Introduction
Glaciers and ice caps have dominated the cryospheric component to global
average sea level rise during the past century (0.5 mm yr-1 or about
70 % of the total cryospheric component for the period 1961–2003; Solomon
et al., 2007) due to their relatively short response times to climate
variability. However, the largest freshwater reservoir in the Northern
Hemisphere is the Greenland ice sheet, which would cause a sea level rise of
7.4 m if completely melted (Bamber et al., 2013). The average sea level rise
contribution from the ice sheet has increased from 0.09 mm yr-1 over
the period 1992–2001 to 0.6 mm yr-1 over the period 2002–2011,
according to the latest IPCC report (Vaughan et al., 2013). The sheer volume
of the ice sheet and the relatively large warming of the polar regions may
yield an increasingly dominant contribution to cryospheric mass loss in coming decades.
An increasingly important driver of this accelerating mass loss is surface
melt and subsequent run-off (Shepherd et al., 2012). Between 2009 and 2012,
roughly 84 % of the Greenland ice sheet's increased mass loss was due to
enhanced surface run-off and reduced surface mass budget (SMB) (Ettema et al., 2009, 2010; Enderlin
et al., 2014). Increased melt is primarily the result of atmospheric warming
(Huybrechts and de Wolde, 1999; Huybrechts et al., 2011) and the darkening
of the ice sheet (Bøggild et al., 2010; Wientjes and Oerlemans, 2010; Box
et al., 2012; Van As et al., 2013). It has been postulated that the sea
level rise associated with an increase in meltwater production can be
substantially buffered by water refreezing in snow and firn (Harper et al.,
2012). However, it has also been suggested that under moderate warming the
ice sheet will lose 50 % of its capacity to retain water by the end of
the century (Van Angelen et al., 2013), although there is considerable
uncertainty involved in retention estimates based on SMB simulations (Vernon et al., 2013).
In situ measurements are essential for understanding the impact of the
changing atmospheric conditions on the ice sheet. In the Kangerlussuaq
region, West Greenland, seven automatic weather stations (AWSs) and nine SMB
stakes constitute a relatively dense network of in situ measurements (Van de
Wal et al., 1995; Greuell et al., 2001; Van den Broeke et al., 2008a; Van As
et al., 2012). The uppermost AWS, KAN_U, was established on 4 April 2009
(67∘0′0′′ N, 47∘1′1′′ W; Fig. 1). Located
approximately 140 km inland from the ice margin and at about 1840 m a.s.l. (above sea level),
KAN_U is one of the few AWSs in Greenland located in the lower accumulation
area, where small changes in climate forcing will likely have the largest
impact on ice sheet near-surface stratigraphy.
In the Kangerlussuaq region, approximately 150 km of mountainous tundra
separates the ice sheet from the ocean. Characteristic for the ice sheet in
this region is a relatively wide (∼ 100 km) ablation area. The
equilibrium line altitude (ELA), where annual accumulation and ablation are
equal, was estimated to be 1535 m a.s.l. for the period of 1990–2003 (Van
de Wal et al., 2005) but is reported to have increased to 1553 m a.s.l. for
the period of 1990–2011 (Van de Wal et al., 2012). At 1520 m a.s.l.,
superimposed ice becomes evident at the ice sheet surface at the end of
every ablation season, and its up-glacier extent is estimated to reach about
1750 m a.s.l. (Van den Broeke et al., 2008a). The percolation area is found
at higher elevations, up to about 2500 m a.s.l., which is the lower limit of
the dry snow area.
Sensors and their published accuracies.
Parameter
Sensor
Accuracy
Air pressure
Campbell CS100
2 hPa at -40 to 60 ∘C
Aspirated air temperature
Rotronic MP100H aspirated (Pt100)
0.03 at 0 ∘C
Relative humidity
Rotronic MP100H aspirated (HygroClip R3)
1.5 % at 23 ∘C
Shortwave radiation (incoming and reflected)
Kipp & Zonen CNR4 (Pyranometer)
10 % for daily totals
Longwave radiation (incoming and emitted)
Kipp & Zonen CNR4 (Pyrgeometer)
10 % for daily totals
Wind speed and direction
Young 05103-5
0.3 m s-1; 3∘
Surface height
Campbell SR50A
10-2 m or 0.4 %
The ablation area in this region has been studied extensively. Van den
Broeke et al. (2008a) presented 4 years of radiation measurements below
the ELA. The lowest albedo values are found at the intermediate AWS S6
(1020 m a.s.l.) due to a “dark band” induced by surface meltwater (Greuell, 2000;
Wientjes and Oerlemans, 2010). Melt modelling revealed not only an increase in summer
melt toward the margin, and a decrease in sensible heat flux with increasing
elevation, but also an increase in the importance of shortwave radiation in
the surface energy balance (SEB) during melt at higher elevations (Van den
Broeke et al., 2008b, 2011). An annual cycle in surface roughness length has
been found to exist over a large part of the ablation area (Smeets and van
den Broeke, 2008). This determines part of the variability in the turbulent
heat fluxes during the summer months (Van den Broeke et al., 2009). This
latter study showed that the regional katabatic winds, in combination with
the variable surface roughness at lower elevations, provides significant
year-round turbulent heat transfer in a stable surface layer. An increasing
wind speed with surface elevation was identified, contrary to what would be
expected from katabatically forced wind over an ice surface flattening with
elevation. This is due to the larger surface roughness near the margin
(Smeets and van den Broeke, 2008), the increasing influence of the large-scale pressure gradient force (Van Angelen et al., 2011), and the proximity
of pooled cold air over the tundra that sets up an opposing pressure
gradient force in the boundary layer during winter. Van As et al. (2012)
quantified the extreme surface melt in the Kangerlussuaq region in 2010,
validated by river discharge measurements.
