We use 24 years (1992–2016) of high-quality meteorological observations at
Neumayer Station, East Antarctica, to force a surface energy balance model.
The modelled 24-year cumulative surface melt at Neumayer amounts to 1154 mm
water equivalent (w.e.), with only a small uncertainty (±3 mm w.e.)
from random measurement errors. Results are more sensitive to the chosen
value for the surface momentum roughness length and new snow density,
yielding a range of 900–1220 mm w.e. Melt at Neumayer occurs only in the
months November to February, with a summer average of 50 mm w.e. and large
interannual variability (σ=42 mm w.e.). This is a small value
compared to an annual average (1992–2016) accumulation of 415±86 mm w.e. Absorbed shortwave radiation is the dominant driver of temporal
melt variability at Neumayer. To assess the importance of the
snowmelt–albedo feedback we include and calibrate an albedo parameterisation
in the surface energy balance model. We show that, without the
snowmelt–albedo feedback, surface melt at Neumayer would be approximately
3 times weaker, demonstrating how important it is to correctly represent this
feedback in model simulations of surface melt in Antarctica.
Introduction
The Antarctic ice sheet (AIS) contains more than 25 million km3 of ice,
sufficient to raise global mean sea level by almost 60 m if melted
completely . Between 1992 and 2017, the AIS lost mass
at an accelerated rate, contributing 7.6±3.9 mm to global sea level
. This mass loss is mainly observed in coastal West
Antarctica and the Antarctic Peninsula (AP) and is caused by glaciers that
accelerated after their buttressing ice shelves had thinned or disintegrated
. The interaction between meltwater and
firn, the intermediate product between snow and glacier ice, is hypothesised
to play an important role in ice shelf disintegration
. If the firn layer contains enough air, as is
the case for most of the AIS, meltwater can percolate downwards and refreeze
. If the storage capacity of the firn layer is
reduced, surface meltwater can flow laterally towards the ice shelf edge
, be stored englacially or form
ponds on the ice shelf surface . In all cases,
meltwater can accumulate in crevasses, thereby increasing the hydrostatic
pressure in the crevasse tip, causing it to penetrate farther down. When a
crevasse reaches the bottom of the ice shelf or a basal crevasse, part of the
ice shelf disintegrates, a process called hydrofracturing
. Hydrofracturing has been identified as a
potential precursor of rapid loss of Antarctic ice, accelerating sea level
rise . In combination with enhanced ocean swell under
low sea-ice conditions , hydrofracturing likely caused
the disintegration of the Larsen B ice shelf in the AP in 2002
. In July 2017, a large iceberg calved
from the Larsen C ice shelf, but it is unclear whether this signifies a further
southward progression of ice shelf destabilisation in the AP
.
Improving our predictive capabilities of future ice shelf stability, AIS mass
loss and associated sea level rise thus requires a thorough understanding of
the surface melt process on Antarctic ice shelves. In contrast to meltwater
occurrence, which is readily observed from space
, observational
estimates of surface melt rates on Antarctic ice shelves are rare;
they have been obtained locally through explicit modelling of the surface
energy balance (SEB)
. In
turn, these enabled continent-wide melt rate estimates using calibrated
satellite products based on backscatter strength of radio waves
. These studies invariably
demonstrate that, in most parts of Antarctica, melt is currently a weak and
intermittent process. In this melt regime, the positive snowmelt–albedo
feedback (SMAF) plays a decisive role: when snow melts, meltwater may refreeze in
the cold snowpack, resulting in considerably larger grains (∼1 mm)
than new snow or snow that has been subjected to only dry compaction (∼0.1 mm). Larger snow grains reduce backward scattering of photons into the
snowpack, increasing the probability of absorption and reducing the surface
albedo, especially in the near-infrared
. This further enhances absorption of
solar radiation and melt. For pure, uncontaminated snow, the strength of the
SMAF depends on multiple factors, e.g. the intensity and
duration of the melt and the frequency and intensity of snowfall events,
which provide new snow consisting of smaller grains. We therefore expect the
SMAF to be spatially and temporally variable on Antarctic
ice shelves.
Most studies on the SMAF address the removal of
(seasonal) snow and the appearance of dark soil or water
, leading to
further warming of the air and water. These studies commonly express the
melt–albedo feedback in terms of air and water temperature sensitivity. Our aim
is to quantify the impact on the melt rate of the darkening but not the
disappearance of snow, a process addressed by far fewer studies
. To that end, we implement a snow albedo
parameterisation in an SEB
model, which is then calibrated using observations and used to study the
sensitivity of melt rates to snow properties that influence snow albedo. We
use 24 years of high-quality in situ observations from the
German research station Neumayer (Fig. ) to calculate the SEB
and melt rate. We investigate the effects of measurement uncertainties and
model settings on the modelled cumulative amount of surface melt. We then
analyse the main drivers of surface melt and the magnitude of the
SMAF at Neumayer by switching the feedback process
in the albedo parameterisation on and off.
The SEB model is explained in Sect. , followed by a
description of the albedo parameterisation in Sect. . The
meteorological data used to force the SEB model are described in
Sect. . The results section is split into two parts: in
Sect. we present and discuss the SEB and melt rate that are
obtained using the observed albedo. In Sect. the albedo
parameterisation is used instead and the SMAF is
quantified and discussed. Finally, the results are discussed in
Sect. .
MethodsSurface energy balance model
The one-dimensional energy balance model is a further development of the
models presented by , ,
and ; here only the
main features are described. The energy balance of an infinitesimally thin
surface layer (the “skin” layer) is defined as follows:
M=SW↓+SW↑+LW↓+LW↑+QS+QL+QG,
where positive fluxes are defined to be directed towards the surface.
SW↓ and SW↑ are the incoming and reflected shortwave
radiation, LW↓ and LW↑ are the downward and upward
longwave radiation, QS and QL the turbulent sensible and latent heat
fluxes and QG is the conductive subsurface heat flux. We neglect latent
energy from rain. M is the energy used to melt snow or ice and is non-zero
only when the surface has reached the melting point of ice
(Ts=273.15K). Throughout this paper, melt and accumulation
amounts are expressed in terms of millimetre water equivalent (mm w.e.), which
equals kg m-2. In order to calculate QG and allow for densification,
meltwater percolation and refreezing, a snow–firn model is used, initialised
with 70 layers. The layer thickness varies from 1 cm at the top to 2 m at
the bottom (25 m depth). We impose a no-energy flux boundary condition at
the lowermost model level. New snow density is parameterised following the
expression of , which relates it to the prevailing
surface temperature (Ts) and 10 m wind speed (V10m) and imposes a
lower limit of new snow density ρs,0. Meltwater percolation is based
on the tipping-bucket method e.g., allowing for
immediate downward transport (within a single timestep of 10 s) of remaining
water if a layer has attained its maximum capillary retention, as modelled
using the expressions of . Meltwater refreezing
increases the density and temperature of a layer. At the bottom of the firn
layer, the meltwater is assumed to run off immediately, i.e. the model does
not allow for slush/superimposed ice formation or lateral water movement.
Turbulent fluxes are calculated following the “bulk” method, which is based
on Monin–Obukhov similarity theory (see e.g.
for relevant equations) between a single measurement level (2 m for
temperature and humidity, 10 m for wind) and the surface, assuming the
latter to be saturated with respect to ice and using the stability functions
according to for unstable and
for stable conditions.
