Atmos. Chem. Phys., 16, 1570915740, 2016 www.atmos-chem-phys.net/16/15709/2016/ doi:10.5194/acp-16-15709-2016 Author(s) 2016. CC Attribution 3.0 License.
Mira L. Phlker1, Christopher Phlker1, Florian Ditas1, Thomas Klimach1, Isabella Hrabe de Angelis1, Alessandro Arajo2, Joel Brito3,a, Samara Carbone3,b, Yafang Cheng1, Xuguang Chi1,c, Reiner Ditz1,Sachin S. Gunthe4, Jrgen Kesselmeier1, Tobias Knemann1, Jot V. Lavri5, Scot T. Martin6, Eugene Mikhailov7, Daniel Moran-Zuloaga1, Diana Rose8, Jorge Saturno1, Hang Su1, Ryan Thalman9,d, David Walter1, Jian Wang9, Stefan Wolff1,10, Henrique M. J. Barbosa3, Paulo Artaxo3, Meinrat O. Andreae1,11, and Ulrich Pschl1
1Multiphase Chemistry and Biogeochemistry Departments, Max Planck Institute for Chemistry, 55020 Mainz, Germany
2Empresa Brasileira de Pesquisa Agropecuria (EMBRAPA), Trav. Dr. Enas Pinheiro, Belm, PA, 66095-100, Brazil
3Institute of Physics, University of So Paulo, So Paulo, 05508-900, Brazil
4EWRE Division, Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600036, India
5Department of Biogeochemical Systems, Max Planck Institute for Biogeochemistry, 07701 Jena, Germany
6School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
7St. Petersburg State University, 7/9 Universitetskaya nab, St. Petersburg, 199034, Russia
8Institute for Atmospheric and Environmental Research, Goethe University Frankfurt am Main, 60438 Frankfurt, Germany
9Biological, Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, USA
10Instituto Nacional de Pesquisas da Amazonia (INPA), Manaus, 69083-000, Brazil
11Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92037, USA
anow at: Laboratory for Meteorological Physics, University Blaise Pascal, Clermont-Ferrand, France
bnow at: Federal University of Uberlndia, Uberlndia-MG, 38408-100, Brazil
cnow at: Institute for Climate and Global Change Research & School of Atmospheric Sciences, Nanjing University, Nanjing, 210093, China
dnow at: Department of Chemistry, Snow College, Richeld, UT 84701, USA
Correspondence to: Mira L. Phlker ([email protected])
Received: 16 June 2016 Published in Atmos. Chem. Phys. Discuss.: 23 June 2016
Revised: 18 October 2016 Accepted: 7 November 2016 Published: 20 December 2016
Abstract. 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 2014February 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 ( Ait = 0.14 [notdef] 0.03), higher
values for the accumulation mode ( Acc = 0.22 [notdef] 0.05), and
an overall mean value of mean = 0.17 [notdef] 0.06, consistent with
high fractions of organic aerosol.
The hygroscopicity parameter, , 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 concentra-
Published by Copernicus Publications on behalf of the European Geosciences Union.
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
15710 M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1
tion. We nd 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.
1 Introduction
1.1 Atmospheric aerosols and clouds
In our current understanding of the Earths climate system and its man-made perturbation, the multiscale and feedback-rich life cycles of clouds represent one of the largest uncertainties (Boucher et al., 2013; Stevens et al., 2016). Accordingly, the adequate and robust representation of cloud properties is an Achilles heel in climate modeling efforts (Bony et al., 2015). Atmospheric aerosols are a key ingredient in the life cycle of clouds (known as aerosol indirect effect) as they affect their formation, development, and properties by acting as cloud condensation nuclei (CCN) and ice nuclei (IN) (Lohmann and Feichter, 2005; Rosenfeld et al., 2008).Aerosol particles can originate from various natural and anthropogenic sources and span wide ranges of concentration, particle size, composition, as well as chemical and physical properties (Pschl, 2005). Their activation into cloud droplets depends on their size, composition, and mixing state as well as the water vapor supersaturation (e.g., Khler, 1936;Dusek et al., 2006; McFiggans et al., 2006; Andreae and Rosenfeld, 2008; Su et al., 2010). The microphysical link between clouds and aerosol has been the subject of manifold and long-term research efforts. On one hand, the cycling of CCN as well as their relationship to the aerosol population has been studied in a variety of eld experiments worldwide (e.g., Gunthe et al., 2009; Rose et al., 2010; Jurnyi et al., 2011; Paramonov et al., 2015). On the other hand, the knowledge obtained from the growing body of eld data has been translated into different parametrization strategies that represent the cloudaerosol microphysical processes in modeling studies (e.g., Nenes and Seinfeld, 2003; Petters and Kreidenweis, 2007; Su et al., 2010; Deng et al., 2013; Mikhailov et al., 2013).
