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

被引：110

作者：

Schmale, Julia ^{[1
]}

Henning, Silvia ^{[2
]}

Decesari, Stefano ^{[3
]}

Henzing, Bas ^{[4
]}

Keskinen, Helmi ^{[5
,6
]}

Sellegri, Karine ^{[7
]}

Ovadnevaite, Jurgita ^{[8
,9
]}

Poehlker, Mira L. ^{[10
,11
]}

Brito, Joel ^{[7
,12
]}

Bougiatioti, Aikaterini ^{[13
]}

Kristensson, Adam ^{[14
]}

Kalivitis, Nikos ^{[13
]}

Stavroulas, Iasonas ^{[13
]}

Carbone, Samara ^{[12
]}

Jefferson, Anne ^{[15
]}

Park, Minsu ^{[16
]}

Schlag, Patrick ^{[17
,18
]}

Iwamoto, Yoko ^{[19
,20
]}

Aalto, Pasi ^{[5
]}

Aijala, Mikko ^{[5
]}

Bukowiecki, Nicolas ^{[1
]}

Ehn, Mikael ^{[5
]}

Frank, Goran ^{[14
]}

Frohlich, Roman ^{[1
]}

Frumau, Arnoud ^{[21
]}

Herrmann, Erik ^{[1
]}

Herrmann, Hartmut ^{[2
]}

Holzinger, Rupert ^{[17
]}

Kos, Gerard ^{[21
]}

Kulmala, Markku ^{[5
]}

Mihalopoulos, Nikolaos ^{[13
,22
]}

Nenes, Athanasios ^{[22
,23
,24
,25
]}

O'Dowd, Colin ^{[8
,9
]}

Petaja, Tuukka ^{[5
]}

Picard, David ^{[7
]}

Poehlker, Christopher ^{[10
,11
]}

Poeschl, Ulrich ^{[10
,11
]}

Poulain, Laurent ^{[2
]}

Prevot, Andre Stephan Henry ^{[1
]}

Swietlicki, Erik ^{[14
]}

Andreae, Meinrat O. ^{[10
,11
]}

Artaxo, Paulo ^{[12
]}

Wiedensohler, Alfred ^{[2
]}

Ogren, John ^{[15
]}

Matsuki, Atsushi ^{[19
]}

Yum, Seong Soo ^{[16
]}

Stratmann, Frank ^{[2
]}

Baltensperger, Urs ^{[1
]}

Gysel, Martin ^{[1
]}

机构：

[1] Paul Scherrer Inst, Lab Atmospher Chem, CH-5232 Villigen, Switzerland

[2] Leibniz Inst Tropospher Res, Permoserstr 15, D-04318 Leipzig, Germany

[3] Natl Res Council Italy, Inst Atmospher Sci & Climate, Via Piero Gobetti 101, I-40129 Bologna, Italy

[4] Netherlands Org Appl Sci Res, Princetonlaan 6, NL-3584 Utrecht, Netherlands

[5] Univ Helsinki, Fac Sci, Gustaf Hallstrominkatu 2, Helsinki 00560, Finland

[6] Hyytiala Forestry Field Stn, Hyytialantie 124, Korkeakoski, Finland

[7] Univ Clermont Auvergne, Lab Meteorol Phys LaMP, F-63000 Clermont Ferrand, France

[8] Natl Univ Ireland Galway, Sch Phys, Univ Rd, Galway, Ireland

[9] Natl Univ Ireland Galway, CCAPS, Univ Rd, Galway, Ireland

[10] Max Planck Inst Chem, Multiphase Chem Dept, Mainz, Germany

[11] Max Planck Inst Chem, Biogeochem Dept, Mainz, Germany

[12] Univ Sao Paulo, Inst Fis, Rua Matao 1371, BR-05508090 Sao Paulo, SP, Brazil

[13] Univ Crete, Dept Chem, Iraklion 71003, Greece

[14] Lund Univ, Dept Phys, S-22100 Lund, Sweden

[15] NOAA, Earth Syst Res Lab, 325 Broadway, Boulder, CO USA

[16] Yonsei Univ, Dept Atmospher Sci, Seoul, South Korea

[17] Univ Utrecht, Inst Marine & Atmospher Res, Utrecht, Netherlands

[18] Forschungszentrum Julich, Inst Energy & Climate Res Troposphere IEK 8, Julich, Germany

[19] Kanazawa Univ, Inst Nat & Environm Technol, Kakuma Machi, Kanazawa, Ishikawa 9201192, Japan

[20] Hiroshima Univ, Grad Sch Biosphere Sci, 1-4-4 Kagamiyama, Higashihiroshima 7398528, Japan

[21] Energy Res Ctr Netherlands, Petten, Netherlands

[22] Natl Observ Athens, Athens 15236, Greece

[23] Georgia Inst Technol, Sch Chem Biomol Engn, Atlanta, GA 30332 USA

[24] Georgia Inst Technol, Sch Atmospher Sci, Atlanta, GA 30332 USA

[25] Fdn Res & Technol Hellas, Patras 26504, Greece

来源：

ATMOSPHERIC CHEMISTRY AND PHYSICS | 2018年 / 18卷 / 04期

基金：

欧盟地平线“2020”; 芬兰科学院;

关键词：

AEROSOL MASS-SPECTROMETER; SINGLE-PARAMETER REPRESENTATION; BIOMASS BURNING SMOKE; MEGA-CITY GUANGZHOU; 3580 M A.S.L; ORGANIC AEROSOL; CCN ACTIVITY; MIXING STATE; ATMOSPHERIC AEROSOL; HYGROSCOPIC GROWTH;

D O I：

10.5194/acp-18-2853-2018

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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, kappa, 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 kappa-Kohler 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 kappa. 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 longterm measurements can be achieved.

引用

页码：2853 / 2881

页数：29

共 114 条

[1] Measured and modelled cloud condensation nuclei (CCN) concentration in Sao Paulo, Brazil: the importance of aerosol size-resolved chemical composition on CCN concentration prediction [J].