Publication Abstracts

Fanourgakis et al. 2019, submitted

Fanourgakis, G.S., M. Kanakidou, A. Nenes, S.E. Bauer, T. Bergman, K.S. Carslaw, A. Grini, D.S. Hamilton, J.S. Johnson, V.A. Karydis, A. Kirkevåg, J.K. Kodros, U. Lohmann, G. Luo, R. Makkonen, H. Matsui, D. Neubauer, J.R. Pierce, J. Schmale, P. Stier, K. Tsigaridis, T. van Noije, H. Wang, D. Watson-Parris, D.M. Westervelt, Y. Yang, M. Yoshioka, N. Daskalakis, S. Decesari, M. Gysel Beer, N. Kalivitis, X. Liu, N.M. Mahowald, S. Myriokefalitakis, R. Schrödner, M. Sfakianaki, A.P. Tsimpidi, M. Wu, and F. Yu, 2019: Evaluation of global simulations of aerosol particle number and cloud condensation nuclei, and implications for cloud droplet formation. Atmos. Chem. Phys., submitted, doi:10.5194/acp-2018-1340.

A total of sixteen global chemistry transport models and general circulation models have participated in this study. Fourteen models have been evaluated with regard to their ability to reproduce near-surface observed number concentration of aerosol particle and cloud condensation nuclei (CCN), and derived cloud droplet number concentration (CDNC). Model results for the period 2011-2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations, located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments, and on the seasonal and short-term variability in the aerosol properties.

There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of -24% and -35% for particles with dry diameters > 50nm and > 120nm and -36% and -34% for CCN at supersaturations of 0.2% and 1.0%, respectively. Fifteen models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3nm) and up to about 1 for simulated CCN in the extra-polar regions.

An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter.

Models capture the relative amplitude of seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40% during winter and 20% in summer.

In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB -17% and -22% for updraft velocities 0.3 and 0.6ms-1, respectively). In addition, simulated CDNC is in slightly better agreement with observationally-derived value at lower than at higher updraft velocities (index-of-agreement of 0.47 vs 0.50). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration and to updraft velocity. Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high.

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BibTeX Citation

@unpublished{fa03100n,
  author={Fanourgakis, G. S. and Kanakidou, M. and Nenes, A. and Bauer, S. E. and Bergman, T. and Carslaw, K. S. and Grini, A. and Hamilton, D. S. and Johnson, J. S. and Karydis, V. A. and Kirkevåg, A. and Kodros, J. K. and Lohmann, U. and Luo, G. and Makkonen, R. and Matsui, H. and Neubauer, D. and Pierce, J. R. and Schmale, J. and Stier, P. and Tsigaridis, K. and van Noije, T. and Wang, H. and Watson-Parris, D. and Westervelt, D. M. and Yang, Y. and Yoshioka, M. and Daskalakis, N. and Decesari, S. and Gysel Beer, M. and Kalivitis, N. and Liu, X. and Mahowald, N. M. and Myriokefalitakis, S. and Schrödner, R. and Sfakianaki, M. and Tsimpidi, A. P. and Wu, M. and Yu, F.},
  title={Evaluation of global simulations of aerosol particle number and cloud condensation nuclei, and implications for cloud droplet formation},
  year={2019},
  journal={Atmos. Chem. Phys.},
  doi={10.5194/acp-2018-1340},
  note={Manuscript submitted for publication}
}

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RIS Citation

TY  - UNPB
ID  - fa03100n
AU  - Fanourgakis, G. S.
AU  - Kanakidou, M.
AU  - Nenes, A.
AU  - Bauer, S. E.
AU  - Bergman, T.
AU  - Carslaw, K. S.
AU  - Grini, A.
AU  - Hamilton, D. S.
AU  - Johnson, J. S.
AU  - Karydis, V. A.
AU  - Kirkevåg, A.
AU  - Kodros, J. K.
AU  - Lohmann, U.
AU  - Luo, G.
AU  - Makkonen, R.
AU  - Matsui, H.
AU  - Neubauer, D.
AU  - Pierce, J. R.
AU  - Schmale, J.
AU  - Stier, P.
AU  - Tsigaridis, K.
AU  - van Noije, T.
AU  - Wang, H.
AU  - Watson-Parris, D.
AU  - Westervelt, D. M.
AU  - Yang, Y.
AU  - Yoshioka, M.
AU  - Daskalakis, N.
AU  - Decesari, S.
AU  - Gysel Beer, M.
AU  - Kalivitis, N.
AU  - Liu, X.
AU  - Mahowald, N. M.
AU  - Myriokefalitakis, S.
AU  - Schrödner, R.
AU  - Sfakianaki, M.
AU  - Tsimpidi, A. P.
AU  - Wu, M.
AU  - Yu, F.
PY  - 2019
TI  - Evaluation of global simulations of aerosol particle number and cloud condensation nuclei, and implications for cloud droplet formation
JA  - Atmos. Chem. Phys.
DO  - 10.5194/acp-2018-1340
ER  -

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