1.2 Amazon rain forest and its hydrological cycle
The Amazon rain forest is a unique and important ecosystem for various reasons such as its high density and diversity of life, its role as major carbon storage, and its large recycling rate of energy and water in the Earths hydrological cycle (Brienen et al., 2015; Gloor et al., 2015; Olivares et al., 2015; Yez-Serrano et al., 2015). In times of global change,
the man-made disturbance and pressure on this ecosystem have strongly increased and have started a transition of the Amazon into an uncharted future (Davidson et al., 2012;Lawrence and Vandecar, 2015). In the context of atmospheric composition, the Amazon is unique since it represents one of the last terrestrial locations worldwide that allows at least for part of the year to investigate an relatively undisturbed state of the atmosphere in the absence of major anthropogenic pollution (Roberts et al., 2001; Andreae, 2007;Andreae et al., 2012; Hamilton et al., 2014).
Overall, the troposphere over the Amazon is dened by the alternation of a relatively clean wet season and a polluted dry season, as outlined in more detail in previous studies (e.g., Martin et al., 2010b; Andreae et al., 2012, 2015; Mishra et al., 2015). In this paper, we use the following classication of the Amazonian seasons1: (i) the wet season typically spans February to May and shows the cleanest atmospheric state, (ii) the transition period from wet to dry season typically spans June and July, (iii) the dry season months August to November show the highest pollution levels, and (iv) the transition period from dry to wet season spans December and January (Andreae et al., 2015; Moran-Zuloaga et al., 2017).
A lively discussed aspect of the Amazonian hydrological cycle is the potential impact of changing aerosol regimes, which oscillate between polluted and pristine extremes, on the development of clouds and precipitation (e.g., Roberts et al., 2003; Andreae et al., 2004; Rosenfeld et al., 2008). A variety of pollution-induced changes in cloud properties, such as increased cloud drop concentrations with a corresponding decrease of their average size, intense competition for water vapor, and thus a deceleration of drop growth rates, suppression of supersaturation, reduced coalescence of smaller droplets, increased cloud depths as well as an invigoration of cloud dynamics and rain, are well documented (e.g., Koren et al., 2004, 2012; Freud et al., 2008).
Overall, the aforementioned observations indicate that increasing aerosol concentrations can have substantial impacts on spatial and temporal rainfall patterns in the Amazon (e.g., Martins et al., 2009a; Reutter et al., 2009). In view of the globally increasing pollution levels and the ongoing deforestation in the Amazon, pollution-triggered perturbations of the hydrological cycle are discussed as potential major threats to the Amazonian ecosystem, its forest structure, stability, and integrity (e.g., Coe et al., 2013; Junk, 2013).
1The Amazonian seasons are mostly dened meteorologically with respect to precipitation data (Fu et al., 2001; Fernandes et al., 2015). Note that in this study we use a slightly different denition of the seasons in the central Amazon based on meteorological and aerosol data to emphasize the seasonality in aerosol sources and prevalence. For example, the meteorological wet season typically has its core period in February (maximum in precipitation), whereas the pollution-dened wet season has its core period in April/May (e.g., minimum in carbon monoxide (CO) and black carbon (BC) concentrations) (Andreae et al., 2015).
Atmos. Chem. Phys., 16, 1570915740, 2016 www.atmos-chem-phys.net/16/15709/2016/
resolved CCN measurements in the Amazon were conducted (Gunthe et al., 2009; Martin et al., 2010a). These studies report that aerosol particles in the Aitken and accumulation modes, which represent the CCN-relevant size range, predominantly contain organic constituents and thus have comparably low hygroscopicity levels.The observed hygroscopicity parameter ranges between 0.1 and 0.2, which corresponds to the typical hygroscopicity of secondary organic aerosol (SOA) (Andreae and Rosenfeld, 2008).
2010/11: During several short observational periods, Almeida et al. (2014) measured total CCN concentrations around the city of Fortaleza in northeast Brazil.The selected measurement locations receive wind from changing directions. Accordingly, the response of the CCN population to marine, urban, and rural air masses was investigated.
2013: Recently, Whitehead et al. (2016) reported results from further short-term, size-resolved CCN and HTDMA measurements that were conducted north of Manaus (ground-based, ZF2 site, July 2013) as part of the BrazilUK network for investigation of Amazonian atmospheric composition and impacts on climate (BUNIAACIC) project. The results of this study agree well with Gunthe et al. (2009).
2014/15: As part of the international eld campaign observation and modeling of the green ocean Amazon (GoAmazon2014/5), size-resolved CCN measurements were conducted at three sites in and around Manaus: the Amazon Tall Tower Observatory (ATTO) site (T0a, pristine rain forest), which is discussed in the present study, the T2 site (in Manaus, urban environment), and the T3 site (rural site in the Manaus plume) (Martin et al., 2016; Thalman et al., 2017). All three size-resolved CCN measurements in the context of GoAmazon2014/5 took place in close collaboration. Moreover, CCN measurements were conducted onboard the G-1 aircraft during the GoAmazon2014/5 intensive observation periods IOP1 and IOP2 (Martin et al., 2016).
