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Observations and models agree that the Greenland Ice Sheet (GrIS) surface mass balance (SMB) has decreased since the end of the 1990s due to an increase in meltwater runoff and that this trend will accelerate in the future. However, large uncertainties remain, partly due to different approaches for modelling the GrIS SMB, which have to weigh physical complexity or low computing time, different spatial and temporal resolutions, different forcing fields, and different ice sheet topographies and extents, which collectively make an inter-comparison difficult. Our GrIS SMB model intercomparison project (GrSMBMIP) aims to refine these uncertainties by intercomparing 13 models of four types which were forced with the same ERA-Interim reanalysis forcing fields, except for two global models. We interpolate all modelled SMB fields onto a common ice sheet mask at 1 km horizontal resolution for the period 1980–2012 and score the outputs against (1) SMB estimates from a combination of gravimetric remote sensing data from GRACE and measured ice discharge; (2) ice cores, snow pits and in situ SMB observations; and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting model deficiencies in an accurate representation of the GrIS ablation zone extent and processes related to surface melt and runoff. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of the same order as RCMs compared with observations and therefore remain useful tools for long-term simulations or coupling with ice sheet models. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present-day SMB relative to observations, suggesting that biases are not systematic among models and that this ensemble estimate can be used as a reference for current climate when carrying out future model developments. However, a higher density of in situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 m w.e. yr−1 due to large discrepancies in modelled snowfall accumulation.

With the aim of studying the recent Greenland ice sheet (GrIS) surface mass balance (SMB) decrease relative to the last century, we have forced the regional climate MAR (Modèle Atmosphérique Régional; version 3.5.2) model with the ERA-Interim (ECMWF Interim Re-Analysis; 1979–2015), ERA-40 (1958–2001), NCEP–NCARv1 (National Centers for Environmental Prediction–National Center for Atmospheric Research Reanalysis version 1; 1948–2015), NCEP–NCARv2 (1979–2015), JRA-55 (Japanese 55-year Reanalysis; 1958–2014), 20CRv2(c) (Twentieth Century Reanalysis version 2; 1900–2014) and ERA-20C (1900–2010) reanalyses. While all these forcing products are reanalyses that are assumed to represent the same climate, they produce significant differences in the MAR-simulated SMB over their common period. A temperature adjustment of +1 °C (respectively −1 °C) was, for example, needed at the MAR boundaries with ERA-20C (20CRv2) reanalysis, given that ERA-20C (20CRv2) is ∼ 1 °C colder (warmer) than ERA-Interim over Greenland during the period 1980–2010. Comparisons with daily PROMICE (Programme for Monitoring of the Greenland Ice Sheet) near-surface observations support these adjustments. Comparisons with SMB measurements, ice cores and satellite-derived melt extent reveal the most accurate forcing datasets for the simulation of the GrIS SMB to be ERA-Interim and NCEP–NCARv1. However, some biases remain in MAR, suggesting that some improvements are still needed in its cloudiness and radiative schemes as well as in the representation of the bare ice albedo. Results from all MAR simulations indicate that (i) the period 1961–1990, commonly chosen as a stable reference period for Greenland SMB and ice dynamics, is actually a period of anomalously positive SMB (∼ +40 Gt yr−1) compared to 1900–2010; (ii) SMB has decreased significantly after this reference period due to increasing and unprecedented melt reaching the highest rates in the 120-year common period; (iii) before 1960, both ERA-20C and 20CRv2-forced MAR simulations suggest a significant precipitation increase over 1900–1950, but this increase could be the result of an artefact in the reanalyses that are not well-enough constrained by observations during this period and (iv) since the 1980s, snowfall is quite stable after having reached a maximum in the 1970s. These MAR-based SMB and accumulation reconstructions are, however, quite similar to those from Box (2013) after 1930 and confirm that SMB was quite stable from the 1940s to the 1990s. Finally, only the ERA-20C-forced simulation suggests that SMB during the 1920–1930 warm period over Greenland was comparable to the SMB of the 2000s, due to both higher melt and lower precipitation than normal.

