Higher Antarctic ice sheet accumulation and surface melt rates revealed at 2 km resolution

Domain: earth_science
Published: 2023-12-01
Editorial outcome: minor_revision

Abstract
Antarctic ice sheet (AIS) mass loss is predominantly driven by increased solid ice discharge, but its variability is governed by surface processes. Snowfall fluctuations control the surface mass balance (SMB) of the grounded AIS, while meltwater ponding can trigger ice shelf collapse potentially accelerating discharge. Surface processes are essential to quantify AIS mass change, but remain poorly represented in climate models typically running at 25-100 km resolution. Here we present SMB and surface melt products statistically downscaled to 2 km resolution for the contemporary climate (1979-2021) and low, moderate and high-end warming scenarios until 2100. We show that statistical downscaling modestly enhances contemporary SMB (3%), which is sufficient to reconcile modelled and satellite mass change. Furthermore, melt strongly increases (46%), notably near the grounding line, in better agreement with in-situ and satellite records. The melt increase persists by 2100 in all warming scenarios, revealing higher surface melt rates than previously estimated.

Introduction
The Antarctic ice sheet (AIS) is currently losing mass1–3, as solid ice discharge, including ice shelf basal melting4 and iceberg calving1, exceeds the mass gained from surface processes. The surface mass balance (SMB), i.e., the difference between snow accumulation and surface ablation from sublimation, drifting snow erosion and meltwater runoff5, primarily affects the synoptic, seasonal, interannual and decadal variability of AIS mass change6. Over floating ice shelves, surface melt triggers surface lowering, firn pore space depletion through retention and refreezing, and subsequent ponding of excess meltwater, that is eventually discharged into the ocean via crevasse and lake drainage, or runoff rivers7–12. As for Greenland fjords, increase in surface and subsurface runoff have the potential to enhance sub-shelf basal melting and subsequent destabilisation by thinning ice shelves from below4. These combined processes, i.e., surface meltwater ponding and basal melt, can trigger ice shelf disintegration by hydrofracturing, a mechanism that reduces the buttressing effect on the grounded AIS to eventually accelerate solid ice discharge and sea-level rise13. This has previously occurred in the Antarctic Peninsula over Larsen B ice shelf (March 2002)14. As surface melt is projected to increase exponentially with future atmospheric warming15, it is expected that Antarctic ice shelves become increasingly prone to collapse16,17.

Regional climate models and earth system models, that typically run at spatial resolutions of 25–100 km, are key tools to reconstruct the contemporary SMB and surface melt in Antarctica and project their (future) future evolution5,17–21. However, their relatively coarse grids cannot accurately resolve the complex topography of e.g., the Antarctic Peninsula, the Transantarctic Mountains or Queen Maud Land, where both snowfall accumulation and melt gradients peak in response to steep slopes22. Consequently, climate models generally underestimate orographic precipitation and propagate snowfall too far inland onto the AIS interior plateau5,21. A recent multi-model comparison revealed large inter-model annual SMB differences of 266 Gt year−1 (~10% of the Antarctic-wide total)5, that are primarily controlled by the grid resolution used in regional climate models23. Modele evaluation using in situ measurements from automatic weather stations (AWS) (1992–2018)24 and remote sensing from the satellite radar scatterometer (QuikSCAT) (2000–2009)25 likewise suggests a general melt underestimation in e.g., the regional climate model RACMO2.3p218.

Here we present daily SMB and surface melt products covering the grounded AIS and floating ice shelves at 2 km spatial resolution for the contemporary climate and three scenario projections until 2100. As a first step, present-day climate from the global climate reanalysis ERA526 (1979–2021) and three global climate projections from the Community Earth System Model (CESM2)27 under a low (SSP1-2.6), moderate (SSP2-4.5), and high-end (SSP5-8.5) warming scenario (1950–2099) are used as lateral forcing for the Regional Atmospheric Climate Model (RACMO2.3p2)17,18, which simulates the contemporary and future SMB (components) of Antarctica on a 27km grid (see Methods). As a second step, statistical downscaling is applied to correct these SMB components, including surface melt, runoff, total precipitation, snowfall, sublimation and snow drift erosion, for elevation biases between the relatively coarse RACMO2.3p2 grid at 27km and a high-resolution surface topography from the Reference Elevation Model of Antarctica at 2km (REMA)28. Melt and runoff are further adjusted for local albedo biases, e.g., regions exposing blue ice or darker bare ice mixed with rocks, not included in RACMO2.3p2, based on a 2km albedo map from the Moderate Resolution Imaging Spectroradiometer (MODIS) averaged for 2000–2021 (see “Methods”). The ability of statistical downscaling to refine the spatial distribution of SMB components was first demonstrated for the Greenland ice sheet, where the downscaled product realistically captured high mass loss rates over narrow ablation zones and outlet glaciers that are typically unresolved in RACMO2.3p229. Likewise, statistical downscaling to (sub-) kilometre spatial resolution proved essential to accurately quantify contemporary (and projected) mass change of the Greenland ice sheet30,31, its peripheral ice caps32, glaciers of the Canadian Arctic33, Svalbard34, and Iceland35, and their contribution to global sea-level rise. Using in situ and remote sensing data for model evaluation, we show that our downscaled product for Antarctica at 2km improves upon the original RACMO2.3p2 data at 27km, by resolving SMB and surface melt patterns in unprecedented spatial detail, notably in topographically rough regions including mountain ranges and the vicinity of the grounding line.

Results
Larger accumulation rates across mountain ranges
Figure 1a, b shows the resulting contemporary Antarctic-wide SMB at 2km and 27km spatial resolution averaged for the period 1979–2021. Spatial differences are generally small (<200 mm w.e. year−1) (Fig. 1c), except for the Antarctic Peninsula where precipitation peaks (Fig. 1d, e). Here, local differences can reach up to 2 m w.e. year−1 (Fig. 1f). Note that as the extent of the 2km and 27km ice masks differ at the margins (Supplementary Fig. 1c, d), large local positive (resp. negative) differences occur when the 2km ice mask covers larger (resp. smaller) areas than the 27km product (Fig. 1f). Statistical downscaling overall enhances accumulation across high-elevated mountain ranges that are not properly resolved at 27km resolution (Fig. 1c and Supplementary Fig. 1). In general, the orographic effect on precipitation is enhanced at 2km resolution, with accumulation being higher on the steep, windward marginal slopes of the grounded AIS, and reduced towards the gently sloping inland plateau (Fig. 1c). In particular, accumulation is enhanced above the crest of the Antarctic Peninsula, with reductions relative to the 27km grid at lower levels on both sides of the Peninsula and over Larsen C ice shelf (Fig. 1f), which is in line with the dry foehn winds that prevail in this region36.

Using 14,292 multi-year in situ SMB measurements from the AntSMB dataset37 (white dots in Supplementary Fig. 2a), we show that the 2km product improves on the original 27km data (R2=0.83 vs. 0.80), with both lower bias (−6.6 mm w.e.) and RMSE (−9.4 mm w.e.) (Supplementary Fig. 2b, c). Using a two-sample Kolmogorov-Smirnov (K-S) test on the model bias at both resolutions, we find that although small, these local SMB changes at 2km are significant (p value < 0.01). We integrate SMB over the whole of Antarctica (ANT), including the grounded AIS, adjacent Antarctic islands, and floating ice shelves. Accumulation in the 2km product (black line in Fig. 2a) increases by 72 Gt year−1 (3% for the period 1979–2021 in Fig. 2b) relative to the 27km product (grey band in Fig. 2a). This difference falls well within the multi-model uncertainty of 266 Gt year−1 estimated by Mottram et al.5. Elevation correction onto the 2km grid accounts for 49 Gt year−1 (2%) of the total SMB increase, whereas the remaining 23 Gt year−1 (1%) are attributed to differences in ice mask extent (Supplementary Fig. 1c, d). Dividing Antarctica in sectors, we find that most of the accumulation increase originates from the Antarctic Peninsula (APIS) (31 Gt year−1 in Supplementary Fig. 3a), followed by the floating ice shelves (27 Gt year−1 in Supplementary Fig. 3d), and the East Antarctic ice sheet (EAIS) (16 Gt year−1 in Supplementary Fig. 3c). The West Antarctic ice sheet (WAIS), however, experiences a small and negligible accumulation decrease (2 Gt year−1 in Supplementary Fig. 3b). Regional changes in area-integrated SMB at both resolutions are summarised in Supplementary Table 1.

Contemporary mass change estimates
Mass change (MB) of the grounded AIS can be quantified using the input-output method2, i.e., the difference between modelled SMB (this study) and solid ice discharge (D)1 for the period 1979–2021 (Fig. 2a). Since published D estimates do not extend beyond 2017, we extrapolate the time series to 2021 using a linear regression for 1979–2017 (R2=0.95) (see “Methods”). In the past four decades (1979–2021), we show that modelled SMB at 2km integrated over the grounded AIS, including Antarctic islands (blue region in the inset map of Fig. 3a), remained relatively constant at 2180 ± 107 Gt year−1 of surface accumulation with an insignificant negative trend of −0.001 ± 0.660 Gt year−2 (p value > 0.01) (red line in Fig. 2a). In contrast, solid ice discharge (D) contributed an average mass loss of 2220 ± 142 Gt year−1 for the period 1979–20171, with a significant positive trend of 4.9 ± 0.4 Gt year−2 (p value < 0.01) (orange line in Fig. 2a). For evaluation, we estimate monthly mass change at both spatial resolutions and compare the results with that of the Gravity Recovery and Climate Experiment (GRACE) Follow-On (FO) satellite products for the overlapping period 2002–2021 (see “Methods”). We show that Antarctic-wide mass change from the 2km product (blue line in Fig. 3a) significantly improves on that from the 27km product (cyan line), reducing both mean bias and RMSE by about 395 Gt relative to GRACE/GRACE-FO (red line). We also find significant improvements in the APIS (Fig. 3b) and the EAIS sectors (Fig. 3d). In the WAIS sector, the mean bias and RMSE are similar at both resolutions (Fig. 3c). Overall, the 2km product agrees better with GRACE/GRACE-FO records both AIS-wide (R2=0.98) and over its three sectors (0.96 < R2 < 0.99).

