1
and 6.57×10-4±8.1×10-6 yr-1 for the
piece-wise linear and power-law models, respectively). However, both of these
values differ significantly from the surface GPS-based value of
7.72×10-4±3.1×10-6 yr-1 .
These differences might arise because the modelled vertical strain rates are
estimated using data from only the upper portion of the ice sheet rather than
the full ice sheet thickness.
Snow accumulation history
The mean snow accumulation rate of 0.688 ± 0.130 (1 standard
deviation) myr-1IE (0.682 ± 0.129 myr-1IE
for the piece-wise linear model) is in close agreement with previous
estimates of 0.678 , 0.680 and
0.688 myr-1IE . The assumption of no
long-term trend in snow accumulation rate can be checked using the above
long-term accumulation rate estimates. Specifically, each estimate is based
on data fitting over different epochs; therefore, the similarity of these
estimates suggests that either the assumption of no long-term trend in snow
accumulation rate (see Sect. ) is valid or that any
trend in snow accumulation rate has been linear and constant over the last
2 kyr. Additionally, the uncertainty in the estimated vertical strain
rate (and associated long-term snow accumulation rate) on the accumulation
time history was assessed using a Monte Carlo simulation. For the piece-wise
linear model, uncertainty in the accumulation record increases approximately
linearly with depth, with an average value of 0.70 % and a maximum of
1.77 %. Therefore, the assumption of a zero long-term snow accumulation
trend does not rule out a trend of 0.88 % per millennium.
The snow accumulation rate distribution has a standard deviation of
0.130 myr-1IE and is slightly, but significantly
p<0.001, positively skewed (0.47), i.e. it has a long tail
at higher snow accumulation rates (see Fig. ). Additionally, the
distribution has more mass in the tails than a normal distribution, with a
non-mesokurtic p<0.001, probability density
function with slightly raised excess kurtosis (0.58).
Inferred surface vertical strain rates from the piece-wise linear and power-law models. The surface strain rates are determined from annual layer thickness data from a 100 m depth interval centred on each ice equivalent depth value and the fixed long-term average annual snow accumulation rate specific to each model.
Integrated snowfall excess (Eq. 5) based on the 10-year low-pass filtered
power-law-based DSS snowfall record. Positive values indicate a period with
above-average low-pass filtered snowfall. Only integrated excesses larger in
magnitude than 2 mIE are shown.
Epoch (CE)
Integrated snowfall
excess (mIE)
380–442
3.323
663–704
-2.377
727–783
3.056
1429–1468
-2.008
1970–2009
2.597
Considering the 10-year low-pass filtered snow accumulation time series, the
integrated snow accumulation excess (I) can be defined as
I=∫t0t1a˘(t)-a¯dt,
where a˘(t) is the low-pass filtered snow accumulation time series,
and the epoch t0–t1 defines a contiguous period when a˘(t) is
always above or below the long-term average snow accumulation (a¯).
The recent above-average snow accumulation rate of 1970–2009 CE is the
third largest period of integrated snow accumulation excess throughout the
record (Fig. c), after 380–442 and 727–783 CE
(see Table ); however, it has the strongest anomaly as it
occurs over a shorter time interval. Not only is the strength of the recent
39 year (1970–2009) snow accumulation rate anomaly uncommon, its duration is
also atypical, with only 5 (3 positive and 2 negative) events of equal or
longer duration, although decadal-scale events are common with 74 events (33
positive and 41 negative) of at least a 10-year duration in the record. The
three largest positive anomalies are all larger in magnitude than any of the
negative anomalies in the record, possibly due to the positively skewed
nature of the snow accumulation rate distribution. The three longest
integrated low snowfall periods span 663–704, 933–975 and
1429–1468 CE. The combination of the low snowfall period for 663–704 CE
followed by the high snowfall period of 727–783 CE results in a substantial
trend in snow accumulation rate between the mid-7th century and the end of
the 8th century. The continental-scale low snow-accumulation periods of
1250–1300 and 1420–1550 CE are reflected in the DSS
record with strong negative I (but are interspersed with short periods of
above-average snow fall) for the epochs 1239–1302 and 1415–1522 CE. The
continental-scale low snow-accumulation period of 1660–1790 CE is also
recorded at the DSS site, although with a later commencement (1691 CE) and
one short, but large (I=0.623 mIE), positive anomaly between
1763 and 1772 CE.
Probability density function for the power-law-based annual snow
accumulation rate (solid line) and the equivalent normal distribution (dashed
line). Note excess accumulation in the right hand tail of the power-law
distribution.
There is no obvious relationship between anomalous accumulation periods, or
trends associated with them, and the annually dated volcanic history of
because it is unlikely that the low-pass filtered
accumulation record would reflect the high-frequency effect of atmospheric
sulfate loading due to volcanic activity. Furthermore, there is no
meaningful correlation (r=-0.038, p>0.2) between Law Dome snow
accumulation and the 1008-year Southern Annular Mode (SAM) record of
, consistent with the finding of .
