TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-10-1147-2016Extraordinary runoff from the Greenland ice sheet in 2012 amplified by
hypsometry and depleted firn retentionMikkelsenAndreas Bechbechmikkelsen@gmail.comHubbardAlunMacFerrinMikehttps://orcid.org/0000-0001-8157-7159BoxJason Erichttps://orcid.org/0000-0003-0052-8705DoyleSam H.FitzpatrickAndrewHasholtBentBaileyHannah L.LindbäckKatrinhttps://orcid.org/0000-0002-5941-6743PetterssonRickardhttps://orcid.org/0000-0002-6961-0128Department of Geosciences and Natural Resource Management,
University of Copenhagen, Copenhagen, DenmarkCentre for Permafrost (CENPERM), University of Copenhagen,
Øster Voldgade 10, Copenhagen, 1350, DenmarkCentre for Arctic Gas Hydrate, Environment and Climate,
Department of Geology, University of Tromsø, Dramsveien 201, 9037
NorwayCentre for Glaciology, Department of Geography and Earth
Sciences, Aberystwyth University, Aberystwyth, SY23 3DB, UKCooperative Institute for Research in Environmental
Sciences (CIRES), University of Colorado, Boulder, CO, USADepartment of Glaciology and Climate, Geological Survey of
Denmark and Greenland, Copenhagen, DenmarkAlfred Wegener Institute, Helmholtz Center for Polar and
Marine Research, Periglacial Research Section, 14473 Potsdam,
GermanyDepartment of Earth Sciences, Uppsala Universitet, Villav. 16, 752 36
Uppsala, SwedenAndreas Bech Mikkelsen (bechmikkelsen@gmail.com)30May20161031147115921July20153September201518March201623March2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://tc.copernicus.org/articles/10/1147/2016/tc-10-1147-2016.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/10/1147/2016/tc-10-1147-2016.pdf
It has been argued that the infiltration and retention of meltwater within
firn across the percolation zone of the Greenland ice sheet has the
potential to buffer up to ∼ 3.6 mm of global sea-level rise
(Harper et al., 2012). Despite evidence confirming active refreezing
processes above the equilibrium line, their impact on runoff and proglacial
discharge has yet to be assessed. Here, we compare meteorological, melt,
firn stratigraphy and discharge data from the extreme 2010 and 2012 summers
to determine the relationship between atmospheric forcing and melt runoff at
the land-terminating Kangerlussuaq sector of the Greenland ice sheet, which
drains into the Watson River. The 6.8 km3 bulk discharge in 2012 exceeded
that in 2010 by 28 %, despite only a 3 % difference in net incoming melt
energy between the two years. This large disparity can be explained by a
10 % contribution of runoff originating from above the long-term
equilibrium line in 2012 caused by diminished firn retention. The amplified
2012 response was compounded by catchment hypsometry; the disproportionate
increase in area contributing to runoff as the melt-level rose high into the
accumulation area.
Satellite imagery and aerial photographs reveal an extensive supraglacial
network extending 140 km from the ice margin that confirms active meltwater
runoff originating well above the equilibrium line. This runoff culminated
in three days with record discharge of 3100 m3 s-1 (0.27 Gt d-1) that peaked on 11 July and washed out the Watson River Bridge. Our
findings corroborate melt infiltration processes in the percolation zone,
though the resulting patterns of refreezing are complex and can lead to
spatially extensive, perched superimposed ice layers within the firn. In
2012, such layers extended to an elevation of at least 1840 m and provided a
semi-impermeable barrier to further meltwater storage, thereby promoting
widespread runoff from the accumulation area of the Greenland ice sheet that
contributed directly to proglacial discharge and global sea-level rise.
Introduction
The Greenland ice sheet is losing mass at 0.7 mm yr-1 equivalent of
global sea-level rise, the majority of which is attributed to surface
ablation that is set to increase under atmospheric warming (Enderlin et al.,
2014; Hanna et al., 2013). Although surface-meltwater production can be
readily calculated by regional climate models (e.g. Fettweis et al., 2011),
such estimates do not equate directly to sea-level rise due to the
hydrological processes that buffer and store melt on, within and beneath the
ice sheet. It has been argued that retention at the ice sheet surface has
the greatest capacity to offset future sea-level rise, particularly
refreezing across the wet-snow/percolation zone above the equilibrium line
(Pfeffer et al., 1991). Within the percolation zone, melt generated at the
surface infiltrates and refreezes within the snowpack, increasing its
density, forming firn and thereby retaining potential runoff (Pfeffer et
al., 1991; Braithwaite et al., 1994). Harper et al. (2012) analysed a series
of cores and ground-penetrating radar profiles collected across an 85 km
transect above the equilibrium line at ∼ 69.5∘ N to
quantify the water-storage capacity of the percolation zone. Their analysis
revealed repeated infiltration events in which surface melt penetrated to
more than 10 m depth and refroze as superimposed ice layers. Although the
resulting patterns of vertical densification were complex, they proposed
that over a number of decades such infiltration will fill all of the
available pore space and provide a storage sink of between 322 to 1289 Gt
of melt – equivalent to buffering ∼ 0.9 to ∼ 3.6 mm of global sea-level rise.