At elevations above the superimposed ice area and below the dry snow area
(i.e. ∼ 1750–2500 m a.s.l.), sufficient melt occurs to impact
snow/firn properties but not enough to reveal bare ice. In a warming
climate with melt occurring at higher elevations, this area would comprise
an increasingly large surface area of the ice sheet due to the ice sheet's
flattening with increasing elevation (McGrath et al., 2013). A rare event in
July 2012 caused melt at all elevations of the ice sheet (Nghiem et al.,
2012). Bennartz et al. (2013) partially attributed this Greenland-wide event
of increased near-surface temperatures to thin, low-level liquid clouds.
These clouds, while optically thick and low enough to enhance downward
longwave radiation, were thin enough for solar radiation to reach the ice
sheet surface. They were present at Summit, the highest location on the ice sheet ( 3216 m a.s.l.), about 30 % of the time
during the 2012 summer months.
A large difference between the ablation and accumulation areas is that in
the accumulation area, processes within the snow/firn layers, such as
meltwater percolation and refreezing, significantly impact the mass budget
(e.g. Harper et al., 2012). The melt–albedo feedback (Box et al., 2012) is
also an important process in the accumulation area, since, once activated,
it can result in abnormally high ablation.
Our aim is to assess the sensitivity of SMB to atmospheric forcing in the
lower accumulation area by using AWS measurements as input for a SEB model.
The 5-year period of AWS measurements (2009–2013) spans a wide range
of melting conditions, including the record melt years of 2010 and 2012
(Tedesco et al., 2011, 2013; Van As et al., 2012; Nghiem et al., 2012; Hanna
et al., 2014) and years with moderate melting such as 2009 and 2013. We add
temporal perspective by discussing Kangerlussuaq air temperatures since 1976 and
Moderate Resolution Imaging Spectroradiometer (MODIS) albedo values since
2000. Below, we first describe the observations and SEB calculations, after
which we present atmospheric conditions and surface energy fluxes at
KAN_U and the changes therein due to recent years with
extreme melt. Finally, we investigate the importance of the melt–albedo
feedback on the SMB of the lower accumulation area and discuss how changes
in the firn can yield SMB variability on an interannual timescale.
Methods
AWS measurements
KAN_U is part of the ∼ 20 AWSs comprising the
Programme for Monitoring of the Greenland Ice Sheet (PROMICE) network
(Ahlstrøm et al., 2008). Measurements include ambient air pressure,
relative humidity and aspirated temperature (Ta) at 2.7 m height above
the ice sheet surface, wind speed, and direction at 3.1 m height, as well as
incoming and reflected solar/shortwave (ES↓,
ES↑) and downward and emitted terrestrial/longwave
(EL↓, EL↑) radiation components at
10-minute intervals. Accumulation and ablation are measured by two sonic
rangers, one attached to the AWS and one on a separate stake assembly (Fig. 1b).
Sensor specifications are listed in Table 1. The AWS transmits hourly
measurements during the summer period and daily during winter (Citterio et al., 2015).
AWSs installed on glaciers are prone to tilt due to transient evolution of
the ice or firn surface. The importance of accounting for pyranometer tilt
has been discussed by MacWhorter and Weller (1991). AWSs located in
accumulation areas are comparatively stable due to the accumulated snow on
the base of the tripod. The maximum tilt registered by KAN_U
was 3.0∘. A tilt correction for the solar radiation measurements is
made after Van As (2011).
Linear regression parameters for hourly values of
KAN_U and S10 AWSs: slope (χ), intercept (ψ),
correlation coefficients (R), and root-mean-squared deviations (RMSD).
S10-KAN_U
χ
ψ
R
RMSD
ES↓*
1.010
–
0.99
37.25 (W m-2)
ES↑*
0.987
–
0.99
24.71 (W m-2)
EL↓
1.003
-6.06
0.99
8.92 (W m-2)
EL↑
0.990
-0.25
1.00
3.62 (W m-2)
Ta
0.995
-0.25
1.00
0.50 (∘C)
Ambient air pressure
0.990
7.77
1.00
0.45 (hPa)
Relative humidity
0.899
10.31
0.91
3.78 (%)
Wind speed*
0.928
–
0.99
0.66 (m s-1)
α2010**
–
–
0.93
0.032 (–)
α2011**
–
–
0.94
0.028 (–)
α2012**
–
–
0.91
0.066 (–)
* Regression line forced through 0;
** 24 h running averages for the months May until September.
Two gaps in (sub-)hourly measurements exist due to a malfunctioning memory
card, from 27 October 2010 until 22 April 2011 and from 26 October 2011
until 21 January 2012. During these periods, when only transmitted daily
values are available, measurements from a second AWS, S10 erected on
17 August 2010 at ∼ 50 m distance from KAN_U, were
used and adjusted by linear regression to eliminate systematic offsets due to
different measurement heights. The overlapping records of the two time
series revealed high cross-correlations and low root-mean-squared deviations (RMSD)
for every measured parameter (Table 2). Due to technical issues with
S10, EL↓, EL↑, and Ta measurement
gaps from 9 February 2011 until 30 April 2012 were filled with a similar
approach, using measurements from the AWS S9 located 53 km closer to the ice
sheet margin. Any added uncertainty from using adjusted wintertime
measurements will have minimal impact on the summertime results presented below.
The broadband albedo is the fraction of the incoming shortwave radiation
reflected at the ice sheet surface and an important parameter in studying
the changes in the accumulation area:
α=ES↑ES↓.
To verify its accuracy, albedo was compared for both AWSs KAN_U
and S10 for the warm seasons (May–September) of 2010, 2011, and 2012
(Table 2). For hourly values, the RMSD for 2010 and 2011 was only ∼ 0.03.
The RMSD for 2012 was 0.07 due to the higher spatial variability in surface
reflectance after substantial melt.
Surface radiation budget
The radiation budget at the ice sheet surface is given by the sum of solar
and terrestrial radiation components:
ER=ES↓+ES↑+EL↓+EL↑=ESNet+ELNet.
