On clocks and clouds

Abstract

Abstract. Cumulus clouds exhibit a life cycle that consists of (a) the growth phase (increasing size, most notably in the vertical direction); (b) the mature phase (growth ceases; any precipitation that develops is strongest during this period); and (c) the dissipation phase (cloud dissipates because of precipitation and/or entrainment; no more dynamical support). Although radar can track clouds over time and give some sense of the age of a cloud, most aircraft in situ measurements lack temporal context. We use large eddy simulations of trade wind cumulus cloud fields from cases during the Barbados Oceanographic and Meteorological Experiment (BOMEX) and Rain In Cumulus over the Ocean (RICO) campaigns to demonstrate a potential cumulus cloud "clock." We find that the volume-averaged total water mixing ratio r t is a useful cloud clock for the 12 clouds studied. A cloud's initial r t is set by the subcloud mixed-layer mean r t and decreases monotonically from the initial value due primarily to entrainment. The clock is insensitive to aerosol loading, environmental sounding and extrinsic cloud properties such as lifetime and volume. In some cases (more commonly for larger clouds), multiple pulses of buoyancy occur, which complicate the cumulus clock by replenishing r t . The clock is most effectively used to classify clouds by life phase.

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

“…The power spectra both exhibit significant variability, which might reflect the fact that some clouds are supported by multiple thermals. This might be at least partially related to the documented pulsations in individual trade wind cumulus clouds [ Heus et al , ; Witte et al , ].…”

Characterization of cumulus cloud fields using trajectories in the center of gravity versus water mass phase space: 1. Cloud tracking and phase space description

“…Lastly, only 4.1% of cloud samples reside in the precipitating branch, but they constitute a significant part of the total mass (36%). The number asymmetry favoring growing branch clouds is in line with observations [ Rauber et al ., ; Meerkötter and Bugliaro , ; Nuijens et al ., ] and numerical studies [ M Zhao and Austin , ; Heus et al ., ; Plant , ; Witte et al ., ] of warm cumulus cloud fields, where for the majority of the clouds' lifetimes, cloud bases are at the vicinity of the LCL while cloud growth occurs.…”

Observed and Modeled Warm Rainfall Occurrence and Its Relationships with Cloud Macrophysical Properties

“…Although it is expected that the cloud-top temperature difference methodology used within this study should give a crude indication of the distribution of cloud maturity, the cloud life cycle ideas developed herein would greatly benefit from more direct measurements of cloud evolution. Within RAMS, a cloud-tracking algorithm (e.g., Dawe and Austin 2012;Witte et al 2014) could be developed to trace the full life cycle of simulated warm clouds and identify temporal rainfall onset. Methodologies designed to track cloud features observed by satellites in time are inherently hindered by the nature of the observations themselves.…”

“…Cumulus cloud life cycle is often conceptualized as consisting of three stages: growth, maturity, and dissipation (e.g., Malkus 1952;Byers and Hall 1955;Heus et al 2009;Cotton et al 2010;Witte et al 2014;Katzwinkel et al 2014;Borque et al 2014). Within this construct, incipient clouds increase in depth through their growth stage, cease growth and precipitate any rain hydrometeors they have managed to develop during their mature phase, and break apart as a result of factors such as water mass rainout and/or entrainment in their dissipation stage.…”

“…Composite rainfall statistics derived from instantaneous snapshots of clouds that are in various stages of growth, maturity, and dissipation inherently reflect the amount of time the clouds spend within these life cycle stages. However, as clouds develop and mature in time, it is likely that their associated macrophysical properties (e.g., geometric depth and liquid water path) evolve prior to the onset of rainfall (e.g., Reiche and Lasher-Trapp 2010;Burnet and Brenguier 2010;Witte et al 2014). In an effort to more accurately characterize instantaneous CloudSat observations of identified cumulus congestus clouds, Luo et al (2009) developed a methodology that provided a dynamic context for snapshot observations of this deep convective cloud type.…”