Below the equilibrium line in spring, meltwater is initially stored within
the snowpack, but once the pore space is saturated, it runs off the
previous summer's ice surface (Irvine-Fynn et al., 2011). This runoff either
flows directly into the subglacial environment via supraglacial river
networks and moulins or is temporarily stored in supraglacial lakes. Such
lakes can individually capture up to 107 m3 (0.01 Gt) of water and
are estimated to cover up to 3 % of the western sector of the ice sheet
(Box and Ski, 2007; Fitzpatrick et al., 2014). Hence, these lakes have the
capacity to buffer large volumes of water on timescales from weeks to
months, or potentially years if they do not drain (e.g. Fitzpatrick et al.,
2014; Selmes et al., 2011). Once filled, the lakes contribute directly to
proglacial discharge either by overflowing into downstream moulins or by
rapid in situ drainage into the subglacial environment (e.g. Das et al.,
2008; Doyle et al., 2013; Tedesco et al., 2013a). It is observed that
supraglacial lakes often drain in clusters that could cause major peaks in
proglacial discharge (Doyle et al., 2013; Fitzpatrick et al., 2014).
Ice-dammed proglacial lakes also provide a temporary buffer to proglacial
discharge that can flood rapidly (Carrivick and Quincey, 2014; Mikkelsen et
al., 2013; Russel et al., 2011).
Quantifying these water-storage mechanisms across the ice sheet is important
since the consequence of enhanced melt on mass-balance and sea-level
contribution depends on the fraction of melt that escapes to the ocean. The
area of the ice sheet undergoing melt will expand to higher elevations under
predicted atmospheric warming, and this could force runoff from well within
the ice sheet interior and contribute to enhanced sea-level rise (Hanna et
al., 2008; Huybrechts et al., 2011; Smith et al., 2015). Expansion of the
melt area with warming is further amplified by the ice sheet hypsometry. As
the ice surface flattens toward higher elevations, a linear increase in the
melt level results in a disproportionate gain in the net surface area
exposed to melt conditions. If, however, a significant fraction of that melt
is subsequently intercepted and stored by local percolation and refreezing
within the snowpack above the equilibrium line, or otherwise at lower
elevations in supra- and proglacial lakes, then discharge and sea-level
rise is buffered on a timescale of weeks to decades. Although these storage
terms have been estimated for the ice sheet (Box and Ski, 2007; Carrivick
and Quincey, 2014; Fitzpatrick et al., 2014; Harper et al., 2012; Humphrey
et al., 2012), their combined impact on runoff and proglacial discharge in
an integrated study has yet to be quantitatively assessed.
Here, by reference to the two extreme warm summers of 2010 and 2012, we
quantify the efficacy of surface-melt storage processes across the Greenland
ice sheet using a hydrological-budget approach. We compare the seasonal
production of surface melt with proglacial discharge across a well-defined,
land-terminating catchment that drains the Kangerlussuaq (K-transect) sector
of the ice sheet. By drawing on satellite imagery, photographs and a series
of snow pits and firn cores above the equilibrium line, we relate the
calculated residual difference in the hydrological budget through time to
the spatial extent and effectiveness of potential meltwater retention across
the catchment, with particular attention to the percolation zone.
The exceptional 2010 and 2012 melt seasons
The record warm Greenland summers of 2010 and 2012 have been documented
using regional atmospheric modelling (Tedesco et al., 2013b),
microclimatological observations (Bennartz et al., 2013; van As et al.,
2012), microwave and optical remote sensing (Nghiem et al., 2012; Smith et
al., 2015; Tedesco et al., 2011) and in situ data (McGrath et al., 2013).
In both years, a blocking high pressure system, associated with a strongly
negative summer North Atlantic oscillation (NAO) anomaly, was present in the
mid-troposphere over Greenland (Hanna et al., 2014). The resulting
circulation pattern advected warm southerly winds over the western flank of
the ice sheet, forming an insulating heat bubble over Greenland (Neff et
al., 2014) that promoted enhanced surface heating.
Panel (a) shows the location of the study area (cyan) and catchment
(red) in Greenland is shown on the inset map. Panel (b) shows the map of the study area
overlain with the location of the AWS, gauging station, catchment area and
snow pit sites. The background Landsat 7 image, which was acquired on 16
July 2012, reveals that superglacial lakes and streams formed at an
exceptional and unprecedented elevation of ∼ 1800 m a.s.l. The
non-linear increase in the size of the catchment with increasing elevation
is shown in (c), and (d) shows an example of the impact on melt area with a
rise in the snow line of 250 m with a 500 m displacement in different start
elevations (hypsometric effect).
During summer 2010, higher than average near-surface air temperatures in
western and south-western regions of the ice sheet led to early and
prolonged summer melting and metamorphism of surface snow, significantly
reducing surface albedo and thereby enhancing sunlight absorption (van As,
2012; Box et al., 2012; Tedesco et al., 2013b). Similarly, in summer 2012
high near-surface air temperatures and a low-surface albedo enabled high
melt rates (Ngheim et al., 2012). During 2012, exceptional melt events were
concentrated in two periods in mid-July and late July. On 12 July, a ridge of
warm air stagnated over Greenland and melt occurred over 98.6 % of the
surface of the ice sheet – even extending to the perennially frozen,
high-elevation interior at the ice divide (McGrath et al., 2013; Nghiem et
al., 2012). In the Kangerlussuaq sector, the focus of this study, the 11
July 2012 melt event had a severe and direct hazardous impact with the
washout and partial destruction of the Watson River Bridge on the 11 July
2012 (https://youtu.be/RauzduvIYog), indicating that proglacial discharge
was at its highest stage since the early 1950s when the bridge was
constructed. A second phase of exceptional conditions returned in late July
2012 when over 79 % of the ice-sheet surface was again exposed to
exceptional melt (Nghiem et al., 2012). Bennartz et al. (2013) found that
low-level clouds played an important role by increasing near-surface air
temperatures via their effect on radiative absorption: sufficiently low to
enhance the downward infrared irradiance whilst optically thin enough to
allow solar radiation to penetrate.
