Introduction
Three methods are commonly used to assess the mass balance (MB) of the Greenland
ice sheet (GrIS): gravimetry, radar/laser altimetry and the mass budget method (MBM). Each method has distinct advantages and disadvantages. The main
advantage of gravity observations is that they provide, once corrected and
deconvolved, a relatively direct measure of glacial mass change; as a result,
several years after its launch, the dual-satellite Gravity Recovery and
Climate Experiment (GRACE) mission was able to confirm that the GrIS is
losing mass . The main drawbacks of this technique
are the relative brevity of the time series (since late 2002), the large
footprint of ∼ 200 km and the fact that corrections must be made for mass
movements in atmosphere, ocean, soil and solid earth (e.g. glacial isostatic
adjustment, or GIA) in order to isolate ice mass changes.
Like GRACE, satellite altimetry provides full coverage of the GrIS. The time
series are longer, starting in the mid-1990s, with aerial photography
extending some of the records back to the mid-1980s and earlier
. Difficulties in interpreting radar
altimeter data arise from the variable penetration depth of the radar signal
in firn and, especially for the earlier instruments,
signal loss along the steep coastal margins. The radar altimeter on board
CryoSat-2, launched in 2010, partly mitigates these issues and shows GrIS
elevation changes in unprecedented detail . ICESat's laser
altimeter measured the surface elevation change accurately but was sensitive
to cloud cover and had a relatively large ground track separation. The
various altimeter records must be intercalibrated and spatially interpolated
between ground tracks before a continuous time series is obtained. Both radar
and laser altimeters measure ice sheet volume changes, which must be converted
to mass changes using a model that accounts for vertical bedrock motion and
variability in the depth and mass of the firn layer, which introduces
additional uncertainties .
The MBM estimates the difference between individual mass
sources (mainly snowfall) and sinks (mainly meltwater runoff and solid ice
discharge). Because it resolves the individual components of the MB, this method has the advantage that it identifies the physical
processes that are responsible for the mass change. However, because the mass
change represents the relatively small difference between three large source
and sink terms, it is very sensitive to uncertainties in any of these. This
is especially true for surface mass fluxes such as snowfall and meltwater
runoff; because these cannot be measured from space, they must be
interpolated from scarce in situ measurements and/or simulated using
dedicated regional climate models, which introduces potentially large
uncertainties .
reconciled results of the three methods for the
GrIS to obtain an average GrIS 1992–2011 mass loss of 142 ± 49 Gt yr-1. The fifth assessment report of Working Group I of the
Intergovernmental Panel on Climate Change (IPCC), while weighing the results
of the various studies somewhat differently, arrived at a similar conclusion
and shows that in 2012 the GrIS had become the largest single contributor to
sea level rise . A compilation of GrIS MB
studies covering sub-periods of the satellite era confirms that the mass loss
of the GrIS is accelerating .
Combining two or more methods may reduce uncertainties and may provide
additional insights in the physical processes causing the mass loss.
used GRACE, the MBM and altimetry to assess mass changes
in seven GrIS regions; for the GrIS as a whole, between August 2002 and
August 2010 they found a mass trend of -228 ± 22 Gt yr-1, with an acceleration of -15 ± 7 Gt yr-2.
used a combination of GRACE and
the MBM to show that GrIS mass loss between 1992 and 2010 accelerated by
17–22 Gt yr-2. When combined with surface mass balance (SMB)
fields, satellite altimetry can be used to spatially separate mass losses
arising from ice dynamical (i.e. driven by ice flow) and surface (i.e. driven
by the atmosphere) processes . Using
anomalies relative to a reference period,
showed that 1996–2008 GrIS mass loss was approximately equally partitioned
between increased surface meltwater runoff and ice discharge.
showed that the relative contribution of ice discharge
to total GrIS mass loss decreased from 58 % before 2005 to 32 % between
2009 and 2012, indicating an increasingly important role for surface
processes.
In this paper we combine GRACE data and the MBM, using new SMB and D data
that allow updating the time series to 2015, to identify the causes and
temporal evolution of recent (1958–2015) GrIS mass loss and its contribution
to sea level rise. In Sect. we discuss methods, in
Sect. we discuss results and in Sect. we identify outstanding problems and
avenues for future research.
Methods
Definitions
The MB of an ice sheet, usually expressed in Gt yr-1, represents the change of its mass in time dM/dt.
