TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-12-811-2018Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 1: Greenland (1958–2016)NoëlBricevan de BergWillem Janhttps://orcid.org/0000-0002-8232-2040van WessemJ. Melchiorhttps://orcid.org/0000-0003-3221-791Xvan MeijgaardErikvan AsDirkhttps://orcid.org/0000-0002-6553-8982LenaertsJan T. M.https://orcid.org/0000-0003-4309-4011LhermitteStefhttps://orcid.org/0000-0002-1622-0177Kuipers MunnekePeterhttps://orcid.org/0000-0001-5555-3831SmeetsC. J. P. Paulvan UlftLambertus H.van de WalRoderik S. W.van den BroekeMichiel R.https://orcid.org/0000-0003-4662-7565Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the NetherlandsRoyal Netherlands Meteorological Institute, De Bilt, the NetherlandsGeological Survey of Denmark and Greenland (GEUS), Copenhagen, DenmarkDepartment of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, USADepartment of Geoscience & Remote Sensing, Delft University of Technology, Delft, the NetherlandsBrice Noël (b.p.y.noel@uu.nl)6March20181238118318September20176October201714January201817January2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://tc.copernicus.org/articles/12/811/2018/tc-12-811-2018.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/12/811/2018/tc-12-811-2018.pdf
We evaluate modelled Greenland ice sheet (GrIS) near-surface climate, surface
energy balance (SEB) and surface mass balance (SMB) from the updated regional
climate model RACMO2 (1958–2016). The new model version, referred to as
RACMO2.3p2, incorporates updated glacier outlines, topography and ice albedo
fields. Parameters in the cloud scheme governing the conversion of cloud
condensate into precipitation have been tuned to correct inland snowfall
underestimation: snow properties are modified to reduce drifting snow and
melt production in the ice sheet percolation zone. The ice albedo prescribed
in the updated model is lower at the ice sheet margins, increasing ice melt
locally. RACMO2.3p2 shows good agreement compared to in situ meteorological
data and point SEB/SMB measurements, and better resolves the spatial patterns
and temporal variability of SMB compared with the previous model version,
notably in the north-east, south-east and along the K-transect in south-western
Greenland. This new model version provides updated, high-resolution gridded
fields of the GrIS present-day climate and SMB, and will be used for
projections of the GrIS climate and SMB in response to a future climate
scenario in a forthcoming study.
Introduction
Predicting future mass changes of the Greenland ice sheet
(GrIS) using regional climate models (RCMs) remains challenging
. The reliability of projections depends on the ability of RCMs
to reproduce the contemporary GrIS climate and surface mass balance (SMB),
i.e. snowfall accumulation minus ablation from meltwater run-off, sublimation
and drifting snow erosion . In addition, RCM
simulations are affected by the quality of the re-analysis used as lateral
forcing and by the accuracy of
the ice sheet mask and topography prescribed in the models .
Besides direct RCM simulations, the contemporary SMB of the GrIS has been
reconstructed using various other methods, e.g. positive degree day (PDD)
models forced by statistically downscaled re-analyses
, mass balance models forced by the climatological
output of an RCM (HIRHAM4) and reconstruction
of SMB obtained by combining RCM outputs with temperature and ice core
accumulation measurements . In addition,
and respectively used the Community Earth System Model
(CESM) at 1∘ resolution (∼ 100 km) and the Goddard Earth
Observing System model version 5 (GEOS-5) at 0.5∘ resolution
(∼ 50 km) to estimate recent and future mass losses of the GrIS.
Polar RCMs have the advantage of explicitly resolving the relevant
atmospheric and surface physical processes at high spatial (5 to 20 km) and
temporal (subdaily) resolution. Nonetheless, good RCM performance often
results from compensating errors between poorly parameterised processes, e.g.
cloud physics and turbulent fluxes
. Therefore, considerable efforts have been
dedicated to evaluating and improving polar RCM output in Greenland
, using
in situ SMB observations ,
airborne radar measurements of snow accumulation
and meteorological records
, including radiative fluxes that are
required to close the ice sheet surface energy balance (SEB) and hence
quantify surface melt energy.
For more than two decades, the polar version of the Regional Atmospheric
Climate Model (RACMO2) has been developed to simulate the climate and SMB of
the Antarctic and Greenland ice sheets. In previous versions, snowfall
accumulation was systematically underestimated in the GrIS interior, while
melt was generally overestimated in the percolation zone . At
the ice sheet margins, meltwater run-off is underestimated over narrow
ablation zones and small outlet glaciers that are not accurately resolved in
the model's ice mask at 11 km. Locally, this underestimation can exceed
several m w.e. yr-1, e.g. at automatic weather station (AWS) QAS_L
installed at the southern tip of Greenland . These biases
can be significantly reduced by statistically downscaling SMB components to
1 km resolution . Computational limitations currently hamper
direct near-kilometre-scale simulations of the contemporary GrIS climate,
making it essential to further develop RACMO2 model physics at coarser
spatial resolution. Important modelling challenges and limitations still need
to be addressed in RACMO2 regardless of the spatial resolution used: e.g.
cloud representation , surface albedo and turbulent heat
fluxes (Sect. 6).
Here, we present updated simulations of the contemporary GrIS climate and SMB
at 11 km resolution (1958–2016). The updated model incorporates multiple
adjustments, notably in the cloud scheme and snow module. Model evaluation is
performed using in situ meteorological data and point SEB/SMB measurements
collected across the GrIS. We then compare the SMB of the updated model
version (RACMO2.3p2) with its predecessor (RACMO2.3p1), discussed in
for the overlapping period between the two simulations
(1958–2015). Section 2 discusses the new model settings and initialisation
together with observational data used for model evaluation. Modelled climate
and SEB components are evaluated using in situ measurements in Sect. 3.
Changes in SMB patterns between the new and old model versions are discussed
in Sect. 4, as well as case studies in north-eastern, south-western and
south-eastern
Greenland. Section 5 introduces and evaluates the updated downscaled daily,
1 km SMB product. Section 6 discusses the remaining model uncertainties,
followed by conclusions in Sect. 7. This paper is part of a tandem model
evaluation over the Greenland (present study) and Antarctic ice sheets
.
Model and observational dataThe Regional Atmospheric Climate Model RACMO2
The polar (p) version of the Regional Atmospheric Climate Model (RACMO2)
is specifically adapted to simulate the climate of polar
ice sheets. The model incorporates the dynamical core of the High Resolution
Limited Area Model (HIRLAM) and the physics package cycle
CY33r1 of the European Centre for Medium-Range Weather Forecasts Integrated
Forecast System . It also includes a multilayer snow
module that simulates melt, liquid-water percolation and retention,
refreezing and run-off , and accounts for dry snow
densification following . RACMO2 implements an albedo
scheme that calculates snow albedo based on prognostic snow grain size, cloud
optical thickness, solar zenith angle and impurity concentration in snow
. In RACMO2, impurity concentration, i.e. soot, is
prescribed as constant in time and space. The model also simulates drifting
snow erosion and sublimation following . Previously,
RACMO2 has been used to reconstruct the contemporary SMB of the Greenland ice
sheet and peripheral ice caps
, the Canadian Arctic Archipelago
, Patagonia and Antarctica
.