These conditions had the capacity to force rapid and extreme ice-sheet melt
and runoff that was visible from space and in time-lapse camera sequences
of, for example, proglacial flooding (Smith et al., 2015) and turbulent
plumes active at the fronts of tidewater glaciers (Chauché et al., 2014;
Nick et al., 2012). Nevertheless, the challenge of measuring discharge at
marine-terminating glaciers and the lack of proglacial gauging stations in
Greenland mean that this inference can only be assessed at a broad,
regional scale using satellite-derived estimates of mass balance (e.g.
GRACE; Ewert et al., 2012). Hence, the years of exceptionally warm
atmospheric forcing in 2010 and 2012 present an ideal natural experiment and
opportunity to assess and quantify the catchment-wide efficacy and
spatio-temporal footprint of melt, storage and runoff processes across the
ice sheet.
Study area and methodsStudy area
We focus on the ∼ 12 500 km2 catchment that drains into
the Watson River from the land-terminating Kangerlussuaq sector on the western
margin of the ice sheet. The catchment is 95 % glaciated and comprises
four main outlet glaciers centred on Russell Glacier (Fig. 1). Within this
catchment, the ice surface rises ∼ 90 km from the ice margin
at 550 m a.s.l. to the mean 1990 to 2010 equilibrium line altitude (ELA) of
1553 m a.s.l. (van de Wal et al., 2012, 2015), and extends a further
∼ 150 km across the accumulation area to the ice divide at
∼ 2550 m a.s.l.
Proglacial discharge measurements
Proglacial river discharge was gauged near the Watson River Bridge in
Kangerlussuaq (Fig. 2), located 22 km from the ice-sheet margin and with a
direct outlet into the Kangerlussuaq Fjord. Due to orographic shielding by
Sukkertoppen ice cap the Kangerlussuaq region is exceptionally dry, with a
mean annual precipitation of 149 mm (Box et al., 2004; van den Broeke et
al., 2008). Land-surface water losses from evaporation and sublimation
further minimise the land-area contribution to runoff compared to the ice-sheet component (Hasholt et al., 2013). The Watson River discharge was
determined using the stage–discharge relationship presented in Hasholt et al. (2013). Water stage was recorded by pressure transducers on a stable
cross section ∼ 100 m upstream from the bridge. The discharge
Q is given by the following equation:
Q=V×A,
where V represents the mean velocity in the river cross section and A is the
cross-sectional area. The surface velocity (V) was measured by means of a
float and converted into mean cross-sectional velocity by applying a
reduction factor of 0.95 (Hasholt et al., 2013). The cross-sectional area
(A) used for discharge calculations is based on the deepest sounding of the
channel bottom after the winter ice melts in spring. The combined
uncertainty in the cross-sectional area and velocity measurements is
estimated to be 15 % (Hasholt et al., 2013). However, here we also
conservatively include the possibility of a systematically deeper cross
section due to bed erosion within the deepest of the two channels during the
runoff season. Therefore we estimate the upper limit in the annual
cumulative discharge for 2010 and 2012 at +44 and +32 %
respectively. The instantaneous potential error varies with the discharge
rate and is plotted together with the measured discharge (Fig. 3d and e).
Photograph taken at 18:00 West Greenland Summer Time on
11 July 2012 during the flood with the Watson River Bridge being washed out.
Image courtesy of Jens Christiansson.
During the flood event on 11 July 2012 the water level exceeded the
previously observed maximum water stage by 1.65 m (15 %) and the
stage–discharge relationship was extrapolated accordingly. Our
stage–discharge relationship was also altered by the partial removal of a
road dam (part of the bridge construction), which opened up two new, shallow
channels in between and south of the two original channels (Fig. 2). We
measured the cross-sectional area of the two new channels after the flood
had subsided and, by combining these with measurements of stage from
timestamped time-lapse photographs, we estimate that these new channels
were 1.5 and 2.5 m deep at peak flow.
The surface velocity in these new channels was calculated assuming the
conservation of energy in fluids:
v=2gh,
where v is the surface velocity of the water, g is the gravitational
acceleration (9.82 m s-2) and h is the water level. Uncertainty in v for
the two new channels is mainly attributed to the determination of stage from
time-lapse photos, which we conservatively estimate at ∼ 30 %. The two original bedrock channels remained intact and we assume that
the hydraulic conditions in these channels did not change substantially
during the flood event. For the period after the bridge foundation was
partially washed out, the discharge in the new channels is added to that
calculated based on the stage–discharge relationship for the original
channels. We estimate that the formation of the two new channels during the
flood event resulted in a small relative (i.e. < 3 %) contribution
to the total discharge.
Meteorological records, discharge measurements and
modelled melt runoff for the study area during 2010 and 2012, including
(a) daily average air temperature at AWS_L and
AWS_U. To avoid cluttering, temperatures below -10 ∘C are not shown. Likewise the air temperatures at
AWS_M , which usually lies between that of AWS_
L and AWS_U, are not plotted. Panel (b) shows the calculated
cumulative energy input, (c) the albedo at three different
elevation bands, (d, e) the proglacial discharge, supraglacial lake
drainage volume and modelled melt runoff, and (f) the cumulative
proglacial discharge, modelled melt runoff and residual between the two.
The dashed vertical purple line demarks the bridge washout on 11 July 2012.
The uncertainty in discharge estimates is shown using grey lines on (d) and
(e) and by grey shading on (f). Where the uncertainty estimates for 2010 and
2012 overlap on (f), a darker shade of grey is used.