Similarly, the 1000-year Law Dome CO2 record of
shows no obvious commonalities between CO2 and accumulation.
The accumulation series was compared with the annual δ18O
isotope ratio at the site over the period of 174–2012 CE. Correlation analysis
reveals that the two series are weakly correlated, with r=0.227. While only
representing a common variance of 5 %, the result is highly statistically
significant; the 95 % confidence interval is [0.191–0.262], as computed
using a method which accounts for autocorrelations . A
similar level of variance is found for 10-year low-pass filtered data,
although the reduced effective degrees of freedom result in a larger 95 %
confidence interval [0.036–0.414]. The weak relationship between the isotope
ratio and precipitation is consistent with earlier findings
which demonstrated a strong coupling of the isotope
ratio and accumulation in the glacial period but not in the Holocene.
notes the importance of circulation intensity relative
to thermodynamic control of moisture content in determining precipitation,
and this is particularly important at Law Dome where cyclonic influence is
large. The weak control of temperature over recent centuries also reflects
other findings at other moderate to high accumulation sites
. Furthermore, show the isotope
signal is affected by snow accumulation timing, deposition and surface
reworking on short timescales which may also impact
δ18O-accumulation coherence.
MultiTaper Method power spectrum of power-law-based
snow accumulation rate time series using a resolution of 2 and 3 tapers. The
period of spectral components above 99 % significance are shown.
Spectral analysis of the 2 kyr annual snow accumulation rate record
(Fig. ) shows a number of significant periodicities in the
sub-decadal band of 2.4–8.5 years, while one 29.7-year period is also evident,
which may be related to climate variability. The sub-decadal power at
2.4–8.5 years is in the broadband of ENSO-type frequencies. An analysis of
sea salts at Law Dome has previously shown an ENSO signal in the
summer-period sea salts, with associated ENSO-band significant frequencies of
2.8, 4.4, 6.0 and 7.5 years . It is interesting to note that
ENSO-type frequencies are also evident in the snow accumulation rate record
despite there being no significant correlation between the snow accumulation
rate record presented here and the Southern Oscillation Index over the epoch
of 1870–2012 CE. The 29.7-year period is not seen in the summer sea salt record
but may be related to the IPO , as a
1000-year reconstruction of the IPO has been produced recently using multiple
Law Dome records. Snow accumulation rate was a necessary input parameter to
this IPO reconstruction to produce a high skill reconstruction
. The higher frequencies in the sub-decadal band (2.4 and
2.7 years) are generally more intermittent throughout the 2 kyr period
(Fig. ). The damping of these higher frequency signals
may be a real climate signal, but may also result from noise associated with
surface processes, such as the wind-blown redistribution of snowfall and the
passage of sastrugi over the site. In contrast, the 29.7-year period is more
persistent throughout the record, and there are multi-centennial epochs where
this frequency is quite strong (e.g. 100–550, 750–1000 and 1500–2012 CE).
Therefore, if the 29.7-year period is associated with the IPO, this suggests
that the IPO signal has remained relatively steady at Law Dome for the past
2 kyr. This is further reinforced by , who showed
that both positive and negative phases of the IPO could be reconstructed with
high skill over both the instrumental calibration period (1870–2009 CE) and
the full millennial period spanning 1000–2009 CE.
MultiTaper Method evolutive power spectrum of the calculated power
law DSS snow accumulation rate time series using a resolution of 2 and 3 tapers
and a bandwidth of 256 years.
Correlation between the Law Dome snow accumulation record and
shallow ice core records from Queen Maud Land .
Core
Latitude
Longitude
Epoch
Correlation
(∘ S)
(∘ E)
(CE)
DML94C07_38
-71.162
-6.699
1979–2006
0.389
DML95C07_02
-71.568
-6.667
1979–2006
0.318
DML96C07_39
-71.408
-9.917
1979–2006
0.167
DML97C07_04
-72.064
-9.558
1979–2006
0.423
NM02C02_02
-70.656
-8.254
1980–2001
0.340
Spatial correlation maps for (a) ERA-Interim
precipitation–evaporation for 1979–2013 CE and (b) RACMO2.1/ANT surface mass balance for 1979–2010 CE.
Contours show the 95 % confidence level while yellow stars denote the
location of DSS and the grey box shows Queen Maud Land.
