Content uploaded by Ruth Mottram
Author content
All content in this area was uploaded by Ruth Mottram on Aug 13, 2015
Content may be subject to copyright.
ARTICLES
PUBLISHED ONLINE: 13 JULY 2015 | DOI: 10.1038/NGEO2482
Amplified melt and flow of the Greenland ice
sheet driven by late-summer cyclonic rainfall
Samuel H. Doyle1*, Alun Hubbard2, Roderik S. W. van de Wal3, Jason E. Box4,5, Dirk van As4,
Kilian Scharrer6, Toby W. Meierbachtol7, Paul C. J. P. Smeets3, Joel T. Harper7, Emma Johansson8,9,
Ruth H. Mottram10, Andreas B. Mikkelsen11, Frank Wilhelms12,13, Henry Patton2, Poul Christoersen14
and Bryn Hubbard1
Intense rainfall events significantly aect Alpine and Alaskan glaciers through enhanced melting, ice-flow acceleration
and subglacial sediment erosion, yet their impact on the Greenland ice sheet has not been assessed. Here we present
measurements of ice velocity, subglacial water pressure and meteorological variables from the western margin of the
Greenland ice sheet during a week of warm, wet cyclonic weather in late August and early September 2011. We find that
extreme surface runo from melt and rainfall led to a widespread acceleration in ice flow that extended 140km into the
ice-sheet interior. We suggest that the late-season timing was critical in promoting rapid runo across an extensive bare ice
surface that overwhelmed a subglacial hydrological system in transition to a less-ecient winter mode. Reanalysis data reveal
that similar cyclonic weather conditions prevailed across southern and western Greenland during this time, and we observe
a corresponding ice-flow response at all land- and marine-terminating glaciers in these regions for which data are available.
Given that the advection of warm, moist air masses and rainfall over Greenland is expected to become more frequent in the
coming decades, our findings portend a previously unforeseen vulnerability of the Greenland ice sheet to climate change.
The Greenland ice sheet (GIS) is the largest cryospheric
contributor to global sea-level rise: responsible for
0.7 mm yr−1—a rate at least double that of the Antarctic
ice sheets combined and one that has steadily accelerated over the
past two decades1,2. Approximately half of this mass loss is attributed
to summer, surface melt processes enhanced through a number of
positive feedbacks, such as surface albedo and hypsometry, which
are relatively well constrained and amenable to modelling under
future climate change1,3. The remaining dynamic mass losses are
attributed to increased ice discharge and are clearly significant yet
remain poorly constrained and difficult to model and predict2. Here
we present evidence for a large, late-summer, cyclonically induced
runoff event that falls outside the surface melt processes included
in mass-loss assessments, and which had a potent and widespread
effect on surface melt and ice dynamics.
In this study, we test the hypothesis that late-season cyclonic
weather systems—which are predicted to increase in frequency
and magnitude4,5 yet have so far been neglected in studies of
GIS dynamics and mass balance1,2—produce sufficient runoff to
overwhelm the ice sheet’s basal drainage system, driving transient,
widespread accelerations in ice flow. We focus our investigation
on the Kangerlussuaq sector of the GIS where a dense network
of Global Positioning System (GPS) receivers and automated
weather stations (AWS), plus borehole water-pressure and proglacial
discharge records, enable a comprehensive analysis of the me-
teorological and glaciological conditions driving a late-summer
acceleration event in 2011. We then use reanalysis data to deter-
mine the synoptic weather and spatial footprint of this specific
event, before interrogating meteorological, regional climate mod-
elling and ice surface velocity records from around Greenland
to determine the magnitude, impact and frequency of this and
other similar events in the past. This analysis provides a frame-
work for the reinterpretation of three well-documented ice-flow
acceleration events that were previously attributed to surface melt
alone. Finally, we discuss the implications of these findings in
the context of predicted changes in Greenland’s climate over the
next century.
Runo and ice dynamics in the Kangerlussuaq sector
Seasonal and inter-annual acceleration of GIS flow is governed by
the dynamic response of the basal hydrologic system to variability in
meltwater delivery to the bed6–14. The highest ice velocities typically
occur shortly after melt onset, when an inefficient drainage system
is overwhelmed by the first major inputs of surface water of the melt
1Centre for Glaciology, Department of Geography & Earth Sciences, Aberystwyth University, Aberystwyth SY23 3DB, UK. 2Centre for Arctic Gas Hydrate,
Environment and Climate, Department of Geology, The Arctic University of Norway, N-9037 Tromsø, Norway. 3Institute for Marine and Atmospheric
Research Utrecht, Utrecht University, PO Box 80005, 3508TA Utrecht, Netherlands. 4Geological Survey of Denmark and Greenland, Øster Voldgade 10,
1350 Copenhagen K, Denmark. 5Department of Geography, The Ohio State University, 1036 Derby Hall, 154 North Oval Mall, Columbus, Ohio 43210-1361,
USA. 6ENVEO IT GmbH, Innsbruck 6020, Austria. 7Department of Geosciences, University of Montana, Missoula, Montana 59812, USA. 8Department of
Physical Geography and Quaternary Geology, Bert Bolin Centre for Climate Research, Stockholm University, SE-106 91 Stockholm, Sweden. 9Swedish
Nuclear Fuel and Waste Management Co, Box 250, SE-101 24 Stockholm, Sweden. 10 Danish Meteorological Institute, Lyngbyvej 100, DK-2100
Copenhagen Ø, Denmark. 11Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, DK-1350
Copenhagen, Denmark. 12Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Am Handelshafen 12, 27570 Bremerhaven,
Germany. 13Department of Crystallography, Geoscience Centre, University of Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany. 14 Scott Polar
Research Institute, University of Cambridge, Lensfield Road, Cambridge CB2 1ER, UK. *e-mail: sdd08@aber.ac.uk
NATURE GEOSCIENCE | VOL 8 | AUGUST 2015 | www.nature.com/naturegeoscience 647
© 2015 Macmillan Publishers Limited. All rights reserved
ARTICLES NATURE GEOSCIENCE DOI: 10.1038/NGEO2482
010
km
0 25 50 75 125 150100 175+
Event acceleration (%)
67
50
Longitude (° W)
Latitude (° N)
51 49
R13/M13
1,200
1,000
800
R2
M0
Kangerlussuaq R52
R38
I46
I27
I14
R140
Snowline
R88
ELA
M61
R52
R38
R13
R2
1,280
1,520 1,696
1,110
1,010
732
383
1,840 m
100 m yr−1
Flow velocity
GPS
AWS
S11
Greenland
A20
R140
a
b
Figure 1 | The study area. a, Map of the lower ablation area of the
Kangerlussuaq sector. The background MODIS image was acquired
on 17 August 2011. Acceleration during the late-August event
(23 August–3 September 2011) relative to the preceding period
(1–23 August 2011) was derived from TanDEM-X velocity maps. The GPS
symbol colours represent the percentage of acceleration during the event
(Supplementary Table 1). The velocity vector shows the mean velocity
during the late-August acceleration. The red square in the inset map shows
the location in Greenland. b, Russell Glacier’s elevation profile with the ELA
and snowline marked.
season, with the lowest velocities usually observed in late summer
and autumn when declining melt inputs are easily accommodated
by an efficient and well-developed basal hydrologic system7–11.
