Year: 2025
Type: article
Abstract: The conceptual landscape of convection has two simple gateways: optimal function and random form. Optimal convection adjusts toward a univariate ideal called neutrality. Convection’s form involves elements (parcels, bubbles, and drafts) whose most parsimonious assumption is random. Between these gates lies a wilderness of realizable flow configurations. The only simple principle is natural selection by “fitness,” a scalar whose gradient is a local direction in an abstract configuration space . Random or high-entropy patterns occupy most of the configuration space and occur spontaneously. With time, convection can discover less facile but more efficient (“organized”) configurations, by sequential selection. Here, two data exercises explore the self-organization process, in shallow and deep moist convection. For shallow convection, causal network postulates are explored in a large set of cyclic-domain large-eddy simulations (LESs; the “Cloud Botany” set). When an evolutionary pathway (mainly layer deepening in these simulations) leads to precipitation, mesoscale patterns blossom rapidly. For deep convection, expanding rings of conditional cell probability around prior cells are estimated from satellite imagery over South America and the South Pacific. In a Monte Carlo model iterating such a conditional probability kernel, hundreds of hourly cells take days to discover a self-sustaining “squall” configuration the kernel affords. Larger-scale implications include overshoots (redefinition of neutrality) and tens-of-hours time scales to both adjustment and noise (indeterminacy). If functional organization can be inferred from horizontal patterns, the abundance of horizontal texture information in satellite cloud imagery could find quantitative value.
Author: Brian Mapes
Institution: University of Miami
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4412473150