Fluxes are here taken as positive when directed toward the ice sheet
surface. By the inclusion of albedo and utilizing the Stefan–Boltzmann law,
this can be rewritten as
ER=(1-α)ES↓+εEL↓-εσTS4,
with σ being the Stefan–Boltzmann constant (5.67 × 10-8 W m-2 K-4)
and TS the surface temperature. The longwave emissivity ε
for snow/firn is assumed equal to 1 (black-body assumption).
SEB model
Various studies have applied SEB models in glaciated areas under different
climatic conditions, such as the high Antarctic plateau (Van As et al., 2005)
and the Greenland ablation area (Van den Broeke et al., 2008b, 2011). The
energy balance at the atmosphere–surface interface is
EM=ER+EH+EE+EG+EP,
where EH, EE, EG, and EP are the turbulent sensible,
turbulent latent, subsurface conductive, and rain-induced energy fluxes respectively.
Rainfall is assumed to be at melting-point temperature (T0 = 273.15 K),
and thus EP is non-zero when Ts is below freezing:
EP=ρwcwr˙T0-Ts,
where cw is the specific heat of water (4.21 kJ kg-1 K-1 at
0 ∘C and 999.84 kg m-3) and r˙ is the rainfall rate. The rainfall rate
is assumed to be non-zero under conditions of heavy cloud cover during
periods with non-freezing air temperatures (see below).
The energy balance is solved for the one unknown variable Ts, which is limited to the melting-point temperature (273.15 K), and the
imbalance in Eq. (4) is attributed to melt (EM). For sub-freezing
Ts values all other SEB components are in balance and surface melt
does not occur. EH and EE are calculated using the “bulk method”
as described by Van As et al. (2005). This method uses atmospheric
stability and thus depends on Ts, implying that Eq. (4) has to
be solved iteratively.
The average surface roughness length for momentum z0 at this elevation
would realistically be ∼ 10-4 m (Smeets and van den
Broeke, 2008). During summer, the ice sheet surface melts occasionally,
and thus smoothes while attaining a smaller z0 (∼ 10-5 m).
Slightly increased roughness is expected during wintertime due to
sastrugi, while drifting snow (Lenaerts et al., 2014) can increase z0 in
cases up to 10-3 m. In the present study, z0 is kept constant at
10-4 m. A series of test runs showed that the results of this study
were not very sensitive to the range of plausible z0 values. The scalar
roughness lengths for heat and moisture are calculated according to Andreas (1987).
SEB model validation: (a) observed and simulated relative surface
height for the period of observations. (b) Simulated against
observed
Ts (R2 = 0.98; (ΔTs)avg = 0.11 ∘C;
RMSE = 1.43 ∘C).
Subsurface heat transfer is calculated with 0.1 m spatial resolution (20 m
depth; 200 layers) and is forced by temperature changes at the surface and
latent heat release when water refreezes within the firn. Heat conduction is
calculated using effective conductivity as a function of firn density (Sturm
et al., 1997) and specific heat of firn as a function of temperature (Yen,
1981). The calculations are initialized using thermistor string temperatures
from April 2009 and depth-adjusted firn core densities measured on 2 May 2012.
The subsurface part of the model includes a percolation/refreezing
scheme based on Illangasekare et al. (1990), assuming active percolation
within snow/firn. Provided that there is production of meltwater at the
surface, the amount of refreezing is limited either by the available pore
volume or by the available cold content at each level. The scheme simulates
water transport and subsequent refreezing as the progression of a uniform
warming front into the snow/firn and is active for all melt seasons except
for 2012. In 2012, surface run-off dominated water movement after 14 July, as
clearly visible on Landsat imagery (not shown). This coincided with the
surfacing of a 6 m thick ice layer in the model, which was also found in
firn cores (Machguth et al., 2015). Consistent with these observations,
we use 6 m of ice (density of 900 kg m-3) as a threshold that causes
meltwater to run off horizontally, shutting down vertical percolation.
Solid precipitation is added in the model based on KAN_U
sonic ranger measurements, assuming a rounded average snow density of 400 kg m-3
observed in snow-pit measurements. Although rain occurs
infrequently at 1840 m a.s.l., a rain estimate is incorporated with
prescribed precipitation rates for each year during hours with thick cloud
cover producing EL↓ values that exceed black-body
radiation using the air temperature (EL↓ > σTa4)
and Ta is above freezing. Evaluating this against
winter accumulation, the following precipitation rates were derived and
prescribed to the rain calculation: 2.0 mm h-1 for
2009–2010 and 2012–2013, 3.5 mm h-1 for
2010–2011, and 0.5 mm h-1 for 2011–2012. Using
this approach, the model produces liquid precipitation during the summer
months only; by the end of the 5-year period it amounts to a total of
0.26 m w.e. (water equivalent), 15 % of the total precipitation over
the 5 years. The contribution of rain in the energy balance is minor; the
total energy added to the surface for the whole study period is
approximately 1.15 MJ m-2, which could yield a total of 9 mm of melted snow.
The performance of the model in terms of ablation is illustrated by
comparing simulated surface changes with the measured surface height changes
due to ablation and accumulation (Fig. 2a). The model accurately reproduces
the melt rates during every melt season, yet this validation does not cover
the whole melt season. We found that the AWS tripod and stake assembly are
prone to sinking somewhat into warm, melting firn during the second part of
the melt season (note the measurement gaps). In a second model validation
exercise, we compare simulated and measured Ts (inferred from the
EL↑) in Fig. 2b and find they correlate well (R2 = 0.98)
with an average difference of 0.11 ∘C and root-mean-squared error (RMSE)
of 1.43 ∘C. Part of this difference can be attributed to the
seemingly overestimated 10 % EL↑ measurement uncertainty
as reported by the sensor manufacturer, which would yield a RMSE of 6.2 ∘C of Ts values.