Neglecting basal melting of grounded ice, which typically does not exceed
several mm per year, and assuming the grounding line position to remain
unchanged, the MB of the grounded ice sheet is governed by the difference
between SMB and solid ice discharge across the
grounding line (D):
MB=dMdt=SMB-D.
SMB represents the sum of mass fluxes towards and away from the ice sheet
surface:
SMB=Ptot-SUtot-ERds-RU,
where Ptot is total precipitative flux (sum of snowfall, SN, and
rainfall, RA), SUtot is total sublimation (from the surface and from
drifting snow particles), ERds is erosion of snow by divergence of
the drifting snow transport and RU is meltwater runoff. The
accumulation/ablation zones of an ice sheet are defined as the areas where
SMB > 0 and SMB < 0, respectively. These two zones are separated by the
equilibrium line, where SMB = 0.
The amount of RU from the ice sheet is determined by the liquid water
balance (LWB), the sum of all sources (mainly rainfall and melt) and sinks
(mainly refreezing and capillary retention) of liquid water in the column of
firn and/or ice:
RU=RA+CO+ME-RT-RF,
where CO is condensation of water vapour at the ice sheet surface, ME is
surface meltwater production, RT is retention of liquid water in the
snow/firn by capillary forces and RF is refreezing of liquid water at or
below the surface. Equation () in first order includes processes
associated with the formation of perennial firn aquifers
but neglects the delay in
runoff by storage of meltwater in semipermanent supra-, sub- and englacial
lakes and channels, which is potentially significant .
In summary, to quantify the MB of the GrIS, the MBM relies on the
quantification of mass sinks and sources in Eqs. (),
() and ().
Data sources
To calculate GrIS MB using the MBM we combine SMB components
calculated with the regional atmospheric climate model RACMO2.3 with annual
estimates of D, updated from . The latter data represent
discharge summed over all marine-terminating glaciers wider than 1 km, and
cover the 16-year period 2000–2015. Between 1996 and 2000 we assume a linear
increase in D of in total 38 Gt yr-1 following
. In the absence of data, no changes in D are assumed
between 1958 and 1996. A seasonal cycle in ice-sheet-wide D is not
considered, although it is well known that marine-terminating outlet glaciers
can show pronounced (sub-)seasonal velocity oscillations .
Because seasonal acceleration leads to thinning, the effect on D is smaller
than based on velocity changes alone. Moreover, by using the median annual
velocity, seasonal variability is accounted for to a large extent
. Land-terminating glaciers also exhibit a seasonal
cycle in their velocity , but this does not influence D.
RACMO2.3 Greenland domain with ice sheet mask (white), land mask
(green) and elevation contours (dashed lines, every 500 m a.s.l.). Boundary
relaxation zone indicated by dots every other grid point.
SMB components are derived from a run with RACMO2.3 for the period January 1958–December 2015, using 40 vertical layers and a horizontal resolution of
∼ 11 × 11 km2 .
Figure shows the Greenland domain of RACMO2.3, which consists of 312 (latitude) × 306 (longitude) grid cells and includes
Iceland, the Svalbard archipelago and the Canadian Arctic. The model is forced at the lateral boundaries by the 40-year European Centre for
Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA-40) for the period January 1958–December 1979 and the ECMWF Interim Reanalysis
(ERA-Interim) afterwards. In previous versions of RACMO2, the impact of using inhomogeneous forcing before and after 1979 (ERA-40 vs. ERA-Int)
was found to be small . According to , the regional climate model MAR shows precipitation to be 5 %
greater when forced by ERA-40 compared to ERA-Interim for the period 1980–1999, probably a result of biases in the ERA-40 humidity scheme that
were corrected in ERA-Interim. This uncertainty falls within the error bar used here.
The polar version of RACMO2.3 has been especially developed to simulate SMB
of glaciated regions and is an update of RACMO2.1 . It is interactively coupled to a multilayer
one-dimensional (column) firn model that simulates (sub-)surface processes
like vertical heat transport, grain growth, firn densification, meltwater
percolation, retention and refreezing. RACMO2.3 uses a prognostic calculation
of snow grain size from which broadband snow albedo is derived
. Ice albedo is not explicitly calculated, as it
is influenced by poorly known processes such as dust accumulation and
biological activity; to account for its considerable spatial heterogeneity
, ice albedo is prescribed from the Moderate Resolution
Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites
.