Subsurface penetration of shortwave radiation is calculated using a spectral
model , based on the parameterisation by
, which is in turn based on the two-stream radiation
model of . The impact on modelled melt and the
quantification of the SMAF is discussed in the relevant
sections.
The terms in Eq. () are either based on observations or can be
expressed as a function of the skin temperature Ts. The SEB is
solved iteratively by looking for a value of Ts that closes the
SEB to within 0.005 K between iterations: if Ts>273.15K, it is reset to 273.15 K and excess energy M is used for
surface melt. To evaluate model
performance, the modelled value of Ts is compared to observed
Ts calculated from LW↑, using
Stefan–Boltzmann's law for a longwave emissivity ϵ=1:
LW↑=σTs4,
where σ=5.67⋅10-8 W m-2 K-4 is the
Stefan–Boltzmann constant.
Surface roughness lengths for momentum, heat and moisture are related through
the expression of :
lnz0*z0,m=a1+a2lnRe*+a3lnRe*2,
where z0* represents either z0,h or z0,q, the roughness
lengths for heat and moisture respectively, and a1, a2 and a3 are
coefficients determined by for various regimes of the
roughness Reynolds number Re*=u*z0,mν with
kinematic viscosity ν and friction velocity u*.
Albedo parameterisation
Because the shortwave radiation sensor faces the sky and includes a
significant direct component, measured SW↓ suffers from relatively
large uncertainties owing to poor sensor cosine response, sensor tilt and/or
rime formation . In order to improve the accuracy of
observed net shortwave radiation used in the SEB calculations
(Sect. ), we calculate SWnet based on SW↑,
which is diffuse and hence much less sensitive to these errors. To further
decrease the impact of these errors, we use a 24 h moving average albedo,
as described in . In Sect. , in
which albedo is parameterised to study melt–albedo feedbacks, for consistency
we use measured SW↑ in combination with parameterised albedo to
estimate SWnet.
In Sect. , the parameterised surface albedo α is
described as a base albedo αS, modified by perturbations describing
the effect of changing solar zenith angle θ (dαu),
the cloud optical thickness τ (dατ) and the
concentration of black carbon in the snow (dαc)
:
α=αS+dαu+dατ+dαc.
For Antarctica, we neglect the impact of impurities in the snow
(dαc=0); dαu and dατ
both depend on the base albedo αS, dαu in addition
depends on the solar zenith angle (u=cosθ) and
dατ on the cloud optical thickness τ:
5dαu=0.53αS(1-αS)(1-0.64x-(1-x)u)1.2,6dατ=0.1τ(αS+dαc)1.3(1+1.5τ)αS,
where x=minτ3u,1. The base albedo depends
on the snow grain size re (in metres):
αS=1.48-1.27048re0.07,
in which the snow grain size re on time step t is parameterised as
re(t)=re(t-1)+dre,dry+dre,wetfo+re,0fn+re,rfr.
Here, dre,dry and dre,wet describe the
metamorphism of dry and wet snow respectively, fo, fn and fr are the
fractions of old, new and refrozen snow, and re,0 and re,r are the
grain sizes of new and refrozen snow. Dry snow metamorphism is parameterised
following :
dre,drydt=dredt0η(re-re,0)+η1/κ,
where re,0 is the new snow grain size, and the coefficients
dredt0, η and κ are
obtained from a look-up table. This look-up table is compiled based on
simulations with the SNICAR model , which calculates
the snow metamorphism resulting from temperature gradient metamorphism.
dre,wet is a function of the snow grain size re itself and
the liquid water content fliq:
dre,wetdt=Cfliq34πre2,
where C is a constant (4.22⋅10-13 m3 s-1).
The fractions fo, fn and fr are derived from the snow/firn model,
and the grain sizes of new and refrozen snow are constants; the method for
determining their values from a tuning exercise is described in
Sect. .
(a) Downward longwave radiation vs. air temperature. The
red lines are quadratic fits of the upper and lower 5 percentile boundaries.
The longwave-equivalent cloud cover is determined by linear interpolation
between these bounds. (b) Optical thickness vs. cloud cover. The
red line resembles the best fit to a function
τ=c1ec2Nϵ-1. The shaded area indicates the
95 % uncertainty range.
To determine cloud optical thickness τ, an empirical relation between
τ and the longwave-equivalent cloud cover Nϵ is used following
:
τ=c1exp(c2Nϵ)-1,
with fitting parameters c1 and c2. Nϵ is determined using a
method described by , which relates hourly
values of downward longwave radiation LW↓ to near-surface air
temperature T2m as illustrated in Fig. a. Red lines
indicate quadratic fits through the upper and lower 5 percentiles of the data,
assumed to represent fully cloudy and clear conditions, respectively.
Nϵ is obtained by linearly interpolating between these upper and
lower bounds, yielding values between 0 and 1. Hourly values for cloud cover
are then used to obtain values for τ (Fig. b). The values
used for the fit parameters c1=5.404 and c2=2.207 (both dimensionless)
differ somewhat from , who used daily values
for the fit.
Observational data
The SEB model is forced with data from the meteorological observatory at the
German research station Neumayer, situated on the Ekström ice shelf
. The observatory has been operational since 1981 and was
relocated in 1992 and 2009. In 2016, its location was 70∘40′ S,
8∘16′ W (Fig. ). The observatory is one of only four
Antarctic stations – and the only one situated on an ice shelf – that is part
of the Baseline Surface Radiation Network (BSRN), a global network of
stations with high-quality (artificially ventilated) radiation observations,
coordinated by the Alfred Wegener Institute (AWI). We use hourly averages of
2 m temperature (T2m) and specific humidity (q2m), 10 m wind
speed (V10m), surface pressure (p) and radiation fluxes for the period
April 1992–January 2016 (24 years) to force the SEB model; their uncertainty
ranges are provided in Table . Approximately 4.1 % of the data
points contained at least one missing variable, which mostly come from daily
performed visual inspection of the data. To obtain a continuous data set, all
missing data were replaced: pressure, relative humidity, wind speed,
temperature and longwave radiation were simply linearly interpolated. In the case
of shortwave radiation, the missing value was replaced by imitating the
average daily cycle of the 2 preceding days. As the measurement station is
visited and maintained every day, the impact of rime formation is limited, as
is the tilt of the observation mast, resulting in a high-quality
meteorological data set.
Listing of used measurement variables and their associated
measurement uncertainties.
VariableUncertainty rangeV10mmax (0.5 m s-1, 5 %)SW↓5 W m-2SW↑5 W m-2LW↓5 W m-2LW↑5 W m-2T2m0.1 ∘CRH2m5 %p0.5 hPa
Accumulation observations are only available from stake measurements,
provided by AWI, which were performed weekly for the period April
1992–January 2009. As timing of precipitation is important for correctly
simulating the effects of new snow on snow albedo, we combined the stake
observations with precipitation predicted by the regional atmospheric climate
model RACMO2.3p2 to obtain realistic timing of
precipitation in between stake observations, as well as for the post-2009
period. The amount of precipitation modelled by RACMO2 was scaled such that
the modelled surface height changes agree with stake measurements; this
required a 15.3 % upward adjustment of the modelled precipitation flux.