2014: Furthermore, as part of the GermanBrazilian ACRIDICON (Wendisch et al., 2016) and CHUVA (Machado et al., 2014) projects, airborne CCN measurements were made over the entire Amazon Basin (September 2014). The results of this study are currently being analyzed for an upcoming publication and represent an ideal complement to the long-term data of the present study.
In addition to the aforementioned CCN measurements, some further studies relied on HTDMA measurements to probe the aerosol hygroscopicity and particle growth factors below 100 % relative humidity (RH), which can be used
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M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1 15711
1.3 Previous CCN measurements in the Amazon
Ground-based and airborne CCN measurements have been conducted in a number of eld campaigns in the Amazon Basin as outlined below in chronological order, constituting the baseline and context for the present study.
1998: Roberts and coworkers (Roberts et al., 2001; Roberts et al., 2002) conducted the rst CCN measurements in the Amazon in the context of the LBA/CLAIRE-98 campaign (ground-based, Balbina site, March and April 1998) and pointed out that under clean conditions the CCN concentration NCCN(S) (at a certain supersaturation S) in the green ocean Amazon is surprisingly similar to conditions in the maritime blue ocean atmosphere. Regarding the low natural NCCN(S), which is dominated by mostly organic particles, they further suggested that cloud and precipitation properties may react sensitively to pollution-induced increases of the total aerosol load.
1999: In the context of the LBA-EUSTACH campaign in 1999, ground-based CCN measurements at three different sites in the Amazon Basin were conducted (Andreae et al., 2002; Roberts et al., 2003). This was the rst study on CCN properties and cloud dynamics under the inuence of strong biomass burning emissions in the Amazon.
2001: In the follow-up study LBA/CLAIRE-2001 in July 2001, ground-based (Balbina site) and airborne measurements (around Manaus) were conducted. For the ground-based study, Rissler et al. (2004) combined a hygroscopicity tandem differential mobility analyzer (HTDMA) with CCN measurements, focusing on the CCN-relevant water-soluble fraction in the particles, and provided a CCN closure and parametrization for model approaches. In addition, an airborne analysis of the aerosol and CCN properties was conducted, focusing on the contrast between the Amazonian background air and the Manaus plume (Kuhn et al., 2010).
2002: Subsequently, in the course of the LBA-SMOCC-2002 campaign in southern Brazil during major biomass burning episodes (Rondnia state, September and October 2002), ground-based and airborne CCN measurements were performed (Vestin et al., 2007; Martins et al., 2009b). A major nding of this study was that the CCN efciency of natural biogenic and man-made pyrogenic (cloud-processed) aerosols is surprisingly similar (Andreae et al., 2004). Furthermore, NCCN(0.5 %)
was found as a valuable predictor for the required cloud depth of warm rain formation, which is an important property for cloud dynamics (Freud et al., 2008).
2008: During the AMAZE-08 campaign (ground-based, ZF2 site, February and March 2008), the rst size-
15712 M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1
to extrapolate the CCN activity in supersaturation regimes (Zhou et al., 2002; Rissler et al., 2006).
1.4 Aims and scope of this study
All of the previously published CCN measurements in the Amazon have been conducted over relatively short time periods of up to several weeks. In addition, size-resolved CCN measurements are still sparse in the Amazon region. In this study, we present the rst continuous, long-term, and size-resolved CCN data set from the Amazon Basin, which spans a full seasonal cycle and therefore represents the CCN properties during contrasting seasonal conditions.
The focus of this study is on presenting major trends and characteristics of the CCN population in the Amazon Basin. Thus, our study contributes to a global inventory of CCN properties, representing this unique and climatically important ecosystem. We extract key CCN properties and parameters that help to include CCN predictions in the Amazon region into future modeling studies. Based on our data set, different parametrization strategies for CCN prediction are compared and discussed. Moreover, we present a novel and generalized CCN parametrization, which allows efcient modeling of CCN concentrations based on a minimal set of basic aerosol properties.
This paper represents part 1 of a comprehensive analysis of the CCN cycling in the central Amazon. It covers the overall trends and presents annually averaged CCN parameters as well as characteristic differences in the CCN population between the Amazonian seasons. A companion paper (Part 2) provides in-depth analyses of particularly interesting events through short-term case studies and aims for a more emission- and process-related understanding of the CCN variability (M. L. Phlker et al., 2017a).
