TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-10-271-2016The modelled surface mass balance of the Antarctic Peninsula at 5.5 km horizontal resolutionvan WessemJ. M.j.m.vanwessem@uu.nlhttps://orcid.org/0000-0003-3221-791XLigtenbergS. R. M.ReijmerC. H.https://orcid.org/0000-0001-8299-3883van de BergW. J.https://orcid.org/0000-0002-8232-2040van den BroekeM. R.https://orcid.org/0000-0003-4662-7565BarrandN. E.ThomasE. R.https://orcid.org/0000-0002-3010-6493TurnerJ.WuiteJ.https://orcid.org/0000-0001-9333-1586ScambosT. A.https://orcid.org/0000-0003-4268-6322van MeijgaardE.https://orcid.org/0000-0003-4657-2904Institute for Marine and Atmospheric Research Utrecht, Utrecht University, Utrecht, the NetherlandsSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UKBritish Antarctic Survey, Cambridge, UKENVEO IT GmbH, Innsbruck, AustriaNational Snow and Ice Data Center, University of Colorado, Boulder, CO, USARoyal Netherlands Meteorological Institute, De Bilt, the NetherlandsJ. M. van Wessem (j.m.vanwessem@uu.nl)3February20161012712854September201529September20157January20168January2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://tc.copernicus.org/articles/10/271/2016/tc-10-271-2016.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/10/271/2016/tc-10-271-2016.pdf
This study presents a high-resolution (∼ 5.5 km) estimate of surface mass balance (SMB) over the period 1979–2014 for the Antarctic Peninsula
(AP), generated by the regional atmospheric climate model RACMO2.3 and a firn
densification model (FDM). RACMO2.3 is used to force the FDM, which
calculates processes in the snowpack, such as meltwater percolation,
refreezing and runoff. We evaluate model output with 132 in situ SMB
observations and discharge rates from six glacier drainage basins, and find
that the model realistically simulates the strong spatial variability in
precipitation, but that significant biases remain as a result of the highly
complex topography of the AP. It is also clear that the observations
significantly underrepresent the high-accumulation regimes, complicating a
full model evaluation.
The SMB map reveals large accumulation gradients, with precipitation values
above 3000 mm we yr-1 in the western AP (WAP) and below
500 mm we yr-1 in the eastern AP (EAP), not resolved by coarser
data sets such as ERA-Interim. The average AP ice-sheet-integrated SMB,
including ice shelves (an area of 4.1 × 105 km2), is
estimated at 351 Gt yr-1 with an interannual variability of
58 Gt yr-1, which is dominated by precipitation (PR)
(365 ± 57 Gt yr-1). The WAP (2.4 × 105 km2)
SMB (276 ± 47 Gt yr-1), where PR is large
(276 ± 47 Gt yr-1), dominates over the EAP
(1.7 × 105 km2) SMB (75 ± 11 Gt yr-1) and PR
(84 ± 11 Gt yr-1). Total sublimation is
11 ± 2 Gt yr-1 and meltwater runoff into the ocean is
4 ± 4 Gt yr-1. There are no significant trends in any of the
modelled AP SMB components, except for snowmelt that shows a significant
decrease over the last 36 years (-0.36 Gt yr-2).
Introduction
The Antarctic Peninsula (AP) is one of the most rapidly changing regions on
Earth . Over the last 50 years, the AP has
experienced a warming of up to 3 K which is among the highest on
Earth , and has increasingly contributed to global sea
level rise . In addition, the AP is the only
region of Antarctica that is warm enough for widespread surface melt to
occur, which has increased unprecedentedly over the past 1000 years. Likely as a result of the increased melt rates, many of
the ice shelves that fringe the AP have (partly) disintegrated in recent
years , potentially following hydrofracturing of surface
crevasses . In combination with warmer
ocean currents melting the ice shelves from below
, the ice shelves lose their buttressing
effect , accelerating the AP glaciers flowing into the ice
shelves, and raising sea level .
Increased snowfall in a warming climate is only partly
expected to compensate for increasing dynamical mass loss in the future
. In order to quantify the mass changes of the AP and the
associated sea level rise, an accurate estimate of the AP contemporary
surface mass balance is essential.
The surface mass balance (SMB in mmweyr-1) is defined as the
sum of all mass gains and losses of the ice sheet surface:
SMB=∫year(PR-SUs-SUds-ERds-RU)dt,
where PR represents total precipitation (snowfall plus rain), SU surface
(SUs) plus drifting snow (SUds)
sublimation, ERds drifting snow erosion/deposition (caused
by divergence/convergence in the horizontal drifting snow flux) and RU
meltwater runoff, the amount of liquid water (melt and rain) that is not
retained or refrozen in the snowpack. The mean area averaged SMB of the AP is 6 times larger than that of the total Antarctic ice sheet (AIS)
, and AP melt rates are also relatively large due to its
northerly location . The high accumulation rates are
a result of the AP acting as an efficient mountain barrier, forcing large
amounts of orographic precipitation along its windward slopes.
