Meltwater ponding has an underestimated radiative effect on the surface of the Greenland Ice Sheet

Domain: earth_science
Published: 2025-09-12
Editorial outcome: minor_revision

Abstract
Ponding of meltwater on the surface of the Greenland Ice Sheet has the potential to reduce ice sheet albedo and amplify mass loss. However, this process remains poorly constrained and is absent from models that project ice sheet mass balance. Here we demonstrate that meltwater ponding considerably increases the amount of energy available for melting the Greenland Ice Sheet. We first use satellite-derived products to show that meltwater ponding has a significant impact on spatial albedo patterns, particularly in the lower percolation zone. We then use drone imagery to demonstrate that, in the upper ablation zone, there are thousands of narrow streams and small pools (<100 m²) that collectively account for >50% of the total meltwater area. These small meltwater features are not resolved by surface water maps derived from medium-resolution satellite imagery, signifying that the radiative effect of meltwater ponding is three to four times stronger than predicted by satellite-based approaches. Our findings therefore place lower bounds on the radiative effect of meltwater ponding that could be used to advocate for the inclusion of this process into models that forecast Greenland Ice Sheet’s contribution to sea-level rise.

Introduction
The Greenland Ice Sheet has been losing mass since the 1990s and was responsible for ~20% of global sea-level rise during the 2006–2018 period1. This mass loss has largely been driven by an increase in surface melt and runoff caused by higher air temperatures2–4. Radiative feedbacks between melt and ice sheet surface albedo make the ice sheet especially sensitive to increasing air temperatures (also known as the “melt-albedo feedback”)5–7. As the climate warms, melt increases, reducing albedo, leading to more shortwave energy absorption and more melting. Constraining the strength of this feedback is therefore critical for accurate modeling of current and future meltwater runoff from the Greenland Ice Sheet to the global ocean.

Several processes (e.g., snowline fluctuations, ice algae, snow grain size) are responsible for ice sheet radiative feedbacks, many of which have been extensively studied6,8–10. One process that has received less attention is meltwater ponding. During the Northern Hemisphere summer, vast amounts of meltwater are produced on the Greenland Ice Sheet surface11–13. Much of this meltwater is temporarily stored on the surface at a variety of scales, from small cylindrical cryoconite holes with diameters of centimeters to large supraglacial lakes with areas of up to ~10 km2 14–17. Remote sensing and field measurements demonstrate that supraglacial meltwater features have low albedo and melt rates that are 110–170% higher than nearby non-inundated ice sheet surfaces18–20. However, despite evidence that meltwater ponding is a key control on albedo and melt in the ablation zone21,22, these local measurements have not been upscaled to the entire ice sheet. The radiative effect of meltwater ponding, and its importance relative to other processes that reduce ice sheet albedo, therefore remains unconstrained.

The increasing availability of medium-resolution satellite imagery now enables meltwater ponding to be mapped accurately at large spatial scales. Several studies have used Landsat and Sentinel-2 to quantify the extent of supraglacial lakes and rivers in specific sectors of the ice sheet at a spatial resolution of 10–30 m23–28. More recently, Landsat and Sentinel-2 were used to map supraglacial lakes29,30 and rivers31 across the entire ice sheet. Finally, one study mapped all surface water contained in lakes, rivers, and water-filled crevasses with 10 m Sentinel-2 imagery32. These maps represent a snapshot of surface water coverage for a cool (2018) and a warm (2019) summer and provide a first opportunity to directly investigate the radiative effect of meltwater ponding.

Here, we provide observational constraints on the radiative effect of meltwater ponding using a combination of satellite and drone remote sensing. We first introduce an empirical method to quantify the reduction of surface albedo (Δα) caused by meltwater ponding. To do this, we intersect surface water maps from Sentinel-232 (from Jul 25–30, 2018, and Jul 29–Aug 5, 2019) with coincident MCD43A3 albedo data derived from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard NASA’s Aqua and Terra satellites. Next, we combine Δα with downward shortwave radiation and near-surface air temperatures from MERRA-2 atmospheric reanalysis to quantify the radiative effect of meltwater ponding and its strength relative to other processes that reduce the ice sheet albedo. Finally, we evaluate our findings using high-resolution (30 cm/pixel) drone imagery from 2015 covering almost 300 km2 of the ice sheet. Our drone imagery reveals many narrow streams and small pools that are not resolved by medium-resolution satellite imagery (i.e., Sentinel-2), implying that satellite-based estimates of the radiative effect caused by meltwater ponding should be considered a lower-bound.

Results
Contribution of meltwater ponding to spatial albedo variability
By intersecting satellite-derived surface water maps with surface albedo products (see Methods), we confirm that meltwater ponding considerably influences spatial albedo variability in late-Jul/early-Aug (Fig. 1a–d). The strongest impact occurs between 1000–2000 m a.s.l., where ponding accounts for up to 51% of albedo variability at short length scales (i.e., <3 km; Fig. 2a). Here “length scale” refers to the side length of the search window used for quantifying albedo variability (see Methods). At longer length scales (25 km), ponding still accounts for 24.2% of albedo variability at 1000 m a.s.l., consistent with a previous study reporting 15% in the western ice sheet at the same elevation (length scale also 25 km)16. At lower elevations (0–600 m a.s.l.), ponding accounts for a smaller proportion of albedo variability (8.1% at 25 km), likely because steep slopes at the margins of the ice sheet promote efficient drainage of meltwater (Fig. 2b)33. Above 2000 m a.s.l., the effect of meltwater ponding on albedo variability diminishes (3.4% at 25 km), likely reflecting less surface melt (Fig. 2b). Our findings therefore suggest that meltwater ponding contributes considerably to spatial albedo variability, especially in the upper ablation and lower percolation zones.

Radiative effect of meltwater ponding
Surface albedo is strongly negatively correlated with the area of meltwater ponding (mean r=−0.63) at short length scales (i.e. ≤ 10 km; Fig. S1). The slopes of these local linear regressions between surface water fraction and albedo average −0.11±0.06 (μ±σ) at length scales ≤ 10 km, meaning that for every 10% increase in surface water fraction, albedo is reduced by 0.011± 0.006 (see Methods). We combine this albedo-lowering effect with satellite-derived surface water maps and downward shortwave radiation from MERRA-2 reanalysis to quantify the radiative effect of meltwater ponding (for a detailed discussion of the uncertainties associated with the use of a static albedo-lowering effect, refer to the Assumptions and Uncertainties section in the Methods). We find that the radiative effect of meltwater ponding exhibits large elevational variations. At 1000–2000 m a.s.l., where large amounts of meltwater accumulate on the surface, the radiative effect averaged 0.30± 0.16 W m−2 in the summer of 2019 (Fig. 2b, c). At lower (<600 m a.s.l.) and higher (>2000 m a.s.l.) elevations, the radiative effect of meltwater ponding is smaller, averaging 0.13 ± 0.07 W m−2 and 0.002 ± 0.001 W m−2, respectively (Fig. 2b, c). Summed across the ice sheet, we find that meltwater ponding enhances energy absorption by 5.7±3.1 PJ d−1 in Jul 25–30, 2018 to 15.7±8.6 PJ d−1 in Jul 29–Aug 5, 2019 (Fig. 2d). For context, spatiotemporal albedo variability enhanced ice sheet energy absorption by 2176 PJ d−1 in the summer of 20196. Therefore, our satellite-based analysis indicates that the radiative effect caused by meltwater ponding accounted for 0.7±0.4% of the total ice sheet surface shortwave radiative effect. We note that this value represents mid- to late-summer hydrological conditions and that the contribution of meltwater ponding to total shortwave radiative effect may be higher earlier in the summer.

