the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Aug 2025
Constraining urban CO2 emissions in Seoul using combined ground and satellite observations with Bayesian inverse modelling
Abstract. Accurate carbon emission estimates are essential for guiding climate action toward net zero emissions by 2050. The Bayesian inverse method, combined with atmospheric CO2 measurements and the transport model, can serve as an independent verification approach to improve accuracy. In this study, we developed a Bayesian inverse modelling framework using ground- and space-based measurements and applied it to Seoul to test the framework and constrain its CO2 emissions. By leveraging the high temporal resolution of ground-based in situ observations and the broad spatial coverage of satellite data, we improved the accuracy of emission estimates. Our results indicate a 4.43 % increase in posterior emissions compared to prior estimates, suggesting that the prior emissions were slightly underestimated. The spatiotemporal variability of posterior emissions increased significantly, enabling us to track CO2 fluctuations and assess the impact of carbon reduction policies over time and space. Additionally, the mean absolute error was reduced, improving the agreement between simulated and observed CO2 enhancements. We thoroughly investigated the performance of the inverse model through a sensitivity analysis that considered different observational network configurations. The most substantial reductions in uncertainties (19.2 %) were observed when all available observations were used. The extensive coverage of satellite observations enabled further corrections in areas not covered by ground observations. Overall, this study highlights the importance of combining multiple observational sources to better constrain urban CO2 emissions. The framework also shows strong potential for application in other cities and can support the development of effective climate mitigation policies.
- Preprint
(11379 KB) - Metadata XML
-
Supplement
(1016 KB) - BibTeX
- EndNote
Status: open (until 14 Oct 2025)
- RC1: 'Comment on egusphere-2025-3367', Anonymous Referee #1, 27 Aug 2025 reply
-
RC2: 'Comment on egusphere-2025-3367', Anonymous Referee #2, 18 Sep 2025
reply
Review of “Constraining urban CO2 emissions in Seoul using combined ground and satellite observations with Bayesian inverse modelling” by Sim and Jeong, 2025.
This study presents an atmospheric inverse modelling framework to estimate fossil fuel CO2 emissions in Seoul megacity. The study is timely and well within the scope of ACP, addressing the critical need for independent verification of urban CO2 emissions. The main novelty is the combined assimilation of both in situ and satellite observations within an urban-scale inversion. The framework is clearly described and general enough to be of value for the design of other urban inversion systems. The inversion is applied for the month of December 2021, finding a slight increase in total CO2 emissions compared to the prior ODIAC inventory. While the study is generally well-executed, several aspects would benefit from further clarification or discussion. In particular, the suitability of the background definition, the potential influence of other model errors, and a more detailed interpretation of the posterior flux estimates would provide stronger support for the conclusions. Nonetheless, the study demonstrates sufficient merit as a proof-of-concept of the inversion framework. I therefore recommend publication after minor revisions, provided the authors address the following comments.
General comments:
- Background definition: The definition of the background for both in situ and satellite observations is a critical component of the urban-scale inversion. At present, no clear justification for the chosen background definitions is provided, making it difficult to assess their validity. For the in situ sites, the background is defined as the 5th percentile within a 24-hour moving window. Could the influence of biospheric uptake within the domain, or periods when the wind direction shifts within the window, bias this estimate compared to the true background? For satellite observations, the background value is defined as the daily median XCO2 over non-urban areas within a 500,000 km2 domain centered on Seoul. It is not immediately obvious whether this definition is appropriate. Given the spatial pattern of the posterior flux corrections, it seems plausible that these patterns may, at least in part, reflect biases in the background definition. A short analysis demonstrating why the chosen background is suitable, or even a sensitivity test using an alternative background, would help in evaluating the robustness of the results.
- Sensitivity tests: The overall analysis of inversion model performance is somewhat limited. The robustness of the inversion results could be improved by additional sensitivity tests, especially including variations in background concentrations, the influence of biospheric fluxes, prior uncertainties, and transport model uncertainties. I do not view a full suite of new experiments as strictly necessary for publication, but at a minimum the manuscript should include more explicit acknowledgement and discussion of how these potential sources of error could affect the inversion results. Without these sensitivity tests, the strength of the conclusions is limited and should not be overstated.
- Study period: The study focuses only on the month of December 2021. The rationale for selecting such a short study period is not sufficiently justified in the current manuscript. The short study period limits the strength of the conclusions and frames the work more as a proof-of-concept.
