the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Sep 2025
Uncertainty and retrieval sensitivity in TROPOMI-based methane inversions over the North Slope of Alaska
Abstract. The Arctic is experiencing unprecedented environmental changes with rapidly rising temperatures. Emissions of methane (CH4) – a potent greenhouse gas – may be increasing from the region, making accurate monitoring essential. The TROPOspheric Monitoring Instrument (TROPOMI) instrument offers high spatial and temporal coverage of CH4 column mole fractions. However, its data in the Arctic has historically exhibited seasonal and latitudinal biases and low-quality retrievals. A major challenge is the lack of ground-based validation data in high-latitude regions, which are used to improve satellite retrievals. This study evaluates inverse modelling to estimate CH4 emissions using TROPOMI measurements over the North Slope of Alaska. Using two retrieval products – the operational SRON product and the scientific WFMD product from the University of Bremen – we assess the alignment of derived emissions with surface measurement-derived inversions over 2018–2020 and test their robustness through sensitivity analyses. Our results show that tundra emissions from SRON inversions align more closely with surface measurement-derived emissions than WFMD inversions. Both TROPOMI-product derived emissions have anomalously low emissions in August 2018 compared to surface measurement-derived emissions, likely due to low data density resulting from high cloud cover. TROPOMI inversions provided stronger constraints on fugitive anthropogenic emissions compared to surface inversions. However, each retrieval produced different emission estimates, highlighting retrieval-dependent differences. Sensitivity tests revealed a strong prior dependence in both retrievals, raising concerns about robustness in northern high latitudes. This study highlights the importance of using multiple retrievals and rigorous sensitivity testing in high-latitude satellite inversions.
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Status: open (until 17 Nov 2025)
- RC1: 'Comment on egusphere-2025-4230', Anonymous Referee #1, 29 Oct 2025 reply
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RC2: 'Comment on egusphere-2025-4230', Nicole Montenegro, 14 Nov 2025
reply
Overall, the study appears to be well justified. The research gap is clearly articulated, as is the importance of investigating this topic and the substantial challenges associated with it. The use of recent observations positions the study as a meaningful and timely contribution, particularly in regions known to contain significant gaps in observational coverage and monitoring infrastructure, and which represent an important source of methane emissions.
I think it is a great contribution that the study examines uncertainty through the sensitivity of inversions to various configurations. However, I believe that these results could have been given greater prominence, especially considering that one of the stated objectives of the manuscript is precisely the quantification of uncertainty. The analysis places more emphasis on RMSE reduction than on RMSE spread, which is the quantity that more directly reflects uncertainty. I expected the manuscript to be more tightly centered on uncertainty quantification, in alignment with what the title suggests.
It is also noteworthy that the study demonstrates that, although TROPOMI observations provide a significant advancement, important observational gaps persist. The period from October to March remains essentially unobserved, suggesting that future missions could meaningfully support Arctic monitoring. Presenting the progress that can be achieved with the current observational record—while also highlighting the remaining potential—is highly valuable.
Below, I provide my comments section by section, indicating numbering at both general and detailed levels, and referring to specific lines when applicable:
0. Abstract: The abstract is concise and clearly presents the study. It effectively communicates the context, objectives, and main findings.
1. Introduction: The introduction appropriately presents the key elements that frame the research, identifies the gaps and challenges, and convincingly justifies the importance of studying the Arctic. Given that a substantial portion of the analysis focuses on tundra regions, I suggest including a land-cover map of the study domain, or alternatively a map distinguishing tundra and anthropogenic emission sources.
2.1 The work of Ward et al. (2024) is cited frequently throughout the manuscript. In several instances, the manuscript is not sufficiently self-contained, and the citation alone does not provide enough context for the reader to fully understand the methodological or conceptual point being referenced. While it is reasonable to avoid restating the entire content of Ward et al. (2024), the manuscript should nonetheless include the essential information needed for it to be understandable on its own.
Line 99: The methods section begins by citing Ward et al. (2024), followed by an explanation of the method. It is unclear whether the description corresponds directly to Ward et al. or whether it represents an adaptation or extension. A brief summary would help clarify this, as well as an explicit statement that further methodological details can be found in Ward et al. (2024).2.4 Figure 3, which presents the average footprints, is not referenced in the discussion. It would be valuable to connect the inversion results to the sensitivity patterns shown in this figure, particularly to assess how the inversions based on each product relate to sensitivities over tundra and anthropogenic regions. It would also be helpful to include the sensitivity of the BRW station in the figure.
2.6 The domain used for the inversions—particularly in the sensitivity tests—was not entirely clear. Figure 1 defines domain, region, and measurement sites; however, since multiple configurations are tested, it is important to explicitly clarify whether the same domain is consistently applied (if that is the case). It would also be helpful to explicitly state that the analysis focuses on the NSA region, even if this seems implied. Furthermore, to more directly assess uncertainty, it would have been interesting to include an experiment involving, for example, 10 perturbed prior ensembles and to evaluate the resulting spread.