Surface energy budget and surface mass balance
In RACMO2, the skin temperature (Tskin) of snow and ice is derived by closing
the surface energy budget (SEB), using the linearised dependencies of all
fluxes to Tskin and further assuming, as a first approximate, that no melt
occurs at the surface (M=0). If the obtained Tskin exceeds the melting
point, Tskin is set to 0 ∘C; all fluxes are then recalculated and
the melt energy flux (M>0) is estimated by closing the SEB in Eq. (1),
assuming that no solar radiation can directly penetrate the snow or ice
interface.
M=SWd-SWu+LWd-LWu+SHF+LHF+Gs=SWn+LWn+SHF+LHF+Gs,
where SWd and SWu are the shortwave down-/upward
radiation fluxes, LWd and LWu are the longwave
down-/upward radiation fluxes, SHF and LHF are the net sensible and latent
turbulent heat fluxes, and Gs is the subsurface heat flux.
SWn and LWn are the net short-/longwave radiation at
the surface. All fluxes are expressed in W m-2 and are defined
positive.
In the percolation zone of the GrIS, liquid-water mass from melt (ME) and
rainfall (RA) can percolate through the firn column, and is either retained
by capillary forces as irreducible water (RT) or refreezes (RF). Combined
with dry snow densification, this progressively depletes firn pore space
until the entire column turns into ice (900 kg m-3). The fraction not
retained is assumed to immediately run-off (RU) to the ocean:
RU=ME+RA-RT-RF.
The climatic mass balance , hereafter referred to as SMB, is
estimated as
SMB=Ptot-RU-SUtot-ERds,
where Ptot is the total amount of precipitation, i.e. solid and
liquid, RU is meltwater run-off, SUtot is the total sublimation
from drifting snow and surface processes, and ERds is the erosion
by the process of drifting snow. All SMB components are expressed in millimetre water equivalent
(mm w.e.) for point-specific SMB values, or in Gt yr-1
when integrated over the GrIS.
Model updates
In the cloud scheme, parameters controlling precipitation formation have been
modified to reduce the negative snowfall bias in the GrIS interior
(∼ 40 mm w.e. yr-1) . To correct for this, the
critical cloud content (lcrit) governing the onset of effective
precipitation formation for liquid-mixed and ice clouds has been increased by
factors of 2 (Eqs. 5.35 and 6.39 in ) and 5 (Eq. 6.42 in
). As a result, moisture transport is
prolonged to higher elevations and precipitation is generated further inland.
The values of lcrit adopted in RACMO2 were obtained after
conducting a series of sensitivity experiments, i.e. 1-year simulations, to
test the dependence of precipitation formation efficiency, spatial
distribution and cloud moisture content on lcrit and other cloud
tuning parameters. From these experiments, we found a linear relationship
between lcrit for mixed and ice clouds, the vertical integrated
cloud content, i.e. liquid and ice water paths that also affect the SEB
through changes in cloud optical thickness, and the integrated precipitation
over Greenland. These new settings were then tested for a longer period and
proved to almost cancel the dry bias observed in RACMO2.3p1 (see Sect. 5.1).
This led to larger but realistic vertical integrated cloud content and did
not strongly affect the SEB and surface climate of the GrIS. For instance,
the induced changes of surface downward shortwave and longwave radiation are
only about -4 and 7 W m-2, peaking in central
Greenland. While the obtained increase in lcrit is relatively
large, especially for ice clouds, it is important to note that it is also
strongly adjusted in the original ECMWF physics compared to commonly used
values in the literature: e.g. set lcrit to
1×10-3 kg kg-1 for ice clouds, while the ECMWF physics, tuned
for GCM sized grid cells, uses a value of 0.3×10-4 kg kg-1. As
lcrit depends on model grid resolution, i.e. GCMs running at
lower spatial resolution require lower values of lcrit, the use of a larger lcrit, e.g. for ice
clouds (1.5×10-4 kg kg-1) in RACMO2, is deemed reasonable.
In addition, this value remains well within the range of values previously
presented in the literature .
Furthermore, the previous model version overestimated snowmelt in the
percolation zone of the GrIS . With the aim of minimising this
bias, the following parameters have been tuned in the snow module:
The model soot concentration, accounting for dust and black carbon
impurities deposited on snow, has been reduced from 0.1 to 0.05 ppmv, more
representative of observed values . A lower soot
concentration yields a higher surface albedo; hence melt decreases
.
The size of refrozen snow grains has been reduced from 2 to 1 mm .
Consequently, the surface albedo of refrozen snow increases, as smaller particles enhance
scattering of solar radiation back to the atmosphere .
In previous model versions, the albedo of superimposed ice, i.e. the
frozen crust forming at the firn surface, was set equal to the albedo of bare
ice (∼ 0.55), underestimating surface albedo and hence overestimating
melt. The snow albedo scheme now explicitly calculates the albedo of
superimposed ice layers (∼ 0.75), following .
The saltation coefficient of drifting snow has been approximately
halved from 0.385 to 0.190 . Saltation occurs when near-surface
wind speed is sufficiently high to lift snow grains from the surface.
In RACMO2, this coefficient determines the depth of the saltation layer, i.e.
typically extending 0 to 10 cm above the surface, that directly controls the
mass of drifting snow transported in the suspension layer aloft (above
10 cm). This revision does not affect the timing and frequency of drifting
snow events, which are well modelled , but
only reduces the horizontal drifting snow transport and sublimation,
preventing a too-early exposure of bare ice during the melt season,
especially in the dry and windy north-eastern GrIS (Sect. 4.2).
Initialisation and set-up
To enable a direct comparison with previous runs, RACMO2.3p2 is run at an
11 km horizontal resolution for the period 1958–2016, and is forced at its
lateral boundaries by ERA-40 (1958–1978) and ERA-Interim
(1979–2016) re-analyses on a 6-hourly basis over the model
domain shown in Fig. . The forcing consists of temperature,
specific humidity, pressure, wind speed and direction being prescribed at
each of the 40 vertical atmosphere hybrid model levels. To better capture SMB
inter-annual variability in this new model version, upper atmosphere
relaxation (UAR or nudging) of temperature and wind fields is applied every
6 h for model atmospheric levels above 600 hPa, i.e. ∼ 4 km a.s.l.
