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Abstract Greenland Ice Sheet (GrIS) mass loss has accelerated in recent decades, primarily due to enhanced surface melt. Identifying the causal dependencies of surface melt remains challenging with conventional correlations. Using the causal discovery algorithm applied to CESM2 large‐ensemble simulations and evaluated against two high‐resolution regional climate models, we identify significant contemporaneous positive links from melt to net shortwave radiation (reflecting melt–albedo feedback) and from sensible and latent heat fluxes to melt. These results highlight shortwave radiation and turbulent heating as dominant drivers of GrIS summer melt anomalies over the ablation zone at monthly timescales. Compared with correlations, isolates fewer but more physically interpretable dependencies. By the end of the century (SSP3‐7.0), these links persist but the turbulent heat‐related ones become undirected, indicating reduced statistical identifiability and possible stronger instantaneous surface–atmosphere coupling in a warmer climate. 

Abstract Supraglacial lakes on the Greenland Ice Sheet (GrIS) can impact both the ice sheet surface mass balance and ice dynamics. Thus, understanding the evolution and dynamics of supraglacial lakes is important to provide improved parameterizations for ice sheet models to enable better projections of future GrIS changes. In this study, we utilize the growing inventory of optical and microwave satellite imagery to automatically determine the fate of Greenland‐wide supraglacial lakes during 2018 and 2019; low and high melt seasons respectively. We develop a novel time series classification method to categorize lakes into four classes: (a) Refreezing, (b) rapidly draining, (c) slowly draining, and (d) buried. Our findings reveal significant interannual variability between the two melt seasons, with a notable increase in the proportion of draining lakes, and a particular dominance of slowly draining lakes, in 2019. We also find that as mean lake depth increases, so does the percentage of lakes that drain, indicating that lake depth may influence hydrofracture potential. We further observe rapidly draining lakes at higher elevations than the previously hypothesized upper‐elevation hydrofracture limit (1,600 m), and that non‐draining lakes are generally deeper during the lower melt 2018 season. Our automatic classification approach and the resulting 2‐year ice‐sheet‐wide data set provide new insights into GrIS supraglacial lake dynamics and evolution, offering a valuable resource for future research. 

Abstract This study shows the impact of black carbon (BC) aerosol atmospheric rivers (AAR) on the Antarctic Sea ice retreat. We detect that a higher number of BC AARs arrived in the Antarctic region due to increased anthropogenic wildfire activities in 2019 in the Amazon compared to 2018. Our analyses suggest that the BC AARs led to a reduction in the sea ice albedo, increased the amount of sunlight absorbed at the surface, and a significant reduction of sea ice over the Weddell, Ross Sea (Ross), and Indian Ocean (IO) regions in 2019. The Weddell region experienced the largest amount of sea ice retreat ($$ \sim \mathrm{33,000} $$km²) during the presence of BC AARs as compared to$$ \sim \mathrm{13,000} $$ km²during non-BC days. We used a suite of data science techniques, including random forest, elastic net regression, matrix profile, canonical correlations, and causal discovery analyses, to discover the effects and validate them. Random forest, elastic net regression, and causal discovery analyses show that the shortwave upward radiative flux or the reflected sunlight, temperature, and longwave upward energy from the earth are the most important features that affect sea ice extent. Canonical correlation analysis confirms that aerosol optical depth is negatively correlated with albedo, positively correlated with shortwave energy absorbed at the surface, and negatively correlated with Sea Ice Extent. The relationship is stronger in 2019 than in 2018. This study also employs the matrix profile and convolution operation of the Convolution Neural Network (CNN) to detect anomalous events in sea ice loss. These methods show that a higher amount of anomalous melting events were detected over the Weddell and Ross regions. 

