Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e

van Caspel, Willem Elias; Simpson, David; Jonson, Jan Eiof; Benedictow, Anna M.K.; Ge, Yao; di Sarra, Alcide; Pace, Giandomenico; Vieno, Massimo ORCID: https://orcid.org/0000-0001-7741-9377; Walker, Hannah L.; Heal, Mathew R.. 2023 Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e. Geoscientific Model Development Discussions, gmd-2023-147. 44, pp. https://doi.org/10.5194/gmd-2023-147

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Abstract/Summary

The present work describes the implementation of the state of the art Cloud-J v7.3 photolysis rate calculation code in the EMEP MSC-W chemical transport model. Cloud-J calculates photolysis rates and accounts for cloud and aerosol optical properties at model run-time, replacing the old system based on tabulated values. The performance of Cloud-J is evaluated against aerial photolysis rate observations made over the Pacific Ocean, and against surface observations from three measurement sites in Europe. Numerical experiments are performed to investigate the sensitivity of the calculated photolysis rates to the spatial and temporal model resolution, input meteorology model, simulated ozone column, and cloud effect parameterization. These experiments indicate that the calculated photolysis rates are most sensitive to the choice of input meteorology model and cloud effect parameterization, while also showing that surface ozone photolysis rates can vary by up to 20 % due to daily variations in total ozone column. Further analysis investigates the impact of Cloud-J on the oxidizing capacity of the troposphere, aerosol radiative effect, and surface air quality predictions. Results find that the total tropospheric hydroxyl budget is increased by 26 %, while the radiative impact of aerosols is mostly limited to large tropical biomass burning regions. Overall, Cloud-J represents a major improvement over the tabulated system, leading to improved model performance for predicting carbon monoxide and daily maximum ozone surface concentrations. The bias is worsened for nitrogen dioxide, however, possibly hinting at model shortcomings elsewhere.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.5194/gmd-2023-147
UKCEH and CEH Sections/Science Areas:	Atmospheric Chemistry and Effects (Science Area 2017-)
ISSN:	1991-962X
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
NORA Subject Terms:	Atmospheric Sciences Data and Information
Related URLs:	Dataset Dataset Dataset Other
Date made live:	09 Nov 2023 12:50 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/536034

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