Articles | Volume 26, issue 7
https://doi.org/10.5194/acp-26-4863-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-4863-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Apr 2026
Research article |
| 14 Apr 2026
| 14 Apr 2026
Idealized particle-resolved large-eddy simulations to evaluate the impact of emissions spatial heterogeneity on CCN activity
Department of Climate, Meteorology, and Atmospheric Sciences, University of Illinois Urbana–Champaign, 1301 W. Green St., Urbana, IL 61801, USA
Matin Mohebalhojeh
Department of Mechanical Science and Engineering, University of Illinois Urbana–Champaign, 1206 W. Green St., Urbana, IL 61801,USA
Jeffrey H. Curtis
Department of Climate, Meteorology, and Atmospheric Sciences, University of Illinois Urbana–Champaign, 1301 W. Green St., Urbana, IL 61801, USA
Department of Mechanical Science and Engineering, University of Illinois Urbana–Champaign, 1206 W. Green St., Urbana, IL 61801,USA
Matthew West
Department of Mechanical Science and Engineering, University of Illinois Urbana–Champaign, 1206 W. Green St., Urbana, IL 61801,USA
Department of Climate, Meteorology, and Atmospheric Sciences, University of Illinois Urbana–Champaign, 1301 W. Green St., Urbana, IL 61801, USA
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Cited articles
Brasseur, G. P., Barth, M., Kazil, J., Patton, E. G., and Wang, Y.: Segregation of Fast-Reactive Species in Atmospheric Turbulent Flow, Atmosphere, 14, 1136, https://doi.org/10.3390/atmos14071136, 2023. a
Burkhardt, U. and Kärcher, B.: Process-Based Simulation of Contrail Cirrus in a Global Climate Model, J. Geophys. Res.-Atmos., 114, https://doi.org/10.1029/2008JD011491, 2009. a
Ching, J., Riemer, N., and West, M.: Impacts of Black Carbon Mixing State on Black Carbon Nucleation Scavenging: Insights from a Particle-Resolved Model, J. Geophys. Res.-Atmos., 117, https://doi.org/10.1029/2012JD018269, 2012. a
Ching, J., Fast, J., West, M., and Riemer, N.: Metrics to quantify the importance of mixing state for CCN activity, Atmos. Chem. Phys., 17, 7445–7458, https://doi.org/10.5194/acp-17-7445-2017, 2017. a, b
Crippa, P., Sullivan, R. C., Thota, A., and Pryor, S. C.: The impact of resolution on meteorological, chemical and aerosol properties in regional simulations with WRF-Chem, Atmos. Chem. Phys., 17, 1511–1528, https://doi.org/10.5194/acp-17-1511-2017, 2017. a
Curtis, J. H., Riemer, N., and West, M.: A single-column particle-resolved model for simulating the vertical distribution of aerosol mixing state: WRF-PartMC-MOSAIC-SCM v1.0, Geosci. Model Dev., 10, 4057–4079, https://doi.org/10.5194/gmd-10-4057-2017, 2017. a, b
Curtis, J. H., Riemer, N., and West, M.: Explicit stochastic advection algorithms for the regional-scale particle-resolved atmospheric aerosol model WRF-PartMC (v1.0), Geosci. Model Dev., 17, 8399–8420, https://doi.org/10.5194/gmd-17-8399-2024, 2024. a, b, c, d
Deardorff, J. W.: Stratocumulus-capped mixed layers derived from a three-dimensional model, Bound.-Lay. Meteorol., 18, 495–527, https://doi.org/10.1007/BF00119502, 1980. a
de Bruine, M., Krol, M., Vilà-Guerau de Arellano, J., and Röckmann, T.: Explicit aerosol–cloud interactions in the Dutch Atmospheric Large-Eddy Simulation model DALES4.1-M7, Geosci. Model Dev., 12, 5177–5196, https://doi.org/10.5194/gmd-12-5177-2019, 2019. a
