Fire carbon emission constraints from space-based carbon monoxide retrievals during the 2019 intense burning season in Brazil

van den Berg, Anne-Wil; Hooghiem, Joram J. D.; van der Woude, Auke M.; Rijsdijk, Pieter; Vernooij, Roland; Botia, Santiago; van der Werf, Guido R.; Miller, John B.; Luijkx, Ingrid T.; Krol, Maarten C.; Peters, Wouter

doi:10.5194/egusphere-2025-6213

Preprints

https://doi.org/10.5194/egusphere-2025-6213

Preprints

05 Feb 2026

| 05 Feb 2026

Fire carbon emission constraints from space-based carbon monoxide retrievals during the 2019 intense burning season in Brazil

Anne-Wil van den Berg, Joram J. D. Hooghiem, Auke M. van der Woude, Pieter Rijsdijk, Roland Vernooij, Santiago Botia, Guido R. van der Werf, John B. Miller, Ingrid T. Luijkx, Maarten C. Krol, and Wouter Peters

Abstract. In 2019, Brazil experienced an intensive fire season, despite the absence of major climate anomalies to enhance fire activity. Existing carbon monoxide (CO) emission estimates from state-of-the-art fire emission inventories differ by a factor two for the period and region. We provide a top-down estimate on CO emissions from these fires using the new CarbonTracker Europe – Long/Short Window (CTE-LW/SW) inverse modelling framework driven by column-averaged dry air CO mole fractions (X_CO) from the MOPITT and TROPOMI satellite instruments.

Our analysis indicates that the 2019 fires in Brazil released approximately 47 TgCO. Although structural atmospheric-chemistry related uncertainties remain (±15 TgCO), the inversions converged strongly to a common posterior (46–48 TgCO) independent of the prior emission inventory (GFED5.1, GFAS v1.2) or assimilated dataset (TROPOMI, MOPITT).

Posterior fire emissions closely resemble the newly released GFED5.1, supporting recent advances in bottom-up fire modelling. Nonetheless, at the biome level, our results reveal a systematic underestimation—by roughly a factor of two—in the Cerrado and Caatinga savannas relative to both fire emission priors. While more targeted uncertainty assessments are required, we speculate that this emission gap unlikely stems from inversion choices alone and may indicate an underestimation of fuel loads or emission factors.

Overall, we demonstrate CTE-LW/SW effectively leverages X_CO to complement existing fire emission monitoring capacities at increasingly fine spatial resolution—a capability that is especially valuable in Brazil, where different fire regimes occur in close proximity and fire activity has intensified in recent years.

Received: 12 Dec 2025 – Discussion started: 05 Feb 2026

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

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Journal article(s) based on this preprint

06 Oct 2026

Fire carbon emission constraints from space-based carbon monoxide retrievals during the 2019 intense burning season in Brazil

Anne-Wil van den Berg, Joram J. D. Hooghiem, Auke M. van der Woude, Pieter Rijsdijk, Roland Vernooij, Santiago Botía, Guido R. van der Werf, John B. Miller, Ingrid T. Luijkx, Maarten C. Krol, and Wouter Peters

Atmos. Chem. Phys., 26, 13929–13958, https://doi.org/10.5194/acp-26-13929-2026,https://doi.org/10.5194/acp-26-13929-2026, 2026

Short summary

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-6213', Anonymous Referee #1, 26 Feb 2026