- Interpretation and policy relevance: The inversion finds a slight increase in net emissions relative to the prior ODIAC inventory, along with an interesting spatial pattern of flux corrections. The manuscript would benefit from a brief discussion of plausible emission sources, processes, or model errors that could explain these differences. Without such context, it is difficult to conclude whether the posterior flux estimate represents a real emissions signal or could instead reflect model biases e.g., a background or transport model error. It would also be useful to expand the conclusions to discuss what the results could mean for current mitigation efforts in Seoul.
Specific comments:
- Line 126-129: Include an explanation for why only the month of December 2021 was selected for analysis.
- Line 147-152: A general description of the typical availability of OCO-2 and OCO-3 observations over Seoul (or urban areas in general) would add useful context. For instance, is December 2021 representative in terms of data coverage, or was this a favorable period for high-quality retrievals? Since the study emphasizes the utility of satellite observations and the potential extension of the framework to other urban areas, providing this information would help readers assess how broadly the approach might be applied.
- Line 164-167: A thorough explanation and justification for the background is required.
- Line 170-191: Some aspects of the transport model configuration need clarification. In the case of WRF-STILT: Are the particles released at the same height as the measurement inlet? Is 24 hours sufficient for particles to leave the modeled domain? What is the vertical output layer of the footprints (surface layer, PBL, or other)? In the case of WRF-XSTILT: How are the averaging kernel and a priori profile applied? What happens above 6000 m a.g.l.?
- Line 170-191: How do modeled meteorological fields compare with observations? Even small biases (e.g., wind speed or planetary boundary layer systematically higher/lower) could strongly influence the inversion results. This information appears in the supplement, but this should be discussed in the main text.
- Line 223: “the daytime (1-7 UTC) emissions for December 2021 serve as the state vector”. Throughout the text, it would be worth acknowledging the full diurnal cycle of fluxes, and some of the limitations of the current approach for constraining net CO2 For example, morning and evening commuter rush hours are not included in the state vector and therefore not optimized. To what extent does this reduce the ability to constrain net CO2 emissions?
Technical corrections:
- Line 1: Consider revising “urban CO2 emissions” to “urban fossil fuel CO2 emissions” to avoid ambiguity, since the current phrasing could also include biospheric fluxes.
- Line 8: Define on first use: “CO2” to “carbon dioxide (CO2)”.
- Line 8: “the transport model” to “a transport model”.
- Line 10: Again, would be useful to specify fossil fuel CO2
- Line 15: “mean absolute error was reduced” to “mean absolute error between simulated and observed CO2 enhancements was reduced”.
- Line 17: “The most substantial reductions in uncertainties (19.2%) were observed when all available observations were used”. This goes without saying. It would be more insightful to report the uncertainty reduction for the different observational network configurations in the abstract, so that the contribution from both in situ and satellite observations is clear at the outset.
- Line 55: Drop “the” before “large point sources”.
- Line 63-64: “the uncertainty reduction” to “uncertainty reductions”.
- Line 92: Suggest writing “0.01° (approximately 1 km)” for easier interpretation.
- Line 114: I recommend replacing the italic bold “Error reduction” in the equation with a defined variable e.g., “The uncertainty reduction (UR) is defined as…”.
- Line 149: “volume mixing ratio” to “mole fraction”.
- Figure 3: Can the unit also be displayed on the colorbar. Also make sure that all colormaps used in the paper are colorblind-friendly.
- Line 223: Ensure consistency for reported times, either use a single time zone throughout the text or provide both UTC and KST.
- Line 264-266: Phrasing is incorrect “the time/distance at which errors in the prior emissions are considered uncorrelated”. Technically, correlation at the e-folding correlation lengths is non-zero.
- Figure 7: Please add units to Fig. 7b (ppm?).
- Figure 8: It is very hard to see any difference between Fig. 8a and Fig. 8b.
- Line 420: The provided URL https://db.cger.nies.go.jp/dataset/ODIAC does not appear to be active.
- Line 499: “IPCC: GLOBAL WARMING OF 1.5°C an IPCC special report on the impacts of global, Ipcc, 2018”. Incorrectly formatted reference.