. UAR is not applied to atmospheric humidity fields in order
not to alter clouds and precipitation formation in RACMO2. As the model does
not incorporate a dedicated ocean module, sea surface temperature and sea ice
cover are prescribed from the re-analyses . The
model has about 40 active snow layers that are initialised in September 1957
using estimates of temperature and density profiles derived from the offline
IMAU Firn Densification Model (IMAU-FDM) . These
profiles are obtained by repeatedly running IMAU-FDM over 1960–1979 forced
by the outputs of the previous RACMO2.3p1 climate simulation until the firn
column reaches an equilibrium. The data spanning the winter season up to
December 1957 serve as an additional spin-up for the snowpack and are
therefore discarded in the present study.
SMB (mm w.e. yr-1) modelled by RACMO2.3p2 at 11 km
resolution for 2016. Black dots delineate the relaxation zone (24 grid cells)
where the model is forced by ERA re-analyses. Ablation sites (213) are
displayed as yellow dots, accumulation sites (182) as white dots, and AWS
locations (23) are represented in green.
Relative to previous versions, the integration domain extends further to the
west, north and east (Fig. ). This brings the northernmost sectors
of the Canadian Arctic Archipelago and Svalbard well inside the domain
interior and further away from the lateral boundary relaxation zone (24 grid
cells, black dots in Fig. ). In addition, RACMO2.3p2 utilises the
90 m Greenland Ice Mapping Project (GIMP) digital elevation model (DEM)
to better represent the glacier outlines and the surface
topography of the GrIS. Compared to the previous model version, which used
the 5 km DEM presented in , the GrIS area is reduced by
10 000 km2 (Fig. a). This mainly results from an improved
partitioning between the ice sheet and peripheral ice caps, for which the
ice-covered area has, in equal amounts, decreased and increased,
respectively. In RACMO2, a grid cell with an ice fraction ≥0.5 is
considered fully ice covered. The updated topography shows significant
differences compared to the previous version, especially over marginal outlet
glaciers where surface elevation has considerably decreased
(Fig. b). Bare ice albedo is prescribed from the 500 m
Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day albedo version 5
product (MCD43A3v5) as the lowest 5 % surface albedo records for the
period 2000–2015 (vs. 2001–2010 in older versions; Fig. c). In
RACMO2, minimum ice albedo is set to 0.30 for dark ice in the low-lying
ablation zone, and a maximum value of 0.55 for bright ice under perennial
snow cover in the accumulation zone. In previous RACMO2 versions, bare ice
albedos of glaciated grid cells without valid MODIS estimates were set to 0.47
.
Difference in (a) ice mask (b) surface elevation
and (c) bare ice albedo between RACMO2.3p2 and RACMO2.3p1. In
panel (a), the common ice mask for both model versions is displayed
in grey, the ice sheet area is outlined in yellow; additional and removed
ice-covered cells in RACMO2.3p2 are shown in red and blue.
Observational data
To evaluate the modelled contemporary climate and SMB of the GrIS, we use
daily average meteorological records of near-surface temperature, wind speed,
relative humidity, air pressure and down-/upward short-/longwave radiative
fluxes, retrieved from 23 AWSs for the period 2004–2016 (green dots in
Fig. ). Erroneous radiation measurements, e.g. caused by sensor
riming, were discarded by removing daily records showing SWdbias>6σbias, where SWdbias is the difference between
daily modelled and observed SWd, and σbias is the
standard deviation of the daily SWd bias for all measurements. In
addition, measurements affected by sensor heating in summer, i.e. showing
LWu>318 W m-2, were eliminated as these values represent
Ts>0∘C for ϵ≈0.99, where
Ts is the surface temperature and ϵ the selected
emissivity of snow or ice. We only used daily records that were
simultaneously available for each of the four radiative components. Eighteen
of these AWS sites are operated as part of the Programme for Monitoring of
the Greenland Ice Sheet (PROMICE, www.promice.dk) covering the period
2007–2016 . Four other AWS sites, namely S5, S6, S9 and S10
(2004–2016), are located along the K-transect in south-western Greenland
(67∘ N, 47–50∘ W) . Another AWS
(2014–2016) is situated in south-eastern Greenland (66∘ N;
33∘ W) at a firn aquifer site . The
latter five sites are operated by the Institute for Marine and Atmospheric
research at Utrecht University (IMAU).
We also use in situ SMB measurements collected at 213 stake sites in the GrIS
ablation zone (yellow dots in Fig. ; ) and at
182 sites in the accumulation zone (white dots in Fig. ) including
snow pits, firn cores , and airborne radar
measurements . We exclusively selected measurements that
temporally overlap with the model simulation (1958–2016). To match the
observational period, daily modelled SMB is cumulated for the exact number of
measuring days at each site.
For model evaluation, we select the grid cell nearest to the observation site
in the accumulation zone. In the ablation zone, an additional altitude
correction is applied by selecting the model grid cell with the smallest
elevation bias among the nearest grid cell and its eight adjacent neighbours.
One ablation site and seven PROMICE AWS sites presented an elevation bias in
excess of > 100 m compared to the model topography and were discarded
from the comparison.
In addition, we compare modelled SMB with annual glacial ice discharge (D)
retrieved from the combined Zachariae Isstrøm and Nioghalvfjerdsbrae
glacier catchments in north-eastern Greenland (1975–2015; yellow line in
Fig. a), presented in .
Comparison between modelled (RACMO2.3p2, 2004–2016) and observed
(a) 2 m temperature (T2m, ∘C),
(b) 2 m specific humidity (q2m, g kg-1),
(c) 10 m wind speed (w10m, m s-1) and
(d) surface pressure (Psurf, hPa) collected at 23 AWSs
(green dots in Fig. ). For each variable, the linear regression
including all records is displayed as a red dashed line. Statistics including
number of records (N), regression slope (b0) and intercept (b1),
determination coefficient (R2), bias and RMSE are listed for each
variable.
Results: near-surface climate and SEB
We evaluate the modelled present-day near-surface climate of the GrIS in
RACMO2.3p2 using data from 23 AWS sites (see Sect. 2.5). Then, we discuss in
more detail the model performance at four AWSs along the K-transect and compare
RACMO2.3p2 outputs to those of RACMO2.3p1.
