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We evaluate modelled Antarctic ice sheet (AIS) near-surface climate, surface mass balance (SMB) and surface energy balance (SEB) from the updated polar version of the regional atmospheric climate model, RACMO2 (1979–2016). The updated model, referred to as RACMO2.3p2, incorporates upper-air relaxation, a revised topography, tuned parameters in the cloud scheme to generate more precipitation towards the AIS interior and modified snow properties reducing drifting snow sublimation and increasing surface snowmelt. Comparisons of RACMO2 model output with several independent observational data show that the existing biases in AIS temperature, radiative fluxes and SMB components are further reduced with respect to the previous model version. The model-integrated annual average SMB for the ice sheet including ice shelves (minus the Antarctic Peninsula, AP) now amounts to 2229 Gt y−1, with an interannual variability of 109 Gt y−1. The largest improvement is found in modelled surface snowmelt, which now compares well with satellite and weather station observations. For the high-resolution (∼ 5.5 km) AP simulation, results remain comparable to earlier studies. The updated model provides a new, high-resolution data set of the contemporary near-surface climate and SMB of the AIS; this model version will be used for future climate scenario projections in a forthcoming study.

The Antarctic ice sheet mass balance is a major component of the sea level budget and results from the difference of two fluxes of a similar magnitude: ice flow discharging in the ocean and net snow accumulation on the ice sheet surface, i.e. the surface mass balance (SMB). Separately modelling ice dynamics and SMB is the only way to project future trends. In addition, mass balance studies frequently use regional climate models (RCMs) outputs as an alternative to observed fields because SMB observations are particularly scarce on the ice sheet. Here we evaluate new simulations of the polar RCM MAR forced by three reanalyses, ERA-Interim, JRA-55, and MERRA-2, for the period 1979–2015, and we compare MAR results to the last outputs of the RCM RACMO2 forced by ERA-Interim. We show that MAR and RACMO2 perform similarly well in simulating coast-to-plateau SMB gradients, and we find no significant differences in their simulated SMB when integrated over the ice sheet or its major basins. More importantly, we outline and quantify missing or underestimated processes in both RCMs. Along stake transects, we show that both models accumulate too much snow on crests, and not enough snow in valleys, as a result of drifting snow transport fluxes not included in MAR and probably underestimated in RACMO2 by a factor of 3. Our results tend to confirm that drifting snow transport and sublimation fluxes are much larger than previous model-based estimates and need to be better resolved and constrained in climate models. Sublimation of precipitating particles in low-level atmospheric layers is responsible for the significantly lower snowfall rates in MAR than in RACMO2 in katabatic channels at the ice sheet margins. Atmospheric sublimation in MAR represents 363 Gt yr−1 over the grounded ice sheet for the year 2015, which is 16 % of the simulated snowfall loaded at the ground. This estimate is consistent with a recent study based on precipitation radar observations and is more than twice as much as simulated in RACMO2 because of different time residence of precipitating particles in the atmosphere. The remaining spatial differences in snowfall between MAR and RACMO2 are attributed to differences in advection of precipitation with snowfall particles being likely advected too far inland in MAR.

We present a reconstruction of historical (1950–2014) surface mass balance (SMB) of the Greenland ice sheet (GrIS) using a high-resolution regional climate model (RACMO2; ∼11 km) to dynamically downscale the climate of the Community Earth System Model version 2 (CESM2; ∼111 km). After further statistical downscaling to 1 km spatial resolution, evaluation using in situ SMB measurements and remotely sensed GrIS mass change shows good agreement. Comparison with an ensemble of previously conducted RACMO2 simulations forced by climate reanalysis demonstrates that the current product realistically represents the long-term average and variability of individual SMB components and captures the recent increase in meltwater runoff that accelerated GrIS mass loss. This means that, for the first time, climate forcing from an Earth system model (CESM2), which assimilates no observations, can be used without additional corrections to reconstruct the historical GrIS SMB and its recent decline that initiated mass loss in the 1990s. This paves the way for attribution studies of future GrIS mass loss projections and contribution to sea level rise.

