Search

The Antarctic firn layer provides pore space in which an estimated 94% to 96% of the surface melt refreezes or is retained as liquid water. Future depletion of firn pore space by increased surface melt, densification and formation of low-permeability ice slabs can potentially lead to meltwater ponding, hydrofracturing and ice-shelf disintegration. Here, we investigate the 21st-century evolution of total firn air content (FAC) and accessible FAC (i.e. the pore space that meltwater can reach) across Antarctic ice shelves. We use the semi-empirical IMAU Firn Densification Model (IMAU-FDM) with an updated dynamical densification expression to cope with changing climate forcing. The firn model is forced by general circulation model output of the Community Earth System Model version 2 (CESM2) for three climate emission scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5), dynamically downscaled to a 27km horizontal resolution by the Regional Atmospheric Climate Model version 2.3p2 (RACMO2.3p2). To estimate accessible FAC, we prescribe a relationship between ice-slab thickness and permeability. In our simulations, ice shelves on the Antarctic Peninsula and the Roi Baudouin Ice Shelf in Dronning Maud Land are particularly vulnerable to total FAC depletion (>50% decrease by 2100), even for low-emission (SSP1-2.6) and intermediate-emission (SSP2-4.5) scenarios. In the high-emission (SSP5-8.5) scenario in particular, the formation of ice slabs further reduces accessible FAC on ice shelves with low accumulation rates (current rates of <500mmw.e.yr-1), including many East Antarctic ice shelves and the Filchner-Ronne, Ross, Pine Island and Larsen C ice shelves. These results underline the potentially large vulnerability of low-accumulation ice shelves to firn air depletion through ice-slab formation.

Surface melt on the Greenland ice sheet has been increasing in intensity and extent over the last decades due to Arctic atmospheric warming. Surface melt depends on the surface energy balance, which includes the atmospheric forcing but also the thermal budget of the snow, firn and ice near the ice sheet surface. The temperature of the ice sheet subsurface has been used as an indicator of the thermal state of the ice sheet's surface. Here, we present a compilation of 4612 measurements of firn and ice temperature at 10m below the surface (T10m) across the ice sheet, spanning from 1912 to 2022. The measurements are either instantaneous or monthly averages. We train an artificial neural network model (ANN) on 4597 of these point observations, weighted by their relative representativity, and use it to reconstruct T10m over the entire Greenland ice sheet for the period 1950-2022 at a monthly timescale. We use 10-year averages and mean annual values of air temperature and snowfall from the ERA5 reanalysis dataset as model input. The ANN indicates a Greenland-wide positive trend of T10m at 0.2°C per decade during the 1950-2022 period, with a cooling during 1950-1985 (-0.4°C per decade) followed by a warming during 1985-2022 (+0.7° per decade). Regional climate models HIRHAM5, RACMO2.3p2 and MARv3.12 show mixed results compared to the observational T10m dataset, with mean differences ranging from -0.4°C (HIRHAM) to 1.2°C (MAR) and root mean squared differences ranging from 2.8°C (HIRHAM) to 4.7°C (MAR). The observation-based ANN also reveals an underestimation of the subsurface warming trends in climate models for the bare-ice and dry-snow areas. The subsurface warming brings the Greenland ice sheet surface closer to the melting point, reducing the amount of energy input required for melting. Our compilation documents the response of the ice sheet subsurface to atmospheric warming and will enable further improvements of models used for ice sheet mass loss assessment and r

Correction to: Nature Reviews Earth & Environmenthttps://doi.org/10.1038/s43017-023-00507-9, published online 23 January 2024. In the version of the article initially published, in Fig. 5, under “Radar altimeter”, “O(16–160 m)” previously read “O(16–160 km)”. This has now been corrected in the HTML and PDF versions of the article.

