the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Heterogeneous impacts of fire-sourced ozone (O3) pollution on global crop yields in the future climate scenarios
Abstract. Wildfire smoke often aggravates the ozone (O3) pollution and negatively affect crop yields. To date, the global impact of fire-sourced O3 exposure on crop yields still remained unknown. To address this issue, a multi-stage model was developed to quantify the global wildfire-induced ambient O3 concentrations in the future scenarios. The results suggested that the relationship between observed K⁺ level and simulated fire-sourced maximum daily MDA 8-hour average (MDA8) O3 concentration reached 0.67, indicating the robustness of fire-sourced O3 estimate. In both of historical and future scenarios, Sub-Sahara Africa (SS: 14.9 ± 8.4 (historical) and 18.3 ± 9.6 (mean of the future scenarios) μg/m3) and South America (SA: 4.0 ± 2.5 and 4.7 ± 3.2 μg/m3) showed the highest fire-sourced MDA8 O3 concentrations among all of the regions. However, the crop production losses (CPL) caused by O3 exposure reached the highest values in China due to very high total crop yields and relatively high wildfire-induced MDA8 O3 levels. Moreover, CPL in China was sensitive to emission scenario, indicating the effective emission control could largely decrease fire-sourced O3 damage to crop. In contrast, both of SS and SA even showed the higher CPL in low-carbon scenario (SSP1-2.6), suggesting more stringent control measures are required to offset the wildfire contribution. Our findings call for attention on the threat to future global food security from the absence of pollution mitigation and the persistence of global warming.
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Status: open (until 24 Jul 2025)
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RC1: 'Comment on egusphere-2025-847', Anonymous Referee #3, 26 Jun 2025
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This manuscript proposes a three-stage modeling framework to estimate the current and future global distributions of wildfire-sourced ozone under various SSP scenarios. By combining GEOS-Chem simulations with XGBoost, the authors (1) calibrate modeled ozone using historical observations, (2) project future ozone using CMIP6 data, and (3) estimate fire-sourced ozone through a ratio-based approach. The methodology is rigorous and the study addresses an important but underexplored topic, namely the future concentrations of wildfire-induced ozone and their impacts on crop yields. However, several methodological assumptions and data consistency issues require further clarification or revision.
Major Comments
- Data Consistency Between Model Stages
While the authors avoid using GEOS-Chem for future total ozone due to uncertainties in emission inventories, they still apply the fire-to-total ozone ratio derived from GEOS-Chem to CMIP6-based projections. This raises two issues: (1) the manuscript does not evaluate whether this ratio is stable across different models or emission scenarios, and whether it can be reliably transferred from GEOS-Chem outputs to CMIP6-driven ozone fields; and (2) it does not explain why GEOS-Chem total ozone is considered unreliable, while the fire contribution ratio, derived from the same emission inputs, is assumed to be trustworthy.
- Estimation of Fire-Induced Ozone in GEOS-Chem
The manuscript states that fire-related ozone was estimated by comparing GEOS-Chem simulations with and without fire emissions. However, key details of this implementation are missing. The authors should provide more information on how the simulations were configured in order to ensure transparency and reproducibility.
- Attribution without internal evidence
The manuscript attributes regional ozone differences to known processes by citing external literature. For example, it attributes high wildfire-induced ozone in the SS region to higher fuel consumption and burned area, yet presents no corresponding model outputs. Similarly, the discussion of low ozone enhancement over the US refers to temperature and NOₓ concentrations but does not show any temperature or NOₓ fields from the model. The authors are encouraged to support such interpretations using variables directly derived from their simulation.
Minor Comments
- Figures 1 and 2 show wildfire-induced MDA8 O₃ for the 2010s and 2040s, but the temporal averaging method is unclear. Please specify whether values represent multi-year means, seasonal averages, or other metrics.
- The AOT40 formula is provided, but key details are missing, such as the daily 8-hour window used, how growing seasons were defined, and whether values were averaged annually or over multiple years.
- Line 77: The text refers to Figure S1 as showing the monitoring site distribution, but it only presents the modeling workflow.
Citation: https://doi.org/10.5194/egusphere-2025-847-RC1
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