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Using modelled relationships and satellite observations to attribute modelled aerosol biases over biomass burning regions

Biomass burning (BB) is a major source of aerosols that remain the most uncertain components of the global radiative forcing. Current global models have great difficulty matching observed aerosol optical depth (AOD) over BB regions. A common solution to address modelled AOD biases is scaling BB emis...

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Autores principales: Zhong, Qirui, Schutgens, Nick, van der Werf, Guido R., van Noije, Twan, Bauer, Susanne E., Tsigaridis, Kostas, Mielonen, Tero, Checa-Garcia, Ramiro, Neubauer, David, Kipling, Zak, Kirkevåg, Alf, Olivié, Dirk J. L., Kokkola, Harri, Matsui, Hitoshi, Ginoux, Paul, Takemura, Toshihiko, Le Sager, Philippe, Rémy, Samuel, Bian, Huisheng, Chin, Mian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9547058/
https://www.ncbi.nlm.nih.gov/pubmed/36207322
http://dx.doi.org/10.1038/s41467-022-33680-4
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author Zhong, Qirui
Schutgens, Nick
van der Werf, Guido R.
van Noije, Twan
Bauer, Susanne E.
Tsigaridis, Kostas
Mielonen, Tero
Checa-Garcia, Ramiro
Neubauer, David
Kipling, Zak
Kirkevåg, Alf
Olivié, Dirk J. L.
Kokkola, Harri
Matsui, Hitoshi
Ginoux, Paul
Takemura, Toshihiko
Le Sager, Philippe
Rémy, Samuel
Bian, Huisheng
Chin, Mian
author_facet Zhong, Qirui
Schutgens, Nick
van der Werf, Guido R.
van Noije, Twan
Bauer, Susanne E.
Tsigaridis, Kostas
Mielonen, Tero
Checa-Garcia, Ramiro
Neubauer, David
Kipling, Zak
Kirkevåg, Alf
Olivié, Dirk J. L.
Kokkola, Harri
Matsui, Hitoshi
Ginoux, Paul
Takemura, Toshihiko
Le Sager, Philippe
Rémy, Samuel
Bian, Huisheng
Chin, Mian
author_sort Zhong, Qirui
collection PubMed
description Biomass burning (BB) is a major source of aerosols that remain the most uncertain components of the global radiative forcing. Current global models have great difficulty matching observed aerosol optical depth (AOD) over BB regions. A common solution to address modelled AOD biases is scaling BB emissions. Using the relationship from an ensemble of aerosol models and satellite observations, we show that the bias in aerosol modelling results primarily from incorrect lifetimes and underestimated mass extinction coefficients. In turn, these biases seem to be related to incorrect precipitation and underestimated particle sizes. We further show that boosting BB emissions to correct AOD biases over the source region causes an overestimation of AOD in the outflow from Africa by 48%, leading to a double warming effect compared with when biases are simultaneously addressed for both aforementioned factors. Such deviations are particularly concerning in a warming future with increasing emissions from fires.
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spelling pubmed-95470582022-10-09 Using modelled relationships and satellite observations to attribute modelled aerosol biases over biomass burning regions Zhong, Qirui Schutgens, Nick van der Werf, Guido R. van Noije, Twan Bauer, Susanne E. Tsigaridis, Kostas Mielonen, Tero Checa-Garcia, Ramiro Neubauer, David Kipling, Zak Kirkevåg, Alf Olivié, Dirk J. L. Kokkola, Harri Matsui, Hitoshi Ginoux, Paul Takemura, Toshihiko Le Sager, Philippe Rémy, Samuel Bian, Huisheng Chin, Mian Nat Commun Article Biomass burning (BB) is a major source of aerosols that remain the most uncertain components of the global radiative forcing. Current global models have great difficulty matching observed aerosol optical depth (AOD) over BB regions. A common solution to address modelled AOD biases is scaling BB emissions. Using the relationship from an ensemble of aerosol models and satellite observations, we show that the bias in aerosol modelling results primarily from incorrect lifetimes and underestimated mass extinction coefficients. In turn, these biases seem to be related to incorrect precipitation and underestimated particle sizes. We further show that boosting BB emissions to correct AOD biases over the source region causes an overestimation of AOD in the outflow from Africa by 48%, leading to a double warming effect compared with when biases are simultaneously addressed for both aforementioned factors. Such deviations are particularly concerning in a warming future with increasing emissions from fires. Nature Publishing Group UK 2022-10-07 /pmc/articles/PMC9547058/ /pubmed/36207322 http://dx.doi.org/10.1038/s41467-022-33680-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Zhong, Qirui
Schutgens, Nick
van der Werf, Guido R.
van Noije, Twan
Bauer, Susanne E.
Tsigaridis, Kostas
Mielonen, Tero
Checa-Garcia, Ramiro
Neubauer, David
Kipling, Zak
Kirkevåg, Alf
Olivié, Dirk J. L.
Kokkola, Harri
Matsui, Hitoshi
Ginoux, Paul
Takemura, Toshihiko
Le Sager, Philippe
Rémy, Samuel
Bian, Huisheng
Chin, Mian
Using modelled relationships and satellite observations to attribute modelled aerosol biases over biomass burning regions
title Using modelled relationships and satellite observations to attribute modelled aerosol biases over biomass burning regions
title_full Using modelled relationships and satellite observations to attribute modelled aerosol biases over biomass burning regions
title_fullStr Using modelled relationships and satellite observations to attribute modelled aerosol biases over biomass burning regions
title_full_unstemmed Using modelled relationships and satellite observations to attribute modelled aerosol biases over biomass burning regions
title_short Using modelled relationships and satellite observations to attribute modelled aerosol biases over biomass burning regions
title_sort using modelled relationships and satellite observations to attribute modelled aerosol biases over biomass burning regions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9547058/
https://www.ncbi.nlm.nih.gov/pubmed/36207322
http://dx.doi.org/10.1038/s41467-022-33680-4
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