Organization Development in Precipitating Shallow Cumulus Convection: Evolution of Turbulence Characteristics

Abstract

Horizontal organization or mesoscale variability is an important mechanism in the interaction of the boundary layer with the large-scale conditions. The development of organization in a precipitating cumulus trade-wind boundary layer is studied using large-eddy simulations with extensive horizontal domains, up to 160×160 km and fine grid resolution (40 m). The cloud fields vary between different computational domain sizes. Mean profiles and vertical velocity statistics do not vary significantly, both with respect to the domain size and when large-scale organization develops. Turbulent kinetic energy (TKE) rapidly increases when organization develops. The increase of TKE is attributed to the horizontal component, whereas the vertical velocity variance does not change significantly. The large computational domains blend the boundary between local convective circulations and mesoscale horizontal motions leading to the dependence of horizontal TKE on the LES domain size. Energy-containing horizontal length scales are defined based on the premultiplied spectra. When large-scale organization develops, the premultiplied spectra develop multiple peaks corresponding to the characteristic horizontal scales in the boundary layer. All flow variables have a small length scale of 1–2 km, which corresponds to local convective motions, e.g., updrafts and cumulus clouds. Organization development creates additional larger length scales. The growth rate of the large length scale is linear and it is about 3–4 km h−1, which agrees well with the growth rate of the cold pool radii. A single energy containing length scale is observed for vertical velocity for the entire run (even after organized convection develops) that is fairly constant with height.

The Time Scale of Shallow Convective Self‐Aggregation in Large‐Eddy Simulations Is Sensitive to Numerics

“…( 2021 ) and observed by George et al. ( 2022 ), (b) radiative heterogeneity (Klinger et al., 2017 ) and (c) cold‐pool dynamics (e.g., Anurose et al., 2020 ; Lamaakel & Matheou, 2022 ; Seifert & Heus, 2013 ; Seifert et al., 2015 ), all of which appear important pathways to develop the mesoscale cumulus patterns observed in nature.…”

Computational Domain Size Effects on Large-Eddy Simulations of Precipitating Shallow Cumulus Convection

“…LES is a "turbulence resolving" model that yields a physically consistent description of the spatial variability over a large range of scales [5,6]. For example, in investigations of processes in shallow cumulus convection [7][8][9][10][11][12]. The domain size in LES can significantly impact the simulated cloud fields and boundary layer structure [13,14].…”

“…For instance, refs. [12,14] demonstrated that increasing domain size could lead to the formation of larger convective structures.…”

“…The interplay between domain size and precipitation remains relatively unexplored. Lamaakel and Matheou [12] showed that larger domains were required to accurately capture the cloud organization in precipitating shallow convection. Whereas [12] studied the development of shallow convective organization in relatively large LES computational domains, up to 160 × 160 km 2 , presently we focus on the boundary layer steady state, which occurs after mesoscale organization has been formed.…”

Cloud Botany: Shallow Cumulus Clouds in an Ensemble of Idealized Large‐Domain Large‐Eddy Simulations of the Trades

“…The goal of improving our understanding of the mesoscale, marine trades has mobilized an entire community, centered around the EUREC 4 A (Elucidating the role of clouds-circulation coupling in climate) field campaign (Stevens et al, 2021). Fortunately, advances in computational capabilities now allow these observations to be complemented by (a) global and regional models running at a sufficiently fine resolution to begin resolving shallow convection (e.g., Stevens et al, 2019) and (b) detailed process models-"classical" LES codes-running on sufficiently large domains to capture the mesoscale (e.g., Lamaakel & Matheou, 2022;Narenpitak et al, 2021;Seifert et al, 2015). In particular, these models facilitate understanding of the degree to which mesoscale cloud patterns originate in larger-scale dynamics, which set the environment in which clouds form, or small-scale processes, which govern individual cumulus structures.…”