Preprints
https://doi.org/10.5194/gmd-2021-129
https://doi.org/10.5194/gmd-2021-129
Submitted as: model description paper
 | 
17 May 2021
Submitted as: model description paper |  | 17 May 2021
Status: this preprint has been withdrawn by the authors.

Particle dry deposition algorithms in CMAQ version 5.3: characterization of critical parameters and land use dependence using DepoBoxTool version 1.0

Qian Shu, Benjamin Murphy, Jonathan E. Pleim, Donna Schwede, Barron H. Henderson, Havala O.T. Pye, Keith Wyat Appel, Tanvir R. Khan, and Judith A. Perlinger

Abstract. This study investigates particle dry deposition by characterizing critical parameters and land-use dependence in a 0-D box model as well as quantifying the resulting impact of dry deposition parameterizations on regional-scale 3-D model predictions. A publicly available box model configured with several land-use dependent dry deposition schemes is developed to evaluate predictions of several model approaches with available measurements. The 0-D box model results suggest that current dry deposition schemes in 3-D regional models underestimate particle dry deposition velocities, but this varies with size distribution properties and land-use categories. We propose two revised schemes to improve dry deposition performance in air quality models and test them in the Community Multiscale Air Quality (CMAQ) model. The first scheme improves the previous CMAQ scheme by preserving the original dry deposition impaction calculation but turning off redundant integration across particle size for each aerosol mode. The second scheme adds a dependence on leaf area index (LAI) to better estimate uptake to vegetative surfaces while using a settling velocity that is integrated across particle size for the Stokes number calculation. CMAQ model performance was evaluated for a month in July 2011 for the conterminous U.S. based on available observations of ambient sulfate (SO4) aerosol concentrations from multiple routine particulate matter monitoring networks. Incorporation of the first scheme has a larger impact on coarse particles than fine particles, systematically reducing monthly domain-wide average particle dry deposition velocities (Vd) by approximately 96% and 35%, respectively, and increasing monthly average SO4 concentrations by 395% and 21%. After incorporating LAI into the boundary layer resistance (Rb), the second scheme creates more spatial diversity of Vd and changes SO4 concentrations (coarse = −76% to +336%; fine = −7% to +18%) with land-use categories. These modifications are incorporated into the current publicly available version of CMAQ (v5.3 and beyond).

This preprint has been withdrawn.

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Qian Shu, Benjamin Murphy, Jonathan E. Pleim, Donna Schwede, Barron H. Henderson, Havala O.T. Pye, Keith Wyat Appel, Tanvir R. Khan, and Judith A. Perlinger

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-129', Anonymous Referee #1, 16 Jun 2021
  • RC2: 'Comment on gmd-2021-129', Anonymous Referee #2, 21 Sep 2021

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-129', Anonymous Referee #1, 16 Jun 2021
  • RC2: 'Comment on gmd-2021-129', Anonymous Referee #2, 21 Sep 2021
Qian Shu, Benjamin Murphy, Jonathan E. Pleim, Donna Schwede, Barron H. Henderson, Havala O.T. Pye, Keith Wyat Appel, Tanvir R. Khan, and Judith A. Perlinger
Qian Shu, Benjamin Murphy, Jonathan E. Pleim, Donna Schwede, Barron H. Henderson, Havala O.T. Pye, Keith Wyat Appel, Tanvir R. Khan, and Judith A. Perlinger

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This preprint has been withdrawn.

Short summary
We have bridged the gap between dry deposition measurement and modeling by rigorous use of box and regional transport models and field measurements, but more efforts are needed. This study highlights that deviation among deposition schemes is most pronounced for small and large particles. This study better links model predictions to available real-world observations and incrementally reduces uncertainties in the magnitude of loss processes important for the lifecycle of air pollutants.