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Astroclimes -- measuring the abundance of CO$_2$ and CH$_4$ in the Earth's atmosphere using astronomical observations

T0 review · 1 major / 7 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Astroclimes shows that ground-based astronomical spectra of telluric standard stars can recover the long-term rise of atmospheric CO2 and CH4.

desk verdict A well-built feasibility study for a genuinely new way to get nighttime greenhouse-gas columns from archival telluric spectra, but the profile-shape scaling and CAMS-based shift keep the CO2/CH4 numbers from being trusted yet. read the letter →

arxiv 2509.10258 v1 pith:PCEWT7RM submitted 2025-09-12 astro-ph.EP astro-ph.IMphysics.ao-ph

Marcelo Aron Fetzner Keniger , David Armstrong , Matteo Brogi , Siddharth Gandhi , Marina Lafarga This is my paper · ORCID
classification astro-ph.EPastro-ph.IMphysics.ao-ph
keywords greenhousegasestelluricspectroscopyCO2retrievalCH4MCMCCARMENESatmosphericcolumnnighttimemeasurements
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper presents a new algorithm, Astroclimes, that measures the abundance of CO2 and CH4 in Earth's atmosphere by fitting model telluric transmission spectra to near-infrared observations of telluric standard stars taken by ground-based telescopes. Because these stars are routinely observed for calibration purposes, the method could turn archival astronomical data into a nighttime greenhouse-gas monitoring network, complementing sunlight-dependent satellites and ground networks. Running the algorithm on 436 CARMENES spectra from 2016 to 2023, the paper finds that the retrievals reproduce the known long-term increase in both CO2 and CH4 but do not recover their seasonal cycles. Benchmarked against the CAMS EGG4 reanalysis, the retrievals show an overall vertical offset and long-term scatter of about ±5 ppm (ground CO2), ±9 ppm (column CO2), ±31 ppb (ground CH4), and ±42 ppb (column CH4), while scatter within a single night is much smaller, around ±1 ppm and ±10 ppb. The paper argues the method is not yet competitive with dedicated state-of-the-art instruments but is a promising proof of concept.

What carries the argument

The central object is the synthetic telluric transmission spectrum, computed from molecular cross-sections (a newly calculated low-temperature grid based on HITRAN), an atmospheric profile of pressure, temperature, and molecular abundances, and the Beer-Lambert law with additional terms for Rayleigh scattering, aerosol extinction, and collision-induced absorption by O2. The free parameters are the ground-level abundances of CO2, CH4, H2O, and O2; each molecular abundance profile from the GGG2020 pipeline is multiplied by a single scaling factor. The model is compared to observations through a cross-correlation-based log-likelihood, and the parameters are estimated with an MCMC using the emcee sampler. Column-averaged dry mole fractions are derived by integrating the retrieved number-density profiles relative to O2, whose atmospheric fraction is assumed constant.

What would settle it

A direct test would be to generate synthetic telluric spectra with the ESO Sky Model but with deliberately altered CO2 and CH4 vertical profile shapes (same ground value, different distribution with height), run the Astroclimes retrieval on them, and check whether the retrieved column-averaged dry mole fractions recover the known inputs or instead show a shape-dependent bias. Independently, comparing Astroclimes column-averaged retrievals to collocated aircraft flask profiles or TCCON measurements over the same nights at a site such as Calar Alto would reveal whether profile-shape errors produce the observed vertical offset and missing seasonal cycle.

Watch

Extended reading notes

Core claim

The paper's central claim is that astronomical observations of telluric standard stars, which are routinely taken for calibration and contain strong telluric absorption lines, can be used to estimate the column-averaged and ground-level dry mole fractions of CO2 and CH4 in Earth's atmosphere. Using a Markov-Chain Monte Carlo fit of synthetic transmission spectra to CARMENES near-infrared data, the authors recover the long-term upward trend of both gases over 2016 to 2023, though the seasonal cycle is not detected at a statistically significant level. The paper also quantifies the current precision limits: on hourly timescales the scatter is comparable to the individual measurement uncertainties, indicating that the residual long-term scatter is dominated by systematic effects rather than random noise.