Supplementary Fig. 4 extends our mass change estimates back to 1979. Using the 2km product, we find that the grounded AIS remained in approximate mass balance until the mid-2000s (blue line in Supplementary Fig. 4a), with SMB mass gain (2187 ± 107 Gt year−1 for 1979–2005) compensating for D mass loss (2187 ± 142 Gt year−1) on average (Fig. 2a), though with large interannual variability. Thereafter, D (2301 ± 142 Gt year−1 for 2006–2021) persistently exceeds SMB (2168 ± 107 Gt year−1) (Fig. 2a), driving the recent mass loss (Supplementary Fig. 4a). We estimate that the grounded AIS has lost 2272 ± 243 Gt of ice since 2002, contributing 6.3 ± 0.7 mm to global sea-level rise (Fig. 3a). The APIS and WAIS sectors (Fig. 3b, c), contributed 634 ± 61 Gt and 2930 ± 132 Gt to the total mass loss, respectively. In contrast, persistent mass gains in the EAIS sector since 2002 mitigate mass losses from the other two sectors by 1292 ± 158 Gt (Fig. 3d). The climatic signal of the EAIS mass gain remains actively debated1–3,6,38. Nevertheless, our downscaled (regional) SMB estimates are supported by good agreement with both in situ (Supplementary Fig. 2) and remote sensing records (Fig. 3d). Regional contributions to global sea level change since 2002 are listed in Supplementary Table 2.

Increased surface melt rates at the grounding line
Among other processes4, floating ice shelves are vulnerable to (local) high surface melt rates17 that can result in meltwater ponding, trigger ice shelf destabilisation and rapidly accelerate solid ice discharge. It is therefore critical to accurately represent surface melt rates, notably near the grounding line that separates the grounded AIS from the floating ice shelves. Statistical downscaling to 2km has a pronounced effect on surface melt rate and distribution across Antarctica, especially over the ice shelves where melt peaks (Fig. 4a). Differences between modelled melt at both resolutions locally reaches up to 500 mm w.e. year−1 (Fig. 4c). As for SMB, varying ice mask extent between both grids can lead to large differences, notably at the ice mask edge (Supplementary Fig. 1c, d). Overall, melt changes mirror the surface topography difference between the 2km and 27km grids, reaching up to 500 m (Supplementary Fig. 1a). As a general pattern, surface elevation in RACMO2.3p2 is overestimated over narrow, low-lying valleys and at the grounding line (Supplementary Fig. 1a). In contrast, surface elevation at 27km is underestimated over the divide of the steep mountain ranges, including the Antarctic Peninsula, Queen Maud Land and the Transantarctic Mountains (Supplementary Fig. 1a). These mountain ranges with their networks of narrow peaks and valleys locally show large elevation biases (~500 m) between the 2km and 27km grid (Supplementary Fig. 1a). Small negative elevation differences are generally found over the relatively flat floating ice shelves (Supplementary Fig. 1a). Combined with the strong surface melt gradients found nearby the grounding lines, this leads to an overall melt increase that locally peaks over crevasses that are not resolved at 27km (Fig. 4c). In contrast, the Amery ice shelf shows a small positive elevation difference at 2km, leading to a local melt decrease compared to the 27km grid (Fig. 4c and Supplementary Fig. 1a). Locally, the albedo correction implies that surface melt further increases in regions where blue ice is exposed. Lower elevations in the downscaled product result in higher melt rates relative to native RACMO2.3p2, and vice-versa (Fig. 4c). Such patterns are clearly visible in the APIS sector, with increased melt rates over low-lying valley glaciers, crevasses, and in the vicinity of the grounding line, while melt is reduced toward the elevated interior (Fig. 4f).

We evaluate modelled surface melt at 2km and 27km using 81 annual records from ten AWS (yellow stars in Supplementary Fig. 2a) spanning the period 1992–201824 (see “Methods”). Four sites are situated on the Larsen C ice shelf and six in Dronning Maud Land. We find that the downscaled product shows better agreement with observations than the native 27km product (Supplementary Fig. 2d, e), with reduced bias (−7.9 mm w.e.) and similar RMSE. Since the AWS network is spatially limited, we complement our evaluation with an Antarctic-wide point comparison between mean annual melt modelled on both grids and derived from QuikSCAT25 at 4.45km resolution, averaged for the period 2000–2009 (Supplementary Fig. 5a) (see “Methods”). We find that high melt rates are better resolved on the 2km grid with reduced bias (−8.3 mm w.e.) and RMSE (−7.2 mm w.e.) (Supplementary Fig. 5b, c). Notably, the 2km product improves upon the native RACMO2.3p2 product at capturing high melt rates measured in the vicinity of the grounding line (Supplementary Fig. 5d, e). Integrated over the whole of Antarctica (ANT), melt in the 2km product increases by 51 Gt year−1 (46% for 1979–2021 in Fig. 2d) relative to the 27km product (black line and grey band in Fig. 2c). The elevation correction contributes 38 Gt year−1 (34%) to the total surface melt increase (Supplementary Fig. 1a, b). This effect is particularly important near the grounding line where surface elevation is generally reduced at 2km, and steep topographic gradients were not accurately captured at 27km. Over low-lying ice shelves, the combined elevation difference and strong melt gradients locally enhance surface melt at 2km. Spatial refinement of the ice mask from 27km to 2km contributes 10 Gt year−1 (9%) to the total melt increase (Supplementary Fig. 1c, d), while the remaining 3 Gt year−1 (3%) stem from albedo correction over blue ice areas. The downscaled product aligns with QuikSCAT estimates within uncertainties for the period 2000–2009 (orange line and band in Fig. 2c). Supplementary Fig. 3e–h shows similar results for individual sectors, highlighting a 42 Gt year−1 (62%) increase in surface melt over floating ice shelves that mainly occurs along the grounding line (Fig. 4c). For the grounded AIS, melt and there with the melt increase is smaller with 1 Gt year−1 in the WAIS (39%), 2 Gt year−1 in the APIS (9%), and 6 Gt year−1 in the EAIS sector (45%) (Supplementary Fig. 3e, f). Regional changes in area-integrated surface melt at both resolutions are shown in Supplementary Table 3.

Persistent surface melt increase at high-resolution until 2100
To explore whether the surface melt underestimate found in the contemporary RACMO2.3p2 simulation at 27km persists in the future, we use one CESM2 historical reconstruction (HIST, 1950–2014) extended by three climate projections under a low (SSP1-2.6), moderate (SSP2-4.5) and high-end (SSP5-8.5) warming scenario (1950–2099). These simulations are first dynamically downscaled to 27km using RACMO2.3p2, and further statistically downscaled onto the 2km grid (see “Methods”). Figure 5a shows time series of Antarctic-wide (ANT) integrated melt at 2km (dark coloured lines) and 27km (lower margin of the light coloured bands) for the period 1950–2099. We first ascertain that melt in the HIST period (green, 1950–2014) aligns with the contemporary ERA5 simulation (black, 1979–2021). We find good agreement with an averaged melt at 2km of 139 ± 27 Gt year−1 in the HIST run compared to 167 ± 33 Gt year−1 in the ERA5 simulation for the overlapping period 1979–2014 (Fig. 5a). In the period 1950–2014, Antarctic-wide (ANT) melt in the HIST run remained steady with a small positive trend of 0.6 ± 0.4 Gt year−2. In line with previous studies15,17,20, melt increases linearly under low (3.5 ± 0.6 Gt year−2 for 2015–2099) and moderate scenarios (5.4 ± 0.6 Gt year−2), with a stronger acceleration under a high-end warming trajectory (17.4 ± 1.3 Gt year−2). Figure 5b compares the Antarctic-wide (ANT) melt between both model resolutions for all warming scenarios. We find that, irrespective of the warming trajectory, a systematic melt underestimate of 45% persists in the 27km product (Fig. 5b), corroborating our results for the contemporary simulation (Fig. 2d). Dividing Antarctica in sectors, Supplementary Fig. 6 shows that the relative melt increase over floating ice shelves (61%) is larger than for individual AIS sectors (11–24%).

Methods
Regional Atmospheric Climate Model
The polar (p) Regional Atmospheric Climate Model version 2.3p2 (RACMO2.3p2) incorporates the dynamical core of the High Resolution Limited Area Model (HIRLAM)40 and the physics package cycle CY33r1 of the European Centre for Medium-Range Weather Forecasts-Integrated Forecast System (ECMWF-IFS)41. The model is specifically adapted to represent surface processes of polar ice sheets and ice caps including the Greenland ice sheet30, Canadian Arctic33, Svalbard34, Iceland35, and Antarctica18. The model incorporates a 40-layer snow module (up to 100 m depth) simulating melt, percolation and retention into firn and subsequent surface runoff42. The model represents dry-snow densification43, drifting snow erosion44, and snow albedo based on grain size, cloud optical thickness, solar zenith angle, and impurity content45. Here we use the benchmark RACMO2.3p218 model at 27km spatial resolution to dynamically downscale the latest ERA5 climate reanalysis (1979–2021)26 from the European Centre for Medium-Range Weather Forecasts (ECMWF). This reference run is further complemented by one historical simulation (1950–2014) and three climate projections from the Community Earth System Model (CESM2) under a low-end (SSP1-2.6), moderate (SSP2-4.5) and high-end (SSP5-8.5) Shared Socioeconomic Pathways (SSP) emission scenarios (2015–2099), dynamically downscaled to 27km. RACMO2.3p2 is forced by ERA5 reanalysis and CESM2 climate outputs within a 24-grid-cell-wide relaxation zone at the lateral model boundaries. Forcing consists of temperature, pressure, specific humidity, wind speed and direction being prescribed at the 40 model atmospheric levels every 3 and 6 h, for the ERA5 and CESM2-forced setting respectively. Upper atmospheric relaxation is active46. Sea ice extent and sea surface temperature are also prescribed from the ERA5 reanalysis or CESM2 outputs on a 3 or 6-h basis. Firn is initialised in January 1979 (ERA5) and 1950 (CESM2) using snow temperature and density profiles from the offline Institute for Marine and Atmospheric research Utrecht-Firn Densification Model (IMAU-FDM)43. Ice albedo is prescribed as a constant field in space and time at 0.55. Surface topography is prescribed from the 1 km digital elevation model of Bamber et al.47, downsampled to 27km. In the absence of interactive ice dynamics in RACMO2.3p2, ice mask and topography are kept fixed. The RACMO2.3p2 model settings are discussed and evaluated in van Wessem et al.18.

