Land Surface Sensitivity of Warm Season MCS Nocturnal Transition

Characterizing the behavior of mesoscale convective systems (MCS) during the transition from day-to-night (D2N) and night-to-day (N2D) remains a forecasting and research challenge. The primary impetus for changes in storm dynamics, leading to changes in intensity, lifespan and speed during these transition periods is due to changes in the planetary boundary layer (PBL). PBL dynamics, including the transition from a daytime, convective boundary layer, to a stable nocturnal boundary layer, and vice versa, are driven by intrinsic and external factors. The intrinsic factors are characteristics of the land surface, including surface roughness, albedo, soil type and many others that may vary spatially but not temporally on the time scale of the MCS. When considering a modeled PBL, other intrinsic factors include land surface model and turbulence parameterization empirical factors, which may vary by parameterization. For the purposes of this study, external factors driving PBL dynamics include those independent of the MCS, such as pre-existing soil moisture conditions and those dependent on the MCS, such as anvil cloud cover. A 13-year climatology of both D2N and N2D MCS transition was compiled and a first classification was made based on MCSs that weakened, maintained or strengthened through the transition. Next, an objective classification of transitioning MCSs was made based on synoptic conditions. An idealized modeling study was then conducted to determine the sensitivity of MCS lifespan, strength and speed to variations in PBL dynamics. First, a set of experiments was conducted to examine the sensitivity of PBL dynamics including PBL height, vertical velocity and temperature characteristics, in the idealized modeling environment to variations in intrinsic and external factors. Next a set of idealized numerical experiments was conducted to examine the sensitivity of the different classes of MCSs, based on the synoptic environment, to changes in the PBL, both from changing the magnitude and distribution of intrinsic and external factors.