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, 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 -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 . 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.
Bibliographical noteFunding Information:
Acknowledgements. The authors acknowledge funding from the European FP7 project BACCHUS (grant agreement no. 49603445) and the Horizon 2020 research and innovation programme ACTRIS-2 Integrating Activities (grant agreement no. 654109). Long-term measurements at Jungfraujoch are supported by the International Foundation High Altitude Research Stations Jungfraujoch and Gornergrat and MeteoSwiss in the framework of the Global Atmosphere Watch (GAW) programme. This work was supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 15.0159-1. The opinions expressed and arguments employed herein do not necessarily reflect the official views of the Swiss Government. Measurements at Mace Head are supported by HEA-PRTLI4 Environment and Climate Change: Impact and Responses programme and EPA Ireland. The research at Cabauw has received funding from the European Union Seventh Framework Programme (FP7, grant agreement no. 262254). We appreciate the support from KNMI in hosting the experiment at Cabauw and for the access to meteorological data from the tower. We also thank Philip Croteau (Aerodyne Research) for his support on the Cabauw ACSM measurements regarding the data acquisition and evaluation. The research at Noto was supported by JSPS Grant-in-Aid for Young Scientists (A, grant no. JP26701001). The research in Seoul was supported by Grant KMIPA 2015-1030. For the operation of the ATTO site, we acknowledge the support by the German Federal Ministry of Education and Research (BMBF contract 01LB1001A) and the Brazilian Ministério da Ciência, Tec-nologia e Inovação (MCTI/FINEP contract 01.11.01248.00) as well as the Amazon State University (UEA), FAPEAM, LBA/INPA and SDS/CEUC/RDS-Uatumã. This paper contains results of research conducted under the Technical/Scientific Cooperation Agreement between the National Institute for Amazonian Research, the State University of Amazonas, and the Max-Planck-Gesellschaft e.V.; the opinions expressed are the entire responsibility of the authors and not of the participating institutions. We highly acknowledge the support by the Instituto Nacional de Pesquisas da Amazô-nia (INPA). We thank the Max Planck Society (MPG) and the Max Planck Graduate Center with the Johannes Gutenberg University Mainz (MPGC). The research at Vavihill has been supported by the Swedish research councils VR and FORMAS, the Swedish Environmental Protection Agency, and the strategic research area MERGE. The research at Hyytiälä was supported by the Academy of Finland’s Centres of Excellence Programme (grant no. 307331). Aerosol property measurements performed at the PUY station are partly supported by the Service National d’Observation (SNO) CLAP. We thank all observatories’ operational teams for their continuous efforts. Athanasios Nenes thanks the Georgia Power faculty Chair and Cullen-Peck faculty fellowship funds.
All Science Journal Classification (ASJC) codes
- Atmospheric Science