We find that the radiative effect of meltwater ponding is strongest within the percolation zone34,35. In North Greenland, for example, the radiative effect of meltwater ponding is higher at 1000–1200 m a.s.l. (1.0±0.5 W m−2) than at lower elevations (e.g., 0.1±0.06 W m−2 at 200–400 m a.s.l.; Fig. 2e). Likewise, in Southwest Greenland, the radiative effect of meltwater ponding is highest at 1600–1800 m a.s.l. (1.9±1.0 W m−2; Fig. 2e). This is over six times higher than the radiative effect of meltwater ponding in the dark zone, which is located at 1000–1400 m a.s.l. (0.3±0.2 W m−2; Figs. 1c, 2e). It is plausible that the prevalence of meltwater ponding in the percolation zone may be explained by recent evidence of ice layers or ‘slabs’36–38. When infiltrated meltwater refreezes in porous snow or firn, impermeable ice layers form just below the surface that can promote surface runoff into supraglacial streams and lakes31,39. Processes that determine meltwater drainage and infiltration therefore appear to be the primary control on spatial patterns of the radiative effect caused by meltwater ponding.

Interannual variability in the radiative effect of meltwater ponding appears to be largely driven by air temperatures. Satellite-derived surface water maps reveal that more of the ice sheet surface is covered by meltwater during warm summers32 (Fig. 2b, f). Reference 32 found that meltwater covered 4903 km2 in 2018 (a cool summer) and 9988 km2 in 2019 (a warm summer). Given that ice sheet summer (JJA) near-surface (2 m) air temperatures were 1.76 K higher in 2019 than 2018, we estimate that meltwater ponding increased by +1715 km2 K−1, implying that the radiative effect of meltwater ponding is sensitive to air temperatures. We find that the slope of this relationship is +5.7±5.2 PJ d−1 K−1, although we note that the uncertainty is relatively large (Fig. 2f; see Methods). We also note that this relationship may be more complex than identified by our analysis. For example, a melting ice sheet surface will have a larger cooling effect on near-surface air temperatures than a surface that is below freezing. Likewise, the sensitivity of meltwater ponding to air temperatures likely exhibits spatiotemporal variability. For example, the formation of extensive near-surface semi-impermeable ice layers by refrozen meltwater reduces firn meltwater storage capacity, as observed following the extreme 2012 melt season37,39. It is therefore possible that meltwater ponding is more sensitive to air temperatures immediately following extreme melt events, rather than before38. Further mapping of surface water before and after 2012 would be required to investigate such non-linear relationships between air temperatures and meltwater ponding.

Meltwater ponding in high-resolution drone imagery
The accuracy of our findings largely depends on the quality of the satellite-derived water products we used to characterize the area of meltwater ponding. To investigate the accuracy of these products, we surveyed two sites in the ice sheet’s ablation zone at high spatial resolution (0.3 m) using a series of drone surveys in Jul 2015 (Fig. 3a, b). The first site includes both Russell Glacier and Isunguata Sermia (~180 km2) with an elevational range of 150–660 m a.s.l. (Fig. 3c). The second site (~110 km2) is situated in the dark zone with an elevational range of 1170–1290 m a.s.l. (Fig. 3d). At Russell Glacier and Isunguata Sermia, we find that only 0.001% of the surface was ponded on Jul 11–12, 2015 (Fig. 4a, b; see Methods). This is consistent with ref. 32 who did not identify any supraglacial lakes, ponds, or streams in the same sector using Sentinel-2 imagery in Jul 25–30, 2018 and Jul 29–Aug 5, 2019. Our high-resolution surface water maps therefore confirm that there is negligible meltwater ponding at the margins of the ice sheet.

In contrast, our high-resolution surface water maps reveal substantial meltwater ponding in the ablation zone at elevations higher than our ice-sheet margin field site (Fig. 4d–f). At our dark zone field site, 3.6% of the ice sheet surface was inundated on Jul 21, 2015 – three to four times more than previously estimated using Sentinel-2 imagery in Jul 25–30, 2018 (0.9%) and Jul 29–Aug 5, 2019 (1.2%)32. Some of these differences may be attributed to interannual variability. However, cumulative runoff was similar between Jul 21, 2015 and Jul 25–30, 2018 at the time of mapping, and substantially more runoff occurred before the Jul 29–Aug 5, 2019 mapping (Fig. S3), rendering our conclusion conservative.

A more plausible explanation for the differences in surface water coverage between the drone and satellite products is improved spatial resolution. With a spatial resolution of 0.3 m, we document thousands of meltwater ponds and streams <100 m2 in our drone imagery (Fig. 5). Collectively, these features account for 53.7% of all surface water at our field site. In contrast, the satellite-derived surface water maps (with a spatial resolution of 10 m) resolve none of these <100 m2 ponds32. These maps also appear to underestimate the number of ponds <1000 m2 (Fig. 5). The satellite-derived water maps therefore substantially underestimate the total area of meltwater ponding implying that, in the dark zone, its radiative effect may be stronger than previously thought.

Our drone imagery reveals that the radiative effect of meltwater ponding was 0.9±0.5 W m−2 at our field site in the dark zone on Jul 21, 2015. For context, the radiative effect caused by glacier ice albedo change has been previously estimated to be 19.1 W m−2 during the same period6, indicating that 4.7±2.6% of the radiative effect caused by reductions in glacier ice albedo can be attributed to meltwater ponding. This is substantially larger than that predicted by satellite-derived maps of surface water (i.e., 0.2±0.1 W m−2 [1.0±0.5%] in Jul 25–30, 2018 or 0.3±0.2 W m−2 [1.7±0.9%] in Jul 29–Aug 5, 2019). Furthermore, we note that the radiative effect of meltwater ponding is weak in Southwest Greenland at 1250 m a.s.l. relative to the lower percolation zone (1600–1800 m a.s.l.; Fig. 2e). If satellite-derived maps underestimate meltwater ponding by similar magnitudes in the lower percolation zone (i.e., by a factor of four), then the radiative effect caused by meltwater ponding would account for up to ~9% of the total surface shortwave radiative effect in this area of the ice sheet (which is 38.2 W m−2). High-resolution drone imagery in the percolation zone would be required to evaluate this inference. Even without such evidence, our findings demonstrate that it is likely that the radiative effect of meltwater ponding represents a considerable source of energy for ice sheet melt in the upper ablation and lower percolation zones.

Methods
Data
We obtained binary surface water coverage maps from ref. 32 which applied a semi-automated water extraction algorithm to 271 Sentinel-2 images to map surface water across the entire Greenland Ice Sheet. These maps represent two distinct time periods: Jul 25–30, 2018, and Jul 29–Aug 5, 2019 when most of the imagery was acquired. We primarily used the 2019 map for our analysis. ref. 32 categorizes surface water into three groups: supraglacial lakes, supraglacial rivers, and water-filled crevasses. After comparison with drone imagery from a tidewater glacier, we found that this product substantially overestimates the area of water-filled crevasses, so we decided to exclude water-filled crevasses from our analysis (Fig. S5, S6). Our ice sheet-wide analysis therefore focuses on the radiative effect of meltwater in supraglacial lakes, ponds, rivers, and streams.