2 Methods
2.1 Measurement site and period
The measurements reported in this study were conducted at the ATTO site (02 08.602[prime] S, 59 00.033[prime] W; 130 m a.s.l.), which is located in an untouched rain forest area in the central Amazon, about 150 km northeast of the city of Manaus, Brazil. An overview of the atmospheric, geographic, and ecological conditions at the ATTO site has been published recently by Andreae et al. (2015), where a detailed description of the aerosol setup for the long-term measurements can be found. The instrumentation for CCN measurements is part of a broad aerosol measurement setup, which also covers aerosol size and concentration, absorptivity, scattering, uorescence, as well as chemical composition (Andreae et al., 2015). The aerosol inlet is located at a height of 60 m, which is about 30 m above the forest canopy. The sample air is dried by silica gel diffusion dryers at the main inlet, which keeps the RH below 40 %. For the CCN setup, a second diffusion
dryer decreases the RH even further to < 20 %, which ensures reliable hygroscopicity measurements.
The CCN measurements are ongoing since the end of March 2014. This study covers the measurement period from the end of March 2014 to February 2015, representing almost a full seasonal cycle. Also, the measurement period overlaps with the international large-scale eld campaign GoAmazon2014/5 that was conducted in and around the city of Manaus from 1 January 2014 through 31 December 2015. During GoAmazon2014/5, comprehensive CCN measurements were conducted at different sites (see Sect. 1.3) (Martin et al., 2016). The ATTO site served as a clean background (T0a) site during GoAmazon2014/5. Furthermore, the measurement period of this study encompasses the GermanBrazilian ACRIDICON-CHUVA eld measurement campaign in September 2014 (Machado et al., 2014; Wendisch et al., 2016), where (non-size-resolved) CCN measurements at multiple supersaturation levels were performed onboard the high-altitude and long-range research aircraft (HALO) ying over the Amazon Basin.
2.2 Size-resolved CCN measurements
The number concentration of CCN was measured with a continuous-ow streamwise thermal gradient CCN counter (CCNC, model CCN-100, DMT, Boulder, CO, USA) (Roberts and Nenes, 2005; Rose et al., 2008b). The inlet ow rate of the CCNC was 0.5 L min1 with a sheath-to-aerosol ow ratio of 11. The water pump was operated at a rate of 4 mL h1 corresponding to the CCNC setting of low liquid ow. The supersaturation (S) of the CCNC was cycled through 10 different S values between 0.11 and 1.10 % (see Table 1), which are dened by controlled temperature gradients inside the CCNC column. Particles with a critical supersaturation (Sc) S in the column are activated and form
water droplets. Droplets with diameters 1 m are detected
by an optical particle counter (OPC) at the exit of the column.
Size-resolved CCN activation curves (for nomenclature, see Sect. 2.3) were measured based on the concept of Frank et al. (2006), following the procedures in Rose et al. (2008a) and Krger et al. (2014) by combining the CCNC with a differential mobility analyzer (DMA, model M, Grimm Aerosol Technik, Ainring, Germany). The DMA was operated with a sheath-to-aerosol ow ratio of 5. The DMA selects particles with a certain diameter (D) in the size range of 20 to 245 nm (sequence of D value has been optimized for every S), which are then passed into the two instruments: (i) the CCNC system and (ii) a condensation particle counter (CPC, model 5412, Grimm Aerosol Technik), which measures the number concentration of aerosol particles with selected D(NCN(D)), while the CCNC measures the number concentration of CCN with selected D for the given S(NCCN(S,D)). The cycle through a full CCN activation curve (NCCN(S,D)/NCN(D))
for one S level took 28 min, including 40 s equilibra
tion time for every new D, and 2 min equilibration time
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Table 1. Characteristic CCN parameters as a function of the supersaturation S, averaged over the entire measurement period: midpoint activation diameter Da(S), hygroscopicity parameter (S,Da), width of CCN activation curve (S), heterogeneity parameter (S)/Da(S), maximum activated fraction MAF(S), CCN number concentration NCCN(S), total particle concentration (> 10 nm) NCN,10, CCN efciencies
NCCN(S)/NCN,10, and number of data points n. S is shown as set value [notdef] the experimentally derived deviation in S. All other values are
given as arithmetic mean [notdef] 1 standard deviation. All values are provided for ambient conditions (temperature 28 C; pressure 100 kPa).