Pigmented microalgae inhabiting snow and ice environments lower the albedo of glacier and ice-sheet surfaces, significantly enhancing surface melt. Our ability to accurately predict their role in glacier and ice-sheet surface mass balance is limited by the current lack of empirical data to constrain their representation in predictive models. Here we present new empirical optical properties for snow and ice algae and incorporate them in a radiative transfer model to investigate their impact on snow and ice surface albedo. We found ice algal cells to be more efficient absorbers than snow algal cells, but their blooms had comparable impact on surface albedo due to the different photic conditions of their habitats. We then used the model to reconstruct the effect of ice algae on bare ice albedo spectra collected at our field site in southern Greenland, where blooms dropped the albedo locally by between 3 and 43%, equivalent to 1–10 L m$^{-2}$ d$^{-1}$ of melted ice. Using the newly parametrized model, future studies could investigate biological albedo reduction and algal quantification from remote hyperspectral and multispectral imagery.

Melting of the Greenland Ice Sheet (GrIS) is the largest single contributor to eustatic sea level and is amplified by the growth of pigmented algae on the ice surface, which increases solar radiation absorption. This biological albedo-reducing effect and its impact upon sea level rise has not previously been quantified. Here, we combine field spectroscopy with a radiative-transfer model, supervised classification of unmanned aerial vehicle (UAV) and satellite remote-sensing data, and runoff modelling to calculate biologically driven ice surface ablation. We demonstrate that algal growth led to an additional 4.4–6.0 Gt of runoff from bare ice in the south-western sector of the GrIS in summer 2017, representing 10 %–13 % of the total. In localized patches with high biomass accumulation, algae accelerated melting by up to 26.15±3.77 % (standard error, SE). The year 2017 was a high-albedo year, so we also extended our analysis to the particularly low-albedo 2016 melt season. The runoff from the south-western bare-ice zone attributed to algae was much higher in 2016 at 8.8–12.2 Gt, although the proportion of the total runoff contributed by algae was similar at 9 %–13 %. Across a 10 000 km2 area around our field site, algae covered similar proportions of the exposed bare ice zone in both years (57.99 % in 2016 and 58.89 % in 2017), but more of the algal ice was classed as “high biomass” in 2016 (8.35 %) than 2017 (2.54 %). This interannual comparison demonstrates a positive feedback where more widespread, higher-biomass algal blooms are expected to form in high-melt years where the winter snowpack retreats further and earlier, providing a larger area for bloom development and also enhancing the provision of nutrients and liquid water liberated from melting ice. Our analysis confirms the importance of this biological albedo feedback and that its omission from predictive models leads to the systematic underestimation of Greenland's future sea level contribution, especially because both the bare-ice zones available for algal colonization and the length of the biological growth season are set to expand in the future.

Dark (low albedo) surface ice on the Greenland Ice Sheet enhances melting and subsequent runoff, a major mass loss contributor during the ablation season. The accumulation of both biological (e.g., glacier ice algae) and abiotic (e.g., mineral dust) light‐absorbing particulates are important darkening factors, that are potentially influenced by the duration of snow‐free, bare ice (a phenological factor), and other geo‐topographical factors such as elevation, slope, aspect and the distance from the ice margin. Here, we present the first medium‐resolution (30 m) analysis of the phenological and geo‐topographical controls on the distribution of dark ice in SE and SW Greenland from statistical analysis of data derived from a harmonized satellite albedo product and ArcticDEM. The duration of bare ice primarily controls the distribution of dark surface ice, allowing for algae growth on inland ice surfaces in particular, whereas geo‐topographical factors are only secondary controls. Plain Language Summary Meltwater that runs off the Greenland Ice Sheet is currently the largest single glaciological contributor to global sea level rise. Recent darkening of the surface ice has contributed significantly to increased melt rates. We used medium resolution (30m) satellite data to link ice surface darkness (albedo) to the topography (elevation, slope, and aspect) of the ice sheet surface, the distance from the ice margin, and the duration of bare or snow‐free surface ice. Our analysis shows that the duration of bare ice or snow‐free conditions was the most significant factor in driving the darkening of ice. We also found that the impact of topographic factors on the ice darkening varied depending on whether abiotic or biological processes were driving the darkening. This information helps us to better explain where the ice sheet is melting fastest and to predict the loss of ice mass, particularly considering the expected increase in the area of bare, snow‐free ice in our warming climate. Key Points The duration of snow‐free, bare ice is the primary control on the distribution of dark ice Longer durations of bare ice (median = 40 days) are prerequisites for ice to become dark Surface slope and aspect influence darkening by modulating the duration of bare ice and glacier ice algae growth