Community Earth System Model
The Community Earth System Model version 2.1 (CESM2.1)27 is an earth system model from the Climate Model Comparison Project phase 6 (CMIP6) that simulates interactions between atmosphere-land-ocean systems on the global scale. The model includes the Community Atmosphere Model version 6 (CAM6)48, the Parallel Ocean Program model version 2.1 (POP2.1)49, the Los Alamos National Laboratory Sea Ice Model version 5.1 (CICE5.1)50, the Community Land Model version 5 (CLM5)51, and the Community Ice Sheet Model version 2.1 (CISM2.1)52 to simulate interactions between atmosphere-ocean-land and sea ice dynamics as well as snow/ice surface processes in a fully-coupled fashion. Here all components are active except for land-ice dynamics, i.e., excluding basal melting, calving, collapse of ice shelves and subsequent ice sheet thickening/thinning and advance/retreat. We use outputs from the CMIP6 CESM2 model for one historical reconstruction (HIST; 1950–2014), and three climate projections (SSP1-2.6, SSP2-4.5 and SSP5-8.5; 2015–2099)53 at a spatial resolution of 0.9 × 1.25°. In CESM2, CAM6 and CLM5 exclusively prescribe land use changes; atmospheric greenhouse gas and aerosol emissions are derived from the selected SSP warming scenarios54. CLM5 has been adapted to simulate runoff, melt, firn retention and refreezing in a 10m w.e. snowpack55. Model settings are discussed and evaluated in Dunmire et al.21.

Statistical downscaling
Following Noël et al.29, the RACMO2.3p2 contemporary climate (1979–2021, ERA5) and projections (1950–2099, CESM2) are statistically downscaled from the native model resolution of 27km to an ice mask and topography defined on a 2km grid. A 2km grid was selected as a trade-off between computational efficiency and high spatial resolution, while maintaining manageable file size. The ice mask combines the grounded AIS mask from the Ice Sheet Mass Balance Inter-comparison Exercise-2 (IMBIE-2)2, the refined floating ice shelf mask from the Antarctic Bedrock Mapping version 2 (Bedmap2)56, and sub-Antarctic glaciers from the Randolph Glacier Inventory version 6 (RGIv6)57 down-sampled to a 2km grid. Surface topography at 2km is derived from the Reference Elevation Model of Antarctica (REMA)28 digital elevation model at 200m resolution. The downscaling procedure corrects individual SMB components for elevation on the 2km topography, using daily-specific vertical gradients estimated on the RACMO2.3p2 grid at 27km. SMB components (X in Eq. (1)) include total precipitation (PR), total sublimation (SU), snow drift erosion (ER), total melt (ME), and runoff (RU). Vertical gradients are estimated as linear regressions using at least six grid-cells, i.e., the current one and five (or more) adjacent pixels. To obtain realistic local estimates, the regression slope (a) is applied to the current grid-cell to compute an intercept (b), i.e., value at sea level. These two regression coefficients are bi-linearly interpolated from the 27km grid onto the 2km one, and applied to the high-resolution topography at 2km (h) as:

X2km = a2km × h2km + b2km (1)

Melt and runoff are further corrected for surface albedo in regions exposing blue ice or darker bare ice mixed with rocks (albedo < 0.55) in summer, that are unresolved in RACMO2.3p2 at 27km. To that end, we use a 2km MODIS 16-day product averaged for the period 2000–2021. The albedo correction accounts for solar zenith angle, surface slope and aspect as discussed in Noël et al.29. Since runoff is spatially limited in Antarctica, i.e., surface melt is mostly retained or refrozen in firn, we bi-linearly interpolate runoff in space onto the 2km grid for days with insufficient number of grid-cells experiencing runoff (<6 adjacent grid-cells). This resolution issue becomes less pronounced as the runoff zone expands following atmospheric warming, notably in the three SSP scenarios. Statistical downscaling of total precipitation is motivated by the fact that the native RACMO2.3p2 underestimates orographic precipitation, notably on the steep and rugged terrain of the Antarctic Peninsula. In addition, daily snowfall fraction (SFfrac), i.e., the fraction of snowfall on total precipitation, is statistically downscaled to 2km following Huai et al.58. Snowfall is thus estimated as:

SF2km = PR2km × SFfrac2km (2)

Rainfall is estimated as a residual as:

RA2km = PR2km − SF2km (3)

SMB is reconstructed using individual components statistically downscaled to 2km as:

SMB2km = PR2km − RU2km − SU2km − ER2km (4)

and refreezing (RF) is estimated as a residual following:

RF2km = ME2km + RA2km − RU2km (5)

The above statistical downscaling procedure is not mass conservative, enabling mass flux corrections that significantly improve the representation of individual SMB components relative to the native RACMO2.3p2 outputs. For a detailed description of the statistical downscaling technique, we refer to Noël et al.29.

Limitations and uncertainties
The statistical downscaling technique used in this study builds on previous RACMO2-based downscaling efforts over glaciated surfaces in Greenland29–32, the Canadian Arctic33, Svalbard34, and Iceland35. For all these land ice masses, the downscaled product accurately represents the spatial and temporal SMB variations, as demonstrated by high and improved correlations with in situ and remotely sensed observations. The statistical downscaling technique uses elevation and albedo as primary downscaling predictors and applies the downscaling at very high (daily) temporal resolution. Three factors explain the robustness of the downscaling procedure. First, the spatial variability of individual SMB components shows strong average correlation with elevation, also in Antarctica (Supplementary Fig. 7). Second, local albedo-driven surface melt anomalies are captured by implementing a spatial correction for ice albedo in the downscaling procedure, which accounts for solar zenith angle, slope, and aspect of the underlying terrain as detailed in Noël et al.29. For instance, the albedo correction locally enhances surface melt over darker blue ice areas. As blue ice currently remains spatially limited in Antarctica, albedo correction only contributes ~1/15th (3%) to the total surface melt increase obtained after downscaling (+46%). Finally, statistical downscaling estimates elevation gradients locally (using the current and at least five adjacent grid-cells) and at high temporal resolution (daily). This preserves the modelled spatial patterns and synoptic timescales enabling to capture melt and accumulation spatiotemporal variability due to local surface slope, distance to the coast, and atmospheric circulation direction and strength affecting e.g., foehn events. Sources of uncertainty that persist in the current statistical downscaling routine stem from local biases in: (1) the modelled elevation gradients (RACMO2.3p217,18 or CESM221) notably in regions characterised by steep slopes and sharp gradients, (2) the high-resolution surface topography (REMA)28 used for the elevation correction, (3) the ice sheet (IMBIE-2)2, glaciers (RGIv6)57 and ice shelf masks (Bedmap2)56 used for spatial integration, and (4) the satellite albedo product (MODIS) used for ice albedo correction.

RACMO2.3p217,18 and none of the CESM2 scenarios21 currently available implement the effect of ice dynamics in Antarctica, i.e., fixed present-day conditions were prescribed in our future projections. Under climate warming, the AIS and floating ice shelves are expected to retreat, calve and/or thicken/thin, which would affect the circulation and associated precipitation distribution as well as melt patterns. For instance, marginal ice thinning could trigger melt-elevation feedback, further enhancing the projected surface melt rates, while snowfall-driven ice thickening could reduce melt. These competing mechanisms will be investigated by statistically downscaling future melt projections from forthcoming fully-coupled earth system models that include Antarctic ice dynamics when these become available, as was previously done for the Greenland ice sheet35. In addition, we prescribe the present-day MODIS blue ice albedo with a base value of 0.55 when firn retreats to expose blue ice both in RACMO2.3p2 at 27km and at 2km. This approach remains valid in the absence of future surface darkening due to biological growth (algae)59 or impurity deposition (black carbon, dust, ashes)60 as e.g., observed in Greenland. To our knowledge, ice darkening is not yet ongoing in Antarctica. In view of the above, our projected, statistically downscaled surface melt rates should be interpreted as conservative estimates.

SMB and mass change observations
For point SMB evaluation, we use in situ measurements from the AntSMB dataset37 that compiles multi-year records from stakes, snow pits, ice cores, ultrasonic sounders and ground penetrating radar covering the past 1000 years (white dots in Supplementary Fig. 2a). Here, we discarded records that were located outside the ice mask at 2km. For a meaningful comparison with observations that did not overlap with our benchmark ERA5-forced RACMO2.3p2 simulation, we averaged modelled SMB at 2km and 27km over the period 1979–2021. To reduce the noise in our evaluation, we spatially averaged all point measurements that fell within a same 2km grid-cell in the downscaled product. In total, 14,292 multi-annual records were compared to RACMO2.3p2 at 27km and statistically downscaled to 2km using the closest model grid-cell (blue dots in Supplementary Fig. 2b, c). For completeness, we also show the full data set as grey dots (274,154 records).

For large-scale SMB evaluation, we compare monthly mass change from the Gravity Recovery and Climate Experiment (GRACE, 2002–2018) and Follow-On mission (GRACE-FO, 2018–2021) (red lines in Fig. 3 and Supplementary Fig. 4) with combined sector integrated SMB statistically downscaled to 2km (1979–2021) and corresponding solid ice discharge from Rignot et al.1 (1979–2017). As the solid ice discharge data set does not extend beyond 2017, we apply a linear regression on the 1979–2017 period to extrapolate the time series until 2021. Although potentially missing discharge events after 2017, using a linear regression is most appropriate provided its high statistical significance (R2=0.95, p value < 0.01), and the fact that AIS mass change variability is primarily driven by SMB processes6. Sectors include the APIS, WAIS, EAIS and surrounding Antarctic islands, i.e., detached from the grounded AIS, as well as the whole grounded AIS. For a meaningful comparison with remote sensing records, i.e., that do not discriminate surrounding islands from the AIS sectors, we partition the islands’ solid ice discharge and associated uncertainty (D=77 ± 5 Gt year−1 in 1979–2017) into the three AIS sectors. Best agreement with remote sensing is found by attributing 20% of the flux to the APIS, 10% to the EAIS and the remainder to the WAIS sector. Note that these adjustments are small relative to the total flux in each sector (1–7% of the regional total), which only marginally affects our mass change estimates. Uncertainty in regional solid ice discharge is derived from Rignot et al.1. Uncertainty in modelled SMB at 2km and 27km is estimated as one standard deviation around the 1979–2021 mean. To estimate modelled mass change uncertainty, we sum the corresponding uncertainty from SMB and D.

