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

Abstract

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 > 20 nm) 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.

External and internal cloud condensation nuclei (CCN) mixtures: controlled laboratory studies of varying mixing states

“…σ s/a is the surface tension, M w is the molecular weight of water, R is the universal gas constant, T is the temperature at activation, and ρ w is the density of water. Surface tension and density of water were calculated according to temperaturedependent parameterizations presented by Seinfeld and Pandis (1998) and Pruppacher and Klett (1997). The surface tension of the solution is assumed to be that of pure water.…”

Instrument artifacts lead to uncertainties in parameterizations of cloud condensation nucleation

“…At 5 × 10 4 cm −3 , the detector saturates and cannot detect higher concentrations. By comparison, the TSI 3025 is effective at counting higher particle concentrations, of up to 2.5 × 10 4 cm −3 (Hameri et al, 2002;Sem, 2002).…”

“…Predicting the cloud forming capacity of various air masses based on the properties of the aerosol they contain is essential for evaluating relative contributions from pollution, continental background, and marine aerosol sources (Brooks and Thornton, 2018;Carslaw et al, 2013). Long-term CCN measurements are available from numerous locations globally (Schmale et al, 2018). However, understanding regional and temporal variability in CCN populations requires the ability to assess whether observed differences reflect true physical differences or simply variations in CCN sampling strategies.…”

“…We note that this study considers sized CCN measurements, which may be used for the determination of κ app . In contrast, a number of earlier CCN studies were conducted on the full ambient aerosol population without sizing the aerosol (Jennings et al, 1996;Hudson and Xie, 1998;Modini et al, 2015;Duan et al, 2017;Schmale et al, 2018;Leng et al, 2013). While useful, such studies do not produce the data required for accurate determination of κ app from the CCN measurements.…”

The SMART‐s Trace Gas and Aerosol Inversions: I. Algorithm Theoretical Basis for Column Property Retrievals

“…The combination of aerosol chemical information with trace gas retrievals from sufficient measurements can contribute reliable source apportionment of the air pollutants (e.g., Jeong et al, ; Schauer & Cass, ). Furthermore, accurate chemical and size information of aerosols are also critical to estimate hygroscopicity of different types of aerosols and their impact on cloud formation (e.g., Chang et al, ; Dusek et al, ; Eck et al, ; Facchini et al, ; Hao et al, ; Mochida et al, ; Schmale et al, ; Väisänen et al, , and references therein). Therefore, simultaneous retrievals of trace gases (e.g., NO 2 , SO 2 , H 2 O, and O 3 ) and aerosol properties by SMART‐s can provide unique opportunities to monitor the evolution processes of atmospheric trace gases and aerosols including emission, gas‐to‐particle conversion, and information on cloud formation.…”