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

We document the density and hydrologic properties of bare, ablating ice in a mid-elevation (1215 m a.s.l.) supraglacial internally drained catchment in the Kangerlussuaq sector of the western Greenland ice sheet. We find low-density (0.43–0.91 g cm−3, μ = 0.69 g cm−3) ice to at least 1.1 m depth below the ice sheet surface. This near-surface, low-density ice consists of alternating layers of water-saturated, porous ice and clear solid ice lenses, overlain by a thin (< 0.5 m), even lower density (0.33–0.56 g cm−3, μ = 0.45 g cm−3) unsaturated weathering crust. Ice density data from 10 shallow (0.9–1.1 m) ice cores along an 800 m transect suggest an average 14–18 cm of specific meltwater storage within this low-density ice. Water saturation of this ice is confirmed through measurable water levels (1–29 cm above hole bottoms, μ = 10 cm) in 84 % of cryoconite holes and rapid refilling of 83 % of 1 m drilled holes sampled along the transect. These findings are consistent with descriptions of shallow, depth-limited aquifers on the weathered surface of glaciers worldwide and confirm the potential for substantial transient meltwater storage within porous low-density ice on the Greenland ice sheet ablation zone surface. A conservative estimate for the  ∼  63 km2 supraglacial catchment yields 0.009–0.012 km3 of liquid meltwater storage in near-surface, porous ice. Further work is required to determine if these findings are representative of broader areas of the Greenland ice sheet ablation zone, and to assess the implications for sub-seasonal mass balance processes, surface lowering observations from airborne and satellite altimetry, and supraglacial runoff processes.

Meteorological station records, ice cores, and regional climate model output are combined to develop a continuous 171-yr (1840–2010) reconstruction of Greenland ice sheet climatic surface mass balance (Bclim) and its subcomponents including near-surface air temperature (SAT) since the end of the Little Ice Age. Independent observations are used to assess and compensate errors. Melt water production is computed using separate degree-day factors for snow and bare ice surfaces. A simple meltwater retention scheme yields the time variation of internal accumulation, runoff, and bare ice area. At decadal time scales over the 1840–2010 time span, summer (June–August) SAT increased by 1.64°C, driving a 59% surface meltwater production increase. Winter warming was +2.0°C. Substantial interdecadal variability linked with episodic volcanism and atmospheric circulation anomalies is also evident. Increasing accumulation and melt rates, bare ice area, and meltwater retention are driven by increasing SAT. As a consequence of increasing accumulation and melt rates, calculated meltwater retention by firn increased 51% over the period, nearly compensating a 63% runoff increase. Calculated ice sheet end of melt season bare ice area increased more than 5%. Multiple regression of interannual SAT and precipitation anomalies suggests a dominance of melting on Bclimand a positive SAT precipitation sensitivity (+32 Gt yr−1K−1or 6.8% K−1). The Bclimcomponent magnitudes from this study are compared with results from Hanna et al. Periods of shared interannual variability are evident. However, the long-term trend in accumulation differs in sign.