We obtained surface albedo data for the same time periods from the MCD43A3 version 6.1 product. This albedo product provides black- and white-sky albedo on a daily basis using a 16-day retrieval window based on MODIS data from Terra and Aqua47. We used black-sky albedo following previous studies that have demonstrated that there is little difference between black- and white-sky albedo for typical solar zenith angles at local noon during summer48,49. We resampled both the surface water and surface albedo data onto the ISMIP6 grid by averaging values within each target grid cell. The ISMIP6 grid has an NSIDC Sea Ice Polar Stereographic North (EPSG:3413) projection and a grid cell resolution of 1×1 km50.

We retrieved downward all-sky shortwave radiation data for the periods Jul 25–30, 2018, and Jul 29–Aug 5, 2019, from the MERRA-2 radiation diagnostics product (M2T1NXRAD). This product is provided at an hourly temporal resolution so we averaged these data to daily means. We also statistically downscaled M2T1NXRAD from 0.625×0.5° resolution to the ISMIP6 1×1 km grid following an elevation-based approach described by previous studies6,51. For each MERRA-2 grid cell, we performed a local linear regression between elevation and downward longwave (or shortwave) radiation using the 24 nearest grid cells (~100 km search radius). We then applied the regression coefficients to higher spatial resolution elevation data (1×1 km). If the correlation between elevation and downward longwave (or shortwave) radiation was not significant (p < 0.1), we did not apply a correction.

We acquired high-resolution aerial imagery using a fixed-wing drone similar to that described by ref. 52. The drone was equipped with a 16.1 MP Sony NEX-5N digital camera and programmed to capture RGB imagery in RAW mode every 50 m of horizontal distance traveled. Each image was georeferenced using the onboard L1 GPS along with attitude (pitch, roll, yaw) data recorded by the accelerometer. The drone collected imagery at two separate field sites. The first site includes both Russell Glacier and Isunguata Sermia (180 km2) with an elevational range of 150–660 m a.s.l53. During six surveys, the drone collected 9732 overlapping images of the glacier surface on Jul 11–12, 2015. The second site (110 km2) is situated in the dark zone with an elevational range of 1170–1290 m a.s.l. Here, the drone collected a total of 3795 images during three surveys between Jul 20–22, 2015. We used Agisoft Metashape Pro v2.2.0 to generate orthomosaics from the overlapping aerial imagery. The final products have a spatial resolution of 0.30 m, offering unprecedented capability for quantifying the area of meltwater ponding on the ice sheet surface.

Local albedo variability and the radiative effect of meltwater ponding
We quantified the impact of meltwater ponding on albedo variability using an empirical approach based on the satellite-derived maps of surface water and albedo. To do this, we compute the standard deviation of albedo within a series of search windows with lengths varying from 1 to 100 km (Fig. S7). In each search window, we compute the standard deviation of albedo for all grid cells (σall) and the standard deviation of albedo for grid cells that do not contain any surface water (σdry). Albedo variability (σalbedo) caused by meltwater ponding can then be defined as

σalbedo = 1 − (σdry / σall) (1)

This metric is best understood by considering a window in which there is little meltwater ponding. In this window, σdry will be similar to σall. Hence, σalbedo will be low. Alternatively, when there is a lot of meltwater ponding, σall will be larger than σdry, signifying that more of the albedo variability can be attributed to meltwater ponding. We compute this metric for 32,000 randomly selected grid cells that are evenly spread across sixteen elevation bins ranging from 0–3,400 m a.s.l. with an interval of 200 m (i.e., 2000 grid cells per elevation bin). This approach allows us to investigate how σalbedo varies with both length scale and elevation.

We derived the albedo-lowering effect of meltwater ponding using a similar approach. However, instead of computing the standard deviation of albedo in each search window, we linearly regressed albedo (α) against surface water fraction (fwater):

α = β0 + β1 * fwater (2)

The slope of this local linear regression (β1) represents the response of albedo to the area of meltwater ponding. We applied this approach to a random sample of grid cells in our coincident maps of surface water and albedo from Jul 25–30, 2018, and Jul 29–Aug 5, 2019 (n=9079). We find high correlation coefficients between meltwater ponding and surface albedo at length scales <= 10 km (mean r=−0.63), indicating that spatial albedo patterns can primarily be attributed to meltwater ponding at these length scales (Fig. S1 and S2). At greater length scales (i.e., >10 km), other ice sheet properties, such as snow grain size, become more important for spatial albedo patterns, and the impact of meltwater ponding becomes weaker. For example, the correlation coefficients (r) between meltwater ponding and surface albedo decline to −0.56±0.21 at 25 km, −0.45±0.22 at 50 km, and −0.36±0.20 at 100 km. We therefore define the albedo-lowering effect of meltwater ponding as the mean sensitivity of surface albedo to meltwater ponding (β1) at length scales <= 10 km (Fig. S2). The total albedo change caused by meltwater ponding (Δα) for any grid cell can subsequently be computed as

Δα = β1 * fwater (3)

We quantified the radiative effect of meltwater ponding (F) for each grid cell as

F = (1 − Δα) * SW↓ (4)

where SW↓ is downward all-sky shortwave radiation from M2T1NXRAD and F is in units of W m−2. Next, we computed the sensitivity of the radiative effect caused by meltwater ponding (λ) from

λ = ΔF / ΔT (5)

where ΔF is the difference in the radiative effect between the summers of 2018 and 2019, ΔT is the difference in near-surface air temperature between the summers of 2018 and 2019. λ is in units of W m−2 K−1. Since we were interested in the radiative effect at the surface, we used 2 m air temperatures from MERRA-2 (M2T1NXSLV). We computed the sensitivity of the radiative effect to air temperature in 200 m elevation bins ranging from 0 to 3400 m a.s.l. and converted λ into units of energy (in J d−1 K−1) to account for variations in area within each elevation bin.

Meltwater ponding from drone orthomosaics
We mapped meltwater ponding in the orthomosaic produced over the dark zone (1170–1290 m a.s.l.) using a semi-automated classification approach. To do this, we first generated a validation dataset by manually digitizing polygons representing water and non-water. Next, we segmented the orthomosaic using Simple Non-Iterative Clustering (SNIC) to remove noise in the imagery54. We also computed a Normalized Difference Water Index (NDWI) from our orthomosaic using the red and blue bands. The median NDWI value of each segment was then assigned to all pixels within that segment. To find the optimal threshold for distinguishing between water and non-water segments, we iterated through a range of NDWI values (from 0 to 0.3 with a 0.05 interval). The optimal threshold was defined as the threshold with the highest combination of accuracy and precision relative to our validation dataset (Fig. S8). By explicitly accounting for precision (which improves as the number of false positives decreases) during the optimization process, we ensured that our classification does not overestimate meltwater ponding. Once we identified this threshold (0.175), we applied it to every pixel in the segmented orthomosaic to classify water vs. non-water automatically. This approach classified meltwater ponding with an accuracy of 91.5% relative to our validation dataset (Fig. S8).

Our semi-automated approach performed poorly for the Russell Glacier and Isunguata orthomosaic (150–600 m a.s.l.) because self-shadowed pixels caused by crevassing were characterized by high NDWI values. Therefore, for these study sites, we manually digitized ponded areas using the Geo-SAM QGIS plugin, an interactive segmentation tool based on the Segment Anything Model (SAM) foundation AI model55,56. It is possible that the use of different methods to map surface water could introduce some uncertainties. However, based on visual inspection of the imagery and given that both approaches segment water pixels using NDWI values, it is unlikely that these uncertainties are responsible for the large differences in water coverage between the dark zone and margin field sites. We consider the high-resolution (0.3 m/pixel) maps of surface water from the drone imagery to represent “ground-truth” for comparisons with the satellite-derived surface water maps.