Cumulative surface mass balance (SMBGreenland) for the full
island (indicated by subscript “Greenland”, amber line), i.e. including
marginal glaciers and ice caps (GIC) and seasonal snow on the tundra;
cumulative ice discharge D (blue line) and resulting cumulative mass balance
MBGreenland (red line). GRACE time series included (grey line) has
been offset by 1000 Gt for clarity.
RACMO2.3 includes a routine for drifting snow sublimation, which removes on
average 25 Gt yr-1 from the ice sheet ,
with little interannual variability; when compared to scarce observations,
this scheme was found to accurately predict the occurrence of drifting snow,
but overestimate drifting snow transport, and thus likely also
overestimate drifting snow sublimation, although no direct observations are
available to verify this. For a more detailed description of recent changes
in RACMO2.3 model physics and how they impact the modelled SMB of the GrIS,
the reader is referred to and references therein.
(a) As Fig. but for the GRACE epoch
(2003–2014), cumulative MBGreenland (red line) and the GRACE time
series (grey lines, offset by 1000 Gt for clarity). (b) Scatter plot
shows direct linear correlation between the GRACE time series and cumulative
MBGreenland.
Annual values of main SMB components integrated over the contiguous
GrIS: total precipitation (Ptot), melt (ME), runoff (RU), refreezing
(RF)
and rainfall (RA). Dashed lines indicate 1991–2015
trends.
Other RACMO2.3 modelled SMB components and atmospheric parameters have been
extensively evaluated against in situ observations both in the
accumulation and ablation zone of the GrIS .
From these comparisons, typical uncertainties of 9 and 15 % were found
for ice-sheet-wide integrated accumulation and ablation, respectively. These
are combined into an uncertainty for ice-sheet-wide SMB assuming accumulation
and ablation to be independent. We note that this assumption is debatable, as
ablation and accumulation tend to be connected via surface albedo, especially
in summer . The scarcity of accumulation and
ablation measurements does not allow for a further regionalization of the
uncertainty, but obviously uncertainties can be significantly larger for
smaller areas and sub-climatological time periods. For the trend in
cumulative SMB-D, an uncertainty is derived following
.
We compare ice sheet MB obtained from the MBM, i.e.
MB = SMB-D, with monthly GRACE
gravity field solutions. Mass variations for the GrIS were derived from the
CSR RL05 data release (April 2002–September 2015), following the method
described in . In brief, regional mass anomalies are
adjusted in a model consisting of eight pre-defined GrIS basins and the
resulting gravity disturbance is computed. The modelled mass anomalies are
then adjusted until convergence with the actual GRACE observations is
reached. All standard corrections are applied to the GRACE data, including a
correction for GIA, based on the model of . The
uncertainties in monthly GRACE values and mass trend are estimated at 40 Gt
and 20 Gt yr-1, respectively. For the monthly values, the
uncertainty is computed by conservatively assuming that all observed signals
with a periodicity smaller than 6 months are due to noise .
For the trends, the quoted uncertainty takes into account methodological
differences in the processing of the level-1 GRACE data, the uncertainty in
the GIA correction, the formal error of the least-square fit and aliasing of
ocean tides . To assess the methodological uncertainty
in the GRACE time series, results were compared to mass anomalies from the
Jet Propulsion Laboratory (JPL) mascons using a fully independent approach
. No significant differences in trend and interannual
variability were found.
GRACE data are typically provided for mid-month, while cumulated SMB values
from RACMO2.3 represent the end of the month, so we linearly interpolated the
GRACE data to the end of the month. Missing monthly values were linearly
interpolated. Because GRACE provides mass anomalies in time δMB(t)
rather than mass balance dM/dt, SMB and D must be integrated in time before
being subtracted and compared with GRACE. Alternatively, we could also use
the temporal derivative of the GRACE time series to obtain dM/dt, but given
the inherent noise in the GRACE data this would introduce large
uncertainties.
Results
Comparing MBM and GRACE
In this paper we are principally interested in the MB of the
contiguous ice sheet, the main reason being that Greenland's peripheral
glaciers and ice caps (GIC) are usually assumed to be part of the global
population of glaciers when a MB assessment is made, e.g.