Meteorological measurements
Automatic weather stations (AWS) are located at three elevations: 732
(AWS_L), 1280 (AWS_M) and 1840 m a.s.l.
(AWS_U; see van As et al., 2012). Each AWS, recorded
near-surface (2–3 m) air temperature, humidity, wind speed, upward and
downward short-wave and long-wave irradiance as well as air pressure.
Snow and ice albedo
Surface albedo was determined from the Moderate
Resolution Imaging Spectroradiometer (MODIS) by NASA's Terra Satellite, interpolated onto a 5 km grid
from 1 May 2010 to 31 September 2012. An 11-day running median was taken to
reject noise caused by contrails and cloud shadows (Box et al., 2012). From
these data, an albedo time series was formed for the glaciated part of the
Watson River catchment area defined as 67 ± 0.2∘ N and west
of 44∘ W. The data were averaged in 100 m elevation intervals on
the basis of Scambos and Haran (2002). The resulting albedo product was divided
into three approximately equal area bands corresponding to the physiographic
regions dominated by surface-impurity darkness (1000 to 1450 m a.s.l.),
lakes (1500 to 1650 m a.s.l.) and wet snow (1700 to 1850 m a.s.l.; Fig. 1; see also Wientjes et al., 2012; Wientjes and Oerlemans, 2010).
Surface energy budget model
The surface energy budget (SEB) was calculated daily across the glacierized
catchment following van As et al. (2012). The model calculates radiative,
turbulent, rain and subsurface (conductive) energy fluxes using data from
the three AWS measurements as input, interpolated into the same 100 m
elevation bins as the albedo data. The MODIS albedo data were used in the
calculation of net short-wave radiation. The sensible and latent energy
fluxes were calculated from near-surface gradients of wind speed,
temperature and humidity using a stability correction. The surface mass
balance (SMB) was calculated as the sum of solid precipitation, surface melt
and sublimation. The model was validated against independent K-transect
measurements (e.g. van de Wal et al., 2012) and its performance was found to
be within 4 % of the observed values. The net energy available for melt
across the entire glacierized catchment was determined by integrating the
calculated energy flux (W m-2) for each elevation interval by area. For
the purpose of quantifying the potential net melt available for runoff,
refreezing and retention, parameterisations were disabled.
Firn-saturation model
Based upon firn core stratigraphy and density measurements at
AWS_U, a mass conservation model was used to determine when
horizontal water flow might occur if meltwater were not permitted to
percolate beneath the massive 2010 ice layers. Water generated by melt at
the surface, minus evaporation/sublimation, fills the available pore space
of the firn beneath and raises the saturated-water table level. In situ
measurements and/or reasonable ranges were assigned for model input values,
including the density of fresh snow, the average depth and density of the
packed snow layer above the firn, the density of refrozen ice and the amount
of water attributed to sublimation and evaporation. Ten million (107)
Monte Carlo model iterations were run over the range of input variables to
produce 95 % confidence intervals of the daily water levels and potential
firn-saturation dates at AWS_U.
Supraglacial lake drainage
To determine the extent and timing of supraglacial lake drainage events
within the Watson River catchment, an automatic lake classification was
applied to daily MODIS MOD09 imagery following Fitzpatrick et al. (2014).
Fifty-two cloud-free MODIS images with an initial resolution of 500 m were
sharpened to 250 m and processed to derive the surface area and volume of
supraglacial lakes. The smallest lake classified was 0.0625 km2, which
equates to a single 250 × 250 m pixel. Lake areas were classified using an
empirically determined threshold of the normalised difference water index
(NDWI; Huggell et al., 2002). Lake volume was derived using a reflective
index approach after Box and Ski (2007) calibrated against lake bathymetry
data acquired in 2010 (Doyle et al., 2013) and subsequently validated
against in situ depths from an independent supraglacial lake at
67∘ N, 48∘ W, at ∼ 1420 m.a.s.l.
(Fitzpatrick et al., 2014). The error in our lake area and depth is an
estimated ±0.2 km2 per lake and 1.5 m per pixel respectively.
Change in stored volume in each lake was converted to mean discharge rates
between cloud-free observations (Fig. 3d and e).
Catchment delineation
A well-documented source of uncertainty in calculating runoff stems from the
delineation of hydrologically complex watersheds with rapidly evolving
supraglacial stream, river and lake networks (e.g. van As et al., 2012;
Fitzpatrick et al., 2014; Smith et al., 2015). Furthermore, supraglacial
drainage plays a relatively minor part (albeit a readily observable one) of
the entire water transport story and the subsequent routing of meltwater
into the subglacial hydrological system via moulins and fractures remains
unconstrained. Here we adopt a novel watershed delineation approach based on
catchment and drainage routing determined from subglacial hydraulic
potential analysis presented by Lindbäck et al. (2015). Lindbäck et al. (2015) demonstrate that the subglacial footprint of the Watson River
catchment can migrate northward and capture up to ∼ 30 % of
the area of the adjacent Isunnguata Sermia catchment, under varying
subglacial water-pressure conditions during the melt season. However, the
study also reveals that despite significant hydrological piracy between
adjacent catchments, the actual contributing area of the Watson River
subglacial catchment, along with its surface hypsometry, remains effectively
constant. Lindbäck et al. (2015) also demonstrate that across the lower
ablation area (500 to 1250 m a.s.l.) where meltwater production rates are
highest, the subglacial footprint is fixed even under transient
water-pressure conditions. Hence, we are confident that the catchment
delineation adopted in this study, based on subglacial hydropotential
analysis and the associated melt and runoff calculations, are robust and
within error of data sets used.
