Long-term observations of cloud condensation nuclei in the Amazon rain forest - Part 1: Aerosol size distribution, hygroscopicity, and new model parametrizations for CCN prediction

被引:103
作者
Poehlker, Mira L. [1 ,2 ]
Poehlker, Christopher [1 ,2 ]
Ditas, Florian [1 ,2 ]
Klimach, Thomas [1 ,2 ]
de Angelis, Isabella Hrabe [1 ,2 ]
Araujo, Alessandro [3 ]
Brito, Joel [4 ,13 ]
Carbone, Samara [4 ,14 ]
Cheng, Yafang [1 ,2 ]
Chi, Xuguang [1 ,2 ,15 ,16 ]
Ditz, Reiner [1 ,2 ]
Gunthe, Sachin S. [5 ]
Kesselmeier, Juergen [1 ,2 ]
Koenemann, Tobias [1 ,2 ]
Lavric, Jost V. [6 ]
Martin, Scot T. [7 ]
Mikhailov, Eugene [8 ]
Moran-Zuloaga, Daniel [1 ,2 ]
Rose, Diana [9 ]
Saturno, Jorge [1 ,2 ]
Su, Hang [1 ,2 ]
Thalman, Ryan [10 ,17 ]
Walter, David [1 ,2 ]
Wang, Jian [10 ]
Wolff, Stefan [1 ,2 ,11 ]
Barbosa, Henrique M. J. [4 ]
Artaxo, Paulo [4 ]
Andreae, Meinrat O. [1 ,2 ,12 ]
Poeschl, Ulrich [1 ,2 ]
机构
[1] Max Planck Inst Chem, Multiphase Chem Dept, D-55020 Mainz, Germany
[2] Max Planck Inst Chem, Biogeochem Dept, D-55020 Mainz, Germany
[3] Empresa Brasileira Pesquisa Agr EMBRAPA, BR-66095100 Belem, PA, Brazil
[4] Univ Sao Paulo, Inst Phys, BR-05508900 Sao Paulo, Brazil
[5] Indian Inst Technol, Dept Civil Engn, EWRE Div, Madras 600036, Tamil Nadu, India
[6] Max Planck Inst Biogeochem, Dept Biogeochem Syst, D-07701 Jena, Germany
[7] Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA
[8] St Petersburg State Univ, 7-9 Univ Skaya Nab, St Petersburg 199034, Russia
[9] Goethe Univ Frankfurt Main, Inst Atmospher & Environm Res, D-60438 Frankfurt, Germany
[10] Brookhaven Natl Lab, Biol Environm & Climate Sci Dept, Upton, NY 11973 USA
[11] INPA, BR-69083000 Manaus, Amazonas, Brazil
[12] Univ Calif San Diego, Scripps Inst Oceanog, La Jolla, CA 92037 USA
[13] Univ Blaise Pascal, Lab Meteorol Phys, Clermont Ferrand, France
[14] Univ Fed Uberlandia, BR-38408100 Uberlandia, MG, Brazil
[15] Nanjing Univ, Inst Climate & Global Change Res, Nanjing 210093, Jiangsu, Peoples R China
[16] Nanjing Univ, Sch Atmospher Sci, Nanjing 210093, Jiangsu, Peoples R China
[17] Snow Coll, Dept Chem, Richfield, UT 84701 USA
关键词
SECONDARY ORGANIC AEROSOL; SEA-SURFACE TEMPERATURES; BIOMASS BURNING SMOKE; MEGA-CITY GUANGZHOU; TRACE GASES; WET SEASON; SUBMICROMETER AEROSOL; RESOLVED MEASUREMENTS; CHEMICAL-COMPOSITION; POLLUTED AIR;
D O I
10.5194/acp-16-15709-2016
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Size-resolved long-term measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations and hygroscopicity were conducted at the remote Amazon Tall Tower Observatory (ATTO) in the central Amazon Basin over a 1-year period and full seasonal cycle (March 2014-February 2015). The measurements provide a climatology of CCN properties characteristic of a remote central Amazonian rain forest site. The CCN measurements were continuously cycled through 10 levels of supersaturation (S = 0.11 to 1.10 %) and span the aerosol particle size range from 20 to 245 nm. The mean critical diameters of CCN activation range from 43 nm at S = 1.10% to 172 nm at S = 0.11 %. The particle hygroscopicity exhibits a pronounced size dependence with lower values for the Aitken mode (kappa(Ait) = 0.14 +/- 0.03), higher values for the accumulation mode (kappa(Acc) = 0.22 +/- 0.05), and an overall mean value of kappa(mean) = 0.17 +/- 0.06, consistent with high fractions of organic aerosol. The hygroscopicity parameter, kappa, exhibits remarkably little temporal variability: no pronounced diurnal cycles, only weak seasonal trends, and few short-term variations during long-range transport events. In contrast, the CCN number concentrations exhibit a pronounced seasonal cycle, tracking the pollution-related seasonality in total aerosol concentration. We find that the variability in the CCN concentrations in the central Amazon is mostly driven by aerosol particle number concentration and size distribution, while variations in aerosol hygroscopicity and chemical composition matter only during a few episodes. For modeling purposes, we compare different approaches of predicting CCN number concentration and present a novel parametrization, which allows accurate CCN predictions based on a small set of input data.
引用
收藏
页码:15709 / 15740
页数:32
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