. However, because GRACE has a footprint similar to the
maximum width of the ice-free tundra in Greenland (∼ 200 km), it cannot
readily separate mass changes of the contiguous ice sheet from those of GIC.
Moreover, GRACE mass signals also include the waxing and waning of the tundra
seasonal snow cover and hydrological signals. Although the latter two
processes in principle do not contribute to long-term mass changes, because
of their seasonal character they do represent a significant seasonal cycle
in mass loading over Greenland that varies from year to year, modifying the
amplitude of the GRACE signal . To enable a meaningful
comparison with GRACE therefore requires integrating surface mass fluxes over
the entire island, including the GIC and ice-free tundra. This is rather
straightforward, because both are explicitly modelled in RACMO2.3, albeit the
seasonal snow cover with a simpler (single-layer, no refreezing) snow model
. This island-integrated SMB, only used in
Sect. (), is indicated by
SMBGreenland. No marine-terminating outlet glaciers wider than 1 km
wide originate from detached ice caps, so we neglect their contribution to
D
and assume mass changes in GIC to be solely caused by SMB, i.e.
DGreenland=D and MBGreenland = SMBGreenland-D.
The most direct way to compare MBM and GRACE is to cumulate
SMBGreenland and D in time and calculate the difference to get
cumulative MBGreenland, i.e. δMB relative to 1 January
1958. Figure shows these cumulative values
(expressed in Gt), starting at 0 on 1 January 1958 when our time series of
SMBGreenland starts. For reference, the equivalent mass required
for 5 cm global mean sea level change is indicated on the left. Until
mid-1996, cumulative D represents a straight line, because its annual value
is assumed constant at the 1996 value of 411 Gt yr-1. The average value
of SMBGreenland over the period 1958–1995 is within 2 % of this
value (418 Gt yr-1), resulting in an estimated pre-1996 cumulative MB (red line) that remains close to 0, in line with previous results
of . The fact that the estimated 1995 value of D and the
1958–1995 average value of SMB are similar suggests that ice flow in the
mid-1990s was well adjusted to the average annual mass input of the
previous decades, reminiscent of an ice sheet in approximate balance
. Because we do not include a seasonal cycle in D, the
mass curve shows a gradual wintertime increase, when SMBGreenland
exceeds D in magnitude and Greenland gains mass, and a steep summer drop,
when SMBGreenland becomes strongly negative and acts together with
D to remove mass from Greenland.
After 1995, following the acceleration of several large outlet glaciers in
the southeast and northwest , D
increased while simultaneously SMBGreenland decreased. As a result,
their cumulated values in Fig. curve upward
and downward, respectively, and cumulative MBGreenland becomes
persistently negative as a result. According to Fig. , the most significant mass loss derives from
the last 1–2 decades, and it is a fortunate coincidence that the GRACE
mission covered most of this period. The recent Greenland mass evolution from
the MBM agrees qualitatively well with the GRACE observations, represented by
the dark grey line in Fig. . Note that,
because GRACE measures mass changes rather than absolute mass, the time
series have been vertically offset by 1000 Gt for clarity without losing
information.
Figure a zooms in on the time
series of cumulative MBGreenland and GRACE during the overlapping
period (2002–2015). Although the seasonal oscillations in the GRACE time
series have lost some of their amplitude because of interpolation, Fig. b confirms the good
agreement (R2 > 0.995) between the fully independent time
series. The seasonal and interannual variations in the GRACE time series are
qualitatively well reproduced, with the largest summertime mass losses in
2007, 2010 and 2012, limited mass loss in 2013 and large interannual
variability in wintertime accumulation. The magnitude of a fitted linear
trend over the period 2003–2014 is also similar, -294 ± 5
in MBGreenland and -270 ± 4 Gt yr-1 in GRACE. These
errors represent fitting uncertainties; the real uncertainties in the trends
are estimated at 20 Gt yr-1 in both time series
. This good agreement between both methods is
partly fortuitous due to compensating biases in distinct SMB components
(see Sect. ). Nonetheless, Figs. and
inspire sufficient confidence to
support a more quantitative analysis of the components of year-to-year
contributions of the GrIS to global average sea level rise.
Contiguous ice sheet (GrIS) averages (1961–1990, Gt yr-1 with
standard deviation) and trends (1961–1990 and 1991–2015, Gt yr-2, with
standard error) of SMB components, discharge (D) and mass balance (MB).