The seasonal velocity cycles recorded at nine GPS sites on
Russell Glacier and Isunngata Sermia in West Greenland during
2011 (Fig. 1) are consistent with these systematic variations with
one exception: a prominent flow acceleration between 24 August
and 1 September (Fig. 2d,e). Surface velocities derived from offset
tracking with TanDEM-X data show widespread acceleration of
up to 220% across a large portion of the ice-sheet margin for
the interval 23 August–3 September compared with the preceding
period (1–23 August; Fig. 1 and Supplementary Fig. 1). During
the event, velocities at Russell Glacier GPS sites R2, R13, R38, R52
and R88 (where the number denotes kilometres from the glacier’s
terminus) increased by 117, 140, 71, 64 and 46% above those of
the preceding week (Supplementary Table 1). The acceleration was
particularly large at site R13 (Fig. 2d) where the daily mean velocity
of 365 m yr−1during the event exceeded the site’s melt onset peak
of 337 m yr−1on 8 June, which typically represents the highest
velocity within any given year7,8,11. The pronounced flow response
was present across all neighbouring glaciers, including Isunngata
Sermia, which accelerated by 22%, 58% and 55% at I14, I27 and I46
respectively (Figs 1 and 2d and Supplementary Table 1), and was
also detectable—albeit muted—140 km into the ice-sheet interior,
well within the accumulation zone at R140 (Fig. 2e).
We ascribe this flow acceleration to an intense period of surface
runoff composed of both rainfall and melt. Measured rainfall
during the event accounted for 20% (23.6 mm) of the annual total
(115.1 mm) and despite such rainfall being uncommon for the
Kangerlussuaq region15, surface melt was the primary contributor
to runoff. Furthermore, although the highest melt rates during the
event (for example, 3.2mm water equivalent (w.e.) h−1at M13
on 27 August, Fig. 3b) were less than peak summer values (for
example, 6 mm w.e. h−1at M13 on 30 July 2011), they were unusually
sustained throughout both day and night by enhanced long-wave
radiation and turbulent heat fluxes16,17 associated with the advection
of warm, moist air into the region (Figs 3 and 4 and Supplementary
Fig. 2; see Meteorological measurements in Methods). This
unusually sustained period of continuous melt totalled 331 mm w.e.
at M13 between 24 August and 1 September, representing 10% of
the annual total and twice that of the preceding week (Fig. 3b).
Given the upper estimate of rainfall of 24mm, ice surface melt
accounted for at least 93% of the overall runoff at M13 (732 m above
sea level; asl) during the event. Measured melt was only slightly
lower at higher elevations: at M61 (1,280 m asl) 315 mm w.e. of
melt during the event accounted for a disproportionate percentage
(15%) of the annual total (Fig. 2b). Melting extended throughout the
entire ablation area, and beyond the mean (1990–2011) equilibrium
line altitude18 (ELA) of 1,553m asl. Although rain-induced ice
melt was minimal at M13 (1.1–1.8 mm w.e.), the heat released by
rain freezing into the surface snowpack enhanced melt above the
snowline (1,696 m asl). For instance, 14 mm of rain at 2 ◦C freezing
in the snowpack would bring 15cm w.e. of snow at −15 ◦C to the
melting point (see Meteorological measurements in Methods). At
site M140 (1,840 m asl), situated well above the snowline, 50 mm w.e.
of melt (8% of the annual total) was recorded between 24–29 August,
concurrent with above-freezing air temperatures (Supplementary
Fig. 2). Virtually all precipitation occurred during this period and
precipitation estimates (13–23 mm) suggest that at higher elevations
rainfall contributed a larger proportion (for example, 18–32% at
M140) to the runoff than at lower elevations. The highest daily
precipitation totals on 27 August (8.4mm in Kangerlussuaq and
3.9 mm at M0) were coincident with the highest daily total melt
(for example, 53mm w.e. at M13), and a transient excursion of the
freezing level from ∼1,000 m asl to 2,450 m asl, which is ∼900 m
above the long-term ELA (Supplementary Fig. 2c).
Owing to the ice sheet’s hypsometry, the surface area that receives
precipitation as rain and is exposed to melt increases nonlinearly
with a rising freezing level (Fig. 1b). We attribute the extremely high
freezing level on 27 August (Supplementary Fig. 2), which indicates
that melt and rainfall occurred up to 280 km inland, to the advection
of warm, moist air over the GIS from the southwest (Fig. 4). As a
saturated air mass rises its cooling rate decreases owing to the latent
heat released by condensation. Accordingly, the lapse rate during
the event was significantly lower (0.48 ◦C 100 m−1) than the annual
mean (0.70 ◦C 100 m−1) and condensation at the ice surface resulted
in an increased—and abnormally positive—latent heat flux that
contributed 18% to the energy available for melting on 27 August
(Fig. 3a). Melting consequently extended well into the accumulation
area and across almost a third of the entire GIS on this day19.
Crucially, the event occurred in late summer when buffering of
surface runoff in snowpack and firn was at an annual minimum.
The decadal-high, end-of-season snowline (1,696 m asl) exposed
a large expanse of bare ice (Fig. 1b), and runoff during the event
would have been efficiently concentrated into a mature network
of channels and moulins developed during the preceding summer
months, thereby facilitating rapid discharge into the subglacial
environment. Nevertheless, total runoff during the late-August
event was lower than mid-summer values when ice velocities were
below the annual mean, implying that runoff volume is not the only
factor governing the acceleration we observe (Fig. 2). The seasonal
evolution of subglacial drainage from an inefficient/distributed
to an efficient system explains low mid-summer ice velocities
at times of high melt input7–12, and previous studies20,21 suggest
that the antecedent subglacial conditions modulate basal sliding.
It is therefore significant that the event reported here occurred
in late summer, immediately following a period of sub-zero air
648 NATURE GEOSCIENCE | VOL 8 | AUGUST 2015 | www.nature.com/naturegeoscience
© 2015 Macmillan Publishers Limited. All rights reserved
NATURE GEOSCIENCE DOI: 10.1038/NGEO2482 ARTICLES
0
4
8
4
8
12
Daily total precipitation
(mm)
Discharge (m3 s−1)
−100
0
100
0
100
200
−100
0
100
0
100
200
Speed above annual mean (%)
0
100
200
300
Melt (mm w.e.)
−100
0
100
200
−20
−10
0
24-h mean M61 temperature
1-h mean M61 temperature
Air temperature
(°C)
Speed above annual mean (%)
0
No data
a
R13
R38
R52
R88
R2
Precipitation at M0
Melt at M61
d
A20
S11
−20
20
0
No data
−100
0
100
1 June 15 June 1 July 15 July 1 August 15 August 1 September 15 September
2011
0
100 I27
0
100
I46
0
100
I14
b
c0
20
40
0
2
4
−2
e
%
SAJJMO
R140
Kangerlussuaq precipitation
Leverett River discharge
Figure 2 | Records of meteorology, proglacial discharge, and ice velocity for the 2011 melt season. a, Air temperature at M61 with a 1- and 24-h average
applied. b, Daily total melt at M61 and precipitation at M0. c, Precipitation in Kangerlussuaq and proglacial Leverett River discharge13.d,e, Daily averaged
velocity at 11 GPS sites on land-terminating Russell Glacier (R) and Isunngata Sermia (I) and marine-terminating Store Glacier30 (S) and Sermeq
Avannarleq6(A), expressed as speed above the annual mean. Note the dierent y-axis scaling for S11 and R140. The timing of the late-August acceleration
is shaded in grey.
temperatures (Fig. 2a) and rapidly declining meltwater production:
no melt was recorded at M61 for the week before the event (Fig. 2b).
On 21 August, temperatures at the lowest-elevation AWS, M13,
dropped below freezing for the first time since melt onset on
1 June resulting in the lowest daily melt rates, proglacial discharge
and ice velocities since that date (Figs 2 and 3). We interpret the
gradual reduction in diurnal variability and simultaneous increase
in borehole water pressure at R13 during the preceding period
(Fig. 3) as evidence of a pre-event transition of the basal hydrologic
system to a less efficient winter-type mode6,12. The 3-day-lagged
peak in proglacial discharge (Fig. 2c), of unprecedented magnitude
in 2011 and which represents 13% of the annual total, supports this
hypothesis as water transit is retarded under inefficient subglacial
drainage conditions22. Hence, the late-summer timing of the event
critically dictated the ice sheet’s dynamic response: the preceding
week of sub-zero air temperatures (Fig. 2a) and declining melt
(Figs 2 and 3) primed the ice sheet for a high-magnitude flow
response to this late-August runoff perturbation.
