Preprints
https://doi.org/10.5194/egusphere-2024-1487
https://doi.org/10.5194/egusphere-2024-1487
31 May 2024
 | 31 May 2024
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Biomass Burning Emissions Analysis Based on MODIS AOD and AeroCom Multi-Model Simulations

Mariya Petrenko, Ralph Kahn, Mian Chin, Susanne E. Bauer, Tommi Bergman, Huisheng Bian, Gabriele Curci, Ben Johnson, Johannes Kaiser, Zak Kipling, Harri Kokkola, Xiaohong Liu, Keren Mezuman, Tero Mielonen, Gunnar Myhre, Xiaohua Pan, Anna Protonotariou, Samuel Remy, Ragnhild Bieltvedt Skeie, Philip Stier, Toshihiko Takemura, Kostas Tsigaridis, Hailong Wang, Duncan Watson-Parris, and Kai Zhang

Abstract. We assessed the performance of 11 AeroCom models in simulating biomass burning (BB) smoke aerosol optical depth (AOD) in the vicinity of fires over 13 regions globally. By comparing multi-model outputs and satellite observations, we aim to: (1) assess the factors affecting model-simulated, BB AOD performance using a common emissions inventory, (2) identify regions where the emission inventory might underestimate or overestimate smoke sources, and (3) identify anomalies that might point to model-specific smoke emission, dispersion, or removal, issues. Using satellite-derived AOD snapshots to constrain source strength works best where BB smoke from active sources dominates background aerosol, such as in boreal forest regions and over South America and southern-hemisphere Africa. The comparison is poor where the total AOD is low, as in many agricultural burning areas or where background, non-BB AOD is high, such as parts of India and China. Many inter-model BB AOD differences can be traced to differences in model-assumed values for the mass ratio of organic aerosol to organic carbon, the BB aerosol mass extinction efficiency, and the aerosol loss-rate. The results point to the need for increased numbers of available BB cases for study in some regions, and especially to the need for more extensive, regional-to-global-scale measurements of aerosol loss rates and of detailed microphysical and optical properties; this would better constrain models and help distinguish BB from other aerosols in satellite retrievals. More generally, there is the need for additional efforts at constraining aerosol source strength and other model attributes with multi-platform observations.

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Mariya Petrenko, Ralph Kahn, Mian Chin, Susanne E. Bauer, Tommi Bergman, Huisheng Bian, Gabriele Curci, Ben Johnson, Johannes Kaiser, Zak Kipling, Harri Kokkola, Xiaohong Liu, Keren Mezuman, Tero Mielonen, Gunnar Myhre, Xiaohua Pan, Anna Protonotariou, Samuel Remy, Ragnhild Bieltvedt Skeie, Philip Stier, Toshihiko Takemura, Kostas Tsigaridis, Hailong Wang, Duncan Watson-Parris, and Kai Zhang

Status: open (until 12 Jul 2024)

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  • RC1: 'Comment on egusphere-2024-1487', Anonymous Referee #1, 21 Jun 2024 reply
Mariya Petrenko, Ralph Kahn, Mian Chin, Susanne E. Bauer, Tommi Bergman, Huisheng Bian, Gabriele Curci, Ben Johnson, Johannes Kaiser, Zak Kipling, Harri Kokkola, Xiaohong Liu, Keren Mezuman, Tero Mielonen, Gunnar Myhre, Xiaohua Pan, Anna Protonotariou, Samuel Remy, Ragnhild Bieltvedt Skeie, Philip Stier, Toshihiko Takemura, Kostas Tsigaridis, Hailong Wang, Duncan Watson-Parris, and Kai Zhang
Mariya Petrenko, Ralph Kahn, Mian Chin, Susanne E. Bauer, Tommi Bergman, Huisheng Bian, Gabriele Curci, Ben Johnson, Johannes Kaiser, Zak Kipling, Harri Kokkola, Xiaohong Liu, Keren Mezuman, Tero Mielonen, Gunnar Myhre, Xiaohua Pan, Anna Protonotariou, Samuel Remy, Ragnhild Bieltvedt Skeie, Philip Stier, Toshihiko Takemura, Kostas Tsigaridis, Hailong Wang, Duncan Watson-Parris, and Kai Zhang

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Short summary
We compared smoke plume simulations from 11 global models to each other and to satellite smoke-amount observations, aimed at constraining smoke source strength. In regions where plumes are thick and background aerosol is low, models and satellites compare well. However, the input emission inventory tends to underestimate in many places, and particle property and loss-rate assumptions vary enormously among models, causing uncertainties that require systematic in-situ measurements to resolve.