SMB
Average
Trend
Average
Trend
component
(1961–1990)
(1961–1990)
(1991–2015)
(1991–2015)
Ptot
695 ± 79
2.1 ± 1.6
712 ± 64
-1.7 ± 1.8
SN
673 ± 77
1.9 ± 1.6
684 ± 61
-2.0 ± 1.7
RA
23 ± 6
0.3 ± 0.1
28 ± 9
0.3 ± 0.2
SUtot
41 ± 6
0.1 ± 0.1
42 ± 4
0.0 ± 0.1
ERds
1 ± 0
0.0 ± 0.0
1 ± 0
-0.0 ± 0.0
ME
433 ± 68
1.9 ± 1.4
581 ± 145
11.4 ± 3.4
RF
200 ± 27
0.9 ± 0.6
245 ± 59
3.2 ± 1.5
RU
256 ± 51
1.3 ± 1.1
363 ± 102
8.4 ± 2.3
SMB
398 ± 112
0.8 ± 2.4
306 ± 120
-10.2 ± 2.3
D
–
–
477 ± 51
6.6 ± 0.4
MB
–
–
-171 ± 157
-16.8 ± 2.8
Modelled 1961–1990 average melt (a) and 1991–2015 minus 1961–1990
difference (b). Stippled areas indicate differences that are not significant
at the 95 % level. Dashed contours are 500 m elevation intervals; thick
solid contour represents glacier mask.
Temporal SMB variability
In order to combine SMB with annual D in Sect. we integrated SMB components over the contiguous GrIS ice mask
of RACMO2.3 and over calendar years. Figure
shows the resulting time series of the main SMB components, with 1991–2015
trends included as dashed lines. Table
lists the averages and trends over the climate period 1961–1990, for which
the GrIS was assumed to be in approximate balance, and the recent melting
period 1991–2015.
Total annual precipitation (Ptot) typically varies between
600 and 800 Gt yr-1, without significant trend over the
full period. A small positive trend in Ptot of 0.3 % per
year for the period 1961–1990 is followed by an insignificant negative trend
in the subsequent decades. Although total precipitation on the GrIS has not
significantly changed over the last 6 decades, the RA fraction has
increased in response to a warmer atmosphere. During 1961–1990, 3.3 % or 23 Gt yr-1 of the modelled precipitation on the ice sheet fell
as rain, increasing to 3.9 % or 28 Gt yr-1 in 1991–2015.
For the full period, the annual rain fraction varied between 2.0 and
7.0 %, the latter value occurring in 2012. Total sublimation and drifting
snow erosion are relatively constant from year to year and do not show
significant trends.
Modelled 1961–1990 average refreezing (a) and 1991–2015 minus
1961–1990 difference (b). Stippled areas indicate differences that are not
significant at the 95 % level. Dashed contours are 500 m elevation
intervals; thick solid contour represents glacier mask. Note that refreezing
is not considered outside of the glacier mask.
During 1961–1990, the small positive trends in ME and RU were
offset by a similarly small precipitation increase, resulting in an
insignificant trend in SMB. This changed dramatically in the ensuing period
(1991–2015), during which ME and RU trends increased 6- to 7-fold. In
combination with a small decrease in precipitation, this led to a sharp
decrease in SMB of 3.3 % per year. A synchronous increase in RF
has limited the mass loss: based on annual values, the refrozen fraction
RF/(RA+ME) varied between 32 % and 57 %, underlining the great importance
of firn processes for contemporary GrIS MB. During 1991–2015, the
refrozen fraction averaged 41 %, down from 44 % in 1961–1990, signalling
a decrease in the retention efficiency of the GrIS firn layer. In RACMO2.3
this is mainly caused by a decrease in firn pore space
, but in reality this effect is exacerbated by
the formation of impenetrable ice lenses during warm summers, preventing the
surface meltwater from reaching the deeper firn layers and using the full
retention potential .