The rapid increases in subglacial water pressure recorded in
two boreholes on Russell Glacier (at R13), as well as those
measured on Isunngata Sermia23 and Sermeq Avannarleq6, to levels
exceeding the ice overburden pressure suggest that the ice sheet
was hydraulically decoupled from its bed during the acceleration
event (Fig. 3c). This is confirmed by decimetre-scale surface uplift
recorded at R13 (Fig. 3c) and at site A20 on Sermeq Avannarleq6.
Uplift was sustained at R13 for ∼22h before the surface lowered,
NATURE GEOSCIENCE | VOL 8 | AUGUST 2015 | www.nature.com/naturegeoscience 649
© 2015 Macmillan Publishers Limited. All rights reserved
ARTICLES NATURE GEOSCIENCE DOI: 10.1038/NGEO2482
96
98
99
100
Pressure (percentage of overburden)
0
100
200
300
400
500
Velocity (m yr−1)
10 15 20 25 30 4
0
2
4
6
Melt rate (mm w.e. h−1)
0
20
40
60
Daily total melt
(mm w.e.)
0.1
0.3
0.4
0.5
0.0
Detrended uplift (m)
97
5
August September2011
0.2
Daily total
Rate
101
Overburden pressure
c
b
SWnet
LWnet
SHF
LHF
GF
a
Total energy flux (MJ m−2 d−1)
−10
−5
0
5
10
15
20
Figure 3 | The surface energy budget, melt, borehole water pressure and ice surface velocity and uplift at R13. a, The surface energy budget at M13 at a
daily time step. The components: net short-wave (SWnet) and net long-wave (LWnet) radiation, the sensible (SHF) and latent (LHF) heat fluxes and the
ground flux (GF) are defined as positive when they add heat to the surface. b, Surface melt rate and total daily melt. c, Borehole water pressure, ice surface
velocity and ice surface uplift. The timing of the late-August acceleration is shaded in grey.
L992
L1012
L1000
H1024
H1022
996
1,000
1,004
1,008
1,012
1,012
1,020
1,020
1,016
1,016
1,020
1,020
1,016
1,012
0
20
40
80
60
76
84
68
60
52
44
Sea level pressure (hPa)
TAS anomaly
26 August−1 September 2011
27 August 2011
Precipitation
24−30 August 2011
100−119
80−99
60−79
40−59
20−39
0−19
mm
6
°C
5
4
3
2
1
0
−1
R140
A20
S11
R140
A20
S11
R140
A20
S11
Figure 4 | Reanalysis data for the August/September 2011 event including sea-level pressure for 27 August 2011, the near-surface air temperature
(TAS) anomaly for the period 26 August–1 September versus the same period in the 1981–2010 baseline, and total precipitation for the week
26–30 August. The red arrow indicates the direction of warm air advection. Data from ref. 27.
650 NATURE GEOSCIENCE | VOL 8 | AUGUST 2015 | www.nature.com/naturegeoscience
© 2015 Macmillan Publishers Limited. All rights reserved
NATURE GEOSCIENCE DOI: 10.1038/NGEO2482 ARTICLES
Month
1990−1999
Year
500
1,000
1,500
2,000
2,500
F
M
A
M
J
J
A
S
O
N
D
Elevation (m) Elevation (m) Elevation (m)
2000−2009
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Rain fraction
500
1,000
1,500
2,000
2,500
0.0
Dierence
J
500
1,000
1,500
2,000
2,500
−0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
Dierence
a
c
1990−1999 2000−2009
b1980−1989
P25
P10
P50
P75
P90
IDR = 90
IQR = 58
IQR = 51
IQR = 61
IDR = 97
IDR = 106
June July August September
160
180
200
220
240
260
Day of year
0.0
0.1
0.2
0.3
0.4
0.5
Month
F
M
A
M
J
J
A
S
N
D
Fortnightly total rainfall
normalized by annual total
J
1980 1990 2000 20101985 1995 2005
2 3 4
O
Palmer et al., 20111
Zwally et al., 2002
2
van de Wal et al., 2008
3
August/September (2011)
4
1
Figure 5 | Long-term trends in rainfall seasonality and elevation. a, Fortnightly total rainfall in Kangerlussuaq normalized by the annual total between 1980
and 2012. Arrows indicate the 2011 event and three events evident in recent studies14,33,34.b, The decadal mean day of year on which the percentiles in
total annual rainfall in Kangerlussuaq were achieved. c, HIRHAM5 regional climate model simulations of the rain fraction across the K-transect at a weekly
time step. Heavy rainfall events appear on aas dark red blocks. The interquartile (IQR) and interdecile (IDR) ranges with units of days are annotated on b.
returning to its pre-event height by 12:30 UTC on 3 September
(Fig. 3c). Consistent with the theory of cavity opening24, peak
flow acceleration was coincident with the highest rates of water
pressure and uplift change, not their maxima. The sudden drops in
water pressure and ice velocity on 31 August to levels lower than
those before the acceleration event suggest that the rapid post-event
decline in surface runoff delivered a markedly reduced water flux to
a drainage system with temporary overcapacity12,21.
It is worth noting that rainfall events occurring earlier in the
summer (for example, 10mm in Kangerlussuaq on 18 July) had no
pronounced dynamic response (Fig. 2) as they did not challenge
the capacity of what can be interpreted as an efficient subglacial
drainage system at this time7–12. Importantly, the glaciological
and meteorological conditions inherent to the late-August event
preferentially occur in late summer when the drainage system is
closing down and cannot efficiently drain high runoff volumes. The
advection of cyclonic weather systems over the GIS is most frequent
in August25, and accordingly August is the wettest15 and cloudiest26
month in this region, with the heaviest rainfall (Fig. 5a) and peak
net long-wave radiation26.
The spatial extent and frequency of these events
The acceleration event in late August 2011 provides a unique natural
experiment to investigate the dynamic response of the GIS to a
spatially extensive and well-defined runoff perturbation. For eight
days during late August and early September 2011, reanalysis data27
indicate that a cyclone (minimum surface pressure of 992 hPa)
centred on Baffin Bay off the west coast of Greenland advected
warm, southwesterly airflow over the GIS, bringing extensive
precipitation, which was especially heavy in southeast Greenland
(Fig. 4 and Supplementary Fig. 9). These observations are consistent
with lee cyclogenesis whereby an Icelandic low-pressure system
forms in the lee of the GIS off the southeast coast, while the parent
cyclone delivers precipitation to west Greenland as it tracks north
up the Davis Strait28,29. Occurring on 3.5% of all days between
1961–1999, and 6.3% of days in summer, Baffin Bay cyclones
NATURE GEOSCIENCE | VOL 8 | AUGUST 2015 | www.nature.com/naturegeoscience 651
© 2015 Macmillan Publishers Limited. All rights reserved
ARTICLES NATURE GEOSCIENCE DOI: 10.1038/NGEO2482
represent the most frequent synoptic pattern to deliver precipitation
to Greenland29. Reanalysis data confirm that the weather conditions
driving the late-August acceleration prevailed across southern and
western Greenland (Fig. 4).
