Improving the particle dry deposition scheme in the CMAQ photochemical modeling system

Shu, Qian; Murphy, Benjamin N.; Schwede, Donna; Henderson, B. H.; Pye, Havala O. T.; Appel, K. Wyat; Khan, Tanvir; Perlinger, Judith A.

doi:10.1016/j.atmosenv.2022.119343

Cited by 8 publications

(13 citation statements)

References 43 publications

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“…However, V d trends for all schemes start to shift to the left bringing the minimum V d from about 2 to 0.1 μm as σ g is increased from 1.01 (Figure 12a) to 2.5 (Figure 12c), showing that predicted V d is sensitive to σ g . This characteristic change is expected, as reported in Shu et al (2022) because when bigger particles (D p > 10 m) affect the integration method (S or M), where V d is orders of magnitude larger, the scheme with a larger σ g (1.7 or 2.5) tends to predict a higher V d at a mid-range particle size (0.2 < D p < 1 m) than the scheme with a lower σ g (1.01). Meanwhile, at smaller sizes (D p < 0.1 m), the scheme (1.7 or 2.5) predicts a lower V d than that with a lower σ g (1.01) as they are influenced by particles of midsize (0.2 < D p < 1 m) where V d is orders of magnitude smaller.…”

Section: Intercomparison Of Results From S21 and C21 Formulationssupporting

confidence: 85%

“…Formulations of Shu et al (2022), referred to as S21 (See Text S2 in Supporting Information S1), are based on Pleim and Ran (2011) where S21 added a new term to R b to calculate leaf area index (LAI)-dependent vegetative surface uptake. S21 showed that the new formulation introduced a vegetation dependence that is directionally consistent with the observed impact of vegetation on particle dry deposition and this update has resulted in relatively better results in the estimation of particle dry deposition.…”

Section: The S21 and The New C21 Schemesmentioning

confidence: 99%

“…Equations Used to Represent Several Processes in Each of the Schemes (Shu et al, 2022;Zhang et al, 2001) 2). Thus, usage of such 𝐴𝐴 𝐴𝐴 * without utilizing a stability function can underrepresent turbulence magnitude in process formulations and thus will lead to underestimation of dry deposition velocity.…”

Section: Tablementioning

confidence: 99%

“…To the best of our knowledge, temporal variation, and magnitude of W f was neither compared with nor evaluated against other similar turbulence factor estimates and thus our new formulation fills this scientific gap. Saylor et al (2019) have shown Scheme Description Z01-B Base Z01 scheme (Zhang et al, 2001) C01-E Base Z01 scheme using e * C01-ETF Base Z01 scheme using e * and T f S21-BS Base S21 scheme (sectional) (Shu et al, 2022) S21-BM Base S21 scheme (modal)…”

Section: Characterization Of Turbulence Parametersmentioning

confidence: 99%

“…Numerical model used in this study is the same single-point model (DepoBoxToolv1.0) that was used byShu et al (2022) to test different particle dry deposition schemes for different land use. DepoBoxToolv1.0 was configured as an open-source Python tool, which can be easily modified to incorporate updates on the dry deposition schemes.…”

mentioning

confidence: 99%

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Dry Deposition Methods Based on Turbulence Kinetic Energy: 2. Extension to Particle Deposition Using a Single‐Point Model

et al. 2022

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Atmospheric aerosols (liquid and solid), hereafter referred to as particles, have substantial influences on the air quality, radiative balance of the earth, climate, and ecosystem (e.g., Fowler et al., 2009;Lee et al., 2013). Dry deposition is the process that removes gases and aerosols from the air (e.g., Pryor et al., 2008) and is a source of many nutrients to natural ecosystems. Large uncertainties exist in the modeling of particle dry deposition leading to more uncertainties in the estimation of (a) aerosol direct, semi-direct, and indirect effects in climate models; (b) damages to environment via impairment and shifts in biodiversity and health of vegetation; (c) critical loads of nutrients affecting terrestrial ecosystem; and (d) human health impacts via exposure to pollutants. Thus, refined deposition formulations and updated comprehensive measurements are needed to improve estimation of particle dry deposition to address these important topics (Farmer et al., 2021).

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Section: Intercomparison Of Results From S21 and C21 Formulationssupporting

confidence: 85%

Section: The S21 and The New C21 Schemesmentioning

confidence: 99%

Section: Tablementioning

confidence: 99%

Section: Characterization Of Turbulence Parametersmentioning

confidence: 99%

mentioning

confidence: 99%

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Dry Deposition Methods Based on Turbulence Kinetic Energy: 2. Extension to Particle Deposition Using a Single‐Point Model

et al. 2022

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3DVar sectoral emission inversion based on source apportionment and machine learning

Huang,

Niu,

Wang

et al. 2024

Environmental Pollution

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Influence of More Mechanistic Representation of Particle Dry Deposition on 1850–2000 Changes in Global Aerosol Burdens and Radiative Forcing

Clifton,

Bauer,

Tsigaridis

et al. 2024

J Adv Model Earth Syst

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Robust estimates of historical changes in aerosols are key for accurate constraints on climate sensitivity. Dry deposition is a primary sink of aerosols from the atmosphere. However, most global climate models do not accurately represent observed strong dependencies of dry deposition following turbulent transport on aerosol size. It is unclear whether there is a substantial impact of mischaracterized aerosol deposition velocities on historical aerosol changes. Here we describe improved mechanistic representation of aerosol dry deposition in the NASA Goddard Institute for Space Studies (GISS) global climate model, ModelE, and illustrate the impact on 1850–2000 changes in global aerosol burdens as well as aerosol direct and cloud albedo effects using a set of 1850 and 2000 time slice simulations. We employ two aerosol configurations of ModelE (a “bulk” mass‐based configuration and a configuration that more explicitly represents aerosol size distributions, internal mixing, and microphysics) to explore how model structural differences in aerosol representation alter the response to representation of dry deposition. Both configurations show larger historical increases in the global burdens of non‐dust aerosols with the new dry deposition scheme, by 11% in the simpler mass‐based configuration and 23% in the more complex microphysical configuration. Historical radiative forcing responses, which vary in magnitude from 5% to 12% as well as sign, depend on the aerosol configuration.

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Improving the particle dry deposition scheme in the CMAQ photochemical modeling system

Cited by 8 publications

References 43 publications

Dry Deposition Methods Based on Turbulence Kinetic Energy: 2. Extension to Particle Deposition Using a Single‐Point Model

Dry Deposition Methods Based on Turbulence Kinetic Energy: 2. Extension to Particle Deposition Using a Single‐Point Model

3DVar sectoral emission inversion based on source apportionment and machine learning

Influence of More Mechanistic Representation of Particle Dry Deposition on 1850–2000 Changes in Global Aerosol Burdens and Radiative Forcing

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