Fierce, L., Riemer, N., and Bond, T. C.: When is cloud condensation nuclei activity sensitive to particle characteristics at emission?, J. Geophys. Res.-Atmos., 118, 13476–13488, https://doi.org/10.1002/2013JD020608, 2013. a
Fierce, L., Riemer, N., and Bond, T. C.: Explaining variance in black carbon's aging timescale, Atmos. Chem. Phys., 15, 3173–3191, https://doi.org/10.5194/acp-15-3173-2015, 2015. a
Fierce, L., Yao, Y., Easter, R., Ma, P.-L., Sun, J., Wan, H., and Zhang, K.: Quantifying structural errors in cloud condensation nuclei activity from reduced representation of aerosol size distributions, J. Aerosol Sci., 181, 106388, https://doi.org/10.1016/j.jaerosci.2024.106388, 2024. a
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D., Mauritsen, T., Palmer, M., Watanabe, M., Wild, M., and Zhang, H.: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 923–1054, https://doi.org/10.1017/9781009157896.009, 2021. a
Frederick, S., Mohebalhojeh, M., Curtis, J., West, M., and Riemer, N.: WRF-PartMC-MOSAIC-LES, Zenodo [code], https://doi.org/10.5281/zenodo.18644307, 2026a. a
Frederick, S., Mohebalhojeh, M., Curtis, J., West, M., and Riemer, N.: Data for “Idealized Particle-Resolved Large-Eddy Simulations to Evaluate the Impact of Emissions Spatial Heterogeneity on CCN Activity”, University of Illinois Urbana-Champaign [data set], https://doi.org/10.13012/B2IDB-9622921_V3, 2026b. a
Golaz, J., Van Roekel, L. P., Zheng, X., Roberts, A. F., Wolfe, J. D., Lin, W., Bradley, A. M., Tang, Q., Maltrud, M. E., Forsyth, R. M., Zhang, C., Zhou, T., Zhang, K., Zender, C. S., Wu, M., Wang, H., Turner, A. K., Singh, B., Richter, J. H., Qin, Y., Petersen, M. R., Mametjanov, A., Ma, P., Larson, V. E., Krishna, J., Keen, N. D., Jeffery, N., Hunke, E. C., Hannah, W. M., Guba, O., Griffin, B. M., Feng, Y., Engwirda, D., Di Vittorio, A. V., Dang, C., Conlon, L. M., Chen, C., Brunke, M. A., Bisht, G., Benedict, J. J., Asay‐Davis, X. S., Zhang, Y., Zhang, M., Zeng, X., Xie, S., Wolfram, P. J., Vo, T., Veneziani, M., Tesfa, T. K., Sreepathi, S., Salinger, A. G., Reeves Eyre, J. E. J., Prather, M. J., Mahajan, S., Li, Q., Jones, P. W., Jacob, R. L., Huebler, G. W., Huang, X., Hillman, B. R., Harrop, B. E., Foucar, J. G., Fang, Y., Comeau, D. S., Caldwell, P. M., Bartoletti, T., Balaguru, K., Taylor, M. A., McCoy, R. B., Leung, L. R., and Bader, D. C.: The DOE E3SM Model Version 2: Overview of the Physical Model and Initial Model Evaluation, J. Adv. Model. Earth Sy., 14, e2022MS003156, https://doi.org/10.1029/2022MS003156, 2022. a
Gustafson Jr., W. I., Qian, Y., and Fast, J. D.: Downscaling aerosols and the impact of neglected subgrid processes on direct aerosol radiative forcing for a representative global climate model grid spacing, J. Geophys. Res.-Atmos., 116, https://doi.org/10.1029/2010JD015480, 2011. a, b
Huszar, P., Cariolle, D., Paoli, R., Halenka, T., Belda, M., Schlager, H., Miksovsky, J., and Pisoft, P.: Modeling the regional impact of ship emissions on NOx and ozone levels over the Eastern Atlantic and Western Europe using ship plume parameterization, Atmos. Chem. Phys., 10, 6645–6660, https://doi.org/10.5194/acp-10-6645-2010, 2010. a
Kaser, L., Karl, T., Yuan, B., Mauldin III, R. L., Cantrell, C. A., Guenther, A. B., Patton, E. G., Weinheimer, A. J., Knote, C., Orlando, J., Emmons, L., Apel, E., Hornbrook, R., Shertz, S., Ullmann, K., Hall, S., Graus, M., de Gouw, J., Zhou, X., and Ye, C.: Chemistry-turbulence interactions and mesoscale variability influence the cleansing efficiency of the atmosphere, Geophys. Res. Lett., 42, 10894–10903, https://doi.org/10.1002/2015GL066641, 2015. a
