Collocated observations of cloud condensation nuclei, particle size distributions, and chemical composition

Schmale, Julia; Henning, S.; Henzing, Bas; Keskinen, Helmi; Sellegri, Karine; Ovadnevaitė, Jurgita; Bougiatioti, A.; Kalivitis, N.; Stavroulas, Iasonas; Jefferson, Anne; Park, Minsu; Schlag, Patrick; Kristensson, Adam; Iwamoto, Yoko; Pringle, K. J.; Reddington, Carly L.; Aalto, Pasi; Äijälä, Mikko; Baltensperger, Urs; Białek, Jakub; Birmili, W.; Bukowiecki, Nicolas; Ehn, Mikael; Fjæraa, Ann Mari; Fiebig, Markus; Frank, Göran; Fröhlich, Roman; Frumau, Arnoud; Masaki, Furuya; Hammer, E.; Heikkinen, Liine; Herrmann, Erik; Holzinger, Rupert; Hyono, Hiroyuki; Kanakidou, Maria; Kiendler‐Scharr, Astrid; Kinouchi, Kento; Kos, G.P.A.; Kulmala, Markku; Mihalopoulos, Nikolaos; Motos, Ghislain; Nenes, Athanasios; O’Dowd, Colin; Paramonov, Mikhail; Petäj̈ä, Tuukka; Picard, D.; Poulain, Laurent; Prévôt, Andrê S. H.; Slowik, Jay G.; Sonntag, A.; Swietlicki, Erik; Svenningsson, Birgitta; Tsurumaru, Hiroshi; Wiedensohler, Alfred; Wittbom, Cerina; Ogren, J. A.; Matsuki, Atsushi; Yum, Seong Soo; Myhre, Cathrine Lund; Carslaw, K. S.; Stratmann, Frank; Gysel, M.

doi:10.1038/sdata.2017.3

Cited by 64 publications

(70 citation statements)

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“…Six out of the nine stations provided non-refractory chemical composition data on submicron particles (based on aerosol mass spectrometry), while all stations recorded submicron particle number size distributions and CCN number concentrations over a variety of supersaturations. A detailed discussion of the observational results can be found in Schmale et al (2018).…”

Section: Observational Data For Model Evaluationmentioning

confidence: 99%

“…A total of 16 global models contributed to this study, and multiyear observations of CCN, size-resolved particle number concentration distributions, and particle chemical composition obtained from eight atmospheric monitoring stations in Europe and one in Japan were used as an observational reference, representing distinct atmospheric environments (Schmale et al, 2017(Schmale et al, , 2018.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Abstract. A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as 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 and >120 nm, as well as −36 % and −34 % for CCN at supersaturations of 0.2 % and 1.0 %, respectively. However, they seem to behave differently for particles activating at very low supersaturations (<0.1 %) than at higher ones. A total of 15 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. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2 % (CCN0.2) compared to that for N3, maximizing over regions where new particle formation is important. 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 the 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 −13 % 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 values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). 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 (∂Nd/∂Na) and to updraft velocity (∂Nd/∂w). 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. Discrepancies are found in sensitivities ∂Nd/∂Na and ∂Nd/∂w; models may be predisposed to be too “aerosol sensitive” or “aerosol insensitive” in aerosol–cloud–climate interaction studies, even if they may capture average droplet numbers well. This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain inter-model biases on the aerosol indirect effect.

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Section: Observational Data For Model Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…The VBS scheme assumes that a certain fraction of POA can evaporate and be distributed in the semi-volatile range (saturation concentration between 0.1 and 1000 µg m −3 ), while most of the emission inventories only include POA in the particle phase. According to the partitioning theory (Donahue et al, 2006), the ratio between gas and particle phase in the semi-volatile range is roughly 3, and therefore many modeling studies increase the POA emissions by a factor of 3 to compensate for the missing SVOCs (Ciarelli et al, 2017a;Shrivastava et al, 2011;Tsimpidi et al, 2010). This approach agrees well with the emission study in Europe which shows that the revised residential wood combustion emissions accounting for the semi-volatile components are higher than those in the previous inventory by a factor of 2-3 on average (Denier van der Gon et al, 2015).…”

