Articles | Volume 17, issue 3
https://doi.org/10.5194/gmd-17-1111-2024
https://doi.org/10.5194/gmd-17-1111-2024
Model evaluation paper
 | 
12 Feb 2024
Model evaluation paper |  | 12 Feb 2024

Implementation of the ISORROPIA-lite aerosol thermodynamics model into the EMAC chemistry climate model (based on MESSy v2.55): implications for aerosol composition and acidity

Alexandros Milousis, Alexandra P. Tsimpidi, Holger Tost, Spyros N. Pandis, Athanasios Nenes, Astrid Kiendler-Scharr, and Vlassis A. Karydis

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Cited articles

Alvarez, D.: JUWELS cluster and booster: Exascale pathfinder with modular supercomputing architecture at juelich supercomputing Centre, Journal of large-scale research facilities JLSRF, 7, A183, https://doi.org/10.17815/jlsrf-7-183, 2021. 
Andreae, M. O., Jones, C. D., and Cox, P. M.: Strong present-day aerosol cooling implies a hot future, Nature, 435, 1187–1190, https://doi.org/10.1038/nature03671, 2005. 
Ansari, A. S. and Pandis, S. N.: The effect of metastable equilibrium states on the partitioning of nitrate between the gas and aerosol phases, Atmos. Environ., 34, 157–168, https://doi.org/10.1016/S1352-2310(99)00242-3, 2000. 
Astitha, M., Lelieveld, J., Abdel Kader, M., Pozzer, A., and de Meij, A.: Parameterization of dust emissions in the global atmospheric chemistry-climate model EMAC: impact of nudging and soil properties, Atmos. Chem. Phys., 12, 11057–11083, https://doi.org/10.5194/acp-12-11057-2012, 2012. 
Bacer, S., Sullivan, S. C., Karydis, V. A., Barahona, D., Krämer, M., Nenes, A., Tost, H., Tsimpidi, A. P., Lelieveld, J., and Pozzer, A.: Implementation of a comprehensive ice crystal formation parameterization for cirrus and mixed-phase clouds in the EMAC model (based on MESSy 2.53), Geosci. Model Dev., 11, 4021–4041, https://doi.org/10.5194/gmd-11-4021-2018, 2018. 
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Short summary
This study aims to evaluate the newly developed ISORROPIA-lite aerosol thermodynamic module within the EMAC model and explore discrepancies in global atmospheric simulations of aerosol composition and acidity by utilizing different aerosol phase states. Even though local differences were found in regions where the RH ranged from 20 % to 60 %, on a global scale the results are similar. Therefore, ISORROPIA-lite can be a reliable and computationally effective alternative to ISORROPIA II in EMAC.
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