Random forest predictions of tundra snow density elevate Arctic soil temperatures in CLM5.0

Abstract. Arctic snow exerts a critical control on winter soil temperature and carbon exchange, however representation of its properties in Earth System Models (ESMs) remains simplified. In the Community Land Model v5.0 (CLM5.0), recent updates to snow compaction schemes have led to overly dense tundra snow, increasing snow thermal conductivity and conductive heat loss, producing a persistent cold-soil bias. Here we developed a Random Forest (RF) regression model to derive tundra bulk snow density from meteorological variables, trained on six winter seasons (Sep – May) of Arctic SVS2-Crocus (ASC) simulations constrained by in-situ observations from Trail Valley Creek (TVC), Northwest Territories, Canada. RF-derived snow densities capture the lower bulk densities of Arctic tundra snowpacks, which CLM5.0’s compaction scheme overestimates. Application of the RF-derived snow densities to CLM5.0 reduces snow thermal conductivity and enhances snowpack insulation, decreasing soil temperature RMSE by approximately 2–3 °C relative to field measurements (2017–2023) and increasing future winter soil temperature projections (2016–2100) by 4–7 °C under RCP4.5 and 8.5. Such increases in simulated soil temperature highlight the strong sensitivity of CLM5.0’s soil thermal regime to snow physical properties. The RF model reproduces ASC-simulated density evolution with a mean absolute error of 32 kg m⁻³ which falls within typical measurement error for volumetric snow density sampling in Arctic tundra (20–40) kg m⁻³ and matches field measurements more closely than default CLM5.0. Future bulk snow density predictions using the RF model driven by bias-corrected North American-Coordinated Regional Downscaling Experiment (NA-CORDEX) meteorology indicate bulk snow densities 200–450 kg m⁻³ lower than CLM5.0 and more consistent with tundra conditions.