Near-surface meteorology
Figure compares daily mean values of 2 m temperature, 2 m
specific humidity, 10 m wind speed, and air pressure collected at 23 AWS
sites with RACMO2.3p2 output. The modelled 2 m temperature is in good
agreement with observations (R2=0.95) and with a RMSE of
∼ 2.4∘C and a small cold bias of ∼ 0.1∘C
(Fig. a). As specific humidity is not directly measured at AWS
sites, it is calculated from measured temperature, pressure and relative
humidity following . The obtained 2 m specific humidity is
accurately reproduced in the model (R2=0.95) with a RMSE
∼ 0.35 g kg-1 and a negative bias of 0.13 g kg-1
(Fig. b). The same holds for daily records of 10 m wind speed
(R2=0.68; Fig. c), with the model exhibiting a small
negative bias and RMSE of ∼ 2 m s-1. Surface pressure is also
well represented (R2=0.99) with a small negative bias of 0.8 hPa and
RMSE <6 hPa (Fig. d). A systematic pressure bias at some
stations results from the (uncorrected) elevation difference with respect to
the model, which can be as large as 100 m. To provide some regional insight
into the model performance, Supplement Table S1 and Figs. S1–S4 compare
modelled meteorological data from RACMO2.3p2 with AWS measurements (green
dots in Fig. ) clustered in four sectors of the GrIS, i.e. NW, NE,
SW and SE. These sectors correspond to the four quadrants
delimited by 40∘ W longitude and 70∘ N latitude. These regional scatter plots unambiguously show that RACMO2.3p2
performs equally well in each of these four sectors of the GrIS.
Difference between daily modelled RACMO2.3p1 (2004–2015) or
RACMO2.3p2 (2004–2016) and observed meteorological data and SEB components
collected at 23 PROMICE AWSs (green dots in Fig. ). Statistics
include model bias (RACMO2.3pX – observations), RMSE of the bias as well as
the determination coefficient of daily mean data. All fluxes are set to
positive.
Table and Fig. S5 compare the agreement of RACMO2.3p2 and version
2.3p1 with in situ measurements. We find an overall improvement in the
updated model version, showing a smaller bias and RMSE as well as an
increased variance explained. Notably, the remaining negative bias in 2 m
temperature (Fig. S5a) and the systematic dry bias (Fig. S5b) in RACMO2.3p1
have almost vanished in the updated model version (Figs. a and b).
Comparison between daily average modelled (RACMO2.3p2, 2004–2016)
and observed (a) shortwave downward, (b) shortwave upward,
(c) longwave downward and (d) longwave upward radiation
(W m-2) collected at 23 AWSs (green dots in Fig. ). For each
variable, a regression including all records is displayed as a red dashed line.
Statistics including number of records (N), the linear regression slope
(b0) and intercept (b1), determination coefficient (R2), bias and RMSE
are listed for each variable.
Radiative fluxes
Figure shows scatter plots of modelled and measured daily mean
radiative fluxes, i.e. short-/longwave down-/upward radiation. Radiative fluxes
are also well reproduced by the model with R2 ranging from 0.83 for
LWd to 0.95 for SWd (Fig. ), showing
relatively small biases of -7.1 and 3.8 W m-2, and RMSEs of 21.2 and
27.1 W m-2. The negative biases in LWd and
2 m temperature partly lead to an LWu underestimation of
4.4 W m-2 with a small RMSE of 12.1 W m-2. In combination with
a positive bias in SWd this suggests an underestimation of cloud cover
in the ice sheet marginal regions, where most stations are located. The
larger biases and RMSEs in SWu of 6.8 and 32.1 W m-2
can be ascribed to overestimated surface albedo, especially
during summer snowfall episodes, when a bright fresh snow cover is deposited
over bare ice. In RACMO2, precipitation falls vertically, i.e. no horizontal
transport is allowed, and is assumed to be instantly deposited at the
surface. Consequently, the spatial distribution of summer snow patches may be
locally inaccurate, resulting in large albedo discrepancies when compared to
point albedo measurements. Note that these AWS radiation measurements are
also prone to potentially large uncertainties due to preferred location on
ice hills, sensor tilt, riming and snow/rain deposition on the instruments,
leading to spurious albedo and SWu data , e.g. the
upper-left dots in Fig. b. By clustering AWS measurements within four
sectors of the GrIS (Figs. S6–S9 and Table S1), RACMO2.3p2 shows good and
equivalent agreement in NW, NE, SW and SE Greenland.
Compared to the previous model version (Table ), changes in the
cloud scheme have significantly improved the representation of
LWd (Figs. c and S10c), showing a reduced negative bias
and RMSE. These modifications have also somewhat decreased the positive bias
in SWd (Fig. a) relative to RACMO2.3p1 (Fig. S10a). In
addition, LWu is notably improved in RACMO2.3p2: the remaining
negative bias in LWu has almost vanished (Figs. d and
S10d). This can be partly explained by the much better resolved 2 m
temperature in RACMO2.3p2.
Seasonal SEB cycle along the K-transect
The K-transect comprises four AWS sites located in different regions of the
GrIS: S5 and S6 are installed in the lower and upper ablation zones,
respectively, S9 is situated close to the equilibrium line and S10 is in the
accumulation zone. Figure shows monthly mean modelled (continuous
lines, RACMO2.3p2) and observed (dashed lines) SEB components, i.e. net
short-/longwave radiation (SWn/LWn), latent and
sensible heat fluxes (LHF and SHF, respectively), surface albedo and melt measured at these
four AWS sites for the period 2004–2015. Tables – list
statistics calculated at each individual AWS and for the two model versions.
Modelled and observed mean SEB components and statistics of the
differences (2004–2015) at station S5 in the lower ablation zone
(490 m a.s.l.). Statistics include means of measurements collected at S5,
model bias (RACMO2.3pX – observations), RMSE of the bias as well as the
determination coefficient of monthly mean data. All fluxes are set
to positive.
Modelled and observed mean SEB components and statistics of the
differences (2004–2015) at station S6 in the upper ablation zone
(1010 m a.s.l.). Statistics include means of measurements collected at S6,
model bias (RACMO2.3pX – observations), RMSE of the bias as well as the
determination coefficient of monthly mean data. All fluxes are set
to positive.
Modelled and observed mean SEB components and statistics of the
differences (2009–2015) at station S9 close to the equilibrium line
(1520 m a.s.l.). Statistics include means of measurements collected at S9,
model bias (RACMO2.3pX – observations), RMSE of the bias as well as the
determination coefficient of monthly mean data. All fluxes are set
to positive.
Modelled and observed mean SEB components and statistics of the
differences (2010–2015) at station S10 in the accumulation zone
(1850 m a.s.l.). Statistics include means of measurements collected at S10,
model bias (RACMO2.3pX – observations), RMSE of the bias as well as the
determination coefficient of monthly mean data. All fluxes are set
to positive.
Observed and modelled (RACMO2.3p2) monthly mean
(a) turbulent and net shortwave/longwave fluxes (W m-2) and
(b) surface albedo and surface melt energy (W m-2) at site S5
for 2004–2015. Similar results are shown at S6 for
2004–2015 (c, d), S9 for 2009–2015 (e, f) and S10 for
2010–2015 (g, h).