Figure clearly demonstrates the large
interannual variability of the major GrIS SMB components
Ptot, ME and RU. For ME and RU, the peak of 2012 stands out,
with a modelled melt flux in excess of 1000 Gt, i.e. 1 Tt, exceeding the
previous record of 2010 (∼ 800 Gt) by a wide margin. Remarkably, the
summer of 2013 saw a return to near-normal melt conditions, with melt close
to the 1961–1990 average, while summer 2015 saw record melting in the
northern reaches of the ice sheet . This exceptional
interannual variability in the melt climate of the GrIS points towards
important roles for large-scale atmospheric drivers (;
; ) and local feedback
processes. Especially important is the albedo–melt feedback ,
which constitutes the darkening of snow once it has melted, as well as the
lengthening of the exposure of dark, bare ice in the ablation zone once the
winter snow has melted away . However, precipitation, where
feedbacks play a lesser role, is also highly variable from year to year; for
instance, Ptot increased by ∼ 300 Gt yr-1 between 1971 and 1972, a year-to-year change
equivalent to 40 % of the long-term average. Fitting a linear trend to the
standard deviation of running decadal values reveals that precipitation
variability decreased by ∼ 30 Gt yr-1, while that of
runoff increased by approximately the same amount. The reasons for this are
presently not clear.
Spatial SMB variability
In this section we discuss the spatial distribution of changes in LWB and SMB components between the climatic period 1961–1990
and the recent period of GrIS mass loss. For a description of spatial
differences in D the reader is referred to e.g. and
. To maximise the length and to avoid spurious trends, we
let the recent period start in 1991 rather than in 1995, when the first
changes became noticeable. Figures to show the average for 1961–1990 (a) and the
difference between 1991–2015 and 1961–1990 (b) of ME, RF,
RU and SMB, respectively. In these maps, mass flux (difference) is
expressed as kg m-2 yr-1, equivalent to
mm w.e. yr-1. Note that over non-glaciated areas, RACMO2.3
uses a simpler snow model that does not calculate refreezing, only melt and
runoff; SMB is therefore only calculated and physically meaningful over
glaciated areas.
Figure a shows that over tundra, the average
annual ME rate is limited by the annual snowfall, which explains the
generally lower values when compared to the adjacent glaciated areas, where
ablated ice is continuously replenished by glacier flow. Over glaciated
areas, ME increases strongly with decreasing elevation and latitude, reaching
3500 mm w.e. yr-1 in the low-lying parts of the
southwestern GrIS. Although higher observed melt rates have been reported for
the GrIS ablation zone, this concerns mainly locally very dark ice surfaces
and/or isolated glacier tongues surrounded by ice-free land that are not well
resolved at the model resolution of 11 km .
Most of the meltwater produced at higher elevations refreezes in the cold
firn. Figure a shows that RF peaks
in the lower accumulation zone between 1000 and 2000 m a.s.l., where significant
summer melt occurs yet the firn layer still has sufficient pore space to
store the meltwater. As a result, all RU from the GrIS occurs from
ice sheet regions roughly below 2000 m a.s.l. in the south and 1500 m a.s.l.
in the north (Fig. a). The resulting 1961–1990
SMB distribution (Fig. a) shows relatively
wide (50–150 km) ablation zones in the dry southwest, north and northeast of
the GrIS and narrow (10–50 km) ablation zones in the wetter and therefore
steeper-sloped northwest and southeast of the GrIS.
Modelled 1961–1990 average runoff (a) and 1991–2015 minus 1961–1990
difference (b). Stippled areas indicate differences that are not significant
at the 95 % level. Dashed contours are 500 m elevation intervals; thick
solid contour represents glacier mask.
Relative to 1961–1990, melt in 1991–2015 has increased everywhere over the
GrIS (Fig. b). The change is not statistically
significant on the higher ice sheet domes, where melt occurs intermittently.
Integrated over the contiguous GrIS, melt has increased from 433 Gt yr-1 in 1961–1990 to 581 Gt yr-1 in
1991–2015, an increase of 34 % (see Table ). Not all additional liquid water reaches
the ocean: part of the mass loss is buffered by increased RF. Table and Fig.
show that 29 % of the combined increase in ME and RA is buffered by
increased RF. Figure b clearly shows that this
increase in RF is confined to areas where the firn layer has sufficient
storage capacity, i.e. well above the equilibrium line. As a result, the
increase in RU is confined to the ablation zone and the lower accumulation
zone (Fig. b). Resulting changes in the SMB
(Fig. b) have two components: a significant
decrease in the ablation zone and lower accumulation zone that mirrors the
change in RU, and a partially significant increase in the interior, owing to
an increase in snowfall. The result is that SMB gradients have steepened
along the perimeter of the ice sheet, which is also visible in
high-resolution altimetry .