S Da(S) (S,Da) (S) (S)/Da(S) MAF(S) NCCN(S) NCN,10 NCCN(S)/NCN,10 n (%) (nm) (nm) (cm3) (cm3)
0.11 [notdef] 0.01 172 [notdef] 12 0.22 [notdef] 0.05 45 [notdef] 11 0.26 [notdef] 0.06 0.93 [notdef] 0.10 275 [notdef] 219 1100 [notdef] 776 0.24 [notdef] 0.10 1071
0.15 [notdef] 0.02 136 [notdef] 10 0.22 [notdef] 0.05 42 [notdef] 10 0.31 [notdef] 0.06 0.97 [notdef] 0.05 457 [notdef] 384 1093 [notdef] 770 0.39 [notdef] 0.13 1086
0.20 [notdef] 0.02 117 [notdef] 9 0.21 [notdef] 0.05 35 [notdef] 10 0.30 [notdef] 0.07 0.98 [notdef] 0.04 571 [notdef] 482 1096 [notdef] 775 0.48 [notdef] 0.15 1087
0.24 [notdef] 0.03 105 [notdef] 8 0.19 [notdef] 0.05 29 [notdef] 8 0.28 [notdef] 0.07 0.99 [notdef] 0.04 652 [notdef] 550 1098 [notdef] 778 0.55 [notdef] 0.16 1078
0.29 [notdef] 0.03 98 [notdef] 7 0.17 [notdef] 0.04 27 [notdef] 8 0.27 [notdef] 0.08 1.01 [notdef] 0.05 719 [notdef] 601 1103 [notdef] 784 0.60 [notdef] 0.17 1069
0.47 [notdef] 0.04 77 [notdef] 5 0.13 [notdef] 0.03 17 [notdef] 6 0.22 [notdef] 0.07 1.03 [notdef] 0.04 883 [notdef] 744 1101 [notdef] 799 0.74 [notdef] 0.18 1008
0.61 [notdef] 0.06 63 [notdef] 4 0.14 [notdef] 0.03 15 [notdef] 5 0.23 [notdef] 0.07 0.97 [notdef] 0.03 900 [notdef] 719 1089 [notdef] 791 0.78 [notdef] 0.14 922
0.74 [notdef] 0.08 57 [notdef] 4 0.13 [notdef] 0.03 14 [notdef] 6 0.24 [notdef] 0.09 0.96 [notdef] 0.03 941 [notdef] 730 1108 [notdef] 809 0.82 [notdef] 0.12 984
0.92 [notdef] 0.11 49 [notdef] 4 0.13 [notdef] 0.03 12 [notdef] 6 0.24 [notdef] 0.11 0.96 [notdef] 0.04 987 [notdef] 742 1117 [notdef] 814 0.86 [notdef] 0.10 995
1.10 [notdef] 0.08 43 [notdef] 3 0.13 [notdef] 0.03 11 [notdef] 5 0.25 [notdef] 0.10 0.95 [notdef] 0.03 1013 [notdef] 747 1120 [notdef] 792 0.88 [notdef] 0.08 952
for every new S level. The completion of a full measurement cycle comprising CCN activation curves for 1213 D values (number of D depends on S) and 10 different S levels took 4.5 h. The entire CCN system (including the CCNC,
DMA, and CPC) was controlled by a dedicated LabVIEW (National Instruments, Munich, Germany) routine.
The S levels of the CCNC system were calibrated periodically (March, May, and September 2014) using ammonium sulfate ((NH4)2SO4, Sigma Aldrich, St. Louis, MO, USA)
particles generated in an aerosol nebulizer (TSI Inc., Shore-view, MN, USA). The calibration procedure was conducted according to Rose et al. (2008b). All three calibrations gave consistent results and thus conrmed that the S cycling in the CCNC was very stable and reliable throughout the entire measurement period.
All concentration data presented here are given for ambient conditions. During the entire measurement period, no signicant uctuations in temperature ( 28 C) and pressure
( 100 kPa) were observed in the air-conditioned laboratory
container.
2.3 Data analysis, error analysis, and nomenclature of CCN key parameters
The theoretical background and related CCN analysis procedures are comprehensively described elsewhere (Petters and Kreidenweis, 2007; Rose et al., 2008a). For the present study, the following corrections were applied to the data set. (i) The CCN activation curves were corrected for systematic deviations in the counting efciency of the CCNC and CPC according to Rose et al. (2010). (ii) Usually, the double-charge correction of the CCN activation curve is conducted according to Frank et al. (2006). For this study, we developed the following alternative approach, which reconstructs the CCN efciency curves based on data from an independent scanning mobility particle sizer (SMPS, TSI model
3080 with CPC 3772 operating with standard TSI software) at the ATTO site. The activation curve for every D can be described by the following equation:
Pi NCCN (S,Di)
Pi NCN(Di) =
Pi f (Di) [notdef] s (Di) [notdef] a(S,Di)
Pi f (Di) [notdef] s (Di)
. (1)
The index i represents the charge of the particles (typically 1 i 4). The left side of the equation is the measured (non-
corrected) ratio of CCN to condensation nuclei (CN) for one selected D and S. The parameter s(Di) is the multi-charge-corrected particle number size distribution inverted from the SMPS measurements at Di with its different charge states.The parameter f (Di) is the corresponding fraction of particles with the charge i. The function a(S,Di) accounts for the activated fraction of s(Di) at a given supersaturation S. We describe a(S,Di) as a cumulative Gaussian function. Using a nonlinear least-squares t method (LevenbergMarquardt) together with the knowledge of s(Di) and f (Di), the parameters of the function a(S,Di) can be optimized to get an optimal t of the measured CCN activation curve for a given S.The function a(S,D) is the cumulative Gaussian function after the t, which describes the multi-charge-corrected CCN activation curve and has been used as a basis for the further analysis. Because the information on multiple charged particles also contributes to the t results, this approach is superior to previously used methods, where this information is neglected. Based on a(S,D), the critical diameter (Da(S),
where 50 % of the particles are activated) is used to retrieve the effective hygroscopicity parameter ( (S,Da)) according to the -Khler model (Petters and Kreidenweis, 2007). A detailed description of the calculation can be found in Petters and Kreidenweis (2007), Rose et al. (2010), and Mikhailov et al. (2009).