Low ablation zone
At station S5 (490 m a.s.l.), surface melt is well reproduced in
RACMO2.3p2, with a small negative bias of 0.4 W m-2 (Table ;
Fig. b). However, this good agreement results from significant
error compensation between overestimated SWn (bias of
16.2 W m-2) and underestimated SHF in summer (15.3 W m-2;
Fig. a). The bias in SWn is mostly driven by
overestimated SWd (20.7 W m-2; Table ) and to a
lesser extent by underestimated SWu (4.5 W m-2), resulting
from underestimated cloud cover and ice albedo (Fig. b),
respectively. AWSs are often installed on snow-covered promontories, i.e.
hummocks, that maintain higher albedo in summer (∼ 0.55) than their
surroundings where impurities collect. Mixed reflectance from bright ice
cover (∼ 0.55) and neighbouring darker tundra, exposed nunataks or
meltwater ponds (< 0.30), located within the same MODIS grid cell, likely
explains this underestimation. Another explanation stems from the
deterioration of MODIS sensors in time, resulting in underestimated surface
albedo records for the MCD43A3v5 product .
LWn is well reproduced in the model due to similar negative
biases in LWd and LWu (∼ 12 W m-2),
again indicating underestimated cloud cover. The large negative bias in SHF
is attributed to an inaccurate representation of surface roughness in the
lowest sectors of the ablation zone. show that observed
surface roughness for momentum has a high temporal variability at site S5,
with a minimum of 0.1 mm in winter, when a smooth snow layer covers the
rugged ice sheet topography, and a peak in summer (up to 50 mm), when
melting snow exposes hummocky ice at the surface. In RACMO2, surface
aerodynamic roughness is prescribed at 1 mm for snow-covered grid cells and
at 5 mm for bare ice, hence significantly underestimating values over ice in
summer and thus causing too-low SHF . This bias in SHF at
S5 is also partly ascribable to too-cold conditions (2 ∘C). Although
not negligible, LHF contributes little to the energy budget and shows a
positive bias of 3.4 W m-2, notably in winter.
Upper ablation zone
Station S6 is located at 1010 m a.s.l. in the GrIS upper ablation zone.
There, summer melt is overestimated by ∼ 8 W m-2 owing to both
too-high SWn and SHF (9.8 and 7 W m-2;
Fig. c and Table ). As for S5, the bias in
SWn results from overestimated SWd (6 W m-2)
and underestimated SWu (3.8 W m-2). At the AWS location,
surface albedo progressively declines from 0.60 to ∼ 0.40 when bare ice
is exposed in late summer, whereas RACMO2.3p2 simulates bare ice at the
surface throughout summer, with an albedo of 0.40. As a result, modelled
surface albedo is systematically underestimated in summer, especially in July
(Fig. d). Likewise, a small negative bias in LWn
(2.3 W m-2) is obtained as LWd and LWu are
both slightly underestimated (Table ). Here, 2 m temperature is on
average 0.7 ∘C too high, causing SHF to be overestimated
(7 W m-2).
Equilibrium line
Close to the equilibrium line, RACMO2.3p2 slightly underestimates summer melt
(2.4 W m-2; Fig. f and Table ). At station S9
(1520 m a.s.l.), a perennial snow cover maintains a minimum albedo of 0.65
in summer, i.e. when melt wets the snow. A small positive bias in modelled
snow albedo (0.03) combined with a slightly underestimated SWd
(1.5 W m-2) leads to an overestimated SWu
(3.5 W m-2), hence underestimating SWn (5 W m-2).
Although LWd is underestimated by 3.1 W m-2 and
LWu is overestimated by 0.5 W m-2, especially in winter,
LWn agrees well with the measurements. The 2 m surface temperature
shows a 0.5 ∘C positive bias, in turn causing a slightly too-large SHF
(5.2 W m-2; Fig. e and Table ).
Accumulation zone
All SEB components are well reproduced at site S10 (1850 m a.s.l.).
Compensation of minor errors between underestimated SWd and
SWu (∼ 2 W m-2) provides good agreement with
observed SWn (Fig. g and Table ). Modelled
surface albedo also compares well with the measurements, with only a small
positive bias (0.01; Fig. h). LWn is underestimated by
∼ 9 W m-2; this is mainly driven by a too-low LWd
and a too-large LWu (Table ). The turbulent fluxes are
well captured, although a significant positive bias in SHF persists
(∼ 5 W m-2), especially in winter when LWd is
underestimated. As biases in SHF and LWd are almost equal,
modelled melt matches well with observations despite a small negative bias
(∼ 0.2 W m-2).
Model comparison along the K-transect
Tables – compare statistics of SEB components between
RACMO2.3p2 and 2.3p1. Although differences are relatively small, the new
model formulation shows general improvements. The increased cloud cover over
the GrIS reduced the biases in SWd and LWd.
Improvements in the representation of turbulent fluxes is partly attributed
to the new topography prescribed in RACMO2.3p2 and the better resolved
SWd/LWd, although significant biases remain at all
stations.
At site S5 located in the low ablation zone (Table ), smaller
SWd and lower ice albedo significantly reduce the SWu
bias in RACMO2.3p2, and enhanced LWd decreases the negative bias
in LWu. As a result, melt increases substantially, reducing the
negative bias compared to version 2.3p1. Note that SWd remains
overestimated in RACMO2.3p2. This is compensated by underestimated SHF, i.e.
partly caused by underestimated LWd, providing realistic surface
melt. In the upper ablation zone, similar improvements are obtained at site
S6 (Table ). At site S6, all SEB components show smaller biases
except for SWu, as underestimated surface albedo increases the
negative SWu bias.
Above the equilibrium line, enhanced cloud cover also reduces the SW and LW
biases at sites S9 and S10 (Tables and ). However,
surface albedo overestimation in RACMO2.3p2 causes a small increase in melt
underestimation.
Results: regional SMB
In Sect. 3, we discussed the overall good ability of RACMO2.3p2 to reproduce
the contemporary climate of the GrIS, which is essential for estimating
realistic SMB patterns. Here we compare SMB from RACMO2.3p2 and RACMO2.3p1
over the GrIS. For further evaluation, we focus on three regions where there
are large differences in SMB between the two versions.