Measuring AP SMB is a complicated task, as the high precipitation rates and
the steep mountainous (and inaccessible) terrain leave large regions devoid
of observational data; most observations are therefore located in relatively
dry areas and/or over the flat ice shelves which are easily accessible
. Ice and firn core observations have identified a doubling
of accumulation over the WAP since 1850 , an increase in AP
SMB over the last 50 years, and a large spatial
variability of the SMB . To solve the limited coverage of
these observations, remote sensing techniques are pivotal. However, remote
sensing techniques, such as radar backscatter to identify melt episodes
or satellite products like the Gravity Recovery and
Climate Experiment (GRACE; ), often don't resolve the
small-scale features of the AP SMB. Other methods, such as using ice
discharge estimates to calculate the mass balance of the ice sheet, or
satellite altimetry to measure elevation changes, need detailed SMB fields
, or require a correction for firn
processes , which is dependent on the SMB. Therefore, for
a realistic mass balance estimate, continuous SMB fields are necessary.
Regional atmospheric climate models (RCMs) realistically simulate the climate
and SMB of the larger glaciated regions, such as Greenland
and Antarctica
. Moreover, RCMs have, in combination with
a firn densification model (FDM, ), been used to
simulate the interaction of the snowpack with the atmosphere and a changing
climate , providing detailed
information about meltwater percolation, refreezing and the stability of ice
shelves .
The SMB and climate of the AP have been simulated before, at a relatively
high (14 km) resolution, but for a short timespan (6 years)
. Higher resolution simulations have also been
performed, but without a high-resolution snow-routine
. Recently, the regional atmospheric climate
model RACMO2.3 was used at a horizontal resolution of 5.5 km to
simulate the SMB of Patagonia . In this study we use the
same model and resolution to simulate the SMB of the AP for 1979–2014, the
period for which reliable forcing data are available, coupling it to a FDM to
calculate processes in the firnpack and, eventually, runoff. We discuss the
SMB and its components, with a particular focus on meltwater and
precipitation. We present the model and the observations used in
Sect. , and evaluate the modelled SMB with in situ
observations and discharge estimates in Sect. . In
Sect. , we discuss gridded climate maps of SMB and its
components, and discuss spatial and temporal variability. Finally, we present
a discussion of the results and conclusions in Sects.
and .
Data and methodsRegional Atmospheric Climate Model RACMO2.3
The Regional Atmospheric Climate Model RACMO2.3 combines the dynamics package
of the High Resolution Limited Area Model (HIRLAM) with the
physics package of the European Centre for Medium-range Weather Forecasts
(ECMWF) Integrated Forecast System (IFS) . RACMO2.3 has been
adapted for use over the large ice sheets of Greenland and Antarctica: it
includes a multi-layer snow model to calculate melt, percolation, refreezing
and runoff of liquid water ; a prognostic scheme to
calculate surface albedo based on snow grain size ;
and a routine that simulates the interaction of drifting snow with the
surface and the lower atmosphere . ERA-Interim
re-analysis data with 6-hourly resolution from January 1979 to December 2014
are used to force the model at the lateral atmospheric
boundaries as well as at the lower ocean boundaries by prescribing sea ice
fraction and sea surface temperatures. The model domain interior
(Fig. ) is allowed to evolve freely and a model time step of
2 min is used.
RACMO2.3 Antarctic Peninsula model domain (black box in inset map of
Antarctica), boundary relaxation zone (dotted area, 16 grid points) and model
surface topography (m). Locations of in situ SMB observations are marked
(dots); ice cores, as used in Sect. 3.2, are highlighted (black circles).
Colours of the dots indicate whether the observation lies in a WAP (red), EAP
(blue) or spine (purple) height bin (see Sect. ). Model
topography is based on digital elevation models from for the
region north of 70∘ S, and south of
70∘ S. White areas represent floating ice shelves, colours represent
the elevation of the grounded ice sheet. drainage basins
24, 25, 26 and 27 are shown (black lines). One EAP in situ observation is
shown in light blue for visibility. Inset denotes the six drainage basins of
pre-acceleration Larsen B outlet glaciers: Starbuck (S), Flask (F),
Leppard (L), Crane (C), Jorum (J) and Hektoria-Green (HG) from
.
RACMO2.3 is a hydrostatic model that we run at a horizontal resolution of
∼5.5km and 40 vertical levels. At this resolution we assume
the assumption of hydrostatic balance to hold, an assumption that is
justified to some extent by earlier studies
, although a non-hydrostatic model version
will likely further improve the model output in terms of better resolved
processes over sloping surfaces, such as foehn and katabatic winds that
influence (drifting snow) sublimation and snowmelt fluxes
. The surface topography is based on a combination of the
100 m digital elevation model (DEM) from and the
1 km DEM from . The ice sheet mask is kept constant
through the simulation, and includes the (former) Larsen B and Larsen A ice
shelves. All integrated SMB estimates use basins number 24–27 from the basin
definition from , and include Larsen B, but for
calculations (e.g. yearly averages) after its disintegration (2003), Larsen B
is excluded. For 1979–2014 averages, the full ice sheet mask including
Larsen B is used. For further details of the model the reader is referred to
.
The model is initialized on 1 January 1979, with the atmospheric state and
sea surface boundary conditions adopted from ERA-Interim reanalysis. The
initial firnpack of the AP is inferred from a simulation with an offline firn
densification model (FDM, ), which was driven by an
earlier climatological simulation of the AP by RACMO2.3, largely comparable
to the one used in this study. Runoff estimates are taken from the FDM, which
was forced by RACMO2.3: the other SMB components are taken directly from
RACMO2.3.