In Figure 4b, the color scheme overlaps with that of Figure 1, which generated confusion. For instance, the Ward domain appears in orange in Figure 4a but in black in Figure 1.
I recommend relocating Figure 4 to the Results section, as it corresponds to the first point addressed there.
3.2.1 In this subsection, the inversions correspond to the NSA, but Alaska is used as the domain—is this correct?
3.2.2 – Figure 6 The shift between TROPOMI-OPER and BRW-based inversions is not addressed in either the results or the discussion. Although the manuscript comments on monthly matches, the phase differences are particularly striking and, in my view, merit discussion. This may even be connected to the sensitivity analysis previously conducted for TROPOMI.
3.3 – Figure 7 The legend lists “Uniform prior” twice. I assume that the solid line corresponds to the posterior of the Uniform-prior experiment and the dashed line to the prior.
3.3.4 – Lines 387–388 No Figures 9A or 9B are present.
3.6 To address uncertainty more directly, I recommend evaluating the spread of RMSE values for each TROPOMI product—that is, assessing how wide the RMSE range is across the experiments. The discussion currently emphasizes the RMSE magnitude for individual experiments and highlights improvements, but the variability among experiments is not addressed.
3.7 Tables S1 and S2 should be included in the main manuscript.
Line 435: The reference does not correspond to Figure S15; I understand that Figure S16 is intended instead.6. Conclusions Line 684: The recommendations from Tsuruta should be briefly summarized; including new citations in the conclusions is not advisable. The same applies to line 695.
Citation: https://doi.org/10.5194/egusphere-2025-4230-RC2
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- 1
1) General comments
Methane is second most important greenhouse gas in Earth's atmosphere,
and the Arctic region is a potential large source of CH4 emission.
Emission detection and monitoring is therefore an interesting and
relevant research question. The authors compare emission estimates
based on surface-only and TROPOMI based inversions using two different
retrieval products. A sensitivity study is conducted to asses the
robustness of the results. Since the article adresses a relevant
sicientific topic from an inversion modelling perspective, it
should be published after consideration of the specific comments
given below.
2) Specific comments
*) page 4, line 99: the inversion method used in this study is the same
as in Ward (2024). A few lines about the general setup of this method
would make the rest of the section easier to follow for those readers
who are not familiar with this method and don't have the time to read
the referenced paper (e.g. what is the "tuning phase" (line 115) or
"MCMC sample" (line 119), or how is the uncertainty interval (line 118)
calculated?).
*) page 5, line 108: in some models negative emissions are interpreted
as uptake instead. Why is that not considered here?
*) page 6, lines 156-158: I interpret regridding as calculating the
average of all observations falling into the gridcell of the
mentioned dimensions. This already reduces the data density of the
observations, so why is additional reduction step necessary? Would
using larger regridded "observations" without the additional
reduction step have the same effect as the current approach?
*) page 9 line 203-205: the XCH4^{model}_{pert} is apparently valid
from surface to maxlev (right-hand side of supplement eq 4 =
left-hand side of supplement eq. 7). That should be made clear in the
text as well, since now it's just "we have equation 2 and subtract
two other terms".
So these lines could be clarified by first stating why you would
need XCH4^{model}_{pert}, and then how you calculate it.
*) page 9, equation 4: if the first summation is from "1 till maxlev",
shouldn't the second one be from "maxlev+1 till n"? In other words,
which of the two summations includes maxlev? Related to that is
equation 3 and the rest of the derivation in the Supplement.
*) page 9, line 220: please clarify the CAMS dataset that you're using
and why you use an outdated version (based on the "data availability"
section, you could have downloaded one in 2023).
The problem with a "CAMS dataset" is that there are multiple datasets
providing methane estimates, see
https://ads.atmosphere.copernicus.eu/datasets?q=methane&limit=30
Neither of the datasets listed here is from 2019. The only dataset
that I found using a "v19r1" or similar versioning scheme is called
"CAMS global inversion-optimised greenhouse gas fluxes and concentrations"
for which the "Evaluation and quality assurance (EQA) reports" on the
documentation tab point to a report by Segers, not to Inness.
*) page 10, line 229: why use EDGAR v6? As you mention in section 4.1
(line 492-494), emission totals over Alaska may vary significantly
according to the version of the database being used.
3) Technical corrections
*) page 1, line 80: Since the acronym HBMCMC is used here, I would
capitalise the word "hierarchical" as well.
*) page 5, line 125: the 78W should be 78N?
*) page 6, line 156: please clarify if the regridding resolution is
latitude x longitude or the other way around? The same holds for
the other spatial resolutions mentioned hereafter.
*) Supplement page 1, eq. 6: the second summation term should have
n instead of 1 as it's upper level
*) page 19, line 387-388: there is no figure 9A or 9B.