Changes in SMB patterns
Figure a shows SMB from RACMO2.3p2 for the overlapping model period
1958–2015. Differences with the previous version 2.3p1 are shown in
Fig. b and the changes in individual SMB components are depicted
in Fig. . Owing to the modifications in the cloud scheme, clouds
are sustained at higher elevations, enhancing precipitation further inland,
while it decreases in low-lying regions. Changes are especially large in
south-eastern Greenland where the decrease locally exceeds
300 mm w.e. yr-1. Precipitation in the interior increases by up to
50 mm w.e. yr-1 (Fig. a). This pattern of change is clearly
recognisable in the SMB difference (Fig. b). In addition, the
shallower saltation layer in the revised drifting snow scheme is responsible
for reduced sublimation (∼ 50 mm w.e. yr-1; Fig. b)
that reinforces the overall increase in SMB (Fig. b). Although
drifting snow erosion changes locally, patterns are heterogeneous and the
changes remain small when integrated over the GrIS (Fig. c). This
process has only a limited contribution to SMB (∼ 1 Gt yr-1)
resulting from drifting snow being transported away from the ice sheet
towards the ice-free tundra and ocean.
(a) SMB (mm w.e. yr-1) averaged for the period
1958–2015. The combined Zachariae Isstrøm and Nioghalvfjerdsbrae
(79∘ N) glacier basins are delineated by
the yellow line. Yellow dots locate the K-transect measurement sites in
western Greenland and the single AWS operated in south-eastern Greenland.
(b) SMB difference (mm w.e. yr-1) between RACMO2.3p2 and
RACMO2.3p1 for the period 1958–2015. Areas showing significant difference
are stippled in panel (b): difference exceeds 1 standard
deviation of the difference between the two model versions.
Difference in SMB components (mm w.e. yr-1) between
RACMO2.3p2 and RACMO2.3p1 averaged for the period 1958–2015. Areas showing
significant difference are stippled: the difference exceeds 1
standard deviation of the difference between the two model
versions.
(a) Modelled basin-integrated SMB in RACMO2.3p2 (blue dots)
and RACMO2.3p1 (red dots) and ice discharge estimates (black dots,
) from the glacier basins of Zachariae Isstrøm and
Nioghalvfjerdsbrae (79∘ N) in
north-eastern
Greenland (yellow line in b and c) for the period
1975–2015. Dashed lines represent average SMB for 1975–2001. Mean SMB as
modelled by (b) RACMO2.3p2 and (c) RACMO2.3p1 in
north-eastern Greenland for the period 1958–2015.
In the percolation zone, the decrease in run-off (Fig. d) is
governed by reduced surface melt (Fig. e), mostly resulting from
the smaller grain size of refrozen snow and the lower soot concentration in
snow that have increased surface albedo (not shown), further increasing SMB
(Fig. b). In western and north-eastern Greenland, this decrease in run-off
even exceeds that of melt by 50 to 100 mm w.e. yr-1, a result of
combined enhanced precipitation and reduced summer melt (delaying the
disappearance of the seasonal snow cover) that increased the snow refreezing
capacity (Fig. f). At higher elevations, the decrease in refreezing
is exclusively driven by melt reduction (Fig. e and f), while at
the extreme margins of the GrIS, the lower ice albedo used in RACMO2.3p2
(Fig. c) locally increases run-off (Fig. d), in turn
decreasing SMB (Fig. b).
North-eastern Greenland
For north-eastern Greenland's two main glaciers, Zachariae Isstrøm and
Nioghalvfjerdsbrae (79∘ N glacier; yellow
line in Fig. a), solid ice discharge (D) estimates are available
for the period 1975–2015 . Assuming that this glacier
catchment draining of ∼ 12 % of the GrIS area remained in approximate
balance until ∼ 2000 , i.e. D ≈ SMB,
measurements of D at the grounding line of these marine-terminating glaciers
can be used to evaluate modelled SMB.
In these two catchments, model updates significantly improve the
representation of SMB, which was substantially underestimated in the previous
version. Figure a compares ice discharge (black dots) with modelled
SMB (RACMO2.3p2 as blue dots and 2.3p1 in red) integrated over the two
glacier basins for 1958–2015. In a balanced system, i.e. before discharge
accelerated in 2001, SMB equals ice discharge. Averaged over 1975–2001,
modelled SMB in RACMO2.3p2 (20.5 Gt yr-1) is similar to the estimated
glacial discharge of 21.2 Gt yr-1, significantly improving upon
version 2.3p1 (15.8 Gt yr-1). The negative bias in RACMO2.3p2
(0.7 Gt yr-1; dashed blue line) is reduced by almost a factor of 8
relative to the previous version (5.4 Gt yr-1) and SMB now equals
discharge within the uncertainty. However, it is important to note that,
while good agreement is obtained between averaged SMB and D before 2001,
suggesting a glacier catchment in approximate balance as in
, this does not necessarily confirm that spatial and
temporal variability of north-eastern Greenland SMB is accurately resolved by the
model. Averaging over 2001–2015 showed that basin mass loss accelerated due to enhanced
surface run-off, decreasing SMB by 4.2 Gt yr-1, and increasing ice
discharge (2.8 Gt yr-1).
Figures b and c show mean SMB for 1958–2015 as modelled by
RACMO2.3p2 and 2.3p1. In the percolation zone, the difference
between the two model versions primarily results from the smaller refrozen
snow grain size that reduces melt and run-off through increased surface albedo
in RACMO2.3p2. To a smaller extent, reduced soot concentration delays the
onset of melt in summer. In the ablation zone, snow cover persists longer
before bare ice is exposed in late summer, in turn reducing run-off
(Fig. d). Superimposed onto this, precipitation has increased over
the whole glacier basin (Fig. a), allowing for enhanced refreezing
in snow (Fig. f) and increasing SMB by 4.7 Gt yr-1 in
RACMO2.3p2 (Fig. b). Note the large inter-annual variability in
modelled SMB showing maximum and minimum values of approximately 30 and
8.5 Gt yr-1 in RACMO2.3p2 vs. 25 and 0 Gt yr-1 in the previous
version and stressing the importance of accurately modelling individual SMB
components. In this dry region, underestimation of snowfall accumulation in
RACMO2.3p1 initiated a pronounced feedback decreasing SMB: active drifting
snow processes erode the shallow snow cover, exposing bare ice prematurely
and moving the equilibrium line too far inland (Fig. b and c).
(a) Observed and simulated SMB (m w.e. yr-1) along
the K-transect in western Greenland (67∘ N), averaged for the period
1991–2015. The observed SMBs (grey dots) at S4, S5, SHR, S6, S7, S8, S9 and
S10 are based on annual stake measurements; S10 observations cover
1994–2015. The coloured bars represent the standard deviation (1σ)
around the 1991–2015 modelled and observed mean value. Modelled SMB at stake
sites are displayed for RACMO2.3p2 (blue dots) and RACMO2.3p1 (red dots).
Panel (b) shows time series of modelled (continuous lines) and
observed (dashed lines) annual SMB at stakes S4, SHR, S7 and S8 for the
period 1991–2016. Similar time series are shown for S5, S6, S9 and S10 in
panel (c). At S10, modelled SMB is estimated as the difference
between total precipitation and melt.