(a) Seasonal cycles of 2 m temperature (red, left axis),
10 m wind speed (green, right axis) and 2 m specific humidity (blue, right
axis). Shaded areas indicate the standard deviations of monthly means.
(b) Same as panel (a) for melt (red), net shortwave
radiation (blue), net longwave radiation (orange), sensible heat (black),
latent heat (magenta) and ground heat (green).
Local near-surface climate
Neumayer station is located on an ice shelf ∼20 km from Halvfarryggen
ice rise to the south-east, ∼100 km from the ice shelf break
(grounding line) to the south, ∼20 km from open water and sea ice to
the north and ∼5 km to open water and sea ice to the east. As a
result, Neumayer experiences relatively mild conditions without significant
impact from katabatic winds but with a pronounced influence of synoptic
low-pressure systems passing mainly from west to east in the South Atlantic
Ocean to the north of the station. The seasonal cycles of 2 m temperature,
10 m wind and 2 m specific humidity are presented in
Fig. a. Summer temperatures around -4∘C and
winter temperatures around -25∘C imply a substantial (>20 K)
seasonal temperature amplitude based on monthly mean values. This is in line
with the formation of a surface-based temperature inversion in winter, a
phenomenon that is representative for the flat ice shelves as well as the
interior ice domes and in contrast to the topographically steeper escarpment
zone, where the quasi-continuous mixing by katabatic flow limits the
formation of such an inversion . As expected from
the strong link to the air temperature through the Clausius–Clapeyron
relation and a high annual mean relative humidity of 82 % (relative to
either water or ice, depending on the air temperature), because of the
proximity of a saturated snow surface and the ocean, the seasonal cycle of
q2m closely follows that of temperature.
Results: surface energy balance and meltSEB model performance and uncertainties
There are several SEB model parameters for which the exact values or
formulations are unknown, e.g. the surface roughness length for momentum
z0,m, the density of new snow ρs, the stability functions
(required to calculate the turbulent scales) and the effective conductivity,
which couples the magnitude of QG to the temperature gradient in the snow.
We estimated the impact of observational and model uncertainties on modelled
melt by running the model 600 times while randomly varying all hourly
observations within the specified measurement uncertainty ranges (Table ) and using multiple expressions for the heat conductivity and
stability functions. Model performance is quantified by comparing modelled
with observed Ts and assessing the changes in modelled 24-year cumulative
melt. Note that in this section, the albedo based on observations is used to
obtain SWnet.
The choice of expressions for the stability functions and heat conductivity
did not significantly impact the modelled amount of melt (total within
30 mm w.e. or 2.7 %, not shown). The model outcome is more sensitive to
the choice of surface roughness length for momentum z0,m and
the lower limit of density of new snow ρs,0: when
z0,m is varied between 0.5 and 50 mm and ρs,0
between 150 and 500 kg m-3, the cumulative amount of surface melt over
the 24-year period varies between 900 and 1220 mm w.e., with higher melt
values for smaller values of z0,m and ρs,0.
Optimal values in terms of simulated Ts are
z0,m=1.65mm and
ρs,0=280 kg m-3, resulting in a Ts bias of
0.01 K and an RMSD of 0.79 K (Fig. ). We use these
values in the remainder of this study. Figure a and b show
modelled 24-year cumulative melt and annual melt (March–February) at
Neumayer, combined with uncertainties associated with model parameters. The
annual values for year X are obtained by summing monthly values for
March of year X until February of year X+1. The total melt amounts to
1154 mm w.e., with a small uncertainty associated with measurement
uncertainties (1σ≈3mmw.e., i.e. 0.3 %). The
method adopted to estimate this uncertainty has its limitations, as
measurement errors are probably autocorrelated: if a measurement at one time
is disturbed in some way, it is probably disturbed in a similar way at the
next time step. Therefore, this result could be interpreted as a lower bound
of the uncertainty range, which is supported by the larger uncertainty
estimates (∼15 %) by , who applied a
constant systematic error which can be interpreted as an upper bound on the
modelled uncertainty range. This also explains why the model outcome is much
more sensitive to different values of z0,m, as these runs
effectively introduce a systematic error between the true (unknown) value and
the chosen value. Furthermore, this approach assumes the true value to be
constant, which likely is an oversimplification .
Daily values of modelled vs. measured Ts for the
parameter settings used in this study: z0,m=1.65mm,
ρs,0=280 kg m-3.
Effect of model uncertainties on (a) cumulative melt and
(b) seasonal melt. The shaded area indicates the 1σ range due
to model uncertainties (changing z0,m and ρs,0
between their respective values). The vertical grey patches in
panel (a) indicate November–February of each season. Note that
panel (b) ends earlier than panel (a) because the
observations do not cover the 2015–2016 melt season
entirely.
The sensitivity of modelled cumulative melt to z0,m is somewhat
unexpected. Following Eq. () both z0,h and z0,q decrease
for increasing z0,m; in combination with the bulk method this acts to
dampen the effect of z0,m on the magnitude of the turbulent fluxes. Our
interpretation of this result is that decreasing z0,m and ρs,0
lead to a decrease in the turbulent fluxes as well as the ground heat flux
QG. This reduces the efficiency with which heat is removed from the
surface, in turn allowing more energy to be invested in melt. The obtained
value of z0,m=1.65mm is high compared to the average value of
z0,m=0.1mm found during a field campaign at Neumayer in 1982
but it is not uncommon for snow surfaces
.
Measured values of Ts in excess of the melting point in
Fig. only occurred in the first six seasons; from
1998–1999 onwards they were removed by additional post-processing. These
measurements mainly reflect uncertainties in the adopted unit value of
longwave emissivity and in measured LW↑, e.g. from sensor window
heating and the fact that the downward-facing
radiation sensor also measures longwave radiation emitted by the relatively
warm air between the surface and the sensor.
Surface energy balance
Annual (March–February) mean values of near-surface meteorological
quantities and SEB components are presented in Table , with
seasonal cycles of SEB components presented in Fig. b.
These show that the summertime SEB is dominated by the radiation fluxes;
despite the high albedo of the snow surface, SWnet is
the dominant heat source for the skin layer, whereas
LWnet extracts energy from the surface, most efficiently
so in summer, when the surface is heated by the sun. In summer,
QL becomes a significant source of heat loss in the SEB (sublimation),
preventing strong negative QS (convection). The seasonal cycle of
QG is small, indicating a small net transport of heat away from
the surface in summer and towards the surface in winter. The net annually
integrated amount is less than zero as a result of the refreezing of
meltwater, warming the subsurface snow layers.
Mean annual values and interannual variability (calculated as
standard deviations of monthly means) of meteorological variables and SEB
components. For precipitation and melt, total annual values are
given.