The polar version of the regional climate model RACMO2, version 2.3p1, is used to study the effect of model resolution on the simulated climate and surface mass balance (SMB) of south Greenland for the current climate (2007–2014). The model data at resolutions of 60, 20, 6.6, and 2.2 km are intercompared and compared to SMB observations using three different data refinement methods: nearest neighbour, bilinear interpolation, and a statistical downscaling method utilising the local dependency of fields on elevation. Furthermore, it is estimated how the errors induced by model resolution compare to errors induced by the model physics and initialisation. The results affirm earlier studies that SMB components which are tightly linked to elevation, like runoff, can be refined successfully, as soon as the ablation zone is reasonably well resolved in the source dataset. Precipitation fields are also highly elevation dependent, but precipitation has no systematic correlation with elevation, which inhibits statistical downscaling to work well. If refined component-wise, 20 km resolution model simulations can reproduce the SMB ablation observations almost as well as the finer-resolution model simulations. Nonetheless, statistical downscaling and regional climate modelling are complementary; the best results are obtained when high-resolution RACMO2 data are statistically refined. Model estimates in the accumulation zone do not benefit from statistical downscaling; hence, a resolution of about 20 km is sufficient to resolve the majority of the accumulation zone of the Greenland Ice Sheet with respect to the limited measurements we have. Furthermore, we demonstrate that using RACMO2, a hydrostatic model, at 2.2 km resolution led to invalid results as topographic and synoptic vertical winds exceed 10 m s−1, which violates the hydrostatic model assumptions. Finally, additional tests show that model resolution is as important as properly resolving spatial albedo patterns, correctly initialising the firn column, and uncertainties in the modelled precipitation and turbulent exchange.

We assess the recent contribution of the Greenland ice sheet (GrIS) to sea level change. We use the mass budget method, which quantifies ice sheet mass balance (MB) as the difference between surface mass balance (SMB) and solid ice discharge across the grounding line (D). A comparison with independent gravity change observations from GRACE shows good agreement for the overlapping period 2002–2015, giving confidence in the partitioning of recent GrIS mass changes. The estimated 1995 value of D and the 1958–1995 average value of SMB are similar at 411 and 418 Gt yr−1, respectively, suggesting that ice flow in the mid-1990s was well adjusted to the average annual mass input, reminiscent of an ice sheet in approximate balance. Starting in the early to mid-1990s, SMB decreased while D increased, leading to quasi-persistent negative MB. About 60 % of the associated mass loss since 1991 is caused by changes in SMB and the remainder by D. The decrease in SMB is fully driven by an increase in surface melt and subsequent meltwater runoff, which is slightly compensated by a small ( <  3 %) increase in snowfall. The excess runoff originates from low-lying ( <  2000 m a.s.l.) parts of the ice sheet; higher up, increased refreezing prevents runoff of meltwater from occurring, at the expense of increased firn temperatures and depleted pore space. With a 1991–2015 average annual mass loss of  ∼  0.47 ± 0.23 mm sea level equivalent (SLE) and a peak contribution of 1.2 mm SLE in 2012, the GrIS has recently become a major source of global mean sea level rise.

Mass budget calculations, validated with satellite gravity observations [from the Gravity Recovery and Climate Experiment (GRACE) satellites], enable us to quantify the individual components of recent Greenland mass loss. The total 2000-2008 mass loss of approximately 1500 gigatons, equivalent to 0.46 millimeters per year of global sea level rise, is equally split between surface processes (runoff and precipitation) and ice dynamics. Without the moderating effects of increased snowfall and refreezing, post-1996 Greenland ice sheet mass losses would have been 100% higher. Since 2006, high summer melt rates have increased Greenland ice sheet mass loss to 273 gigatons per year (0.75 millimeters per year of equivalent sea level rise). The seasonal cycle in surface mass balance fully accounts for detrended GRACE mass variations, confirming insignificant subannual variation in ice sheet discharge.