Measurements of firn and snowpack density
To assess firn and snowpack densification, 15 snow pits and three 7.6 cm
diameter ice cores were obtained from eight sites between 1280 and 1840 m a.s.l. in April 2012. Two cores were drilled 10 m apart
near AWS_U whilst the third core was drilled at a site located 400 m to the south of AWS_U. Core stratigraphy was analysed at
∼ 1 cm vertical resolution before cores were cut into 10 cm
sections and weighed to determine the density profile of the snowpack and
firn. A transect of 0.5 to 1 m-deep snow pits between AWS_M
and AWS_U were examined to investigate spatial variations in
firn and snowpack density (Fig. 1).
Results
Near-surface air temperatures from three AWS reveal insightful differences
in the temporal and altitudinal distribution of energy available for melt
between 2010 and 2012. Melt commenced earlier in 2010 with the lowest
AWS_L reaching 6 ∘C daily average air temperature
by mid-May (Fig. 3a). At AWS_L, melt with air temperature
5 ∘C above the seasonal average persisted until 15 September. The
duration of the 2010 melt season (119 days) was without precedent for the
Kangerlussuaq sector of the ice sheet since 1973 (van As et al., 2012). At
the uppermost AWS_U, located ∼ 300 m above the
1991–2009 baseline ELA of 1524 m (van de Wal et al., 2012), above-freezing
temperatures did not prevail until 8 July 2010. Thereafter mean daily
temperatures periodically remained above freezing until September, making
2010 exceptional for melt compared to the long-term average.
During the 2012 melt season, air temperatures above the equilibrium line indicate
widespread surface melting from mid-June onwards, including two week-long periods
with mean daily air temperatures at AWS_U of 3 ∘C (Fig. 3a) during high pressure
and clear sky conditions. In the five days leading up to the extreme mid-July 2012 melt
event, air temperatures at AWS_M and AWS_U
were within 1 ∘C despite 70 km horizontal and 500 m vertical
separation. Hence, from mid-June through to July 2012, the environmental
lapse rate was exceptionally low, indicating that melting conditions likely
prevailed across an extensive, relatively flat accumulation area. By 12
July, surface melting extended across the entire accumulation area up to the
ice sheet divide and indeed, the entire ice sheet including Summit Camp
and the NEEM drill site where wet-snow conditions halted airborne
ski-equipped CH130 operations (McGrath et al., 2013; Nghiem et al., 2012).
Below 1000 m a.s.l., the mean 2012 summer air temperatures were in contrast
0.75 ∘C lower than in 2010, though still higher than the long-term
mean. This in part is explained by the delayed 2012 melt onset that
commenced in late May (Fig. 3a).
Energy inputs in 2010 and 2012 (TW).
Energy inputs – 020102012Difference 2012to 1850 m a.sl.to 2010Energy available for melt2.43 × 1062.37 × 106-3 %
Somewhat surprisingly, the net cumulative energy available for surface melt
across the catchment is virtually equivalent by the end of the 2010 and 2012
summers despite quite different prevailing weather conditions (Fig. 3b).
The total energy available for melt across the catchment in 2010 and 2012
calculated from the SEB model up to an elevation of 1840 m a.s.l. was only
3 % less in 2010 compared to 2012 (Table 1; Supplement for
yearly energy balances for the three weather station sites for 2010 and 2012).
MODIS albedo time series (Fig. 3c) binned into three elevation bands
equating to the extent of the dark, lake and wet-snow zones.
Fig. 3 exhibits complex patterns of change through space and time. In
2012, the albedo decline lags behind 2010 (Fig. 3c) due to the early
melt season onset in May 2010 promoted by low 2009/2010 winter snow
accumulation (van As et al., 2012). By mid-June, albedo across the dark zone
for both years declined to 0.4. For the remainder of the melt season, the
2010 dark zone albedo was ∼ 0.05 lower than in 2012 (Fig. 3c), consistent with warmer temperatures and enhanced melt at low elevations
in summer 2010. Across the lake and wet-snow zones, a similar pattern of
albedo decline is observed up until mid-June. From this time onwards, in
contrast to the dark zone, it is the 2012 albedo that is consistently as
much as 0.2 lower than 2010, with the exception of a week-long period when
the albedo was reset due to snowfall on 5 August 2012.
The seasonal evolution of daily Watson River discharge and
catchment-integrated melt varies considerably between 2010 and 2012 (Fig. 3d to f). In 2010 the integrated melt and proglacial discharge increased at
a lower rate than in 2012, despite higher cumulative energy input aided by
elevated temperatures combined with lower albedo. Mean daily integrated
discharge in 2012 peaked at 3100 m3 s-1 (equivalent to
∼ 0.27 km3 d-1; Fig. 4e) in mid-July, that
washed out Watson River Bridge. With lower temperatures during the week
commencing 15 July, melt and discharge dropped to below 2010 levels but
returned to high values of at least 1500 m3 s-1 for 11 days from
26 July 2012, coinciding with the second phase of exceptionally warm
conditions. By the end of the melt season, the final total annual discharge
in 2012 of 6.8 km3 exceeded that of 5.3 km3 in 2010 by
∼ 28 %.
The cumulative measured discharge as a function of the
calculated energy input for the catchment up to 1850 m a.s.l. The flooding
period of 11 to 14 July is marked with a bold red line.