We find that a concomitant flow response is evident in all
available velocity records from these regions, including three
major marine-terminating glaciers located up to 370 km north
of Kangerlussuaq: ice flow increased by 9% and 95% above
the preceding week at GPS sites on Store Glacier30 (S11) and
Sermeq Avannarleq6(A20) respectively (Fig. 2d and Supplementary
Table 1). Repeat InSAR data31 reveal that ice flow also increased
by 10% on Jakobshavn Isbræ between 23 August and 3 September
2011 compared with 2–13 August (Supplementary Fig. 3). The lower
relative increase in ice velocity for the lower tongues of Store Glacier
and Jakobshavn Isbræ (that is, up to 10–15 km from the calving
front) is not unexpected because it is well established that the ter-
mini of fast marine-terminating glaciers experience smaller relative
increases in velocity in response to surface water inputs compared
with interior and land-terminating regions of the ice sheet that are
remote from tidewater influences32. Simultaneous ice acceleration
at all sites with contemporaneous data indicates that runoff dur-
ing the late-August 2011 event overwhelmed the basal hydrologic
system of at least eight glaciers in southern and western Greenland
(Figs 1 and 2), including both land- and marine-terminating outlets.
The acceleration in ice flow extended at least 140 km into the ice-
sheet interior (Fig. 2e), and we infer that this flow response was
not just restricted to those sectors of the ice sheet with available
velocity measurements.
Moreover, three previous late-summer acceleration and uplift
events identified on the GIS in recent studies14,33,34 can, with hind-
sight, now be reinterpreted as cyclonic rainfall/melt events (Fig. 5a).
Previously, these pronounced acceleration events were interpreted
as characteristic of late-summer melt-induced acceleration14,33,34,
which given their timing does not conform with the typical seasonal
velocity cycle identified by subsequent studies7–11. Reanalysis data
and meteorological records (Supplementary Figs 4–7 and 10–12
and Supplementary Section 1) reveal that these events were
all driven by cyclonic conditions similar to those during late
August 2011. Many additional late-summer rainfall events are ap-
parent from analysis of precipitation records (Fig. 5a), although, un-
fortunately, velocity data are not available to determine the specific
flow response to each of these perturbations.
Future impact on ice-sheet mass balance and dynamics
Our findings lead us to the question of whether annual ice flow
will increase in the future given predictions of a warmer, wetter
climate. The delivery of surface water to the bed accelerates ice
flow over the course of summer14 yet recent studies13,34–36 suggest
that annual ice flow in the ablation area is regulated by the melt-
induced seasonal transition from inefficient/distributed to efficient
subglacial drainage. Our observations support the hypothesis that
high-magnitude inputs of water to the bed have the capacity to
reorganize the basal drainage system, resulting in lower velocities
following the event than preceded it20,21,37. It follows that, by estab-
lishing efficient subglacial drainage, such events will yield reduced
post-event velocities and could therefore regulate ice flow over an-
nual timescales13,34–36,38. Although this self-regulation model seems
to hold across the ablation area13 and on Alaskan glaciers38, where
melt inputs to the basal drainage system are high and thin ice results
in low basal conduit closure rates, this generalized model is unlikely
to hold under a succession of late-season, cyclonic perturbations as
described herein. Nor will flow within the interior of the GIS self-
regulate as thicker ice, lower net surface melt rates and shallower
surface and bed slopes hinder the development of efficient subglacial
drainage and its regulating influence23,39,40. Multi-year observations
of increasing ice flow within the accumulation area of the GIS
(ref. 41) provide support for this premise. Furthermore, higher-
order three-dimensional ice-flow modelling42 indicates that the
net annual flow and discharge of the GIS is indeed sensitive to
and will increase in response to a greater frequency and spa-
tial extent of high-magnitude runoff events anticipated under a
warmer climate.
The advection of warm, moist air masses and rainfall over
Greenland is predicted to become more frequent through the
twenty-first century, in response to a warmer and cloudier regional
climate and a northward shift in storm tracks4,5,43–45. Consistent with
modelling experiments43, analysis of Kangerlussuaq precipitation
records and HIRHAM5 model simulations reveal that the seasonal
distribution of rainfall has already increased over the past thirty
years, with a tendency for a higher proportion of rain falling later
in the season (Fig. 5 and Supplementary Fig. 8) when the subglacial
drainage system is likely to be highly sensitive to water inputs. A
larger fraction of precipitation already falls as rain across the GIS
(ref. 46), and rain now falls at higher elevations (Fig. 5c) where
the ice sheet is responsive to increased runoff41. Cyclonic-induced
runoff events may therefore play a more prominent role in the mass
balance and dynamics of the GIS than they have previously, and
their importance will increase if predicted changes in Greenland’s
climate4,5,43–45,47 are realized.
Methods
Methods and any associated references are available in the online
version of the paper.
Received 13 April 2015; accepted 10 June 2015;
published online 13 July 2015
References
1. Hanna, E. et al. Ice-sheet mass balance and climate change. Nature 498,
51–59 (2013).
2. Vaughan, D. G. et al. in Climate Change 2013: The Physical Science Basis
(eds Stocker, T. F. et al.) Ch. 4 (IPCC, Cambridge Univ. Press, 2013).
3. van den Broeke, M. et al. Partitioning recent Greenland mass loss. Science 326,
984–986 (2009).
4. Schuenemann, K. C. & Cassano, J. J. Changes in synoptic weather patterns and
Greenland precipitation in the 20th and 21st centuries: 2. Analysis of 21st
century atmospheric changes using self-organizing maps. J. Geophys. Res. 115,
D05108 (2010).
5. Vavrus, S. J. Extreme Arctic cyclones in CMIP5 historical simulations. Geophys.
Res. Lett. 40, 6208–6212 (2013).
6. Andrews, L. C. et al. Direct observations of evolving subglacial drainage
beneath the Greenland Ice Sheet. Nature 514, 80–83 (2014).
7. Bartholomew, I. et al. Seasonal evolution of subglacial drainage and
acceleration in a Greenland outlet glacier. Nature Geosci. 3, 408–411 (2010).
8. Bartholomew, I. et al. Seasonal variations in Greenland Ice Sheet motion:
Inland extent and behaviour at higher elevations. Earth Planet. Sci. Lett. 307,
271–278 (2011).
9. Bartholomew, I. et al. Short-term variability in Greenland Ice Sheet motion
forced by time-varying meltwater drainage: Implications for the relationship
between subglacial drainage system behavior and ice velocity. J. Geophys. Res.
117, F03002 (2012).
10. Colgan, W. et al. The annual glaciohydrology cycle in the ablation zone of the
Greenland ice sheet: Part 1. Hydrology model. J. Glaciol. 57, 697–709 (2011).
11. Hoffman, M. J., Catania, G. A., Neumann, T. A., Andrews, L. C. & Rumrill, J. A.
Links between acceleration, melting, and supraglacial lake drainage of the
western Greenland Ice Sheet. J. Geophys. Res. 116, F04035 (2011).
12. Schoof, C. Ice-sheet acceleration driven by melt water supply variability. Nature
468, 803–806 (2010).
13. Sole, A. et al. Winter motion mediates dynamic response of the Greenland Ice
Sheet to warmer summers. Geophys. Res. Lett. 40, 3940–3944 (2013).
14. Zwally, J. H. et al. Surface melt-induced acceleration of Greenland ice-sheet
flow. Science 297, 218–222 (2002).
15. Cappelen, J., Jørgensen, B. V., Laursen, E. V., Stannius, L. S. & Thomsen, R. S.
The Observed Climate of Greenland, 1958–99—with Climatological
Standard Normals, 1961–90 Technical Report 00-18 (Danish
Meteorological Institute, 2001); http://www.dmi.dk/fileadmin/user_upload/
Rapporter/TR/2000/tr00-18.pdf
652 NATURE GEOSCIENCE | VOL 8 | AUGUST 2015 | www.nature.com/naturegeoscience
© 2015 Macmillan Publishers Limited. All rights reserved
NATURE GEOSCIENCE DOI: 10.1038/NGEO2482 ARTICLES
16. Gascon, G., Sharp, M. & Bush, A. Changes in melt season characteristics on
Devon Ice Cap, Canada, and their association with the Arctic atmospheric
circulation. Ann. Glaciol. 54, 101–110 (2013).
