Shin-ichiro Shima

Abstract The clouds covering Venus globally, that are primarily composed of ‐O droplets, strongly influence the thermal structure and dynamics of the atmosphere. However, the mechanism governing their growth and long‐term maintenance remains poorly understood. In this study, the bifurcation structure of the droplets' growth dynamics through the co‐condensation of O and is investigated by constructing a box model under the assumption of a monodisperse droplet population. Our analysis reveals that the phase portrait which is a diagram with the masses of O and liquids in the droplet as the axes, showing how each evolves over time, depends on the saturation ratios of the O and gases and that the mass of O in the droplet varies much more rapidly than that of under conditions near the Venusian cloud base. The condition for the stable existence of Venusian cloud droplets is also investigated in terms of the saddle‐node bifurcation. Based on these findings, we simulate the droplets' growth under the thermodynamic conditions near the Venusian cloud base and find that the small cloud droplets, such as Mode 1, may rapidly grow into larger ones, such as Modes 2 or 3, depending on the droplet number density.

Abstract Clouds play a central role in climate physics by interacting with precipitation, radiation, and circulation. Despite being a fundamental issue in convective organization, the self-aggregation of clouds lacks a theoretical explanation due to its complexity. In this study, we introduce an idealized mathematical model where the system’s state is represented solely by the vertically integrated water vapor content of atmospheric columns under the weak temperature gradient approximation. By analyzing the nonlinear dynamics of this simplified system, we mathematically elucidate the mechanisms that determine the onset of self-aggregation and the spatial scale of the self-aggregated state. Nonlocal coupling between atmospheric columns induces bistability, leading to dry and moist equilibria. This reflects the circulation effects driven by horizontal differential heating due to convection and radiation. The bistable self-aggregated state realizes when destabilization by nonlocal coupling, triggered by finite-amplitude disturbances in the uniform state, overcomes stabilization by diffusion. For globally coupled systems where all columns are equally coupled, perturbations with the maximum wavelength exhibit the highest growth rate. This results in a solution with an infinitely long wavelength, understood as the dynamical system’s heteroclinic trajectories describing the steady state’s spatial evolution. Conversely, for nonlocally coupled systems with finite filter lengths, perturbations with wavelengths close to the characteristic length of the coupling are preferred. The results reveal that the balance between nonlocal coupling and diffusion is essential for understanding convective self-aggregation. Moreover, this study suggests a physical similarity between convective self-aggregation and the moisture mode. Significance Statement Cloud self-organization is a longstanding fundamental problem in climate physics, and its representation in climate models may contribute to uncertainties in future climate projections. Clouds are complex phenomena, intricately connected to latent heat release during water phase changes, buoyancy-driven fluid motion, and interactions with radiation. As a result, their detailed modeling is ongoing. Efforts have also been undertaken to understand the macroscopic behavior of clouds through simple mathematical descriptions. In this study, we semianalytically reveal the mechanisms underlying the spontaneous clustering of clouds and the characteristic distances between clusters using an idealized mathematical model that describes the spatiotemporal variation of water vapor content in tropical atmospheric columns.

Abstract This study presents the first model intercomparison of aerosol‐cloud‐turbulence interactions in a controlled cloudy Rayleigh‐Bénard Convection chamber environment, utilizing the Pi Chamber at Michigan Technological University. We analyzed simulated cloud chamber‐averaged statistics of microphysics and thermodynamics in a warm‐phase, cloudy environment under steady‐state conditions at varying aerosol injection rates. Simulation results from seven distinct models (DNS, LES, and a 1D turbulence model) were compared. Our findings demonstrate that while all models qualitatively capture observed trends in droplet number concentration, mean radius, and droplet size distributions at both high and low aerosol injection rates, significant quantitative differences were observed. Notably, droplet number concentrations varied by over two orders of magnitude between models for the same injection rates, indicating sensitivities to the model treatments in droplet activation and removal and wall fluxes. Furthermore, inconsistencies in vertical relative humidity profiles and in achieving steady‐state liquid water content suggest the need for further investigation into the mechanisms driving these variations. Despite these discrepancies, the models generally reproduced consistent power‐law relationships between the microphysical variables. This model intercomparison underscores the importance of controlled cloud chamber experiments for validating and improving cloud microphysical parameterizations. Recommendations for future modeling studies are also highlighted, including constraining wall conditions and processes, investigating droplet/aerosol removal (including sidewall losses), and conducting simplified experiments to isolate specific processes contributing to model divergence and reduce model uncertainties.

Abstract. The phenomenon of electric fields applied to droplets, inducing droplet coalescence, is called the electro-coalescence effect. An analytic expression for electro-coalescence with the accurate electrostatic force for a pair of droplets with opposite-sign charges is established by treating the droplets as conducting spheres (CSs). To investigate this effect, we applied a weak electric field to a cumulus cloud using a cloud model that employs the super-droplet method, a probabilistic particle-based microphysics method. This study employs a two-dimensional (2D) large-eddy simulation (LES) in a flow-coupled model to examine aerosol microphysics (such as collision–coalescence enhancement, velocity fluctuations, and supersaturation fluctuations) in warm cumulus clouds without relying on subgrid dynamics. In the simulation, we assume that droplets carry opposite-sign charges and are well mixed within the cloud. The charge is not treated as an individual particle attribute. To assess fluctuation effects, we conducted 50 simulations with varying pseudo-random number sequences for each electro-coalescence treatment. The results show that, with CS treatment, the electrostatic force contributes a larger effect on cloud evolution than in previous research. With a lower charge limit of the maximum charge amount on the droplet, the domain total precipitation with CS treatment for droplets with opposite signs is higher than that with the no-charge (NC) setting. Compared to previous work, the multi-image dipole treatment of CS results in higher precipitation. It is found that the electro-coalescence effect could affect rain formation even when the droplet charge is at the lower charge limit. High pollution levels result in greater sensitivity to electro-coalescence. The results show that, when the charge ratio between two droplets is over 100, the short-range attractive electric force due to the multi-image dipole would also significantly enhance precipitation for the cumulus. It is indicated that, although the accurate treatment of the electrostatic force with the CS method would require 30 % longer computation time than before, it is worthwhile to include it in cloud, weather, and climate models.

Abstract Estimating the states of error-growing (sensitive to initial state) cellular automata (CA) based on noisy imperfect data is challenging due to the discreteness of the dynamical system. This paper proposes particle filter (PF)–based data assimilation (DA) for three-state error-growing CA and demonstrates that the PF-based DA can predict the present and future state even with noisy and sparse observations. The error-growing CA used in the present study comprised a competitive system of land, grass, and sheep. To the best of the authors’ knowledge, this is the first application of DA to such CA. The performance of DA for different observation sets was evaluated in terms of observational error, density, and frequency, and a series of sensitivity tests of the internal parameters in the DA was conducted. The inflation and localization parameters were tuned according to the sensitivity tests.