This manuscript investigates the 2019 Brazilian fire season under non-extreme climate conditions using a two-step CTE-LW/SW inversion framework constrained by TROPOMI and MOPITT XCO observations. The topic is scientifically relevant and timely. The inversion design is carefully structured, and several sensitivity experiments are conducted to assess robustness. The study provides valuable insights into regional fire CO emissions and bottom-up inventory discrepancies.
The manuscript is generally well organized and supported by data. However, several methodological aspects require further clarification and quantitative support before publication.
Major Comments
1. Identifiability of the Two-Step Inversion Framework
The manuscript applies LW to optimize background CO and SW to optimize fire emissions. However: How does the LW step avoid absorbing part of the fire signal?
Does LW optimization alter OH or NMVOC production patterns regionally?
Could the SW step compensate for residual background bias?
The manuscript mentions a remaining ~20 ppb positive bias in the free troposphere but argues it does not dominate SW results. This claim requires quantitative sensitivity tests. Please clarify information flow and provide supporting diagnostics.
2. Justification of the XCO ≥125 ppb Filtering Threshold
The chosen threshold is used to isolate fire plumes, but: How sensitive are total emissions to this threshold? Could low-intensity or diffuse fires be excluded? Please provide emission estimates under different thresholds (e.g., 100, 125, 150 ppb). Also include statistics of excluded observations (<125 ppb) to demonstrate that the filtering does not bias total fire emissions.
3. Observation Error Inflation Strategy
TROPOMI errors are inflated (transport error doubled + 4 ppb added) to achieve χ² ≈ 1. While common in data assimilation, further clarification is needed:Show χ² distributions before and after inflation (not only the mean). Explain how the “×2 + 4 ppb” parameters were chosen. Provide sensitivity tests to alternative inflation factors. Importantly, TROPOMI native uncertainty (~3 ppb) is much smaller than MOPITT (~7 ppb). Inflation changes their relative weighting. Please discuss whether the reported convergence between TROPOMI and MOPITT inversions is partly influenced by this weighting adjustment.
4. CO-to-C Conversion Assumption
CO emissions are converted to carbon emissions using a fixed CO/CO₂ ratio. However, emission factors vary by fuel type and biome. Please provide sensitivity analysis (e.g., ±20% in CO/CO₂ratio). Quantify the resulting uncertainty in carbon estimates. Clarify the limitations of using CO-only inversion for total carbon quantification.
5. Underestimation over Cerrado/Caatinga
The manuscript reports posterior emissions nearly twice the inventory values in this region. While fuel pool explanations are discussed, the attribution remains qualitative.
Please consider: Showing spatial posterior/prior ratios. Providing innovation statistics by biome. Clarifying whether transport redistribution contributes to the regional signal.
Minor Comments
1.Clarify how the two-month MOPITT data gap in 2019 affects results.
2.State earlier in the methods that the system cannot constrain super-local plumes due to resolution limits.
3.Clarify the origin of NMVOC and CH₄ pre-scaling factors.
4.In the conclusions, better define the applicable scale (regional totals vs. event-scale emissions) to avoid overgeneralization.
Technical / Presentation Issues
1.The “thick semi-transparent black line” in Figure 1 is difficult to identify; please adjust visibility.
2.Provide bias and RMSE metrics for ATTO validation.
3.Add y-axis labels in Figure 5 for clarity.

Citation: https://doi.org/10.5194/egusphere-2025-6213-RC1
- AC1: 'Reply on RC1', Anne-Wil van den Berg, 30 Apr 2026
  
  Please find our response in the attached document.
  
  Citation: https://doi.org/10.5194/egusphere-2025-6213-AC1
RC2:
'Comment on egusphere-2025-6213', Ying Li, 28 Feb 2026
Constraining fire carbon emissions is essential for understanding the carbon budget and carbon cycle. This study presents satellite-based estimates of CO emissions from fires in Brazil using inverse modeling. Different inversion configurations lead to consistent posterior estimates, demonstrating the robustness of the framework. The manuscript is generally well-structured and clearly written. This study demonstrates physical consistency of fire emission monitoring through an innovative two-step inversion framework (long-window and short-window inversion), multi-source satellite data fusion, dynamic error modeling, and detailed biome-level analysis. It not only provides reliable emission estimates for the 2019 fires in Brazil but also offers important methodological references for the design of future fire monitoring systems, the improvement of emission inventories, and carbon cycle research. The manuscript is valuable and suitable for publication after moderate revisions. My comments are as follows:
Comments
The entireframework hinges on the premise that the long-window (LW) inversion can effectively optimize non-fire sources (NMVOC oxidation, CH4 oxidation, anthropogenic) to provide a pristine background for the short-window (SW) inversion, which then exclusively optimizes fire emissions. There is a risk of "aliasing," where fire signals not fully captured by the prior fire inventory could be absorbed by the LW-optimized terms (e.g., through adjustments to NMVOC production), or conversely, that residual errors in the LW background could be erroneously attributed to fires in the SW step. The authors should include a dedicated sensitivity analysis or a set of diagnostics to validate this decoupling.

