Diffusion of Martian Methane in Concert with Destruction Via Adsorption Onto UV-Activated Perchlorate in Martian Dust
Bibliographic record
Abstract
The observation of temporal and spatial variability in methane concentration on Mars is puzzling, as the current 300-year photochemical lifetime of methane should ensure that it is well mixed throughout the atmosphere (Atreya et al., 2007; Wong et al., 2003). For instance, the spectrometer onboard the Mars Science Laboratory (MSL) rover has observed a significantly higher methane concentration near the surface of Mars than has been observed above 5 km by the instruments aboard the Trace Gas Orbiter (TGO). Near the surface, MSL sees approximately a 0.4 ppbv concentration of background methane that varies on diurnal and seasonal timescales with periodic spikes of over 20 ppbv (Webster et al., 2018, 2021). By contrast, in the middle and upper atmosphere, no methane is observed by TGO to below the detection threshold of ~0.02 ppbv (Korablev et al., 2019; Montmessin et al., 2021).Previously, these differences were explained by considering the different timing and airmasses being sampled by these measurements – at night and near the surface for MSL and at sunset or sunrise and at altitude for TGO (Moores, et al., 2019). This framework required that the methane decrease from its relatively high surface concentration to a much lower concentration at altitude. While this is possible purely through atmospheric mixing and dilution, if the amount of methane being injected into the atmosphere near the surface is sufficiently large this methane will eventually build up in the upper atmosphere.In this work, we consider the effect of a recently quantified destruction mechanism described by Zhang et al., (2022). Their derived destruction rates using a simplified model of the atmosphere suggest that methane adsorbed onto a surface with UV-activated perchlorate can contribute to the destruction of methane on the order of hours to days. This model is appealing for several reasons: (1) it can rapidly destroy methane, (2) it operates only during the day when both MSL and TGO observe methane to be low and not at night when MSL observes methane concentration to be high, and (3) destruction occurs on the surfaces of dust grains which are in abundance in the lower atmosphere. To evaluate the effects of this destruction mechanism with a more realistic representation of the Martian atmosphere, we couple its effects into a martian vertical diffusion model (VDM) that was previously developed by Walters et al (2024). Walters et al. (2024) previously investigated methane flux from the surface of Mars in a 1-D VDM that incorporated eddy diffusivity coefficients derived from GEM-Mars, a global climate model, to model the effects of transport and diffusion of methane in the VDM (Neary & Daerden, 2018; Stroud et al., 2005). The methane is injected at the surface, and the methane mixing ratio is tracked throughout the vertical column using 1 m layers in half-hour time steps for select sols. While Walters et al. (2024) were able to closely replicate the required flux to replicate the SAM-TLS measurements, the diffusion and removal at the top layer was insufficient to decrease methane below 0.02 ppbv at the top of their model at 5 km above the surface.As part of the inclusion of a destruction mechanism in the VDM simulation procedure (Fig. 1), we made the following changes to the model of Walters et al. (2024): (1) we incorporated a Conrath profile to describe the dust distribution in the atmosphere (Conrath, 1975), (2) we added in a radiative transfer model to estimate the available UVC for the destruction mechanism (Smith & Moores, 2020) at each step in the VDM, and (3) we also added a simple box model on top of the VDM to track the methane concentration in the portion of the atmosphere that is visible to TGO. Diffusion into this upper box is dependent on the relative concentration of methane in the top layer in the VDM and the box.Figure 1. Plots showing the concentration of methane in the atmosphere repeated over Ls=180o in MY 35 between 0 and 6000 m above the surface. The y-axis shows the height in the simulation, and the x-axis shows the time during the sol after a 1-day spinup. On the left, we show the simulation using the method produced by Walters et al. (2024) where methane can build up in the atmosphere if methane is continuously provided into the system. This model demonstrates the effect of diffusion alone, where there is a build up at night, and diffusion throughout the atmosphere. On the right, we show the same simulation with the inclusion of the destruction mechanism from Zhang et al. (2022).We report our preliminary results incorporating the effects of oxidation of activated perchlorate surfaces as our methane destruction mechanism for select sols and compare them to the available methane measurements.ReferencesAtreya, S. K., Mahaffy, P. R., & Wong, A. S. (2007). PSS, 55(3), 358–369. Conrath, B. J. (1975). Icarus, 24(1), 36–46. Korablev, O., Vandaele, A. C., et al. (2019). Nature, 568(7753), 517–520. Montmessin, F., Korablev, O. I., et al. (2021). A&A, 650, A140. Moores, J. E., Gough, R. V., et al. (2019). Nature Geoscience, 12(5), 321–325. Moores, J. E., King, P. L., et al. (2019). GRL, 46(16), 9430–9438. Neary, L., & Daerden, F. (2018). Icarus, 300, 458–476. Smith, C. L., & Moores, J. E. (2020). Icarus, 338, 113497. Stroud, C., Makar, P., et al. (2005). JGR D: Atmospheres, 110(17), 149–162. Walters, M. E., Dong, E. et al. (2024). 10th Mars.Webster, C. R., Mahaffy, P. R., et al (2018). Science, 360(6393), 1093–1096. Webster, C. R., Mahaffy, P. R., et al. (2021). A&A, 650, A166. Wong, A. S., Atreya, S. K., & Encrenaz, T. (2003). JGR: Planets, 108(E4), 5026. Zhang, X., Berkinsky, D., et al. (2022). Icarus, 376, 114832.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".