VariableYearly meanVariabilityT2m (K)257.10.7Ts (K)256.00.8q2m (g kg-1)1.10.1V10m (m s-1)8.90.6p (hPa)981.62.0SWnet (W m-2)202SW↓ (W m-2)1273SW↑ (W m-2)1072LWnet (W m-2)-283LW↓ (W m-2)2185LW↑ (W m-2)2464QS (W m-2)14.52.7QL (W m-2)-6.31.2QG (W m-2)0.70.4M (W m-2)0.50.4Precipitation (mm w.e.)41586Melt (mm w.e.)5042
Statistically significant and previously unreported trends over the full
24-year period (not shown) are detected in LW↑ (-0.28±0.14 W m-2 yr-1) and QS (+0.21±0.07 W m-2 yr-1). Both of these are a result of wintertime
trends. LW↑ is linked directly to Ts, which shows a statistically
insignificant negative trend (-0.029±0.026 K yr-1), which
in magnitude exceeds the negative trend in T2m (-0.0045±0.02 K yr-1; assuming a normal distribution, the probability
that the negative trend in Ts is greater in magnitude than the trend in
T2m is 0.76). As a result, the air temperature gradient near the surface
has increased, enhancing QS. The negative trend in Ts originates from a
decrease in LW↓ (-0.26±0.17 W m-2 yr-1),
which is in turn driven by a slight decrease in cloud cover (-0.003±0.001 yr-1). This is suggested independently by the decrease in
average winter humidity (-0.004±0.002 g kg-1 yr-1).
These findings agree with and
, who determined from satellite observations
that summer cloud cover has decreased over that part of coastal Antarctica in
the period 1979–2011.
Melt season
Melt occurs at Neumayer from November to February (Fig. ) but
is highly variable from year to year. The mean annual amount of melt is
50 mm w.e. with an interannual variability of 42 mm w.e. and a range of
2 mm w.e. in 1999–2000 to 176 mm w.e. in 2012–2013. Most melt occurs in
December and January and the surface only sporadically reaches melting point
in February. Only in 2007 did melt occur in November, and no melt occurs
outside these 4 months. The cumulative melt occurring at Neumayer shows
stepwise increases (Fig. a), which represent the peaked melt
seasons, in which melt occurs on average on 18±10 d. The uncertainty in
the number of melt days due to the chosen values of z0,m and
ρs,0 is relatively small compared to the interannual
variability in melt totals (Fig. ), implying that this choice
does not significantly affect the modelled melt duration, but it does affect
the total melt.
Average number of melt days per month at Neumayer. The inner error
bars (with larger caps) indicate the 1σ uncertainty range resulting
from the runs performed with different settings for roughness length z0
and lower limit of new snow density ρs,0
(Sect. ). The outer error bars (with smaller caps) indicate
the 1σ range of the interannual variability.
To investigate the link between melt and climate, we compare the two summers
with the highest (2003–2004 and 2012–2013, on average 145 mm w.e.) and
lowest (1999–2000 and 2014–2015, on average 4 mm w.e.) melt amounts.
Figure shows the meteorological and SEB components for
these years, averaged over December and January. The largest differences are
found in T2m (+2.3 K) and SWnet
(+17 W m-2); based on the measurement uncertainties
(Table ), these differences are significant. In cold summers, the
low T2m corresponds to a stronger temperature inversion
(T2m-Ts), more longwave cooling, less sublimation
and a larger QS. SW↓ and
LW↓ show almost no difference between high and low melt
seasons; therefore, the difference in SWnet cannot be
caused by a change in cloud cover and is likely caused solely by surface
albedo, which suggests an important role for the SMAF.
This will be elaborated upon in the next section. Finally, the direction of
QG is reversed: in high melt years, the surface is warmed from
below, while in low melt years the surface loses heat to the subsurface. More
refreezing of meltwater in high melt years warms the near surface snow
layers, which in turn leads to a conductive heat flux towards the surface.
Average values of some SEB components (a, b) and some
meteorological variables (c) for December and January in the years
with the highest (2003–2004 and 2012–2013, in light grey) and lowest
(1999–2000 and 2014–2015, in dark grey) amount of melt, as identified in
Sect. . Note that SW↓,
SW↑, LW↓ and LW↑ are
scaled by a factor of 10 in panel (a) for
clarification.
Using the subsurface radiation model of , the
influence of subsurface penetration of shortwave radiation is estimated. Its
inclusion increases the modelled cumulative amount of melt by 13 %, from
1154 to 1326 mm w.e. The absorbed shortwave radiation heats the
subsurface layers, but the heat cannot be transported away as effectively as
would happen at the surface by turbulent fluxes and longwave radiation. This
leads to an increase in total melt.
The findings presented in this section are in good agreement with
, who used a similar approach to calculate the SEB
at Neumayer but used a lower value for z0,m=0.32mm and a
higher snow density that was assumed constant with depth (420 kg m-3 in
their study vs. 280 kg m-3 in this study). Compared to melt
estimates from the Larsen C ice shelf, obtained through a similar modelling
approach by , melt at Neumayer is weak. Owing to
its more northerly location, on the Larsen C ice shelf an annual (2009–2011)
average melt energy of 2.8 W m-2 is obtained, compared to the
2009–2011 annual average of 0.7 W m-2 obtained at Neumayer.
Furthermore, in November and February melt occurs much more frequently on
the Larsen C ice shelf.
Results: the snowmelt–albedo feedback
The SMAF is a well-known phenomenon but has not before
been quantified for Antarctica. The feedback occurs after the rapid growth of
snow grains when meltwater penetrates into the subsurface and refreezes.
Because a photon travels farther through snow with large particles
than in new snow with smaller particles on average, the probability of it being absorbed
is increased, effectively lowering the surface albedo
. Even without melt, albedo decreases when snow ages,
following grain growth from dry snow metamorphism, but this is a much slower
process which mainly depends on temperature gradients in the snow, favouring
moisture transport onto larger grains. Precipitation of new, fine-grained
snow has been shown to inhibit the albedo decrease by metamorphism on the
Antarctic plateau .
To quantify the SMAF at Neumayer, we need to be able to switch on and off the
albedo dependency on melt-driven grain growth. To that end, we implemented an
albedo parameterisation in the SEB model, as described in
Sect. . Because no data on grain size are available from
Neumayer, we optimised the albedo model performance by maximising the
correspondence between (1) modelled and observed hourly SW↑
and (2) the total melt obtained from the calculations based on observed
albedo (Sect. ). We compare SW↑ instead
of the albedo itself because by doing so the hourly values are naturally
weighted with its contribution to
SWnet and hence its importance
for the SEB. We then perform several runs with different processes switched
on and off affecting the surface albedo to investigate the importance of the
SMAF for melt at Neumayer (Sect. ).
Optimising the albedo parameterisation
The albedo parameterisation, and especially the expression for snow grain
size (Eq. ), contains several parameters that are not well
constrained, such as new snow grain size re,0 and refrozen snow grain
size re,r. These parameters were varied within reasonable ranges to
optimise the results: new snow grain sizes between 0.04 and 0.3 mm and
refrozen snow grain sizes between 0.1 and 10 mm. The best comparison with
observed albedo was achieved when using the look-up table for dry snow
metamorphism, dre,dry, corresponding to a grain size of
0.055mm.
The first step in optimising the parameterisation was to split the summer
season into two parts, the “dry” and the “wet” season. The respective starts
of the dry and wet seasons are the first day on which the sun rises more than
15∘ above the horizon and the first day that surface melt occurs. The
wet season ends when the sun no longer rises higher than 15∘. For the
dry season, we varied the dry snow metamorphism factor and the new snow grain
size to best match observed SW↑. This resulted in a new snow grain
size of 0.25mm. This value is then used in the second step, in
which the refrozen snow grain size re,r is varied to best match the
modelled cumulative melt using observed albedo. This was achieved for a
refrozen snow grain size of 1.45 mm.