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The CCN size distribution (NCCN(S,D)) was calculated by
NCCN (S,D) = s (D) [notdef] a (S,D). (2)
In this equation, s(D) represents the particle number size distribution of the SMPS at D (10 D 450 nm).
The CCN efciencies (NCCN(S)/NCN,10; for nomenclature, see end of Sect. 2.3) have been calculated based on the integral concentration of CN with lower size cutoff Dcut = 10 nm (NCN,10)2 and CCN (NCCN(S)) as
NCCN(S)
NCN,10 = [integraltext]
DNCCN (S,D) [notdef] dD
RDs(D) [notdef] dD
. (3)
In addition to Da(S), the maximum activated fraction (MAF(S)) can be obtained from a(S,D). MAF(S) typically equals unity, except for completely hydrophobic particles(i.e., fresh soot). The third parameter that can be derived from a(S,D) is the width of the CCN activation curve (S), which strongly depends on Da(S). The ratio between (S) and Da(S) ((S)/Da(S)) is called heterogeneity parameter and can be used as an indicator for the chemical and geometric diversity of the aerosol particles.
The error in S was calculated based on the uncertainty according to the commonly used calibration procedure (Rose et al., 2008b). Overall, the error [Delta1]S of S equals approximately 10 %; however, in the following analysis, we have used the specic [Delta1]S values for every S (see Table 1). The uncertainty of the selected D of the DMA ([Delta1]D) was obtained as the mean width of the Gaussian t of polystyrene latex (PSL) beads and equals 5.3 nm. For NCCN(S,D) and NCN(D), the standard error of the counting statistic was used. By Gaussian error propagation we determined [Delta1](NCCN(S,D)/NCN(D))
and then repeated the data analysis for the upper and lower bounds (1 [notdef] [Delta1]) [notdef] (NCCN(D,S)/NCN(D)). The result
ing relative errors of the values NCCN(S), NCN,10, and NCCN(S)/NCN,10 do not depend on S and equal 6 %. The errors of Da(S) and (S,Da) depend on S and can be described as
[Delta1]Da(S) = Da(S) [notdef] (S [notdef] 0.07 + 0.03) (4)
[Delta1] (S,Da) = (S,Da) [notdef] (S [notdef] 0.17 + 0.10). (5)
Throughout this study, we observed a slight systematic deviation of the results for the supersaturation S = 0.47 %. This
effect can be seen, for example, in MAF(0.47 %) values exceeding unity in Fig. 1 and NCCN(0.47 %,D)/NCN(D) values exceeding unity in Fig. 5. The effect persists even after applying all aforementioned corrections to the data and is most pronounced during the dry season. Yet, since we did not nd any evidence of these data being erroneous, we decided to keep them in the study.
2Note that NCN,10 usually corresponds to the total CPC-detectable aerosol particle number concentration for the characteristic size distribution at the ATTO site because the particle population in the nucleation-mode range (i.e., < 10 nm) is negligibly small.
The use of certain terms in the context of CCN measurements is not uniform in the literature. For clarity, we summarize the key parameters and terms applied in this study as follows: (i) the value NCCN(S,D)/NCN(D) is called
CCN activated fraction, while (ii) NCCN(S,D)/NCN(D) plotted against D is called CCN activation curve;(iii) NCCN(S) plotted against S is called CCN spectrum;(iv) NCCN(S)/NCN,Dcut at a certain S level is called CCN efciency; (v) NCCN(S)/NCN,Dcut plotted against S is called
CCN efciency spectrum.
