| 12 Nov 2025
Hydrological drivers of hydrogen cyanide wildfire emissions from Indonesian peat fires during the 2015, 2019, and 2023 El Niño events
Abstract. Indonesian peatlands store vast amounts of carbon that are highly vulnerable to fire during El Niño-driven droughts. When ignited, peats release large quantities of greenhouse gases and other species with significant environmental impacts, including hydrogen cyanide (HCN), a sensitive tracer of smouldering combustion. In this work, we use satellite retrievals from the Infrared Atmospheric Sounding Interferometer (IASI), TOMCAT atmospheric model simulations, hydrological information, and fire activity observations to evaluate the factors driving trace gas emissions during the 2015, 2019, and 2023 El Niño events.
The 2015 El Niño produced large burdens of HCN and CO unprecedented in the satellite observational era, driven by exceptionally low soil moisture, depressed groundwater levels, and deep burn depths. In contrast, the 2019 and 2023 events exhibited markedly weaker emissions despite similar Oceanic Niño Index (ONI) anomalies, reflecting more favourable hydrological conditions. Comparisons of the satellite trace gas observations with simulations of the TOMCAT model show that burned-area-based inventories such as GFED substantially overestimate emissions from peat fires in 2015, while a new peat-specific database, FINNpeatSM, better represents fire season timing and burn depth by incorporating soil moisture constraints. From satellite-derived HCN:CO enhancement ratios, we provide new emission factors for HCN that offer benchmarks for new emission inventories.
Our results show that peat fire intensity and emissions are driven not only by El Niño strength but also by local hydrological conditions such as soil water content and precipitation. Integrating hydrological indicators with satellite observations of atmospheric composition is therefore critical for improving fire emission inventories.
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.