What is the practical benefit of the LW inversion in this application? While correcting background concentrations is conceptually beneficial, Fig. 2b shows substantial background bias distributed across the entire domain rather than localized in fire regions, which may limit the effectiveness of the LW inversion. Please further illustrate the prior residuals before applying the LW inversion to clarify its contribution.

MOPITT data are missing between 26 June and 24 August (L203), covering roughly one quarter of the inversion period. Since Fig. 5 shows that deforestation fire emissions peak in mid-August, this gap may introduce non-negligible bias. Notably, the posterior mean is averaged across three inversions, whereas GFED5.1_MOPITT represents the prior before 24 August only. This could shift the posterior mean of deforestation fires toward the GFED5.1 prior (see Fig. A4). Please clarify this issue.

2.5.1: How are observations within the 3-day assimilation window treated, given that data are available at roughly three timesteps?

Line 248: An outlier threshold of three is standard in many inversion systems. However, for rapidly varying fire emissions, large model-data mismatches at individual timesteps may result in the rejection of valid observations. The missing peaks in Fig. 3 suggest this may be occurring. Please discuss the potential impact of the filtering strategy.

2: It would be helpful to include the spatial pattern of prior emissions to facilitate comparison between emission and concentration patterns.

Lines 404–405: Fig. 2d does not present an observation-simulation comparison. Fig. 2e shows residuals, but residuals over B1 remain substantial. Please clarify or correct the description.

The inversion domain extends beyond Brazil (Fig. 1), yet only Brazilian results are discussed. Given the significant CO fire signals in surrounding regions (Fig. 2a), how do emission adjustments in neighboring countries influence the Brazilian estimates?

Relatedly, could biases in emissions from surrounding biomes contribute to the inferred increases in the Cerrado and Caatinga? For example, Fig. 2b shows substantial underestimation over deforestation and Pantanal regions, which would likely drive emission increases during inversion. Although a biome-dependent covariance structure may limit such cross-region influence, the Amazon savanna fire region is not separated in Fig. 1.

The authors do a good job explaining the inflation of the model-data mismatch to achieve a χ² near 1. It would be useful to state the mean χ² value afterinflation, not just that it "approached one." Was it 1.05? 0.98? Providing the number adds precision.

Author presents a clear mass balance equation. I may miss it somewhere, the cost of function and configuration of the prior error covariance matrix B is not clearly described. Given that the specification of B can significantly influence the posterior results, the authors should provide a detailed explanation of its construction (e.g., variances and auto-correlations).
Citation: https://doi.org/10.5194/egusphere-2025-6213-RC2
- AC2: 'Reply on RC2', Anne-Wil van den Berg, 30 Apr 2026
  
  Please find our response in the attached document.
  
  Citation: https://doi.org/10.5194/egusphere-2025-6213-AC2

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload

AR by Anne-Wil van den Berg on behalf of the Authors (30 Apr 2026) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (19 May 2026) by Jason Cohen

RR by Anonymous Referee #1 (12 Jun 2026)

RR by Ying Li (14 Jul 2026)

Suggestions for revision or reasons for rejection

The authors have made substantial revisions in response to the first-round comments. The additions of the 100/150 ppb threshold sensitivity tests (Sect. 2.5.4), the free-tropospheric bias experiment (Appendix A3), the quantification of MOPITT data gaps (Sect. 3.3), and the 6-day rolling correlation analysis (Appendix A6) are valuable contributions that address my RC2 concerns. I am generally satisfied with the responses to my original 11 comments, particularly the new quantitative analyses for comments 1, 3, 5, 8, and 9. The remainder of this review focuses on a few issues that I noticed upon reading the revised manuscript, which I believe warrant further attention before the manuscript is finalized.

