Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.509 IF 5.509
  • IF 5-year value: 5.689 IF 5-year
  • CiteScore value: 5.44 CiteScore
  • SNIP value: 1.519 SNIP 1.519
  • SJR value: 3.032 SJR 3.032
  • IPP value: 5.37 IPP 5.37
  • h5-index value: 86 h5-index 86
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 161 Scimago H
    index 161
Discussion papers
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 18 Jan 2019

Research article | 18 Jan 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Evaluation of global simulations of aerosol particle number and cloud condensation nuclei, and implications for cloud droplet formation

George S. Fanourgakis1, Maria Kanakidou1, Athanasios Nenes2,3,4,5, Susanne E. Bauer6,7, Tommi Bergman8, Ken S. Carslaw9, Alf Grini10, Douglas S. Hamilton11, Jill S. Johnson9, Vlassis A. Karydis12,13, Alf Kirkevåg14, John K. Kodros15, Ulrike Lohmann16, Gan Luo17, Risto Makkonen18,19, Hitoshi Matsui20, David Neubauer16, Jeffrey R. Pierce15, Julia Schmale21, Philip Stier22, Kostas Tsigaridis7,6, Twan van Noije8, Hailong Wang23, Duncan Watson-Parris22, Daniel M. Westervelt24,6, Yang Yang23, Masaru Yoshioka9, Nikos Daskalakis25, Stefano Decesari26, Martin Gysel Beer21, Nikos Kalivitis1, Xiaohong Liu27, Natalie M. Mahowald11, Stelios Myriokefalitakis28, Roland Schrödner29, Maria Sfakianaki1, Alexandra P. Tsimpidi12, Mingxuan Wu27, and Fangqun Yu17 George S. Fanourgakis et al.
  • 1Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, Heraklion, 70013, Greece
  • 2Laboratory of Atmospheric Processes and their Impacts, School of Architecture, Civil & Environmental Engineering, Ecole Polytechnique Federale de Lausanne, Lausanne, 1015, Switzerland
  • 3IERSD, National Observatory of Athens, P. Penteli 15236, Athens, Greece
  • 4ICE-HT, Foundation for Research and Technology – Hellas, Greece
  • 5School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, 30332-0340, USA
  • 6NASA Goddard Institute for Space Studies, New York NY USA
  • 7Center for Climate Systems Research, Columbia University, New York NY USA
  • 8Royal Netherlands Meteorological Institute (KNMI) , DeBilt, the Netherlands
  • 9School of Earth and Environment, University of Leeds, UK
  • 10independent researcher
  • 11Department of Earth and Atmospheric Sciences, Atkinson Center for a Sustainable Future, Cornell University, Ithaca, NY, USA
  • 12Department of Atmospheric Chemistry, Max Planck Institute for Chemistry, Mainz, Germany
  • 13Forschungszentrum Jülich, Inst Energy & Climate Res IEK-8, 52425 Jülich, Germany
  • 14Norwegian Meteorological Institute, Oslo, Norway
  • 15Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA
  • 16ETH Zurich, Institute for Atmospheric and Climate Science, Zurich, Switzerland
  • 17The Atmospheric Sciences Research Center (ASRC), of the State University of New York at Albany
  • 18System Research, Finnish Meteorological Institute, P.O. Box 503, 00101, Helsinki, Finland
  • 19Institute for Atmospheric and Earth System Research/Physics, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland
  • 20Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan
  • 21Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen, Switzerland
  • 22Atmospheric, Oceanic & Planetary Physics, Department of Physics, University of Oxford, UK
  • 23Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
  • 24Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA, 10964
  • 25Laboratory for Modeling and Observation of the Earth System (LAMOS ) Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany
  • 26Institute of Atmospheric Sciences and Climate, National Research Council of Italy, Via Piero Gobetti, 101, 40129 Bolonga, Italy
  • 27Department of Atmospheric Science, University of Wyoming, Laramie, Wyoming, USA
  • 28Institute for Environmental Research and Sustainable Development, National Observatory of Athens, Penteli, Greece
  • 29Centre for Environmental and Climate Research, Lund University, Sweden

Abstract. 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 > 50 nm and > 120 nm 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 3 nm) 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 120 nm). 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 0.6 m s−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.

George S. Fanourgakis et al.
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for Authors/Co-Editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
George S. Fanourgakis et al.
George S. Fanourgakis et al.
Total article views: 615 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
445 167 3 615 27 8 6
  • HTML: 445
  • PDF: 167
  • XML: 3
  • Total: 615
  • Supplement: 27
  • BibTeX: 8
  • EndNote: 6
Views and downloads (calculated since 18 Jan 2019)
Cumulative views and downloads (calculated since 18 Jan 2019)
Viewed (geographical distribution)  
Total article views: 480 (including HTML, PDF, and XML) Thereof 475 with geography defined and 5 with unknown origin.
Country # Views %
  • 1
No saved metrics found.
No discussed metrics found.
Latest update: 20 Apr 2019
Publications Copernicus
Short summary
Effects of aerosols on clouds are important for climate studies but are among the largest uncertainties in climate projections. This study evaluates the skill of global models to simulate aerosol, cloud condensation nuclei (CCN) and cloud droplet (CDNC) number concentrations. Model results show reduced spread in CDNC compared to CCN, due to the negative correlation between the sensitivities of CDNC to aerosol number concentration (air pollution) and updraft velocity (atmospheric dynamics).
Effects of aerosols on clouds are important for climate studies but are among the largest...