Analysis of albedo versus cloud fraction relationships in liquid water clouds using heuristic models and large eddy simulation

Abstract

The relationship between the albedo of a cloudy scene scriptA and cloud fraction fc is studied with the aid of heuristic models of stratocumulus and cumulus clouds. Existing work has shown that scene albedo increases monotonically with increasing cloud fraction but that the relationship varies from linear to superlinear. The reasons for these differences in functional dependence are traced to the relationship between cloud deepening and cloud widening. When clouds deepen with no significant increase in fc (e.g., in solid stratocumulus), the relationship between scriptA and fc is linear. When clouds widen as they deepen, as in cumulus cloud fields, the relationship is superlinear. A simple heuristic model of a cumulus cloud field with a power law size distribution shows that the superlinear scriptA‐fc behavior is traced out either through random variation in cloud size distribution parameters or as the cloud field oscillates between a relative abundance of small clouds (steep slopes on a log‐log plot) and a relative abundance of large clouds (flat slopes). Oscillations of this kind manifest in large eddy simulation of trade wind cumulus where the slope and intercept of the power law fit to the cloud size distribution are highly correlated. Further analysis of the large eddy model‐generated cloud fields suggests that cumulus clouds grow larger and deeper as their underlying plumes aggregate; this is followed by breakup of large plumes and a tendency to smaller clouds. The cloud and thermal size distributions oscillate back and forth approximately in unison.

Aerosol‐Cloud Interactions in Trade Wind Cumulus Clouds and the Role of Vertical Wind Shear

“…This is also true for the fit slope of CSDs and the total area of large clouds (Figure ). The coherence between the time series of rain and CSDs lends credence to the idea that the similar frequencies in charge‐discharge cycles (Dagan et al, ) and CSD variability (Feingold et al, ) are indeed related. While the aforementioned papers identified a dominant frequency of 80 – 90 min for BOMEX simulations, the period of the precipitation cycle in our deeper cumulus simulations is not exactly captured with periods between 1 and 2 hr, and it is found that inclusion of low frequency modes is important.…”

“…Time series analysis showed that the build‐up of cloud water leads that of rain water build‐up by approximately 20 min. For the same case, Feingold et al () found an oscillation in the fit slope of CSDs with similar periodicity (≈80 and ≈15 min). These results indicate a physically based link between the precipitation cycle and the cloud size distribution cycle.…”

Bounding Global Aerosol Radiative Forcing of Climate Change

“…Calculating average cloud albedo across the global oceans based on CERES data for cases where the ice cloud fraction is zero, following Bender et al (), yield a scaling factor of 0.3 to 0.5 for marine boundary‐layer clouds. The linear scaling is appropriate for stratocumulus clouds where clouds are capped by the inversion and therefore deepen relatively little as they widen (Feingold et al, ).…”

Power-Law Scaling in the Internal Variability of Cumulus Cloud Size Distributions due to Subsampling and Spatial Organization

“…The spatial organization in a cloud field can add to this stochastic variability by introducing pertur bations in the cloud populations on scales much larger than the boundary layer depth (e.g., Seifert and Heus 2013). Physical-dynamical processes that drive spatial organization include cold pool formation (e.g., Schlemmer and Hohenegger 2014) and oscillations (Sakradzija et al 2015;Feingold et al 2017).…”