Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 1- Greenland (1958–2016) - Noel et al , 2018

Modelling the Climate and Surface Mass Balance of Polar Ice Sheets

Evaluation of the updated regional climate model RACMO2 (1958–2016) on the Greenland ice sheet (GrIS).
The new model version, RACMO2.3p2, incorporates updated glacier outlines, topography, and ice albedo fields.
Parameters in the cloud scheme 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.
The model better resolves the spatial patterns and temporal variability of SMB compared with the previous model version.
This new model version provides updated, high-resolution gridded fields of the GrIS present-day climate and SMB.
The model 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).
SMB is snowfall accumulation minus ablation from meltwater run-off, sublimation, and drifting snow erosion.
RCM simulations are affected by the quality of the re-analysis used as lateral forcing.
Simulations are also affected 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.
- Positive degree day (PDD) models forced by statistically downscaled re-analyses.
- Mass balance models forced by the climatological output of an RCM (HIRHAM4).
- Reconstruction of SMB obtained by combining RCM outputs with temperature and ice core accumulation measurements.
Vizcaíno et al. (2013) and Cullather et al. (2014) respectively used the Community Earth System Model (CESM) at 1 $^{\circ}$ resolution ( $\approx$ 100 km) and the Goddard Earth Observing System model version 5 (GEOS-5) at 0.5 $^{\circ}$ resolution ( $\approx$ 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.
Good RCM performance often results from compensating errors between poorly parameterised processes, e.g. cloud physics and turbulent fluxes.
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.
- 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.
The study presents 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.
The SMB of the updated model version (RACMO2.3p2) is compared with its predecessor (RACMO2.3p1) 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 and Antarctic ice sheets.

Model and Observational Data

The 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 (ECMWF-IFS).
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.
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.
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 ( $T{skin}$ ) of snow and ice is derived by closing the surface energy budget (SEB), using the linearised dependencies of all fluxes to $T{skin}$ and further assuming, as a first approximate, that no melt occurs at the surface ( $M = 0$ ).
If the obtained $T{skin}$ exceeds the melting point, $T{skin}$ is set to 0 $^{\circ}$ C; all fluxes are then recalculated and the melt energy flux (M > 0) is estimated by closing the SEB in equation (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.
  - $Gs$ 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 (Cogley et al., 2011), hereafter referred to as SMB, is estimated as:
- $SMB = P{tot} - RU - SU{tot} - ER_{ds}$
 - Where:
 - $P_{tot}$ is the total amount of precipitation, i.e. solid and liquid.
 - $RU$ is meltwater run-off.
 - $SU_{tot}$ is the total sublimation from drifting snow and surface processes.
 - $ER_{ds}$ 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 ( $l_{crit}$ ) 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 ECMWF-IFS, 2008) and 5 (Eq. 6.42 in ECMWF-IFS, 2008).
As a result, moisture transport is prolonged to higher elevations, and precipitation is generated further inland.
The values of $l{crit}$ 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 $l{crit}$ and other cloud tuning parameters.
From these experiments, a linear relationship was found between $l_{crit}$ 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.
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 $l_{crit}$ 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:
- Lin et al. (1983) set $l_{crit}$ to $1 \times 10^{-3}$ kg kg $^{-1}$ for ice clouds.
- The ECMWF physics, tuned for GCM sized grid cells, uses a value of $0.3 \times 10^{-4}$ kg kg $^{-1}$ .
As $l{crit}$ depends on model grid resolution, i.e. GCMs running at lower spatial resolution require lower values of $l{crit}$ , the use of a larger $l_{crit}$ , e.g. for ice clouds ( $1.5 \times 10^{-4}$ kg kg $^{-1}$ ) in RACMO2, is deemed reasonable.
In addition, this value remains well within the range of values previously presented in the literature (Lin et al., 1983).
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:
- a. 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.
- b. 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.
- c. 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).
- d. 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.

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.
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.
Relative to previous versions, the integration domain extends further to the west, north, and east.
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).
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 Bamber et al. (2001), the GrIS area is reduced by 10 000 km $^2$ .
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 $\geq$ 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.
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).
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.

Observational Data

To evaluate the modelled contemporary climate and SMB of the GrIS, 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 are used.
Erroneous radiation measurements, e.g. caused by sensor riming, were discarded by removing daily records showing $SWd{bias} > 6\sigma{bias}$ , where $SWd{bias}$ is the difference between daily modelled and observed $SWd$ , and $\sigma{bias}$ 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 $Ts > 0$ $^{\circ}$ C for $\epsilon \approx 0.99$ , where $Ts$ is the surface temperature and $\epsilon$ the selected emissivity of snow or ice.
Only daily records that were simultaneously available for each of the four radiative components were used.
Eighteen of these AWS sites are operated as part of the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) 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 $^{\circ}$ N, 47–50 $^{\circ}$ W).
Another AWS (2014–2016) is situated in south-eastern Greenland (66 $^{\circ}$ N; 33 $^{\circ}$ W) at a firn aquifer site.
Also, in situ SMB measurements collected at 213 stake sites in the GrIS ablation zone and at 182 sites in the accumulation zone including snow pits, firn cores, and airborne radar measurements are used.
Exclusively measurements that temporally overlap with the model simulation (1958–2016) were selected.
To match the observational period, daily modelled SMB is cumulated for the exact number of measuring days at each site.
For model evaluation, the grid cell nearest to the observation site in the accumulation zone is selected.
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, modelled SMB is compared with annual glacial ice discharge (D) retrieved from the combined Zachariae Isstrøm and Nioghalvfjerdsbrae glacier catchments in north-eastern Greenland (1975–2015), presented in Mouginot et al. (2015).

