Exploring TRAPPIST-1 Climate States with an Energy Balance Model
TLDR
A new energy balance model (HEXTOR) explores climate states of tidally-locked TRAPPIST-1 planets, suggesting ice cover for 'e' and 'f' without high CO2.
Key contributions
- Adapted HEXTOR energy balance model to study habitable planets orbiting low-mass stars.
- Validated model for Earth-like planets, then calibrated for tidally-locked TRAPPIST-1 e.
- Characterized climate states of synchronously rotating planets across instellation and CO2 levels.
- Predicted partial ice cover for TRAPPIST-1 e and full ice cover for 'f' without ~1 bar CO2.
Why it matters
This paper provides a simplified, yet effective, tool for understanding exoplanet climates, especially for tidally-locked worlds. Its findings on TRAPPIST-1 e and f offer crucial insights for future, more complex climate models and observational campaigns.
Original Abstract
This paper presents a version of the HEXTOR energy balance model that has been configured for the study of habitable terrestrial planets orbiting low-mass stars. The model is validated for rapidly-rotating Earth-like planets using latitudinal coordinates, which shows expected patterns of bistability. A tidally-locked coordinate transformation is then applied to the model, which is calibrated to match mean values of the minimum, average, and maximum surface temperatures from a general circulation model ensemble of TRAPPIST-1 e. This calibrated energy balance model is used to characterize the possible climate states of such a synchronously rotating planet across a parameter space of instellation and carbon dioxide partial pressure. These calculations suggest a state of partial ice cover for TRAPPIST-1 e and complete ice cover for TRAPPIST-1 f, unless carbon dioxide partial pressure is ~1 bar or greater. This approach demonstrates the capability of a simplified one-dimensional model to study the climates of terrestrial planets in synchronous rotation, which can help guide more complex models and observations toward the most promising targets of interest.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.