Assumptions and uncertainties
In this study, we assume that the albedo-lowering effect of meltwater ponding (β1) is static and can be applied across the ice sheet. This decision is justified by low correlation coefficients between the β1 and latitude (R2=0.002) and elevation (R2=0.008). Likewise, we find small correlation coefficients between β1 and ponded area (R2=0.07), which can be considered a proxy for pond depth. ref. 29, for example, found that pond area is significantly correlated with depth with an R2=0.83. We speculate that the lack of correlation between β1 and pond area (depth) is because most of the albedo reduction occurs in the first 20–30 cm of the melt pond18,57. In contrast, albedo exhibits little variation with pond depths >30 cm. ref. 32 find that the mean depth of meltwater features in their dataset 0.66±0.10 m (μ±σ). There is therefore unlikely to be much variation in β1 with depth for most of the meltwater features in our study, justifying our use of a static value (Fig. S9).

We also assume that meltwater ponding is the only process responsible for the albedo-lowering effects we observe in our local linear regression analysis. However, it is possible that other processes are also responsible for the difference in albedo between inundated and non-inundated ice sheet surfaces. For example, if the concentrations of LAPs are consistently higher in supraglacial lakes than in surrounding areas, then the direct effect of meltwater ponding on albedo will be overestimated. Instead, meltwater ponding would also have an indirect effect on albedo by enhancing the concentration of LAPs. While we are not aware of data to systematically evaluate this assumption, drone imagery indicates that the ice sheet surfaces surrounding supraglacial lakes can be both darker or brighter than the bottoms of supraglacial lakes (Fig. S10; see also Fig. 5c in ref. 16). Therefore, while we cannot dismiss indirect effects, we assume that inundated and non-inundated ice sheet surfaces have similar LAP concentrations and that most of the observed albedo-lowering is attributable to meltwater ponding.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review of\nMeltwater ponding is an underestimated source of radiative forcing on the 2 Greenland Ice Sheet\nby Jonathan C. Ryan and others\nGeneral\nIn a recent paper, Ryan (2024) quantified the radiative effects of clouds and surface darkening (due to snowline retreat,\nalgae, ponds and lakes) on Greenland ice sheet surface melt, using a reference dry snow albedo value (0,84), relatively\ncoarse satellite / climate model products and a regional climate model (MAR) to convert the estimated radiative forcing to a\nmelt amount. The current study uses a combination of satellite albedo products and drones to show that the satellite-based\nproducts are too coarse to fully capture small scale supraglacial melt features, and therefore represent a lower bound on\ntheir radiative effect. The combination of existing and new observational techniques (intersecting pond detection with albedo\nproducts, dedicated drone observations) make that the findings of the paper are original, and it is convincingly\ndemonstrated that the impact on Greenland ice sheet melt, although not of first order, can in second order be significant. The paper is well\nwritten and the figures are of good quality. My comments are relatively minor.\nMajor comments\nl. 34: Please specify the albedo-melt feedback addressed in this paper. In my view the melt-albedo feedback takes on\nvarious forms. There is the effect that reduced sea/land ice cover exposes water/bare land which lowers the surface albedo\nand leads to further warming. There is the effect that the presence of (ponding) meltwater reduces the albedo of bare ice/firn,\nleading to increased melting. And there is the effect that snowmelt reduces the snow albedo because it leads to grain\ngrowth. It may be worthwhile to point out early on which of these feedbacks is considered in this study (i.e. the second one).\nl. 129: \" We find that spatial patterns in radiative forcing caused by meltwater ponding cannot be fully explained by air\ntemperatures\". I do not see why temperature would be a useful predictor to start with, as more melt/runoff does not\nnecessarily mean more ponds (these are mostly enabled by small surface slopes in combinations with shallow depressions\nin the topography that partly reflect bed topography).\nl. 143 and further: this analysis of ponding area in terms of temperature is somewhat simplistic (as the authors themselves\nstate in l. 150). What temperature does the difference in l. 147 refer to, is it a Greenland wide value? It is well known that\nabsorbed shortwave radiation is the dominant source for melt energy away from the very ice margin. Near surface air\ntemperatures are moreover depressed in the vicinity of a melting ice surface (which cools the air through downward sensible\nheat transport). I understand that 2 m air temperature is used because it is an intuitive predictor that is also relatively easy to\nmeasure/model, potentially leading to parametrisations in regional climate models, but mentioning these caveats helps\nhighlighting the complexity of the real melt process.\nl. 239: \"there is a radiative feedback between meltwater ponding and air temperatures\" I would not call this a feedback, air\ntemperatures will not react if the additional shortwave radiation is absorbed by a melting surface which cannot increase its\ntemperature further.\nMinor and textual comments\nl. 33: I would be indebted to the authors if 'higher/lower temperatures' is used rather than 'warmer/colder temperatures'.\nSame for 'warming/cooling air temperatures' -> 'increasing/decreasing air temperatures'.\nl. 45 and further: Maybe mention that Tedesco et al. (2012) observed directly but only locally that indeed surface melt\nincreases in the presence of ponds, which required measurements of the pond bottom height change in the presence of a\nwater body above the ice surface. Then say that these local observations have never been upscaled to the full ice sheet,\nwhich is the purpose of this work.\nl. 128: radiative forcing -> shortwave radiative forcing\nReference 6 is incomplete.", "reviewer_2": "Reviewer Comments on: \"Meltwater ponding is an underestimated source of radiative\nforcing on the Greenland Ice Sheet\"\nThis is a high-quality and timely study that combines multi-resolution satellite and drone\nobservations to constrain the impact of meltwater ponding on surface albedo and radiative\nforcing across the Greenland Ice Sheet. The manuscript addresses a significant knowledge\ngap – quantifying the radiative impact of supraglacial hydrological features – and makes a\nstrong case for incorporating radiative forcing due to meltwater ponding into climate and ice-\nsheet mass balance models. The work is novel, methodologically sound, and clearly written.\nThe results are carefully interpreted, and the figures are particularly effective in supporting\nthe conclusions.\nA strength of the study is its recognition that small meltwater features (i.e., narrow streams\nand small ponds) are widespread and substantially enhance radiative forcing yet are largely\nunresolved by satellite-based surface water maps. The use of drone imagery to capture\nthese finer-scale features provides important new insight.\nI recommend publication pending minor revisions outlined below.\n1. Interpretation of drone data as an upper bound\nThe integration of 2015 drone data with 2018 and 2019 satellite-derived surface\nwater and albedo products is a pragmatic decision, given the logistical challenges of\nacquiring very-high-resolution imagery across the ice sheet. However, the manuscript\nrepeatedly presents the drone-based estimates as providing an \"upper bound\" on\nmeltwater-induced radiative forcing, despite the fact that the drone surveys were\ncollected during a different melt season to the satellite-based maps. It is plausible,\nperhaps even likely, that meltwater ponding was more extensive in 2019 (a high melt\nyear), meaning that the true upper bound may actually lie above the 2015 drone-\ninferred value. Furthermore, the drone surveys are spatially limited to a single sector\nof the ice sheet and do not capture variability across higher-elevation regions, where\nponding could be even more pronounced. For these reasons, I recommend reframing\nthe drone results as a local or site-specific upper bound, or presenting them as an\nillustrative case study, rather than suggesting they define a true upper bound for the\nice sheet as a whole. Revising the language accordingly would more accurately\nreflect the spatial and temporal limitations of the drone dataset.