The most important issue I would like to raise is the absence of any discussion of aerosol interference on the satellite XCO retrievals. The 125 ppb XCO threshold used for fire plume selection necessarily identifies the most intense burning conditions, which are also the conditions under which aerosol optical depths are highest. Both TROPOMI SWIR and MOPITT TIR retrievals are known to be susceptible to aerosol-induced biases under high-AOD conditions, and the direction of these biases depends on aerosol type, vertical distribution, and single-scattering albedo. The manuscript applies a quality filter (TROPOMI qa_value > 0.5) and inflates the model–data mismatch (Sect. 2.5.4), but neither step specifically addresses retrieval bias caused by aerosols in dense fire plumes. The free-tropospheric bias sensitivity experiment in Appendix A3 tests the impact of a uniform 20 ppb CO bias and is informative for model-related errors, but it does not speak to whether the satellite retrievals themselves are biased under the very conditions selected for assimilation. I would appreciate seeing at least a discussion of the potential direction and magnitude of aerosol-induced biases in the TROPOMI and MOPITT XCO retrievals under high-AOD fire plume conditions. An examination of the AOD distribution associated with the assimilated (XCO ≥ 125 ppb) observations, cross-referenced with independent aerosol products such as MODIS AOD, would be ideal, but even a qualitative assessment of whether aerosol effects would lead to systematic over- or underestimation of XCO, and thus of inferred fire emissions, would materially strengthen the manuscript.

A second issue relates to the representativeness of the fire types sampled by the 125 ppb threshold. The 100/150 ppb sensitivity tests are a welcome addition and demonstrate that total fire season emissions are robust to the threshold choice (1.1 TgCO, or 2–3%). However, the authors' own conclusion that "the primary mass constraint is driven by the heavier CO enhancements" (Sect. 2.5.4) suggests that low-intensity, diffuse fires contribute little to the mass constraint. This raises a question that: do the fires excluded by the 125 ppb threshold contribute a significant fraction of total emissions? If a substantial portion of emissions originates from low-intensity smoldering or understory fires that rarely produce XCO enhancements above 125 ppb, then the threshold would systematically miss these sources. A comparison of the FRP or active fire detection distributions for assimilated versus non-assimilated TROPOMI observations, or an evaluation of what fraction of GFED5.1/GFAS prior emissions is associated with low-intensity fire types, would help clarify whether the sampled fire types are representative of the full emission spectrum.

In summary, the revision has meaningfully improved the manuscript, and the responses to my RC2 comments are substantive and well-documented. The issues raised above, particularly the aerosol interference question, are important enough to warrant further attention, but I leave the decision on the appropriate revision category to the editor.

Hide

ED: Reconsider after major revisions (27 Jul 2026) by Jason Cohen

Thank you for your revisions and detailed responses to the first round of reviews. Both reviewers acknowledge that your substantial efforts have addressed many of their technical questions.

I have now received the second-round reviews, and while both reviewers recognize your improvements, they also identify remaining issues that they believe should be further addressed. I share the view that these concerns are substantial enough to require further revision.

From my perspective, the reviewers’ concerns are grouped into three areas:

1. Observational uncertainty and retrieval biases.
Have the uncertainties in your inversion framework been fully characterized, particularly regarding the satellite XCO retrievals? They both are concerned whether retrieval uncertainties may be larger than assumed, not random, or even biased due to co‑emitted aerosols under high‑AOD conditions.

2. Representativeness of sampled fires.
While your sensitivity tests demonstrate robustness of the season‑total emissions, questions remain about whether the inversion framework can adequately constrain low‑intensity or diffuse fire emissions. Furthermore, whether the system can identify missing sources (including large fires) when they are absent from the prior. Not being able to scale a zero prior is a known limitation in this type of inversion.