K-transect
The K-transect in south-western Greenland consists of eight stake sites where SMB
is measured annually (yellow dots in Fig. a)
. Figure a compares modelled (RACMO2.3p2
as blue dots and RACMO2.3p1 in red) with observed SMB (black dots) along the
transect, averaged for the period 1991–2015. Using mean annual SMB at each
station, the updated model shows a decreased RMSE from 606 mm w.e. in
RACMO2.3p1 to 424 mm w.e. in version 2.3p2, and reduced bias from -133 to
-54 mm w.e., and an increased R2 from 0.92 to 0.97. In the low
ablation zone (< 600 m a.s.l.), the lower ice albedo increases run-off in
summer, locally reducing SMB. Decreased run-off in the upper ablation zone,
i.e. between 600 and 1500 m a.s.l., increases SMB, improving the agreement
at all sites except SHR. A negative bias in SMB remains at site S6 where ice
albedo in summer (0.45 in July) is underestimated by up to 0.1
(Fig. d). Above the equilibrium line (> 1500 m a.s.l.), in
situ stake SMB measurements systematically underestimate climatic SMB, as
they do not or only partly account for internal accumulation, i.e. refreezing
in the firn. For comparison at S10, we therefore use the difference between
modelled total precipitation and melt instead of SMB, decreasing the bias by
260 to -40 mm w.e. yr-1 and the RMSE by 200 to
210 mm w.e. yr-1. Measured and modelled SMB-to-elevation gradients
are estimated using a linear regression: 3.21 mm w.e. m-1 from
the observations, 2.62 mm w.e. m-1 in RACMO2.3p1, and
3.16 mm w.e. m-1 in RACMO2.3p2, indicating a notable improvement in
model performance along the K-transect.
Figures b and c show time series of measured (dashed lines) and
modelled SMB (continuous lines; RACMO2.3p2) at each site along the K-transect
for the period 1991–2016. The model realistically captures inter-annual
variability in the SMB signal, although substantial biases remain at stations
SHR and S6 (Table ).
Modelled and observed mean annual SMB (m w.e. yr-1) and
statistics of the differences at S4, S5, SHR, S6, S7, S8 and S9 over
1991–2015; measurements at S10 are compared to modelled total precipitation
minus melt for the period 1994–2015. Spatial coordinates of each site are
listed.
South-eastern Greenland experiences topographically forced precipitation maxima
in winter, followed by high melt rates in summer, allowing for the formation
of perennial firn aquifers . In April 2014, an
AWS was installed in the aquifer zone of the south-eastern GrIS (yellow dot in
Fig. a). In August 2015, the AWS was relocated from 1563 m a.s.l
(66.18∘ N and 39.04∘ W) to 1663 m a.s.l
(66.36∘ N and 39.31∘ W). Figure shows time
series of snow albedo and cumulative snowmelt energy (expressed
in mm w.e.) modelled by RACMO2.3p2 (blue lines) and RACMO2.3p1 (red lines),
and calculated from the AWS data (grey lines) for the summer of 2014. The
comparison is limited to 2014 because of a 3-month data gap in summer 2015.
Time series of (a) daily snow albedo, and
(b) cumulative surface melt (mm w.e. day-1) modelled by
RACMO2.3p2 (blue lines), RACMO2.3p1 (red lines) and measured (grey lines) at
the south-eastern AWS (66∘ N; 33∘ W; 1563 m a.s.l.) during
summer 2014.
Comparison between (a) modelled, i.e. RACMO2.3p2 (blue) and
RACMO2.3p1 (red) at 11 km, and observed SMB (m w.e. yr-1) collected
in the GrIS accumulation zone (white dots in Fig. ). Regressions
for RACMO2.3p2 (blue) and version 2.3p1 (red) are displayed as dashed lines.
Comparison between SMB measurements from the GrIS ablation zone (yellow dots
in Fig. ) and (b) original RACMO2.3p2 data at 11 km,
(c) downscaled product at 1 km. Orange stars correspond to
measurements collected at station QAS_L at the southern tip of Greenland.
Regression including all records is displayed as orange dashed line in
panels (b) and (c). Main statistics including number of
records (N), regression slope (b0) and intercept (b1), determination
coefficient (R2), bias and RMSE are listed for each graph.
As melt wets the snow in summer, surface albedo gradually decreases from
values typical for dry fresh snow (0.85) to wet old snow (∼ 0.75) in
late summer, before sharply increasing again when a new fresh snow cover is
deposited (grey line in Fig. a). In the previous model version,
surface albedo could drop to values as low as ∼ 0.66 in summer (JJA),
e.g. days 152 to 243, underestimating albedo by 0.04 on average. The bias is
reduced to 0.01 in RACMO2.3p2 as combined lower soot concentration and
decreased grain size of refrozen snow increase the surface albedo. The
remaining small negative bias is mostly ascribable to a too-rapid snow
metamorphism from fresh to old snow that leads to a premature drop in surface
albedo, e.g. days 140 to 160. Sporadic fresh snow deposition over older snow,
characterised by sharp peaks in surface albedo during summer, is well timed
by the model. Consequently, the cumulative melt obtained at the end of summer
(702 mm w.e.; blue line in Fig. b) is reduced by
∼ 100 mm w.e. relative to RACMO2.3p1 (red line), a significant
improvement when compared to the observations (639 mm w.e.; grey line).
Results: SMB of the contiguous ice sheetModelled SMB at 11 km
In Fig. , we evaluate modelled SMB in RACMO2.3p2 using 182
measurements collected in the GrIS accumulation zone (white dots in
Fig. ) and 1073 stake observations from 213 sites located in the
ablation zone (yellow dots in Fig. ). The increased precipitation
in the GrIS interior reduces the negative bias in the 11 km product (blue
dots in Fig. a) compared to the previous model version (red dots
in Fig. a). For the full data set, a significant bias of
-22 mm w.e. yr-1 and RMSE of 72 mm w.e. yr-1 remain in
RACMO2.3p2. Sites experiencing the highest precipitation rates on the steep
slopes of south-eastern Greenland (> 0.5 m w.e. yr-1) primarily
contribute to this bias. If only values < 0.5 m w.e. yr-1 are
considered (156 measurements), the bias and RMSE decrease from -26 and
52 mm w.e. yr-1 in RACMO2.3p1 to only -7 and
49 mm w.e. yr-1 in RACMO2.3p2. In the ablation zone
(Fig. b), the updated model performs as well as the previous
version, i.e. with a bias of 1.20 m w.e. yr-1 and RMSE of
0.47 m w.e. yr-1, although SMB remains overestimated
in the lower sectors, caused by inaccurately resolved steep slopes, low ice
albedo and relatively large turbulent fluxes at the GrIS margins, which
require further downscaling (see Sect. 5.2).
