The Contributions of Shear and Turbulence to Cloud Overlap for Cumulus Clouds

Abstract

The causes of vertical cloud overlap, the ratio between the cloud fraction by area and the cloud fraction by volume, are studied for fields of cumulus convection. Using large-eddy simulations, the structure of individual clouds within the cloud field is inspected and connected to the overall cloud cover of the entire cloud field. First, it is shown that the cloud overlap of the entire field can be well approximated by the total cloud overlap of the individual clouds. It is then shown that a simple maximum overlap assumption, which is commonly used for cloud fields with small layer depth, can only account for roughly half of the overlap inefficiency. This is due to the fact that maximum overlap only represents variations in cloud width as a function of depth but does not take other factors into account, most notably shear or turbulence. The effects of turbulence and wind shear on the overlap of individual clouds are first estimated as a function of cloud depth, which closes the discrepancy between observed cloud overlap and the maximum overlap assumption. Then, an empirical model is developed for the shear and turbulence contributions on overlap as a function of cloud depth and of synoptic conditions. Finally, it is shown that our model is capable of accurately predicting overlap for a large number of cloud fields and under a variety of meteorological conditions.

Process‐Based Climate Model Development Harnessing Machine Learning: III. The Representation of Cumulus Geometry and Their 3D Radiative Effects

“…This would mean that a sub-parameterization should be developed to make this parameter depend on atmospheric conditions. Such parameterizations exist for example to predict cloud perimeter length in Fielding et al (2020), or the degree of overlap in for example, Sulak et al (2020). New parameters appear in these formulations, which can in turn be calibrated using the same procedure as described in this work.…”

“…Such parameterizations exist for example to predict cloud perimeter length in Fielding et al (2020), or the degree of overlap in e.g. Sulak et al (2020).…”

Improved Representation of Clouds in the Atmospheric Component LMDZ6A of the IPSL‐CM6A Earth System Model

“…The cloud fraction by surface is more appropriate for coupling with radiative transfer schemes but most climate models do not yet distinguish between the two quantities and by doing so, assume that the cloudy area of a gridbox fills the entire gridbox in the vertical. The difference between the cloud fraction by volume and the cloud fraction by surface can be computed by a parameterization of subgrid‐scale heterogeneities that will depend on the vertical resolution and various physical information, such as wind shear (Sulak et al., 2020). Work is underway to implement such parameterization in LMDZ (Jouhaud et al., 2018).…”

Reconciling Chord Length Distributions and Area Distributions for Fields of Fractal Cumulus Clouds

“…However, this generates only one-dimensional transects through the clouds. In other words, a vertically pointing measurement would result in a chord length distribution, while a cloud area distribution is arguably more relevant one for parameterizations of convective mass flux and of cloud radiative transfer [13]. A good understanding of the chord length distribution also helps in comparing the cloud base properties between observations and simulations [14].…”