Additional measurements
For a study with a 5-year time span, it is useful to provide a longer
temporal perspective. For this, we use the air temperature record from
Kangerlussuaq airport observed by the Danish Meteorological Institute (DMI)
since 1973 in support of aircraft operations (Cappelen, 2013). Full
observational suite coverage is available since 1976. Monthly Ta from
the airport correlates well with the KAN_U time series (R = 0.97),
indicating that Kangerlussuaq measurements can be used for providing
temporal perspective, despite the 160 km distance that separates the two
measurement sites. Finally, we use the pixel nearest to KAN_U
in 5-by-5 km re-gridded MODIS albedo product (MOD10A1) to investigate albedo
variability over the 2000–2013 period.
Annual and summer (June–July–August) average meteorological
parameters at KAN_U.
KAN_U
2009*
2010
2011
2012
2013**
Annual averages
Ta (∘C)
-15.5
-11.6
-18.0
-14.3
-15.4
Ambient air pressure (hPa)
799
804
797
800
799
Specific humidity (g kg-1)
1.5
2.0
1.4
1.9
1.5
Wind speed (m s-1)
7.0
7.0
6.2
6.5
7.0
Albedo
0.85
0.82
0.82
0.79
0.80
Summer (JJA) averages
Ta (∘C)
-4.3
-1.8
-2.9
-1.8
-4.5
Ambient air pressure (hPa)
809
808
811
811
804
Specific humidity (g kg-1)
2.9
3.6
3.3
3.7
2.8
Wind speed (m s-1)
5.3
5.2
5.0
4.6
5.2
Albedo
0.78
0.77
0.78
0.71
0.78
* Average 2010–2013 for January, February, and March;
** average 2009–2012 for October, November, and December.
Average values of (a) wind direction, (b) wind speed,
(c) air pressure, and (d) air temperature at KAN_U.
Surface height changes and mass budgets (measured in winter and
calculated in summer) at KAN_U in metres and m w.e.
respectively and ablation duration. The uncertainty associated with surface
height change is estimated to be 0.2 m. The mass budgets are calculated with
an assumed snow density of 360 kg m-3 (the average density of the
uppermost 0.9 m measured on 26 April 2013), with uncertainty estimated at
40 kg m-3 (standard deviation among the snow-pit measurements). The snow
density assumption was not needed in 2012 and 2013, when actual density
measurements were conducted.
Winter
Winter budget
Summer
Summer
Net
Ablation period
height
height
budget
budget
change
change
2008–2009
+1.6*
+0.59* ± 0.15
-0.7
-0.26 ± 0.08
+0.34* ± 0.12
1 Jun–19 Aug
2009–2010
+0.7
+0.25 ± 0.08
-1.2
-0.44 ± 0.09
-0.19 ± 0.12
30 Apr–5 Sep
2010–2011
+1.0
+0.37 ± 0.08
-1.1
-0.41 ± 0.09
-0.04 ± 0.12
28 May–13 Aug
2011–2012**
+0.7
+0.25 ± 0.08
-1.8
-0.86 ± 0.14
-0.61 ± 0.16
27 May–24 Aug
2012–2013***
+1.2
+0.45 ± 0.09
-0.8
-0.27 ± 0.08
+0.18 ± 0.12
29 May–17 Aug
* Value inferred from Van de Wal et al. (2012); ** estimate
based on snow-pit densities from May 2012; *** estimate based on snow-pit
densities from May 2013.
Results
Meteorological observations
The importance of katabatic and synoptic forcing on near-surface wind
direction are roughly equivalent at the elevation of KAN_U
(Van Angelen et al., 2011). The average wind direction is south-southeast
(∼ 150∘; Fig. 3a). However, in a case study of the
2012/2013 winter (Van As et al., 2014), the prevailing wind direction was
∼ 135∘ (southeast), suggesting an influential
katabatic regime in which air drains down-slope and is deflected by the
Coriolis effect. Wind speed is higher during winter (Fig. 3b); annual
average values are 6–7 m s-1, whereas summer (June–July–August) average values are around
5 m s-1 (Table 3). Winds exceeding 15 m s-1 occur primarily during
the winter period and rarely exceed 20 m s-1 when averaged over
24 h. The barometric pressure of about 800 hPa exhibits an annual cycle
with relatively high pressure in summer (Fig. 3c), favouring more stable,
clear-sky conditions. The specific humidity also varies annually; it peaks
in summer with annual average about 1.7 g kg-1.
The year 2010 was the warmest year of the record (Table 3), with the winter
(December–January–February) of 2009–2010 being 4.0 ∘C warmer than the
2009–2013 average and the summer only being equaled by
2012 (-1.8 ∘C; Table 3). May 2010 was especially warm, at -6.2 or
5.1 ∘C above the 2009–2013 average. Positive Ta
persisted during the end of the melt season resulting in a -1.1 ∘C
monthly average for August. The high 2010 temperatures influenced surface
ablation by inducing the early onset of melt. In 2010, ablation at
KAN_U occurred from late April until early September, whereas, for instance,
the 2009 melt season at KAN_U spanned early June until mid-August.
The average SMB over the period 1994–2010 at KAN_U is
+0.27 m w.e. (Van de Wal et al., 2012). Melt at this elevation occurs
during each melt season. The winter 2009/2010 accumulation of 0.25 m w.e. was
relatively low, amounting to just 65 % of the 2009–2013 average
(Table 4). During the 2010 melt season, all the snow that had accumulated
since the end of the previous melt season ablated, including part of the
underlying firn, resulting in the first negative SMB year on record (Table 4).
The stake measurements from Van de Wal et al. (2012) document a 2-year
surface height change of +0.42 m on average for 2008–2010 at the same
location (S10), corresponding to +0.15 m w.e. assuming a snow-pit density
of 360 kg m-3. From this estimate, we infer the winter and net SMB for
2009 to be +0.59 and +0.34 m w.e. respectively.
(a) Running average values for 31 days of all radiation budget
components at KAN_U. Solid lines indicate the net solar and
terrestrial radiation components. (b) Same as (a) but for
all surface energy balance components.