This value for refrozen snow grain size is compatible with the typical
largest grains in dry metamorphosed snow of O(1 mm) and which
used as a lower limit for refrozen snow
grains. and present observations
of snow grain sizes on the Antarctic plateau during field campaigns in
2012–2013 and 2013–2014 as well as estimates from satellite observations. On
the plateau, summer temperatures are comparable to Neumayer winter
temperatures. report summertime snow grain size
estimates of approximately 0.11 mm (Fig. 6 in their study, reported as a
specific surface area SSA=3ρire, where ρi is
the density of ice and re is the snow grain size). In our study,
wintertime snow grain sizes approach 0.21 mm. The difference is expected as
the plateau is generally much colder than Neumayer. The seasonal cycle of
modelled average specific surface area in the upper 7 cm
(Fig. ) is comparable to the one presented in
, although the wintertime values are probably too low.
For the purpose of this study, however, the accurate representation of surface
albedo during winter is less relevant as there is no shortwave radiation in
winter.
Seasonal cycle of modelled average grain size in the upper 7 cm for
the period 2000–2014. The grain size is expressed in terms of specific
surface area (SSA=3ρire) rather
than grain size itself to allow for a comparison with Fig. 6 of
. The vertical grey patches indicate November–February
of each season.
When the adopted albedo values are combined with the observations of
SW↑, the model adequately reproduces the incoming shortwave
radiation (Fig. , bias=+0.93 W m-2, RMSD=7.3 W m-2), providing confidence in the modelled albedo.
Measured vs. modelled daily average incoming shortwave radiation
(SW↓). The modelled SW↓ is obtained
by dividing the hourly measured SW↑ by the hourly modelled
albedo.
Magnitude of the snowmelt–albedo feedback
Three experiments with the SEB model were carried out in addition to the
original run (R0), which uses the measured albedo:
R1: the average measured albedo (0.84, determined by adding all SW↓ and SW↑ for all
measurements when the sun is higher than 15∘ above the horizon and taking the ratio between the two) is prescribed for the entire
period.
R2: the full albedo parameterisation is used.
R3: refrozen snow does not contribute to the changing snow characteristics, i.e. fr=0 in Eq. ().
Figure a and b show time series of modelled cumulative
and seasonal surface melt for the four experiments. Experiment R1
underpredicts melt in most seasons, yielding a mean annual amount of surface
melt of 39±27 mm w.e. yr-1 (compared to 50±42 mm w.e. yr-1 for experiment R0). More melt was modelled
in the 1995–1996 melt season, which was characterised by frequent
precipitation events and cloudy conditions, keeping observed albedo higher
than the long-term mean. Because the albedo parameterisation (used in
experiment R2) has been calibrated to match observed albedo, experiment
R2 adequately reproduces the amount of seasonal melt (50±34 mm w.e. yr-1), although melt, e.g. in the 2012 melt season,
is underestimated. Run R3 represents the situation in which the
SMAF has been switched off, leading to significantly
underpredicted melt (21±16 mm w.e. yr-1).
(a) Time series of the modelled cumulative amount of melt for
the run with measured albedo (R0, blue), a constant albedo of 0.84 (R1,
red), a run in which refrozen snow impacts snow grain size (R2,
yellow) and a run in which snow grain size is not influenced by refrozen snow
(R3, purple). (b) Same as panel (a) but for seasonal
amount of melt. (c) Ratio of modelled surface melt between yellow
and purple lines in panels (a) and (b) (runs R2 and
R3 respectively). The grey area indicates the uncertainty coming from the
uncertainty in the determination of τ (Fig. b), ±5 W m-2 measurement uncertainty in SW↑ and the
inclusion of shortwave radiation penetration.
Defining the strength of the SMAF as the ratio
between the total seasonal surface melt in experiments R2 and R3, we
obtain an average value of 2.6, with a range of 1.3 (1996–1997) to 4.8
(1993–1994; see Fig. c). The effect of subsurface
penetration of shortwave radiation on this result is estimated by repeating
the above experiments with an inclusion of the radiation penetration model of
. This yielded an average SMAF of 2.3, ranging
from 1.5 (2005–2006) to 3.2 (2002–2003). The main difference between the two
experiments is the reduced interannual variability: including penetration of
shortwave radiation does not yield SMAF values larger than 3.5. Shortwave
radiation penetration heats the subsurface, causing subsurface melt which is
less affected by the SMAF because the radiative flux is
smaller in the subsurface. Therefore, the “extreme” years in the sense of
SMAF are less distinct in the experiment with shortwave radiation
penetration. The effect of shortwave radiation penetration is included in the
uncertainties indicated in Fig. c. Combining this with
the uncertainties in observed SW↑ and the determination of τ
(Fig. b) leads to uncertainties in the determination of the
SMAF of typically 15 %, with a range of 4 % (1995–1996) to 32 %
(1993–1994).
A weak positive correlation was found between SMAF and SW↓
(R2=0.15, p=0.07): if SW↓ increases, more energy is available
at the surface for melting, which is then in turn further intensified by
SMAF. Another weak negative correlation was found between SMAF and summer
precipitation (R2=0.13, p=0.1): snowfall inhibits SMAF as it effectively
“resets” the surface albedo as was also shown by in a
dry region.
Only few studies report on the SMAF concerning the
darkening of snow rather than disappearance of it. provide
relationships between anomalies of seasonal T2m and SWnet (Figs. 5
and 12 of ). They find a negative relationship for
accumulation regions, i.e. lower 2 m temperatures are associated with
smaller SWnet. No such relationship is found for Neumayer (not shown).
Conclusions
In this study, we used 24 years of high-quality meteorological and radiation
observations from the BSRN station Neumayer, situated on the Ekström ice
shelf, East Antarctica, to force a surface energy balance model. The primary
goal was to calculate the amount of melt at Neumayer and to investigate the
importance of the snowmelt–albedo feedback (SMAF). Model performance was
evaluated based on the difference between modelled and measured surface
temperature, and the modelled melt was tested for measurement and model
parameter uncertainties. We found that measurement uncertainties, when
considered random in time, do not significantly impact modelled melt at
Neumayer over the full 24-year period (<0.5 % difference). However, melt
amount and model performance are sensitive to the values chosen for the
surface roughness length for momentum z0,m and lower limit of
new snow density ρs,0; thus accurate measurements of these
values would further improve future modelling studies. Our results confirm
that melt at Neumayer is an intermittent process, occurring on average on
only 18 d each summer, totalling 50 mm w.e. and with an interannual
variability of 42 mm w.e. Melt occurs mainly in December and January,
sporadically in February and only once melt was modelled in November.
Significant and previously unreported trends were found in the net longwave
radiation (decreasing) and the sensible heat flux (increasing), but these are
unrelated to the melt at Neumayer as they mainly occur in winter and are
attributed to a decrease in cloud cover.
The main difference between high and low melt years was found to be surface
albedo, implying an important role for the SMAF.
