Johnston et al. (2026) The snow meteorology and phenology classification (SnowMAP): global snow cover observations enhance snow’s representation

Identification

• Journal: Scientific Reports
• Year: 2026
• Date: 2026-03-18
• Authors: Jeremy Johnston, Jennifer M. Jacobs, Megan Vardaman, Eunsang Cho
• DOI: 10.1038/s41598-026-44321-x

Research Groups

• Earth Systems Research Center, University of New Hampshire, Durham, NH, USA
• Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH, USA
• Ingram School of Engineering, Texas State University, San Marcos, TX, USA

Short Summary

This study introduces SnowMAP, a novel global snow classification system that integrates meteorological controls (snowfall, temperature, wind) with snow phenology (seasonal presence, melt timing), providing a more complete and decision-relevant view of global snow conditions. The system identifies 18 distinct snow classes that reflect variations in snow depth, geography, land cover, and infrastructure, enhancing the understanding of snowpack formation and evolution.

Objective

• To develop and demonstrate SnowMAP, a global snow classification system that combines snow meteorological and phenology classes to provide a more complete view of global snow conditions and enhance its decision-relevance for various applications.

Study Configuration

• Spatial Scale: Global, with primary data at 30 arc-second (~1 km) and 0.01° (~1 km) resolution. Regional analyses for specific areas like Washington State and US watersheds.
• Temporal Scale: Meteorological data from 1981–2019; snow cover phenology from 2000–2023; snow depth observations from 1981–2025.

Methodology and Data

• Models used:
  • SnowMAP: A classification framework combining existing meteorological and phenological snow classes.
  • MicroMet: Used to downscale meteorological variables for the seasonal snow classification.
• Data sources:
  • Snow Classifications:
    • Global Seasonal-Snow Classification, Version 1 (Sturm and Liston 2021): Meteorological snow classes (7 classes).
    • MODIS/Terra Global Annual 0.01Deg CMG Snow Cover Climatology, Version 1 (Johnston et al., 2023): Snow phenology classes (5 classes).
  • Meteorological Data:
    • European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis, 5th Generation Land (ERA5-Land): Air temperature and precipitation (1981–2019).
  • Snow Depth Observations:
    • Global Historical Climatology Network daily dataset (GHCNd): Daily snow depth records (1981–2025).
  • Geospatial Variables:
    • Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010): Elevation data (30 arc-second).
    • MODIS/Terra+Aqua Global Land Cover Yearly Version 6.1 (MCD12Q1.061): Land cover (500 m, aggregated to 30 arc-second).
    • Crowther et al. (2015): Forest density (30 arc-second).
    • WorldPop (2020): Population density (30 arc-second).
    • Global Roads Inventory Project (GRIP4): Road density (5 arc-minute, resampled to 30 arc-second).
    • OpenSkiMap.org: Global ski area inventory (as of April 2025).
    • Geospatial Attributes of Gages for Evaluating Streamflow (GAGES-II): Hydrologic reference basin boundaries.
    • United States Geological Survey (USGS): Streamflow data for GAGES-II basins.

Main Results

• SnowMAP integrates 7 meteorological and 5 phenology classes to produce 18 distinct global snow classes.
• "No snow" areas constitute the largest extent, covering over 50% (76 million square kilometers) of global land.
• Prairie snow is predominantly ephemeral (42%) or transitional (56%), indicating infrequent persistent snowpacks.
• Maritime snow classes exhibit significant coverage across ephemeral, transitional, and seasonal phenology classes due to steep elevation gradients.
• Montane forest is primarily transitional (78%), while boreal forest is mostly seasonal (69%), with transitional snowpacks at lower latitudes.
• Tundra regions are predominantly seasonal (70%), but ephemeral tundra (16%) is notable in cold, dry, and windy areas like the Tibetan Plateau.
• Increasing snow phenology from ephemeral to seasonal is associated with higher snowfall rates, lower air temperatures, and longer core snow seasons (from 1-2 weeks to over 6 months).
• Observed snow depths and peak snow depth magnitudes increase, and peak timing shifts later in the season, as snow phenology transitions from ephemeral to seasonal (median peak depths: <10 cm for ephemeral, 15-30 cm for transitional, 51-93 cm for seasonal).
• Elevation and latitude are strong drivers of snow seasonality, with seasonal snow more prevalent at higher latitudes and a negative linear relationship between elevation and latitude for class development.
• SnowMAP classes exhibit a diverse range of land cover types, with ephemeral and transitional snow climates hosting higher population densities and more transportation infrastructure compared to seasonal snow regions.
• Ski areas are most common in transitional (61%) and ephemeral (27%) snow classes, with larger resorts typically found in transitional maritime areas.
• SnowMAP effectively identifies distinct runoff seasonality in adjacent watersheds with differing dominant snow phenology classes, demonstrating its utility for hydrologic benchmarking.

Contributions

• Introduces SnowMAP, a novel global snow classification system that unifies existing meteorological and phenological snow datasets into a comprehensive framework.
• Provides a more complete and decision-relevant understanding of global snow conditions by integrating both physical properties and seasonal dynamics.
• Reveals new insights into the spatial variability of snow depth, the influence of geographic controls, land cover, and human interactions (population, infrastructure, recreation) across different snow regimes.
• Offers a practical tool for scientists, planners, and communities to better understand snow conditions and their impacts on natural and built environments.
• Demonstrates the utility of SnowMAP through case studies in winter recreation and hydrologic benchmarking, highlighting its value beyond cryosphere research.
• Provides a dynamic representation of snow, offering insights into the uncertainty of snow properties across different phenological classes.

Funding

• Broad Agency Announcement Program
• Cold Regions Research and Engineering Laboratory (ERDC-CRREL) under Contract No. W913E523C0004

Citation

@article{Johnston2026snow,
  author = {Johnston, Jeremy and Jacobs, Jennifer M. and Vardaman, Megan and Cho, Eunsang},
  title = {The snow meteorology and phenology classification (SnowMAP): global snow cover observations enhance snow’s representation},
  journal = {Scientific Reports},
  year = {2026},
  doi = {10.1038/s41598-026-44321-x},
  url = {https://doi.org/10.1038/s41598-026-44321-x}
}