Throughout the 2010 melt season there is a steady increase in the difference
between calculated integrated melt across the catchment and cumulative
measured discharge, which by the end of the season equates to 36 %
(∼ 1.9 km3) of residual melt retained (R′) within the
catchment (Fig. 3f). In the period leading up to 11 July 2012, a similar
increase in residual R′ as 2010 indicates substantial meltwater storage
within the catchment. However, after 11 July 2012 the residual R′ drops by
40 % equating to 1 km2 of bulk discharge released within
5 days. Throughout the remainder of the summer, R′ further diminishes so
that only ∼ 0.2 km3 of meltwater is retained by the end
of the melt season. This contrasting catchment response to forcing between
the two years is demonstrated by plotting cumulative energy input versus
cumulative discharge for 2010 and 2012 (Fig. 4). The resulting slope of
energy forcing against discharge response is considerably steeper in 2012
than 2010. Hence, for a given energy input, there is a disproportionately
larger catchment runoff and discharge response in 2012 compared to 2010,
particularly so during the 11 to 14 July 2012 flooding.
The melt totals for each elevation band along with bulk Watson River
discharge and their differences are listed in Table 2. Below the long-term
ELA of 1550 m, the 2010 and 2012 calculated melt totals are within 7 % of each
other. By contrast, in the two elevations bands 1550–1850 and 1850–2050 m a.s.l., calculated melt was respectively 75 and 200 % larger in
2012 compared to 2010 (only melt up to 1850 m a.s.l. is included in Fig. 3d and f). Despite this, the absolute difference in total calculated melt
between the two years is still only 3 %, yet the difference in proglacial
discharge between the two years is 28 %. Thus, the runoff response to
atmospheric forcing is again demonstrated to be more pronounced in 2012,
reflected in the larger residual between calculated melt and measured
proglacial discharge (Fig. 3f).
Panels (a–c) show the density profiles of three shallow firn cores drilled at AWS_U in April 2012. The water table
is indicated in light blue and ice lenses observed in the core stratigraphy
are indicated in cyan. Magenta and red lines indicate two potential sets of
“blocking” ice lenses observed in the firn. Panel (d) shows a model simulation of the
near-surface water table at AWS_U for each of the two
blocking lens assumptions in (a–c), with 95 % confidence intervals in grey.
Red ticks on the horizontal axes indicate days above freezing when surface
melt would occur. As snow melts above the blocking lenses the water table
rises simultaneously until it meets the lowering snow surface. Light blue is
free air. The daily snow surface is observed by the adjacent
AWS_U AWS. The two dashed orange vertical lines indicate 11
July, the date of the Watson River Bridge destruction and 16 July, when the
Landsat image from Fig. 1 shows horizontal water transport in the vicinity
of AWS_U.
Melt contributions (km3) from different elevation
intervals integrated through to the end of the melt season, 1 October each year.
20102012Differencekm3km3%Below mean ELA6.86.3-71550 to 1850 m0.40.7751850 to 2050 m0.10.3200Total – up to 1850 m7.27.0-3Total – up to 2050 m7.37.30% melt above mean ELA (1550 to 1850 m)6 (%)10 (%)67Measured proglacial discharge at Oct. 15.36.828Integrated melt up 1850 m – measured discharge1.90.2-89Integrated melt up 2050 m – measured discharge2.00.5-75
Examination of the timing between of catchment-integrated melt and
proglacial discharge (Fig. 3d and e) reveals that meltwater routing
through the glacial and proglacial system has a lag of between one and five days
during each melt season. In June 2012, the proglacial discharge response to
melt was dampened and delayed. Prior to the 11 July 2012 extreme melt and
discharge, the integrated modelled melt closely resembles the proglacial
discharge hydrograph but with a ∼ 3 day lag. Henceforth,
during the remainder of July and the beginning of August 2012, there is a
significantly shorter lag between discharge response to melt production. The
implication here is that once local meltwater production had been mobilised,
even at high elevations above the equilibrium line, the resulting runoff
transits through a drainage network up to 160 km long within three days, thereby
contributing to the proglacial discharge peak. Such rapid transit times
imply supra- and subglacial flow velocities in excess of 2 km h-1
(∼ 0.6 m s-1) through an efficient – linked – drainage
system. These results are comparable to similar transit velocities derived
from tracer experiments conducted up to 57 km from the ice margin in 2011
(Chandler et al., 2013). The second phase of intense melt, commencing on 26
July 2012 was followed by a rapid rise in proglacial discharge with a lag of
just two days. Peak melt during this period occurred on 3 August 2012 with the
associated peak in proglacial discharge occurring on the 5 August 2012. The
onset of discharge abatement was concurrent with declining air temperatures
from 6 August 2012 onwards.
The release of water stored in supraglacial lakes accounts for a minor
component of proglacial discharge. In 2012 the majority of lake drainages
occurred well before any peaks in proglacial discharge (Fig. 3e and f).
The calculated mean drainage rate of < 100 m3 s-1 for 2012
indicates that the volume of lake drainage water contributed less than 2 %
of the total bulk discharge (Fig. 3d and e). The maximum short-term
contribution from lake drainage (0.10 km3) occurred on 23 June 2012
with the synchronous drainage of a local cluster of five lakes (Fig. 3e).
Over the following week, approximately 70 % of all water stored in
supraglacial lakes across the entire catchment was released (Fig. 3e),
which could have accounted for half of the Watson River discharge. However,
this multiple lake drainage event occurred ∼ 12 days before
the proglacial discharge peak of 11 July 2012. Supraglacial lakes drain in
as little as 2 h (Das et al., 2008; Doyle et al., 2013) and it is likely
that this stored water discharged out of the catchment well before 11 July.