Firn densification model
The firn densification model (FDM) is a high-resolution version (∼3000 layers in the vertical) of the internal snow model that is
interactively coupled to RACMO2.3. It is a single column time-dependent model
that describes the evolution of the firn layer. It calculates firn density,
temperature and liquid water content evolution based on forcing at the
surface by RACMO2.3 variables at 3-hourly resolution: surface temperature,
accumulation and wind speed. Surface meltwater percolates into the model firn
layer, where it can refreeze, be stored or percolate further down. The
retention of meltwater is based on the “tipping-bucket” method (i.e. liquid
water is stored in the first available layer and transported downwards only
when it exceeds capillary forces). Liquid water that reaches the bottom of
the firn layer and can neither refreeze nor be stored is removed as runoff (RU
in Eq. ). We do not use output from the internal snow model,
because of small coding errors, but instead run the FDM offline, with the
additional benefit of its higher vertical resolution. We found no significant
differences due to, e.g. the interaction of the atmosphere with subsurface
processes in the snow column. A detailed analysis of subsurface processes is
beyond the scope of this study; here we focus on the integrated mass budget
and the SMB. More details on the FDM can be found in .
Observational dataIn situ observations
We evaluate modelled SMB using 132 in situ observations, originating from
various sources and methods, e.g. ice cores, stake arrays and bomb horizons
(Fig. ) . Included
in these data are five unpublished in situ observations from high elevations,
obtained from a shallow ice core, sonic snow height measurements and camera
observations of a snow stake over time (T. A. Scambos, personal
communication, 2014). Also included are six ice core accumulation records:
James Ross Island , Gomez , two cores from
Dyer Plateau, one to the east of the ice divide , one to
the west of the ice divide (, E. R. Thomas, unpublished
data), Bryan Coast and Ferrigno (E. R. Thomas, unpublished data). These
records are additionally used to assess model interannual variability in
Sect. 3.2.
We compare modelled SMB with in situ observations only for overlapping time
periods, when available. When observations date from before 1979, or when the
time of measurement is not known, they are compared with the climatological
(1979–2014) modelled SMB. Only 38 SMB observations have an observation
length >5years and these are used for model evaluation in
Sect. . Note that for all comparisons, the observational data
are compared with data from the nearest model grid point; no interpolation is
performed.
Discharge estimates
We evaluate modelled SMB using 1995 pre-collapse discharge (D) estimates of
six Larsen B outlet glaciers in the north-eastern AP. We assume these to be
in balance (SMB-D=0), as the data date from well before the
significant thinning and retreat of the Larsen B ice shelf, that culminated
in its disintegration in 2002 ; we exclude basins that are
too small to be resolved by the model resolution, or were not in balance.
This leaves six basins: Starbuck, Flask, Leppard, Crane, Jorum and
Hektoria-Green, shown in Fig. . Assuming balance of these
basins, a comparison with the climate average SMB for 1979–2014 is
justified, and we directly compare discharge with RACMO2.3 SMB for this
period.
Results: model evaluationIn situ observations
Figure compares modelled (red) and observed (black) SMB
for the locations of the in situ observations (averaged in elevation bins).
As many SMB observations are clustered, i.e. mostly located on the ice
shelves and locations of relatively low accumulation, we have binned the
observations in 11 surface elevation bins, selected such that they span
at least ∼75m in elevation and contain at least 10 SMB data
points. The bins are arranged from left to right in eight western AP (WAP)
bins (W1–W8), one bin for the spine (S) and two bins for the eastern AP
(EAP; E1–E2). For all other observations that are not located in any of the
basins, but situated within the model domain, we define a 65∘ W
longitude threshold: all observations west of this boundary represent the
WAP, the others the EAP. Figure shows that the model and
the observations show relatively constant SMB values for the WAP and the EAP
(<1000mmweyr-1), and a pronounced peak in SMB over the
spine. Variability between the different bins is caught well and simulated
SMB is always within the standard deviation of the observations. For bins W2,
W6 and W7 there is a slightly larger discrepancy with the observations. To
explain this, we calculated the average of all model grid points within the
respective bins (orange points). These averages are considerably higher than
both the observed and modelled values, showing that the observations are
biased towards regions of low accumulation.
Modelled RACMO2.3 5.5 km (red), RACMO2.3 27 km (blue), ERA-Interim
(green) and observed (black) SMB as a function of 11 elevation bins for
all in situ SMB observations. The orange dots are the average SMB of all
RACMO 5.5 km grid points in the elevation bins. The bins are arranged from
west to east in three separate classes (WAP (W1–8), the spine (S) and the
EAP (E1–2)), using the drainage basin definition from .
Error bars represent 1 standard deviation within the bin and amount of
observations in each bin is denoted within the plot. Further details are
provided in the text (Sect. 2.3.1).
(a) Modelled RACMO2.3 SMB at 5.5 km as a function of
in situ SMB observations (black dots), and the height bins as defined in
Fig. for RAMCO2.3 5.5 km (red dots), RACMO2.3 27 km
(blue squares) and ERA-Interim (green triangles) in mmweyr-1.
