Leveraging Weekly Snow Cover Time Series for Improved Glacier Monitoring and Modeling

Abstract

Seasonal snow and ice melt strongly influence glacier mass balance, yet sparse sub-annual observations limit our understanding of seasonal dynamics. Here we construct and analyze weekly snow cover time series for 200 glaciers across western North America from 2013 to 2023 using an automated image processing pipeline. Snow cover varied widely across the region: snow minima timing varied with latitude — from $\sim$August from 62 to 64$°$N to $\sim$October from 48 to 50$°$N—and accumulation area ratios ranged from near-zero to 0.92 (median of 0.52). A comparison of snowlines from observations and the PyGEM glacier mass balance model revealed seasonally evolving but spatially consistent biases in modeled snowlines: observed snowlines rose earlier, but at a slower rate throughout the melt season, than modeled snowlines. Beyond capturing glacier state, snowline observations efficiently provide sub-seasonal mass balance constraints and empirically represent unresolved processes like snow redistribution, refining model gradients and improving projections.