Mass anomalies of the AIS and its three sectors are derived from monthly GRACE and GRACE-FO spherical harmonic gravity field solutions from four processing centres, i.e., CSR RL06 from the Center for Space Research61, JPL RL06 from the Jet Propulsion Laboratory62, GFZ RL06 from the German Research Centre for Geosciences63 and ITSG-Grace2018 from TU Graz64. We added degree-1 spherical harmonics as in Swenson et al.65, and replaced the poorly resolved C20 and C30 coefficients by values estimated from satellite laser ranging, following the recommendations of the processing centres. Mass variations are estimated in 27 Antarctic basins following the method described in van Wessem et al.18, for each of the four GRACE/GRACE-FO datasets. The resulting time series are then combined using error-weighted averaging as in Wouters et al.66. The correction for glacial isostatic adjustment is based on the model by Ivins and James67.

Surface melt observations
For local melt evaluation, we use 81 annual melt records from ten AWS covering the period 1992–201824 (see “Methods”). Six stations are installed in Dronning Maud Land and four on the Larsen C ice shelf. We use the closest model grid-cell at 2km and 27km resolution for comparison with observations (Supplementary Fig. 2d, e). We also perform an Antarctic-wide point comparison between each grid-cell of the 2km and 27km products with QuikSCAT data at 4.45km resolution, averaged for the period 2000–2009 (Supplementary Fig. 5a) (see “Methods”). To that end, native RACMO2.3p2 and QuikSCAT data are interpolated onto the 2km grid to enable a grid-cell to grid-cell comparison. To remove spatial noise due to the different grid resolutions used, we apply an average filter of seven grid-cells (three pixels around the current one) to both high-resolution QuikSCAT and 2km downscaled melt data sets before performing the point comparison (Supplementary Fig. 5b–e). These point comparisons are performed both Antarctic-wide and in the vicinity of the grounding line, i.e., within a RACMO2.3p2-derived grounding line contour at 27km. We also compare Antarctic-wide (ANT) integrated melt from the 2km (black line in Fig. 2c) and 27km products (grey band in Fig. 2) with that of QuikSCAT (2000–2009, orange line in Fig. 2c)25. Uncertainty in QuikSCAT melt is estimated as one standard deviation around the 2000–2009 mean (orange band in Fig. 2c).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Please see uploaded file.", "reviewer_2": "Review of Noël et al., “Higher Antarctic ice sheet accumulation and melt rates revealed at 2 km\nresolution”\nWhat are the noteworthy results?\nStatistical downscaling at 2km resolution is an effective tool to produce estimates of Antarctic surface\nmass balance (SMB) and melt in the present-day and future. It improves upon models with coarser\nspatial resolution by better capturing the topography of the Antarctic ice sheet, particularly on the\nAntarctic Peninsula. Over the historical period, statistical downscaling at 2km increases SMB by 2% over\nthe Antarctic Ice Sheet compared to 27km resolution RACMO2.3p2. When this downscaled SMB is\ncombined with discharge using the input-output method from 1979-2021, the calculated mass balance\nis much closer to remote sensing observations of mass balance from GRACE. Also in the present day, it is\nfound that RACMO2.3p2 underestimates surface melting near the grounding line, due to an\noverestimate of elevation. In low, middle, and high-end projections for the future climate, the\nsystematic underestimate of melt persists in the 27km product compared to the 2km.\nWill the work be of significance to the field and related fields? How does it compare to the established\nliterature?\nThe work is original and highlights the important impacts of elevation on modeled SMB and surface\nmelt. Most climate models have a spatial resolution on the order of 10—100km, and this study\nemphasizes that these grids cannot resolve the complex topography of the Antarctic Ice Sheet, building\non Lenaerts et al. (2019). This leads to an underestimation of accumulation on the windward side of\nmountainous regions and an underestimation of melt near the grounding line. The paper contextualizes\nthe research gap by discussing results from Mottram et al. (2020) and Hansen et al. (2022), which show\nlarge variation in annual SMB estimates among multiple regional climate models linked to their grid\nresolutions. It builds on Dunmire et al. (2022), which evaluates SMB and melt in CESM2 in the historical\nperiod and examined future changes under three projected warming scenarios, providing important\ncontext for this work. Both Dunmire et al. (2022) and Kittel et al. (2021) highlight the contrast between\nmelt and SMB on the grounded Antarctic Ice Sheet vs ice shelves in future climate scenarios, and this\nwork expands on these studies by closely examining melt patterns along the grounding line at 2km\nresolution. The results are in agreement with previous studies on regional present and future SMB and\nmelt in Antarctica, for example Donat-Magnin et al. (2021). While prior studies like Gilbert and Kittel\n(2021) have quantified Antarctic-wide future melt and SMB, this work does so at unprecedented spatial\ndetail, enabling the authors to highlight the topographical effects missed in coarser resolution grids.\nDoes the work support the conclusions and claims, or is additional evidence needed?\nThe paper is clear regarding its scope and aims, explicitly stating that the primary purpose of statistical\ndownscaling is to correct SMB components for elevation biases between the coarse topography of\nRACMO2.3p2 (27km) and the 2km REMA topography. Strong evidence is provided to support the claims,\nincluding the statistical significance of results comparing the 27km product to the 2km product,\nuncertainty metrics for SMB and discharge, and thorough comparisons to multiple observational\ndatasets. The results will serve as a step forward in moving the field towards using Antarctic SMB\nproducts with finer spatial resolutions to examine the past, present, and future climate.\nAre there any flaws in the data analysis, interpretation, and conclusions? Do these prohibit publication\nor require revision?\nThe authors present context for uncertainties in modelled SMB among multiple regional climate models\nin the introduction, and thoroughly compare modelled SMB and melt to observations during the\nhistorical period in the results. However, I think this paper would benefit from additional discussion on\nthe uncertainties and biases that can be introduced or enhanced by the statistical downscaling method,\nin reference to existing biases in RACMO2.3p2, and how much these may influence the result – as well\nas in the future simulations, which rely on CESM2 and then RACMO2.3p2 prior to the downscaling.\nAdditionally, the methods describe that the land-ice dynamics component of CESM2 is not active,\nincluding changes in ice sheet thickness. This implies that SMB- and melt-elevation feedbacks due to\ndynamical changes the ice sheet are not accounted for in the future scenarios. The authors showed\nevidence of the critical importance of elevation in producing snowfall and melt estimates for Antarctica,\nespecially the Antarctic Peninsula. If the future projections do not account for changing ice sheet\nelevations in a warming climate, how might that impact the projected melt scenarios presented in the\nresults?\nRegarding the linear regression used to extend the discharge time series to 2021 (P3 L100), is there a\nprecedent for this method? From Figures 2a and 3, it is clear that extending the discharge time series\nprovides more data for comparison to GRACE-observed mass change, and that SMB is the primary driver\nfor short-term variability in the mass balance during the 2018-2021 period, while discharge experiences\na more linear increase. However, because the discharge and thus the mass change data are synthetic\nbeyond 2017, I think it would be important to indicate this in the figures. I would strongly recommend\nadapting the line style, changing the shading, or adding a vertical line in the timeseries to indicate the\nperiod when discharge data are extrapolated.\nNeither of these points prohibits publication. I would recommend minor revisions to address the\nuncertainty, the presentation of discharge extrapolated data, and minor comments below.\nIs the methodology sound? Does the work meet the expected standards in your field? Is there enough\ndetail provided in the methods for the work to be reproduced?\nThe methodology is sound and meets the expected standards in my field. The methods section is\ndetailed, enabling reproducibility of the work regarding the use of RACMO2.3p2, CESM2, and\nobservations from the AntSMB data set, GRACE, and melt records from AWS. Please see limitations\nbelow regarding the statistical downscaling method.\nLimitations\nMy primary limitation as a reviewer is that I do not have expertise in statistical downscaling. I cannot\ncomment on the approach nor the reproducibility of that part of the method.\nMinor Comments\nP1 L4: “Surface processes are thus essential to quantify AIS mass change” groups ice shelf viability in\nwith mass change; I would recommend either mentioning that ice shelf collapse can accelerate\ndischarge or more distinctly separate ice shelf viability from mass changes.\nP1 L12: It looks like the absolute melt increase is much greater in the high-end warming scenario than\nthe low and middle scenarios (particularly on the Antarctic Peninsula and ice shelves in Supplementary\nFigure 6), so perhaps it would be better to use “in all scenarios” instead of “irrespective”.\nP2 L41: “general melt underestimation in a regional climate model” – since multiple regional climate\nmodels are mentioned earlier in the paragraph, specificity would improve clarity here. Does this refer to\nRACMO2.3p1 and RACMO2.3p2?\nP3 L66 and Figure 1f: It would be helpful if the difference in the 2km and 27km ice masks at the margins\ncould be displayed in Figure 1f (as an outline of the 27km ice mask, or hatching, etc.), especially since\nthe effect is highlighted in the text. Is it clear to see the resolution difference in Figure 1e, but it is less\nclear in the difference plot between 2km and 27km.\nP3 L70 and Supplementary Figure 1: “2km – 27km” could be added at the top of each plot in\nSupplementary Figure 1, like in Figures 1c and 1f, to improve clarity.\nP3 L83: Including both the definition of ANT and the results describing an increase in SMB from the\n27km product to the 2km product forms a very long sentence here – would recommend simplifying or\nsplitting in two.\nP3 L85 and Figure 2: I find the colors used in Figures 2a and 2c slightly confusing, because almost the\nsame 3 colors are used in each figure to represent different things. For example, to me it is non-intuitive\nthat SMB AIS is portrayed in red in 2a and Melt AIS is portrayed in red in 2c, while at the same time SMB\nANT is in black in 2a and Melt ANT is in yellow in 2c. It would be helpful if the authors could explain the\nreasoning for these color choices in a response or modify the colors to improve the consistency of color\nchoices between the two figures.