Preprints
https://doi.org/10.5194/egusphere-2024-1059
https://doi.org/10.5194/egusphere-2024-1059
21 May 2024
 | 21 May 2024
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

CCN estimations at a high-altitude remote site: role of organic aerosol variability and hygroscopicity

Fernando Rejano, Andrea Casans, Marta Via, Juan Andrés Casquero-Vera, Sonia Castillo, Hassan Lyamani, Alberto Cazorla, Elisabeth Andrews, Daniel Pérez-Ramírez, Andrés Alastuey, Francisco Javier Gómez-Moreno, Lucas Alados-Arboledas, Francisco José Olmo, and Gloria Titos

Abstract. High-altitude remote sites are unique places to study aerosol-cloud interactions since they are located at the altitude where clouds may form. At these remote sites, organic aerosols (OA) are the main constituents of the overall aerosol population, playing a crucial role in defining aerosol hygroscopicity (κ). To estimate the CCN budget at OA dominated sites, it is crucial to accurately characterize OA hygroscopicity (κOA) and how its temporal variability affects the CCN activity of the aerosol population since κOA is not well established due to complex nature of ambient OA. In this study, we performed CCN closures at a high-altitude remote site during summer season to investigate the role of κOA in predicting CCN concentrations under different atmospheric conditions. In addition, we performed an OA source apportionment using Positive Matrix Factorization (PMF). Three OA factors were identified from the PMF analysis: hydrocarbon-like OA (HOA), less-oxidized oxygenated OA (LO-OOA) and more-oxidized oxygenated OA (MO-OOA), with average contributions of 5 %, 36 % and 59 % of the total OA, respectively. This result highlights the predominance of secondary organic aerosol with high degree of oxidation at this high-altitude site. To understand the impact of each OA factor on the overall OA hygroscopicity we defined three κOA schemes that assume different hygroscopicity values for each OA factor. Our results show that the different κOA schemes lead to similar CCN closure results between observations and predictions (slope and correlation ranging between 1.08–1.40 and 0.89–0.94, respectively). However, the predictions were not equally accurate across the day. During nighttime, CCN predictions underestimated observations by 6–16 %, while during morning and midday hours, when the aerosol was influenced by vertical transport of particles and/or new particle formation events, CCN concentrations were overestimated by 0–20 %. To further evaluate the role of κOA in CCN predictions, we established a new OA scheme that uses the OA oxidation level (parameterized by the f44 factor) to calculate κOA and predict CCN. This method also shows a large bias, especially during midday hours (up to 40 %), indicating that diurnal information about the oxygenation degree does not improve CCN predictions. Finally, we used a neural network model with four inputs: N80 (number concentration of particles with diameter >80 nm), OA fraction, f44 and surface global radiation) to predict CCN. This model matched the observations better than the previous approaches, with a bias within ±10 % and with no daily variation, reproducing the CCN variability along the day. Therefore, neural network models seem to be an appropriate tool to estimate CCN concentrations using ancillary parameters accordingly.

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Fernando Rejano, Andrea Casans, Marta Via, Juan Andrés Casquero-Vera, Sonia Castillo, Hassan Lyamani, Alberto Cazorla, Elisabeth Andrews, Daniel Pérez-Ramírez, Andrés Alastuey, Francisco Javier Gómez-Moreno, Lucas Alados-Arboledas, Francisco José Olmo, and Gloria Titos

Status: open (until 02 Jul 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-1059', Anonymous Referee #3, 21 May 2024 reply
  • RC2: 'Comment on egusphere-2024-1059', Anonymous Referee #2, 05 Jun 2024 reply
Fernando Rejano, Andrea Casans, Marta Via, Juan Andrés Casquero-Vera, Sonia Castillo, Hassan Lyamani, Alberto Cazorla, Elisabeth Andrews, Daniel Pérez-Ramírez, Andrés Alastuey, Francisco Javier Gómez-Moreno, Lucas Alados-Arboledas, Francisco José Olmo, and Gloria Titos
Fernando Rejano, Andrea Casans, Marta Via, Juan Andrés Casquero-Vera, Sonia Castillo, Hassan Lyamani, Alberto Cazorla, Elisabeth Andrews, Daniel Pérez-Ramírez, Andrés Alastuey, Francisco Javier Gómez-Moreno, Lucas Alados-Arboledas, Francisco José Olmo, and Gloria Titos

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Short summary
This study provides valuable insights to improve cloud condensation nuclei (CCN) estimations at a high-altitude remote site which is influenced by nearby urban pollution. Understanding the factors that affect CCN estimations is essential to improve the CCN data coverage worldwide and assess aerosol-cloud interactions in a global scale. This is crucial for improving climate models since aerosol-cloud interactions are the most important source of uncertainty in climate projections.