James M. Lea

Ice-marginal lakes are important components of glacierised landscapes, storing meltwater, influencing glacier dynamics, and regulating freshwater transfer to the ocean. Greenland (Kalaallit Nunaat) hosts a large concentration of ice-marginal lakes along the edge of the Greenland Ice Sheet, and its peripheral glaciers and ice caps. Here, we present a Greenland-wide remote sensing assessment of ice-marginal lakes between 2016 and 2025 to provide a comprehensive overview, and evaluate their role with ongoing glacier change. We identify 4038 lakes covering 4734.9 km², equivalent to 1 % of Greenland's ice-free land area. Lake number, area, and surface temperature show negligible change over the decade, indicating broadly stable conditions. However, in total these ice-marginal lakes store an estimated 462.5 Gt of meltwater, of which 67.0 Gt (14 %) has the potential to be rapidly released through glacial lake outburst floods (GLOFs). Our results demonstrate that ice-marginal lakes are a major and spatially heterogeneous component of Greenland’s meltwater system, functioning both as long-term water storages and as sources of episodic, high-magnitude outflow events. Their role remains under-represented in assessments of Greenland hydrology and freshwater fluxes, with implications for constraining Greenland’s contribution to global sea-level estimates.

Crevasse field drainage transfers at least half of the seasonal runoff from the surface to the bed of the Greenland Ice Sheet, but the patterns of drainage are complex and spatio‐temporally heterogenous. To better understand controls on crevasse drainage processes, we use an automated deep learning method to map the seasonal filling and drainage of water‐filled crevasses at Sermeq Kujalleq (Store Glacier) in 2019 using 10‐m‐resolution Sentinel‐2 MSI imagery, alongside RACMO reanalysis melt estimates and strain rates inferred from ITS_LIVE velocity observations. The timing of crevasse drainage correlates strongly with seasonal peaks in surface strain rates, suggesting that seasonal variation in surface stress exerts a strong control on the exact timing of drainage to the bed. Linear elastic fracture mechanics (LEFM) modeling suggests that a seasonal transition to higher tensile stress regimes can trigger rapid full‐depth hydrofracture at the point at which stresses exceed the threshold necessary to initiate initial fracture, provided that meltwater from early season snowmelt is abundant. We suggest that this causal behavior is distinct from both (a) supraglacial lake drainage, which depends on short‐term (hourly daily) transient accelerations to trigger drainage; and (b) crevasse fields that are not observed to pond, which exist in high tensile regimes and likely readily supply water into the englacial or subglacial system from melt‐season onset. The distinct mode of drainage described here requires unique parameterization to predict spatio‐temporal patterns of water transfer to the bed of ice sheets and drive models of subglacial hydrology.

We present a new version of the PROMICE | GC-NET automatic weather station (AWS) data product, combining observations from two Greenland AWS networks; PROMICE and GC-NET. As of late 2025, the dataset integrates records from 52 active and historical AWS sites across the Greenland Ice Sheet, peripheral glaciers and land areas. This new version includes improvements in station design, sensor configuration, and data processing. Two primary station types are used: dual-boom masts in the accumulation area, and free-standing tripods with a single instrument boom in the ablation area. Data are processed with pypromice, an open-source Python package designed for standardized, transparent, and reproducible workflows, including calibration, filtering, variable derivation, and correction. The resulting products are distributed in CF-compliant NetCDF and CSV formats and include both measured and derived variables for applications in polar meteorology, climatology, and glaciology. Access is open under license CC-BY 4.0. A GitHub-based issue tracker (https://github.com/GEUS-Glaciology-and-Climate/PROMICE-AWS-data-issues, last access: 12 November 2025) supports community-driven quality control within a living data framework. The datasets are openly available at https://doi.org/10.22008/FK2/IW73UU (How et al., 2022a).

