Fully time-dependent cloud formation from a non-equilibrium gas-phase in exoplanetary atmospheres (2311.03244v1)
Abstract: Recent observations suggest the presence of clouds in exoplanet atmospheres but have also shown that certain chemical species in the upper atmosphere might not be in chemical equilibrium. The goal of this work is to calculate the two main cloud formation processes, nucleation and bulk growth, consistently from a non-equilibrium gas-phase. The aim is further to explore the interaction between a kinetic gas-phase and cloud micro-physics. The cloud formation is modeled using the moment method and kinetic nucleation which are coupled to a gas-phase kinetic rate network. Specifically, the formation of cloud condensation nuclei is derived from cluster rates that include the thermochemical data of (TiO$_2$)$_N$ from N = 1 to 15. The surface growth of 9 bulk Al/Fe/Mg/O/Si/S/Ti binding materials considers the respective gas-phase species through condensation and surface reactions as derived from kinetic disequilibrium. The effect of completeness of rate networks and the time evolution of the cloud particle formation is studied for an example exoplanet HD 209458 b. A consistent, fully time-dependent cloud formation model in chemical disequilibrium with respect to nucleation, bulk growth and the gas-phase is presented and first test cases are studied. This model shows that cloud formation in exoplanet atmospheres is a fast process. This confirms previous findings that the formation of cloud particles is a local process. Tests on selected locations within the atmosphere of the gas-giant HD 209458 b show that the cloud particle number density and volume reach constant values within 1s. The complex kinetic polymer nucleation of TiO$_2$ confirms results from classical nucleation models. The surface reactions of SiO[s] and SiO$_2$[s] can create a catalytic cycle that dissociates H$_2$ to 2 H, resulting in a reduction of the CH$_4$ number densities.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Collections
Sign up for free to add this paper to one or more collections.