Effects of land surface parameterizations on cold‐fog simulation in Heber Valley: Insights from the CFACT field campaign

Abstract

The goal of this study is to investigate how different land surface models (LSMs) affect mountain cold‐fog simulations and to study the sensitivity of key land surface parameters and clarify the associated physical mechanisms. Observations were collected during January–February 2022 as part of the Cold Fog Amongst Complex Terrain (CFACT) project conducted in Heber Valley, Utah, USA. Sensitivity simulations are conducted using the Weather Research and Forecasting (WRF) model at 1‐km resolution to investigate the role of land surface processes in cold‐fog evolution. Three LSMs including the five‐layer thermal diffusion model (SLAB), Noah land surface model (Noah), and Noah land surface model with multiparameterization options (Noah‐MP) are employed to examine the impacts of snow depth ( H d ), soil thermal conductivity (STC), and soil moisture content (SMC). Results indicate that H d derived from the fifth‐generation ECMWF reanalysis (ERA5) introduces cold biases in default Noah and Noah‐MP simulations. Reducing or removing the initial H d obtained from ERA5 significantly improves near‐surface simulations. Further sensitivity experiments show that STC primarily regulates nocturnal surface cooling, whereas SMC mainly modulates near‐surface moisture availability. Both SMC and STC, therefore, affect the cold‐fog life cycle. Specifically, adjusting STC and SMC resulted in good simulation performance at 2‐m height, with air temperature ( T a ) showing a correlation coefficient ( R 2 ) of 87%, and relative humidity (RH) with R 2 of 74%. As a result, WRF more realistically represents time and space variability of cold fog. The simulated fog droplet number concentration ( N d ) is found to be within a reasonable range of observations (up to 200 cm −3 ); however, the liquid water content (LWC) peaks are relatively higher, ranging from 0.1 to 0.4 g·m −3 based on various microphysics schemes. It is concluded that cold‐fog predictions are sensitive to surface conditions related to snow cover, soil properties and microphysics.