Lightning activity on a tidally locked terrestrial exoplanet in storm-resolving simulations for a range of surface pressures
Bibliographic record
Abstract
ABSTRACT Cloudy atmospheres produce electric discharges, including lightning. Lightning, in turn, provides sufficient energy to break down air molecules into reactive species and thereby affects the atmospheric composition. The climate of tidally locked rocky exoplanets orbiting M-dwarf stars may have intense and highly localized thunderstorm activity associated with moist convection on their day side. The distribution and structure of lightning-producing convective clouds is shaped by various climate parameters, of which a key one is atmospheric mass, i.e. surface air pressure. In this study, we use a global storm-resolving climate model to predict thunderstorm occurrence for a tidally locked exoplanet over a range of surface pressures. We compare two lightning parametrizations: one based on ice cloud microphysics and one based on the vertical extent of convective clouds. We find that both parametrizations predict that the amount of lightning monotonically decreases with surface pressure due to a weaker convection and fewer ice clouds. The spatial distribution of lightning on the planet changes with respect to the surface pressure, responding to the changes in the large-scale circulation and the vertical stratification of the atmosphere. Our study provides revised high-resolution estimates for lightning activity on a tidally locked Earth-like exoplanet, with implications for global atmospheric chemistry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".