Nearly all meltwater produced on the Greenland Ice Sheet surface is routed through the interior of the ice, and the spatial and temporal patterns – as well as the mode of delivery – of discharge to the bed can have significant consequences for processes including ice fracture, rheology, and basal sliding. Existing suggest that a majority of meltwater in Greenland is transferred to the bed via surface crevasse fields, rather than lakes or moulins. However, in contrast to well-observed phenomena such as supraglacial lake drainages, little work has been done to explore how this process should be parameterised in regional-scale models that route meltwater from the surface to the bed. Here, we explore: (i) how well observations of crevasse field filling and drainage support existing parameterisations based upon linear elastic fracture mechanics (LEFM); (ii) what modifications may need to be implemented to better represent crevasse field hydrology, including the choice of proxy for resistive stress (Rxx) and the inclusion of seasonally-varying stress; and (iii) the potential consequences for effective and sliding at the glacier bed, as represented through subglacial hydrological models.

The Greenland Ice Sheet (GrIS) is a hotspot for sediment transport, where glaciers and their meltwaters deliver sediment and associated nutrients to proglacial outwash plains and the ocean. This outflux supports proglacial and marine ecosystems, where enhanced primary production can contribute to atmospheric CO2 drawdown and negative carbon feedback loops. As field measurements of sediment export from the GrIS are limited both spatially and temporally, we use Google Earth Engine to extract suspended sediment concentrations from Sentinel-2 near infrared reflectance measurements of meltwater flows exiting land terminating glacier systems in Greenland (ground truthed by field data from Watson River, Kangerlussuaq), where sediment export analysis is facilitated by meltwater estimates from the Regional Atmospheric Climate Model (RACMO). Over the summer meltwater seasons of 2016 – 2024 we estimate a combined mean annual sediment flux of ~0.44 Gt yr-1, where we note that export has increased at an average rate of ~0.001 Gtyr-1 over the study period. Land terminating glaciers of the GrIS exported the greatest suspended sediment flux of 0.51 Gtyr-1 in 2022 and the lowest export of 0.24 Gtyr-1 in 2018. Regionally, southwest Greenland provides the greatest suspended sediment export per area, accounting for ~41% of total export over the study period. Our analysis provides the first estimate of proglacial sediment export from all land-terminating glacier systems in Greenland using Sentinel-2 imagery, allowing us to examine drivers of change and establish a baseline for assessing future changes in sediment delivery, as a result of climate warming. These changes will alter land systems in front of glaciers and impact sediment-derived nutrient delivery/ ecosystem response, with resultant implications for CO2 drawdown.

We present a new version of the PROMICE | GC-NET automatic weather station (AWS) data product, combining observations from two Greenland AWS networks; PROMICE and GC-NET. The dataset integrates records from more than 50 active and historical AWS sites across the Greenland Ice Sheet, peripheral glaciers and land areas. This new version includes improvements in station design, sensor configuration, and data processing. Two primary station types are used: dual-boom masts in the accumulation area, and free-standing tripods with a single instrument boom in the ablation area. Data are processed with pypromice, an open-source Python package designed for standardized, transparent, and reproducible workflows, including calibration, filtering, variable derivation, and correction. The resulting products are distributed in CF-compliant NetCDF and CSV formats and include both measured and derived variables for applications in polar meteorology, climatology, and glaciology. Access is open under license CC-BY 4.0. A GitHub-based issue tracker (https://github.com/GEUS-Glaciology-and-Climate/PROMICE-AWS-data-issues) supports community-driven quality control within a living data framework. The datasets are openly available at https://doi.org/10.22008/FK2/IW73UU (How et al., 2022a).