Load-bearing premise

The retrieval assumes the vertical shape of each molecular abundance profile from the GGG2020 pipeline is correct and only scales that entire profile by a single constant; if the true shape differs, for example because the underlying flask records are extrapolated through an El Niño year, the column-averaged values will be biased even when the ground-level value fits well.

Editorial extensions

If this is right

  • Archival telluric standard star spectra from high-resolution spectrographs at multiple observatories could become a new data source for nighttime greenhouse-gas column monitoring.
  • The short-timescale scatter (about ±1 ppm for CO2 and ±10 ppb for CH4 within a night) suggests that once systematic offsets are understood, the method could approach the precision needed to detect regional carbon-cycle signals.
  • The inability to recover seasonal cycles with the current dataset indicates that longer time baselines or better control of systematic biases are required before the method can be used for trend attribution studies.
  • Applying the same algorithm to other near-infrared instruments, especially those covering the stronger O2 A-band in the visible, could improve the column-averaged retrievals and reduce scatter.
  • The overall vertical shift of the retrieved abundances relative to the CAMS reanalysis points to a correctable bias that, if removed, would make the method directly comparable to established column networks.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the nightly precision holds after correcting the systematic offset, the method could turn existing exoplanet-atmosphere telluric calibration data into a climate dataset spanning years, enabling retrospective studies of how CO2 and CH4 columns evolved at dozens of observatory sites.
  • The assumed constancy of the vertical profile shape is likely the main source of the seasonal-cycle failure; a retrieval that fits profile shape parameters (e.g., a scale height or a stratification parameter) rather than a single ground-level scaling factor might recover the seasonal signal.
  • Combining the near-infrared CO2 and CH4 lines with the visible O2 A-band in a joint retrieval would anchor the dry-air column directly from the same stellar spectrum, reducing the reliance on external O2 assumptions and potentially lowering the column-averaged scatter.
  • A direct test of the method's sensitivity to profile shape could be made by applying Astroclimes to spectra taken during a known El Niño event, when the extrapolated MLO and SMO flask records are most likely to be in error.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

1 major / 7 minor

Summary. The paper presents Astroclimes, a synthetic telluric transmission code that generates model spectra from atmospheric profiles and molecular cross-sections, then fits them to high-resolution near-infrared spectra of telluric standard stars using an MCMC likelihood. The method retrieves ground-level and column-averaged dry mole fractions of CO2, CH4, H2O, and O2. The authors apply it to 436 CARMENES observations from 2016 to 2023, using GGG2020 prior profiles scaled by a single ground-level factor per molecule. Benchmarking against the CAMS EGG4 reanalysis, they report long-term scatter of ±5 ppm (ground CO2), ±9 ppm (column CO2), ±31 ppb (ground CH4), and ±42 ppb (column CH4), plus vertical offsets that they correct using the same CAMS comparison. The paper claims recovery of the long-term CO2 and CH4 trends but not their seasonal cycles, and demonstrates short-timescale scatter of order ±1 ppm and ±10 ppb that is comparable to individual MCMC uncertainties.

Significance. If the central claim holds, the method would turn routine telluric-standard observations into a nighttime greenhouse-gas column measurement capability, reusing archival data from multiple observatories. The paper has clear strengths: it uses a large, real dataset; includes a dedicated weather-balloon profile; makes honest and explicit statements about known limitations (e.g., unpropagated GGG2020 extrapolation errors in Section 3.2.4); and provides a SKYCALC benchmark for H2O that quantitatively tests the retrieval of precipitable water vapour. However, the CO2 and CH4 channels are not validated against synthetic spectra or an independent reference free of circularity, and the recovered long-term trend may be inherited from the smooth extrapolation of the GGG2020 priors. These gaps are load-bearing for the paper's main claim, so the contribution is currently more a promising proof-of-concept than a demonstrated measurement technique.