Error bars represent 1 standard deviation within the height bins (for
clarity error bars for RACMO2.3 27 km and ERA-Interim are not plotted).
Dashed lines represent the linear regressions lines. Two locations with
either observed or modelled SMB values >3000mmweyr-1, as
well as one binned 27 km location, are off the chart and are not shown for
clarity. (b) Modelled RACMO2.3 SMB at 5.5 km (with 1 standard
deviation) as a function of drainage basins discharge estimates (with
uncertainty) in Gtyr-1. The red line represents
the regression line of modelled SMB with discharge estimates. Basin locations
are shown in Fig. .
Figure a presents the direct correlation between the
model and the observations, both for the individual locations as for the
11 elevation bins. The SMB over the AP is highly variable and modelled
SMB is poorly correlated with the individual observations (r2= 0.25,
rc = 0.46), although the average bias is low
(bias = 28.4 mmweyr-1). The binned averages strongly
improve the correlation (r2= 0.8) and the slope (rc = 0.97),
demonstrating that larger-scale spatial variability is generally well
captured. The EAP SMB is well simulated, especially the low SMB over the ice
shelves, even though few EAP observations are available. For the WAP, the
model overestimates SMB in relatively dry locations (George VI ice shelf and
southern Palmer Land), while underestimating SMB in the wetter locations
(coastal zones). This is likely related to an insufficient representation of
orographic precipitation at 5.5 km. This could be a result of overly
simple cloud physics (e.g. precipitation falls to the surface instantly, and
is not transported across grid-boxes), and/or the model being hydrostatic,
not effectively simulating vertical atmospheric motion, and/or the remaining
underestimation of the surface slope . The model does
simulate the peak in SMB (∼2000mmweyr-1) of the
northern spine bin (containing WAP and EAP observations >1500m
elevation), despite the few observations and the very large spatial
variability.
Modelled and observed yearly (1979–2014) SMB
(mmweyr-1) at locations of Gomez (a, 73∘ S,
70∘ W), James Ross Island (b, 64.12∘ S,
57.54∘ W), Dyer Plateau East (c, 70.4∘ S,
64.5∘ W), Dyer Plateau West (d, 70.7∘ S,
64.9∘ W), Bryan Coast (e, 74.3∘ S,
81.4∘ W) and Ferrigno (f, 74.3∘ S,
86.5∘ W) ice core sites. Statistics (r2, bias and RMSD) are also
given. Ice core locations are shown in Figs. and
.
Figures and a clearly present the
main advantage of the high-resolution SMB product over low-resolution
products such as RACMO2.3 at 27 km horizontal resolution
, or the ERA-Interim re-analysis . At
27 km horizontal resolution the low accumulation regions are well
represented, resulting in a similar correlation (r2= 0.8) as at
5.5 km. In two elevation bins the SMB is actually better represented than at
5.5 km, likely due to error compensation of positive and negative SMB
biases. However, at 27 km the SMB at high elevations is poorly represented
and the 5.5 km product shows a considerable improvement, especially over the
spine. The advantage of the high-resolution product is more evident when
compared to the re-analysis: even though the re-analysis captures the SMB at
the middle elevation bins reasonably well, as a result of its coarse
resolution (∼80km), it lacks the ability to resolve the steep
SMB gradients, and the large SMB values at the higher elevations, as well as
the low SMB over the WAP and EAP ice shelves. Another obvious difference is
the small spatial variability in ERA-Interim, compared to RACMO2.3 and the
observations.
Interannual variability
Figure shows yearly modelled (red) and observed (black)
SMB at the location of six ice cores. Modelled SMB to a large extent
reproduces the observations. The model mainly underestimates the absolute SMB
east and west of the ice divide at Dyer Plateau
(Fig. c and d), where spatial variability is large,
although for the western Dyer Plateau record the modelled and observed
variability matches relatively well, but both these records are very short.
The records of Gomez, Bryan Coast and Ferrigno over the WAP, and James Ross
Island over the EAP, match particularly well; the magnitude is simulated well
by the model at these locations, and correlation coefficients are larger as
well, although the model has difficulties in timing the SMB maxima at the
Gomez and James Ross ice cores, especially earlier in the record. For James
Ross Island this is likely related to the ice core being located on a small
island with large elevation differences, and due to the observational record
being compared with data from the nearest model grid point.
Discharge
We assessed modelled climatological SMB by comparing it to solid ice
discharge estimates from glacier basins, for a time period when these were in
approximate balance (pre-1995, ).
Figure b shows that modelled SMB matches the discharge
estimates, especially for the smaller basins (Jorum, Starbuck and Flask). For
the larger basins the representation is still generally good (bias <30 %), especially considering the relatively small size of these basins
(Fig. ), and the large spatial SMB variability along the
northern AP mountain spine.
Figure presents average 1979–2014 AP SMB components
(Eq. ) and Fig. a the resulting SMB.