One small ∼ 0.02 km3 lake drainage event between 5 and 8
July would have contributed ∼ 2 % to the extraordinary
discharge measured between July 10 and 14 (0.9 km3).
Analysis of MODIS and Landsat imagery indicate that no ice-dammed proglacial
lakes within the catchment drained prior to the mid-July flood event,
including one that appears to drain regularly in August/September each year.
On 11 September 2010 and 12 August 2012, a partially filled proglacial lake
did drain (described in Mikkelsen et al., 2013) and even though it is
recorded in the Watson River hydrograph, the net contribution to proglacial
discharge is minor in 2010 and 2012 (Fig. 3d and e).
Discussion
Our analysis reveals that even though the net atmospheric forcing
represented by the total incoming energy flux for 2010 and 2012 was similar,
the ensuing runoff response was markedly different (Fig. 4). Widespread
melt in 2010 has been ascribed to atmospherically sourced heating coupled
with a strong albedo feedback promoted by low winter snowfall and early melt
onset (Tedesco et al., 2011; Box et al., 2012; van As et al., 2012). Yet low
albedo and high air temperatures alone do not explain the 28 % increase in
discharge in 2012 compared to 2010. Our analysis also confirms that the
release of stored water from supraglacial lakes played a relatively minor
role in peak and total proglacial discharge in 2012 (Fig. 3d and e). At
most, the supraglacial lake contribution to the 11 July 2012 peak discharge
of 3100 m s-1 was ∼ 2 %. Our results indicate that only
a relatively small proportion of the total melt generated at the surface was
stored in supra- and proglacial lakes and that the buffering effect of lakes
on runoff and discharge is thus limited (Fig. 3d and e). That is not to
dismiss the key role of supraglacial lakes in ice sheet hydrology, since it
is the critical storage of large volumes of meltwater in them that initiate
new hydrofractures and allow them to propagate to the bed – which eventually
develop into moulins (Krawczynski et al., 2009; Doyle et al., 2013; Tedesco
et al., 2013a). Supraglacial lakes are hence a prerequisite to establishing
efficient pathways for injecting surface water into the subglacial
environment (Das et al., 2008; Doyle et al., 2013).
We invoke three mutually compatible explanations for the exceptional
discharge response observed in 2012: (1) significant melt occurred above the
equilibrium line in addition to below it, (2) ice-surface hypsometry
amplified the total melt originating from the accumulation zone by
disproportionately increasing the contributing area as melt-levels rose and
(3) firn retention and storage capacity was reduced within the accumulation
zone, thereby promoting widespread runoff. It is significant that such a
large runoff contribution from the percolation zone could only have been
attained if firn retention capacity was either filled or otherwise severely
reduced in 2012 and it is this hypothesis that herein forms the central
tenet of our discussion. In support of this we present three lines of
evidence: (a) snow pit observations and firn core stratigraphy acquired in
April 2012 from the percolation zone, (b) observations of surface water
networks obtained from satellite imagery and oblique photographs in the
vicinity of AWS_U (Fig. 6) and (c) results of our
SEB-modelling experiments where total integrated melt is assumed to runoff
without any retention or refreezing.
Our core stratigraphic analysis (Fig. 5a to c) reveals significant perched
superimposed ice layers that could be capable of blocking surface meltwater
infiltration into deeper unsaturated firn layers across the percolation
zone. In addition to the shallow firn cores presented (Fig. 5), a
persistent and continuous decimetre-thick layer of refrozen, superimposed
ice was also observed in 15 snow pits dug along a transect extending from
the equilibrium line to AWS_U (Fig. 1). Severely reduced
firn retention due to such a superimposed, perched ice lens is further
supported by mass conservation modelling of the near-surface water table at
AWS_U (Fig. 5d). Here, two potential sets of blocking
layers at different levels within the snowpack equate to the thick
superimposed ice lenses observed in the firn cores acquired at
AWS_U (Fig. 5a to c). For the shallowest of these
scenarios, melt and retention calculations predict complete saturation and
free surface water available for active runoff by 11 July 2012. These
results are consistent with a recent study by Machguth et al. (2016) who
also demonstrate reduced meltwater retention across the percolation zone of
western sector of the Greenland ice sheet.
Panel (a) is a zoomed-in Landsat 7 image from 16 July 2012 showing
free surface water in the area around AWS_U. The extent is
marked on Fig. 1. The scan line correction failure was interpolated using
the ENVI “replace bad data” routine based on Band 8 and visible surface
water was enhanced using a modified normalized difference water index
(Fitzpatrick et al., 2014). (b, c) Oblique aerial photographs of the
active supraglacial channel network emerging from AWS_U well
within the accumulation zone at 1840 m a.s.l. and 140 km from the ice-sheet
margin on 13 August 2012. Image courtesy of Paul Smeets.
Evidence for firn saturation and active surface runoff are furnished
independently by the identification of an active supraglacial channel
network in Landsat satellite imagery and from oblique photographs taken 13
August 2012 in the vicinity of AWS_U (Fig. 6). Landsat
imagery indicates that wet snow, meltwater channels and lakes can be
identified up to at least 1750 m a.s.l. on 23 June 2012 and an active
stream network to at least 1800 m a.s.l. from 5 July 2012 onwards. In early
August, 2012 an active channel network was confirmed first-hand during a
scheduled maintenance visit to AWS_U (Fig. 6b and c). That
a well-developed supraglacial hydrological network is clearly observed well
above the long-term equilibrium line in the period leading up to the 2012
peak discharge event confirms the assessment of firn retention conditions
and the snowpack modelling presented here. Moreover, aerial photos of
stream networks to 1840 m a.s.l. provide clear evidence of widespread runoff
from the percolation zone across the western sector of the Greenland ice
sheet.