In some areas of Antarctica, blue‐colored bare ice is exposed at the surface. These blue ice areas (BIAs) can trap meteorites or old ice and are vital for understanding the climatic history. By combining multi‐sensor remote sensing data (MODIS, RADARSAT‐2, and TanDEM‐X) in a deep learning framework, we map blue ice across the continent at 200‐m resolution. We use a novel methodology for image segmentation with “noisy” labels to learn an underlying “clean” pattern with a neural network. In total, BIAs cover ca. 140,000 km2 (∼1%) of Antarctica, of which nearly 50% located within 20 km of the grounding line. There, the low albedo of blue ice enhances melt‐water production and its mapping is crucial for mass balance studies that determine the stability of the ice sheet. Moreover, the map provides input for fieldwork missions and can act as constraint for other geophysical mapping efforts. Plain Language Summary While most of the continent of Antarctica is covered by snow, in some areas, ice is exposed at the surface, with a typical blue color. At lower elevations, blue ice enhances melt‐water production, which is important for studying the future of the ice sheet. Moreover, scientific teams frequently visit blue ice areas (BIAs) as they act as traps for meteorites and very old ice. In this study, we map the extent and the exact location of BIAs using various satellite observations. These diverse observations are efficiently combined in an artificial intelligence algorithm. We develop the algorithm so that it can learn to map blue ice even though existing training labels, which teach the algorithm what blue ice looks like, are imperfect. We quantify that the new map scores better on various performance metrics compared to the current most‐used blue ice map. Moreover, for the first time, we estimate uncertainties of the detection of blue ice. The map indicates that ca. 1% of the surface of Antarctica exposes blue ice and will be important for fieldwork missions and understanding surface processes leading to melt and potential sea level rise. Key Points We map blue ice areas in Antarctica by combining multi‐sensor satellite observations in a convolutional neural network Blue ice covers ca. 140,000 km2 (∼1%) of Antarctica, of which ca. 50% located in the grounding zone Our map will improve mass balance estimates and studies on ice‐shelf stability, and will support searches for meteorites or old ice

Accurate modeling of cryospheric surface albedo is essential for our understanding of climate change as snow and ice surfaces regulate the global radiative budget and sea-level through their albedo and mass balance. Although significant progress has been made using physical principles to represent the dynamic albedo of snow, models of glacier ice albedo tend to be heavily parameterized and not explicitly connected with physical properties that govern albedo, such as the number and size of air bubbles, specific surface area (SSA), presence of abiotic and biotic light absorbing constituents (LACs), and characteristics of any overlying snow. Here, we introduce SNICAR-ADv4, an extension of the multi-layer two-stream delta-Eddington radiative transfer model with the adding–doubling solver that has been previously applied to represent snow and sea-ice spectral albedo. SNICAR-ADv4 treats spectrally resolved Fresnel reflectance and transmittance between overlying snow and higher-density glacier ice, scattering by air bubbles of varying sizes, and numerous types of LACs. SNICAR-ADv4 simulates a wide range of clean snow and ice broadband albedo (BBA), ranging from 0.88 for (30 µm) fine-grain snow to 0.03 for bare and bubble-free ice under direct light. Our results indicate that representing ice with a density of 650 kg m−3 as snow with no refractive Fresnel layer, as done previously, generally overestimates the BBA by an average of 0.058. However, because most naturally occurring ice surfaces are roughened “white ice”, we recommend modeling a thin snow layer over bare ice simulations. We find optimal agreement with measurements by representing cryospheric media with densities less than 650 kg m−3 as snow and larger-density media as bubbly ice with a Fresnel layer. SNICAR-ADv4 also simulates the non-linear albedo impacts from LACs with changing ice SSA, with peak impact per unit mass of LACs near SSAs of 0.1–0.01 m2 kg−1. For bare, bubble-free ice, LACs actually increase the albedo. SNICAR-ADv4 represents smooth transitions between snow, firn, and ice surfaces and accurately reproduces measured spectral albedos of a variety of glacier surfaces. This work paves the way for adapting SNICAR-ADv4 to be used in land ice model components of Earth system models.