Heat and Moisture Transport Dynamics within the Convective Boundary Layer: Insights from the Southern Great Plains ARM Site

Abstract

Increasing our understanding of moisture transport within the convective boundary layer (CBL) is crucial for improving weather and climate forecasting. Turbulent moisture fluxes within the CBL are critical for precipitation, cloud formation, the surface energy budget, and the initiation of deep convection. Despite the importance of moisture transport, most previous studies have focused on dry CBLs in the absence of humidity. Furthermore, the transport of moisture and other scalars such as temperature can be highly dissimilar, with their statistics largely determined by their respective entrainment zone structure. Thus, many aspects of moisture transport in the CBL remain poorly understood. This study aims to address two key research questions: (1) How do moisture-related processes influence vertical profiles of turbulent statistics in the CBL? (2) Do large-scale turbulent motions (turbulent flows in the CBL that span the entire boundary layer depth or more) exhibit similar spectral properties for temperature and humidity? Using high-resolution Doppler lidar, Raman lidar, and eddy correlation flux measurements from the U.S. Department of Energy (DOE) Southern Great Plains (SGP) Atmospheric Radiation Measurement (ARM) site in Lamont, Oklahoma, collected during summer 2018, we analyzed the data as a function of global stability (quantified by $-z_i/L$) and evaporative fraction ($E_f$, a parameter not accounted for in classical mixed-layer similarity theory). Results show that moisture fluctuations are generally more vertically confined than temperature and are sensitive to surface evaporative conditions. Spectral analysis reveals that temperature variance is dominated by LSMs spanning the boundary layer depth, whereas humidity variance is concentrated at smaller scales and lower altitudes. These findings highlight differences in scalar transport mechanisms and emphasize the importance of incorporating humidity-specific entrainment and large-scale variability in turbulence parameterizations to improve weather and climate models.