Kilometer-scale Climate Modeling of TRAPPIST-1e Using ICON-Sapphire: Peering Through the High Clouds

Recent advances in kilometer-scale modeling and exascale computing have made it possible to simulate Earth's climate with unprecedented detail. On the other hand, global climate simulations of exoplanets have largely relied on coarse-resolution models (grid spacings >100 km), which require parameterizations of convection and clouds. These parameters not only determine the general radiative balance of the planet but are also particularly important when the planet is tidally locked since we can then only observe vertical atmospheric profiles of the terminator: the boundary between the day and night side. The distribution of water vapor and the characteristics of high clouds in this location, shaped by the atmospheric circulation, can therefore determine our ability to observationally characterize a planet's atmosphere and climate.

In this work, we focus on one such tidally locked exoplanet, TRAPPIST-1e, a rocky planet slightly smaller than Earth orbiting in the habitable zone of an ultra-cool red dwarf star 40 light-years away from our solar system. We carry out global climate simulations of TRAPPIST-1e’s atmosphere at 5 km horizontal resolution using ICON-Sapphire, a kilometer-scale model previously applied only to Earth's climate. In order to do so, we adapted the model to reflect the planetary parameters of TRAPPIST‑1e, including its size, rotation rate, stellar irradiation, and an idealized atmospheric composition consistent with the THAI model intercomparison project.

We examine how these factors shape the simulated climate, with particular emphasis on the structure and prevalence of high clouds at the terminator. By comparing our convection-resolving simulation with lower-resolution simulations from the existing literature, we further assess how kilometer-scale modeling changes the representation of atmospheric circulation and cloud processes. This work highlights the potential of high-resolution exoplanet climate modeling to help refine the interpretation of future observational data.