Surface meltwater production impacts the mass balance of the Greenland and Antarctic ice sheets in several ways, both directly (e.g., through runoff in Greenland) and indirectly (e.g., through cryo-hydrologic warming and frontal melt of marine-terminating glaciers in Greenland and hydrofracturing of ice shelves in Antarctica). Despite its importance, the spatial and temporal patterns in melt rates on both ice sheets are still relatively poorly understood. In this contribution we review and contrast surface melt ‘weather ‘(i.e., short term, intra- and interdiurnal variability) and surface melt ‘climate’ (i.e., longer term, interannual variability and future melt) of both ice sheets. We find that in situ observations using suitably equipped (automatic or staffed) weather stations are invaluable for a complete understanding of the melt process, which represents the complex transport of energy by radiation, turbulence, and molecular conduction between the lower atmosphere, the ice/snow surface, and the subsurface ice/snow layers. We provide example time series of ice sheet melt ‘weather’ for the marginal Greenland ice sheet, where warm and humid air masses tend to increase surface melt rate, and for coastal East Antarctica, where the opposite happens. Apart from process understanding, these in situ observations, which especially in Antarctica are scarce in space and time, are also invaluable to validate, evaluate and calibrate satellite- and model-based estimates of ice sheet surface melt rate. We provide examples of modelled melt maps for both ice sheets, and melt projections for a high-warming, fossil-fuelled development scenario. Although important milestones in melt observations (both in-situ and remotely sensed) and melt models (both global and regional) have recently been reached, we identify multiple outstanding research questions pertaining to current and future ice sheet surface melt rates.

Firn simulations are essential for understanding Antarctic ice sheet mass change, as they enable us to convert satellite altimetry observed volume changes to mass changes and column thickness to ice thickness and to quantify the meltwater buffering capacity of firn. Here, we present and evaluate a simulation of the contemporary Antarctic firn layer using the updated semi-empirical IMAU Firn Densification Model (IMAU-FDM) for the period 1979–2020. We have improved previous fresh-snow density and firn compaction parameterizations and used updated atmospheric forcing. In addition, the model has been calibrated and evaluated using 112 firn core density observations across the ice sheet. We found that 62 % of the seasonal and 67 % of the decadal surface height variability are due to variations in firn air content rather than firn mass. Comparison of simulated surface elevation change with a previously published multi-mission altimetry product for the period 2003–2015 shows that performance of the updated model has improved, notably in Dronning Maud Land and Wilkes Land. However, a substantial trend difference (>10 cm yr−1) remains in the Antarctic Peninsula and Ellsworth Land, mainly caused by uncertainties in the spin-up forcing. By estimating previous climatic conditions from ice core data, these trend differences can be reduced by 38 %.

Surface melt is an important driver of ice shelf disintegration and its consequent mass loss over the Antarctic Ice Sheet. Monitoring surface melt using satellite remote sensing can enhance our understanding of ice shelf stability. However, the sensors do not measure the actual physical process of surface melt, but rather observe the presence of liquid water. Moreover, the sensor observations are influenced by the sensor characteristics and surface properties. Therefore, large inconsistencies can exist in the derived melt estimates from different sensors. In this study, we apply state-of-the-art melt detection algorithms to four frequently used remote sensing sensors, i.e., two active microwave sensors, which are Advanced Scatterometer (ASCAT) and Sentinel-1, a passive microwave sensor, i.e., Special Sensor Microwave Imager/Sounder (SSMIS), and an optical sensor, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS). We intercompare the melt detection results over the entire Antarctic Ice Sheet and four selected study regions for the melt seasons 2015-2020. Our results show large spatiotemporal differences in detected melt between the sensors, with particular disagreement in blue ice areas, in aquifer regions, and during wintertime surface melt. We discuss that discrepancies between sensors are mainly due to cloud obstruction and polar darkness, frequency-dependent penetration of satellite signals, temporal resolution, and spatial resolution, as well as the applied melt detection methods. Nevertheless, we argue that different sensors can complement each other, enabling improved detection of surface melt over the Antarctic Ice Sheet.