\n2. Radiative forcing estimates\nThe radiative forcing analysis is a strong component of the paper, and the authors\npresent it clearly. However, there are a few important caveats that should be more\nexplicitly addressed:\n• First, the use of a single, average regression slope linking surface water\nfraction to albedo reduction across the ice sheet may oversimplify the\nrelationship. This slope likely varies depending on surface type (e.g., snow,\nbare ice, algae-covered ice), and acknowledging this potential variability\nwould strengthen the analysis.\n• Second, the ~0.7% contribution of meltwater ponding to total surface radiative\nforcing represents a lower-bound estimate, derived from a short, peak-season\nsnapshot (late July–early August). Since this does not reflect the full melt\nseason, the actual contribution may be higher. It would be helpful if the\nauthors clarified this limitation more directly in the results section.\n• Third, while 0.7% may seem small, the manuscript would benefit from some\ndiscussion of whether this level of energy input is glaciologically significant.\nFor example, is it sufficient to meaningfully increase melt or runoff at local or\nregional scales?\n3. Patterns of radiative forcing from meltwater ponding\nThe observed elevation patterns in radiative forcing are interesting and highlight\ndistinct physical controls. However, several areas would benefit from clarification or\nadditional caution. Specifically, the interpretations regarding infiltration, refreezing,\nand runoff processes in the percolation zone are inferred from spatial patterns in the\nsatellite data, rather than directly observed. Without supporting firn model outputs or\nin situ measurements, it would be more appropriate to frame these mechanisms as\nplausible explanations grounded in prior literature, rather than definitive conclusions.\nI suggest that these interpretations be clearly presented as hypotheses or discussed\nprimarily in the Discussion section, where proposed mechanisms are typically\nexplored in more detail.\n4. Omission of slush and saturated firn:\nThe analysis focuses on surface water features visible in satellite imagery – primarily\nponded water and rivers – but does not consider slush or saturated firn, which may\nalso contribute to albedo reduction, particularly in the percolation zone where\nmeltwater often remains at the surface without forming distinct ponds. Slush is clearly\nvisible in the imagery shown in Figure 1 of the manuscript. Figure 1a and 1c\ncorrespond to areas previously studied by Rawlins et al. (2023) and Glen et al.\n(2025), respectively. These prior studies demonstrated that slush is widespread\nduring the melt season, particularly in high melt years such as 2019. Given the visible\npresence of slush in Figure 1, and its widespread occurrence documented in earlier\nwork, the omission of slush from the current analysis is notable. Its exclusion could\ncontribute to an underestimation of radiative forcing, particularly in regions where\nslush is prevalent but not well captured by standard surface water classification\nmethods, such as those employed by Zhang et al. (2023). Incorporating slush data –\nor at least explicitly acknowledging its potential contribution – would help\ncontextualize the current results and clarify the scope of the estimates presented.\nSpecific Comments\nLine 54: Update “at spatial resolution of 30 m” to “at spatial resolution of 10–30 m” to\naccurately reflect the resolution of Sentinel-2 (10 m) and Landsat (30 m) used in prior\nmeltwater mapping studies.\nLines 55–56: I recommend clarifying that 2018 and 2019 represent a low and high melt year,\nrespectively, to help contextualize the analysis of interannual variability in meltwater\nponding and radiative forcing. Additionally, The current wording implies that Zhang et al. (2023)\nmapped the full summer surface water extent, whereas the dataset represents ~1-week\npeak melt snapshots. I suggest clarifying this point here and elsewhere in the manuscript to\navoid misinterpretation.\nLines 67–70: I recommend adding “from 2015” after “drone imagery” and rephrasing\n“provides an upper-bound” to “offers an illustrative upper estimate” or “helps constrain an\nupper estimate” to better reflect the temporal mismatch.\nLine 76: This section discusses “summer” broadly, but the data are from very specific\nweeks in late July/early August. Albedo variability caused by ponding earlier or later in the melt\nseason might differ.\nFigure 1: The right-hand y-axes do not have labels, and the interpretation of \"water\ncoverage\" (whether it represents fractional area or another metric) is unclear; both issues\nshould be clarified.\nFigure 2: Panels 2e and 2f use the same colour for different variables – was this intentional?\nIf not, I suggest adjusting the colours to avoid confusion.\nLines 123-124: The phrase “5.7 ± 3.1 to 15.7 ± 8.6 PJ d⁻¹” sounds like a range. I suggest\nrewording to make it clear these are separate values for 2018 and 2019 – e.g., “...in 2018\nand 15.7 ± 8.6 PJ d⁻¹ in 2019.”\nLine 147: The sentence “Given that summer near-surface (2 m) air temperatures were 1.76\nK higher in 2019 than 2018...” would benefit from greater specificity. I recommend clarifying\nthe exact time window over which this temperature difference was calculated. Additionally, it\nwould be helpful to state whether the temperature anomaly represents a spatial mean over\nthe entire ice sheet, a specific elevation band, or a region. Providing this context would\nensure that the stated radiative feedback is interpreted appropriately.\nLine 150: While the derived feedback estimate (+5.7 ± 5.2 PJ d⁻¹ K⁻¹) is useful, the large\nuncertainty could be acknowledged more directly.\nFigure 5: Please add \"2015\" after \"drone\" to maintain consistency with the other figure labels\nand to clearly indicate the year of imagery acquisition.\nLine 179: The text refers to the 1,170–1,290 m site as “higher elevation,” but this phrasing\nmay be misleading in the context of the entire Greenland Ice Sheet. I suggest clarifying that\nthis is higher relative to the margin site, but still within the lower to mid-elevation ablation\nzone, particularly given the role of elevation in interpreting radiative forcing.\nLine 286: Please revise the sentence to avoid suggesting that drone imagery captures\nmeltwater features at all scales, as the surveys are spatially and temporally limited.\nLine 315: Please clearly define \"meltwater ponding coverage\" – for example, does it\nrepresent the fractional surface area of a pixel covered by water, or something else?\nLines 329–331: The method for averaging surface water and albedo datasets onto the\nISMIP6 grid is not clearly described; please clarify.\nLine 421: Please briefly discuss potential uncertainties introduced by switching from\nsemi-automated to manual classification in the Russell and Isunguata sites.\nMethods: Consider tightening some phrasing for consistency with the manuscript’s otherwise\nformal tone. For example, “to provide some intuition” (line 365) feels out of place.\nReferences not in manuscript\nGlen, E., Leeson, A., Banwell, A. F., Maddalena, J., Corr, D., Atkins, O., Noël, B., and\nMcMillan, M.: A comparison of supraglacial meltwater features throughout contrasting melt\nseasons: southwest Greenland, The Cryosphere, 19, 1047–1066", "reviewer_author_response": "Response to reviewer comments\nReviewer #1\nIn a recent paper, Ryan (2024) quantified the radiative effects of clouds and surface darkening\n(due to snowline retreat, algae, ponds and lakes) on Greenland ice sheet surface melt, using a\nreference dry snow albedo value (0,84), relatively coarse satellite / climate model products and\na regional climate model (MAR) to convert the estimated radiative forcing to a melt amount. The\ncurrent study uses a combination of satellite albedo products and drones to show that the\nsatellite-based products are too coarse to fully capture small scale supraglacial melt features,\nand therefore represent a lower bound on their radiative effect. The combination of existing and\nnew observational techniques (intersecting pond detection with albedo products, dedicated\ndrone observations) make that the findings of the paper are original, and it is convincingly\ndemonstrated that the impact on Greenland ice sheet melt, although not of first order, can in\nsecond order be significant. The paper is well written and the figures are of good quality. My\ncomments are relatively minor.