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Volume 18, issue 4
Atmos. Chem. Phys., 18, 2853-2881, 2018
https://doi.org/10.5194/acp-18-2853-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: BACCHUS – Impact of Biogenic versus Anthropogenic emissions...

Atmos. Chem. Phys., 18, 2853-2881, 2018
https://doi.org/10.5194/acp-18-2853-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 28 Feb 2018

Research article | 28 Feb 2018

Long-term cloud condensation nuclei number concentration, particle number size distribution and chemical composition measurements at regionally representative observatories

Julia Schmale1, Silvia Henning2, Stefano Decesari3, Bas Henzing4, Helmi Keskinen5,6, Karine Sellegri7, Jurgita Ovadnevaite8, Mira L. Pöhlker9, Joel Brito10,7, Aikaterini Bougiatioti11, Adam Kristensson12, Nikos Kalivitis11, Iasonas Stavroulas11, Samara Carbone10, Anne Jefferson13, Minsu Park14, Patrick Schlag15,16, Yoko Iwamoto17,18, Pasi Aalto5, Mikko Äijälä5, Nicolas Bukowiecki1, Mikael Ehn5, Göran Frank12, Roman Fröhlich1, Arnoud Frumau19, Erik Herrmann1, Hartmut Herrmann2, Rupert Holzinger15, Gerard Kos19, Markku Kulmala5, Nikolaos Mihalopoulos11,20, Athanasios Nenes21,20,22, Colin O'Dowd8, Tuukka Petäjä5, David Picard7, Christopher Pöhlker9, Ulrich Pöschl9, Laurent Poulain2, André Stephan Henry Prévôt1, Erik Swietlicki12, Meinrat O. Andreae9, Paulo Artaxo10, Alfred Wiedensohler2, John Ogren13, Atsushi Matsuki17, Seong Soo Yum14, Frank Stratmann2, Urs Baltensperger1, and Martin Gysel1 Julia Schmale et al.
  • 1Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, 5232 Villigen, Switzerland
  • 2Leibniz Institute for Tropospheric Research, Permoserstrasse 15, 04318 Leipzig, Germany
  • 3Institute of Atmospheric Sciences and Climate, National Research Council of Italy, Via Piero Gobetti, 101, 40129 Bologna, Italy
  • 4Netherlands Organisation for Applied Scientific Research, Princetonlaan 6, 3584 Utrecht, the Netherlands
  • 5Faculty of Science, University of Helsinki, Gustaf Hällströminkatu 2, 00560 Helsinki, Finland
  • 6Hyytiälä Forestry Field Station, Hyytiäläntie 124, Korkeakoski, Finland
  • 7Laboratory for Meteorological Physics (LaMP), Université Clermont Auvergne, 63000 Clermont-Ferrand, France
  • 8School of Physics and CCAPS, National University of Ireland Galway, University Road, Galway, Ireland
  • 9Multiphase Chemistry and Biogeochemistry Departments, Max Planck Institute for Chemistry, Mainz, Germany
  • 10Instituto de Física, Universidade de São Paulo, Rua do Matão 1371, CEP 05508-090, São Paulo, SP, Brazil
  • 11Department of Chemistry, University of Crete, Voutes, 71003 Heraklion, Greece
  • 12Department of Physics, Lund University, 221 00 Lund, Sweden
  • 13Earth System Research Laboratory, National Oceanic and Atmospheric Administration, 325 Broadway, Boulder, CO 80305, USA
  • 14Department of Atmospheric Science, Yonsei University, Seoul, South Korea
  • 15Institute for Marine and Atmospheric Research, University of Utrecht, Utrecht, the Netherlands
  • 16Institute for Energy and Climate Research (IEK-8): Troposphere, Forschungszentrum Jülich, Jülich, Germany
  • 17Institute of Nature and Environmental Technology, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan
  • 18Graduate School of Biosphere Science, Hiroshima University, 1-4-4, Kagamiyama, Higashi-Hiroshima 739-8528, Japan
  • 19Energy Research Centre of the Netherlands, Petten, the Netherlands
  • 20National Observatory of Athens, P. Penteli 15236, Athens, Greece
  • 21School of Chemical & Biomolecular Engineering and School of Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, 30332-0340, USA
  • 22Foundation for Research and Technology – Hellas, Patras, 26504, Greece

Abstract. Aerosol–cloud interactions (ACI) constitute the single largest uncertainty in anthropogenic radiative forcing. To reduce the uncertainties and gain more confidence in the simulation of ACI, models need to be evaluated against observations, in particular against measurements of cloud condensation nuclei (CCN). Here we present a data set – ready to be used for model validation – of long-term observations of CCN number concentrations, particle number size distributions and chemical composition from 12 sites on 3 continents. Studied environments include coastal background, rural background, alpine sites, remote forests and an urban surrounding. Expectedly, CCN characteristics are highly variable across site categories. However, they also vary within them, most strongly in the coastal background group, where CCN number concentrations can vary by up to a factor of 30 within one season. In terms of particle activation behaviour, most continental stations exhibit very similar activation ratios (relative to particles >20nm) across the range of 0.1 to 1.0% supersaturation. At the coastal sites the transition from particles being CCN inactive to becoming CCN active occurs over a wider range of the supersaturation spectrum.

Several stations show strong seasonal cycles of CCN number concentrations and particle number size distributions, e.g. at Barrow (Arctic haze in spring), at the alpine stations (stronger influence of polluted boundary layer air masses in summer), the rain forest (wet and dry season) or Finokalia (wildfire influence in autumn). The rural background and urban sites exhibit relatively little variability throughout the year, while short-term variability can be high especially at the urban site.

The average hygroscopicity parameter, κ, calculated from the chemical composition of submicron particles was highest at the coastal site of Mace Head (0.6) and lowest at the rain forest station ATTO (0.2–0.3). We performed closure studies based on κ–Köhler theory to predict CCN number concentrations. The ratio of predicted to measured CCN concentrations is between 0.87 and 1.4 for five different types of κ. The temporal variability is also well captured, with Pearson correlation coefficients exceeding 0.87.

Information on CCN number concentrations at many locations is important to better characterise ACI and their radiative forcing. But long-term comprehensive aerosol particle characterisations are labour intensive and costly. Hence, we recommend operating migrating-CCNCs to conduct collocated CCN number concentration and particle number size distribution measurements at individual locations throughout one year at least to derive a seasonally resolved hygroscopicity parameter. This way, CCN number concentrations can only be calculated based on continued particle number size distribution information and greater spatial coverage of long-term measurements can be achieved.

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Collocated long-term observations of cloud condensation nuclei (CCN) number concentrations, particle number size distributions and chemical composition from 12 sites are synthesized. Observations cover coastal environments, the Arctic, the Mediterranean, the boreal and rain forest, high alpine and continental background sites, and Monsoon-influenced areas. We interpret regional and seasonal variability. CCN concentrations are predicted with the κ–Köhler model and compared to the measurements.
Collocated long-term observations of cloud condensation nuclei (CCN) number concentrations,...
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