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Record W4412120852 · doi:10.5194/epsc-dps2025-594

Effect of climate change on meteorite dropping fireballs

2025· preprint· en· W4412120852 on OpenAlexaff
Eloy Peña‐Asensio, Denis Vida, Ingrid Cnossen, Esteban Ferrer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsWestern University
Fundersnot available
KeywordsMeteoriteClimate changeAstrobiologyEnvironmental scienceGeologyPhysical geographyGeographyPhysicsOceanography

Abstract

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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.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.013
GPT teacher head0.269
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
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