17. van den Broeke, M. R., Smeets, C. J. J. P. & van de Wal, R. S. W. The
seasonal cycle and interannual variability of surface energy balance and
melt in the ablation zone of the west Greenland ice sheet. Cryosphere 5,
377–390 (2011).
18. van de Wal, R. S. W. et al. Twenty-one years of mass balance observations along
the K-transect, West Greenland. Earth Syst. Sci. Data 4, 31–35 (2012).
19. Mernild, S. H. & Liston, G. E. Surface melt extent for the Greenland Ice Sheet,
2011. Geogr. Tidsskr. 112, 84–88 (2012).
20. Fudge, T. J., Harper, J. T., Humphrey, N. F. & Pfeffer, W. T. Rapid glacier sliding,
reverse ice motion and subglacial water pressure during an autumn rainstorm.
J. Glaciol. 50, 101–107 (2009).
21. O’Neel, S., Echelmeyer, K. A. & Motyka, R. J. Short-term flow dynamics of a
retreating tidewater glacier: LeConte Glacier, Alaska U.S.A. J. Glaciol. 47,
567–578 (2001).
22. Chandler, D. M. et al. Evolution of the subglacial drainage system
beneath the Greenland Ice Sheet revealed by tracers. Nature Geosci. 6,
195–198 (2013).
23. Meierbachtol, T., Harper, J. & Humphrey, N. Basal drainage system response
to increasing surface melt on the Greenland ice sheet. Science 341,
777–779 (2013).
24. Iken, A. The effect of the subglacial water pressure on the sliding
velocity of a glacier in an idealized numerical model. J. Glaciol. 27,
407–421 (1981).
25. Serreze, M., Box, J., Barry, R. & Walsh, J. Characteristics of Arctic synoptic
activity, 1952–1989. Meteorol. Atmos. Phys. 51, 147–164 (1993).
26. van den Broeke, M., Smeets, P., Ettema, J. & Munneke, P. K. Surface radiation
balance in the ablation zone of the west Greenland ice sheet. J. Geophys. Res.
113, D13105 (2008).
27. Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am.
Meteorol. Soc. 77, 437–471 (1996).
28. Chen, Q.-s., Bromwich, D. H. & Bai, L. Precipitation over Greenland retrieved
by a dynamic method and its relation to cyclonic activity. J. Clim. 10,
839–870 (1997).
29. Schuenemann, K. C., Cassano, J. J. & Finnis, J. Synoptic forcing of
precipitation over Greenland: Climatology for 1961–99. J. Hydrometeorol. 10,
60–78 (2009).
30. Ahlstrøm, A. P. et al. Seasonal velocities of eight major marine-terminating
outlet glaciers of the Greenland ice sheet from continuous in situ GPS
instruments. Earth Syst. Sci. Data 5, 277–287 (2013).
31. Joughin, I., Smith, B., Howat, I. & Scambos, T. MEaSUREs Greenland Ice
Velocity: Selected Glacier Site Velocity Maps from InSAR (NASA National
Snow and Ice Data Center, 2011); http://dx.doi.org/10.5067/MEASURES/
CRYOSPHERE/nsidc-0481.001
32. Joughin, I. et al. Seasonal speedup along the western flank of the Greenland Ice
Sheet. Science 320, 781–783 (2008).
33. Palmer, S., Shepherd, A., Nienow, P. & Joughin, I. Seasonal speedup of the
Greenland Ice Sheet linked to routing of surface water. Earth Planet. Sci. Lett.
302, 423–428 (2011).
34. van de Wal, R. S. W. et al. Large and rapid melt-induced velocity changes in the
ablation zone of the Greenland Ice Sheet. Science 321, 111–113 (2008).
35. Tedstone, A. J. et al. Greenland ice sheet motion insensitive to exceptional
meltwater forcing. Proc. Natl Acad. Sci. USA 110, 19719–19724 (2013).
36. van de Wal, R. S. W. et al. Self-regulation of ice flow varies across the ablation
area in south-west Greenland. Cryosphere 9, 603–611 (2015).
37. Gudmundsson, G. H. et al. High-resolution measurements of spatial and
temporal variations in surface velocities of Unteraargletscher, Bernese Alps,
Switzerland. Ann. Glaciol. 31, 63–68 (2000).
38. Burgess, E. W., Larsen, C. F. & Forster, R. R. Summer melt regulates winter
glacier flow speeds throughout Alaska. Geophys. Res. Lett. 40,
6160–6164 (2013).
39. Dow, C. F., Kulessa, B., Rutt, I. C., Doyle, S. H. & Hubbard, A. Upper bounds on
subglacial channel development for interior regions of the Greenland ice sheet.
J. Glaciol. 60, 1044–1052 (2014).
40. Dow, C. F. et al. Modeling of subglacial hydrological development following
rapid supraglacial lake drainage. J. Geophys. Res. 2014JF003333 (2015).
41. Doyle, S. H. et al. Persistent flow acceleration within the interior of the
Greenland ice sheet. Geophys. Res. Lett. 41, 899–905 (2014).
42. Bougamont, M. et al. Sensitive response of the Greenland Ice Sheet to surface
melt drainage over a soft bed. Nature Commun. 5, 5052 (2014).
43. Bintanja, R. & Selten, F. M. Future increases in Arctic precipitation linked to
local evaporation and sea-ice retreat. Nature 509, 479–482 (2014).
44. Fettweis, X., Belleflamme, A., Erpicum, M., Franco, B. & Nicolay, S. in Climate
Change: Geophysical Foundations and Ecological Effects (eds Blanco, J. &
Kheradmand, H.) 503–520 (InTech, 2011).
45. Franco, B., Fettweis, X. & Erpicum, M. Future projections of the Greenland Ice
Sheet energy balance driving the surface melt. Cryosphere 7, 1–18 (2013).
46. Box, J. E. et al. Greenland ice sheet albedo feedback: Thermodynamics and
atmospheric drivers. Cryosphere 6, 821–839 (2012).
47. IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.)
(Cambridge Univ. Press, 2013).
Acknowledgements
This research was financially supported by: SKB/Posiva through the Greenland Analogue
Project (GAP); UK National Environment Research Council (NERC) grants
NE/G005796/1, NE/G010595/1, NE/H024204/1; a Royal Geographical Society Gilchrist
Fieldwork Award; and The Netherlands Organisation for Scientific Research
(NOW/PPP)—the last of which generously supported the K-transect measurements.
TanDEM-X data were provided by the German Aerospace centre (DLR) within the
framework of the XTI_GLAC0433 project. We thank UNAVCO, the National Snow and
Ice Data Center, the Danish Meteorological Institute, MIT, J. Cappellen, R. Pettersson,
K. Lindback and A. Fitzpatrick for help with data collection and processing. The crew of
SV Gambo are thanked for help in the deployment of the Store Glacier GPS. A.H. and
H.P. were supported at the Centre for Arctic Gas Hydrate, Environment and Climate by
funding from the Research Council of Norway (Grant No. 223259). S.H.D. was supported
by an Aberystwyth University doctoral scholarship and NERC grant NE/K006126.
Author contributions
A.H., S.H.D., T.W.M. and J.T.H. collected the dual-frequency GPS data. S.H.D. processed
the dual-frequency GPS data, collated the data sets, prepared the figures and wrote the
original manuscript. R.S.W.v.d.W. and P.C.J.P.S. provided the single-frequency GPS data,
and together with F.W. acquired the borehole water pressure record. J.E.B. provided and
interpreted the reanalysis data and advised on meteorology. D.v.A. collected and
processed the AWS data sets and modelled the surface energy balance. K.S. processed the
TanDEM-X data sets. E.J. applied the correction to the precipitation records. R.H.M.
performed the HIRHAM5 regional climate modelling. B.H. advised on the analysis of
borehole water pressure records and their relationship to ice velocity. H.P. processed the
Terra SAR-X data for Jakobshavn Isbræ. P.C. and A.B.M. provided additional advice on
data interpretation and analysis. All authors contributed to the subsequent editing of the
manuscript. A.H. was the P.I. of the main project that conceived the study and
co-developed it with S.H.D.
