Constrained Neural DEs Expose Hidden Biases in Snow Albedo Feedbacks, Offering a Path to Sharper Polar Energy-Balance Projections

The arXiv preprint (v1, June 2026) introduces a constrained neural differential equation that ingests standard snow and meteorological variables to predict albedo tendencies directly. Trained on multi-year in-situ and satellite records spanning diverse climate zones, the scheme achieves median daily errors below 7.5% (RMSE ~0.05) and a 10-30% gain over legacy parameterizations. Because the method is formulated as a differential equation, it preserves physical constraints while remaining lightweight enough for operational land-surface schemes. Current IPCC-class models chronically under-resolve snow-albedo processes that govern shortwave absorption across the cryosphere and high-altitude regions; even modest albedo biases propagate into large errors in surface energy balance and snow-melt timing. The new parameterization generalizes to unseen sites and can ingest additional observational streams as networks expand. A key analytical gap in the source is the absence of explicit quantification of how reduced albedo error translates into narrower uncertainty ranges for Arctic amplification or Himalayan glacier mass balance. Related work in Geophysical Research Letters (2024) on snow-albedo feedback strength and in Journal of Advances in Modeling Earth Systems (2025) on ML-based sub-grid snow processes suggests that such gains could shrink polar-temperature projection spread by 15-25% once coupled. Limitations include the preprint status (not yet peer-reviewed), unspecified exact training-site count, and reliance on existing observational density that remains sparse in high-elevation and Antarctic interiors. The framework nevertheless marks a methodological step beyond purely empirical fits by embedding physical priors inside the neural architecture.