During winter 2011/2012, accumulation was similar to that in winter
2009/2010. In spring 2012, positive Ta was first recorded during April
(at -12.8 ∘C April 2012 was the warmest April on record), followed by
a relatively warm May (-8.6 ∘C). Ablation
rates were already high in late May 2012 (7.2 mm w.e. day-1; Charalampidis and van As, 2015). June
and July were the warmest of the 5-year record with -1.5 and
-0.6 ∘C monthly average Ta respectively. With the summer of 2012
on average as warm as that of 2010, but the ablation period shorter by 39
days (Table 4), the summer SMB was -0.86 m w.e., making 2012 the most
strongly negative SMB year (-0.61 m w.e.) to be recorded at this location
(Van de Wal et al., 2005, 2012).
Surface energy fluxes
Solar radiation exhibits a strong annual cycle at this location above the
Arctic Circle (Fig. 4a). In the absence of topographic shading or a
significant surface slope (< 1∘) the day-to-day variability in
incoming shortwave radiation at this elevation is dominated by cloudiness
and the solar zenith angle. The highest daily ES↓ values
occur in June and exceed 400 W m-2, while at the ELA they are just
below 400 W m-2 (Van den Broeke et al., 2008a) due to more frequent
cloud cover and a thicker overlying atmosphere. Whereas
ES↓ increases with elevation from the ELA to
KAN_U, ESNet obtains daily values of up to 100 W m-2
both at the ELA and at KAN_U, implying that solar energy input is regulated
by surface reflectance.
Terrestrial radiation exhibits an annual cycle of smaller amplitude (Fig. 4a).
The annual variations of the downward and emitted longwave radiation
are governed by the temperature and emissivity variations of the atmosphere
and the ice sheet surface respectively. Hence, the absolute magnitudes of
both components are larger during the summer period. EL↓
fluctuations depend primarily on cloud cover. EL↑ is a sink
to the SEB and during summer is limited by the melting surface with the
maximum energy loss being 316 W m-2. This results in predominantly
negative ELNet values throughout the year. The energy loss
peaks during June and July.
The ER annual cycle displays an energy gain at the ice sheet surface
during May to August and energy loss the rest of the year (Fig. 4b). This
winter energy loss is primarily compensated by downward sensible heat flux.
Calculated EH is typically positive throughout the year, with highest
values in winter when ER is most negative, heating the ice sheet surface
while cooling the atmospheric boundary layer (Fig. 4b). This facilitates the
katabatic forcing and thus enhances wind speed and further turbulent energy
exchange between the atmosphere and the ice sheet surface. The contribution
of EH to melt is smaller than at lower elevations (Van den Broeke et
al., 2011). The dominant melt energy source at KAN_U is therefore ER.
EE changes sign from winter to summer and is on average a small
contributor to the annual SEB. During the summer period, EE is
comparable to EH but with opposite sign, enabling surface cooling by
sublimation and/or evaporation (Henneken et al., 1994). In winter, EE is directed mostly
toward the cold ice sheet surface, resulting in heating from deposition.
The annually averaged EG is mostly negative and of the same magnitude as
EE (3–4 W m-2) but with no distinct annual cycle. Melt seasons
with substantial refreezing exhibit increased positive summer-averaged
EG since the near-surface firn temperature is on average higher than
Ts, leading to conductive heat transport toward the ice sheet surface.
Low EG values in summer indicate limited refreezing in the firn just
below the ice sheet surface.
EP is non-zero but still negligible in summer, when positive air
temperatures occur and thus precipitation is liquid.
Interannual variability of the SEB and implications for melt
With the exception of August 2009, when predominantly clear skies caused
ES↓ to be 40 W m-2 larger and EL↓
36 W m-2 smaller than in the other years, monthly average values of
ES↓ at this site are fairly invariant
(difference < 25 W m-2; Fig. 5a). Often ER increases when clouds are
present over an ice sheet; this is the so-called radiation paradox (Ambach, 1974),
as it was observed in April 2012.
Seasonal cycles for the years 2009–2013 based on monthly averages
of (a) incoming shortwave energy flux, (b) surface albedo,
and (c) net shortwave energy flux.
Seasonal cycles for the years 2009–2013 based on monthly averages
of (a) incoming, (b) emitted, and (c) net longwave energy flux.
Seasonal cycles for the years 2009–2013 based on monthly averages
of (a) sensible heat flux, (b) latent heat flux, and
(c) subsurface heat flux.
Figure 5b illustrates the annual cycle of monthly averaged albedo, excluding
the winter months. From October to February shortwave radiation values are
too low for accurate albedo estimation. Nevertheless, the albedo is expected
to be characteristic of fresh dry snow values (0.8–0.9) during winter. High
albedo persists until May due to fresh snow deposited on the ice sheet
surface. An exception occurred during March and April 2013, when the monthly
albedo of 0.78 suggests reduced precipitation input for a prolonged period
and the presence of ageing dry snow on the ice sheet surface (Cuffey and
Paterson, 2010). In the years 2009–2011 and 2013 the albedo gradually
decreased beginning late May and during the summer due to the effects of
relatively high temperatures and melt on snow metamorphism. During summer,
albedo still exceeded 0.75. Although melt at KAN_U still
occurs intermittently during August, such melt does not counteract the
effect of snowfall events that increase the surface albedo.
The anomalously warm period in June and July 2012 (Fig. 3d) coincided with a
larger decrease in surface albedo than in the other years. The combination
of enhanced melting, heat-induced snow metamorphosis, and firn saturation
reduced the albedo from 0.85 in April to 0.67 in July, reaching a value that
is characteristic of soaked snow facies close to the lower elevation snow/firn
line (Cuffey and Paterson, 2010). As a consequence, ESNet increased
by approximately 25 W m-2 in June and July (32 %; Fig. 5c). This
darkening thus functioned as an amplifier of melt (Box et al., 2012; Van As
et al., 2013) and contributed to the large observed ablation (Table 4).