We quantified SMAF by implementing and tuning an albedo parameterisation in
the SEB model, which includes the effects of snowfall and wet and dry snow
metamorphism on albedo. The albedo parameterisation adequately reproduces the
seasonal variability in snow grain size, compared to measurements on the
Antarctic Plateau . Our derived wintertime snow grain
sizes at Neumayer are somewhat smaller than the satellite-derived summertime
snow grain sizes at the Antarctic Plateau owing to the
lower temperatures on the plateau. Our main finding is that SMAF on average
enhances surface melt at Neumayer by a factor of 2.6±0.8.
Weak correlations were found of SMAF with summertime SW↓ and
precipitation (0.1<R2<0.2). To assess how the importance of the
snowmelt–albedo feedback varies spatially and temporally, the next step in
this research will be applying this method to other sites in Antarctica and a
regional climate model .
Code and data availability
The Neumayer data are available upon
request via the website of AWI
(https://bsrn.awi.de/data/data-retrieval-via-pangaea/, last access:
9 June 2016). The model output is available upon request by the
authors.
Author contributions
CLJ performed the study and wrote the manuscript. PKM
assisted with the implementation of the albedo parameterisation. GKL was in
charge of the Neumayer data. CHR, PKM, GKL and MRvdB have commented on the
manuscript.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
We would like to thank the AWI for maintaining the station and the Baseline
Surface Radiation Network (BSRN) for providing the data, with special thanks
to Amelie Driemel for creating a citation reference and Holger Schmithüsen
for helping us to interpret the data. Michiel R. van den Broeke acknowledges
support from the Netherlands Earth System Science Centre (NESSC). We would
like to thank Ghislain Picard and Achim Heilig for their constructive
comments.
Financial support
This research has been supported by the Nederlandse
Organisatie voor Wetenschappelijk Onderzoek (grant no. 866.15.204).
Review statement
This paper was edited by Mark Flanner and reviewed by
Ghislain Picard and Achim Heilig.
ReferencesAmory, C., Gallée, H., Naaim-Bouvet, F., Favier, V., Vignon, E., Picard,
G., Trouvilliez, A., Piard, L., Genthon, C., and Bellot, H.: Seasonal
Variations in Drag Coefficient over a Sastrugi-Covered Snowfield in Coastal
East Antarctica, Bound.-Lay. Meteorol., 164, 107–133,
10.1007/s10546-017-0242-5, 2017.Andreas, E. L.: A theory for the scalar roughness and the scalar transfer
coefficients over snow and sea ice, Bound.-Lay. Meteorol., 38, 159–184,
10.1007/BF00121562, 1987.Bell, R. E., Chu, W., Kingslake, J., Das, I., Tedesco, M., Tinto, K. J.,
Zappa,
C. J., Frezzotti, M., Boghosian, A., and Lee, W. S.: Antarctic ice shelf
potentially stabilized by export of meltwater in surface river, Nature, 544,
344–348, 10.1038/nature22048, 2017.Box, J. E., Fettweis, X., Stroeve, J. C., Tedesco, M., Hall, D. K., and
Steffen, K.: Greenland ice sheet albedo feedback: thermodynamics and
atmospheric drivers, The Cryosphere, 6, 821–839,
10.5194/tc-6-821-2012, 2012.Brandt, R. E. and Warren, S. G.: Solar-heating rates and temperature profiles
in Antarctic snow and ice, J. Glaciol., 39, 99–110,
10.3189/S0022143000015756, 1993.Brun, E., Martin, E., Simon, V., Gendre, C., and Coléou, C.: An Energy
and
Mass Model of Snow Cover Suitable for Operational Avalanche Forecasting,
J. Glaciol., 35, 333–342, 10.3189/S0022143000009254, 1989.DeConto, R. M. and Pollard, D.: Contribution of Antarctica to past and future
sea-level rise, Nature, 531, 591–597, 10.1038/nature17145, 2016.Dyer, A. J.: A review of flux-profile relationships, Bound.-Lay.
Meteorol., 7, 363–372, 10.1007/BF00240838, 1974.Flanner, M. G. and Zender, C. S.: Linking snowpack microphysics and albedo
evolution, J. Geophys. Res., 111, D12208, 10.1029/2005JD006834, 2006.Flanner, M. G., Zender, C. S., Randerson, J. T., and Rasch, P. J.:
Present-day
climate forcing and response from black carbon in snow, J.
Geophys. Res., 112, D11202, 10.1029/2006JD008003, 2007.Fretwell, P., Pritchard, H. D., Vaughan, D. G., Bamber, J. L., Barrand, N.
E., Bell, R., Bianchi, C., Bingham, R. G., Blankenship, D. D., Casassa, G.,
Catania, G., Callens, D., Conway, H., Cook, A. J., Corr, H. F. J., Damaske,
D., Damm, V., Ferraccioli, F., Forsberg, R., Fujita, S., Gim, Y., Gogineni,
P., Griggs, J. A., Hindmarsh, R. C. A., Holmlund, P., Holt, J. W., Jacobel,
R. W., Jenkins, A., Jokat, W., Jordan, T., King, E. C., Kohler, J., Krabill,
W., Riger-Kusk, M., Langley, K. A., Leitchenkov, G., Leuschen, C., Luyendyk,
B. P., Matsuoka, K., Mouginot, J., Nitsche, F. O., Nogi, Y., Nost, O. A.,
Popov, S. V., Rignot, E., Rippin, D. M., Rivera, A., Roberts, J., Ross, N.,
Siegert, M. J., Smith, A. M., Steinhage, D., Studinger, M., Sun, B., Tinto,
B. K., Welch, B. C., Wilson, D., Young, D. A., Xiangbin, C., and Zirizzotti,
A.: Bedmap2: improved ice bed, surface and thickness datasets for Antarctica,
The Cryosphere, 7, 375–393, 10.5194/tc-7-375-2013, 2013.Gardner, A. S. and Sharp, M. J.: A review of snow and ice albedo and the
development of a new physically based broadband albedo parameterization,
J. Geophys. Res.-Earth, 115, F01009,
10.1029/2009JF001444, 2010.Hall, A.: The Role of Surface Albedo Feedback in Climate, J. Climate,
17, 1550–1568, 10.1175/1520-0442(2004)017<1550:TROSAF>2.0.CO;2, 2004.Herman, J., DeLand, M. T., Huang, L.-K., Labow, G., Larko, D., Lloyd, S. A.,
Mao, J., Qin, W., and Weaver, C.: A net decrease in the Earth's cloud,
aerosol, and surface 340 nm reflectivity during the past 33 yr
(1979–2011), Atmos. Chem. Phys., 13, 8505–8524,
10.5194/acp-13-8505-2013, 2013.Hogg, A. E. and Gudmundsson, G. H.: Impacts of the Larsen-C Ice Shelf calving
event, Nat. Clim. Change, 7, 540–542, 10.1038/nclimate3359, 2017.Holtslag, A. A. M. and De Bruin, H. A. R.: Applied Modeling of the Nighttime
Surface Energy Balance over Land, J. Appl. Meteorol., 27,
689–704, 10.1175/1520-0450(1988)027<0689:AMOTNS>2.0.CO;2, 1988.Kingslake, J., Ely, J. C., Das, I., and Bell, R. E.: Widespread movement of
meltwater onto and across Antarctic ice shelves, Nature, 544, 349–352,
10.1038/nature22049, 2017.König, G.: Roughness Length of an Antarctic Ice Shelf, Polarforschung,
55, 27–32, 10.2312/polarforschung.55.1.27, 1985.König-Langlo, G.: Basic and other measurements, and meteorological
synoptical observations from Neumayer Station, 1992-04 to 2016-01, reference
list of 572 datasets, Alfred Wegener Institute, Helmholtz Centre for Polar
and Marine Research, Bremerhaven, PANGAEA, 10.1594/PANGAEA.874984,
2017.Kuipers Munneke, P., van den Broeke, M. R., Reijmer, C. H., Helsen, M. M.,
Boot, W., Schneebeli, M., and Steffen, K.: The role of radiation penetration
in the energy budget of the snowpack at Summit, Greenland, The Cryosphere, 3,
155–165, 10.5194/tc-3-155-2009, 2009.Kuipers Munneke, P., Reijmer, C. H., and Van den Broeke, M. R.: Assessing the
retrieval of cloud properties from radiation measurements over snow and ice,
Int. J. Climatol., 31, 756–769, 10.1002/joc.2114,
2011a.Kuipers Munneke, P., Van den Broeke, M. R., Lenaerts, J. T. M., Flanner,
M. G.,
Gardner, A. S., and Van de Berg, W. J.: A new albedo parameterization for use
in climate models over the Antarctic ice sheet, J. Geophys.