If predicted future atmospheric warming is realised, then the combined
impact of reduced firn retention capacity and ice sheet hypsometry will
become increasingly apparent through amplification of runoff and discharge
response with interior melting. If, as we hypothesise, the extraordinary
2012 discharge was partly derived from runoff originating above the
equilibrium line due to an impermeable, superimposed ice lens that formed
during previous warm summers, then the 2012 record-warm event itself will
lead to the formation of even thicker superimposed ice layers extending yet
further into the interior. Hence, we infer a strong positive feedback where
a disproportionate and amplified runoff response to future melt events leads
to yet more abrupt and severe proglacial discharge, as the 11 July 2012
flood documented here.
In light of these findings, the firn-buffering mechanism proposed for the
EGIG line some 120 km north of our study area and extrapolated across the
entire ice sheet by Harper et al. (2012) would appear to be somewhat
diminished, at least in the Kangerlussuaq sector. Based on their data and
analysis (Fig. 3b and c in Harper et al., 2012) and assuming an
equivalent location, our AWS_U site, located 50 km beyond
and 300 m above the ELA, should have had a buffering capacity equating to a
fill-depth of between 2 and 10 m of meltwater equivalent. In July 2012,
up to and including AWS_U at 1840 m a.s.l. this was not the
case and saturated snowpack conditions forced melt to runoff from the
percolation zone into a well-developed river network that directly
contributed to proglacial discharge and sea-level rise. The next decade will
reveal if 2010 and 2012 were exceptions or are part of an emerging new
trend. The three years subsequent to the 2012 melt and runoff extreme, i.e.
2013–2015, have been marked by low temperatures, reduced melting and
anomalously high accumulation which will have, to some extent, recharged the
buffering capacity of the lower accumulation area. Either way, it will be
critical to understand the future runoff response to variable atmospheric
forcing and to determine what portion of the melt generated is intercepted
and stored and what fraction contributes directly to proglacial discharge
and global sea-level rise.
Conclusions
Comparison of melt and discharge across the Kangerlussuaq sector in 2010 and
2012 has enabled us to assess and attribute the contrasting runoff response
of the Greenland ice sheet to extreme atmospheric forcing. The measured bulk
discharge of 6.8 km3 and flooding of the Watson River in 2012 was
unprecedented since the Kangerlussuaq Bridge was constructed in the early
1950s, and exceeded the previous record set in 2010 by ∼ 28 %. Throughout the 2010 melt season, there was a steady increase in the
residual difference between calculated melt across the catchment and
cumulative proglacial discharge, which by the end of the season equated to
36 % (∼ 1.9 km3) melt retained within the catchment up
to an elevation of 1850 m a.s.l. In the period up to 11 July 2012, a
similar pattern of storage indicates significant catchment retention.
However, after 11 July the residual fell by 40 % and diminished further by
the end of September, with only 3 % (∼ 0.2 km3) of melt
generated within the catchment retained. Surface-melt energy versus
proglacial discharge demonstrates an amplified response to forcing in 2012
as compared to 2010, particularly during 11–14 July flood. In 2010 local
melting from above the equilibrium line infiltrated and was stored within
the firn as superimposed ice layers; hence it did not contribute to
proglacial discharge. By contrast, in 2012 our analysis and modelling
reveals severely reduced firn-layer infiltration and retention capacity due
an extensive perched, thick and semi-impermeable ice lens that formed in
previous, anomalously warm melt seasons, including 2010. This resulted in a
near-instantaneous runoff and proglacial discharge response from above the
accumulation area contributing directly to global sea-level rise.
The Supplement related to this article is available online at doi:10.5194/tc-10-1147-2016-supplement.
Acknowledgements
We thank Dirk van As and Horst Machguth for assistance in the field and
during preparations of the manuscript, including provision of the surface
energy balance model, logistics, comments on an initial draft and
supervision of Andreas Mikkelsens PhD project. We also thank Paul Smeets,
Institute for Marine and Atmospheric Research, Utrecht University for
providing oblique areal photographs taken at AWS_U on 13
August 2011. We acknowledge the Greenland Analogue Project (GAP) – Sub
Project A that funded the weather stations and field logistics, the
commission on scientific investigations in Greenland, grant no. 07-015998,
09-064628 and 2138-08-0003 and the Danish National Research Foundation
founding Centre for Permafrost (CENPERM), funded by the Danish National
Research Foundation, DNRF number 100, Department of Geosciences and Nature
Resource Management, University of Copenhagen, Denmark for financial support
of the discharge measurements. Jason Eric Box is supported by Denmark's “Det Frie
Forskningsråd”, Nature and Universe grant DFF – 4002-00234. The
National Aeronautics and Space Administration (NASA) award NNX10AR76G
provided funding for firn table modelling work through the Cooperative
Institute for Research in Environmental Sciences, University of Colorado at
Boulder, USA. Andrew Fitzpatrick and Sam H. Doyle were supported by NERC and Aberystwyth University
doctoral scholarships respectively and fieldwork infrastructure was further
funded by NERC Projects NE/G005796/1, NE/G010595/1, NE/H024204/1 and a Royal
Geographical Society Gilchrist Fieldwork Award. Alun Hubbard acknowledges salary from
the Centre for Arctic Gas Hydrate, Environment and Climate funded through
the Research Council of Norway (Grant no. 223259).
Edited by: M. Tedesco
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