Ice-marginal lakes form at the edge of the Greenland Ice Sheet and its surrounding peripheral glaciers and ice caps (PGIC), where outflowing glacial meltwater is trapped by a moraine, or by the ice itself, and create a reservoir that is in contact with the adjacent ice. While glacial meltwater is typically assumed to flow directly into the ocean, ice-marginal lakes temporarily store a portion of this runoff, influencing glacier dynamics and ablation, ecosystems, and downstream hydrology. Their presence, and change in abundance and size, remain under-represented in projections of sea level change and glacier mass loss. Here, we present an eight-year (2016–2023) inventory of 2918 automatically classified ice-marginal lakes (≧0.05 km2) across Greenland, tracking changes in lake abundance, surface extent, and summer surface temperature over time. Fluctuations in lake abundance were most pronounced at the north (22 %) and northeast (14 %) PGIC margins and the southwest Ice Sheet margin (8 %). Over the study period, an increase in surface lake area was evident at 283 lakes, a decreasing trend was evident at 240 lakes, and 1373 remained stable (±0.05 km2). The northeast region contained the largest lakes, with a median size of 0.40 km2 at the ice sheet margin and 0.24 km2 at PGIC margins. Average summer surface temperatures fluctuated between 3.8 °C (2018) and 5.3 °C (2023), with spatial and temporal trends identified with possible links to lake setting and size. Validation against manually identified lakes showed 64 % agreement, yielding an error estimate of −809 lakes (36 %), while lake area uncertainty was ±5 %. Surface temperature estimates showed strong agreement with in situ measurements (r2=0.87, RMSE =1.68 °C, error ±1.2 °C). This dataset provides a crucial foundation for quantifying meltwater storage at ice margins and refining sea level contribution projections while supporting research on glacier-lake interactions, Arctic ecology, and environmental management. The inventory series is openly accessible on the GEUS Dataverse (https://doi.org/10.22008/FK2/MBKW9N, How et al., 2025) with full metadata and documentation, and a reproducible processing workflow.

Surface meltwater is predicted to become increasingly important for Antarctic mass loss as atmospheric temperatures rise, due to its potential to influence ice dynamic, hydrofracture and radiative processes. However, our understanding of Antarctic surface meltwater is limited, with previous studies restricted in spatial or temporal scope. Here, using cloud computing, we produce an Antarctic-wide, monthly dataset of surface meltwater for 2006 to 2021. Surface meltwater covered 3,732 km² across Antarctica on average during each melt season, with 30% on grounded ice. High interannual variability in meltwater area across the Antarctic Peninsula and in East Antarctica correlates with large-scale modes of climate variability. In west Antarctica, meltwater area is comparatively low and this correlation is absent. An increase in the magnitude and variability of surface meltwater area without a coincident increase in modelled snowmelt in East Antarctica indicates that the ice-sheet surface might be becoming more favourable to meltwater ponding.

Loss of glacier ice is contributing substantially to rising sea levels, and is negatively impacting up to 1.9 billion people globally who rely on meltwater for agriculture, drinking water, hydropower and other ecosystem services. Quantifying how glaciers are responding to ongoing climate change therefore has far-reaching implications, though a global observational assessment of this at an individual glacier scale is currently lacking. Here, we leverage the Randolph Glacier Inventory v7.0 (RGI) dataset (baseline date: 2000), and imagery from the Sentinel-2 archive between 2020 and 2024 to establish the change in extent of the 181,402 small ice masses (area

Greenland’s crevasses are responsible for transferring the majority of seasonal runoff to the bed of the ice sheet in fast-flowing regions, with implications for ice rheology, subglacial hydrology, and ice dynamic feedbacks. However, their drainage mechanics are poorly understood, particularly relative to other transfer mechanisms such as lake drainage and moulins. Here, we use remote-sensing products to identify relationships between strain rates and crevasse drainage at Greenland’s fast-flowing outlet glaciers. We map the time-series evolution of water-filled crevasses by training and applying a convolutional neural network (CNN) to 10 metre resolution Sentinel-2 MSI imagery, and extract contemporaneous logarithmic strain rates from NASA MEaSUREs ITS_LIVE velocity data. We test the time-evolving relationship between strain rates and crevasse ponding across a range of outlet glaciers, and examine whether significant relationships between the two processes can be detected. We find that crevasse drainage displays a unique response to seasonal strain rate evolution not detectable in analogous lake drainage studies, with drainage events occurring following a seasonal transition from compressive to tensile strain rate regimes. We aim to use these relationships to parameterise dynamic controls on crevasse drainage into coupled models of Greenland Ice Sheet hydrology-dynamics.