Additional information
Supplementary information is available in the online version of the paper. Reprints and
permissions information is available online at www.nature.com/reprints.
Correspondence and requests for materials should be addressed to S.H.D.
Competing financial interests
The authors declare no competing financial interests.
NATURE GEOSCIENCE | VOL 8 | AUGUST 2015 | www.nature.com/naturegeoscience 653
© 2015 Macmillan Publishers Limited. All rights reserved
ARTICLES NATURE GEOSCIENCE DOI: 10.1038/NGEO2482
Methods
GPS measurements of ice surface motion. We used five dual-frequency GPS
receivers (R2, R13, I14, I27 and I46) capable of resolving three-dimensional ice
surface velocities at a high temporal resolution (<1h), and three single-frequency
GPS receivers (R38, R52, and R88) capable of resolving horizontal ice surface
velocities at a daily time step (Fig. 1).
Data from the dual-frequency receivers were processed kinematically48 at a 30-s
interval relative to bedrock-mounted reference stations using the carrier-phase
differential-positioning software Track v. 1.24 (ref. 49) and final precise ephemeris
from the International GNSS Service50. Reference GPS stations were located 1 km
from the terminus of Russell Glacier (BASE) and at Kellyville (KELY) giving
baseline lengths of 5 to 46 km. Assuming steady ice motion, uncertainties in the
positions were estimated at <0.02 m in the horizontal and <0.05 m in the vertical
by examining the detrended position time series for GPS receiver R13 in early
June 2011. High-frequency noise was filtered with a two-pole, low-pass
Butterworth filter with a 12-h cutoff period. To reduce the effect of bed-parallel
motion the surface height record presented in Fig. 3c was linearly detrended.
A sixth dual-frequency receiver was located at R140 (co-located with M140)
but owing to the low velocity at this site (∼52m yr−1), which is located 50 km above
the long-term mean ELA, and the long baseline length (>140 km) a different, more
rigorous, processing strategy was used to process data from this site and the
methods are detailed in ref. 41.
The single-frequency receivers recorded horizontal position every hour and the
resulting time series were filtered with a 48-h-period average51. We did not attempt
to resolve vertical motion from the single-frequency GPS records as the detection
limit is close to the magnitude of ice surface uplift51.
Daily averaged horizontal velocity was calculated by differencing the filtered
positions at a daily time step. We calculated the annual velocity (Supplementary
Table 1) from positions of the antenna on 6 June 2010 and 6 June 2011 for Russell
Glacier GPS and from 2 September 2011 and 2 September 2012 for Isunngata
Sermia GPS. These dates were selected on the basis of available data and to avoid
times when antenna poles were relocated (for example, 7 June 2011 for GPS R13
and R2).
To investigate whether ice-flow acceleration occurred in other regions of the
GIS during the late-August/September 2011 event, we examined all available GPS
records from published studies. Flow acceleration was evident in all available
records with contemporaneous data, and these records are presented in Fig. 2d as
daily averaged speed above the annual mean. These data from sites S11 and A20 are
from Store Glacier and Sermeq Avannarleq and are adapted from refs 30 and 6
respectively. S11 consisted of an L1 GPS receiver located 11 km from the calving
front of marine-terminating Store Glacier30 and A20 consisted of a dual-frequency
GPS receiver located at the FOXX borehole and AWS site6,∼20 km from the
terminus of marine-terminating Sermeq Avannarleq (Supplementary Table 1),
which is ∼26 km down-glacier of Swiss Camp14. S11 flows an order of magnitude
faster than all of the other GPS sites in Fig. 2d owing to its proximity to the calving
front of this fast marine-terminating outlet glacier. The seasonal velocity cycle at
S11 is also markedly different and can be explained by the influence of sea ice
melange52. The magnitude and seasonal variation in velocity at A20 is, on the other
hand, entirely consistent with the sites on Russell Glacier and Isunngata Sermia
(Fig. 2d).
TanDEM-X methods and verification against GPS records. We applied offset
tracking on repeat-pass TanDEM-X data to derive surface velocities for the
ice-sheet margin in August and September 2011. TanDEM-X (TerraSAR-X add-on
for Digital Elevation Measurement) is a bistatic Synthetic Aperture Radar (SAR)
mission launched in June 2010 with the two satellites orbiting in a closely
controlled formation with typical distances between 250 and 500m. The satellites
circle on an 11-day repeat orbit, acquiring SAR images, with a 30×50 km footprint,
at a 3 m spatial resolution53.
We used three ascending images acquired on 1 August, 23 August, and
3 September 2011. We combined the first two acquisitions (1 August, 23 August;
22-day separation) to derive the ‘average August’ ice velocities, and the images
recorded on 23 August and 3 September (11-day separation) for detecting the
late-August acceleration (Supplementary Fig. 1). We computed SAR amplitude
images for each of the scenes and applied an image-to-image cross-correlation
technique to track the motion of features on the glacier surface54.
The accuracy of the derived flow speeds was investigated by a comparison with
velocities for the same time intervals at two GPS locations: R2 and R13 (see inset in
Supplementary Fig. 1). We found good agreement for both periods, with
differences ranging between 0.011 md−1(R2, 1–23 August) and 0.058 m d−1(R13,
23 August–3 September).
In addition to the TanDEM-X measurements described above for the
Kangerlussuaq sector, InSAR speed differences across the Jakobshavn Isbræ
catchment were calculated from velocity maps derived from TerraSAR-X image
pairs31 for a pre-event period (2–13 August 2011) and a period representing the
event (23 August–3 September 2011; see Supplementary Fig. 3). Potential
control-related errors along the ice-sheet margin are presumed small (<10 m a−1)
relative to variations in ice-flow speed, although height errors in the digital
elevation model used for topographic correction in the original processing can
yield absolute errors of up to ∼3% of speed55 .
Borehole water pressure. We present data from a wired pressure sensor installed
0.5 m from the ice/bed interface at site R13/M13, and express borehole water
pressure as a percentage of the ice overburden pressure assuming an ice thickness
of 610 m and a density of ice of 917 kgm−3(Fig. 3c). Further description of the
methods used to drill and instrument the borehole is detailed in a previous study56.
Meteorological measurements. Meteorological measurements were made by three
on-ice AWS located 13 km (M13), 61km (M61) and 140 km (M140) from the
terminus of Russell Glacier, and one on-tundra AWS (M0) located 1km west of the
ice margin (Fig. 1 and Supplementary Table 2). To keep consistency with the GPS
site names, the numbers in the AWS site labels refer to the distance from the
terminus of Russell Glacier. The on-ice AWS were previously referred to as KAN_L
(M13; co-located with R13), KAN_M (M61) and KAN_U (M140; co-located with
R140), and we direct the reader to ref. 57 for further information on the AWS
methods. The AWS recorded surface height change due to accumulation and
ablation, air pressure, temperature and humidity, wind speed and direction, and
downward and upward short-wave and long-wave radiation at 2–3 m above the
surface. The AWS sampled at a 10-min interval, from which hourly averages were
calculated. The energy available for melt was determined using a surface energy
balance model58, validated using the AWS measurements of surface temperature
and surface height change57.
Estimating cloud cover and the freezing level. Cloud cover was approximated
making use of the strong dependence of downwards long-wave radiation on
atmospheric moisture58. A full cloud cover was assumed for high downward
long-wave radiation values at a certain air temperature, and clear skies for low
values, with a linear transition for values in between. We estimated the elevation
of the freezing level using lapse rates calculated from M61 and M140 air
temperature measurements.
