Articles | Volume 26, issue 17
https://doi.org/10.5194/acp-26-12925-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
Hydrological drivers of hydrogen cyanide wildfire emissions from Indonesian peat fires during the 2015, 2019, and 2023 El Niño events
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- Final revised paper (published on 14 Sep 2026)
- Supplement to the final revised paper
- Preprint (discussion started on 12 Nov 2025)
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
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RC1: 'Comment on egusphere-2025-5109', Anonymous Referee #1, 12 Dec 2025
- AC1: 'Reply on RC1', Antonio Giovanni Bruno, 19 May 2026
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RC2: 'Comment on egusphere-2025-5109', Anonymous Referee #2, 02 Mar 2026
- AC2: 'Reply on RC2', Antonio Giovanni Bruno, 19 May 2026
Peer review completion
AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Antonio Giovanni Bruno on behalf of the Authors (07 Jun 2026)
Author's response
Author's tracked changes
Manuscript
ED: Referee Nomination & Report Request started (17 Jun 2026) by Andreas Hofzumahaus
RR by Anonymous Referee #2 (08 Jul 2026)
RR by Anonymous Referee #1 (09 Jul 2026)
ED: Reconsider after major revisions (11 Jul 2026) by Andreas Hofzumahaus
AR by Antonio Giovanni Bruno on behalf of the Authors (29 Jul 2026)
Author's response
EF by Katja Gänger (30 Jul 2026)
Manuscript
Author's tracked changes
ED: Publish subject to minor revisions (review by editor) (09 Aug 2026) by Andreas Hofzumahaus
AR by Antonio Giovanni Bruno on behalf of the Authors (12 Aug 2026)
Author's response
Author's tracked changes
Manuscript
ED: Publish as is (15 Aug 2026) by Andreas Hofzumahaus
AR by Antonio Giovanni Bruno on behalf of the Authors (16 Aug 2026)
Bruno et al. analyse the conditions that drive Indonesian peat fire emissions. They identify the importance of hydrological conditions like soil water content and precipitation in addition to El Niño. While the results are interesting and advance our understanding of Indonesian peatland fires, the study misses key aspects required for publication in ACP. At its core, the study relies on newly derived CO and HCN retrievals based on IASI observations. The authors fail to provide a satisfactory explanation as to why alternative retrievals, in addition to the methodologies that are already in place, are required. No retrieval data are provided, the retrieval description is insufficient, and the retrievals have not been evaluated. The only reference provided is to another paper by the authors which is 'in preparation'. I recommend rejecting the manuscript for the time being, and reconsidering it once the new IASI retrievals for CO and HCN have been published and evaluated, and the retrieval method has undergone proper peer review. Before reconsidering the manuscript after this process has been finished, the authors need to address the following comments.
Further major comments:
I have major concerns about the TOMCAT model setup used, as it seems inadequate for the analysis performed:
(1) The authors themselves highlight the importance of circulation patterns and vertical transport (e.g. Figure 3), which are based on ERA5 data. However, their model setup relies on outdated ERA-Interim reanalysis data for meteorological forcing, which lacks improved vertical resolution (Hersbach et al., 2020). I strongly suggest switching to ERA5 reanalysis data as the meteorological driver. This would increase the temporal resolution of the meteorological input from 6 hours to hourly, greatly improving the representation of transport processes.
(2) During the 2015 peatland fires, the Asian monsoon anticyclone was ongoing. The model resolution used in this study (T42, 2.8° by 2.8°) is too coarse to adequately resolve these transport patterns. Please increase the model resolution as frequently employed in global model studies.
(3) The authors state that the TOMCAT model uses monthly averaged emissions, which are then resampled in time. This approach may be valid for emission sources that are not highly time-variable, but it is inappropriate for fire emissions due to their high time-variability. How is this interpolation performed for peatland emissions, and how representative is this approach? Why not rely on datasets that include daily wildfire emissions based on daily fire activity (e.g. GFEDv5; van der Werf et al., 2025)? What influence does your approach have on the overestimation of HCN emissions in September 2015?
The outline and style of the current version need improvement. The order in which the figures are presented does not reflect their usage in the main body of the text. Section 2 provides a simple listing of all the datasets used in this study. The manuscript would benefit greatly if, at the beginning of Section 2, an overview was provided illustrating which datasets will be used and how they will be analysed. For example, the use of the precipitation and soil moisture data is unclear, as is its relevance to the study which only becomes evident later on.
Minor comments:
- Overall, not all abbreviations are properly introduced. In particular, the abstract introduces some abbreviations, while others are assumed to be familiar.
- Lines 28–30: Please provide references for these statements.
- Line 51: HCN has already been introduced on line 29.
- Lines 51–53: Provide reasons and references explaining why these are poorly suited.
- Line 95: How does the TOMCAT model compare to other atmospheric chemistry models for the troposphere? What uncertainty in the retrieval would be introduced if another model would be used?
- Lines 93–97: How representative are the derived profiles?
- Line 109: This is unclear. Does 'fixed' refer to a fixed yearly emission profile, or are the emissions averaged over a year and then fixed?
- Lines 111–113: Provide details on the HCN chemistry used, as well as on other physical loss processes.
- Lines 157–159: Please provide a source for this statement.
- Line 197: The plume of HCN is clearly visible in the 'left' panel, not the 'right' panel, of Figure 1.
- Lines 197–198: This statement reads more like a report than a scientific paper.
- Figure 1: How is the monthly average calculated? How is it ensured that the monthly average at a given location is not skewed towards a single observation, or a few observations, if all other observations within the month have been invalid due to for example cloud cover?
- Figure 2: Why are there two gaps between November and December? Was no output created for these periods by TOMCAT?
- Line 212: Please introduce the ERA5 data in Section 2.
- Lines 227–237: Should this not be included in Section 2.1.2?
- Line 231: Is the daily sample from the model the same as the IASI overpass time? Does the model provide averaged or instantaneous data?
- Line 241: Replace 'observed' with 'simulated'.
- Line 253: This 75% reduction comes out of the blue. How was this number obtained? What could be the reason for such a significant overestimation of emissions?
- Figure 3: Please ensure that the x-axis and y-axis labels are shared across all subplots. Remove the colour bars from (a), (c) and (e).
- Figure 3: Comment on how well ERA5 represents vertical velocities at the equator.
- Figure 4: Add labels (a) and (b).
- Figure 5: Correct the unit in the caption.
- Line 270: Please provide a reference for this statement.
References:
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
van der Werf, G. R., Randerson, J. T., van Wees, D., Chen, Y., Giglio, L., Hall, J., Roland, V., Mu, M., Binte Shahid, S., Barsanti, K. C., Yokelson, R., and Morton, D. C.: Landscape fire emissions from the 5th version of the Global Fire Emissions Database (GFED5), Sci Data, 12, 1870, https://doi.org/10.1038/s41597-025-06127-w, 2025.