2.4 Aerosol mass spectrometry
In addition to the CCN measurements, aerosol chemical speciation monitor (ACSM, Aerodyne Research Inc., Billerica, MA, USA) measurements are being performed at the ATTO site (Andreae et al., 2015). The ACSM routinely characterizes nonrefractory submicron aerosol species such as organics, nitrate, sulfate, ammonium, and chloride (Ng et al., 2011). Particles are focused by an aerodynamic lens system into a narrow particle beam, which is transmitted through three successive vacuum chambers. In the third chamber, the particle beam is directed into a hot tungsten oven (600 C)
where the particles are ash vaporized, ionized with a 70 eV electron impact ionizer, and detected with a quadrupole mass spectrometer. In this study, a time resolution of 30 min was used. The measurements provide a total mass concentration of the chemical composition of the aerosol particles. Further details about the ACSM can be found in Ng et al. (2011).
2.5 Carbon monoxide measurements
Carbon monoxide (CO) measurements are conducted continuously at the ATTO site using a G1302 analyzer (Picarro Inc. Santa Clara, CA, USA). The experimental setup from the point of view of functioning and performance is a duplication of the system described in Winderlich et al. (2010).
3 Results and discussion
3.1 Time series of CCN parameters for the entire measurement period
Over the entire measurement period from 25 March 2014 to 5 February 2015 we recorded size-resolved CCN activation curves at 10 different levels of water vapor supersaturation S with an overall time resolution of approximately4.5 h. A total of 10 253 CCN activation curves were tted and analyzed to obtain parameters of CCN activity as detailed above (Sect. 2.3). Table 1 serves as a central reference in the course of this study and summarizes the annual mean values and standard deviations of the following key parameters, resolved by S: Da(S), (S,Da), (S), (S)/Da(S), MAF(S), NCCN(S),NCN,10, and NCCN(S)/NCN,10. In Fig. 1, some of these CCN key parameters are presented as time se-
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Figure 1. Seasonal trends in time series of precipitation rate P , total aerosol concentration NCN,10, carbon monoxide mole fraction (cCO), and CCN key parameters for three selected supersaturations S for the entire measurement period (shown in original time resolution). (a) Precipitation rates from tropical rainfall measuring mission (TRMM) PTRMM and in situ measurements at the ATTO site PATTO. The PTRMM seasonal cycles are derived from an area upwind of the ATTO site (59.5 W, 2.4 N, 54.0 W, 3.5 S), covering a long-term period from 1 January 1998 to 30 June 2016 (aqua shading), and the period of the CCN measurements from 1 March 2014 to 28 February 2015 (blue line).
(b) Time series of pollution tracers NCN,10 and cCO. (c) CCN concentrations NCCN(S), (d) hygroscopicity parameter (S,Da), (e) CCN efciencies NCCN(S)/NCN,10, and (f) maximum activated fraction MAF(S). Three different types of shading represent (i) the seasonality in the Amazon atmosphere according to Andreae et al. (2015) (wet vs. dry seasons with transition periods, illustrated at the top of the graph),(ii) periods of IOP1 and IOP2 during GoAmazon2014/5, (iii) seasonal periods of interest in context of the present study as dened in Sect. 3.3 (shading in background of time series).
ries over the entire measurement period to provide a general overview of their temporal evolution and variability. Concentration time series of the pollution tracers NCN,10 and CO are added to illustrate the pollution seasonality at the ATTO site.
Figure 1a presents precipitation data from satellite and in situ measurements at the ATTO site to illustrate the meteorological seasonality for the measurement period. The precipitation rates in the Amazon Basin can show pronounced
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anomalies due to teleconnections with the Atlantic and/or Pacic sea surface temperatures (SSTs) (Fu et al., 2001;Fernandes et al., 2015). The most prominent example here is the El NioSouthern Oscillation (ENSO) and its various impacts on the Amazonian ecosystem (e.g., Asner et al., 2000; Ronchail et al., 2002). For the measurement period, the Oceanic Nio Index (ONI) ranged between 0.4
and 0.6 C, conrming that only towards the end of the measurement period a slightly positive anomaly was observed.3 In Fig. 1a, satellite data from the tropical rainfall measurement mission (TRMM) are presented for the area around the ATTO site. The TRMM data are provided for an extended time period (January 1998 until June 2016) and, for comparison, for the CCN measurement period (March 2014 until February 2015). This comparison shows that the 2014/15 precipitation rates do not deviate substantially from the 18-year average data, and thus further conrms that the measurement period can be regarded as a typical year with typical seasons and no pronounced hydrological anomalies.