Precipitation measurements. Precipitation records from site M0 and
Kangerlussuaq (DMI station 04231; ref. 59) were corrected for wind and adhesion
loss effects, using site-specific correction factors60,61. We applied correction factors
of 33% for snow and 16% for rain for the automatic GEONOR gauge deployed
<1 km from the ice margin at M0, a site that is exposed to the wind. For the
manual gauge at Kangerlussuaq, which is more sheltered from the wind, we applied
correction factors of 12% for snow and 4.5% for rain. At both sites the adhesion loss
was set to 0.1 mm per precipitation event, and mass loss due to evaporation was
assumed to be zero.
Decomposing the surface energy budget. We gained further insight into the
abnormal runoff caused by this weather event by decomposing the surface energy
budget (SEB) for the AWS at site M13 (Fig. 3a) on Russell Glacier. The net
short-wave (SWnet) and net long-wave (LWnet) radiation, the sensible (SHF) and
latent (LHF) heat fluxes and the ground flux are defined as positive when they add
heat to the surface.
Although clouds reduce net short-wave radiation, through the so-called
long-wave cloud effect they can increase LWnet as a larger fraction of the outgoing
long-wave radiation is absorbed by clouds and re-radiated back to the surface17,62.
LWnet typically represents a heat sink in the SEB but under certain atmospheric
conditions16,63 it can be positive resulting in higher net radiation
(Rnet =SWnet +LWnet) than under clear skies. Accordingly, although daily total
SWnet on 27 August was less than half (4.9MJ m−2d−1) that recorded under
clear-sky conditions seven days previously on 21 August (10.3MJ m−2d−1), net
radiation was greater on the 27 August (5.2MJ m−2d−1) compared with 21 August
(4.5 MJ m−2d−1) owing largely to the LWnet being positive (Fig. 3a). The largest
energy source during the event was, however, the SHF, which accounted for more
than 50% of the surface energy budget between 26 to 28 August—a marked
increase on the pre-event values (for example, 19% on 21 August 2011) under clear
skies (Supplementary Fig. 2a) and with low wind speeds (Supplementary Fig. 2b).
The increase in SHF can be attributed to the high near-surface temperature
resulting from the advection of warm air over the ice sheet (Fig. 4). Moisture
condensation onto the ice surface due to high specific humidity and wind speed
resulted in a positive LHF (for example, 3.0MJ m−2d−1, or 18% of the SEB on
27 August), as opposed to surface evaporative cooling (for example,
−0.73 MJ m−2d−1on 21 August), which is more frequent under the prevalent
clear-sky conditions (Fig. 3a). Both turbulent heat fluxes (SHF and LHF) are
enhanced by high wind speeds (for example, 8m s−1on 27 August; Supplementary
Fig. 2b), which increase the vertical mixing of air17,64.
For a given air temperature, moist conditions have lower lapse rates and
therefore higher freezing levels than dry conditions. The lapse rate during the
NATURE GEOSCIENCE |www.nature.com/naturegeoscience
© 2015 Macmillan Publishers Limited. All rights reserved
NATURE GEOSCIENCE DOI: 10.1038/NGEO2482 ARTICLES
late-August event (24–31 August) was much lower (0.48 ◦C 100 m−1) than the
annual mean (0.70 ◦C 100 m−1), resulting in the 0 ◦C isotherm attaining an
exceptionally high elevation of ∼2,450 m asl on 27 August 2011. Air temperatures
at M61 (1,280 m asl) were continuously above freezing during the late-August event
(Fig. 2a). Even at M140, ∼50 km inland from the mean 1990–2011 ELA, positive
air temperatures suggest that precipitation was liquid at least 140 km inland
(Supplementary Fig. 2c).
The surface energy balance model does not account for the heat delivered by
rain, which we found was minimal. The heat flux of rain QRis given by:
QR=ρwCwR(Tr−Ts)
where ρwis the density of water, Cwis the specific heat capacity of water
(4.2 kJ kg−1K−1), Ris the rainfall rate, and Trand Tsare the temperatures of rain
and the surface respectively65. Given a surface temperature of a melting ice surface
of 0 ◦C, a rain temperature of 6 ◦C, and the lower (15mm) and upper (24 mm)
estimates of rainfall during the event we estimate that the rain heat flux contributed
0.06–0.1 MJ m−2d−1. This is equivalent to 1.1–1.8 mm w.e. of rain-induced ice melt
during the entire event, which represents a small component of the runoff during
the event (for example, 0.5% at M13).
At higher elevations the sensible and latent heat released by rainfall cooling and
freezing in a surface snowpack may, however, have played an important role in
bringing the sub-freezing snowpack to the melting point. The energy flux QR
supplied by rain freezing in a snowpack is given by:
QR=ρwCsR(Tr−Ts)+ρwλwR
where Csis the specific heat capacity of snow (2,090 Jkg−1K−1), and λwis the
latent heat of fusion (334 kJ kg−1). The rain temperature of 2 ◦C is taken
to be the air temperature at M140 during the rainfall event and the temperature
of the snowpack of −15 ◦C is based on the mean air temperature during the
preceding week. Given these estimates, 14mm of rain at 2 ◦C would bring
a 15 cm w.e. snowpack at −15 ◦C to the melting point. Hence, the energy
released by rain cooling and freezing is a very effective heating mechanism
for sub-freezing snowpacks and this will have enhanced melt rates at
high elevations.
All of these energy sources—an abnormally positive net long-wave radiation,
the sensible heat flux, the latent heat flux from condensation and the rain heat
flux—contributed to high melt conditions peaking on 27 August, coincident
(Fig. 2b) with the highest precipitation (Fig. 2a,b), wind speeds (Supplementary
Fig. 2b), and freezing level (Supplementary Fig. 2c), which combined to produce
abnormally high-magnitude runoff for this time of year (Fig. 2).
Calculating the elevation of the snowline. The elevation of the snowline—the
maximum elevation that snow remains at the end of the melt season—was retrieved
from end-of-melt-season visible-band Moderate Resolution Imaging Spectrometer
(MODIS) images. The ice-sheet-wide, MODIS-retrieved snowlines, which are
calibrated against the K-transect surface mass balance observations18, are available
at https://sites.google.com/site/jboxgreenland/greenland-ice-sheet-snowline.
Reanalysis. We used data from the National Centers for Environmental
Prediction/National Centre for Atmospheric Research (NCEP/NCAR) reanalysis
project27 to track weather systems, and to estimate precipitation and the
near-surface air temperature anomaly over Greenland (Fig. 4 and Supplementary
Figs 4–6 and 9–12). These reanalysis data reveal that a low-pressure system
(minimum surface pressure of 992hPa) tracking across Baffin Bay between 24 and
30 August 2011 caused warm southwesterly airflow (Fig. 4), which resulted in
cloud cover (Supplementary Fig. 2a) and precipitation (Fig. 4) over southern and
western Greenland. Such Baffin Bay cyclones represent the most common synoptic
pattern to bring precipitation to Greenland29. They often bifurcate over the
southern GIS forming an Icelandic Low by lee cyclogenesis on the southeast coast,
while the parent cyclone tracks north delivering precipitation to the west coast of
Greenland28,29. They advect warm moist air onshore from the open North Atlantic,
which is lifted orographically over the ice sheet to an elevation of 2,800m asl in less
than 200 km (ref. 29). As the air rises, it cools adiabatically becoming saturated and
releasing precipitation. Baffin Bay cyclones occur more frequently during the
summer (6.35% of days) than during winter (2.90% of days)29 and atmospheric
models4predict that they will increase in frequency from 3% of days in 1961–1999
to 4% of days in 2081–2100. The widespread nature of the precipitation and heat
delivered by the August–September 2011 event is demonstrated by the reanalysis
data (Fig. 4 and Supplementary Fig. 9) and acceleration in ice flow is evident in all
available velocity records from southern and western Greenland during this period
(Figs 1 and 2d and Supplementary Fig. 3). Substantial acceleration was measured
on three major marine-terminating glaciers in West Greenland, including
Sermeq Avannarleq6, Store Glacier30 and Jakobshavn Isbræ (Fig. 2d and
Supplementary Fig. 3).