In line with observations, the neighbouring ice caps in the Canadian Arctic,
Iceland and Svalbard have also experienced strongly increased melt rates. ME
decreased only over some non-glaciated areas, mainly as a result of decreased
(winter) snow accumulation. Only the interior parts of the highest and
coldest ice caps in the northern Canadian Arctic remain free of runoff, while
ice caps in the southern Canadian Arctic, Iceland and Svalbard all produce
runoff from the entire ice surface.
Modelled 1961–1990 average surface mass balance (SMB) (a) and
1991–2015 minus 1961–1990 difference (b). Stippled areas indicate differences
that are not significant at the 95 % level. Dashed contours are 500 m
elevation intervals; thick solid contour represents glacier mask. Note that
SMB is only defined for the glacier mask.
Temporal MB variability
Figure combines GrIS integrated values of SMB
and D into ice sheet MB with uncertainties as defined in
Sect. . Linear trends for the period 1991–2015 are
indicated by dashed lines. The equivalent sea level rise (eq. SLR) for
negative MB is provided on the lower right axis. The MB values before 1996
are uncertain because reliable estimates of D are missing, although previous
work reported little difference between discharge estimates from the early
1960s and the mid-1990s . Before 1995, under the
assumption of constant ice discharge, we see that MB typically varied between
+200 and -200 Gt yr-1, with an average close to 0. After
1995, MB becomes persistently negative, with a minimum in 2012 of -446 ± 114 Gt yr-1, equivalent to an SLR of
1.2 ± 0.3 mm yr-1. In 2013, MB sharply increased in response to a summer
with near-normal surface melt conditions; this temporarily limited GrIS mass
loss but did not eliminate it, because D remained elevated. After 2013, ME
and RU increased once more, reducing SMB and increasing the mass loss to
values again approaching 1 mm eq. SLR in 2015.
Annual values of D, SMB and MB integrated over the contiguous GrIS.
Dashed lines indicate 1991–2015 trends. The equivalent sea level rise (eq.
SLR) for negative MB is provided on the right
axis.
The period-average mass loss we obtain here can for instance be compared to
the Ice sheet Mass Balance Intercomparison Experiment (IMBIE) results in
, which used data of gravimetry, altimetry and MBM
to reconcile differences in ice sheet MB. For the periods 1992–2000
and 2000–2011, average values for GrIS MB in that (this) study are -51 ± 65 (-35 ± 79) and
-211 ± 37 Gt yr-1 (-236 ± 86 Gt yr-1). Although not fully independent, this good agreement
suggests that the uncertainties in these numbers may actually be smaller than
stated.
The decrease in MB since 1991 is significant and indicates an acceleration of
the mass loss of 16.8 ± 2.8 Gt yr-2. This is somewhat
less than the 21.9 ± 1 Gt yr-2 reported by
for the period 1992–2010, which is obviously caused by
the inclusion of years after 2012 with higher MB. For the same period
1992–2010 we obtain 21.8 ± 3.7 Gt yr-2, i.e. a number
very close to . Using trends in SMB and D as reliable
indicators for mass loss partitioning, the acceleration is caused for 61 %
by a decrease in SMB and for the remainder by an increase in D. Again owing
to the inclusion of the years 2013–2015, this partitioning between the two
components is somewhat closer to equality than reported in
.
Discussion and conclusions
These results show that GrIS MB has been persistently negative since 1998
and continues to be negative in spite of a temporary rebound in 2013. The mass
loss that occurred each year between 2006 and 2012 was unprecedented since
1958. How significant are these recent mass losses and how do they impact the
future resilience of the ice sheet?
First we note that the capacity of the firn layer to buffer runoff
is compromised by increased meltwater refreezing. The
associated release of latent heat warms the firn, reducing its cold content
and enhances dry firn compaction, which together with the refrozen mass
reduces the pore space where meltwater can be stored and refrozen. Figure shows results of calculations with an offline
firn densification model forced with RACMO2.3 output. It shows that firn
temperatures in response to enhanced melt and refreezing have increased by up
to 5 K in the lower accumulation area (Fig. a).
Firn air content, defined as the vertically integrated depth of the air
column (expressed in m), decreased by up to 6 m in the same areas (Fig. b). These changes are highly significant
considering that typical values for total firn air content in the dry snow
zone range between 20 and 25 m . Recent research has
shown that warm summers can generate thick ice layers that prevent meltwater
from reaching the deeper pores, further reducing the meltwater buffering
capacity .