The largest longwave radiation surface emissions occurred during August 2010
and June–July 2012, approaching the theoretical limit of -316 W m-2
for a continuously melting ice sheet surface (Fig. 6b). The concurrent high
EL↓ (Fig. 6a; Table 5) was related to high atmospheric
temperatures. This caused summer ELNet in 2010 and 2012 to exceed
its value in other years (Table 5; Van As et al., 2012). While summer
ESNet was similar in 2009 and 2010, summer ER was 69 %
larger in 2010 than in 2009, primarily due to the high atmospheric
temperatures. During 2012, summer ELNet was similar as in 2010. The
large summer ESNet resulted in summer ER 67 % higher than
in 2010 (Table 5). The highest daily ER attained 100 W m-2 on
9 July and coincided with the start of a Greenland-wide warm event. On
12 July, nearly the entire ice sheet surface was reported to melt (Nghiem et
al., 2012), followed shortly after by the highest meltwater discharge in
56 years on 12 July 2012, as inferred by the partial destruction of a bridge
constructed over the Watson River in Kangerlussuaq in 1956. At
KAN_U, well above the long-term ELA, not only a strongly
negative SMB was recorded in 2012, but it was the only year with a positive
annual radiation budget (ER = +4 W m-2; Table 5).
EH was largest during 2010 and smallest during 2011 (Table 5), the
years of highest and lowest annual Ta respectively (Table 3). Sensible
heat transfer toward the ice sheet surface was also low on average in 2012,
owing to the cold winter months. The high July 2011 EH was due to warm
air advection that occurred over a cold surface, yielding large near-surface
temperature gradients and sensible heat exchange (Fig. 7a). During summer
2013, when air temperatures remained relatively low, the ice sheet surface
exhibited the lowest sensible heat gain compared to the other melt seasons.
In all, EH did not contribute to SEB interannual variability as much as
the radiative components.
Annual and summer (June–July–August) average energy fluxes at
KAN_U (W m-2).
2009*
2010
2011
2012
2013**
Annual averages
ES↓
155
153
150
145
151
ES↑
-125
-121
-121
-110
-119
ESNet
30
32
29
35
32
EL↓
207
224
205
223
212
EL↑
-246
-262
-239
-254
-248
ELNet
-39
-38
-34
-31
-36
ER
-9
-6
-5
4
-4
EH
17
18
12
12
14
EE
-2
-1
-2
-1
-3
EG
-2
-3
1
-2
-2
EP
0.004
0.006
0.009
0.012
0.005
EM
4
8
6
13
5
Summer (JJA) averages
ES↓
322
305
302
296
313
ES↑
-252
-234
-236
-208
-242
ESNet
70
71
66
88
71
EL↓
237
259
252
260
245
EL↑
-291
-303
-299
-303
-292
ELNet
-54
-44
-47
-43
-47
ER
16
27
19
45
24
EH
6
6
8
7
5
EE
-9
-9
-7
-5
-13
EG
2
4
4
2
1
EP
0.014
0.025
0.035
0.049
0.021
EM
15
28
24
49
17
* Average 2010–2013 for January, February, and
March;
** average 2009–2012 for October, November, and December.
Summer EE values are correlated with summer atmospheric pressure
(R = 0.96), which influences the gradients in near-surface specific humidity and
wind speed. During summer 2012, pressure and specific humidity were
relatively high (811 hPa and 3.7 g kg-1 respectively; Table 3), while
the wind speed was reduced, thus contributing to the lowest absolute summer
EE with the lowest cooling rates due to evaporation/sublimation. The
maximum latent heat loss that year occurred in May. Thereafter, the moisture
content in the near-surface air became relatively large, with EE
decreasing in absolute value until July. Summer 2013 was conversely
characterized by relatively low pressure and specific humidity (804 hPa and
2.8 g kg-1 respectively) resulting in high evaporation/sublimation
rates especially in June and July (Fig. 7b).
Monthly EG values were small and displayed small interannual variability,
especially in summer. The summers of 2010 and 2011 exhibited the most
positive EG as a consequence of substantial refreezing (Fig. 7c), which
influenced near-surface firn temperature gradients. Summer EG in 2009
and 2013 (Table 5) was lower due to the moderate melt seasons of smaller
duration. Summer EG was lower in 2012 due to both a warm ice sheet
surface conducting heat into the firn and the absence of refreezing.
The melt rates in 2009 and 2013 were similar. In both years the largest
monthly
EM occurred in July and did not exceed 30 W m-2 (Fig. 8).
EM peaked similarly in 2010 and 2011, in June reaching about
20 W m-2 and in July exceeding 35 W m-2. May and August 2010 exhibited
significant melt in response to the warm atmospheric conditions (Van As et
al., 2012). Both 2010 and 2012 exhibited significant melt in May (10 W m-2).
During summer 2012, EM far exceeded all other years, with a
July value of 68 W m-2, leading to the largest ablation reported in Table 4.
The radiative fluxes dominate the interannual variability of melt at
KAN_U, with variations in ELNet being most
influential over the amount of available EM in the years 2009–2011 and
2013. In 2012, it was the large ESNet that mainly contributed to
the melt anomaly.