Integrated over the GrIS, modelled SMB has increased by 66 Gt yr-1
(415 Gt yr-1; +19 %) compared to the previous version. This
difference is dominated by a significant increase in SMB in the percolation
zone of the GrIS, driven by reduced meltwater run-off (61 Gt yr-1 or
-22 %) and reduced sublimation (10 Gt yr-1 or -24 %),
while precipitation decreased by less than 1 % (5 Gt yr-1); the
latter can be explained by the smaller GrIS area (∼ 10 000 km2
or 0.6 %) in the new ice mask. We deem these changes in the 11 km fields
to be realistic. For the poorly resolved marginal areas, the SMB product
requires further statistical downscaling to reproduce the high melt rates in
these rugged regions at the ice sheet margins. At 11 km resolution, run-off
is locally underestimated by up to 6 m w.e. yr-1, e.g. station
QAS_L in southern Greenland (orange stars in Fig. b).
Downscaled SMB to 1 km
To solve these issues at the margins, we apply the downscaling technique
described in , which includes elevation and ice albedo
corrections. As a result, modelled run-off increases by 82 Gt yr-1
(∼ 37 %) to 305 Gt yr-1 for the period 1958–2015, compared
to the 11 km product, and the SMB biases and RMSEs in the GrIS ablation zone
are reduced by 480 and 460 mm w.e. yr-1. The error at
QAS_L is reduced to ∼ 2 m w.e. yr-1 (orange stars in
Fig. c), i.e. biases and RMSEs of 2.21 and 2.35 m w.e. yr-1.
A major improvement upon is that no additional
precipitation correction is required here as the remaining negative bias in
the GrIS interior has been almost eliminated in RACMO2.3p2
(Fig. a). At 1 km resolution, precipitation contributes
693 Gt yr-1 to GrIS SMB. Relative to the 11 km product,
GrIS-integrated SMB at 1 km decreases by 59 Gt yr-1 (-14 %) to
356 Gt yr-1, in line with our previous estimate of 338 Gt yr-1
(+5 %) . This confirms once more that an 11 km resolution
is insufficient to resolve run-off patterns over narrow ablation zones and
small outlet glaciers, and that further downscaling is essential for
obtaining realistic GrIS SMB.
Remaining limitations and challengesModel resolution
Extensive model evaluation confirms that RACMO2.3p2 realistically reproduces
the contemporary climate and SMB of Greenland, although significant biases
remain. However, while a 11 km grid is sufficient to resolve large-scale
inland SMB patterns, it does not well resolve irregular, low-lying regions at
the GrIS margins where run-off peaks. There, the remaining issue is to
accurately resolve total run-off of meltwater from the narrow ablation zone
and small outlet glaciers. This demonstrates the need for higher-resolution
(statistically or dynamically) downscaled products, e.g. the 1 km product as
presented here, for regional mass balance studies.
An alternative approach is to carry out a dedicated Greenland simulation at
higher spatial resolution, e.g. 5.5 km . This
increase in resolution does lead to better resolved SMB gradients over
marginal glaciers, without exceeding the physics constraints of a hydrostatic
model like RACMO2. Subsequently applying the statistical downscaling
technique to this 5.5 km product would likely result in further
improvements.
Turbulent fluxes
Another model limitation stems from the turbulent fluxes scheme. While LHF
remains generally small and contributes little to the energy budget, accurate
SHF is crucial for capturing extreme melt events along the GrIS margins
, such as those that occurred in summer 2012
. However, SHF shows significant biases in RACMO2.3p2 in
low-lying regions at the GrIS margins. Improving the representation of the
GrIS surface roughness and surface elevation using higher spatial resolution
could reduce these biases.
Surface albedo
Snowmelt rate is highly sensitive to soot concentration in snow
. Although assumed to be constant in time and space in
RACMO2, show a heterogeneous distribution of impurities
(soot, dust, microbiological material) over the GrIS, with a gradual increase
towards lower elevations due to (a) the proximity of dust sources in the
tundra region and (b) downslope transport of previously deposited soot by
meltwater run-off.
Over bare ice, the accumulation of cryoconite and the growth of algae play a
major role in reducing surface albedo .
Therefore, explicitly modelling impurity concentration on ice, as described
in , could substantially improve melt estimates.
Future climate projections should include such a bio-darkening feedback
.
Conclusions
We present a detailed evaluation of the regional climate model RACMO2.3p2
(1958–2016) over the Greenland ice sheet (GrIS). The updated model generates
more inland precipitation at the expense of marginal regions, reducing the
dry bias in the GrIS interior. Impurity concentration in snow, i.e. soot, has
been decreased by a factor of 2, minimising the melt rate overestimation in
the GrIS percolation zone. We demonstrate that the model successfully
reproduces the contemporary climate of the GrIS compared to daily
meteorological records and radiative energy flux measurements from 23 AWS
sites. Apart from the ultimate margins, the model also accurately captures
the seasonal cycle of radiative and turbulent heat fluxes as well as surface
albedo along the K-transect in south-western Greenland. Compared to SMB
observations, RACMO2.3p2 generally improves on the previous version,
especially in the extensive GrIS interior. SMB improvements are also found
along the K-transect as well as in north-eastern and south-eastern Greenland. This
model version will be used for future climate scenario projections at 11 km
resolution. Nonetheless, since run-off from narrow glaciers in the GrIS
margins remains poorly resolved at this resolution, it is necessary to
further statistically downscale present-day and future SMB fields to higher
spatial resolutions for use in regional mass balance studies.
RACMO2.3p2 data at 11 km (1958–2016) and a daily
downscaled product at 1 km resolution are available from the authors without
conditions.
The Supplement related to this article is available online at https://doi.org/10.5194/tc-12-811-2018-supplement.
BN, WJB, JMW and MRB conceived this study, decided on the
new model settings and performed the analysis and synthesis of the data sets.
BN performed the model simulations and led the writing of the manuscript.
JTML, EM, PKM and LHU contributed to the development of the model. DA, SL,
CJPPS and RSWW processed and provided observational data sets. All authors
contributed to discussions on the writing of this manuscript.
The authors declare that they have no conflict of
interest.
Acknowledgements
Brice Noël, Willem Jan van de Berg, J. Melchior van Wessem, Roderik S. W.
van de Wal and Michiel R. van den Broeke acknowledge support from the Polar
Programme of the Netherlands Organization for Scientific Research (NWO/ALW)
and the Netherlands Earth System Science Centre (NESSC), as well as the
European Centre for Medium-Range Weather Forecasts (ECMWF) for hosting
simulations and providing computation time.
Edited by: Xavier Fettweis
Reviewed by: two anonymous referees
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