major comments (1)
  1. [§4, §3.2.4, Eq. (22)] The retrieval in §4 fits only a single ground-level scaling factor that multiplies the full GGG2020 prior profile; if the prior vertical shape is inaccurate, both the fitted ground-level value and the column-averaged DMF from Eq. (22) are biased in a way that a single scale factor cannot correct. Section 3.2.4 acknowledges this directly, stating that the MLO/SMO flask records are extrapolated after 2018, that El Niño conditions can corrupt the profile shape, and that the estimated extrapolation errors (0.25% for CO2, 0.6% for CH4) are not propagated into the reported uncertainties. Since the CARMENES sample spans 2016–2023, roughly half of the data use extrapolated profiles, and the extrapolation functions (19)–(20) are smooth baselines plus a fixed average seasonal cycle. The recovered long-term trend could therefore be an artifact of the smooth extrapolation in the GGG2020 priors rather than a genuine atmospheric signal. The paper provides no sensitivity test that varies the CO2 or CH4 profile shape; Section 4.2 compares only different H2O profile sources. Such a test is necessary before the trend recovery can be attributed to the measurement.
minor comments (7)
  1. [§3.1] The data cut from 600 to 511 observations uses an O2 range of 209500±10000 ppm; the threshold is not clearly justified, and since O2 is itself a fitted parameter, the selection could bias the final sample.
  2. [§3.1.2] The statement that the normalisation parameters 'did not prove to significantly alter the results' is not accompanied by a quantitative check; a brief sensitivity table would support this claim.
  3. [§3.2.4] The sentence reporting the extrapolation errors (0.25% for CO2, 0.6% for CH4) immediately notes that these errors are not propagated; the paper would benefit from a propagation or at least a sensitivity range in the reported uncertainties.
  4. [Abstract] There are several typographical issues in the abstract, such as 'Astroclimescan' and 'therently'; these should be corrected throughout the manuscript.
  5. [Figures 6–9] The BIC comparisons in the figure captions are hard to parse; BIC is a relative measure, so a horizontal line 'centred on 0' is an unusual baseline and the caption should state the two competing models explicitly.
  6. [Eq. (6)] The Rayleigh scattering expression mixes a wavelength in micrometres with an exponent involving λ; please specify the units of λ in this equation.
  7. [§3.3] Using the 800 hPa pressure level for surface pressures of 770–790 hPa introduces a small systematic; please state the approximate magnitude of this effect.

Circularity Check

1 steps flagged · score 2.0 of 10

One minor self-referential step in the CAMS vertical-offset validation; the core retrieval is otherwise self-contained.

  1. other [Section 4.3, CAMS comparison and vertical shift; Figures 10 and 11 captions]
    "Therefore, when comparing our results with the CAMS reanalysis data, we apply a shift to our retrieved values based on c0 values obtained in the long-term trend fitting function. ... the Astroclimes values have been vertically shifted to match the CAMS data based on the c0 values obtained in the long-term trend fitting function described in the text."

    The vertical offset added to Astroclimes before the CAMS comparison is computed from the same Astroclimes-versus-CAMS long-term trend fit, so the post-shift comparison cannot independently validate the absolute abundance level. The comparison remains informative for the long-term trend slope and for scatter, because a constant shift does not change the shape of the series or the BIC comparison, but it is circular to present the shifted match as benchmark confirmation of the offset itself. This is a validation-level circularity, not a by-construction reduction of the central retrieval.