\nWe thank the reviewer for their positive comments and insightful review.\nMajor comments\nR1G1: l. 34: Please specify the albedo-melt feedback addressed in this paper. In my view the\nmelt-albedo feedback takes on various forms. There is the effect that reduced sea/land ice\ncover exposes water/bare land which lowers the surface albedo and leads to further warming.\nThere is the effect that the presence of (ponding) meltwater reduces the albedo of bare ice/firn,\nleading to increased melting. And there is the effect that snowmelt reduces the snow albedo\nbecause it leads to grain growth. It may be worthwhile to point out early on which of these\nfeedbacks is considered in this study (i.e. the second one).\nWe acknowledge there are various forms of the melt-albedo feedback. At this early stage in the\nmanuscript, we conceptualize the melt-albedo feedback as any process that reduces ice sheet\nsurface albedo (as opposed to the ice sheet area). To make this clear we edited the sentence\nto read:\nL34-36: “Radiative feedbacks between melt and ice sheet surface albedo make the ice sheet\nespecially sensitive to increasing air temperatures (also known as the “melt-albedo feedback”)5–\n7.”\nR1G2: l. 129: \" We find that spatial patterns in radiative forcing caused by meltwater ponding\ncannot be fully explained by air temperatures\". I do not see why temperature would be a useful\npredictor to start with, as more melt/runoff does not necessarily mean more ponds (these are\nmostly enabled by small surface slopes in combinations with shallow depressions in the\ntopography that partly reflect bed topography).\nWe agree that this was not the best way to start this paragraph. We revised the first sentence to\nget straight to the point:\nL163-164: “We find that radiative forcing caused by meltwater ponding is strongest within the\npercolation zone34,35.”\nsimplistic (as the authors themselves state in l. 150). What temperature does the difference in l.\n147 refer to, is it a Greenland wide value? It is well known that absorbed shortwave radiation is\nthe dominant source for melt energy away from the very ice margin. Near surface air\ntemperatures are moreover depressed in the vicinity of a melting ice surface (which cools the air\nthrough downward sensible heat transport). I understand that 2 m air temperature is used\nbecause it is an intuitive predictor that is also relatively easy to measure/model, potentially\nleading to parametrisations in regional climate models, but mentioning these caveats helps\nhighlighting the complexity of the real melt process.\nWe apologize for not being clear about the air temperature value which represents the\ndifference in summer (JJA) near-surface air temperatures between 2018 and 2019 averaged\nover the entire ice sheet. Sentence now reads:\nL200-201: “Given that ice sheet summer (JJA) near-surface (2 m) air temperatures were 1.76\nK higher in 2019 than 2018…”\nWe agree that mentioning some caveats would be useful for acknowledging the complexity of\nthis feedback. We added the following sentences:\nL205-208: “We also note that this relationship may be more complex than identified by our\nanalysis. For example, a melting ice sheet surface will have a larger cooling effect on near-\nsurface air temperatures than a surface that is below freezing. Likewise, the sensitivity of\nmeltwater ponding to air temperatures likely exhibits spatiotemporal variability.”\nR1G4: l. 239: \"there is a radiative feedback between meltwater ponding and air temperatures\" I\nwould not call this a feedback, air temperatures will not react if the additional shortwave\nradiation is absorbed by a melting surface which cannot increase its temperature further.\nWe appreciate this perspective. In response we have removed the word “feedback” and revised\nthe sentence to read:\nL203: “…radiative forcing caused by meltwater ponding is sensitive to near-surface air\ntemperatures…”\nMinor and textual comments\nR1S1: l. 33: I would be indebted to the authors if 'higher/lower temperatures' is used rather than\n'warmer/colder temperatures'. Same for 'warming/cooling air temperatures' ->\n'increasing/decreasing air temperatures'.\nWe accept that “higher/lower” is a little more scientific/technical so have replaced\n“warmer/cooler” with “higher/lower” here and elsewhere in the manuscript.\nR1S2: l. 45 and further: Maybe mention that Tedesco et al. (2012) observed directly but only\nlocally that indeed surface melt increases in the presence of ponds, which required\nmeasurements of the pond bottom height change in the presence of a water body above the ice\nsurface. Then say that these local observations have never been upscaled to the full ice sheet,\nwhich is the purpose of this work.\nL46-50: “Remote sensing and field measurements and demonstrate that supraglacial\nmeltwater features have low albedo and melt rates that are 110–170% higher than nearby non-\ninundated ice sheet surfaces18–20. However, despite evidence that meltwater ponding is a key\ncontrol on albedo and melt in the ablation zone21,22, these local measurements have not been\nupscaled to the entire ice sheet.”\nR1S3: l. 128: radiative forcing -> shortwave radiative forcing\nAdded the word “shortwave” to that term.\nR1S4: Reference 6 is incomplete.\nFixed – thanks for spotting.\nThis is a high-quality and timely study that combines multi-resolution satellite and drone\nobservations to constrain the impact of meltwater ponding on surface albedo and radiative\nforcing across the Greenland Ice Sheet. The manuscript addresses a significant knowledge\ngap – quantifying the radiative impact of supraglacial hydrological features – and makes a\nstrong case for incorporating radiative forcing due to meltwater ponding into climate and ice-\nsheet mass balance models. The work is novel, methodologically sound, and clearly written.\nThe results are carefully interpreted, and the figures are particularly effective in supporting\nthe conclusions.\nA strength of the study is its recognition that small meltwater features (i.e., narrow streams\nand small ponds) are widespread and substantially enhance radiative forcing yet are largely\nunresolved by satellite-based surface water maps. The use of drone imagery to capture\nthese finer-scale features provides important new insight.\nI recommend publication pending minor revisions outlined below.\nWe thank the reviewer for their positive comments and insightful review.\nR2G1: Interpretation of drone data as an upper bound\nThe integration of 2015 drone data with 2018 and 2019 satellite-derived surface water and\nalbedo products is a pragmatic decision, given the logistical challenges of acquiring very-high-\nresolution imagery across the ice sheet. However, the manuscript repeatedly presents the\ndrone-based estimates as providing an \"upper bound\" on meltwater-induced radiative forcing,\ndespite the fact that the drone surveys were collected during a different melt season to the\nsatellite-based maps. It is plausible, perhaps even likely, that meltwater ponding was more\nextensive in 2019 (a high melt year), meaning that the true upper bound may actually lie above\nthe 2015 drone-inferred value. Furthermore, the drone surveys are spatially limited to a single\nsector of the ice sheet and do not capture variability across higher-elevation regions, where\nponding could be even more pronounced. For these reasons, I recommend reframing the drone\nresults as a local or site-specific upper bound, or presenting them as an illustrative case study,\nrather than suggesting they define a true upper bound for the ice sheet as a whole. Revising the\nlanguage accordingly would more accurately reflect the spatial and temporal limitations of the\ndrone dataset.\nWe thank the reviewer for this perspective. The main contribution of the drone imagery is that it\nunequivocally demonstrates that satellite-based estimates of radiative forcing caused by\nmeltwater ponding are a lower-bound. And, although we never considered surface water\ncoverage from the drone imagery to be a true “upper-bound” (for the reasons the reviewer\nstates), we agree that some of the text could be mistakenly interpreted that way. To address this\nissue, we edited several sentences in the manuscript.