Mapping Greenland's subglacial topography is critical for projecting the future mass loss of the ice sheet and its contribution to global sea-level rise. However, the complex and sparse nature of observational data, particularly information about the bed topography under the ice sheet, significantly increases the uncertainty in model projections. Bed topography is traditionally measured by airborne ice-penetrating radars that measure the ice thickness directly underneath the aircraft, leaving data gaps of tens of kilometers in between flight lines. This study introduces a deep learning framework, DeepTopoNet, that integrates radar-derived ice thickness observations and BedMachine Greenland data through a novel dynamic loss-balancing mechanism. Among all efforts to reconstruct bed topography, BedMachine has emerged as one of the most widely used datasets, combining mass conservation principles and ice thickness measurements to generate high-resolution bed elevation estimates. The proposed loss function adaptively adjusts the weighting between radar and BedMachine's bed, ensuring robustness in areas with limited radar coverage while leveraging the high spatial resolution of BedMachine's bed estimates. Our approach incorporates gradient-based and trend surface features to enhance model performance and utilizes a convolutional neural network (CNN) architecture (i.e., BedTopoCNN) designed for subgrid-scale predictions. By systematically testing on the Upernavik Isstrøm) region in West Greenland, the model achieves high accuracy (MAE: 12.49 m, RMSE: 19.38 m, and R2: 0.99), outperforming baseline methods in reconstructing subglacial terrain. This work demonstrates the potential of deep learning in bridging observational gaps, providing a scalable and efficient solution to inferring subglacial topography. This framework paves the way for improved predictions of ice sheet flow and sea level rise.

Sea ice extent plays a crucial role in the Arctic system, and thus the study on causal relationships between sea ice extent and other climate variables comes to our interest to better understand the system. To find the causal relationship we applied various state-of-the-art causal discovery techniques from the time-independent and time-dependent domains. Then we employed several causal inference models to quantify the causal effects of different causal relationships in the Arctic system. The NSIDC Sea Ice Concentration observation data and the ERA-5 global reanalysis data were used in our study. Our analysis shows that the GES and VarLiNGAM from causal discovery methods and the conditional instrumental variable (CIV) causal inference model perform better on the Arctic Sea Ice time series dataset.