Citation: https://doi.org/10.5194/egusphere-2025-3367-RC2
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 718 | 40 | 12 | 770 | 31 | 12 | 12 |
- HTML: 718
- PDF: 40
- XML: 12
- Total: 770
- Supplement: 31
- BibTeX: 12
- EndNote: 12
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Review for the manuscript “Constraining urban CO2 emissions in Seoul using combined ground
and satellite observations with Bayesian inverse modelling"
egusphere-2025-3367
General comments
This study presents a Bayesian inversion framework that integrates ground-based and satellite observations of atmospheric CO2 to optimize ODIAC’s emissions estimates over Seoul. The manuscript is clearly written, and the scientific results are well structured and thoughtfully explained. The work has strong scientific merit, given both the timeliness of the topic and the unique characteristics of Seoul as a valuable testbed—its city size, dense observational network, and active engagement of local government and universities. The inclusion of sensitivity analyses to assess uncertainty enhances the credibility and robustness of the findings.
The overall impact of the study could be strengthened with a few additional analyses or clarifications. In particular, it would be useful to (1) relate daily fluctuations in posterior CO2 emissions to specific events or emission cycles in Seoul during the study period, and (2) provide possible explanations for why ODIAC may systematically underestimate or overestimate emissions in different parts of the city. Addressing these points would add depth to the interpretation and broaden the study’s relevance. Further details on these suggestions are provided below.
Specific Comments
Line 15: The sentence “Additionally, the mean absolute error was reduced, improving the agreement between simulated and observed CO2 enhancements.” is somewhat redundant, as the inversion framework is designed to reduce the mismatch between observed and simulated CO2 enhancements. I recommend either removing the sentence or strengthening it by providing quantitative details (e.g., “the mean absolute error decreased by X%, indicating improved agreement”). This would make the point more informative and impactful.
Graphical abstract: Consider adding labels or legends to the three maps within the Footprint box. From context, I assume they represent footprints for ground, OCO-2, and OCO-3 observations, but making this explicit would help readers quickly interpret the figure.
Line 41: There appears to be a minor typo in the reference to (40 Cities, 2022)—please double-check the citation format.
Lines 46–57: It would strengthen the discussion to highlight the importance of self-reported emissions inventories by global cities. For most cities, these self-constructed inventories form the basis of climate action planning and emissions mitigation policies. For example, the Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC) provides the standard framework adopted by many C40 cities. Yet, discrepancies have been reported between GPC-based inventories and bottom-up datasets such as EDGAR and ODIAC. Presenting this context would help situate the study within the broader policy landscape. In particular, framing the comparison as simply “bottom-up vs. top-down” may be too simplified. I suggest expanding this section to include how self-reported inventories are currently used by cities, and how ground & satellite-based top-down approaches can provide complementary value for monitoring, validation, and policy assessment.
Line 133: Please clarify whether the reported altitude values are given above ground level or above sea level.
Figure 1: This is a clear and effective figure. To further enhance it, I suggest including altitude information either directly on the map or in the caption. In addition, it may be helpful to add a box or inset indicating the “background” domain, so readers can easily visualize its scale relative to the city.
Figure 2: This is another strong figure. It would be particularly insightful to add a panel showing the daytime variability of ground-based CO₂ mixing ratios (e.g., 10–16 LT) at each station, plotted as hour versus CO2 concentration. Such a panel would highlight how surface CO₂ concentrations evolve during the day in a megacity like Seoul. An accompanying paragraph in the results or discussion, describing the observed daytime variability and its relationship to planetary boundary layer dynamics and diurnal emission cycles, would add depth. In panel (b), you might also consider marking the five ground stations with yellow dots so that readers can immediately connect the measurements shown in panel (a) with their geographic locations.
Lines 151–152: The statement “In both datasets, the XCO2 values were higher in the southern part of Seoul compared to the northern part” would benefit from some explanation. Could you suggest possible reasons or hypotheses for this observed north–south gradient (e.g., spatial patterns in emissions or transport influences)? Providing context here would strengthen the interpretation.
Lines 164–166: The choice of using 24-hour moving 5th percentile values for background determination may need additional justification, especially since the analysis itself focuses only on 10–16 LT. What assumptions underlie this approach? Including a rationale—or, if possible, a sensitivity analysis using alternative background definitions—would increase the robustness of the results.
Lines 166–167: A similar point applies to the use of the median value within a ~500,000 km² box. Given that Seoul itself is ~600 km², the chosen background domain is quite large. More explanation is needed as to why this choice is appropriate, and ideally, some assessment of its implications. Since background definition is widely recognized as a challenge in urban inversion studies, adding further description or sensitivity testing here would significantly strengthen the scientific merit of the work. As noted above, including this background box in Figure 1 (as an inset) would also help readers visualize its relative scale.
Line 186: In the sentence “For WRF-STILT, one thousand air particles were released from each observation site and tracked backward in time for 24 h (Fig. 3a),” please specify how frequently WRF-STILT was run for each site (e.g., hourly? or once per day?). This detail will improve reproducibility and interpretation.