Total precipitation (PR = snowfall + rainfall,
Fig. a) dominates the AP SMB with values that are
typically 1 order of magnitude larger than the other components, which is
also the case for the whole AIS . PR shows large
gradients (note the nonlinear scale in Figs. a
and ), with values ranging from >1000mmweyr-1 on the western slopes and the adjacent ocean,
towards low values (<300mmweyr-1) on the eastern slopes
and ice shelves. In particular the modelled precipitation rates in the
north-western AP are extreme with rates of up to 5000 mmweyr-1
on the lower windward slopes, equivalent to ∼15m of snowfall
each year. This makes the AP among the wettest regions (in terms of snowfall)
on Earth, comparable with the high snowfall rates
(3500 mmwey-1) for Patagonia simulated with the same model
. These mountainous regions represent a steep barrier
that is almost perpendicular to the strong circumpolar westerlies, causing
very efficient orographically induced precipitation over the windward
mountain slopes, and a strong precipitation shadow on the leeward slopes.
These sharp gradients are widespread, especially in the WAP: for instance,
precipitation rates towards the south are high as well, varying from
2500 mmweyr-1 on the Elgar Uplands towards
1000–2000 mmweyr-1 on the western slopes of Palmer Land, with
a distinct minimum over George VI ice shelf situated at the lee side of
Alexander Island.
1979–2014 average SMB components: total precipitation
(snowfall + rain) (a), snowmelt (b), meltwater
runoff (c), total sublimation (d), sublimation of drifting
snow (e) and erosion of drifting snow (f) in
mmweyr-1. Note that ablation is defined positive
in (d–f). All fluxes are from RACMO2.3, except for runoff,
which is calculated by the FDM that is forced by RACMO2.3 output.
The second largest component is drifting snow sublimation
(SUds, Fig. e), removing ∼50mmweyr-1 of snowfall, peaking in regions of high wind
speed . SUds is smaller at high
elevations and the (western) mountain slopes, where winds are weaker; here a
surface-based temperature inversion, and the associated increasing humidity
with height, favours surface deposition
(SUs≃-20 mmweyr-1,
Fig. d). Surface sublimation
(SUs> 0) mainly occurs over the flat ice shelves
and the lower mountain slopes, typically removing ∼20mmweyr-1. The erosion of drifting snow
(ERds) does not significantly contribute to the
(integrated) SMB, but redistributes mass on a local scale with snow
divergence/convergence rates up to 100 mmweyr-1 on the AP
slopes, closely following the topography. Snowmelt (M) is widespread below
2000 ma.s.l., showing large spatial variability, with maxima up to
500 mmweyr-1 over the north-eastern ice shelves, and with
decreasing values towards higher elevations and/or latitudes. Most meltwater
refreezes or is retained in the snowpack, and only a fraction runs off into
the ocean. This happens mainly over Larsen B and northern Larsen C ice
shelves, but also over Wilkins and northern George VI ice shelves, with
runoff (RU) fluxes up to 300 mmweyr-1. Over the lower slopes
of the north-western mountain range, where both PR and M are large, the
model also simulates small amounts of runoff (∼50mmweyr-1).
SMB
Figure presents the average modelled
(1979–2014) SMB at 5.5 km (panel a) and 27 km (panel b) horizontal
resolution, together with the in situ SMB observations with time spans >5years. SMB is largely similar to PR (Fig. a),
with large west-to-east gradients, and peak SMB values around the WAP coastal
regions, with large spatial variability that is controlled by the orography.
Although a limited number of multi-year in situ observations are available,
the modelled SMB patterns generally match with the observations: the large
SMB values found only in a few observations are simulated well by the model,
as well as the lower SMB values on the George VI and Larsen ice shelves. Over
the former Larsen B ice shelf there is a mismatch: here, simulated RU reaches
its maximum, and modelled SMB is negative (up to
-100 mmweyr-1). This negative SMB is not found in the
pre-break-up observations, which show small but positive values. However, the
model does agree that the minimum in SMB lies in this area. Furthermore, for
unknown reasons, the SMB is considerably overestimated at two locations, on
Adelaide Island, and slightly eastwards, a region of large variability
.
At 27 km horizontal resolution (Fig. ) the
spatial pattern in SMB is largely similar to that at 5.5 km (also see
Fig. ). The main advantage of the high-resolution
product is that it better resolves the SMB gradients, the peak SMB, as well
as the small islands and narrow ice-shelves.
Modelled SMB (colours) and observations (markers) in
mmweyr-1 at 5.5 km (a) and 27 km (b)
horizontal resolution . Observations are from
, and and sources
noted in Sect. , and only shown if they represent a period >5years. Modelled SMB is the 1979–2014 climatological average. At
both resolutions only ice sheet grid points are shown.
Average integrated SMB
Table summarises the integrated WAP (basins 24/25
from ), EAP (basins 26/27) and total (all basins) SMB
values. The total AP SMB amounts to 351 Gtyr-1, which is ∼20 % of the total integrated SMB for the AIS as found in
, even though its area
(4.1 × 105km2) is only 3 % of the total AIS.
The SMB over the WAP (276 Gtyr-1) is considerably larger than
that of the EAP (75 Gtyr-1), although the larger area of the WAP
(2.4 × 105km2) compared to that of the EAP
(1.7 × 105km2) partly accounts for the difference.