\nIn the Abstract (L24-25), we removed the word “upper” and just state that our results place\n“lower bounds on the radiative forcing caused by meltwater ponding.”\nAt the end of the Introduction, we removed the phrase “upper-bound” and edited the final\nsentence to read:\nresolved by medium-resolution satellite imagery (i.e. Sentinel-2), implying that satellite-based\nestimates of radiative forcing caused by meltwater ponding should be considered a lower-\nbound.\nIn the Discussion, we clarified that:\nL391-393: “… (drone imagery) provides a local upper-bound on radiative forcing caused by\nmeltwater ponding (+0.9 ± 0.5 W m-2) at 1,170–1,290 m a.s.l. in Southwest Greenland on Jul\n21, 2015.”\nR2G2: Radiative forcing estimates\nThe radiative forcing analysis is a strong component of the paper, and the authors present it\nclearly. However, there are a few important caveats that should be more explicitly addressed:\n• First, the use of a single, average regression slope linking surface water fraction to\nalbedo reduction across the ice sheet may oversimplify the relationship. This slope likely\nvaries depending on surface type (e.g., snow, bare ice, algae-covered ice), and\nacknowledging this potential variability would strengthen the analysis.\n• Second, the ~0.7% contribution of meltwater ponding to total surface radiative\nforcing represents a lower-bound estimate, derived from a short, peak-season\nsnapshot (late July–early August). Since this does not reflect the full melt\nseason, the actual contribution may be higher. It would be helpful if the authors clarified this\nlimitation more directly in the results section.\n• Third, while 0.7% may seem small, the manuscript would benefit from some\ndiscussion of whether this level of energy input is glaciologically significant.\nFor example, is it sufficient to meaningfully increase melt or runoff at local or\nregional scales?\nWhile we already discuss the sources of variability caused by the use of single value in the\nAssumptions and uncertainties section, we agree that it would be useful to reference this\nsection in the main text. We now state:\nL148-150: “…(for a detailed discussion of the uncertainties associated with the use of a static\nalbedo-lowering effect, refer to the Assumptions and Uncertainties section in the Methods).”\nAs for the representativeness of the 0.7% value, we now caveat this finding in the Results by\nincluding a sentence that reads:\nL160-162: “We note that this value represents mid- to late-summer hydrological conditions and\nthat the contribution of meltwater ponding to total shortwave radiative forcing may be higher\nearlier in the summer.”\nFinally, we did actually compute the amount of runoff that would be caused by 15.7 PJ d-1 in\nFigure S4. Assuming this value is unchanged for the entire summer, it is equivalent to an\nadditional 2.5 Gt or 0.8% of runoff (assuming total is ~300 Gt yr-1). And this is a lower-bound, so\nyes, this process does have the potential to be quite important. However, we are reluctant to\ninclude this finding in the main text because it involves several assumptions that are not well\ntested. For example, the amount of melting at the bed of supraglacial lake depends on the rate\n5\nof heat transfer from the water surface to the bed. This likely varies by the size of the\nsupraglacial lake and mixing processes. Therefore, while we appreciate the encouragement to\ndraw conclusions about melt and runoff, we feel that doing so would distract from the current set\nof conclusions and weaken the manuscript.\nR2G3: Patterns of radiative forcing from meltwater ponding\nThe observed elevation patterns in radiative forcing are interesting and highlight distinct physical\ncontrols. However, several areas would benefit from clarification or additional caution.\nSpecifically, the interpretations regarding infiltration, refreezing, and runoff processes in the\npercolation zone are inferred from spatial patterns in the satellite data, rather than directly\nobserved. Without supporting firn model outputs or in situ measurements, it would be more\nappropriate to frame these mechanisms as plausible explanations grounded in prior literature,\nrather than definitive conclusions. I suggest that these interpretations be clearly presented as\nhypotheses or discussed primarily in the Discussion section, where proposed mechanisms are\ntypically explored in more detail.\nWe agree that the mechanisms we invoke to explain patterns of meltwater ponding come across\na little strongly, as written. We edited the section in the Results to frame this mechanism as a\n“plausible explanation”. The section now reads:\nL169-174: “It is plausible that the prevalence of meltwater ponding in the percolation zone\nmay be explained by recent evidence of ice layers or ‘slabs’36–38. When infiltrated meltwater\nrefreezes in porous snow or firn, impermeable ice layers form just below the surface that can\npromote surface runoff into supraglacial streams and lakes31. Processes that determine\nmeltwater drainage and infiltration therefore appear to be the primary control on spatial patterns\nof radiative forcing caused by meltwater ponding.”\nR2G4: Omission of slush and saturated firn:\nThe analysis focuses on surface water features visible in satellite imagery – primarily\nponded water and rivers – but does not consider slush or saturated firn, which may also\ncontribute to albedo reduction, particularly in the percolation zone where\nmeltwater often remains at the surface without forming distinct ponds. Slush is clearly\nvisible in the imagery shown in Figure 1 of the manuscript. Figure 1a and 1c\ncorrespond to areas previously studied by Rawlins et al. (2023) and Glen et al.\n(2025), respectively. These prior studies demonstrated that slush is widespread\nduring the melt season, particularly in high melt years such as 2019. Given the visible\npresence of slush in Figure 1, and its widespread occurrence documented in earlier\nwork, the omission of slush from the current analysis is notable. Its exclusion could\ncontribute to an underestimation of radiative forcing, particularly in regions where\nslush is prevalent but not well captured by standard surface water classification\nmethods, such as those employed by Zhang et al. (2023). Incorporating slush data –\nor at least explicitly acknowledging its potential contribution – would help\ncontextualize the current results and clarify the scope of the estimates presented.\nOur study focuses on the direct effect of meltwater ponding on ice sheet albedo but we\nacknowledge that, by saturating snow/firn, liquid water also has indirect effects on ice sheet\nalbedo. To some extent, we already discuss these mechanisms in the Discussion. However, we\n6\nagree with the reviewer that more explicit reference to the slush research presented by the\nRawlins et al. (2023) and Glen et al. (2025) papers would strengthen our manuscript.\nWe revised the section to read:\nL343-359: “In this study we focused on meltwater ponding but note that liquid water also\nreduces ice sheet surface albedo in other ways. For example, the presence of meltwater can\nlower snow albedo by increasing effective snow grain size and filling interstitial air-spaces\nbetween snow grains41. Snow albedo is therefore strongly dependent on liquid-water content.\nSuppressed albedo between 20–130 km of the transect in Fig. 1a provides qualitative\nevidence for this process occurring in a region that has been termed the “slush zone”42.\nRecently, studies have demonstrated that the slush zone can grow to a much larger area\nthan other hydrological features (e.g. supraglacial lakes) in North and Southwest\nGreenland during high melt years (e.g. 2019)27,28. While full characterization of the\nrelationship between slush and surface albedo is beyond the scope of our study, we\nanticipate that its impact on radiative forcing could be substantial. For example, at 1,600–\n1,800 m a.s.l. in Southwest Greenland, mean albedo from the MCD43A3 product averages 0.62\nduring Jul 29–Aug 5, 2019, well below that of dry snow (~0.84). Measurements and modeling\nreveal that light-absorbing particulate (LAP) concentrations are too small to substantially reduce\nalbedo in this sector of the ice sheet43. Likewise, our findings indicate that the area of meltwater\nponding is too small to reduce albedo by this magnitude. This implies that most of the albedo\nreduction in the Southwest Greenland percolation zone during Jul 29–Aug 5, 2019 can\nprimarily be attributed to slush formation and snow grain size growth.”