Effect of climate change on meteorite dropping fireballs
Notice bibliographique
Résumé
Climate change, driven by increased greenhouse gas concentrations, not only warming the troposphere (∼0–10 km) but also cooling and contracting the atmosphere above, including the stratosphere (∼10–50 km), mesosphere (∼50–85 km), and thermosphere (∼85–500+ km) [1–3]. This contraction is measurable and has been confirmed by multiple independent datasets and models over the past two decades. In particular, meteor radars, which routinely detect the ablation of small impact micrometeoroids at altitudes between 80 and 100 km, have shown that the peak ablation altitude is decreasing at rates ranging from ~200 to 800 m per decade [4–8]. This observed lowering is consistent with expectations from cooling-induced density changes at fixed altitudes. Although the implications of these changes for satellite drag and orbital debris lifetimes are starting to be explored in detail [9,10], little attention has been paid to their possible influence on larger meteoroids that penetrate deeper into the atmosphere and survive as meteorites.In this study, we investigate whether ongoing climate-driven changes in atmospheric density can significantly affect the atmospheric trajectory and survivability of meteorite dropping fireballs, focusing on the century-scale timescale. To do this, we simulate the atmospheric entry of the Winchcombe meteorite fall, one of the best documented carbonaceous chondrite falls to date, under both atmospheric conditions of 2021 and those projected for the year 2100, using the output of the climate model from the WACCM-X (Whole Atmosphere Community Climate Model - Extended) [11]. The model assumes a moderate emissions scenario (SSP2-4.5) [12], and the density trends are extracted from simulations accounting for solar and geomagnetic activity variations [13].Winchcombe represents an ideal test case. It was a slow low-altitude fireball (entry velocity: 13.9 km/s) with minimal atmospheric deceleration below 40 km, and produced a carbonaceous CM2 chondrite [14]. Its low strength (onset of fragmentation at ~0.07 MPa) and unusually low peak dynamic pressure (~0.6 MPa) make it highly sensitive to changes in atmospheric density. We modeled its entry using a semi-empirical fragmentation and erosion model [15–17], informed by manual identification of fragmentation points and limited by deceleration and photometry data. The simulations were repeated under a projected 2100 atmospheric density profile, obtained by applying regression-derived trends from WACCM-X output to the location and season of the Winchcombe fall. See Figure 1.The comparison between the 2021 and 2100 simulations shows only modest differences in trajectory, light curve, and survivability. The luminous trajectory begins 3 km lower in the 2100 case for a typical +3 magnitude detection threshold. The first fragmentation occurs 820 m lower and the catastrophic fragmentation that produces most of the surviving fragments occurs 300 m lower. However, the final luminous point is actually 190 m higher in 2100 because of slightly faster deceleration in the denser lower stratosphere. The peak brightness remains virtually unchanged, although the fireball is ~0.5 mag fainter at altitudes above 120 km, due to lower densities and reduced drag in the mesosphere. The final surviving mass is reduced by just 0.13 g, or 0.037%, from an initial ~13 kg meteoroid. These variations are small compared to daily and seasonal variations in density [18], and are far below the uncertainties in most meteorite recovery campaigns.Figure 1. Dynamic pressure vs. altitude for the Winchcombe fireball (blue) and its 2100 climate change simulation (red), with eleven fragmentation points marked (crosses). The right panel shows their pressure differences (black).AcknowledgementsEP-A acknowledges financial support from the LUMIO project funded by the Agenzia Spaziale Italiana (2024-6-HH.0). DV was supported in part by the NASA Meteoroid Environment Office under cooperative agreement 80NSSC24M0060. IC was supported by a Natural Environment Research Council (NERC) Independent Research Fellowship (NE/R015651/1). EF acknowledges the funding received by the Grant DeepCFD (Project No. PID2022-137899OB-I00) funded by MICIU/AEI/10.13039/501100011033 and by ERDF, EU. References[1] Roble, R. G., & Dickinson, R. E. (1989). Geophysical Research Letters, 16(12), 1441–1444.[2] Cnossen, I., Emmert, J. T., Garcia, R. R., Elias, A. G., Mlynczak, M. G., & Zhang, S.-R. (2024). Advances in Space Research, 74(11), 5991–6011.[3] Emmert, J. T. (2015). Journal of Geophysical Research: Space Physics, 120(4), 2940–2950.[4] Clemesha, B., & Batista, P. (2006). Journal of Atmospheric and Solar-Terrestrial Physics, 68(17), 1934–1939.[5] Jacobi, C. (2014). Advances in Radio Science, 12, 161–165.[6] Lima, L. M., Araújo, L. R., Alves, E. O., Batista, P. P., & Clemesha, B. R. (2015). Journal of Atmospheric and Solar-Terrestrial Physics, 133, 139–144.[7] Dawkins, E. C. M., Stober, G., Janches, D., et al. (2023). Geophysical Research Letters, 50(2).[8] Venkat Ratnam, M., Teja, A., Pramitha, M., et al. (2024). Advances in Space Research.[9] Brown, M. K., Lewis, H. G., Kavanagh, A. J., & Cnossen, I. (2021). Journal of Geophysical Research: Atmospheres, 126(8).[10] Brown, M., Lewis, H., Kavanagh, A., Cnossen, I., & Elvidge, S. (2024). Journal of Geophysical Research: Space Physics.[11] Cnossen, I. (2022). Geophysical Research Letters, 49(19).[12] O’Neill, B. C., Tebaldi, C., van Vuuren, D. P., et al. (2016). Geoscientific Model Development, 9(9), 3461–3482.[13] Matthes, K., Funke, B., Andersson, M. E., et al. (2017). Geoscientific Model Development, 10(6), 2247–2302.[14] McMullan, S., Vida, D., Devillepoix, H. A. R., et al. (2024). Meteoritics & Planetary Science, 59(5), 927–947.[15] Borovička, J., Tóth, J., Igaz, A., et al. (2013). Meteoritics & Planetary Science, 48(10), 1757–1779.[16] Borovička, J., Spurný, P., & Shrbený, L. (2020). The Astronomical Journal, 160(1), 42.[17] Vida, D., Brown, P. G., Devillepoix, H. A. R., et al. (2023). Nature Astronomy, 7, 318–329.[18] Vida, D., Brown, P. G., Campbell-Brown, M., et al. (2021). Icarus, 354, 114097.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».