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

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 and excessive conductive heat loss, producing a persistent cold-soil bias. Here we developed a Random Forest (RF) regression model to derive tundra snow density from meteorological variables, trained on Arctic SVS2-Crocus (ASC) simulations supported by in-situ observations collected around peak annual SWE from Trail Valley Creek (TVC), Northwest Territories, Canada. The RF model reproduces ASC-simulated density evolution with a mean absolute error of 23 kg m^-3 and an R² of 0.90, matching field measurements more closely than CLM5.0. Future snow density predictions using the RF model driven by bias-corrected NA-CORDEX meteorology (2016 – 2100) indicate bulk snow densities 200 – 450 kg m^-3 lower than CLM5.0 and more consistent with tundra conditions. Application of RF-derived snow densities decreases CLM5.0 winter season 10cm soil temperature RMSE by approximately 2 – 3 °C relative to field measurements (2017 – 2023) and increases future winter soil temperature projections (2016 – 2100) by 4 – 7 °C, highlighting the strong sensitivity of CLM5.0’s soil thermal regime to snow physical properties.