Global Snow Seasonality Regimes from Satellite Records of Snow Cover

Abstract Snow cover provides distinct seasonal controls on the exchange of energy between the Earth’s surface and atmosphere, hydrologic cycling, and holds considerable importance to communities and ecosystems worldwide. In this work, we tackle a comprehensive review of existing snow classification approaches and the development of new globally applicable snow cover-based rules for delineating snow seasonality classes. Snow classification rules are defined using machine learning approaches, which are then applied to the 22-year record of snow cover (2000-2022) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on a 0.01° global grid. For the MODIS period of record, we find the global land surface can be effectively partitioned into five snow seasonality classes: no snow, ephemeral, transitional, seasonal, and perennial snow regimes which on average cover extents of approximately 76 (52% of global land areas), 19 (13%), 16 (11%), 18 (13%), and 16 million km 2 (11%), respectively. Using the multi-decadal dataset, we explore changes within snow regimes and find significant increases in the areal extent of no snow (approximately +70,000 km 2 /year) as well as apparent losses in perennial (‒3,600 km 2 /year) and seasonal snow regime coverage (‒38,000 km 2 /year). The resulting classification maps have strong agreement with in-situ snow depth observations and present similar patterns to existing snow and climate classifications with notable discrepancies in cold arid regions. The framework's ability to accurately capture variations in snow persistence, snow accumulation, and melt cycling is shown, providing a reference to the current state of global snow seasonality.