The large west-to-east differences are due to PR, that is
365 Gtyr-1 in total, 281 Gtyr-1 over the WAP and
84 Gtyr-1 over the EAP. Of PR, 99 % is snowfall
(363 Gtyr-1); rainfall is small (3 Gtyr-1). Total
sublimation is 11 Gtyr-1, dominated by SUds
(9 Gtyr-1). In contrast to the AIS, snowmelt over the AP is
widespread, similar in extent to microwave satellite observations
, and three times larger than the rest of the AIS combined
(34 vs. 11 Gtyr-1, ). Most meltwater
(and rainfall) refreezes in the snowpack (RF = 33 Gtyr-1)
but a small fraction runs off into the ocean
(RU = 4 Gtyr-1); most RU is present over the EAP, in
particular over the Larsen B ice shelf (3 Gtyr-1).
AP integrated values of mean SMB components [Gtyr-1]
with interannual variability σ: total (snow + rain) precipitation
(PR), snow, rain, total sublimation (SUtot), surface
sublimation (SUs), drifting snow sublimation
(SUds), drifting snow erosion (ERds),
runoff (RU), snowmelt (M) and refreezing (RF). All values are calculated
for basins 24–27 of , over a total AP area of
4.1 × 105km2. For WAP and EAP values, basins 24/25
and 26/27 are used, respectively. Values are calculated from yearly averages;
from 2003 onwards integrated values represent the ice shelf excluding
Larsen B ice shelf.
Yearly average SMB (a) and SMB components (b) in
Gtyr-1. For SMB, values for the WAP (red) and the EAP (blue) are
shown. SMB is integrated over basins 24, 25, 26 and 27 from
. Trend lines are only shown if significant. From 2003
onwards integrated values represent the ice shelf excluding Larsen B ice
shelf.
Interannual variability (1 standard deviation, detrended) of SMB
components for 1979–2014: SMB (a), and snowmelt (b), in
mmweyr-1. 1979–2014 trends of precipitation (c) and
snowmelt (d) in mmweyr-2. Stippled pattern represents
trends that are significant >95 %.
Temporal variabilityInterannual variability
Figure shows yearly average SMB values and the
largest SMB components; for integrated values after 2003 we exclude Larsen B
ice shelf. Total AP SMB (Fig. a) has an interannual
variability of 58 Gtyr-1 (15 % of mean SMB, absolute values
shown in Table ) and shows no significant trend.
Precipitation has a similar interannual variability of
57 Gtyr-1. The most positive SMB year is 1989 with
445 Gtyr-1, being significantly wetter than the driest year
(1980, 246 Gtyr-1), relatively a much larger difference than for
the whole AIS , which is expected as over larger regions
differences cancel out. Variability in sublimation and drifting snow
sublimation is low, and comparable for the WAP and the EAP
(1 Gtyr-1). Of the SMB components (other than RU, that is
small), the variability of M is the largest (15 Gtyr-1,
45 % of the mean), reaching its peak in 1992 (73 Gtyr-1),
and minima (∼11Gtyr-1) in 1986 and 2014. EAP melt rates
are higher by 3 Gtyr-1 (relatively even higher as the area of
the EAP is smaller than that of the WAP), but the timing of the maxima in M
is similar. Simulated integrated AP snowmelt is lower than, but the
variability and maxima are comparable to, the melt rates from
, (34 ± 15 and
57 ± 21 Gtyr-1, respectively) that were calculated with
RACMO2.1 at 27 km horizontal resolution. Even though the model
physics has been updated and RACMO2.3 generally simulates less melt over the
AIS , differences between both studies are explained by
the use of a more sophisticated snowpack initialization in this study, and
a different integration domain; i.e. in Larsen B
is included for the whole period and the domain extends further south.
1979–2014 monthly climatology of SMB components: top panel shows
total precipitation (snowfall + rain) averaged for the WAP drainage
basins (red line), the EAP (blue), and total (black). Bottom panel shows
total (WAP + EAP) snowmelt (orange), erosion of drifting snow
ERds (green), drifting snow sublimation
SUds (cyan), and surface sublimation/deposition (blue).
All variables in Gtmo-1.
For none of the variables a significant trend is simulated, except for
snowmelt (significance level >99 %). Snowmelt has decreased by the
same amount (-0.35 Gtyr-2) over the WAP and the EAP, which is
likely related to the significant and widespread cooling over most of the AP
in the last decade . Runoff of meltwater is small but
its variability is as high as its mean (4 Gtyr-1); peak years,
1992 and 1995 in particular, reach values of up to 15 Gtyr-1,
following the peaks in snowmelt. In contrast to M, RU over the WAP and EAP
shows large differences. Despite the higher temperatures, a thick firnpack
exists over the WAP, as a result of the higher snowfall rates, permitting
most meltwater to refreeze in the available pore space. Over the eastern ice
shelves there is hardly any pore space present in the firn and a larger
fraction of meltwater runs off, especially over Larsen B ice shelf before
2003.
Figure shows maps of interannual variability in SMB and
M. SMB variability (Fig. a) shows no unexpected
patterns for most of the domain, mostly following the regions of high
absolute SMB. Relatively, SMB variability peaks over George VI, Larsen B and
the northern part of Larsen C ice shelves (>40 %). This is clearly
related to RU (not shown), which in turn is controlled by snowmelt
variability (Fig. b), that peaks towards the northern
parts of the ice shelves. It is clear that dry regions, which experience
significant melt, show the largest interannual (relative) SMB variability.