\nP5 L143 and Figure 4f: As mentioned previously regarding Figure 1f, it would also be helpful if the\ndifference in the 2km and 27km ice masks at the margins could be displayed in Figure 4f.\nP5 L159 and Supplementary Figure 2a: I count 10 yellow stars in the figure (6 in DML), while 9 are\nmentioned in the text. Also, the stars are difficult to spot in the figure, can you increase their size and/or\nchange the color to make them stand out better?\nP5 L178: “increase in melt over floating ice shelves”\nP12 L401: “(orange band in Figure 2d)”, “(black line in Figure 2d)”, and “(grey band in Figure 2d)” –\nshould this be referring to 2c?\nReferences\nCited in the paper and in the reviewer comments:\nDunmire et al. (2022)\nHansen et al. (2022)\nLenaerts et al. (2019)\nMottram et al. (2020)\nAdditional references:\nDonat-Magnin, Jourdain, Kittel, Agosta, Amory, Gallée, Krinner, and Chekki (2021). “Future surface mass\nbalance and surface melt in the Amundsen sector of the West Antarctic Ice Sheet”, The Cryosphere, doi:\n10.5194/tc-15-571-2021\nGilbert and Kittel (2021). “Surface Melt and Runoff on Antarctic Ice Shelves at 1.5deg C, 2deg C, and\n4deg C of Future Warming”, Geophysical Research Letters, doi: 10.1029/2020GL091733\nKittel, Amory, Agosta, Jourdain, Hofer, Delhasse, Doutreloup, Huot, Lang, Fichefet, and Fettweis (2021).\n“Diverging future surface mass balance between the Antarctic ice shelves and grounded ice sheet”,\nThe Cryosphere, doi: 10.5194/tc-15-1215-2021", "reviewer_3": "This is a very clear and well written manuscript describing a new downscaling of SMB in Antarctica using\nhigher resolution than existing current state of the art downscaled products. The implications of higher\nresolution downscaling are well described and are important. The authors describe in particular\ndifferences that appear in the total surface mass balance integrated over\nAntarctica, and also differences locally, in particular in the Antarctic Peninsula where topographic\ngradients are most important. In addition, the authors demonstrate clearly how the new downscaling is\nable to explain offsets in previous downscaling between remote sensing data and reconstruction\nmass balance time series. This was particularly important, as a simple comparative study between low\nand higher-order resolution would have been difficult to validate. The implications were also\nclearly explained in terms of projections and how this could impact the way surface melt-rates should be\ncomputed for increasingly warm scenarios at 2100 and beyond. I was very intrigued by the impact at the\ngrounding line in particular, though I thought the authors put too strong an emphasis\non hydrofracturing when describing the implications for ice shelf stability. More could be said about the\nimpact on runoff in particular which has been found to be significantly disruptive to\nregional ocean circulation, a controlling factor of basal melt rates, another key component of the\nstability of ice shelves. I suggested changes to the wording in more details (see attached\ncommented pdf). A little bit more effort should also be carried out in terms of anchoring this study\nagainst other existing downscaling efforts, maybe for example drawing from studies done at lower\nresolution for Greenland? Not being an SMB expert, I would like to understand how this anchored into\nthe existing literature.\nOverall, I think this is a worthy scientic addition to our knowledge, and should be shared with the\nCryospheric community, I therefore recommend it be published following minor revisions to address in\nparticular the way the study is introduced and anchored in the existing literature."}, "author_response": "Response letter: Higher Antarctic ice sheet accumulation and surface melt rates revealed at 2 km resolution.\nDear Reviewers, we would like to thank you for your positive and constructive comments, and for giving us the\nopportunity to revise our manuscript. Below you will find our point answers in blue, and textual changes in red.\nReviewer #1 (Remarks to the Author):\nGeneral comments\nIn this clearly-written paper, the authors present estimates of surface mass balance (SMB) and surface melt over\nAntarctica derived from simulations using the RACMO2 regional climate model run at 27 km resolution which\nwere statistically downscaled to an unprecedented 2 km resolution. Results are presented both for the present\nday (1979-2021) and for three future climate (to 2100) scenarios. For present-day climate, the downscaled\nsimulations exhibit a small (3%) increase in Antarctic-wide SMB that, for the first time, brings model SMB\nestimates into agreement with Antarctic Ice Sheet mass changes derived from GRACE satellite gravimetric\nmeasurements. Downscaled surface melt is also in better agreement with satellite-derived melt than is melt\nfrom the 27 km simulation. The 2 km downscaled future climate scenarios show that the underestimate of melt\nin the 27 km present-day simulation persists into the future, independent of scenario.\nThese are important results that will help to reduce uncertainties in estimates of present-day SMB over\nAntarctica and its likely trajectory under future climate scenarios. As changes in Antarctic SMB will have an\nincreasingly important impact on global sea level rise, this is a topic of high socioeconomic importance and I\nwould expect to see this paper prominently cited in the next IPCC report. I strongly recommend its publication\nin Nature Communications subject to attention to the points listed below. Thank you!\nSpecific Comments\n1. The downscaling approach used is based on the assumption that elevation is the most important control on\nthe variables of interest to calculating SMB, so variables from the 27 km model can be downscaled to the 2 km\ngrid by deriving local regression relationships of the variable as a function of elevation from the 27 km model\ndata and then using these, together with 2 km elevation data from REMA, to evaluate the variable on the 2 km\ngrid. This is a fairly standard approach to downscaling but it’s not perfect – there will be some regions where\nparameters other than elevation (e.g. distance from the coast, terrain slope, etc.) will be important. I’m not\nsuggesting that you should include all of these in your study but it might be worth including a few words on why\nyou believe that elevation is probably the strongest control on the relevant variables over most of Antarctica,\nwhile noting that other controls may be important in some limited regions. This is a good point. The revised\nmanuscript now includes new Supplementary Figure 7 and subsection Limitations and uncertainties in the\nMethods. This new subsection now 1) better supports our choices for elevation as main statistical downscaling\npredictor, 2) discusses the benefits of estimating elevation gradients at high spatial and temporal resolution in\nthe downscaling approach, and 3) lists remaining sources of uncertainties, as follows: “The statistical\ndownscaling technique used in this study builds on previous RACMO2-based downscaling efforts over glaciated\nsurfaces in Greenland29–32, the Canadian Arctic33, Svalbard34, and Iceland35. For all these land ice masses, the\ndownscaled product accurately represents the spatial and temporal SMB variations, as demonstrated by high\nand improved correlations with in-situ and remotely sensed observations. The statistical downscaling technique\nuses elevation and albedo as primary downscaling predictors and applies the downscaling at very high (daily)\ntemporal resolution. Three factors explain the robustness of the downscaling procedure. First, the spatial\nvariability of individual SMB components shows strong average correlation with elevation, also in Antarctica\n(Supplementary Fig. 7). Second, local albedo-driven surface melt anomalies are captured by implementing a\nspatial correction for ice albedo in the downscaling procedure, which accounts for solar zenith angle, slope, and\naspect of the underlying terrain as detailed in Noël et al. (2016)29. For instance, the albedo correction locally\nenhances surface melt over darker blue ice areas. As blue ice currently remains spatially limited in Antarctica,\nalbedo correction only contributes ~1/15th (3%) to the total surface melt increase obtained after downscaling\n(+46%). Finally, statistical downscaling estimates elevation gradients locally (using the current and at least five\nadjacent grid-cells) and at high temporal resolution (daily). This preserves the modelled spatial patterns and\nsynoptic timescales enabling to capture melt and accumulation spatiotemporal variability due to local surface\nslope, distance to the coast, and atmospheric circulation direction and strength affecting e.g., foehn events.\nSources of uncertainty that persist in the current statistical downscaling routine stem from local biases in: a) the\nmodelled elevation gradients (RACMO2.3p217,18 or CESM221) notably in regions characterized by steep slopes\nand sharp gradients, b) the high-resolution surface topography (REMA)28 used for the elevation correction,\nc) the ice sheet (IMBIE-2)2, glaciers (RGIv6)57 and ice shelf masks (Bedmap2)56 used for spatial integration, and d)\nthe satellite albedo product (MODIS) used for ice albedo correction.\naSMB bPrecipitation cMelt\ndSublimation eDriftingsnowerosion fRunoff\nCorrelationwithelevation[-]\nSupplementary Figure 7: Spatial correlation with elevation. Spatial correlation between annual modelled SMB\nand its components averaged for 1979-2021 and surface elevation prescribed in RACMO2.3p2 at 27 km. SMB\nand components include a SMB, b total precipitation, c surface melt, d total sublimation, e drifting snow erosion\nand f runoff. Spatial correlation is estimated using annual cumulative values over the current and its eight\nadjacent grid-cells. A positive/negative correlation means that the SMB component increases/decreases with\nelevation. Note that in the present-day climate, runoff is spatially limited with high local negative correlations\non Larsen B and C, Amery, and Wilkins ice shelves.\n2. Lines 74-76: “In particular, accumulation in the Antarctic Peninsula is enhanced on the western windward side\nof the mountain divide, and subsequently reduced on the eastern lee-side including the Larsen C ice shelf (Fig.\n1f), in line with the dry foehn winds that prevail in this region.” I’m not sure that I agree with this description. To\nme, Fig. 1f shows enhanced accumulation in the 2 km product above the crest of the Peninsula, with reductions\nrelative to the 27 km model at lower levels on both sides of the Peninsula. We agree with this observation, and\nreformulated as follows: “In particular, accumulation is enhanced above the crest of the Antarctic Peninsula,\nwith reductions relative to the 27 km grid at lower levels on both sides of the Peninsula and over Larsen C ice\nshelf (Fig. 1f), which is in line with the dry foehn winds that prevail in this region36.”\n3. Line 79: What are the heavy black lines on supplementary fig. 2a? There is no explanation of them in the figure\ncaption. This is now clarified in Supplementary Fig. 2a caption: “In-situ SMB sites (dots) that are situated very\nclose to each other form thick black lines (overlapping dots).”