Melt–albedo feedback
Figure 9a, which depicts total monthly surface energy exchanges throughout
the study period, illustrates that ESNet and ELNet
dominate the SEB from May to September, while ELNet and EH
dominate the SEB during the remainder of the year. During the years
exclusive of 2012 considered here (2009, 2010, 2011, and 2013), the total
summer energy input to the ice sheet surface was 620–650 MJ m-2 each
year. During all years, the energy input peaked in July. For example, in
July 2010 the total energy input reached 246 MJ m-2. By contrast, in
2012, the total summer energy input exceeded 770 MJ m-2, and in July it
reached 304 MJ m-2. The 2012 total energy used for melt was 414 MJ m-2
(65 % higher than in 2010), of which 183 MJ m-2 was used
for melt in July. Figure 9b illustrates the simulated mass fluxes at the ice
sheet surface (note the different y axis scales for positive and negative
values). A total of 40 kg m-2 of mass loss occurs on average by the sum
of sublimation and evaporation during spring and summer. Conversely,
deposition amounts to 10 kg m-2 each winter season. The total snowfall
from April 2009 until September 2013 amounted ∼ 1500 kg m-2
(also Table 4). Up to the end of May 2012, all meltwater had
accumulated internally through percolation into the firn, adding mass of
1158 kg m-2 (1020 kg m-2 from snowfall and 138 kg m-2 from
rainfall). Due to an ice layer blocking vertical percolation in summer 2012,
444 kg m-2 ran off, removing approximately 38 % of accumulated mass
since April 2009.
Seasonal cycle for the years 2009–2013 based on monthly averages
of energy consumed by melt.
The total amount of meltwater generated at the ice sheet surface, equivalent
to the sum of run-off and refreezing minus rainfall, amounted 1232 kg m-2
in 2012. As the calculated surface ablation was 860 kg m-2
(Table 4), 30 % (372 kg m-2) of the produced meltwater was melted
more than once during the ablation season. This suggests that 416 kg m-2
(48 % of the total ablation or 34 % of the produced meltwater) was
effectively retained in near-surface firn layers.
The first year on record during which surface ablation exceeded
accumulation from the preceding winter at KAN_U was 2010 (Table 4; Van
de Wal et al., 2012). Even though atmospheric temperatures were high and the
impact on ablation was large in 2010, the response of the snow surface was
much larger in 2012, when ablation was more than 3 times larger than the
accumulation. In 2012, albedo decreased to ∼ 0.7 by mid-June
(Charalampidis and van As, 2015), implying substantial metamorphosis of the
snow surface, while in all other years this albedo was reached only in July
or August. The albedo reduced even more on 10 July to ∼ 0.6,
signifying the saturation of the ice sheet surface and the exposure of thick
firn. Until 6 August, the albedo value corresponded to that of soaked facies
close to the snow/firn line (Cuffey and Paterson, 2010). It should be noted
that snowfall events increased the albedo during several periods in the
summer season (Charalampidis and van As, 2015).
(a) Total energy per unit surface area. (b) Cumulative fluxes of
all mass components. Note the different y scales in (b).
(a) Albedo anomaly in 2012 measured by KAN_U for the
months May–September; (b) simulated relative surface height
anomaly;
(c) simulated cumulative energy anomalies for all contributing fluxes.
To quantify the impact of a relatively dark ice sheet surface on the SEB,
the average annual cycle in albedo of all years excluding 2012 was used to
replace the low 2012 albedo in dedicated sensitivity analysis. Figure 10a
shows the albedo anomaly of 2012, which resulted in enhanced ablation in
late May/early June (Fig. 10b). At the end of August, the ice sheet surface
lowered an additional 0.64 m due to 58 % more melt energy compared to a
situation with average albedo. The excess EM from the melt–albedo
feedback amounted to 152 MJ m-2, while the excess ESNet
supplied was 213 MJ m-2 (Fig. 10c). The remaining ESNet was
consumed by other fluxes, primarily EH. As the total surface ablation of
2012 was 1.78 m of surface height change (Fig. 2) the remaining 1.14 m was
primarily due to the warm atmospheric conditions and similar to 2010 (1.21 m).
This sensitivity analysis implies that the location would have
experienced a negative SMB in 2012 even without the melt–albedo feedback.
Discussion
Uncertainties
Model performance is limited by the accuracy of the instruments of
KAN_U as given in Table 1. The radiometer uncertainties are
the largest, based on what is reported by the manufacturer (10 % for
daily totals, although this is likely to be an overestimate; Van den Broeke
et al., 2004). Nevertheless, the accurate simulation of surface temperature
and snow ablation rates (Fig. 2) throughout the period of observations
provides confidence in both the measurements and the model.
The model exhibits considerable sensitivity to the subsurface calculations,
suggesting importance of pore volume and firn temperature, and how much more
complicated SEB calculations are in the lower accumulation area than for
bare ice in the ablation area. The model is able to capture the seasonal
variations of temperature in the firn and calculated temperatures are
commonly within 3.6 ∘C of those measured with an average of -0.3 ∘C
(Fig. 11a). The shallow percolation of a wetting front in the
firn is estimated at depths of 1–3 m in the years 2009 and 2013 (Fig. 11b),
while in the years of larger melt, pore volume until 10 m depth is affected,
possibly overestimating the percolation depth given the relative temperature
buildup in the simulated firn below roughly 5 m depth (Fig. 11a;
Charalampidis et al., 2016). In particular for 2012, available simulated
pore volume at ∼ 6 m is significantly affected by meltwater
that percolates below the formed thick ice layer, which may indicate that
the run-off threshold of a 6 m thick ice layer is an overestimate,
highlighting the need for a better run-off criterion. Further investigation
on this criterion and inclusion of water content held in the firn by
capillary forces, saturation of the surface, and proximity of impermeable ice
to the surface is necessary.
The fact that the subsurface calculations are initialized in 2009 by use of
vertically shifted firn densities from a 2012 core does not influence the
calculation of the surface energy fluxes and thus the outcome of this paper.
Importantly, the timing that simulated run-off occurred in July 2012 is in
agreement with satellite observations due to the run-off criterion, thereby
providing confidence in realistic calculation of EG.
Although the subsurface calculations are on a vertical grid of 10 cm (see
also Sect. 2.3), there is a progressive loss of detail in the density profile
through time due to the interpolation scheme that shifts the column
vertically when it needs to account for surface height variations (Fig. 11c).
Increased spatial resolution requires a finer temporal resolution to
avoid model instability. Although the calculated SEB would be unaffected, we
accepted the loss of detail in density because increasing the spatial and
temporal resolution would result in substantially increased computational
time. Nevertheless, during each melt season, when it is important that
refreezing is accounted for, no loss of detail is expected near the surface
since the column is shifted almost continuously upward.