Investigating long-term trends in rainfall seasonality. To investigate changes in
the seasonal distribution of rainfall over the past two to three decades we examined
long-term trends in the Kangerlussuaq precipitation record59 between 1977 and
2012. The phase of precipitation was determined as liquid if the mean temperature
over the corresponding 12-h period was greater than or equal to 2◦C, allowing
daily total rainfall to be calculated.
Following standard statistical measures applied by previous studies66,67 we
calculated the seasonality index (SI) and the day of year on which the 10th (P10),
25th (P25), 50th (P50), 75th (P75) and 90th (P90) percentiles of the annual total
rainfall were achieved. We assessed the dispersion in the seasonal distribution of
rainfall by calculating the interquartile (IQR=P75 −P25) and interdecile range
(IDR=P90−P10). The SI:
SI=1
R
12
X
n=1
Xn−R
12
where Ris the total annual rainfall and Xis the total monthly rainfall in month n, is
a standard measure of rainfall seasonality67. SI theoretically ranges from 0, if all of
the months have equal rainfall to 1.83, if all of the rainfall falls in one month. SI
values of 1–1.19 indicate that most rainfall occurs in 3 months or less with SI >1.2
indicating that most rainfall falls in 2 months or less.
We investigated the timing of heavy rainfall events by calculating the fortnightly
totals of rainfall for the entire Kangerlussuaq precipitation record. To normalize the
influence of exceptional events between years, we divided fortnightly precipitation
sums by their respective annual total (Fig. 5a). Heavy rainfall events, identified as
dark red blocks on Fig. 5a, tend to occur more frequently in late summer and early
autumn, coinciding with the period of highest rainfall15, peak long-wave radiation,
densest cloud cover17 and peak cyclonic activity25 in this region.
HIRHAM5 methods. We examined changes in rainfall across the K-transect18 over
the past two decades using the HIRHAM5 regional climate model68 (RCM). The
HIRHAM5 RCM is driven at the lateral boundaries by the ERA-Interim
reanalysis69 and provides estimates of solid and liquid precipitation at 5 km
horizontal resolution. The simulations are validated against observations from
meteorological stations on the land59 and AWS on the ice sheet57,70 . Generally, the
temperature and precipitation biases are small, indicating a realistic simulation of
the climate over Greenland71. We averaged the rain fraction estimated by the
HIRHAM5 RCM over two periods (1990–1999 and 2000–2009) at each grid cell
along the K-transect at a weekly time step (Fig. 5c).
References
48. King, M. Rigorous GPS data-processing strategies for glaciological
applications. J. Glaciol. 50, 601–607 (2004).
49. Chen, G. GPS Kinematic Positioning for the Airborne Laser Altimetry at Long
Valley, California (Massachusetts Institute of Technology, 1998).
50. Dow, J. M., Neilan, R. E. & Rizos, C. The International GNSS Service in a
changing landscape of Global Navigation Satellite Systems. J. Geod. 83,
191–198 (2009).
51. Den Ouden, M. A. G. et al. Stand-alone single-frequency GPS ice velocity
observations on Nordenskioldbreen, Svalbard. Cryosphere 4, 593–604 (2010).
52. Walter, J. I. et al. Oceanic mechanical forcing of a marine-terminating
Greenland glacier. Ann. Glaciol. 53, 181–192 (2012).
53. Krieger, G. et al. TanDEM-X: A satellite formation for high-resolution
SAR interferometry. IEEE Trans. Geosci. Remote Sensing 45,
3317–3341 (2007).
54. Strozzi, T., Luckman, A., Murray, T., Wegmuller, U. & Werner, C. L. Glacier
motion estimation using SAR offset-tracking procedures. IEEE Trans. Geosci.
Remote Sensing 40, 2384–2391 (2002).
55. Joughin, I., Smith, B. E., Howat, I. M., Scambos, T. & Moon, T. Greenland
flow variability from ice-sheet-wide velocity mapping. J. Glaciol. 56,
415–430 (2010).
56. Smeets, C. J. P. P. et al. Instruments and methods—A wireless subglacial probe
for deep ice applications. J. Glaciol. 58, 841–848 (2012).
57. van As, D. et al. Large surface meltwater discharge from the Kangerlussuaq
sector of the Greenland ice sheet during the record-warm year 2010 explained
by detailed energy balance observations. Cryosphere 6, 199–209 (2012).
58. van As, D. Warming, glacier melt and surface energy budget from weather
station observations in the Melville Bay region of northwest Greenland.
J. Glaciol. 57, 208–220 (2011).
59. Cappelen, J. et al. Weather Observations from Greenland 1958–2012
Technical Report 13-11 (Danish Meteorological Institute, 2013);
http://www.dmi.dk/fileadmin/Rapporter/TR/tr13-11.pdf
60. Alexandersson, H. Korrektion av Nederbord Enligt Enkel Klimatologisk Metodik
(Meteorologi Nr 111, SMHI, 2003).
61. Johansson, E. et al. Hydrological and meteorological investigations in a
periglacial lake catchment near Kangerlussuaq, west Greenland—presentation
of a new multi-parameter dataset. Earth Syst. Sci. Data 7, 93–108 (2015).
NATURE GEOSCIENCE |www.nature.com/naturegeoscience
© 2015 Macmillan Publishers Limited. All rights reserved
ARTICLES NATURE GEOSCIENCE DOI: 10.1038/NGEO2482
62. Ambach, W. The influence of cloudiness on the net radiation balance of a snow
surface with high albedo. J. Glaciol. 13, 73–84 (1974).
63. Bennartz, R. et al. July 2012 Greenland melt extent enhanced by low-level
liquid clouds. Nature 496, 83–86 (2013).
64. van den Broeke, M. et al. Partitioning of melt energy and meltwater fluxes
in the ablation zone of the west Greenland ice sheet. Cryosphere 2,
179–189 (2008).
65. Hock, R. Glacier melt: A review of processes and their modelling. Prog. Phys.
Geogr. 29, 362–391 (2005).
66. Pryor, S. C. & Schoof, J. T. Changes in the seasonality of precipitation over the
contiguous USA. J. Geophys. Res. 113, D21108 (2008).
67. Walsh, R. P. D. & Lawler, D. M. Rainfall seasonality: Description, spatial
patterns and change through time. Weather 36, 201–208 (1981).
68. Christensen, O. B. et al. The HIRHAM Regional Climate Model Version 5
Technical Report 06-17 (Danish Meteorological Institute, 2006);
http://www.dmi.dk/fileadmin/Rapporter/TR/tr06-17.pdf
69. Dee, D. P. et al. The ERA-Interim reanalysis: Configuration and
performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137,
553–597 (2011).
70. van de Wal, R. S. W., Greurell, W., van den Broeke, M. R., Reijmer, C. H. &
Oerlemans, J. Surface mass-balance observations and automatic weather
station data along a transect near Kangerlussuaq, West Greenland. Ann.
Glaciol. 42, 311–316 (2005).
71. Lucas-Picher, P. et al. Very high resolution regional climate model
simulations over Greenland: Identifying added value. J. Geophys. Res.
117, D02108 (2012).
NATURE GEOSCIENCE |www.nature.com/naturegeoscience
© 2015 Macmillan Publishers Limited. All rights reserved
