Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Accurate fire emissions inventories are crucial to predict the impacts of wildland fires on air quality and atmospheric composition. Two traditional approaches are widely used to calculate fire emissions: a satellite-based top-down approach and a fuels-based bottom-up approach. However, these methods often considerably disagree on the amount of particulate mass emitted from fires. Previously available observational datasets tended to be sparse, and lacked the statistics needed to resolve these methodological discrepancies. Here, we leverage the extensive and comprehensive airborne in situ and remote sensing measurements of smoke plumes from the recent Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to statistically assess the skill of the two traditional approaches. We use detailed campaign observations to calculate and compare emission rates at an exceptionally high-resolution using three separate approaches: top-down, bottom-up, and a novel approach based entirely on integrated airborne in situ measurements. We then compute the daily average of these high-resolution estimates and compare with estimates from lower resolution, global top-down and bottom-up inventories. We uncover strong, linear relationships between all of the high-resolution emission rate estimates in aggregate, however no single approach is capable of capturing the emission characteristics of every fire. Global inventory emission rate estimates exhibited weaker correlations with the high-resolution approaches and displayed evidence of systematic bias. The disparity between the low-resolution global inventories and the high-resolution approaches is likely caused by high levels of uncertainty in essential variables used in bottom-up inventories and imperfect assumptions in top-down inventories.

Department

Earth Systems Research Center

Publication Date

12-2-2021

Journal Title

JGR: Atmospheres

Publisher

AGU

Digital Object Identifier (DOI)

https://dx.doi.org/10.1029/2021JD035692

Document Type

Article

Comments

This is an open access article published by AGU in 2021 in JGR: Atmospheres, available online: https://dx.doi.org/10.1029/2021JD035692

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