Revisiting Snow Water Equivalent Bias in the Noah‐MP Land Surface Model in the Western U.S.

Abstract Land surface models (LSMs) are widely used to generate snow records, forecasts, and projections that support water research and management. The complex sources of uncertainty in LSMs motivate evaluations to better understand bias characteristics and identify underlying causes that guide model development. This study evaluates snow water equivalent (SWE) simulations over the western United States (WUS) from the Noah‐MP LSM, a key land component in widely used research and operational modeling systems. Noah‐MP driven by different atmospheric forcing data sets generally underestimates SWE relative to SNOw TELemetry observations, with biases partly explained by scale mismatches between point‐based measurements and model pixels. Despite systematic underestimation, Noah‐MP captures observed interannual variability of peak and mean SWE anomalies very well ( r = 0.99) and shows lower but still significant accuracy in simulating long‐term regional SWE trends and the spatial patterns of SWE trends ( r = 0.60). Comparisons with gridded reference data sets confirm Noah‐MP's high skill in simulating the regional daily timing ( r > 0.99), interannual variability, and pixel‐scale daily SWE ( r = 0.71–0.81). However, the simulations underestimate SWE by 9%–16%, with peak SWE occurring ∼4 days earlier and snow disappearing ∼35 days earlier than observed, reflecting overly rapid ablation across both cold and warm seasons. Spatial clustering analyses reveal regional heterogeneity in SWE bias characteristics and sensitivities to LSM parameterizations and meteorological forcing uncertainties. The presence of regional overestimation suggests that applying uniform model adjustments may degrade performance in certain areas. An updated combination of Noah‐MP physics options shows accuracy improvements, reducing both accumulation and ablation errors.