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 rt is a useful cloud clock for the 12 clouds studied. A cloud's initial rt is set by the subcloud mixed-layer mean rt 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 rt. The clock is most effectively used to classify clouds by life phase.
Highlights
Looking at the sky reveals immense variability in the properties and evolution of clouds, even those adjacent to one another
Six of the clouds are from the “clean” Barbados Oceanographic and Meteorological Experiment (BOMEX) simulation, three are from the “polluted” BOMEX simulation, and three are from the “clean” Rain In Cumulus over the Ocean (RICO) simulation
The goal of this study is to find an observationally relevant, single-measurement cumulus clock
Summary
Looking at the sky reveals immense variability in the properties and evolution of clouds, even those adjacent to one another. Variance among clouds is due to spatial variability in quantities such as temperature and humidity, and to temporal variability – i.e., differences in cloud age. Past analyses of in situ observations have implicitly assumed that temporal variance is minimized by random sampling of many clouds of unknown age to paint a picture of an “averageaged” cloud. Inherent to cumulus evolution are distinctive stages defined by the different physical processes active during each. Knowing the age of a cloud gives insight into which processes are expected to be most important at the time of observation. Using in situ observations of cumulus to explore cloud–aerosol interactions or test theory is more meaningful if cloud age is known because it allows for testing representations of cloud mechanics at the process level
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