\n4. Line 84: “(sub-)Antarctic islands” is a confusing term. “Subantarctic islands” are generally defined as islands\nnorth of 60°S but south of the oceanic polar front (e.g. South Georgia). The islands included in your study would\ngenerally be classed as Antarctic islands. Thank you, we replaced “(sub-)Antarctic islands” by “Antarctic islands”.\n5. Lines 152-153: “No significant elevation difference is found over the flat floating ice shelves, except for local\ncrevasses that are resolved on the 2 km grid.” However, there are some quite large differences in melt between\nthe 27 km and 2 km grids across Larsen C, King George VI, Wilkins etc. If elevation here is the same at 27km and\n2km, what is driving these differences? Good point, we clarified as follows: “Small negative elevation differences\nare generally found over the relatively flat floating ice shelves (Supplementary Fig. 1a). Combined with the strong\nsurface melt gradients found nearby the grounding lines, this leads to an overall melt increase that locally peaks\nover crevasses that are not resolved at 27 km (Figs. 4c). In contrast, the Amery ice shelf shows a small positive\nelevation difference at 2 km, leading to a local melt decrease compared to the 27 km grid (Figs. 4c and\nSupplementary Fig. 1a). Locally, the albedo correction implies that surface melt further increases in regions\nwhere blue ice is exposed.”\n6. Lines 324-325: “To that end, we use a 2 km MODIS 16-day product averaged for the period 2000-2021.” This\nmay be appropriate for the present-day study, but it may become increasingly inaccurate into the future as melt\nregions expand. Here, we prescribe the present-day MODIS “blue ice” albedo with a base value of 0.55 when\nfirn retreats to expose blue ice both in RACMO2.3p2 at 27 km and at 2 km. This assumption loses validity in case\nfuture albedo is e.g., influenced by impurities, which would result in surface darkening (lower albedo), making\nour approach conservative. This is discussed in the new section Limitations and uncertainties as: “In addition,\nwe prescribe the present-day MODIS “blue ice” albedo with a base value of 0.55 when firn retreats to expose\nblue ice both in RACMO2.3p2 at 27 km and at 2 km. This approach remains valid in the absence of future surface\ndarkening due to biological growth (algae)59 or impurity deposition (black carbon, dust, ashes)60 as e.g., observed\nin Greenland. To our knowledge, ice darkening is not yet ongoing in Antarctica. In view of the above, our\nprojected, statistically downscaled surface melt rates should be interpreted as conservative estimates.”\n7. Lines 329-331: Isn’t this just repeating what you have already said in line 315, or am I misunderstanding it?\nYou are right, we reformulated as: “Statistical downscaling of total precipitation is motivated by the fact that the\nnative RACMO2.3p2 underestimates orographic precipitation, …”\nReviewer #2 (Remarks to the Author):\nStatistical downscaling at 2km resolution is an effective tool to produce estimates of Antarctic surface mass\nbalance (SMB) and melt in the present-day and future. It improves upon models with coarser spatial resolution\nby better capturing the topography of the Antarctic ice sheet, particularly on the Antarctic Peninsula. Over the\nhistorical period, statistical downscaling at 2km increases SMB by 2% over the Antarctic Ice Sheet compared to\n27km resolution RACMO2.3p2. When this downscaled SMB is combined with discharge using the input-output\nmethod from 1979-2021, the calculated mass balance is much closer to remote sensing observations of mass\nbalance from GRACE. Also in the present day, it is found that RACMO2.3p2 underestimates surface melting near\nthe grounding line, due to an overestimate of elevation. In low, middle, and high-end projections for the future\nclimate, the systematic underestimate of melt persists in the 27km product compared to the 2km.\nWill the work be of significance to the field and related fields? How does it compare to the established\nliterature? The work is original and highlights the important impacts of elevation on modeled SMB and surface\nmelt. Most climate models have a spatial resolution on the order of 10—100km, and this study emphasizes that\nthese grids cannot resolve the complex topography of the Antarctic Ice Sheet, building on Lenaerts et al. (2019).\nThis leads to an underestimation of accumulation on the windward side of mountainous regions and an\nunderestimation of melt near the grounding line. The paper contextualizes the research gap by discussing results\nfrom Mottram et al. (2020) and Hansen et al. (2022), which show large variation in annual SMB estimates among\nmultiple regional climate models linked to their grid resolutions. It builds on Dunmire et al. (2022), which\nevaluates SMB and melt in CESM2 in the historical period and examined future changes under three projected\nwarming scenarios, providing important context for this work. Both Dunmire et al. (2022) and Kittel et al. (2021)\nhighlight the contrast between melt and SMB on the grounded Antarctic Ice Sheet vs ice shelves in future climate\nscenarios, and this work expands on these studies by closely examining melt patterns along the grounding line\nat 2km resolution. The results are in agreement with previous studies on regional present and future SMB and\nmelt in Antarctica, for example Donat-Magnin et al. (2021). While prior studies like Gilbert and Kittel (2021) have\nquantified Antarctic-wide future melt and SMB, this work does so at unprecedented spatial detail, enabling the\nauthors to highlight the topographical effects missed in coarser resolution grids. Thank you!\nDoes the work support the conclusions and claims, or is additional evidence needed? The paper is clear\nregarding its scope and aims, explicitly stating that the primary purpose of statistical downscaling is to correct\nSMB components for elevation biases between the coarse topography of RACMO2.3p2 (27km) and the 2km\nREMA topography. Strong evidence is provided to support the claims, including the statistical significance of\nresults comparing the 27km product to the 2km product, uncertainty metrics for SMB and discharge, and\nthorough comparisons to multiple observational datasets. The results will serve as a step forward in moving the\nfield towards using Antarctic SMB products with finer spatial resolutions to examine the past, present, and future\nclimate. Thank you for the kind words!\nAre there any flaws in the data analysis, interpretation, and conclusions? Do these prohibit publication or\nrequire revision? The authors present context for uncertainties in modelled SMB among multiple regional climate\nmodels in the introduction, and thoroughly compare modelled SMB and melt to observations during the\nhistorical period in the results. However, I think this paper would benefit from additional discussion on the\nuncertainties and biases that can be introduced or enhanced by the statistical downscaling method, in reference\nto existing biases in RACMO2.3p2, and how much these may influence the result – as well as in the future\nsimulations, which rely on CESM2 and then RACMO2.3p2 prior to the downscaling. The revised manuscript now\nincludes new Supplementary Figure 7 and subsection Limitations and uncertainties in the Methods. This new\nsubsection now 1) better supports our choices for elevation as main statistical downscaling predictor, 2)\ndiscusses the benefits of estimating elevation gradients at high spatial and temporal resolution in the\ndownscaling approach, and 3) lists remaining sources of uncertainties. See specific comment #1 of Reviewer #1.\nAdditionally, the methods describe that the land-ice dynamics component of CESM2 is not active, including\nchanges in ice sheet thickness. This implies that SMB and melt-elevation feedbacks due to dynamical changes\nthe ice sheet are not accounted for in the future scenarios. The authors showed evidence of the critical\nimportance of elevation in producing snowfall and melt estimates for Antarctica, especially the Antarctic\nPeninsula. If the future projections do not account for changing ice sheet elevations in a warming climate, how\nmight that impact the projected melt scenarios presented in the results? RACMO2.3p2 uses a fixed present-day\ntopography and ice geometry. Furthermore, the CESM2 scenario projections used to force RACMO2.3p2 did not\nhave ice dynamics activated (Van Wessem et al., 2023), and to our knowledge, none of the available CMIP6\nCESM2 future projections currently have ice dynamics activated for the AIS (Dunmire et al., 2023). In the revised\nmanuscript, we discuss this limitation and potential impacts on our results in the new Limitations and\nuncertainties section as: “RACMO2.3p217,18 and none of the CESM2 scenarios21 currently available implement\nthe effect of ice dynamics in Antarctica, i.e., fixed present-day conditions were prescribed in our future\nprojections. Under climate warming, the AIS and floating ice shelves are expected to retreat, calve and/or\nthicken/thin, which would affect the circulation and associated precipitation distribution as well as melt\npatterns. For instance, marginal ice thinning could trigger melt-elevation feedback, further enhancing the\nprojected surface melt rates, while snowfall-driven ice thickening could reduce melt. These competing\nmechanisms will be investigated by statistically downscaling future melt projections from forthcoming fully\ncoupled earth system models that include Antarctic ice dynamics when these become available, as was\npreviously done for the Greenland ice sheet35.”\nRegarding the linear regression used to extend the discharge time series to 2021 (P3 L100), is there a precedent\nfor this method? We deem our approach appropriate given the significance of the linear regression (R2=0.95, p-\nvalue<0.01). It is now textually and visually clarified that solid ice discharge data after 2017 were extrapolated\n(see new Figure 3 below). This is now also clarified in the Methods as: “Although potentially missing discharge\nevents after 2017, using a linear regression is most appropriate provided its high statistical significance (R2=0.95,\np-value < 0.01), and the fact that AIS mass change variability is primarily driven by SMB processes6.”\nFrom Figures 2a and 3, it is clear that extending the discharge time series provides more data for comparison to\nGRACE-observed mass change, and that SMB is the primary driver for short-term variability in the mass balance\nduring the 2018-2021 period, while discharge experiences a more linear increase. However, because the\ndischarge and thus the mass change data are synthetic beyond 2017, I think it would be important to indicate\nthis in the figures. I would strongly recommend adapting the line style, changing the shading, or adding a vertical\nline in the timeseries to indicate the period when discharge data are extrapolated. Good point, we clarified this\nby displaying mass change over 2018-2021 as dashed lines. Figures 3 and S4 caption have been modified\naccordingly: “As the solid ice discharge data set does not extend beyond 2017, it is linearly extrapolated\nthereafter. The resulting MB at both resolutions is shown as dashed lines after 2017.”