Results: Near-Surface Climate and SEB

Evaluation of the modelled present-day near-surface climate of the GrIS in RACMO2.3p2 using data from 23 AWS sites.
Detailed discussion of the model performance at four AWSs along the K-transect and comparison of RACMO2.3p2 outputs to those of RACMO2.3p1.

Near-Surface Meteorology

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 are compared.
The modelled 2 m temperature is in good agreement with observations (R^2 = 0.95$) with a RMSE of ∼ 2.4^{\circ} $C and a small cold bias of ∼ 0.1$ ^{\circ}C.
Specific humidity, calculated from measured temperature, pressure and relative humidity, is accurately reproduced in the model (R^2 = 0.95$) with a RMSE ∼ 0.35 g kg $^{-1}$ and a negative bias of 0.13 g kg $^{-1}$ .
The same holds for daily records of 10 m wind speed ( $R^2 = 0.68$ ), with the model exhibiting a small negative bias and RMSE of ∼ 2 m s $^{-1}$ .
Surface pressure is also well represented ( $R^2 = 0.99$ ) with a small negative bias of 0.8 hPa and RMSE < 6 hPa.
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.
Regional scatter plots show that RACMO2.3p2 performs equally well in each of the four sectors of the GrIS, i.e. NW, NE, SW, and SE.
An overall improvement is found 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 and the systematic dry bias in RACMO2.3p1 have almost vanished in the updated model version.

Radiative Fluxes

Scatter plots of modelled and measured daily mean radiative fluxes, i.e. short-/longwave down-/upward radiation are shown.
Radiative fluxes are also well reproduced by the model with R^2 ranging from 0.83 for LWd to 0.95 for SWd, 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.
Also, 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.
By clustering AWS measurements within four sectors of the GrIS, RACMO2.3p2 shows good and equivalent agreement in NW, NE, SW and SE Greenland.
Changes in the cloud scheme have significantly improved the representation of LWd, showing a reduced negative bias and RMSE.
These modifications have also somewhat decreased the positive bias in SWd relative to RACMO2.3p1.
In addition, LWu is notably improved in RACMO2.3p2: the remaining negative bias in LWu has almost vanished.
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.
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.

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 $^{-2}$ .
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 $^{-2}$ ).
The bias in SWn is mostly driven by overestimated SWd (20.7 W m $^{-2}$ ) and to a lesser extent by underestimated SWu (4.5 W m $^{-2}$ ), resulting from underestimated cloud cover and ice albedo, 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.
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 $^{\circ}$ 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.
Summer melt is overestimated by ∼ 8 W m $^{-2}$ owing to both too-high SWn and SHF (9.8 and 7 W m $^{-2}$ ).
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.
Likewise, a small negative bias in LWn (2.3 W m $^{-2}$ ) is obtained as LWd and LWu are both slightly underestimated.
Here, 2 m temperature is on average 0.7 $^{\circ}$ 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 $^{-2}$ ).
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 $^{\circ}$ C positive bias, in turn causing a slightly too-large SHF (5.2 W m $^{-2}$ ).

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.
Modelled surface albedo also compares well with the measurements, with only a small positive bias (0.01).
LWn is underestimated by ∼ 9 W m $^{-2}$ ; this is mainly driven by a too-low LWd and a too-large LWu.
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

Statistics of SEB components between RACMO2.3p2 and 2.3p1 are compared.
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 are 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, 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.
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.
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.
However, surface albedo overestimation in RACMO2.3p2 causes a small increase in melt underestimation.

Results: Regional SMB

The overall good ability of RACMO2.3p2 to reproduce the contemporary climate of the GrIS, which is essential for estimating realistic SMB patterns is shown.
Comparison of SMB from RACMO2.3p2 and RACMO2.3p1 over the GrIS.
Focus on three regions where there are large differences in SMB between the two versions.

Changes in SMB Patterns

SMB from RACMO2.3p2 for the overlapping model period 1958–2015 is shown.
Differences with the previous version 2.3p1 are shown, and the changes in individual SMB components are depicted.
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 $^{-1}$ .
This pattern of change is clearly recognisable in the SMB difference.
In addition, the shallower saltation layer in the revised drifting snow scheme is responsible for reduced sublimation (∼ 50 mm w.e. yr $^{-1}$ ) that reinforces the overall increase in SMB.
Although drifting snow erosion changes locally, patterns are heterogeneous and the changes remain small when integrated over the GrIS.
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.
In the percolation zone, the decrease in run-off is governed by reduced surface melt, mostly resulting from the smaller grain size of refrozen snow and the lower soot concentration in snow that have increased surface albedo further increasing SMB.
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.
At higher elevations, the decrease in refreezing is exclusively driven by melt reduction, while at the extreme margins of the GrIS, the lower ice albedo used in RACMO2.3p2 locally increases run-off, in turn decreasing SMB.

North-Eastern Greenland

For north-eastern Greenland’s two main glaciers, Zachariae Isstrøm and Nioghalvfjerdsbrae (79 $^{\circ}$ N glacier), solid ice discharge (D) estimates are available for the period 1975–2015 (Mouginot