This study presents a data set of daily, 1 km resolution Greenland ice sheet (GrIS) surface mass balance (SMB) covering the period 1958–2015. Applying corrections for elevation, bare ice albedo and accumulation bias, the high-resolution product is statistically downscaled from the native daily output of the polar regional climate model RACMO2.3 at 11 km. The data set includes all individual SMB components projected to a down-sampled version of the Greenland Ice Mapping Project (GIMP) digital elevation model and ice mask. The 1 km mask better resolves narrow ablation zones, valley glaciers, fjords and disconnected ice caps. Relative to the 11 km product, the more detailed representation of isolated glaciated areas leads to increased precipitation over the southeastern GrIS. In addition, the downscaled product shows a significant increase in runoff owing to better resolved low-lying marginal glaciated regions. The combined corrections for elevation and bare ice albedo markedly improve model agreement with a newly compiled data set of ablation measurements.

Surface mass loss from the Greenland ice sheet (GrIS) has accelerated over the past decades, mainly due to enhanced surface melting and liquid water runoff in response to atmospheric warming. A large portion of runoff from the GrIS originates from exposure of the darker bare ice in the ablation zone when the overlying snow melts, where surface albedo plays a critical role in modulating the energy available for melting. In this regard, it is imperative to understand the processes governing albedo variability to accurately project future mass loss from the GrIS. Bare-ice albedo is spatially and temporally variable and contingent on non-linear feedbacks and the presence of light-absorbing constituents. An assessment of models aiming at simulating albedo variability and associated impacts on meltwater production is crucial for improving our understanding of the processes governing these feedbacks and, in turn, surface mass loss from Greenland. Here, we report the results of a comparison of the bare-ice extent and albedo simulated by the regional climate model Modèle Atmosphérique Régional (MAR) with satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the GrIS below 70∘ N. Our findings suggest that MAR overestimates bare-ice albedo by 22.8 % on average in this area during the 2000–2021 period with respect to the estimates obtained from MODIS. Using an energy balance model to parameterize meltwater production, we find this bare-ice albedo bias can lead to an underestimation of total meltwater production from the bare-ice zone below 70∘ N of 42.8 % during the summers of 2000–2021.

Snow and ice albedo schemes in present-day climate models often lack a sophisticated radiation penetration scheme and do not explicitly include spectral albedo variations. In this study, we evaluate a new snow albedo scheme in the Regional Atmospheric Climate Model (RACMO2) for the Greenland ice sheet, version 2.3p3, that includes these processes. The new albedo scheme uses the Two-streAm Radiative TransfEr in Snow (TARTES) model and the Spectral-to-NarrOWBand ALbedo (SNOWBAL) module, version 1.2. Additionally, the bare-ice albedo parameterization has been updated. The snow and ice broadband and narrowband albedo output of the updated version of RACMO2 is evaluated using the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and Kangerlussuaq transect (K-transect) in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) remote-sensing observations. Generally, the modeled narrowband and broadband albedo is in very good agreement with satellite observations, leading to a negligible domain-averaged broadband albedo bias for the interior. Some discrepancies are, however, observed close to the ice margin. Compared to the previous model version, RACMO2.3p2, the broadband albedo is considerably higher in the bare-ice zone during the ablation season, as atmospheric conditions now alter the bare-ice broadband albedo. For most other regions, however, the updated broadband albedo is lower due to spectral effects, radiation penetration or enhanced snow metamorphism.

The Regional Atmospheric Climate Model (RACMO2) has been a powerful tool for improving surface mass balance (SMB) estimates from GCMs or reanalyses. However, new yearly SMB observations for West Antarctica show that the modelled interannual variability in SMB is poorly simulated by RACMO2, in contrast to ERA-Interim, which resolves this variability well. In an attempt to remedy RACMO2 performance, we included additional upper-air relaxation (UAR) in RACMO2. With UAR, the correlation to observations is similar for RACMO2 and ERA-Interim. The spatial SMB patterns and ice-sheet-integrated SMB modelled using UAR remain very similar to the estimates of RACMO2 without UAR. We only observe an upstream smoothing of precipitation in regions with very steep topography like the Antarctic Peninsula. We conclude that UAR is a useful improvement for regional climate model simulations, although results in regions with steep topography should be treated with care.