Evaluating aerosol representation in TM5 chemical transport model using in-situ observations

Aerosol and cloud uncertainty dominates uncertainty in climate prediction (Masson-Delmotte 2021). Aerosol Cloud Interactions (ACIs) are mediated through cloud condensation nuclei (CCN), aerosol particles of large enough size and hygroscopicity to act as seeds upon which cloud droplet can form. Cloud droplet number concentration (CDNC) is determined by both the number of available CCN and the water vapour supersaturation these CCN experience. Variability and uncertainty in CCNC are primarily important in CCN-limited regimes where the CDNC is limited by the number of available CCN. These conditions prevail in much of the boundary layer and lower free troposphere (Rosenfeld et al. 2014). The number concentration of CCN is highly variable, both globally and locally (Schmale et al. 2018), depending on the abundance, size distribution and chemical composition of primary and secondary aerosols from both anthropogenic and biogenic sources. Therefore, CCN and related aerosol observations in different environments are needed to evaluate their representation in global models.

Here we use long-term in-situ observations of CCN number concentrations, particle number size distributions, and particle chemical composition from ground-stations to evaluate CCN representation in the TM5 chemical transport model. We evaluate the default version of TM5 alongside representation of some of the sources of aerosol uncertainty in the model, represented by one-at-a-time sensitivity studies of uncertain aerosol input parameters including emissions of sea salt and biomass burning aerosol, secondary organic aerosol precursors and removal by wet and dry deposition. The model was run for 2017 and 2018, and so ground stations with relevant publicly available observations over at least 9 months of this period are chosen for evaluation including US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) site at Oklahoma, USA (Southern Great Plains) and ARM mobile facility deployment during the Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign on Ascension Island (Andrews et al. 2025), SMEAR II station in Hyytiälä, Finland (Kulmala 2023) and Kennaook/Cape Grim Baseline Air Pollution Station in Tasmania (Keywood 2018).

References

Andrews, E., Zabala, I., Carrillo-Cardenas, G., et al. 'Harmonized aerosol size distribution, cloud condensation nuclei, chemistry and optical properties at 10 sites', Scientific Data, 12: 937.10.1038/s41597-025-04931-y 2025.

Keywood, M., Ward, J., Derek, N., GAW-WDCA. 'Cloud_condensation_nuclei_number_concentration at Kennaook / Cape Grim Baseline Air Pollution Station, data hosted by EBAS at NILU'.10.48597/Z8RB-RDC9 2018.

Kulmala, M., Petäjä, T. "Particle_number_concentration at Hyytiälä, data hosted by EBAS at NILU." In.https://doi.org/10.48597/RHAH-5H7M 2023.

Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou. "IPCC Climate Change 2021: The Physical Science Basis." In.: Cambridge University Press.10.1017/9781009157896 2021.

Rosenfeld, D., Andreae, M.O., Asmi, A., et al. 'Global observations of aerosol-cloud-precipitation-climate interactions', Reviews of Geophysics, 52: 750-808.https://doi.org/10.1002/2013RG000441 2014.

Schmale, J., Henning, S., Decesari, S., et al. 'Long-term cloud condensation nuclei number concentration, particle number size distribution and chemical composition measurements at regionally representative observatories', Atmos. Chem. Phys., 18: 2853-81.10.5194/acp-18-2853-2018 2018.