Figure 3: Okay, now I see each panel is for ground, OCO-2, and OCO-3! To further improve clarity, I recommend adding the representative times for each panel. For example, panel (a) could represent average daytime conditions during December 2021, panel (b) 13:00 LT on December 4, 2021, and panel (c) 11:00 LT on December 5, 2021. Explicitly including this information would make the figure more self-explanatory.
Lines 220–221: Returning to my earlier comment on Figure 2, while I understand that diurnal CO2 variability is not the primary focus of this study, a brief discussion of hourly CO2 changes would add valuable context. Insights derived from ground observations—such as the influence of traffic, heating, or boundary layer growth—would broaden both scientific and policy relevance of the results, connecting the inversion framework to real-world urban dynamics.
Figure 4. In panel (a), there appears to be a dark red pixel which I would assume as the location of a power plant, correct? If so, it would be nice to have a label identifying the power plant on the figure. In addition, I recommend including a note in the caption describing how the power plant location is represented in the original ODIAC dataset. This additional detail would not only improve figure readability but also provide useful context for future studies that make use of ODIAC in the Seoul region.
Line 312: It would be very helpful to translate the reported emissions into mass units on a monthly scale (e.g., million metric tons of CO2 per month). How does your estimate compare to previous studies that estimated Seoul’s CO2 emissions using ground and/or satellite observations? Presenting the results in these terms would provide readers with an intuitive reference point and allow for easier comparison with other inventories or policy-relevant metrics.
Figure 5. Consider adding vertical lines or shaded area to indicate the weekends in panel b.
Line 327-328 The phrase “reflecting a more realistic pattern” could be made more objective. For example, you could refer to temporal variability (1sigma, min, max) from observation-constrained vs. ODIAC/TIMES.
Lines 328-329: As currently written, the text suggests that posterior emissions fluctuate only until December 22, whereas they appear to vary throughout the entire month. If the intent is to emphasize the increase observed during December 27-29, I recommend rephrasing to state that emissions during these days exceed the typical rage of variability (for example, outside of two standard deviations of the monthly fluctuations).
Lines 335-338: The negative correction (i.e., reduced posterior emissions) seems to be centered around the OLP site. It would strengthen the discussion to include a short description of the characteristics of the eastern part of Seoul compared with other areas of the city. For instance, is this region more residential versus commercial, or less of a business district? Providing this context could help explain why ODIAC may underestimate emissions in this particular area. More broadly, it would be useful to reflect on the conditions under which nighttime light imagery—used for ODIAC’s spatial allocation—tends to perform well, and when it may introduce biases. This would add depth to the interpretation and provide guidance for future applications of ODIAC in urban settings.
Figure 7. This is more of a question than a direct comment, as I am not an expert in inversion frameworks. Why is it that the slope between modeled (MOD) and observed (OBS) values in the posterior cannot be optimized to 1 (i.e., perfectly fitting MOD to OBS on the average scale)? Is this related to the configuration of the error covariance matrices—for example, if the uncertainty assigned to prior emissions is relatively strict compared to the observational uncertainties? If so, does this imply that if the inversion were configured to allow a perfect match between MOD and OBS, the resulting posterior emissions would be higher than the current estimates? Including a few sentences in the text to briefly address this point would be helpful for readers who may share this question.
Figure 8. There’s typo in the caption. (b) appears twice in the caption.
Section 3.3: It would be very informative to see how the emission estimates differ across the various sensitivity experiments (all data vs. ground-only vs. OCO-2 vs. OCO-3). One way to highlight this would be to include an additional figure, similar in style to Figure 5, that shows how the average emissions and their fluctuations change under each experiment. Such a comparison would make the relative contributions of ground-based and satellite observations to the inversion more straightforward and would help readers better understand the value added by each dataset.
Lines 397-399: The sentence “Although the averaged CO2 emissions difference between prior and posterior estimates was relatively small (4.43% increase), the inversion revealed significant spatiotemporal variations” may unintentionally understate the impact of the study’s findings. I suggest reframing this point to highlight the value of integrating ground- and satellite-based observations- Beyond improving overall emission estimates, this approach provides insights into spatial and temporal variations at fine scales, which carry important policy implications for urban climate action and emissions mitigation.
Data Availability: The current section appears to be missing on the availability of ground-based observations. Please include a citation for the data source and indicate whether these observations are publicly available (and if so, how they can be accessed).