Snow Cover Plays a Non‐Dominant Role in WRF/Noah‐MP Simulated Surface Air Temperature Cold Biases Over the Western U.S.

Abstract Atmospheric models generally exhibit cold biases in 2‐m air temperature (T2) across mountainous regions during snow seasons. This study evaluates whether snow cover errors are the primary driver for the persistent T2 cold bias in coupled WRF/Noah‐MP simulations across the western U.S (WUS) through analyses of T2, ground snow cover fraction (SCF), snow water equivalent (SWE), and surface albedo. Baseline simulations revealed widespread cold biases in maximum daily T2 (T2 max ), mainly during November–April when snowpack influences the terrestrial energy budget (mean bias = −1.05°C to −1.32°C), with higher elevation areas showing biases up to −5.31°C. Cold biases across the WUS are reduced, on average, by 17%–24% in the simulations using recently enhanced snow compaction and ground SCF parameterizations (hereinafter ARnew) that tend to reduce snow cover and surface albedo. However, notable cold biases still persist in the ARnew simulations, especially in high‐elevation areas, despite underestimates of ground SCF and SWE. Statistical and information theory analyses show very weak connections between T2 max biases and biases in ground SCF and SWE. Further, an experiment that imposes unrealistically low ground SCF (mean bias = −75%) still exhibits cold biases, particularly in high‐elevation areas (mean bias for Z > 2,000 m = −1.46°C). These results indicate that snow cover errors can modestly contribute to cold biases but are not their primary cause. Therefore, resolving the cold bias issue will require addressing factors beyond snow cover, potentially including uncertainties in canopy radiative transfer, surface turbulence, and other atmospheric processes in complex terrain.