Abstract The simulation of ice sheet‐climate interactions, such as surface mass balance fluxes, is sensitive to model grid resolution. Here we simulate the multi‐century evolution of the Greenland Ice Sheet (GrIS) and its interaction with the climate using the Community Earth System Model version 2.2 (CESM2.2) including an interactive GrIS component (the Community Ice Sheet Model v2.1 [CISM2.1]) under an idealized warming scenario (atmospheric increases by 1% until quadrupling the pre‐industrial level and then is held fixed). A variable‐resolution (VR) grid with 1/ regional refinement over the broader Arctic and resolution elsewhere is applied to the atmosphere and land components, and the results are compared with conventional lat‐lon grid simulations to investigate the impact of grid refinement. Compared with the runs, the VR run features a slower rate of surface melt, especially over the western and northern GrIS, where the ice surface slopes gently toward the periphery. This difference pattern originates primarily from higher snow albedo and, thus, weaker albedo feedback in the VR run. The VR grid better captures the CISM ice sheet topography by reducing elevation discrepancies between CAM and CISM and is, therefore, less reliant on the downscaling algorithm, which is known to underestimate albedo gradients. The sea level rise contribution from the GrIS in the VR run is 53 mm by year 150 and 831 mm by year 350, approximately 40% and 20% less than that of the runs, respectively. 

Abstract Predicting the future contribution of the ice sheets to sea level rise over the next decades presents several challenges due to a poor understanding of critical boundary conditions, such as basal sliding. Traditional numerical models often rely on data assimilation methods to infer spatially variable friction coefficients by solving an inverse problem, given an empirical friction law. However, these approaches are not versatile, as they sometimes demand extensive code development efforts when integrating new physics into the model. Furthermore, this approach makes it difficult to handle sparse data effectively. To tackle these challenges, we use the Physics‐Informed Neural Networks (PINNs) to seamlessly integrate observational data and governing equations of ice flow into a unified loss function, facilitating the solution of both forward and inverse problems within the same framework. We illustrate the versatility of this approach by applying the framework to two‐dimensional problems on the Helheim Glacier in southeast Greenland. By systematically concealing one variable (e.g., ice speed, ice thickness, etc.), we demonstrate the ability of PINNs to accurately reconstruct hidden information. Furthermore, we extend this application to address a challenging mixed inversion problem. We show how PINNs are capable of inferring the basal friction coefficient while simultaneously filling gaps in the sparsely observed ice thickness. This unified framework offers a promising avenue to enhance the predictive capabilities of ice sheet models, reducing uncertainties, and advancing our understanding of poorly constrained physical processes. 

Understanding glacier and iceberg dynamics, such as calving, drifting, fragmentation, and melting, is critical in improving climate modeling and prediction. Synthetic Aperture Radar (SAR) has become one of the most important instruments for monitoring these dynamics, as it operates in all weather conditions, day or night, and offers a much higher revisit time compared to other optical satellites. In prior work using SAR for studying calving events, challenges include translating such large volumes of data into meaningful representations that capture both spatial and temporal information. In this work, we explore the use of isotropic graph-based representations of iceberg dynamics over time, extracted from SAR imagery. We use a Vision Graph Neural Network (ViG) architecture to transform the SAR image features into graph structures, enabling the modeling of relationships between small ice objects through dynamically updated neighbor connections. As a proof-of-concept, we use a temporal sequence of SAR images of A-81, a large iceberg that calved off the Brunt Ice Shelf in January 2023. By extracting graphs from multiple ViG blocks, we examine how spatial relationships change within the image. Our preliminary analysis focuses on qualitative visualization and limited quantitative investigation, including variations in patch size, neighborhood size, and simple neighborhood metrics. This work establishes a scalable pipeline that can be extended to include temporal graph connections and comprehensive quantitative analysis, enabling future investigation of fragment connectivity, clustering behavior, aggregation events, and neighborhood motion over time. By laying the groundwork for spatio-temporal graph-based modeling of iceberg dynamics from SAR imagery, this work supports the study of small untracked ice fragments and their contribution to overall iceberg dynamics.