Articles | Volume 15, issue 1
https://doi.org/10.5194/gmd-15-219-2022
https://doi.org/10.5194/gmd-15-219-2022
Development and technical paper
 | 
12 Jan 2022
Development and technical paper |  | 12 Jan 2022

Development of aerosol optical properties for improving the MESSy photolysis module in the GEM-MACH v2.4 air quality model and application for calculating photolysis rates in a biomass burning plume

Mahtab Majdzadeh, Craig A. Stroud, Christopher Sioris, Paul A. Makar, Ayodeji Akingunola, Chris McLinden, Xiaoyi Zhao, Michael D. Moran, Ihab Abboud, and Jack Chen

Related authors

An improved representation of aerosol mixing state for air quality–weather interactions
Robin Stevens, Andrei Ryjkov, Mahtab Majdzadeh, and Ashu Dastoor
Atmos. Chem. Phys., 22, 13527–13549, https://doi.org/10.5194/acp-22-13527-2022,https://doi.org/10.5194/acp-22-13527-2022, 2022
Short summary

Related subject area

Atmospheric sciences
Optimized dynamic mode decomposition for reconstruction and forecasting of atmospheric chemistry data
Meghana Velagar, Christoph Keller, and J. Nathan Kutz
Geosci. Model Dev., 18, 4667–4684, https://doi.org/10.5194/gmd-18-4667-2025,https://doi.org/10.5194/gmd-18-4667-2025, 2025
Short summary
Interpolating turbulent heat fluxes missing from a prairie observation on the Tibetan Plateau using artificial intelligence models
Quanzhe Hou, Zhiqiu Gao, Zexia Duan, and Minghui Yu
Geosci. Model Dev., 18, 4625–4641, https://doi.org/10.5194/gmd-18-4625-2025,https://doi.org/10.5194/gmd-18-4625-2025, 2025
Short summary
Carbon dioxide plume dispersion simulated at the hectometer scale using DALES: model formulation and observational evaluation
Arseniy Karagodin-Doyennel, Fredrik Jansson, Bart J. H. van Stratum, Hugo Denier van der Gon, Jordi Vilà-Guerau de Arellano, and Sander Houweling
Geosci. Model Dev., 18, 4571–4599, https://doi.org/10.5194/gmd-18-4571-2025,https://doi.org/10.5194/gmd-18-4571-2025, 2025
Short summary
Low-level jets in the North and Baltic seas: mesoscale model sensitivity and climatology using WRF V4.2.1
Bjarke T. E. Olsen, Andrea N. Hahmann, Nicolas G. Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
Geosci. Model Dev., 18, 4499–4533, https://doi.org/10.5194/gmd-18-4499-2025,https://doi.org/10.5194/gmd-18-4499-2025, 2025
Short summary
SynRad v1.0: a radar forward operator to simulate synthetic weather radar observations from volcanic ash clouds
Vishnu Nair, Anujah Mohanathan, Michael Herzog, David G. Macfarlane, and Duncan A. Robertson
Geosci. Model Dev., 18, 4417–4432, https://doi.org/10.5194/gmd-18-4417-2025,https://doi.org/10.5194/gmd-18-4417-2025, 2025
Short summary

Cited articles

AERONET: AERONET, NASA [data set], available at: https://aeronet.gsfc.nasa.gov/, last access: 5 February 2020 
Alvarado, M. J., Lonsdale, C. R., Yokelson, R. J., Akagi, S. K., Coe, H., Craven, J. S., Fischer, E. V., McMeeking, G. R., Seinfeld, J. H., Soni, T., Taylor, J. W., Weise, D. R., and Wold, C. E.: Investigating the links between ozone and organic aerosol chemistry in a biomass burning plume from a prescribed fire in California chaparral, Atmos. Chem. Phys., 15, 6667–6688, https://doi.org/10.5194/acp-15-6667-2015, 2015. 
Alvarado, M. J., Lonsdale, C. R., Macintyre, H. L., Bian, H., Chin, M., Ridley, D. A., Heald, C. L., Thornhill, K. L., Anderson, B. E., Cubison, M. J., Jimenez, J. L., Kondo, Y., Sahu, L. K., Dibb, J. E., and Wang, C.: Evaluating model parameterizations of submicron aerosol scattering and absorption with in situ data from ARCTAS 2008, Atmos. Chem. Phys., 16, 9435–9455, https://doi.org/10.5194/acp-16-9435-2016, 2016. 
Anderson, B. E., Cofer, W. R., Bagwell, D. R., Barrick, J. W., Hudgins, C. H., and Brunke, K. E.: Airborne observations of aircraft aerosol emissions 1: total non-volatile particle emission indices, Geophys. Res. Lett., 25, 1689–1692, 1998. 
Anderson, K. and cast of thousands: CFFEPS v2.03, Canadian Forest Service, Natural Resources Canada, Zenodo [code], https://doi.org/10.5281/zenodo.2579383, 2019. 
Download
Short summary
A new lookup table for aerosol optical properties based on a Mie scattering code was calculated and adopted within an improved version of the photolysis module in the GEM-MACH in-line chemical transport model. The modified version of the photolysis module makes use of online interactive aerosol feedback and applies core-shell parameterizations to the black carbon absorption efficiency based on Bond et al. (2006) to the size bins with black carbon mass fraction of less than 40 %.
Share