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利用模拟关系和卫星观测来归因于生物质燃烧区域的模型气溶胶偏差。

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

机构信息

Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Royal Netherlands Meteorological Institute, De Bilt, The Netherlands.

出版信息

Nat Commun. 2022 Oct 7;13(1):5914. doi: 10.1038/s41467-022-33680-4.

DOI:10.1038/s41467-022-33680-4
PMID:36207322
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9547058/
Abstract

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.

摘要

生物质燃烧(BB)是气溶胶的主要来源,气溶胶仍然是全球辐射强迫中最不确定的成分。当前的全球模型很难匹配 BB 地区观测到的气溶胶光学深度(AOD)。解决模型 AOD 偏差的常用方法是对 BB 排放进行缩放。利用气溶胶模型和卫星观测的集合关系,我们表明气溶胶模拟结果的偏差主要来自不正确的寿命和低估的质量消光系数。反过来,这些偏差似乎与不正确的降水和低估的颗粒尺寸有关。我们进一步表明,在源区提高 BB 排放以纠正 AOD 偏差会导致非洲流出物中 AOD 的高估,与同时解决上述两个因素的偏差相比,这会导致双重变暖效应。在未来随着火灾排放的增加而变暖的情况下,这种偏差尤其令人担忧。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/765c/9547058/568589e28b26/41467_2022_33680_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/765c/9547058/2ad0814e70ae/41467_2022_33680_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/765c/9547058/e1a7a2234f0a/41467_2022_33680_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/765c/9547058/fac50b109917/41467_2022_33680_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/765c/9547058/3d8a326b8d5e/41467_2022_33680_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/765c/9547058/389c41b1ac1e/41467_2022_33680_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/765c/9547058/568589e28b26/41467_2022_33680_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/765c/9547058/2ad0814e70ae/41467_2022_33680_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/765c/9547058/e1a7a2234f0a/41467_2022_33680_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/765c/9547058/fac50b109917/41467_2022_33680_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/765c/9547058/3d8a326b8d5e/41467_2022_33680_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/765c/9547058/389c41b1ac1e/41467_2022_33680_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/765c/9547058/568589e28b26/41467_2022_33680_Fig6_HTML.jpg

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