A Next-Generation Snow Albedo Parameterization for Climate Modeling using Constrained Machine Learning

Abstract

We demonstrate a data-driven parameterization for snow albedo using a constrained neural differential equation that directly predicts a range of snow albedo tendencies from standard snow and meteorological inputs. After training with multi-year in-situ and satellite observations from a wide variety of locations, the scheme effectively reproduces daily albedo evolution across diverse climate zones, with median error under 7.5% (RMSE ~0.05), a 10-30% improvement over established models. Furthermore, the model generalizes to sites not seen during training and scales from coarser grids to point locations. The scheme can easily incorporate new features as observational networks expand, offering an adaptive and computationally lightweight framework for next-generation land and climate models.

Initial submission date: November 7th, 2025 Acceptance Date: July 17th, 2026 Supplemental Information has been uploaded as an ancillary .pdf to this submission package. This work has been accepted for the Journal of Geophysical Research: Machine Learning and Computation, with a link to the final paper of https://doi.org/10.1029/2026JH001312