Physics-Guided Multi-Contextual Learning: Understanding the Surface and Subsurface Processes in Southeast Greenland

Abstract

Greenland ice loss contributes approximately 0.7 mm per year to current sea level rise, making process attribution critical for future projections. Mass balance attribution in ice sheets requires distinguishing between surface processes (accumulation, ablation) and subsurface processes (submarine melting, dynamic discharge). Existing approaches lack systematic integration of glaciological process knowledge for robust spatial attribution. We present a physics-guided multi-contextual analysis framework. We construct process-specific variables using fundamental ice sheet mass balance principles. We create spatial neighborhoods through Voronoi polygon construction and feature similarity assessment, then apply Local Indicators of Spatial Association (LISA) to identify process dominance patterns. We advance beyond conventional spatial statistics by systematically deriving physics-informed indicators that isolate distinct mass balance components. We test our framework on Southeast Greenland using 18 years of reanalysis and satellite data (2004–2021). Our results show that subsurface processes control ice loss across 37–46% of the study area, concentrated in northern regions of Southeast Greenland, while surface processes dominate only 6–7% of the area in southern locations of Southeast Greenland. When we compare our spatial findings with the documented glacier behavior from recent studies in Greenland, we find strong agreement that validates our process attribution approach.