\nSpecific Comments\nR2S1: Line 54: Update “at spatial resolution of 30 m” to “at spatial resolution of 10–30 m” to\naccurately reflect the resolution of Sentinel-2 (10 m) and Landsat (30 m) used in prior meltwater\nmapping studies.\nUpdated to “10-30 m”.\nR2S2: Lines 55–56: I recommend clarifying that 2018 and 2019 represent a low and high melt\nyear, respectively, to help contextualize the analysis of interannual variability in meltwater\nponding and radiative forcing. Additionally, The current wording implies that Zhang et al. (2023)\nmapped the full summer surface water extent, whereas the dataset represents ~1-week\npeak melt snapshots. I suggest clarifying this point here and elsewhere in the manuscript to\navoid misinterpretation.\nWe agree that this is an important point to clarify early in the introduction and that the original\nwording was a little misleading. We now state that:\nL59: “These maps represent a snapshot of surface water coverage for a cool (2018) and warm\n(2019) summer…”\nWe also clarified this in other sections of the manuscript, for example:\nabsorption by 5.7 ± 3.1 PJ d-1 in Jul 25–30, 2018 to 15.7 ± 8.6 PJ d-1 in Jul 29–Aug 5, 2019\n(Fig. 2d).”\nR2S3: Lines 67–70: I recommend adding “from 2015” after “drone imagery” and rephrasing\n“provides an upper-bound” to “offers an illustrative upper estimate” or “helps constrain an\nupper estimate” to better reflect the temporal mismatch.\nAdded “2015” after “drone imagery. We appreciate the suggestions for framing the drone\nimagery analysis. Our response to R2G1 describes how we revised the manuscript to address\nthis point.\nR2S4: Line 76: This section discusses “summer” broadly, but the data are from very specific\nweeks in late July/early August. Albedo variability caused by ponding earlier or later in the melt\nseason might differ.\nWe replaced “summer” with “in late-Jul/early-Aug” to be more precise.\nR2S5: Figure 1: The right-hand y-axes do not have labels, and the interpretation of \"water\ncoverage\" (whether it represents fractional area or another metric) is unclear; both issues\nshould be clarified.\nThanks for spotting this. “Water coverage” should have been “fractional water area”. We have\nnow clarified that in the caption. We have also relabeled the legend to read “Water fraction”\ninstead of just “Water” and added “Water fraction” as the y-axis label on Figure 1.\nR2S6: Figure 2: Panels 2e and 2f use the same colour for different variables – was this intentional?\nIf not, I suggest adjusting the colours to avoid confusion.\nWe adjusted the colors in panel 2e to address this issue.\nR2S7: Lines 123-124: The phrase “5.7 ± 3.1 to 15.7 ± 8.6 PJ d⁻¹” sounds like a range. I suggest\nrewording to make it clear these are separate values for 2018 and 2019 – e.g., “...in 2018\nand 15.7 ± 8.6 PJ d⁻¹ in 2019.”\nThis would definitely be clearer. Sentence now reads:\nL155-157: “Summed across the ice sheet, we find that meltwater ponding enhances energy\nabsorption by 5.7 ± 3.1 PJ d-1 in Jul 25–30, 2018 to 15.7 ± 8.6 PJ d-1 in Jul 29–Aug 5, 2019\n(Fig. 2d).”\nR2S8: Line 147: The sentence “Given that summer near-surface (2 m) air temperatures were\n1.76 K higher in 2019 than 2018...” would benefit from greater specificity. I recommend clarifying\nthe exact time window over which this temperature difference was calculated. Additionally, it\nwould be helpful to state whether the temperature anomaly represents a spatial mean over\nthe entire ice sheet, a specific elevation band, or a region. Providing this context would\nensure that the stated radiative feedback is interpreted appropriately.\nsurface air temperatures between 2018 and 2019 averaged over the entire ice sheet. Sentence\nnow reads:\nL200-201: “Given that ice sheet summer (JJA) near-surface (2 m) air temperatures were 1.76\nK higher in 2019 than 2018…”\nR2S9: Line 150: While the derived feedback estimate (+5.7 ± 5.2 PJ d⁻¹ K⁻¹) is useful, the large\nuncertainty could be acknowledged more directly.\nWe agree that acknowledging the relatively large uncertainty makes sense. Sentence now\nreads:\nL204-205: “…although we note that the uncertainty is relatively large (Fig. 2f, see Methods).”\nR2S10: Figure 5: Please add \"2015\" after \"drone\" to maintain consistency with the other figure\nlabels and to clearly indicate the year of imagery acquisition.\nAdded \"2015\" after \"drone\" in figure legend.\nR2S11: Line 179: The text refers to the 1,170–1,290 m site as “higher elevation,” but this\nphrasing may be misleading in the context of the entire Greenland Ice Sheet. I suggest clarifying\nthat this is higher relative to the margin site, but still within the lower to mid-elevation ablation\nzone, particularly given the role of elevation in interpreting radiative forcing.\nWe have clarified this sentence to read:\nL249-250: “In contrast, our high-resolution surface water maps reveal substantial meltwater\nponding in the ablation zone at elevations higher than our ice-sheet margin field site (Fig.\n4d–f).”\nR2S12: Line 286: Please revise the sentence to avoid suggesting that drone imagery captures\nmeltwater features at all scales, as the surveys are spatially and temporally limited.\nWe replaced “at all scales” with “of all sizes” to avoid implying this.\nR2S13: Line 315: Please clearly define \"meltwater ponding coverage\" – for example, does it\nrepresent the fractional surface area of a pixel covered by water, or something else?\nThe Zhang et al. (2023) maps represent binary surface water coverage which we have now\nclarified in this sentence:\nL419: “We obtained binary surface water coverage maps from ref. 31…”\nR2S14: Lines 329–331: The method for averaging surface water and albedo datasets onto the\nISMIP6 grid is not clearly described; please clarify.\nWe added some clarification to that sentence which now reads:\nL433-435: “We resampled both the surface water and surface albedo data onto the ISMIP6 grid\nby averaging values within each target grid cell.”\nsemi-automated to manual classification in the Russell and Isunguata sites.\nWe now state that:\nL531-540: “It is possible that the use of different methods to map surface water could introduce\nsome uncertainties. However, based on visual inspection of the imagery and given that both\napproaches segment water pixels using NDWI values, it is unlikely that these uncertainties are\nresponsible for the large differences in water coverage between the dark zone and margin field\nsites.”\nR2S16: Methods: Consider tightening some phrasing for consistency with the manuscript’s\notherwise formal tone. For example, “to provide some intuition” (line 365) feels out of place\nWe replaced “to provide some intuition” with the more formal “This metric is best understood\nby…”.\n10"}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "I am happy with the way the authors addressed my comments, and recommend the manuscript for publication.", "reviewer_2": "Meltwater ponding is an underestimated source of radiative forcing on the Greenland Ice Sheet\nRyan et al.\nAfter reviewing the revised manuscript by Ryan et al., it is clear that the authors have made sufficient improvements and\ntaken the comments of both reviewers on board. I consider my previous comments to be adequately addressed, and I have\nno concerns remaining. I am happy to recommend the manuscript for publication."}, "author_response": "Response to reviewer comments\nReviewer #1\nI am happy with the way the authors addressed my comments, and recommend the manuscript\nfor publication.\nWe sincerely thank the reviewer for taking the time to thoughtfully evaluate our manuscript.\nReviewer #2\nAfter reviewing the revised manuscript by Ryan et al., it is clear that the authors have made\nsufficient improvements and taken the comments of both reviewers on board. I consider my\nprevious comments to be adequately addressed, and I have no concerns remaining. I am happy\nto recommend the manuscript for publication.\nWe sincerely thank the reviewer for taking the time to thoughtfully evaluate our manuscript.\n1"}]

Decision letter