(a) Difference between firn temperature measured by the
KAN_U thermistor string and simulated firn temperature. The
blue lines indicate the position of the thermistors below the surface. The
white areas near the surface are due to surfaced thermistors. Note that the
thermistor string was replaced by a new one drilled on 28 April 2013.
(b) Simulated refreezing rates. (c) Simulated firn density.
Rainfall is known to occur during summer on the higher elevations of the ice
sheet (Doyle et al., 2015). The exact amount is unknown as in situ
measurements for precipitation are rare and difficult to acquire on the ice
sheet. Therefore, the rainfall calculated by our model should be considered
a first-order estimate. Nevertheless, the amount of rain is expected to be
small and its effect on the SEB is negligible, as shown by the model results.
It is possible that other factors than heat-induced snow metamorphism and
the presence of surface water contributed to the 2012 albedo anomaly. Such
could be aerosol particles or impurities at the snow surface, effectively
reducing its albedo (Doherty et al., 2013). Also, in cases of extreme melt,
microbial activity can develop at the ice sheet surface with the subsequent
production of a dark-coloured pigment (Benning et al., 2014).
Long-term perspective in temperature and albedo
The Kangerlussuaq airport air temperature record since 1976 was used to provide
a temporal perspective to the KAN_U air temperature in recent
years (Fig. 12). The standard deviations reveal variability during the
winter period of more than 10 ∘C while for the months of July and
August standard deviations are ∼ 2.0 ∘C. The temperature
measurements reveal that the region has been warming on average starting in
1996 (not shown). Figure 12c illustrates this for 2000–2013; e.g. the
summers (JJA) were 1.2 ∘C warmer than in the reference period
1976–1999. The warm 2010 and 2012 summers have an anomaly value of +1.9 and
+1.8 ∘C respectively. The high temperatures in recent
years are most apparent for June when in 10 out of 14 years the 1976–1999
standard deviation is exceeded. A further increase of the regional
temperatures, as anticipated by climate models, will likely further increase
the frequency of large melt events and the extent of each melt season,
leading to conditions similar to or more extreme than in 2012 (McGrath et al., 2013).
(a) Monthly air temperature at Kangerlussuaq and at KAN_U.
Correlation coefficients (R): 0.97 for the extent of the KAN_U data and
0.66–0.99 for the months individually, with the minimum being January.
(b) Monthly reference period (1976–1999) air temperature at Kangerlussuaq.
(c) Monthly (May to September) and summer (June–July–August average) air
temperature anomalies at Kangerlussuaq for the years 2000–2013. Error bars
indicate 2 standard deviations.
Eleven-day Gaussian filtered nearest neighbour 5 × 5 km MOD10A1 albedo
(2000–2013) and KAN_U (2009–2013) albedo for the months:
(a) May (R = 0.91, (Δα)avg = -0.02,
RMSD = 0.02), (b) June (R = 0.77, (Δα)avg = -0.05,
RMSD = 0.05), (c) July (R = 0.95, (Δα)avg = -0.05,
RMSD = 0.05), (d) August (R = 0.60, (Δα)avg = -0.03,
RMSD = 0.04), and (e) September (R = -0.19, (Δα)avg = 0.02,
RMSD = 0.04).
The MOD10A1 time series from the years 2000–2013 shows an albedo decrease
of 0.05–0.10 during the 14 years of measurements in response to the
increased temperatures (Fig. 13). In particular, May albedo reached record
low values in 2010 and 2012. July albedo is considerably lower in the years
2007–2013 than it was in the first half of the record. The exceptional
surface conditions in July 2012 were also captured by MODIS with the lowest
monthly albedo (∼ 0.6) of the time series. The albedo in
August is generally higher than in July due to snowfall, but the values
remain sufficiently low to enhance melt. We note that part of the MODIS
based albedo decrease could be the result of the declining instrument
sensitivity of the Terra MODIS sensor (Wang et al., 2012; Lyapustin et al.,
2014) though updated (through 2014) comparisons between MOD10A1 and ground
observations from GC-Net data (Box et al. (2012); not shown) do not indicate
an obvious or statistically significant difference.
Increased meltwater infiltration into the firn during events of increased
melt has led to the formation of thick, near-surface ice lenses between 2 and
7 m, judging from the 2012 firn core (Machguth et al., 2015). This contrasts with the aquifers
(i.e. liquid water storage) that are observed in the firn in southeast Greenland
(Forster et al., 2013; Koenig et al., 2013; Kuipers Munneke et al., 2014).
The southwestern ice sheet receives about one-third of the annual
precipitation received in the southeast (Ettema et al., 2009). This results
in reduced thermal insulation of the infiltrated water as well as reduced
generation of pore volume. The subsequent shallow refreezing during recent
years of enhanced melt has led to the formation of thick impermeable ice,
ultimately enabling run-off in 2012 (Machguth et al., 2015).
The DMI measurements indicate that 2009 is representative of the reference
period 1976–1999 (Fig. 11c; Van As et al., 2012). With respect to summer
2009, the radiation budget in summer 2010 was higher due to lower
ELNet (Table 5; Sect. 3.3). In summer 2012, ELNet was the
same while ESNet was larger than in 2010. Most of this
ESNet excess was consumed by melting (Sect. 3.4). The melt–albedo
feedback (Box et al., 2012) will contribute to the rise of the ELA in a
warming climate (Fettweis, 2007; Van de Wal et al., 2012), and might
transform the lower accumulation area into superimposed ice if warming
prevails. We have shown that the melt–albedo feedback makes that warm
summers can have great impact on melt and run-off in the lower accumulation
area. Our results suggest that if warm atmospheric conditions persist in the
future, the additional input of solar radiation at the ice sheet surface
will be of higher importance to surface changes than atmospheric warming.