3. Broader literature context.
The paper may benefit from a wider engagement with recent work on observationally constrained estimates of emissions, atmospheric composition, and radiative effects.

For these reasons, I believe that a careful revision would substantially strengthen the paper.

I look forward to seeing your revised manuscript.

Hide

AR by Anne-Wil van den Berg on behalf of the Authors (04 Sep 2026) Author's response Author's tracked changes Manuscript

ED: Publish subject to minor revisions (review by editor) (04 Sep 2026) by Jason Cohen

Thank you for your thorough revisions. I am satisfied that the substantive scientific concerns have been addressed. I would like the authors to consider a minor set of clarifications, if they believe that they would enhance the final value of the paper.

In response to reviewer concerns about small fires, you added Figure A5, demonstrating that detected fires with low emission rates only contribute a small fraction of total emissions. However, a known limitation of the inversion framework is that fires may be entirely absent from the prior (undetected fires). As you have discussed, the inversion cannot scale zero prior emissions.

Based on this, could you please think about clarifying that Figure A5 addresses the distribution of detected fires, not the broader issue of undetected fires?

Note that this is a persistent challenge, with small or obscured fires remaining a well documented limitation in the literature. For example, Cohen (2014, ERL) demonstrated that aerosol optical depth can obstruct fire radiative power detection, leading to systematic under-detection of fires. Roteta et al. (2021, PNAS) showed that small fires substantially impact burned area and fire carbon emissions in Africa. High-resolution data has revealed that MODIS-based inventories underestimate emissions in sub-Saharan Africa (Geomatica, 2025).

If you feel that any of this is relevant, or you have other related works, please feel free to provide further context for this limitation. This is purely at your discretion.

This is a minor clarification, and is expected to not take much time.

Please also note that the review file validation staff have also sent a separate notification regarding formatting requirements.

Hide

AR by Anne-Wil van den Berg on behalf of the Authors (11 Sep 2026) Author's response Author's tracked changes Manuscript

ED: Publish as is (20 Sep 2026) by Jason Cohen

AR by Anne-Wil van den Berg on behalf of the Authors (22 Sep 2026)

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-6213', Anonymous Referee #1, 26 Feb 2026

