Quantifying Convective Aggregation Using the Tropical Moist Margin's Length

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.

Weak cooling of the troposphere by tropical islands in simulations of the radiative‐convective equilibrium

“…A first study employing that model version for RCE simulations is presented in Beucler et al . (2020).…”

“…Differences between simulations on CPUs and GPUs relate to rounding errors emerging from re-association of mathematical expressions, mathematical approximations or contraction operators, and architecture-specific implementations of transcendental functions (Arteaga et al, 2014;Schär et al, 2020). A first study employing that model version for RCE simulations is presented in Beucler et al (2020).…”

The Impact of a Land‐Sea Contrast on Convective Aggregation in Radiative‐Convective Equilibrium

“…We present this paper as an investigation of the impacts land‐sea contrasts have on convective aggregation in a global configuration, and provide it as a useful addition to the growing literature on connecting idealized aggregation studies to the manifestation of real world convective organization (e.g., Arnold & Randall, 2015; Becker & Wing, 2020; Beucler et al., 2020; Bony et al., 2020; Hohenegger & Stevens, 2016, 2018; Müller & Hohenegger, 2020; Muller & Romps, 2018; Shamekh et al., 2020a; Tompkins, 2001a). We have shown that, whilst the shape and scale of aggregation change when a continentally sized island is included in an idealized RCE world, many of the features of aggregation remain the same, including its effects on the large‐scale environment.…”

The Influence of Convective Aggregation on the Stable Isotopic Composition of Water Vapor

“…(2022), and the shape‐based BLW method of Beucler et al. (2020). Tompkins and Semie (2017) developed the I org index for use in numerical models, based on statistical comparisons with a pure random process.…”

“…A quantitative metric for the degree of aggregation is essential for a thorough analysis, and several approaches have been developed in recent years, each of which focuses on particular characteristics of aggregated convection. Some of these methods include the Simple Convective Aggregation Index (SCAI) of Tobin et al (2012), which is based on the numbers of convective clusters within a region along with the average distances between clusters; the subsidence fraction method of Coppin and Bony (2015); an approach based on the spatial variance of moist static energy (Wing & Emanuel, 2014); the convective organization potential (COP) of White et al (2017); the morphological index of convective aggregation (Kadoya & Masunaga, 2018); the area-based convective organization potential method of Jin et al (2022), and the shape-based BLW method of Beucler et al (2020). Tompkins and Semie (2017) developed the I org index for use in numerical models, based on statistical comparisons with a pure random process.…”