Kokkola, H., Korhonen, H., Lehtinen, K. E. J., Makkonen, R., Asmi, A., Järvenoja, S., Anttila, T., Partanen, A.-I., Kulmala, M., Järvinen, H., Laaksonen, A., and Kerminen, V.-M.: SALSA – a Sectional Aerosol module for Large Scale Applications, Atmos. Chem. Phys., 8, 2469–2483, https://doi.org/10.5194/acp-8-2469-2008, 2008. a
Kurppa, M., Hellsten, A., Roldin, P., Kokkola, H., Tonttila, J., Auvinen, M., Kent, C., Kumar, P., Maronga, B., and Järvi, L.: Implementation of the sectional aerosol module SALSA2.0 into the PALM model system 6.0: model development and first evaluation, Geosci. Model Dev., 12, 1403–1422, https://doi.org/10.5194/gmd-12-1403-2019, 2019. a
Li, Y., Barth, M. C., Patton, E. G., and Steiner, A. L.: Impact of In-Cloud Aqueous Processes on the Chemistry and Transport of Biogenic Volatile Organic Compounds, J. Geophys. Res.-Atmos., 122, 11131–11153, https://doi.org/10.1002/2017JD026688, 2017. a
Lin, G., Qian, Y., Yan, H., Zhao, C., Ghan, S. J., Easter, R., and Zhang, K.: Quantification of marine aerosol subgrid variability and its correlation with clouds based on high-resolution regional modeling, J. Geophys. Res.-Atmos., 122, 6329–6346, https://doi.org/10.1002/2017JD026567, 2017. a
Liu, Y., Yao, Y., Curtis, J. H., West, M., and Riemer, N.: The impacts of aerosol mixing state on heterogeneous N2O5 hydrolysis, Aerosol Sci. Technol., 59, 402–423, https://doi.org/10.1080/02786826.2024.2443587, 2025. a
Ma, P.-L., Rasch, P. J., Wang, M., Wang, H., Ghan, S. J., Easter, R. C., Gustafson Jr., W. I., Liu, X., Zhang, Y., and Ma, H.-Y.: How does increasing horizontal resolution in a global climate model improve the simulation of aerosol-cloud interactions?, Geophys. Res. Lett., 42, 5058–5065, https://doi.org/10.1002/2015GL064183, 2015. a
Mohebalhojeh, M., Frederick, S., Riemer, N., and West, M.: A Metric for Quantifying Spatial Heterogeneity in Gridded Atmospheric Fields, ESS Open Archive [preprint], https://doi.org/10.22541/essoar.176805046.69348251/v1, 2026. a, b
Ouwersloot, H. G., Vilà-Guerau de Arellano, J., van Heerwaarden, C. C., Ganzeveld, L. N., Krol, M. C., and Lelieveld, J.: On the segregation of chemical species in a clear boundary layer over heterogeneous land surfaces, Atmos. Chem. Phys., 11, 10681–10704, https://doi.org/10.5194/acp-11-10681-2011, 2011. a
Qian, Y., Gustafson Jr., W. I., and Fast, J. D.: An investigation of the sub-grid variability of trace gases and aerosols for global climate modeling, Atmos. Chem. Phys., 10, 6917–6946, https://doi.org/10.5194/acp-10-6917-2010, 2010. a, b
Salmon, L. G., Nazaroff, W. W., Ligocki, M. P., Jones, M. C., and Cass, G. R.: Nitric acid concentrations in southern California museums, Environ. Sci. Technol., 24, 1004–1013, https://doi.org/10.1021/es00077a009, 1990. a
Skamarock, C., Klemp, B., Dudhia, J., Gill, O., Barker, D., Duda, G., Huang, X.-Y., Wang, W., and Powers, G.: A Description of the Advanced Research WRF Version 3, NCAR Technical Notes, https://doi.org/10.5065/D68S4MVH, 2008. a
Tilmes, S., Mills, M. J., Zhu, Y., Bardeen, C. G., Vitt, F., Yu, P., Fillmore, D., Liu, X., Toon, B., and Deshler, T.: Description and performance of a sectional aerosol microphysical model in the Community Earth System Model (CESM2), Geosci. Model Dev., 16, 6087–6125, https://doi.org/10.5194/gmd-16-6087-2023, 2023. a
