What is the process of snow melting?

Seasonal snow and ice melt strongly influences glacier mass balance, yet sparse subannual observations limit our understanding of seasonal dynamics. Here, we generate 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: the timing of minimum snow cover varied with latitude—from around August at 62–64°N to around October at 48–50°N—and the accumulation area ratio ranged from near zero to 0.92 (mean 0.52). 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, during the melt season than modeled snowlines. Beyond recording glacier state, snowline observations effectively provide constraints on subseasonal mass balance and reveal empirically unresolved processes such as snow redistribution, correcting model slopes, and improving predictions. In simple terms, glacier models perform best when calibrated to match observations.

Although model calibration typically focuses on decadal-scale glacier changes, recent advances in satellite image processing and image coverage allow for the generation of detailed observations of glacier snow cover that may be useful for further tuning models. Using nearly weekly satellite imagery to track snow cover over 200 glaciers in western North America from 2013 to 2023, we find that the average timing and extent of minimum snow cover vary widely among glaciers. The seasonal evolution of glacier snow cover is not accurately captured by a state-of-the-art model used for global glacier mass loss projections. However, for a subset of glaciers, we show that the difference between modeled and observed snow cover can be reduced by adjusting the modeled temperature and melt rate by each degree above freezing. Our study highlights the value of accurate satellite-based snow cover time series for observing key measures of glacier health and improving glacier model predictions. Extensive glacier mass loss in the 21st century is consistently projected in global glacier models, but uncertainties about its exact magnitude remain, in part due to differences in the representation of glacier–climate interactions (Marzione et al., 2020). Glacier mass loss outside ice sheets is primarily driven by changes in surface mass balance (SMB; i.e., the net difference between snow accumulation and melt; Larsen et al., 2015; Menounos et al., 2019), so that predictions of glacier mass change are highly dependent on the accuracy of SMB models. These models are often calibrated using time-limited observations of glacier surface elevation and mass balance, which reduces their accuracy and leads to uncertainties in long-term glacier mass balance predictions (Hock et al., 2019; Marzeion et al., 2020). Satellite imagery has revolutionized our ability to monitor important measures of glacier mass balance from space and provides valuable constraints on SMB models. For glaciers in mid-latitudes, the seasonal snowline at the end of the melt season is commonly used to estimate the equilibrium line height (ELA; Østrem, 1975; Rabatel et al., 2005), the height at which the annual net SMB is zero (Cuffey & Paterson, 2010). Similarly, the accumulation area ratio (AAR) represents the portion of a glacier with a positive SMB and is calculated as the area of ​​the glacier covered by snow divided by the total area of ​​the glacier, usually at the end of the melting season. Both metrics have been mapped using satellite imagery at regional scales (e.g., Larocca et al., 2024; Liu et al., 2021; McFadden et al., 2011; Zeller et al., 2025). However, observations are often limited by cloud cover, recent snowfall (Rabatel et al., 2005), and sparse repeat intervals for individual satellites, impeding the identification of annual snow cover minimums. Furthermore, spatiotemporal variability in the seasonal timing of snow cover minimums has not been examined at regional to global scales. Given the recorded and projected changes in the timing of snow accumulation and melt (Barnett et al., 2005; Littell et al., 2018; McCrary et al., 2022; Mote et al., 2018; Musselman et al., 2021; Rhoades et al., 2022; Siirila‐Woodburn et al., 2021), it is increasingly important for observation‐based glacier analyses to consider snow minimum timings in designing sampling strategies and calibrating glacier mass balance models. Here we use a machine learning‐based image classifier. This pipeline is designed to process images from both the Landsat 8/9 and Sentinel‐2 satellite constellations and helps to alleviate the limitations imposed by the repetition intervals of individual sensors. We focus on western North America because it has the highest regional mass loss rates outside of ice sheets (Gardner et al., 2013; Hugonnet et al., 2021; Zemp et al., 201