\nNeither of these points prohibits publication. I would recommend minor revisions to address the uncertainty,\nthe presentation of discharge extrapolated data, and minor comments below. Thank you, we hope that our\nresponses address your remaining comments.\nIs the methodology sound? Does the work meet the expected standards in your field? Is there enough detail\nprovided in the methods for the work to be reproduced? The methodology is sound and meets the expected\nstandards in my field. The methods section is detailed, enabling reproducibility of the work regarding the use of\nRACMO2.3p2, CESM2, and observations from the AntSMB data set, GRACE, and melt records from AWS. Please\nsee limitations below regarding the statistical downscaling method. Thank you!\nLimitations My primary limitation as a reviewer is that I do not have expertise in statistical downscaling. I cannot\ncomment on the approach nor the reproducibility of that part of the method. We hope that the revised Methods\nsection is sufficiently detailed, now including a new subsection on uncertainties. For a full description of the\ndownscaling procedure, we refer to Noël et al. (2016).\nMinor Comments\nP1 L4: “Surface processes are thus essential to quantify AIS mass change” groups ice shelf viability in with mass\nchange; I would recommend either mentioning that ice shelf collapse can accelerate discharge or more distinctly\nseparate ice shelf viability from mass changes. We reformulated as: “…, while meltwater ponding can trigger ice\nshelf collapse potentially accelerating discharge. Surface processes are thus essential to …”\nP1 L12: It looks like the absolute melt increase is much greater in the high-end warming scenario than the low\nand middle scenarios (particularly on the Antarctic Peninsula and ice shelves in Supplementary Figure 6), so\nperhaps it would be better to use “in all scenarios” instead of “irrespective”. We replaced by “in all”.\nP2 L41: “general melt underestimation in a regional climate model” – since multiple regional climate models are\nmentioned earlier in the paragraph, specificity would improve clarity here. Does this refer to RACMO2.3p1 and\nRACMO2.3p2? We clarified as: “… in e.g., the regional climate model RACMO2.3p218.”\nP3 L66 and Figure 1f: It would be helpful if the difference in the 2km and 27km ice masks at the margins could\nbe displayed in Figure 1f (as an outline of the 27km ice mask, or hatching, etc.), especially since the effect is\nhighlighted in the text. It is clear to see the resolution difference in Figure 1e, but it is less clear in the difference\nplot between 2km and 27km. To avoid Figure 1 becoming too crowded, we added two subpanels in\nSupplementary Figure 1 including the ice mask difference between both resolutions. We refer to new\nSupplementary Figures S1 c,d where appropriate and modified the caption accordingly: “c and d Difference in\nice mask between the 2 km and 27 km grids. The common ice mask is shown in grey, added and removed ice\npixels in the 2 km grid are displayed in blue and red, respectively. Black contour lines in a-d represent the ice\nsheet and ice shelves extent at 2 km.”\na2km-27km b2km-27km\nc2km-27km d2km-27km\nP3 L70 and Supplementary Figure 1: “2km – 27km” could be added at the top of each plot in Supplementary\nFigure 1, like in Figures 1c and 1f, to improve clarity. Done.\nP3 L83: Including both the definition of ANT and the results describing an increase in SMB from the 27km product\nto the 2km product forms a very long sentence here – would recommend simplifying or splitting in two. We split\nthe sentence into two as: “We integrate SMB over the whole of Antarctica (ANT), including the grounded AIS,\nadjacent Antarctic islands, and floating ice shelves. Accumulation in the 2 km product …”.\nP3 L85 and Figure 2: I find the colors used in Figures 2a and 2c slightly confusing, because almost the same 3\ncolors are used in each figure to represent different things. For example, to me it is non-intuitive that SMB AIS\nis portrayed in red in 2a and Melt AIS is portrayed in red in 2c, while at the same time SMB ANT is in black in 2a\nand Melt ANT is in yellow in 2c. It would be helpful if the authors could explain the reasoning for these color\nchoices in a response or modify the colors to improve the consistency of color choices between the two figures.\nTo clarify, we show AIS data in red (a SMB, b melt), ANT data in black, and observational data in orange (a\nDischarge, b QuickSCAT). For consistency, discharge data extrapolated beyond 2017 are shown as a dashed line.\nWe modified the figure caption accordingly.\nP5 L143 and Figure 4f: As mentioned previously regarding Figure 1f, it would also be helpful if the difference in\nthe 2km and 27km ice masks at the margins could be displayed in Figure 4f. The ice mask difference is now\nshown in Supplementary Fig. 1c, d.\nP5 L159 and Supplementary Figure 2a: I count 10 yellow stars in the figure (6 in DML), while 9 are\nmentioned in the text. Also, the stars are difficult to spot in the figure, can you increase their size and/or\nchange the color to make them stand out better? Thank you for spotting this, indeed there were 10 instead of 9 sites.\nThis is corrected, and we increased the size of the yellow stars.\nP5 L178: “increase in melt over floating ice shelves”. Done.\nP12 L401: “(orange band in Figure 2d)”, “(black line in Figure 2d)”, and “(grey band in Figure 2d)” –\nshould this be referring to 2c? This is corrected.\nReferences Cited in the paper and in the reviewer comments:\n• Dunmire et al. (2022), Hansen et al. (2022), Lenaerts et al. (2019), Mottram et al. (2020).\nAdditional references:\n• Donat-Magnin, Jourdain, Kittel, Agosta, Amory, Gallée, Krinner, and Chekki (2021). “Future surface mass balance and surface melt in\nthe Amundsen sector of the West Antarctic Ice Sheet”, The Cryosphere, doi: 10.5194/tc-15-571-2021\n• Gilbert and Kittel (2021). “Surface Melt and Runoff on Antarctic Ice Shelves at 1.5deg C, 2deg C, and 4deg C of Future Warming”,\nGeophysical Research Letters, doi: 10.1029/2020GL091733\n• Kittel, Amory, Agosta, Jourdain, Hofer, Delhasse, Doutreloup, Huot, Lang, Fichefet, and Fettweis (2021). “Diverging future surface mass\nbalance between the Antarctic ice shelves and grounded ice sheet”, The Cryosphere, doi: 10.5194/tc-15-1215-2021\nReviewer #3 (Remarks to the Author):\nThis is a very clear and well written manuscript describing a new downscaling of SMB in Antarctica using higher\nresolution than existing current state of the art downscaled products. The implications of higher resolution\ndownscaling are well described and are important. The authors describe in particular differences that appear in\nthe total surface mass balance integrated over Antarctica, and also differences locally, in particular in the\nAntarctic Peninsula where topographic gradients are most important. In addition, the authors demonstrate\nclearly how the new downscaling is able to explain offsets in previous downscaling between remote sensing data\nand reconstruction mass balance time series. This was particularly important, as a simple comparative study\nbetween low and higher-order resolution would have been difficult to validate. The implications were also\nclearly explained in terms of projections and how this could impact the way surface melt-rates should be\ncomputed for increasingly warm scenarios at 2100 and beyond. I was very intrigued by the impact at the\ngrounding line in particular, though I thought the authors put too strong an emphasis on hydrofracturing when\ndescribing the implications for ice shelf stability. More could be said about the impact on runoff in particular\nwhich has been found to be significantly disruptive to regional ocean circulation, a controlling factor of basal\nmelt rates, another key component of the stability of ice shelves. I suggested changes to the wording in more\ndetails (see attached commented pdf). A little bit more effort should also be carried out in terms of anchoring\nthis study against other existing downscaling efforts, maybe for example drawing from studies done at lower\nresolution for Greenland? Not being an SMB expert, I would like to understand how this anchored into\nthe existing literature. Overall, I think this is a worthy scientic addition to our knowledge, and should be shared with\nthe Cryospheric community, I therefore recommend it be published following minor revisions to address in\nparticular the way the study is introduced and anchored in the existing literature. Thank you, we grouped your\nthree main comments and our replies below:\n1. More could be said about the impact on runoff in particular which has been found to be significantly\ndisruptive to regional ocean circulation, a controlling factor of basal melt rates, another key component of\nthe stability of ice shelves. Thank you for providing this insight, in the revised manuscript we now mention\nrunoff as potentially important for ocean circulation and ice shelf stability: “As for Greenland fjords, (future)\nincrease in surface and subsurface runoff have the potential to enhance sub-shelf basal melting and\nsubsequent destabilisation by thinning ice shelves from below4. These combined processes, i.e., surface\nmeltwater ponding and basal melt, can trigger ice shelf disintegration by hydrofracturing, a mechanism that\nreduces the buttressing effect on the grounded AIS to eventually accelerate solid ice discharge and sea-level\nrise13.”\n2. I suggested changes to the wording in more details (see attached commented pdf). Please, find our\nresponses below.\n3. A little bit more effort should also be carried out in terms of anchoring this study against other existing\ndownscaling efforts, maybe for example drawing from studies done at lower resolution for Greenland? Not\nbeing an SMB expert, I would like to understand how this anchored into the existing literature. In the revised\nmanuscript, we now introduce past SMB downscaling studies: “The ability of statistical downscaling to refine\nthe spatial distribution of SMB components was first demonstrated for the Greenland ice sheet, where the\ndownscaled product realistically captured high mass loss rates over narrow ablation zones and outlet\nglaciers that are typically unresolved in RACMO2.3p229. Likewise, statistical downscaling to (sub-)kilometer\nspatial resolution proved essential to accurately quantify contemporary (and projected) mass change of the\nGreenland ice sheet30,31, its peripheral ice caps32, glaciers of the Canadian Arctic33, Svalbard34, and Iceland35,\nand their contribution to global sea-level rise.”\nPoint comments:\nL4 Among other factors. Melt ponding indeed, but in particular basal melt rates, and rifting/cracking.\nSee our response to comment #1.\nL24 Again I agree, but let’s not forget the bottom of the ice shelves, plenty of processes here that could do the\nsame as hydrofracturing. See our response to comment #1.\nL25 Debatable on Larsen A, which was heavily fractured prior to ponding. Larsen B indeed was ponding prior to\ndisintegration. Thank you, we removed the reference to Larsen A: “This has previously occurred in the Antarctic\nPeninsula over Larsen B ice shelf (March 2002)14.”\nL41 Nice transition. Thank you, note the suggestion from Reviewer #2: “… in e.g., the regional climate model\nRACMO2.3p218.”