Year: 2020
Type: article
Abstract: On small scales, the tropical atmosphere tends to be either moist or very dry. This defines two states that, on large scales, are separated by a sharp margin, well identified by the antimode of the bimodal tropical column water vapor distribution. Despite recent progress in understanding physical processes governing the spatiotemporal variability of tropical water vapor, the behavior of this margin remains elusive, and we lack a simple framework to understand the bimodality of tropical water vapor in observations. Motivated by the success of coarsening theory in explaining bimodal distributions, we leverage its methodology to relate the moisture field's spatial organization to its time evolution. This results in a new diagnostic framework for the bimodality of tropical water vapor, from which we argue that the length of the margin separating moist from dry regions should evolve toward a minimum in equilibrium. As the spatial organization of moisture is closely related to the organization of tropical convection, we hereby introduce a new convective organization index (BLW) measuring the ratio of the margin's length to the circumference of a well‐defined equilibrium shape. Using BLW, we assess the evolution of self‐aggregation in idealized cloud‐resolving simulations of radiative‐convective equilibrium and contrast it to the time evolution of the Atlantic Intertropical Convergence Zone (ITCZ) in the ERA5 meteorological reanalysis product. We find that BLW successfully captures aspects of convective organization ignored by more traditional metrics, while offering a new perspective on the seasonal cycle of convective organization in the Atlantic ITCZ.
DOI: 10.1029/2020ms002092
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,800
Funders: National Science Foundation, Bundesministerium für Verkehr und Digitale Infrastruktur, Abdus Salam International Centre for Theoretical Physics, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Eidgenössische Technische Hochschule Zürich, Centro Svizzero di Calcolo Scientifico, National Supercomputing Centre Singapore
Awards: Convective Self-Aggregation in a Hierarchy of Kilometer-Scale Climate Models, Collaborative Research: HDR Elements: Software for a new machine learning based parameterization of moist convection for improved climate and weather prediction using deep learning, Collaborative Research: HDR Elements: Software for a new machine learning based parameterization of moist convection for improved climate and weather prediction using deep learning, Collaborative Research: Role of Cloud Albedo and Land-Atmosphere Interactions on Continental Tropical Climates
OpenAlex ID: W3008060950