Interestingly, these coincide with the EAP ice shelves, which are also
sensitive to disintegration. The other SMB components (not shown) have low
variability and show no pronounced patterns.
There are no trends in integrated precipitation, but
Fig. c shows that large significant trends exist
locally, e.g. on Alexander Island and in the Weddell Sea. The model
simulates large positive trends over the northerly slopes of the WAP
(15 mmweyr-2), but these are not significant as these
locations also experience large interannual variability. These trends
are related to enhanced upper atmosphere northerly winds
. Negative trends in snowmelt
(Fig. d) are more significant on the WAP than on
the EAP ice shelves, and show a uniform pattern.
Correlation of modelled yearly (1979–2014) SMB at locations of
Gomez (a, 73∘ S, 70∘ W), James Ross Island
(b, 64.12∘ S, 57.54∘ W), Dyer Plateau East
(c, 70.4∘ S, 64.5∘ W), Dyer Plateau West
(d, 70.7∘ S, 64.9∘ W), Bryan Coast
(e, 74.3∘ S, 81.4∘ W) and Ferrigno
(f, 74.3∘ S, 86.5∘ W) ice cores, with all other
points in the model domain. Stippled pattern represents significant
correlations >99 %. Locations of ice cores are denoted by blue
circles. Note that over the sea-ice/ocean SMB=PR-SU.
Seasonal cycle
Figure shows the average seasonal cycle
(1979–2014) of SMB components. Integrated AP precipitation has a pronounced
seasonality, with considerably larger values in winter than in summer. In
winter, the stronger westerlies create more orographic precipitation over the
WAP mountain slopes. In addition, WAP (and hence total) precipitation shows
two peaks in March and in October, that are related to the semiannual
oscillation , in agreement with
observational data from surface stations .
Over the EAP, the precipitation seasonality is reversed: here precipitation
peaks in summer, reaching values almost as high as in the WAP summer, and
decreases to a relatively constant minimum in winter 1 order of magnitude
lower than over the WAP. Over the EAP, most summer precipitation originates
from the Weddell Sea; when a minimum of sea-ice cover is reached in summer,
moist ocean air is transported to the eastern ice shelves.
The other SMB components have lower magnitudes and show varying seasonal
patterns. Snowmelt is absent in winter, and reaches its peak
(13 Gtyr-1) in December and January. SUs is
negative in winter (-1 Gtyr-1), when a persistent
surface-based temperature inversion favours surface deposition. This positive
contribution to the SMB is compensated by the stronger wintertime winds
removing mass by SUds. In summer, SUds
drops off as winds are weaker and the snowpack gets warmer and denser.
SUs increases with surface temperature, and up to
3 Gtyr-1 of snow is removed. Finally, ERds is
very low year-round (<0.1Gtyr-1).
Spatial coherence of modelled precipitation
To illustrate the spatial coherence of AP SMB, Fig.
shows the spatial correlation of the modelled yearly time series at the six
ice core locations of Sect. 3.2, with all other points in the model domain.
Figure a shows that the SMB simulated at the location of
the Gomez ice core, in south-western Palmer Land, is strongly and
significantly (>99 %) correlated over the whole WAP, supporting the
strong spatial coherence that was also found for (observed) temperature in
. Simultaneously there is a negative correlation over the
north-eastern AP, and Larsen B and C ice shelves, showing that high WAP SMB
rates are coinciding with dry conditions over this region, a coherence not
found in . Figure b identifies the
spatial coherence of this location as well: when SMB is large at James Ross
Island, it is large over the northern EAP ice shelves and sea-ice, while over
the WAP an insignificant correlation is found. Figure c
shows that the SMB time series simulated at the Dyer Plateau ice core
location, slightly east of the ice divide, is highly correlated with and
representative of the adjacent EAP. Simultaneously, the second ice core at
Dyer Plateau (Fig. d), located only 30 km
westwards, and the Bryan Coast and Ferrigno ice cores
(Fig. e and f), all located on the WAP, show completely
opposite correlations, that closely resemble that of the Gomez ice core.
These correlation maps clearly highlight the different forcing mechanisms of
SMB on the WAP and EAP and the distinct and sharp climatic differences
between both sides of the AP: over the WAP, snowfall is orographically
induced, while over the EAP snowfall events are triggered by low pressure
systems over the Weddell Sea.
Discussion
Although RACMO2.3 simulates the AP SMB with reasonable accuracy, the scarcity of
observational data hampers a more thorough model assessment; more
observational data should therefore be obtained, especially from higher
elevations and areas with large snowfall rates. The comparison suggests that
there are a number of potential problems. Not all local topographic detail
(often at scales of <1km) is resolved at the 5.5 km model
resolution. As a result the slope and the height of the orographic barrier of
the AP are still underestimated, negatively affecting the simulated uplift of
air and, consequently, precipitation and atmospheric foehn winds. In previous
studies , it was suggested
that increasing the horizontal resolution from 27 km to the current
value of 5.5 km would better resolve EAP surface melt, which is
strongly related to the foehn winds. While the increase in resolution is
clearly an improvement for other topography related variables, it appears
that these foehn winds are not yet sufficiently resolved. However, further
increasing the resolution potentially moves the model beyond the limits of
the hydrostatic assumption, and the use of a non-hydrostatic model would then
become mandatory. show that the non-hydrostatic,
high-resolution (1.5 km) UTM model more efficiently resolves these
foehn patterns. discussed the effects of
a non-hydrostatic model on katabatic winds, which are also strongly related
to the topography, and found that biases are relatively independent of the
resolution.
