Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model

Zheng, Zhonghua; West, Matthew; Zhao, Lei; Ma, Po‐Lun; Liu, Xiaohong; Riemer, Nicole

doi:10.5194/acp-21-17727-2021

Cited by 12 publications

(23 citation statements)

References 56 publications

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“…Recently, Zheng et al. (2021) quantified the uncertainty of the aerosol mixing state based on the mixing state index of primary carbonaceous and nonprimary carbonaceous species (χ c ) and the mixing state index of hygroscopic and non‐hygroscopic species (χ h ) in MAM4. They found MAM4 overestimates the mixing state index χ c and χ h at low latitudes to mid‐latitudes, which also points toward the too rapid transfer from the primary carbonaceous mode to the accumulation mode.…”

Section: Resultsmentioning

confidence: 99%

Evaluating BC Aging Processes in the Community Atmosphere Model Version 6 (CAM6)

et al. 2023

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The uncertainty of the climatic effect of Black carbon (BC) remains large. One critical uncertainty source that needs to be captured is BC aging. Here we use the Community Atmosphere Model version 6 (CAM6) configured with the four‐mode version of the Modal Aerosol Module (MAM4) to evaluate the modeled BC aging process with recent laboratory and in‐situ measurements over China. As revealed by the comparison of BC aging timescale and number fraction of aged BC against recent measurements, the modeled condensation aging timescale is estimated to be about 0.8 hr (17%) faster than the chamber measurement, and the diurnal variations of modeled BC aging degree are typically higher than observations mainly due to the fast increase in modeled BC aging degree during daytime. Further analysis shows that the condensation aging dominates (>70%) BC aging across China. More specifically, the condensation of secondary organic aerosol (SOA) vapor contributes most to BC aging over China. Slowing down BC aging increases the modeled surface BC concentration over remote Western China and BC burden, but hardly changes surface BC concentration over Eastern China. Our results suggest that BC aging representation in the MAM4 needs to be further improved toward slowing down the BC aging rate, especially the condensation aging by SOA, to improve the BC simulation over remote areas and its impact on BC transport in MAM4.

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Section: Resultsmentioning

confidence: 99%

Evaluating BC Aging Processes in the Community Atmosphere Model Version 6 (CAM6)

et al. 2023

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“…On the basis of Zheng, West, et al. (2021), we predict χ ML at the runtime of the CAM6 and further use χ ML to improve aerosol representations (as described in Section 2.3.2) and evaluate its impact on aerosol activation (as described in Section 2.3.3). Note that, in the generation of training data (Zheng, West, et al., 2021), the simplification or absence of some physical‐chemical processes in PartMC‐MOSAIC may result in uncertainties for the ML model.…”

Section: Methods and Modelsmentioning

confidence: 99%

“…(2021) and Zhang, Curtis, et al. (2021) extended the approach by Hughes et al. (2018) by defining the mixing state index in three different ways based on the CAM6 (Community Atmosphere Model version 6) model.…”

Section: Introductionmentioning

confidence: 99%

“…(2021), and Zhang, Curtis, et al. (2021) only performed offline diagnosis of the mixing‐state index based on instantaneous outputs of GCMs, and the ML technique for aerosol mixing state index estimates has not been incorporated to the online simulations of GCMs. Moreover, it has not been quantified how changes in mixing state representation may affect other aspects of model simulations, such as CCN activity.…”

Section: Introductionmentioning

confidence: 99%

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Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6)

Shen,

Wang,

Riemer

et al. 2024

J Adv Model Earth Syst

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Representing mixing state of black carbon (BC) is challenging for global climate models (GCMs). The Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4) represents aerosols as fully internal mixtures with uniform composition within each aerosol mode, resulting in high degree of internal mixing of BC with non‐BC species and large mass ratio of coating to BC (RBC, the mass ratio of non‐BC species to BC in BC‐containing particles). To improve BC mixing state representation, we coupled a machine learning (ML) model of BC mixing state index trained on particle‐resolved simulations to the CAM6 with MAM4 (MAM4‐ML). In MAM4‐ML, we use RBC to partition accumulation mode particles into two new modes, BC‐free particles and BC‐containing particles. We adjust RBC to make the modeled BC mixing state index (χmode) match the one predicted by the ML model (χML). On a global average, the mass fraction of BC‐containing particles in accumulation mode decreases from 100% (MAM4‐default) to 48% (MAM4‐ML). The globally averaged χmode decreases from 78% (MAM4‐default) to 63% (MAM4‐ML, 19% reduction) and agrees well with χML (66%). The RBC decreases by 52% for accumulation mode and better agrees with observations. The hygroscopicity drops by 9% for BC‐containing particles in accumulation mode, leading to a 20% reduction in the BC activation fraction. The surface BC concentration increases most (6.9%) in the Arctic, and the BC burden increases by 4%, globally. Our study highlights the application of the ML model for improving key aerosol processes in GCMs.

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“…To better understand the climate effects of soot particles, previous studies have estimated the optical properties of BC (Ocko et al., 2012; Sun et al., 2022; Y. Wang, Pang, et al., 2021), while its radiative forcing varies widely between 0.04 and 0.34 W/m 2 (Bond et al., 2013). One cause of the uncertainties is the limited knowledge of the morphology of soot aggregates, which has a fractal morphology and complex mixing structure in the atmosphere (Adachi et al., 2010; Chakrabarty & Heinson, 2018; Y. Wang, Li, et al., 2021; Yao et al., 2022; Zheng et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Morphology and Fractal Dimension of Size‐Resolved Soot Particles Emitted From Combustion Sources

et al. 2023

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Databases of the morphological parameters of fresh size‐resolved soot particles and their systematic comparisons among various combustion sources are important to trace the soot aging process and evaluate their optical properties. Here, the mixing state, effective density (ρeff), monomer number and diameter (N and dp), and fractal dimension (Df) of size‐resolved soot particles from vehicle emissions (VE), biomass burning (BB), coal combustion (CC), tunnel air (TA) and urban air (UA) were characterized based on electron microscopy observations and analysis. We determined that freshly‐emitted soot particles from combustion sources contain not only bare‐like soot but also some coated‐soot particles. ρeff (157–689 kg/m3) decreased while N (46–1,500) and dp (24–42 nm) increased with the increased diameter of soot particles. The Df of BB and CC were independent of the diameter changes and fluctuated between 1.65 and 1.80, while the Df of VE and TA (1.62–1.71) increased and UA Df (1.87–1.80) decreased with increasing diameter. Based on Df obtained in this study, we found that the ensemble Df of VE, BB, CC, TA, and UA could not represent the finer (<150 nm) soot particles and coarser soot particles (>600 nm) in VE, TA, and UA. This study highlights that the morphological parameters of freshly‐emitted soot particles vary among different combustion sources and have a size effect, particularly for finer and coarser soot particles. The database of size‐resolved soot particles will be helpful to improve soot models based on numerical simulation and better evaluate atmospheric optical properties.

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Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model

Cited by 12 publications

References 56 publications

Evaluating BC Aging Processes in the Community Atmosphere Model Version 6 (CAM6)

Evaluating BC Aging Processes in the Community Atmosphere Model Version 6 (CAM6)

Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6)

Morphology and Fractal Dimension of Size‐Resolved Soot Particles Emitted From Combustion Sources

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