| 18 Mar 2026
Can radon help to improve methane emission estimates? Results from a dual-tracer inversion
Abstract. A major source of uncertainty in inverse modelling of greenhouse gas emissions are deficits in atmospheric transport models, in particular in the description of vertical mixing within the planetary boundary layer (PBL). The properties of radon-222 (Rn) makes it a suitable natural tracer for vertical mixing in the PBL. When comparing the CH4 model-data mismatch (MDM), i.e. the differences between the observed and modelled CH4 concentrations, with the MDM of Rn, we found substantial correlations for several observation sites in central Europe in 2021 (the median CH4-Rn MDM correlation coefficient is 0.6), indicating that a large part of the CH4 and Rn MDM variability can be explained by common errors in the simulated (vertical) transport. We aim to exploit this information in a joint inversion for CH4 and Rn by taking into account prior uncertainties and making use of the fact that the transport model error is correlated between the two gases. We use simultaneous CH4 and Rn observations from 17 sites across central Europe in 2021. The dual-tracer CH4-Rn inversion yields lower CH4 fluxes in several countries covered by the observation sites compared to a single-tracer CH4-only inversion without Rn information. The differences in country-total CH4 fluxes between the dual-tracer and single-tracer inversions are on the order of a few percent and depend on the assumed uncertainty for the Rn prior fluxes. These findings underscore the importance of accurate Rn flux maps for fully leveraging the dual-tracer approach and enhancing the reliability of CH4 flux estimates.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
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Review of “Can radon help to improve methane emission estimates? Results from a dual-tracer inversion”
This manuscript aims to improve the reliability of CH₄ inversions through the inclusion of radon observations. The inversions are performed within the CarboScope framework, which is based on STILT footprints (at least in its standard configuration) and uses an iterative minimization of the cost function with a conjugate-gradient algorithm. The inversions are constrained by observations from 17 in-situ stations across Europe.
The authors use radon as an additional tracer to better characterize transport errors, particularly errors associated with vertical mixing within the planetary boundary layer. This is achieved by introducing correlations between CH₄ and Rn uncertainties in the observational uncertainty covariance matrix within a dual-tracer (CH₄–Rn) inversion framework. The model-data mismatches (MDMs) are estimated from prior model simulations and so-called “leave-one-out” runs.
The study further includes extensive sensitivity analyses, investigating different radon flux models, soil-moisture assumptions, and prior uncertainty settings for radon emissions. The authors discuss both the potential and the limitations of the proposed approach in a comprehensive and transparent manner.
The results indicate that including radon information in dual-tracer inversions generally leads to a smaller increase in CH₄ emissions over Central Europe compared to corresponding single-tracer CH₄ inversions.
Overall, this is an excellent study addressing one of the most important challenges in atmospheric inversions currently: transport uncertainty. Transport errors remain a major limitation for current greenhouse-gas inversions, and the presented work represents an interesting, feasible and scientifically sound contribution towards addressing this issue. The manuscript is comprehensive, methodologically rigorous, clearly written, and provides valuable insights. The authors build upon the well-established CarboScope framework and present a carefully designed experimental setup. I strongly recommend publication after a few minor issues have been clarified.
General comments
1. Lack of independent validation
One aspect that I find missing is an independent validation of the inversion results. As presented, it is difficult to assess whether the dual-tracer inversions actually provide more accurate CH₄ flux estimates than the single-tracer inversions. I fully acknowledge that obtaining an independent validation is challenging in practice. Nevertheless, would it be possible to compare prior and posterior MDMs at stations that are not assimilated in the inversion (at least during periods when the transport model is expected to perform reasonably well)? While such an analysis would still have limitations, it could provide at least some indication of whether the dual-tracer approach improves predictive skill.
2. Adjustments in regions with limited observational coverage
I was surprised by the sometimes substantial flux adjustments occurring in regions with sparse observational constraints, particularly over the Iberian Peninsula and parts of the Balkans. Do the footprints of the assimilated stations provide sufficient sensitivity to these regions to support such adjustments? I see similar behaviour occasionally in other inversion systems as well, something I never fully understood. It would be helpful if the authors could discuss this point and perhaps provide the observational sensitivity of the footprints in these regions.
3. Clarification of the leave-one-out experiments
It is not entirely clear to me whether the leave-one-out experiments are performed separately for each inversion setup or only once and subsequently reused across all inversion experiments. Based on the discussion around line 486 and following, I infer that they are repeated for each setup. Otherwise, it is difficult to understand how changes in the assumed prior uncertainty of radon emissions could lead to larger Rn MDMs that subsequently propagate to CH₄ through the cross-correlated uncertainties. I suggest clarifying this explicitly in the manuscript.
4. Definition of the observational covariance matrix in the dual-tracer inversion
Another point that is not entirely clear to me concerns the treatment of the diagonal elements of Qm. Do the diagonal variances remain identical in the dual-tracer inversion and the corresponding single-tracer inversion? If so, introducing the off-diagonal covariance terms effectively increases the total observational uncertainty represented by Qm, which would generally tend to reduce the strength of the inversion adjustments. Could part of the differences observed, for example for Germany in Fig. 6, be explained by this effect rather than by the transport-error information carried by radon itself?
Specific comments
Figure S2
It is somewhat surprising to me that several stations exhibit a substantial number of observations with strongly negative MDMs at very low mixing heights. Based on the common tendency of models to overestimate vertical mixing, I would have expected predominantly positive MDMs under such conditions.
This behaviour is particularly noticeable at WAO and MHD. For these coastal stations, could wind-direction errors also play an important role in addition to vertical mixing errors? For example, under low-wind conditions, especially during nighttime, the model may incorrectly advect emissions from the continent towards the station, whereas the station may in reality sample relatively clean marine background air.
Furthermore, what effect has the use of the modelled mixing height rather than observed mixing heights in this analysis? How well does the modelled mixing height compare to available observations?
Line 336
In addition, during nighttime there are generally no thermal upwinds along the slopes that would transport emissions from local sources towards mountain stations.