Modelled 1991–2015 minus 1961–1990 difference in 2–10 m average firn
temperature (a) and change in firn air content (b) for the contiguous GrIS.
Dashed contours are 500 m elevation intervals; thick solid contour represents
glacier mask.
The mass overturning rate of an ice sheet is defined as its total mass
divided by the annual mass gain. We approximate the latter by the 1961–1990
average of SN–SUtot = 632 Gt yr-1, assuming most rainfall to
fall on the lower ice sheet margins and to runoff quickly. Combined with the
estimated volume of the GrIS of 2.67 × 106 km3 and an ice density of
900 kg m-3, we obtain a mass overturning rate of 4200 years. If
we interpret this as the reaction timescale of an ice sheet to adjust its
dynamics to changes in accumulation, it is clear that the recent changes
owing to melt, with a typical timescale of decades, by far outpace a
potential ice sheet thickening owing to increased snowfall in a warmer
climate. In spite of that, at the current rate of mass loss, it would still
take over 10 000 years to melt the entire ice sheet.
Alternatively, we may state that the GrIS is significantly out of balance,
noting that the average 1991–2015 mass loss (171 Gt yr-1)
represents a sizeable fraction (27 %) of the annual mass gain. Because D is
definite positive, the situation in which SMB becomes persistently negative
leads to a definite negative MB, even when the ice sheet has lost contact
with the ocean, i.e. D=0. This is therefore sometimes labelled a “tipping
point” for GrIS MB, beyond which the ice sheet will not be able to
recover. At the current rate of SMB decrease (10.2 ± 2.3 Gt yr-2), this tipping point would be reached between 2024 and
2043. The limited length of the time series and our incomplete knowledge of
the main drivers of changes in SMB and D for now preclude firm statements
about how realistic such a scenario is. It is therefore desirable that both
MB components be reconstructed as far back in time as possible; this will
require the smart use of climate archives, such as firn cores from the
accumulation zone of the ice sheet , robust reanalysis
products that cover the full 20th century and
early satellite products and photogrammetry .
Because pre-1995 values of SMB and D are similar, in this study it was not
necessary to define a reference period from which cumulative anomalies are
defined . Instead, absolute mass fluxes could
simply be integrated in time and subtracted to obtain ice sheet MB for
comparison with GRACE (Fig. ). However, this
pre-1995 agreement is almost certainly in part fortuitous, because
uncertainties in especially SMB, which is a modelled quantity, and to a
lesser extent in D, which is largely observed, remain significant. For
instance, current model horizontal resolution of RACMO2.3 (11 km) is
insufficient to resolve the individual, low-lying outlet glaciers of the GrIS
where runoff is especially large; as a result RU increases when the 11 km
field is statistically downscaled to 1 km resolution . This unresolved mass
loss is likely in part error-compensated by snowfall in RACMO2.3 being
underestimated in some regions of the ice sheet . In a
recent study, it was moreover demonstrated that while RACMO2.3 tends to time
drifting snow events well, the model likely overestimates drifting snow
transport and therewith drifting snow sublimation . This
leads to uncertainties in SMB of 60–100 Gt yr-1, clearly
dominating the uncertainty in MB (Fig. ).
To reduce these biases and increase our diagnostic and prediction skills of
GrIS MB, it is imperative that SMB and firn models are further
improved and their horizontal resolution enhanced. This can be achieved
through statistical/dynamical downscaling in combination with targeted
in situ observations. Examples of important processes that are poorly or not
at all represented in current models are interactive snow/ice darkening by
future enhanced dust/black carbon deposition or microbiological processes
, and sub-, supra- and englacial hydrology, including
vertical and horizontal flow of meltwater in firn or over ice lenses
. Other emerging research topics of GrIS melt climate
are the impact of atmospheric circulation changes on Greenland melt
, the
impact of rain on ice sheet motion , the effect of
liquid water clouds on the surface energy balance and melt
and the increased role of
turbulent heat exchange during strong melting episodes over the margins of
the GrIS . Finally, it is desirable that, once developed
and tested, a single, sophisticated snow model is used to simulate both the
deep firn layer over the ice sheet and the seasonal snow cover over the
tundra.