This manuscript investigates the 2019 Brazilian fire season under non-extreme climate conditions using a two-step CTE-LW/SW inversion framework constrained by TROPOMI and MOPITT XCO observations. The topic is scientifically relevant and timely. The inversion design is carefully structured, and several sensitivity experiments are conducted to assess robustness. The study provides valuable insights into regional fire CO emissions and bottom-up inventory discrepancies.
The manuscript is generally well organized and supported by data. However, several methodological aspects require further clarification and quantitative support before publication.
Major Comments
1. Identifiability of the Two-Step Inversion Framework
The manuscript applies LW to optimize background CO and SW to optimize fire emissions. However: How does the LW step avoid absorbing part of the fire signal?
Does LW optimization alter OH or NMVOC production patterns regionally?
Could the SW step compensate for residual background bias?
The manuscript mentions a remaining ~20 ppb positive bias in the free troposphere but argues it does not dominate SW results. This claim requires quantitative sensitivity tests. Please clarify information flow and provide supporting diagnostics.
2. Justification of the XCO ≥125 ppb Filtering Threshold
The chosen threshold is used to isolate fire plumes, but: How sensitive are total emissions to this threshold? Could low-intensity or diffuse fires be excluded? Please provide emission estimates under different thresholds (e.g., 100, 125, 150 ppb). Also include statistics of excluded observations (<125 ppb) to demonstrate that the filtering does not bias total fire emissions.
3. Observation Error Inflation Strategy
TROPOMI errors are inflated (transport error doubled + 4 ppb added) to achieve χ² ≈ 1. While common in data assimilation, further clarification is needed:Show χ² distributions before and after inflation (not only the mean). Explain how the “×2 + 4 ppb” parameters were chosen. Provide sensitivity tests to alternative inflation factors. Importantly, TROPOMI native uncertainty (~3 ppb) is much smaller than MOPITT (~7 ppb). Inflation changes their relative weighting. Please discuss whether the reported convergence between TROPOMI and MOPITT inversions is partly influenced by this weighting adjustment.
4. CO-to-C Conversion Assumption
CO emissions are converted to carbon emissions using a fixed CO/CO₂ ratio. However, emission factors vary by fuel type and biome. Please provide sensitivity analysis (e.g., ±20% in CO/CO₂ratio). Quantify the resulting uncertainty in carbon estimates. Clarify the limitations of using CO-only inversion for total carbon quantification.
5. Underestimation over Cerrado/Caatinga
The manuscript reports posterior emissions nearly twice the inventory values in this region. While fuel pool explanations are discussed, the attribution remains qualitative.
Please consider: Showing spatial posterior/prior ratios. Providing innovation statistics by biome. Clarifying whether transport redistribution contributes to the regional signal.
Minor Comments
1.Clarify how the two-month MOPITT data gap in 2019 affects results.
2.State earlier in the methods that the system cannot constrain super-local plumes due to resolution limits.
3.Clarify the origin of NMVOC and CH₄ pre-scaling factors.
4.In the conclusions, better define the applicable scale (regional totals vs. event-scale emissions) to avoid overgeneralization.
Technical / Presentation Issues
1.The “thick semi-transparent black line” in Figure 1 is difficult to identify; please adjust visibility.
2.Provide bias and RMSE metrics for ATTO validation.
3.Add y-axis labels in Figure 5 for clarity.

Citation: https://doi.org/10.5194/egusphere-2025-6213-RC1
- AC1: 'Reply on RC1', Anne-Wil van den Berg, 30 Apr 2026
  
  Please find our response in the attached document.
  
  Citation: https://doi.org/10.5194/egusphere-2025-6213-AC1
RC2:
'Comment on egusphere-2025-6213', Ying Li, 28 Feb 2026
Constraining fire carbon emissions is essential for understanding the carbon budget and carbon cycle. This study presents satellite-based estimates of CO emissions from fires in Brazil using inverse modeling. Different inversion configurations lead to consistent posterior estimates, demonstrating the robustness of the framework. The manuscript is generally well-structured and clearly written. This study demonstrates physical consistency of fire emission monitoring through an innovative two-step inversion framework (long-window and short-window inversion), multi-source satellite data fusion, dynamic error modeling, and detailed biome-level analysis. It not only provides reliable emission estimates for the 2019 fires in Brazil but also offers important methodological references for the design of future fire monitoring systems, the improvement of emission inventories, and carbon cycle research. The manuscript is valuable and suitable for publication after moderate revisions. My comments are as follows:
Comments
The entireframework hinges on the premise that the long-window (LW) inversion can effectively optimize non-fire sources (NMVOC oxidation, CH4 oxidation, anthropogenic) to provide a pristine background for the short-window (SW) inversion, which then exclusively optimizes fire emissions. There is a risk of "aliasing," where fire signals not fully captured by the prior fire inventory could be absorbed by the LW-optimized terms (e.g., through adjustments to NMVOC production), or conversely, that residual errors in the LW background could be erroneously attributed to fires in the SW step. The authors should include a dedicated sensitivity analysis or a set of diagnostics to validate this decoupling.

What is the practical benefit of the LW inversion in this application? While correcting background concentrations is conceptually beneficial, Fig. 2b shows substantial background bias distributed across the entire domain rather than localized in fire regions, which may limit the effectiveness of the LW inversion. Please further illustrate the prior residuals before applying the LW inversion to clarify its contribution.