Spatial variability
The snow accumulation rate history from the DSS ice-core captures broad-scale
variability across a large region of East Antarctica, well beyond the
immediate vicinity of the Law Dome summit (see Fig. ),
indicated by the spatial coherence of annual snow accumulation rate
correlation from two climate reanalysis models. Temporal correlations at Law
Dome are significant for both ERA-Interim (r=0.6973, p<0.001) and
RACMO2.1/ANT (r=0.7604, p<0.001). The spatial pattern of the correlation
between the modelled snow accumulation rate at Law Dome and elsewhere in
Antarctica agrees well between the two models, although there is a much
larger region of significant positive correlation in Queen Maud Land, East
Antarctica using the RACMO2.1/ANT data set. The correlation with the
RACMO2.1/ANT data set in this region is more likely representative, as
RACMO2.1/ANT is strongly correlated to extensive observational data
and snow accumulation records from shallow cores in the
region are positively correlated with the Law Dome snow accumulation record
(see Table ). The magnitudes of the annual snow accumulation
rates at Law Dome are also in reasonable agreement. For the 1979–2012 CE
period covered by ERA-Interim, the mean calculated snow accumulation rate is
0.749 ± 0.142 compared to the ERA-Interim modelled value of
0.713 ± 0.136 myr-1IE. The comparison with RACMO2.1/ANT
is over the shorter period of 1979–2010 CE, where the calculated mean rate is
0.759 ± 0.141 and the RACMO2.1/ANT mean value is
0.525 ± 0.087 myr-1IE. It is worth noting that this is a
predominantly positive/neutral IPO period and these spatial relationships
could change during strongly negative IPO periods given the clear IPO signal
that is present at Law Dome .
The Law Dome regional accumulation map exhibits large-scale spatial coherence
Fig. , with an average e-folding distance of 900 km
(distance at which the correlation drops to e-1). This suggests that
inter-annual variability in snowfall is dominated by year-to-year changes in
the large-scale atmospheric dynamical forcing, in agreement with a
teleconnection pattern linking Law Dome accumulation with a zonal wave three
index . The spatial correlation between 500 hPa
geopotential height and the snow accumulation rate (Fig. )
shows a quasi-ZW3 pattern, however the Australian and African high pressure
poles are contracted towards Antarctica. Principal component analysis (PC) of
the Southern Hemisphere 500 hPa geopotential height field supports
this. While PC1 (31 %) and PC2 (15 %) represent most of the dominant
annular variability in the Southern Hemisphere 500 hPa geopotential
height, in the Law Dome region there is little correlation between these
first two principle components and accumulation. In contrast, PC3 (11 %)
(r=0.5, p<0.01 for the Law Dome region) shows a strong correlation with
Law Dome accumulation (Fig. inset). This local modulation of
the large-scale variability may represent a tropical signal as demonstrated
by the relationship between PC3 and the Pacific–South American modes
. This is a further line of evidence that Law Dome ice cores
are not only sensitive to the dominant annular signal centred over West
Antarctica, but also preserve tropical and mid-latitude Pacific and Indian
Ocean signals as shown by .
It should be noted that the snow-preserved accumulation record is
influenced by local factors such as wind removal and potential regional
(Law Dome) variations driven by interactions between weather systems and local
orography. Wind speeds at Law Dome Summit are generally low
and net wind removal is not believed to be a major influence at this
site. However, the strong orographically driven accumulation gradient
across Law Dome could conceivably lead to a local signal in
accumulation variability if the climatology of cyclonic systems and
wind tracks changes. Such spatial distribution changes still represent
a climate signal, rather than an amplitude modulation of a relatively
stable spatial distribution. Therefore, these
influences might reduce coherence between the Law Dome accumulation
series and the broader Wilkes Land region. The observed coherence with
the precipitation minus evaporation fields in the reanalyses discussed
above suggests that the local influences are not significant.
Spatial correlation map for ERA-Interim 500 hPa geopotential
height and power-law-based snow accumulation rate for
1979–2012 CE. Contours show the 95 % confidence level while the yellow star
denotes the location of DSS. Inset: third principal component of the Southern
Hemisphere 500 hPa geopotential height correlated with power-law-based snow accumulation rate for 1979–2012 CE.
Conclusions
Two thousand years (22 BCE to 2012 CE) of annual snow accumulation rates have
been calculated for Law Dome, which extends the length of the previous 750-year record. To
deconvolve the effects of ice sheet thinning on calculated snow accumulation
rate profiles, two vertical strain rate models were evaluated, of which a
power-law model proved the most appropriate. The long-term accumulation rate
of 0.688 ± 0.130 myr-1IE for this model is in agreement
with previous estimates, and further supports the notion that there is no
long-term trend in snow accumulation rates, or that any trend is constant and
linear over the period of measurement. Several anomalous periods of
accumulation exist in the record, most notably the periods of 380–442,
727–783 and 1970–2009 CE (high accumulation) and 663–704, 933–975 and
1429–1468 CE (low accumulation). The record has wide-reaching relevance,
indicated by a spatial coherence in correlations with two climate reanalysis
models showing the capture of large-scale variability and possible links to
tropical and higher-latitude dynamical forcing. Furthermore, significant
periodicities were observed in the record which were broadly consistent with
ENSO- and IPO-type variability, suggesting these patterns play an important
role in the delivery of mass to the Law Dome region. The publication of this
accumulation record will allow for the investigation, by both this research
group and others, of relationships between local meteorologically and
environmentally sensitive variables (such as stable water isotopes,
methanesulfonic acid and sea-salt ions) as well as more regional and
hemispheric teleconnections. Specifically this research group has work
underway updating the DSS stable water isotope record (and will publish
comparisons with this accumulation record as part of that work) and is
further studying the use of the DSS accumulation record as a proxy for
eastern Australian palaeoclimate.