Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/acp-2017-798
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
31 Aug 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).
What do we learn from 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, Mikhail Paramonov5,7, Karine Sellegri8, Jurgita Ovadnevaite9, Mira L. Pöhlker10, Joel Brito11,8, Aikaterini Bougiatioti12, Adam Kristensson13, Nikos Kalivitis12, Iasonas Stavroulas12, Samara Carbone11, Anne Jefferson14, Minsu Park15, Patrick Schlag16,17, Yoko Iwamoto18,19, Pasi Aalto5, Mikko Äijälä5, Nicolas Bukowiecki1, Mikael Ehn5, Göran Frank13, Roman Fröhlich1, Arnoud Frumau20, Erik Herrmann1, Hartmut Herrmann2, Rupert Holzinger16, Gerard Kos20, Markku Kulmala5, Nikolaos Mihalopoulos12,21, Athanasios Nenes22,21,23, Colin O'Dowd9, Tuukka Petäjä5, David Picard8, Christopher Pöhlker10, Ulrich Pöschl10, Laurent Poulain2, André Stephan Henry Prévôt1, Erik Swietlicki13, Meintrat O. Andreae10, Paulo Artaxo11, Alfred Wiedensohler2, John Ogren14, Atsushi Matsuki18, Seong Soo Yum15, Frank Stratmann2, Urs Baltensperger1, and Martin Gysel1 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 Bolonga, Italy
4Netherlands Organisation for Applied Scientific Research, Princetonlaan 6, 3584 Utrecht, The Netherlands
5Department of Physics, University of Helsinki, Gustaf Hällströminkatu 2, 00014, Helsinki, Finland
6Hyytiälä Forestry Field Station, Hyytiäläntie 124, Korkeakoski, Finland
7Institute for Atmospheric and Climate Science, ETH Zurich, Universitätsstrasse 16, 8092 Zurich, Switzerland
8Laboratory for Meteorological Physics (LaMP), Université Clermont Auvergne, F-63000 Clermont-Ferrand, France
9School of Physics and CCAPS, National University of Ireland Galway, University Road, Galway, Ireland
10Multiphase Chemistry and Biogeochemistry Departments, Max Planck Institute for Chemistry, Mainz, Germany
11Instituto de Física, Universidade de São Paulo, Rua do Matão 1371, CEP 05508-090, São Paulo, SP, Brazil
12Department of Chemistry, University of Crete, Voutes, 71003 Heraklion, Greece
13Department of Physics, Lund University, 221 00 Lund, Sweden
14Earth System Research Laboratory, National Oceanic and Atmospheric Administration, 325 Broadway, Boulder, CO 80305, USA
15Department of Atmospheric Science, Yonsei University, Seoul, South Korea
16Institute for Marine and Atmospheric Research, University of Utrecht, Utrecht, The Netherlands
17Institute for Energy and Climate Research (IEK-8): Troposphere, Forschungszentrum Jülich, Jülich, Germany
18Institute of Nature and Environmental Technology, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan
19Graduate School of Biosphere Science, Hiroshima University, 1-4-4, Kagamiyama, Higashi-Hiroshima 739-8528, Japan
20Energy Research Center of the Netherlands, Petten, The Netherlands
21National Observatory of Athens, P. Penteli 15236, Athens, Greece
22School of Chemical & Biomolecular Engineering and School of Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, 30332-0340, USA
23Foundation for Research and Technology – Hellas, 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). Numerous observations of CCN number concentration exist, and many closure studies have been performed to predict CCN number concentrations based on particle number size distributions, chemical composition, and the κ-Köhler theory. Most of these studies provide details for short time periods or focus on special environmental conditions. These observations, however, cannot address questions of large-scale temporal and spatial CCN variability. Here we analyze long-term observations of CCN number concentrations, particle number size distributions and chemical composition from twelve sites on three continents. Eight of these stations are part of the European Aerosols, Clouds, and Trace gases Research InfraStructure (ACTRIS).

We group the observatories into categories according to their official classification: coastal background (Barrow, Alaska; Mace Head, Ireland; Finokalia, Crete; Noto Peninsula, Japan), rural background (Melpitz, Germany; Cabauw, the Netherlands; Vavihill, Sweden), alpine sites (Puy de Dôme, France; Jungfraujoch, Switzerland), remote forest sites (ATTO, Brazil; SMEAR, Finland) and the urban environment (Seoul, South Korea). Expectedly, CCN characteristics are highly variable across regions. However, they also vary within categories, 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 behavior, most continental stations exhibit very similar relative activation ratios across the range of 0.1 to 1.0 % supersaturation. At the coastal sites the activation ratios spread more widely across the SS 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 (forest fire influence in fall). 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 the lowest at the rain forest station ATTO (0.2–0.3). We performed closure studies to predict CCN number concentrations from the particle number size distribution and chemical composition measurements. The prediction accuracy for the average concentrations is high. The ratio between predicted and measured CCN concentrations is between 0.87 and 1.4. The temporal variability is also well represented, as reflected by Pearson correlation coefficients > 0.87. We also conducted a series of sensitivity studies for the ratio of predicted versus measured CCN concentration, where we varied the hygroscopicity parameter κ, and made simple assumptions for aerosol particle number concentrations and size distributions. Uncertain particle number concentrations and their size distributions significantly impair the accuracy in predicting temporal variability and hence of absolute concentrations, while the effect of uncertain κ values is limited to the predicted CCN number concentration.