Res.-Atmos., 116, D05114, 10.1029/2010JD015113, 2011b.Kuipers Munneke, P., van den Broeke, M. R., King, J. C., Gray, T., and
Reijmer, C. H.: Near-surface climate and surface energy budget of Larsen C
ice shelf, Antarctic Peninsula, The Cryosphere, 6, 353–363,
10.5194/tc-6-353-2012, 2012.Kuipers Munneke, P., Ligtenberg, S. R. M., Van den Broeke, M. R., and
Vaughan,
D. G.: Firn air depletion as a precursor of Antarctic ice-shelf collapse,
J. Glaciol., 60, 205–214, 10.3189/2014JoG13J183, 2014.Kuipers Munneke, P., Luckman, A. J., Bevan, S. L., Smeets, C. J. P. P.,
Gilbert, E., Van den Broeke, M. R., Wang, W., Zender, C. S., Hubbard, B.,
Ashmore, D., Orr, A., King, J. C., and Kulessa, B.: Intense Winter Surface
Melt on an Antarctic Ice Shelf, Geophys. Res. Lett., 45, 7615–7623,
10.1029/2018GL077899, 2018.Lenaerts, J. T. M., Van den Broeke, M. R., Déry, S. J., Van Meijgaard,
E.,
Van de Berg, W. J., Palm, S. P., and Sanz Rodrigo, J.: Modeling drifting snow
in Antarctica with a regional climate model: 1. Methods and model evaluation,
J. Geophys. Res.-Atmos., 117, D05108,
10.1029/2011JD016145, 2012.Lenaerts, J. T. M., Lhermitte, S., Drews, R., Ligtenberg, S. R. M., Berger,
S.,
Helm, V., Smeets, C. J. P. P., Van den Broeke, M. R., Van de Berg, W. J.,
Van Meijgaard, E., Eijkelboom, M., Eisen, O., and Pattyn, F.: Meltwater
produced by wind-albedo interaction stored in an East Antarctic ice shelf,
Nat. Clim. Change, 7, 58–62, 10.1038/nclimate3180, 2017.Libois, Q., Picard, G., Arnaud, L., Dumont, M., Lafaysse, M., Morin, S., and
Lefebvre, E.: Summertime evolution of snow specific surface area close to the
surface on the Antarctic Plateau, The Cryosphere, 9, 2383–2398,
10.5194/tc-9-2383-2015, 2015.Ligtenberg, S. R. M., Helsen, M. M., and van den Broeke, M. R.: An improved
semi-empirical model for the densification of Antarctic firn, The Cryosphere,
5, 809–819, 10.5194/tc-5-809-2011, 2011.Ligtenberg, S. R. M., Kuipers Munneke, P., and van den Broeke, M. R.: Present
and future variations in Antarctic firn air content, The Cryosphere, 8,
1711–1723, 10.5194/tc-8-1711-2014, 2014.Luckman, A., A., E., Jansen, D., Kulessa, B., Kuipers Munneke, P., King,
J. C.,
and Barrand, N. E.: Surface melt and ponding on Larsen C Ice Shelf and the
impact of föhn winds, Antarct. Sci., 26, 625–635,
10.1017/S0954102014000339, 2014.Massom, R. A., Scambos, T. A., Bennetts, L. G., Reid, P. A., Squire, V. A.,
and
Stammerjohn, S. E.: Antarctic ice shelf disintegration triggered by sea ice
loss and ocean swell, Nature, 558, 383–389, 10.1038/s41586-018-0212-1,
2018.Perovich, D. K., Grenfell, T. C., Light, B., and Hobbs, P. V.: Seasonal
evolution of the albedo of multiyear Arctic sea ice, J. Geophys.
Res., 107, 8044, 10.1029/2000JC000438, 2002.Picard, G., Fily, M., and Gallée, H.: Surface melting derived from
microwave radiometers: a climatic indicator in Antarctica, Ann.
Glaciol., 46, 29–34, 10.3189/172756407782871684, 2007.Picard, G., Domine, F., Krinner, G., Arnaud, L., and Lefebvre, E.: Inhibition
of the positive snow-albedo feedback by precipitation in interior Antarctica,
Nat. Clim. Change, 2, 795–798, 10.1038/nclimate1590, 2012.Picard, G., Libois, Q., Arnaud, L., Verin, G., and Dumont, M.: Development
and calibration of an automatic spectral albedometer to estimate near-surface
snow SSA time series, The Cryosphere, 10, 1297–1316,
10.5194/tc-10-1297-2016, 2016.Qu, X. and Hall, A.: What Controls the Strength of Snow-Albedo Feedback?,
J. Climate, 20, 3971–3981, 10.1175/JCLI4186.1, 2007.Reijmer, C. H. and Oerlemans, J.: Temporal and spatial variability of the
surface energy balance in Dronning Maud Land, East Antarctica, J.
Geophys. Res., 107, 4759, 10.1029/2000JD000110, 2002.Reijmer, C. H., Greuell, W., and Oerlemans, J.: The annual cycle of
meteorological variables and the surface energy balance on Berkner Island,
Antarctica, Ann. Glaciol., 29, 49–54,
10.3189/172756499781821166, 1999.Rignot, E., Casassa, G., Gogineni, P., Krabill, W., Rivera, A., and Thomas,
R.:
Accelerated ice discharge from the Antarctic Peninsula following the collapse
of Larsen B ice shelf, Geophys. Res. Lett., 31, L18401,
10.1029/2004GL020697, 2004.Scambos, T. A., Bohlander, J. A., Shuman, C. A., and Skvarca, P.: Glacier
acceleration and thinning after ice shelf collapse in the Larsen B embayment,
Antarctica, Geophys. Res. Lett., 31, L18402, 10.1029/2004GL020670,
2004.Schlatter, T. W.: The Local Surface Energy Balance and Subsurface Temperature
Regime in Antarctica, J. Appl. Meteorol., 11, 1048–1062,
10.1175/1520-0450(1972)011<1048:TLSEBA>2.0.CO;2, 1972.Schneider, T. and Jansson, P.: Internal accumulation in firn and its
significance for the mass balance of Storglaciären, Sweden, J.