Section: Emissionsmentioning

confidence: 99%

“…One of the most important reasons for the underestimation is the potentially high but unaccounted for contribution of non-traditional vapors. To take these vapors into consideration, the volatility basis set (VBS) scheme has been developed and implemented in several air quality models such as CAMx (Ciarelli et al, 2016(Ciarelli et al, , 2017aKoo et al, 2014), CMAQ (Jathar et al, 2017;Koo et al, 2014;Woody et al, 2016), PMCAMx (Lane et al, 2008;Tsimpidi et al, 2010), CHIMERE (Cholakian et al, 2018;Zhang et al, 2013Zhang et al, , 2015, EMEP (Bergström et al, 2012) and WRF-Chem (Ahmadov et al, 2012;Shrivastava et al, 2011Shrivastava et al, , 2013Shrivastava et al, , 2019. The VBS scheme classifies the firstgeneration oxidation products of vapors according to their volatility.…”

Section: Introductionmentioning

confidence: 99%

Sources of organic aerosols in Europe: a modeling study using CAMx with modified volatility basis set scheme

et al. 2019

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Abstract. Source apportionment of organic aerosols (OAs) is of great importance to better understand the health impact and climate effects of particulate matter air pollution. Air quality models are used as potential tools to identify OA components and sources at high spatial and temporal resolution; however, they generally underestimate OA concentrations, and comparisons of their outputs with an extended set of measurements are still rare due to the lack of long-term experimental data. In this study, we addressed such challenges at the European level. Using the regional Comprehensive Air Quality Model with Extensions (CAMx) and a volatility basis set (VBS) scheme which was optimized based on recent chamber experiments with wood burning and diesel vehicle emissions, and which contains more source-specific sets compared to previous studies, we calculated the contribution of OA components and defined their sources over a whole-year period (2011). We modeled separately the primary and secondary OA contributions from old and new diesel and gasoline vehicles, biomass burning (mostly residential wood burning and agricultural waste burning excluding wildfires), other anthropogenic sources (mainly shipping, industry and energy production) and biogenic sources. An important feature of this study is that we evaluated the model results with measurements over a longer period than in previous studies, which strengthens our confidence in our modeled source apportionment results. Comparison against positive matrix factorization (PMF) analyses of aerosol mass spectrometric measurements at nine European sites suggested that the modified VBS scheme improved the model performance for total OA as well as the OA components, including hydrocarbon-like (HOA), biomass burning (BBOA) and oxygenated components (OOA). By using the modified VBS scheme, the mean bias of OOA was reduced from −1.3 to −0.4 µg m−3 corresponding to a reduction of mean fractional bias from −45 % to −20 %. The winter OOA simulation, which was largely underestimated in previous studies, was improved by 29 % to 42 % among the evaluated sites compared to the default parameterization. Wood burning was the dominant OA source in winter (61 %), while biogenic emissions contributed ∼ 55 % to OA during summer in Europe on average. In both seasons, other anthropogenic sources comprised the second largest component (9 % in winter and 19 % in summer as domain average), while the average contributions of diesel and gasoline vehicles were rather small (∼ 5 %) except for the metropolitan areas where the highest contribution reached 31 %. The results indicate the need to improve the emission inventory to include currently missing and highly uncertain local emissions, as well as further improvement of VBS parameterization for winter biomass burning. Although this study focused on Europe, it can be applied in any other part of the globe. This study highlights the ability of long-term measurements and source apportionment modeling to validate and improve emission inventories, and identify sources not yet properly included in existing inventories.

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“…Globally, however, the CCN-limited regime prevails (Rosenfeld et al, 2014). 15 Among the main factors driving the uncertainty in simulating CCN abundance are the aerosol particle number size distributions, size-dependent removal processes, the contribution of boundary layer new particle formation events to particle number concentration and their size, the particle number size distribution of emitted primary particles, the particle activation diameter, the formation of biogenic and anthropogenic secondary organic aerosol (SOA) as well as the processing of SO 2 in clouds into particulate sulfate (e.g., Croft et al, 2009;Lee et al, 2013;Wilcox et al, 2015). These factors affect the ability of 20 aerosol particles to form CCN at both large-scale and long-term periods as well as at the regional scale and in the short-term.…”

Section: Introductionmentioning

confidence: 99%

What do we learn from long-term cloud condensation nuclei number concentration, particle number size distribution, and chemical composition measurements at regionally representative observatories?

Schmale

Henning

Decesari

et al. 2017

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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 5 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). 10We 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 15 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., atBarrow (Arctic Haze in spring), at the alpine stations (stronger influence of polluted boundary layer air masses in summer), 20 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. 25The 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 c...

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Collocated observations of cloud condensation nuclei, particle size distributions, and chemical composition

Cited by 64 publications

References 100 publications

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

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

Sources of organic aerosols in Europe: a modeling study using CAMx with modified volatility basis set scheme

What do we learn from long-term cloud condensation nuclei number concentration, particle number size distribution, and chemical composition measurements at regionally representative observatories?

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