The firn layer that covers 90 % of the Greenland ice sheet (GrIS) plays an important role in determining the response of the ice sheet to climate change. Meltwater can percolate into the firn layer and refreeze at greater depths, thereby temporarily preventing mass loss. However, as global warming leads to increasing surface melt, more surface melt may refreeze in the firn layer, thereby reducing the capacity to buffer subsequent episodes of melt. This can lead to a tipping point in meltwater runoff. It is therefore important to study the evolution of the Greenland firn layer in the past, present and future. In this study, we present the latest version of our firn model, IMAU-FDM (Firn Densification Model) v1.2G, with an application to the GrIS. We improved the density of freshly fallen snow, the dry-snow densification rate and the firn's thermal conductivity using recently published parametrizations and by calibration to an extended set of observations of firn density, temperature and liquid water content at the GrIS. Overall, the updated model settings lead to higher firn air content and higher 10 m firn temperatures, owing to a lower density near the surface. The effect of the new model settings on the surface elevation change is investigated through three case studies located at Summit, KAN-U and FA-13. Most notably, the updated model shows greater inter- and intra-annual variability in elevation and an increased sensitivity to climate forcing.

We use satellite and airborne altimetry to estimate annual mass changes of the Greenland Ice Sheet. We estimate ice loss corresponding to a sea-level rise of 6.9 ± 0.4 mm from April 2011 to April 2020, with a highest annual ice loss rate of 1.4 mm/yr sea-level equivalent from April 2019 to April 2020. On a regional scale, our annual mass loss timeseries reveals 10–15 m/yr dynamic thickening at the terminus of Jakobshavn Isbræ from April 2016 to April 2018, followed by a return to dynamic thinning. We observe contrasting patterns of mass loss acceleration in different basins across the ice sheet and suggest that these spatiotemporal trends could be useful for calibrating and validating prognostic ice sheet models. In addition to resolving the spatial and temporal fingerprint of Greenland's recent ice loss, these mass loss grids are key for partitioning contemporary elastic vertical land motion from longer-term glacial isostatic adjustment (GIA) trends at GPS stations around the ice sheet. Our ice-loss product results in a significantly different GIA interpretation from a previous ice-loss product.

We use satellite and airborne altimetry to estimate annual mass changes of the Greenland Ice Sheet. We estimate ice loss corresponding to a sea-level rise of 6.9 ± 0.4 mm from April 2011 to April 2020, with a highest annual ice loss rate of 1.4 mm/yr sea-level equivalent from April 2019 to April 2020. On a regional scale, our annual mass loss timeseries reveals 10-15 m/yr dynamic thickening at the terminus of Jakobshavn Isbræ from April 2016 to April 2018, followed by a return to dynamic thinning. We observe contrasting patterns of mass loss acceleration in different basins across the ice sheet and suggest that these spatiotemporal trends could be useful for calibrating and validating prognostic ice sheet models. In addition to resolving the spatial and temporal fingerprint of Greenland's recent ice loss, these mass loss grids are key for partitioning contemporary elastic vertical land motion from longer-term glacial isostatic adjustment (GIA) trends at GPS stations around the ice sheet. Our ice-loss product results in a significantly different GIA interpretation from a previous ice-loss product.

Antarctic blue ice areas are exposed due to erosion and sublimation of snow. At the same time, surface melt can form surface types that are spectrally similar to blue ice, especially at low elevations. These are termed melt-induced blue ice areas. Both types of blue ice are sensitive indicators of climate change. Satellite remote sensing is a powerful technique to retrieve the spatial extent of blue ice areas and their variation in time. Yet, existing satellite-derived blue ice area products are either mono-temporal for the entire Antarctic ice sheet, or multi-temporal for a limited area. Here, we present FABIAN, a product of blue ice fraction over Antarctica, derived from the moderate resolution imaging spectroradiometer (MODIS) archive covering the period 2000–2021. A spectral mixture analysis (SMA) in Google Earth Engine, based on a careful selection of endmember spectra, accurately reconstructs the reflectance observed by MODIS in blue ice areas. Based on a validation with contemporaneous Sentinel-2 images, FABIAN has a root mean square error in blue ice fraction of approximately 10% ∼ 20% in wind-induced blue ice areas, and 20% ∼ 30% in melt-induced blue ice areas across six selected test sites in the coastal East Antarctic ice sheet. FABIAN is challenged in regions with shallow melt streams and lakes, since their spectral profiles are similar to those from blue ice areas in MODIS bands. For further analyses and applications, FABIAN holds the potential for (1) deriving annual blue ice area maps, (2) distinguishing between wind-and melt-induced blue ice types, (3) evaluating and correcting (regional) climate models, and (4) analyzing temporal variations in blue ice abundance and exposure., Mathematical Geodesy and Positioning