Greater climate sensitivity and variability on TRAPPIST-1e than Earth
arXiv:2208.06297 · doi:10.3847/1538-4357/ac866f
Abstract
The atmospheres of rocky exoplanets are close to being characterized by astronomical observations, in part due to the commissioning of the James Webb Space Telescope. These observations compel us to understand exoplanetary atmospheres, in the voyage to find habitable planets. With this aim, we investigate the effect that CO partial pressure (pCO) has on exoplanets' climate variability, by analyzing results from ExoCAM model simulations of the tidally locked TRAPPIST-1e exoplanet, an Earth-like aqua-planet and Earth itself. First, we relate the differences between the planets to their elementary parameters. Then, we compare the sensitivity of the Earth analogue and TRAPPIST-1e's surface temperature and precipitation to pCO. Our simulations suggest that the climatology and extremes of TRAPPIST-1e's temperature are 1.5 times more sensitive to pCO relative to Earth. The precipitation sensitivity strongly depends on the specific region analyzed. Indeed, the precipitation near mid-latitude and equatorial sub-stellar regions of TRAPPIST-1e is more sensitive to pCO, and the precipitation sensitivity is 2 times larger in TRAPPIST-1e. A dynamical systems perspective, which provides information about how the atmosphere evolves in phase-space, provides additional insights. Notably, an increase in pCO, results in an increase in atmospheric persistence on both planets, and the persistence of TRAPPIST-1e is more sensitive to pCO than Earth. We conclude that the climate of TRAPPIST-1e may be more sensitive to pCO, particularly on its dayside. This study documents a new pathway for understanding the effect that varying planetary parameters have on the climate variability of potentially habitable exoplanets and on Earth.
Accepted at ApJ
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Cited by in corpus (3)
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- Traveling planetary-scale waves cause cloud variability on tidally locked aquaplanets
- Chemistry, Climate, and Transmission Spectra of TRAPPIST-1 e Explored with a Multimodel Sparse Sampled Ensemble