Tonttila, J., Maalick, Z., Raatikainen, T., Kokkola, H., Kühn, T., and Romakkaniemi, S.: UCLALES–SALSA v1.0: a large-eddy model with interactive sectional microphysics for aerosol, clouds and precipitation, Geosci. Model Dev., 10, 169–188, https://doi.org/10.5194/gmd-10-169-2017, 2017. a
Topping, D. O. and McFiggans, G.: Tight coupling of particle size, number and composition in atmospheric cloud droplet activation, Atmos. Chem. Phys., 12, 3253–3260, https://doi.org/10.5194/acp-12-3253-2012, 2012. a
Toro, C., Sonntag, D., Bash, J., Burke, G., Murphy, B. N., Seltzer, K. M., Simon, H., Shephard, M. W., and Cady-Pereira, K. E.: Sensitivity of Air Quality to Vehicle Ammonia Emissions in the United States, Atmos. Environ., 327, 120484, https://doi.org/10.1016/j.atmosenv.2024.120484, 2024. a
Vignati, E., Wilson, J., and Stier, P.: M7: An efficient size-resolved aerosol microphysics module for large-scale aerosol transport models, J. Geophys. Res.-Atmos., 109, https://doi.org/10.1029/2003JD004485, 2004. a
Weigum, N., Schutgens, N., and Stier, P.: Effect of aerosol subgrid variability on aerosol optical depth and cloud condensation nuclei: implications for global aerosol modelling, Atmos. Chem. Phys., 16, 13619–13639, https://doi.org/10.5194/acp-16-13619-2016, 2016. a, b
Zaveri, R. A. and Peters, L. K.: A new lumped structure photochemical mechanism for large-scale applications, J. Geophys. Res.-Atmos., 104, 30387–30415, https://doi.org/10.1029/1999JD900876, 1999. a
Zaveri, R. A., Easter, R. C., and Peters, L. K.: A computationally efficient Multicomponent Equilibrium Solver for Aerosols (MESA), J. Geophys. Res.-Atmos., 110, https://doi.org/10.1029/2004JD005618, 2005a. a
Zaveri, R. A., Easter, R. C., and Wexler, A. S.: A new method for multicomponent activity coefficients of electrolytes in aqueous atmospheric aerosols, J. Geophys. Res.-Atmos., 110, https://doi.org/10.1029/2004JD004681, 2005b. a
Zaveri, R. A., Easter, R. C., Fast, J. D., and Peters, L. K.: Model for Simulating Aerosol Interactions and Chemistry (MOSAIC), J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2007JD008782, 2008. a, b, c
Zaveri, R. A., Barnard, J. C., Easter, R. C., Riemer, N., and West, M.: Particle-resolved simulation of aerosol size, composition, mixing state, and the associated optical and cloud condensation nuclei activation properties in an evolving urban plume, J. Geophys. Res.-Atmos., 115, https://doi.org/10.1029/2009JD013616, 2010. a
Zaveri, R. A., Easter, R. C., Singh, B., Wang, H., Lu, Z., Tilmes, S., Emmons, L. K., Vitt, F., Zhang, R., Liu, X., Ghan, S. J., and Rasch, P. J.: Development and Evaluation of Chemistry-Aerosol-Climate Model CAM5-Chem-MAM7-MOSAIC: Global Atmospheric Distribution and Radiative Effects of Nitrate Aerosol, J. Adv. Model. Earth Sy., 13, e2020MS002346, https://doi.org/10.1029/2020MS002346, 2021. a
Short summary
We show with detailed computer simulations that spatial patterns of emissions strongly affect aerosols and their ability to seed clouds. Highly variable emissions can raise cloud-forming particle concentrations in the boundary layer by up to 25 %. Because clouds regulate climate and precipitation, these findings underscore the need to represent realistic emission patterns to improve climate predictions.
We show with detailed computer simulations that spatial patterns of emissions strongly affect...
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