MOPITT data are missing between 26 June and 24 August (L203), covering roughly one quarter of the inversion period. Since Fig. 5 shows that deforestation fire emissions peak in mid-August, this gap may introduce non-negligible bias. Notably, the posterior mean is averaged across three inversions, whereas GFED5.1_MOPITT represents the prior before 24 August only. This could shift the posterior mean of deforestation fires toward the GFED5.1 prior (see Fig. A4). Please clarify this issue.

2.5.1: How are observations within the 3-day assimilation window treated, given that data are available at roughly three timesteps?

Line 248: An outlier threshold of three is standard in many inversion systems. However, for rapidly varying fire emissions, large model-data mismatches at individual timesteps may result in the rejection of valid observations. The missing peaks in Fig. 3 suggest this may be occurring. Please discuss the potential impact of the filtering strategy.

2: It would be helpful to include the spatial pattern of prior emissions to facilitate comparison between emission and concentration patterns.

Lines 404–405: Fig. 2d does not present an observation-simulation comparison. Fig. 2e shows residuals, but residuals over B1 remain substantial. Please clarify or correct the description.

The inversion domain extends beyond Brazil (Fig. 1), yet only Brazilian results are discussed. Given the significant CO fire signals in surrounding regions (Fig. 2a), how do emission adjustments in neighboring countries influence the Brazilian estimates?

Relatedly, could biases in emissions from surrounding biomes contribute to the inferred increases in the Cerrado and Caatinga? For example, Fig. 2b shows substantial underestimation over deforestation and Pantanal regions, which would likely drive emission increases during inversion. Although a biome-dependent covariance structure may limit such cross-region influence, the Amazon savanna fire region is not separated in Fig. 1.

The authors do a good job explaining the inflation of the model-data mismatch to achieve a χ² near 1. It would be useful to state the mean χ² value afterinflation, not just that it "approached one." Was it 1.05? 0.98? Providing the number adds precision.

Author presents a clear mass balance equation. I may miss it somewhere, the cost of function and configuration of the prior error covariance matrix B is not clearly described. Given that the specification of B can significantly influence the posterior results, the authors should provide a detailed explanation of its construction (e.g., variances and auto-correlations).
Citation: https://doi.org/10.5194/egusphere-2025-6213-RC2
- AC2: 'Reply on RC2', Anne-Wil van den Berg, 30 Apr 2026
  
  Please find our response in the attached document.
  
  Citation: https://doi.org/10.5194/egusphere-2025-6213-AC2

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload

AR by Anne-Wil van den Berg on behalf of the Authors (30 Apr 2026) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (19 May 2026) by Jason Cohen

RR by Anonymous Referee #1 (12 Jun 2026)

RR by Ying Li (14 Jul 2026)

Suggestions for revision or reasons for rejection

Hide

ED: Reconsider after major revisions (27 Jul 2026) by Jason Cohen

Hide

AR by Anne-Wil van den Berg on behalf of the Authors (04 Sep 2026) Author's response Author's tracked changes Manuscript

ED: Publish subject to minor revisions (review by editor) (04 Sep 2026) by Jason Cohen

Hide

AR by Anne-Wil van den Berg on behalf of the Authors (11 Sep 2026) Author's response Author's tracked changes Manuscript

ED: Publish as is (20 Sep 2026) by Jason Cohen

AR by Anne-Wil van den Berg on behalf of the Authors (22 Sep 2026)

Journal article(s) based on this preprint

06 Oct 2026

Fire carbon emission constraints from space-based carbon monoxide retrievals during the 2019 intense burning season in Brazil

Atmos. Chem. Phys., 26, 13929–13958, https://doi.org/10.5194/acp-26-13929-2026,https://doi.org/10.5194/acp-26-13929-2026, 2026

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We estimate 2019 Brazilian fire carbon monoxide (CO) emissions using CO retrievals from different satellite instruments in a new top-down inversion framework. The retrievals strongly constrain fire emissions and complement existing inventories. National-scale posterior emissions align well with the new GFED5.1 inventory, supporting progress in fire emission modelling. Yet, posterior emissions in the Cerrado and Caatinga are systematically higher than inventories, we discuss potential drivers.


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