\nL48 Do not assume that the community understand regional downscaling. Please explain it with 1-2 sentences\nof introduction. Especially that you have RACMO output, ERA5 reanalysis in the past, CESM2 in the future, who\nis forcing what? who is regional, who is global? Thank you, we reformulated as: “Here we present new, daily\nSMB and surface melt products covering the grounded AIS and floating ice shelves at 2 km spatial resolution for\nthe contemporary climate and three scenario projections until 2100. As a first step, present-day climate from\nthe global climate reanalysis ERA526 (1979-2021) and three global climate projections from the Community Earth\nSystem Model (CESM2)27 under a low (SSP1-2.6), moderate (SSP2-4.5), and high-end (SSP5-8.5) warming\nscenario (1950-2099) are used as lateral forcing for the Regional Atmospheric Climate Model (RACMO2.3p2)17,18,\nwhich simulates the contemporary and future SMB (components) of Antarctica on a 27 km grid (see Methods).\nAs a second step, statistical downscaling is applied to correct these SMB components …”\nL52 I would put this statement earlier on in the introduction, would make the article more readable earlier on.\nWe modified the text as suggested in the comment in L48. We hope that our changes also address this comment.\nL65 Fig1 You could reduce the number of insets here, because i honestly can't tell the difference between\nAntarctica at 2 or 27km, but I can definitely see on the diff image what is going on. It would declutter your\nfigures, which have a lot of content. Although we see your point, we also feel that it is important to show both\nthe low-resolution model and the high-resolution downscaled products to visualize the spatial improvements.\nThis is less evident on a difference plot, so we kept Figures 1 and 4 as are.\nL101 Do you really need to do this? To quantify mass change estimates beyond 2017, we needed a relevant way\nto extend the solid ice discharge times series. A linear regression was the most relevant approach given its high\nstatistical significance (R2=0.95, p-value<0.01). This is now also clarified in the Methods as: “Although potentially\nmissing discharge events after 2017, using a linear regression is most appropriate provided its high statistical\nsignificance (R2=0.95, p-value < 0.01), and the fact that AIS mass change variability is primarily driven by SMB\nprocesses6.” We followed Reviewer #2 suggestion to display the extrapolated D data as dashed lines.\nI understand scientists want the latest and greatest, but I can accommodate the estimates until 2017 instead of\n2021. Why not then until 2023? Our GRACE mass change estimates do not currently extend to 2023, the\n(downscaled) RACMO2.3p2-ERA5 product is not extended to 2023 yet.\nI think taking the risk of a linear regression won't add to your paper here. You could miss big events in D by doing\nthis (ex: calving events, increases in calving, as demonstrated recently by Greene et al, 2023). We agree that the\napproach could miss discharge events but deem that our approach, in the absence of updated and published\ndischarge data, still provides relevant estimates. See our answer to comment in L101.\nL136 if you talk about ice shelf melt rates, to me this means basal. I would pay attention to avoiding biasing the\nwriteup of the paper too much to the surface community. This is a nice paper, very clear, but there are trigger\nwords here that make me think a lot about the bottom of the ice shelves, when it's not the goal of the paper.\nThank you, we clarified this where appropriate. The term “surface melt” now appears in the revised title: “Higher\nAntarctic ice sheet accumulation and surface melt rates revealed at 2 km resolution”\nL137 I can think also of water runoff completely modifying ocean circulation und\ner ice shelves, consequentially impacting basal melt rates, and sometimes restabilizing an ice shelf (Poinelli et\nal, 2022). Or interactions of rifts with ocean circulation. So be careful in accepting hydrofracturing as the only\nprocess destabilizing/stabilizing ice shelves. To clarify, we start the sentence with: “Among other processes4, …”.\nWhile keeping the study focused and concise, this avoids digressions from our main message on surface melt\nand SMB.\nL144 This is one of the points you are raising that worry me. There are significant changes in calving front position\nin the Peninsula. At 2 km, they are well captured, at 27 km, not so much. Unless I missed it, I would like to a\ndiscussion of how the 2 km and 27 km masks were built. Good point. RACMO2.3p2 ice mask and topography at\n27 km are down-sampled from the 1 km resolution digital elevation model of Bamber et al. (2009). We clarified\nas: “Surface topography is prescribed from the 1 km digital elevation model of Bamber et al. (2009)47, down-\nsampled to 27 km.” The Methods section provides resources used in the downscaled product, which combines\nthe ice sheet mask from IMBIE-2, land ice on Antarctic islands from RGIv6, mask of floating ice shelves from\nBedmap2, and the digital elevation model REMA down-sampled to 2 km.\nWere they changing in time, which would impact your conclusions I would guess. We distinguish two ice mask\nchanges: (1) those that result from the downscaling procedure (27 km to 2 km), (2) those resulting from a\nchanging Antarctic ice sheet mask in a future warmer climate. The ice mask extent difference referred to in the\ntext is the former (1), which is now made explicit in new Supplementary Figures 1c,d (see below). Regarding the\nclimate-related change, note that neither RACMO2.3p2 nor any of the available CESM2 scenarios had Antarctic\nice dynamics activated, i.e., ice mask and topography were kept fixed in the present-day and future projections.\nTo clarify, we added the following to the text of the Methods section: “In the absence of interactive ice dynamics\nin RACMO2.3p2, ice mask and topography are kept fixed.”\na2km-27km b2km-27km\nc2km-27km d2km-27km\nL175 Again I would like to know whether the mask is dynamic. If fixed, how does this work? How much mass are\nyou losing at the edges. For ISMIP6, this was a significant concern when we used RACMO for mass balance\nassessments. We have to figure out a strategy to avoid losing mass between intercompared model results. I\nneed a little bit more explanation and numbers on the transient mask evolution and the impact on your\ncomparisons. See our previous response, the ice mask is not dynamic, i.e., ice is fixed and cannot retreat,\nthicken/thin, or calve. The main reason for doing this is that ice dynamics is not included in any of the models\nused in this paper (and in CMIP6 for that matter). The only ice mask difference discussed in the paper is that\noccurring when downscaling the 27 km grid onto the 2 km grid, see new Supplementary Figures 1c,d (also\npresented above). In the revised manuscript, we now elaborate on this in the new Limitations and uncertainty\nsection: “RACMO2.3p217,18 and none of the CESM2 scenarios21 currently available implement the effect of ice\ndynamics in Antarctica, i.e., fixed present-day conditions were prescribed in our future projections. Under\nclimate warming, the AIS and floating ice shelves are expected to retreat, calve and/or thicken/thin, which would\naffect the circulation and associated precipitation distribution as well as melt patterns. For instance, marginal\nice thinning could trigger melt-elevation feedback, further enhancing the projected surface melt rates, while\nsnowfall-driven ice thickening could reduce melt. These competing mechanisms will be investigated by\nstatistically downscaling future melt projections from forthcoming fully coupled earth system models that\ninclude Antarctic ice dynamics when these become available, as was previously done for the Greenland ice\nsheet35.”\nL182 I'm struggling to understand why? Is it katabatic winds that are better modeled? Pure downscaling effects?\nIt would be nice to understand why, because the implications for grounded line dynamical retreat are significant\npotentially. We broke down the components of the surface melt increase in the revised manuscript, where it is\nnow described as follows: “The elevation correction contributes 38 Gt yr−1 (34%) to the total surface melt\nincrease (Supplementary Fig. 1a,b). This effect is particularly important near the grounding line where surface\nelevation is generally reduced at 2 km and steep topographic gradients were not accurately captured at 27 km.\nOver low-lying ice shelves, the combined elevation difference and strong melt gradients locally enhance surface\nmelt at 2 km. Spatial refinement of the ice mask from 27 km to 2 km contributes 10 Gt yr−1 (9%) to the total melt\nincrease (Supplementary Fig. 1c,d), while the remaining 3 Gt yr−1 (3%) stem from albedo correction over blue ice\nareas.”\nL193 sorry to harp on this, you need to consistently change this to \"surface melt\", or clearly explain somewhere\nin the introduction that you are never referring to basal melt rates. Being a dynamicist, to me dh/dt=div Hu +\nSMB + BMB, so ice thinning is controlled by surface and basal melt rate as well as flux divergence. You are \"only\"\ndealing with 1 or 4 processes here. Avoiding confusion is necessary I think to clarify the impact of your paper.\nThank you, we adjusted the manuscript accordingly.\nL211 Very nice. Thank you!\nFigures I can barely read this, even on my large screen with magnification. This is not readable in A4 format.\nPlease increase font size for every image. We increased the font size when possible.\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have responded positively to comments that I made on an earlier version of this manuscript\nand I am now happy to recommend publication of the paper without further revision.\nReviewer #2 (Remarks to the Author):\nThe authors have addressed my comments in a satisfactory manner. In particular, the authors added a\nLimitations and Uncertainties section in the methods, which addresses my primary comments about\nuncertainties and contextualizes their results in the current state of Antarctic climate modeling. I am\nhappy to recommend publication of the manuscript in its current form.\nResponse letter: Higher Antarctic ice sheet accumulation and surface melt rates revealed at 2 km resolution.\nReviewer #1 (Remarks to the Author):\nThe authors have responded positively to comments that I made on an earlier version of this manuscript and I\nam now happy to recommend publication of the paper without further revision.\nThank you very much for your positive comments that helped us improve the quality and robustness of our\nmanuscript.\nReviewer #2 (Remarks to the Author):\nThe authors have addressed my comments in a satisfactory manner. In particular, the authors added a\nLimitations and Uncertainties section in the methods, which addresses my primary comments about\nuncertainties and contextualizes their results in the current state of Antarctic climate modeling. I am happy to\nrecommend publication of the manuscript in its current form.\nThank you very much for your positive comments that helped us improve the quality and robustness of our\nmanuscript."}]

Decision letter