Preprints
https://doi.org/10.5194/egusphere-2023-1822
https://doi.org/10.5194/egusphere-2023-1822
05 Oct 2023
 | 05 Oct 2023

Diagnosing uncertainties in global biomass burning emission inventories and their impact on modeled air pollutants

Wenxuan Hua, Sijia Lou, Xin Huang, Lian Xue, Ke Ding, Zilin Wang, and Aijun Ding

Abstract. Biomass burning (BB) emission inventories are often used to understand the interactions of aerosols with weather and climate. However, large uncertainties exist among current BB inventories, so the choice of inventories can greatly affect model results. To quantify the differences among BB emission inventories and reveal their reasons, we compared carbon monoxide (CO) and organic carbon (OC) emissions from seven major BB regions globally from 2013 to 2016. The current inventories are based on two basic approaches: (1) bottom-up approach, which establishes inventories based on observed surface data, and (2) top-down approach, which based on the release rate of radiative energy from vegetation burning. In this study, we selected mainstream bottom-up inventories Fire INventory from NCAR 1.5 (FINN1.5) and Global Fire Emissions Database version 4s (GFED4s), and the top-down inventories Quick Fire Emissions Dataset 2.5 (QFED2.5) and VIIRS-based Fire Emission Inventory version 0 (VFEI0). We find that the total global CO emissions fluctuate between 252 and 336 Tg and the regional bias is even larger, which can be up to six times. Dry matter is responsible for most of the regional variation in CO emissions (50–80 %), with emission factors accounting for the remaining 20–50 %. Uncertainties in dry matter often come from biases in the calculation of bottom fuel consumption and burned area, which are closely related to vegetation classification methods and fire detection products. In the tropics, peatlands contribute more fuel loads and higher emission factors than grasslands. At high latitudes, as cloud fraction increases, the bias between burned area (or fire radiative power) increases by 20 %. In addition, due to the corrected emission factors in QFED2.5, global BB OC emissions have higher variability, fluctuating between 14.9 and 42.9 Tg.

Finally, we applied the four sets of BB emission inventories to the Community Atmosphere Model version 6 (CAM6) and compared the model results with observations. Our results suggest that the simulations based on the GFED4s agree best with the MOPITT-retrieved CO. We also compared the simulation results with satellite or ground-based measurments, such as Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and AErosol RObotic NETwork (AERONET) AOD. Our results reveal that there is no global optimal choice for the BB inventories, but we give certain inventory recommendations based on different study areas and spatiotemporal scales. This study has implications for reducing the uncertainties in emissions or improving BB emission inventories in further studies.

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 preprint. The responsibility to include appropriate place names lies with the authors.
Wenxuan Hua, Sijia Lou, Xin Huang, Lian Xue, Ke Ding, Zilin Wang, and Aijun Ding

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1822', Anonymous Referee #1, 16 Nov 2023
    • AC1: 'Reply on RC1', Sijia Lou, 02 Feb 2024
  • RC2: 'Comment on egusphere-2023-1822', Anonymous Referee #2, 27 Dec 2023
    • AC2: 'Reply on RC2', Sijia Lou, 02 Feb 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1822', Anonymous Referee #1, 16 Nov 2023
    • AC1: 'Reply on RC1', Sijia Lou, 02 Feb 2024
  • RC2: 'Comment on egusphere-2023-1822', Anonymous Referee #2, 27 Dec 2023
    • AC2: 'Reply on RC2', Sijia Lou, 02 Feb 2024
Wenxuan Hua, Sijia Lou, Xin Huang, Lian Xue, Ke Ding, Zilin Wang, and Aijun Ding
Wenxuan Hua, Sijia Lou, Xin Huang, Lian Xue, Ke Ding, Zilin Wang, and Aijun Ding

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Short summary
In this study, we diagnose uncertainties in CO and OC emissions from four inventories for seven majorwildfire-prone regions. Uncertainties in vegetation classification methods, fire detection products, and cloud obscuration effects lead to bias in these biomass burning (BB) emission inventories. By comparing simulations with measurements, we provide certain inventory recommendations. Our study has implications for reducing uncertainties in emissions in further studies.