3:30 PM - 3:45 PM
[PAE18-19] Exploring atmospheric chemistry with 3D models: from ultra hot Jupiters to terrestrial planets.
★Invited Papers
Keywords:Atmosphere, Chemistry, Modeling, Exoplanets
Modern photochemical models have evolved into sophisticated tools, coupling 3D general circulation models (GCMs) with kinetic networks of gas-phase and aerosol reactions. This shift enables the study of non-equilibrium processes, such as the vertical and horizontal redistribution of species and the formation of photochemical hazes. Models like SPARC/MITgcm and Exo-FMS have provided critical insights into hot Jupiters, where extreme stellar irradiation drives day-to-night gradients and photodissociation. Meanwhile, tools like CAM-Chem and the UK Met Office Unified Model have been adapted for rocky planets, simulating atmospheres under diverse stellar environments, from M dwarfs to F-type stars. These models illuminate the impact of UV radiation on atmospheric stability, biosignature generation, and surface habitability.
The Generic Planetary Climate Model (G-PCM, previously LMDZ Generic) has been developed with versatility in mind, making it easily applicable to a wide range of planetary environments, from hot Jupiters to temperate terrestrial planets. Its design emphasizes flexibility in incorporating diverse atmospheric compositions, stellar spectra, and orbital configurations. In addition, G-PCM includes a generic photochemical module that can simulate a variety of chemical regimes. Applications to exo-Earth will be presented but the capabilities of this model is wider.
The incorporation of advanced radiative transfer schemes, high-resolution 3D dynamics, and experimental constraints has allowed researchers to address a broader parameter space, including ultra-hot Jupiters, sub-Neptunes, and temperate terrestrial planets. Nevertheless, challenges remain, particularly in the computational cost of coupling detailed photochemistry with dynamics and the limited availability of reaction networks calibrated for exotic conditions. Future developments in machine learning, high-performance computing, and laboratory experiments will be pivotal in refining these models and interpreting observations from next-generation telescopes such as JWST, ELTs, and ARIEL.