Over the WAP there likely is a westward displacement of precipitation due to
the model not taking into account horizontal advection of precipitation. In
the current model version, precipitation is assumed to reach the surface
instantly within the grid box where it is generated. Ideally precipitation,
in particular snow which has a relatively low fall speed, must be modelled as
a prognostic variable in order to capture its fall time and horizontal
displacement. estimated that, with mean wind speeds of
∼7ms-1 for this region, snowfall could be advected over
a distance of ∼10km, i.e roughly two grid boxes at
5.5 km resolution simulation.
Currently, the only prognostic cloud variables are cloud fraction and cloud
condensate that, together with temperature, determine the fractionation of
cloud ice and cloud liquid water. An explicit treatment of liquid and solid
cloud content is more physical than the current implicit treatment using
temperature. Improving this necessitates the inclusion of prognostic
precipitation in future simulations, which potentially leads to more (and
thicker) clouds to be advected over the mountain range, as well as an
improvement of the biases in the downwelling radiative fluxes as found by
.
Summary and conclusions
We used the regional atmospheric climate model RACMO2.3 and a firn
densification model (FDM) to simulate the SMB of the Antarctic Peninsula at
a horizontal resolution of 5.5 km. RACMO2.3 is forced by ERA-Interim
reanalysis at the lateral boundaries, and the snowpack is initialized with
output from the FDM. We have evaluated the simulated SMB by comparison with
132 in situ SMB observations and six glacier discharge basins, both showing
reasonable agreement. Most model biases are likely due to the still limited
horizontal resolution, and limitations in the model formulation, e.g. the
model being hydrostatic and precipitation not being treated as a prognostic
variable which can be advected. However, the observations show a large
over-representation from areas of low accumulation, especially over the
western AP (WAP), and more observations are needed at higher elevations,
regions of high accumulation and from the eastern AP (EAP), for a more robust
model evaluation.
Integrated over four AP drainage basins , the SMB amounts
to 351 ± 58 Gtyr-1 (σ= interannual
variability), more or less equal to total precipitation
(365 ± 57 Gtyr-1), indicating that this is by far the
dominant SMB component. The other components are more than 1 order of
magnitude smaller, with drifting snow sublimation being the largest
(9 ± 1.5 Gtyr-1). Runoff is small
(4 ± 4 Gtyr-1) as most meltwater
(34 ± 15 Gtyr-1) and rainfall refreezes
(33 ± 12 Gtyr-1) in the cold firn, but is locally
important on Larsen B, Larsen C and George VI ice shelves, and over the
north-western AP with values up to 200 mmweyr-1.
Pronounced differences in SMB exist between the WAP and EAP, and the AP spine
acts as a sharp climate barrier. The SMB over the WAP amounts to
276 ± 47 Gtyr-1, nearly a factor of 4 larger than that over
the EAP (75 ± 11 Gtyr-1), resulting from the extreme
orographic precipitation (> 3000 mmweyr-1) the model
simulates over the windward mountain slopes, especially in winter. Over the
EAP, the seasonality in SMB is reversed, peaking in summer when sea-ice
extent in the Weddell Sea is smallest and synoptic weather systems transport
clouds to the AP from the east. The other SMB components do not show these
large west-to-east differences, with the exception of runoff. While melt
rates are relatively similar over both the WAP and the EAP (18.7 ± 9,
15.5 ± 6 Gtyr-1), on the WAP most meltwater is retained or
refreezes in the snowpack, that contains a large amount of pore space as
a result of the large WAP snowfall rates. On the EAP, snowmelt often exceeds
precipitation, and insufficient pore space is available for the meltwater to
refreeze in, resulting in meltwater runoff in the ocean
(4 ± 4 Gtyr-1). This makes total sublimation the largest
ablation term in the integrated AP surface mass budget
(11 ± 2 Gtyr-1), which is primarily determined by drifting
snow sublimation (9 ± 1 Gtyr-1).
This new high-resolution AP data set considerably adds to the current lower
resolution data sets such as ERA-Interim, or lower resolution simulations
with RACMO2.3, as the mountainous terrain is much better resolved. These data
can be used, in combination with satellite products such as GRACE and
radar/laser altimetry, to better understand the changes of AP glaciers and
ice shelves.
In order to further improve model results, a computationally more expensive
non-hydrostatic model might be used for longer climate-scale simulations of
specifically selected small and topographically complex areas of the AP.
Moreover, the FDM can be updated to better represent meltwater percolation
and refreezing, e.g. taking into account non-homogenous meltwater
percolation. Additionally, more observational data of the AP, both remote
sensing and in situ data, should be acquired for improved model evaluation.
Acknowledgements
We are grateful for the financial support of NWO/ALW, Netherlands Polar
Programme and the Netherlands Earth System Science Centre (NESSC). We
thank the ECMWF for the use of their supercomputing facilities. Graphics and
calculations were made using the NCAR Command Language (Version 6.2.1,
). We thank Nerilie Abram for providing us with the
James Ross ice core data. Edited by:
M. Tedesco
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