full rationale

The central retrieval chain is self-contained: Astroclimes builds model telluric spectra from HITRAN cross-sections, atmospheric profiles, and a Beer-Lambert radiative transfer model, then fits the observed CARMENES spectra via an MCMC likelihood, retrieving ground-level abundance scaling factors and deriving column-averaged DMFs from the scaled profiles. No equation in this chain is defined in terms of the final abundance values, and the paper does not rename a known empirical pattern as a new prediction. The SKYCALC benchmark is an independent external check: Astroclimes reproduces the reported PWV values from model spectra before any comparison with CAMS. The GGG2020 profile-shape scaling is a genuine assumption and a correctness risk, but the paper explicitly acknowledges the extrapolation error and does not claim to constrain profile shape; a single scaling factor does not make the fitted trend equal to the prior trend by construction. Self-citations to Gandhi & Madhusudhan and Brogi & Line are methodological and not load-bearing circularity. The only notable circular element is the vertical-shift correction: the offset applied before the CAMS comparison is derived from the same CAMS-referenced trend fit, so the absolute-level agreement is not an independent validation. Because the long-term trend and scatter claims are shape-based and unaffected by a constant shift, the overall circularity is minor, giving a score of 2.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The central retrieval adds four fitted scaling factors per observation plus two ad hoc vertical offsets. The most fragile premise is that the prior profile shapes are exact and only amplitudes are fitted. No invented physical entities are introduced.

free parameters (6)
  • CO2 ground-level abundance scaling factor = Posterior median per observation (not tabulated)
    The MCMC free parameter scaling the GGG2020 CO2 profile; central to the retrieval.
  • CH4 ground-level abundance scaling factor = Posterior median per observation (not tabulated)
    Same as CO2 for methane.
  • H2O ground-level abundance scaling factor = Posterior median per observation (not tabulated)
    Water vapor retrieval, validated against SKYCALC PWV values.
  • O2 ground-level abundance scaling factor = Posterior median per observation (not tabulated)
    Used for column-averaged DMF normalization and for quality cuts.
  • Vertical shift for CO2 = 14 ppm (ground), 15 ppm (column)
    Post-hoc offset derived from c0 of the long-term trend fit to CAMS; without it retrievals are consistently overestimated.
  • Vertical shift for CH4 = 42 ppb (ground), 7 ppb (column)
    Same as CO2, derived from the long-term trend fit to CAMS.
assumptions (6)
  • domain assumption HITRAN line lists and the computed cross-section grid are accurate for Earth's atmosphere.
    All model spectra rely on these cross-sections (Section 2.1). Inaccuracies would bias retrieved abundances.
  • ad hoc to paper The vertical shape of each molecular abundance profile is known from GGG2020 and only its amplitude is free.
    Section 4 states profiles are multiplied by a single scaling factor per molecule. This is the central modeling assumption.
  • domain assumption Telluric standard stars have negligible spectral features after normalization.
    O/B/A stars are selected for this reason (Section 3.1); residual stellar lines are handled by rejection, not modeling.
  • domain assumption CAMS EGG4 reanalysis is an adequate reference for retrieved abundances.
    Used as benchmark in Section 4.3; the model itself has no reported uncertainties.
  • domain assumption The ESO Sky Model correctly predicts the positions of telluric emission lines.
    Emission line masking in Section 3.1.3 uses SKYCALC spectra with a fixed template.
  • standard math The Beer-Lambert law and the adopted Rayleigh, aerosol and CIA parameterizations describe NIR telluric absorption.
    Equations (1) to (10) in Section 2.1.

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Cite this review

Pith. "Pith review of Astroclimes -- measuring the abundance of CO$_2$ and CH$_4$ in the Earth's atmosphere using astronomical observations." pith.science (2026). https://pith.science/paper/PCEWT7RM

@misc{pith2026250910258,
  author       = {Pith},
  title        = {Pith review of: Astroclimes -- measuring the abundance of CO$_2$ and CH$_4$ in the Earth's atmosphere using astronomical observations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PCEWT7RM}},
  note         = {Machine review of arXiv:2509.10258}
}
abstract