Information on CCN number concentrations at many locations is important to better characterize ACI and their radiative forcing. Long-term comprehensive aerosol particle characterizations are labor intensive and costly. For observatories where such efforts are out of scope to obtain nevertheless long-term information of CCN number concentrations, we recommend conducting 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 be calculated based on continued particle number size distribution information only. This approach is a good alternative to deriving kappa from time-resolved chemical composition measurements which are costly and may still not cover the appropriate size range. Additionally, given the variability in observations at sites of the same category, a certain density in spatial coverage of observations is needed, especially along coastlines. We recommend operating "migrating-CCNCs" at priority locations, identified by model evaluation, around the globe where long-term particle number size distribution data are already available.


Citation: Schmale, J., Henning, S., Decesari, S., Henzing, B., Keskinen, H., Paramonov, M., Sellegri, K., Ovadnevaite, J., Pöhlker, M. L., Brito, J., Bougiatioti, A., Kristensson, A., Kalivitis, N., Stavroulas, I., Carbone, S., Jefferson, A., Park, M., Schlag, P., Iwamoto, Y., Aalto, P., Äijälä, M., Bukowiecki, N., Ehn, M., Frank, G., Fröhlich, R., Frumau, A., Herrmann, E., Herrmann, H., Holzinger, R., Kos, G., Kulmala, M., Mihalopoulos, N., Nenes, A., O'Dowd, C., Petäjä, T., Picard, D., Pöhlker, C., Pöschl, U., Poulain, L., Prévôt, A. S. H., Swietlicki, E., Andreae, M. O., Artaxo, P., Wiedensohler, A., Ogren, J., Matsuki, A., Yum, S. S., Stratmann, F., Baltensperger, U., and Gysel, M.: What do we learn from long-term cloud condensation nuclei number concentration, particle number size distribution, and chemical composition measurements at regionally representative observatories?, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-798, in review, 2017.
Julia Schmale et al.
Julia Schmale et al.

Data sets

Collocated observations of cloud condensation nuclei, particle size distributions, and chemical composition
J. Schmale, S. Henning, B. Henzing, H. Keskinen, K. Sellegri, J. Ovadnevaite, A. Bougiatioti, N. Kalivitis, I. Stavroulas, A. Jefferson, M. Park, P. Schlag, A. Kristensson, Y. Iwamoto, K. Pringle, C. Reddington, P. Aalto, M. Äijälä, U. Baltensperger, J. Bialek, W. Birmili, N. Bukowiecki, M. Ehn, A. M. Fjæraa, M. Fiebig, G. Frank, R. Fröhlich, A. Frumau, M. Furuya, E. Hammer, L. Heikkinen, E. Herrmann, R. Holzinger, H. Hyono, M. Kanakidou, A. Kiendler-Scharr, K. Kinouchi, G. Kos, M. Kulmala, N. Mihalopoulos, G. Motos, A. Nenes, C. O'Dowd, M. Paramonov, T. Petäjä, D. Picard, L. Poulain, A. S. H. Prévôt, J. Slowik, A. Sonntag, E. Swietlicki, B. Svenningsson, H. Tsurumaru, A. Wiedensohler, C. Wittbom, J. A. Ogren, A. Matsuki, S. S. Yum, C. Lund Myhre, K. Carslaw, F. Stratmann, and M. Gysel
https://doi.org/10.6084/m9.figshare.c.3471585
Julia Schmale et al.

Viewed

Total article views: 540 (including HTML, PDF, and XML)

HTML PDF XML Total Supplement BibTeX EndNote
363 172 5 540 14 3 13

Views and downloads (calculated since 31 Aug 2017)

Cumulative views and downloads (calculated since 31 Aug 2017)

Viewed (geographical distribution)

Total article views: 540 (including HTML, PDF, and XML)

Thereof 535 with geography defined and 5 with unknown origin.

Country # Views %
  • 1

Saved

Discussed

Latest update: 23 Sep 2017
Publications Copernicus
Download
Short summary
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 enivornments, 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 k-Köhler model and compared to the measurements.
Collocated long-term observations of cloud condensation nuclei (CCN) number concentrations,...
Share