Glaciol., 50, 25–34, 10.3189/172756504781830277, 2004.Shepherd, A., Ivins, E. R., Rignot, E., Smith, B., Van den Broeke, M. R.,
Velicogna, I., Whitehouse, P. L., Briggs, K. H., Joughin, I., Krinner, G.,
Nowicki, S., Payne, A. J., Scambos, T. A., Schlegel, N., A, G., Agosta, C.,
Ahlstrøm, A. P., Babonis, G., Barletta, V. R., Blazquez, A., Bonin, J.,
Csatho, B., Cullather, R. I., Felikson, D., Fettweis, X., Forsberg, R.,
Gallée, H., Gardner, A. S., Gilbert, L., Groh, A., Gunter, B., Hanna, E.,
Harig, C., Helm, V., Horvath, A., Horwath, M., Khan, S., Kjeldsen, K. K.,
Konrad, H., Langen, P., Lecavalier, B., Loomis, B., Luthcke, S. B., McMillan,
M., Melini, D., Mernild, S., Mohajerani, Y., Moore, P., Mouginot, J., Moyano,
G., Muir, A., Nagler, T., Nield, G., Nilsson, J., Noël, B. P. Y.,
Otosaka, I., Pattle, M. E., Peltier, W. R., Pie, N., Rietbroek, R., Rott, H.,
Sandberg Sørensen, L., Sasgen, I., Save, H., Scheuchl, B., Schrama, E.
J. O., Schröder, L., Seo, K.-W., Simonsen, S., Slater, T., Spada, G.,
Sutterly, T. C., Talpe, M., Tarasov, L., Van de Berg, W. J., Van der Wal, W.,
Van Wessem, J. M., Vishwakarma, B. D., Wiese, D., and Wouters, B.: Mass
balance of the Antarctic Ice Sheet from 1992 to 2017, Nature, 558, 219–222,
10.1038/s41586-018-0179-y, 2018.Smeets, C. J. P. P. and Van den Broeke, M. R.: Temporal and Spatial
Variations
of the Aerodynamic Roughness Length in the Ablation Zone of the Greenland Ice
Sheet, Bound.-Lay. Meteorol., 128, 315–338,
10.1007/s10546-008-9291-0, 2008.Smeets, C. J. P. P., Kuipers Munneke, P., Van As, D., Van den Broeke, M. R.,
Boot, W., Oerlemans, J., Snellen, H., Reijmer, C. H., and Van de Wal, R.
S. W.: The K-transect in west Greenland: Automatic weather station data
(1993–2016), Arct. Antarct. Alp. Res., 50, S100002,
10.1080/15230430.2017.1420954, 2018.Tedesco, M.: Assessment and development of snowmelt retrieval algorithms over
Antarctica from K-band spaceborn brightness temperature (1979–2008), Remote
Sens. Environ., 113, 979–997, 10.1016/j.rse.2009.01.009, 2009.Trusel, L. D., Frey, K. E., Das, S. B., Kuipers Munneke, P., and Van den
Broeke, M. R.: Satellite-based estimates of Antarctic surface meltwater
fluxes, Geophys. Res. Lett., 40, 6148–6153,
10.1002/2013GL058138, 2013.Trusel, L. D., Frey, K. E., Das, S. B., Karnauskas, K. B., Kuipers Munneke,
P.,
Van Meijgaard, E., and Van den Broeke, M. R.: Divergent trajectories of
Antarctic surface melt under two twenty-first-century climate scenarios,
Nat. Geosci., 8, 927–934, 10.1038/NGEO2563, 2015.Turner, J., Orr, A., Gudmundsson, G. H., Jenkins, A., Bingham, R. G.,
Hillenbrand, C.-D., and Bracegirdle, T. J.: Atmosphere-ocean-ice interactions
in the Amundsen Sea Embayment, West Antarctica, Rev. Geophys., 55,
235–276, 10.1002/2016RG000532, 2017.
Van As, D., Fausto, R. S., Colgan, W. T., Box, J. E., Ahlstrøm, A. P.,
Andersen, S. B., Andersen, M. L., Charalampidis, C., Citterio, M., Edelvang,
K., Jensen, T. S., Larsen, S. H., Machguth, H., Nielsen, S., Veicherts, M.,
and Weidick, A.: Darkening of the Greenland ice sheet due to the melt-albedo
feedback observed at PROMICE weather stations, Geol. Surv. Den. Greenl., 28, 69–72, 2013.Van den Broeke, M. R.: The semi-annual oscillation and Antarctic climate.
Part 1: influence on near surface temperatures (1957–79), Antarct. Sci., 10,
175–183, 10.1017/S0954102098000248, 1998.Van den Broeke, M. R., Reijmer, C. H., and Van de Wal, R. S. W.: Surface
radiation balance in Antarctica as measured with automatic weather stations,
J. Geophys. Res., 109, D09103, 10.1029/2003JD004394, 2004.Van den Broeke, M. R., Reijmer, C. H., Van As, D., Van de Wal, R. S. W., and
Oerlemans, J.: Seasonal cycles of Antarctic surface energy balance from
automatic weather stations, Ann. Glaciol., 41, 131–139,
10.3189/172756405781813168, 2005.Van den Broeke, M. R., Reijmer, C. H., Van As, D., and Boot, W.: Daily cycle
of
the surface energy balance in Antarctica and the influence of clouds,
Int. J. Climatol., 26, 1587–1605, 10.1002/joc.1323,
2006.Van den Broeke, M. R., König-Langlo, G., Picard, G., Kuipers Munneke, P.,
and Lenaerts, J. T. M.: Surface energy balance, melt and sublimation at
Neumayer Station, East Antarctica, Antarct. Sci., 22, 87–96,
10.1017/S0954102009990538, 2010.Van der Veen, C. J.: Fracture propagation as means of rapidly transferring
surface meltwater to the base of glaciers, Geophys. Res. Lett., 34, L01501,
10.1029/2006GL028385, 2007.van Wessem, J. M., van de Berg, W. J., Noël, B. P. Y., van Meijgaard, E.,
Amory, C., Birnbaum, G., Jakobs, C. L., Krüger, K., Lenaerts, J. T. M.,
Lhermitte, S., Ligtenberg, S. R. M., Medley, B., Reijmer, C. H., van Tricht,
K., Trusel, L. D., van Ulft, L. H., Wouters, B., Wuite, J., and van den
Broeke, M. R.: Modelling the climate and surface mass balance of polar ice
sheets using RACMO2 – Part 2: Antarctica (1979–2016), The Cryosphere, 12,
1479–1498, 10.5194/tc-12-1479-2018, 2018.Wiscombe, W. J. and Warren, S. G.: A Model for the Spectral Albedo of Snow.
I: Pure Snow, J. Atmos. Sci., 37, 2712–2733,
10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2, 1980.Wouters, B., Martin-Español, A., Helm, V., Flament, T., Van Wessem,
J. M., Ligtenberg, S. R. M., Van den Broeke, M. R., and Bamber, J. L.:
Dynamic thinning of glaciers on the Southern Antarctic Peninsula, Science,
348, 899–903, 10.1126/science.aaa5727, 2015.