Monitoring the abundance of greenhouse gases (GHGs) such as carbon dioxide (CO$_2$) and methane (CH$_4$) is necessary to quantify their impact on global warming and climate change. Although a number of satellites and ground-based networks measure the total column volume mixing ratio (VMR) of these gases, they rely on sunlight, and column measurements at night are comparatively scarce. We present a new algorithm, Astroclimes, that hopes to complement and extend nighttime CO$_2$ and CH4 column measurements. Astroclimes can measure the abundance of GHGs on Earth by generating a model telluric transmission spectra and fitting it to the spectra of telluric standard stars in the near-infrared taken by ground-based telescopes. A Markov Chain Monte Carlo (MCMC) analysis on an extensive dataset from the CARMENES spectrograph showed that Astroclimes was able to recover the long term trend known to be present in the molecular abundances of both CO$_2$ and CH$_4$, but not their seasonal cycles. Using the Copernicus Atmosphere Monitoring Service (CAMS) global greenhouse gas reanalysis model (EGG4) as a benchmark, we identified an overall vertical shift in our data and quantified the long term scatter in our retrievals. The scatter on a 1 hour timescale, however, is much lower, and is on par with the uncertainties on individual measurements. Although currently the precision of the method is not in line with state of the art techniques using dedicated instrumentation, it shows promise for further development.

Figures

Figures reproduced from arXiv: 2509.10258 by the authors.

Figure 1
Figure 1. Top: flight path of the weather balloon. Locations of the Calar Alto Observatory, the city of Jaén (launch site) and the municipality of Águilas (crash site) are indicated by the black cross, the black square and the black star, respectively. Map scale is shown on the bottom left corner. Bottom: altitude variation throughout the flight. Symbols indicate the altitudes of the aforementioned sites. The green circles co… view at source ↗
Figure 2
Figure 2. Atmospheric profiles for each of the observations taken with CARMENES on the weather balloon night. Pressure, temperature, CO2 abun￾dance and H2O abundance are shown as a function of height in blue, orange, green and pink, respectively. The profiles are obtained by combining site data, GDAS data and MIPAS data using the simple site merging approach and smoothing out the transition between profiles. contributions of … view at source ↗
Figure 3
Figure 3. Atmospheric profiles for the entire CARMENES data set, generated with ginput. Pressure, temperature, CO2 abundance and H2O abundance are shown as a function of height in blue, orange, green and pink, respectively. 2019; Agustí-Panareda et al. 2023). This dataset is produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) and can be accessed from the CAMS Atmosphere Data Store (ADS; Agustí-Panareda … view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Top: ground level H2O abundance at the night of April 24th 2023 as retrieved by the four different scenarios investigated in the atmospheric profile study carried out in this work: simple site merge, no balloon data (orange); simple site merge, with balloon data (green…
Figure 5
Figure 5. Figure 5: Top: best-fit model telluric transmission spectra computed with Astroclimes (blue) and observational spectra from CARMENES (grey) for a portion of the CARMENES order 25, which contains CO2 lines. Bottom: residuals plot (observed spectra minus model spectra). The RMSE v…
Figure 7
Figure 7. Figure 7: Same as [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Same as [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]
Figure 9
Figure 9. Figure 9: Same as [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 10
Figure 10. Figure 10: Ground level DMFs retrieved by Astroclimes (blue) and from the CAMS global greenhouse gas reanalysis model (orange) for CO2 (top), CH4 (middle) and H2O (bottom), the latter which also shows the humidity measurements taken by the CAHA weather station (green), converted…
Figure 11
Figure 11. Figure 11: Column-averaged DMFs retrieved by Astroclimes (blue) and from the CAMS global greenhouse gas reanalysis model (orange) for CO2 (top) and CH4 (bottom) and H2O (bottom). The Astroclimes values have been vertically shifted to match the CAMS data based on the 𝑐0 values ob…
Figure 12
Figure 12. Figure 12: Ground level DMFs retrieved by Astroclimes (blue) for the ob￾servations of HR5676 on the night of April 24th 2023. The black points correspond to the mean of the retrievals binned by hour, whose uncertainties are the standard deviation of the retrievals in said bin. T…

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.