Figure 1b displays the characteristic seasonal cycle in NCN,10 and the CO mole fraction (cCO). Both pollution tracers reach their maxima during the dry season (NCN,10 = 1400 [notdef] 710 cm3; cCO = 144 [notdef] 45 ppb), whereas
the lowest values are observed during the wet season (NCN,10 = 285 [notdef] 131 cm3; cCO = 117 [notdef] 12 ppb) (given as
mean [notdef] 1 standard deviation). An obvious feature of the
dry season months is the occurrence of rather short and strong peaks (reaching up to NCN,10 = 5000 cm3;
cCO = 400 ppb) on top of elevated background pollution
levels. The pronounced peaks originate from biomass burning plumes, which impact the ATTO site for comparatively short periods (a few hours up to several days). Selected events are discussed in detail in M. L. Phlker et al. (2017a).Figure 1c shows that NCCN(S) follows the same overall trends. A rather close correlation between NCCN(S) and
NCN,10 as well as NCCN(S) and cCO can be observed, as pointed out in previous studies (Andreae, 2009; Kuhn et al., 2010). Figure 1d displays the (S,Da) time series for three exemplary S levels. It shows that the (S,Da) values, which provide indirect information of the particles chemical composition, are remarkably stable throughout the year (see also standard deviations of (S,Da) in Table 1). This illustrates that the dry season maximum in NCCN(S) is mainly related to the overall increase in NCN,10, and not to substantial variations in aerosol composition and therefore (S,Da). The levels of the three (S,Da) time series, with their corresponding Da(S), provide a rst indication that (S,Da) shows a clear size dependence, as further discussed in Sect. 3.2. The pro-
3For the ONI data and specic information on the reference area and time frame, refer to National Oceanic and Atmospheric Administration (NOAA)/National Weather Service, 2016. Historical El Nio/La Nia episodes (1950present) are available at http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml
Web End =http://www.cpc.ncep.noaa.gov/products/analysis_ http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml
Web End =monitoring/ensostuff/ensoyears.shtml (last access: 1 October 2016).
nounced (but rather rare) spikes in (S,Da) (i.e., in April and August) as well as various other specic events in this time series are analyzed in detail in the companion Part 2 paper (M. L. Phlker et al., 2017a). Figure 1e gives an overview of the CCN efciencies NCCN(S)/NCN,10 (for three S levels) and its seasonal trends. This representation shows continuously high fractions of cloud-active particles for higher S (e.g., NCCN(1.10 %) /NCN,10 > 0.9) throughout the entire measurement period with almost no seasonality. For intermediate S, such as 0.47 %, the values of NCCN(0.47 %) /NCN,10
range from 0.6 to 0.9 and reveal a noticeable seasonal cycle, with the highest levels during the dry season. Furthermore, NCCN(0.11 %) /NCN,10 is mostly below 0.4, with clear seasonal trends. These observations can be explained by the characteristic aerosol size distribution at the ATTO site (Andreae et al., 2015), which (i) is dominated by particles in the Aitken (annually averaged peak DAit at 70 nm) and accu
mulation modes (annually averaged peak DAcc at 150 nm),
(ii) shows a sparse occurrence of nucleation-mode particles (< 30 nm), and (iii) reveals a clear seasonality in the relative abundance of Aitken and accumulation modes (see Sect. 3.3 and Fig. 6). Thus, the higher dry season abundance of accumulation-mode particles, which are more prone to act as CCN, results in higher NCCN(S)/NCN,10 levels, particularly at lower S.
Analogous NCCN(S)/NCN results from other continental background sites have been published previously: for example, Levin et al. (2012) reported NCCN(0.97 %) /NCN = 0.40.7,
NCCN(0.56 %) /NCN = 0.250.5, and NCCN(0.14 %) /NCN
< 0.15 for a semi-arid Rocky Mountain site. Jurnyi et al. (2011) reported NCCN(1.18 %)/NCN,16 = 0.6
0.9, NCCN(0.47 %)/NCN,16 = 0.20.6, and
NCCN(0.12 %)/NCN,16 < 0.25 for the high alpine Jungfraujoch site. At both locations, the CCN efciencies tend to be lower than the corresponding results at the ATTO site, which can be explained by the frequent occurrence of new particle formation (NPF) and the related abundance of ultrane particles (with sizes well below Da(S)) at these
sites (Boulon et al., 2010; Ortega et al., 2014). The activated fractions at the Rocky Mountain and Jungfraujoch sites have a stronger seasonality than those at ATTO, probably inversely related to the seasonal cycle in NPF. Overall, we state that the activated fractions in the central Amazon, due the absence of signicant ultrane particle (< 30 nm) populations, tend to be constantly higher than in other continental background locations (Paramonov et al., 2015). The absence of classical NPF (Kulmala et al., 2004) and the corresponding lack of ultrane particles is a unique property of the Amazon atmosphere resulting in the uniquely high CCN efciencies. A systematic study on the abundance, properties, and seasonality of the sparse nucleation-mode bursts in the central Amazon is the subject of an upcoming study.
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cumulation mode. Note that S levels in convective clouds rarely exceed 1.0 %, but that in the presence of precipitation higher S values are possible (Cotton and Anthes, 1989). The step from the activation curves at S = 0.47 % to S = 0.29 %
relates to the position of the characteristic Hoppel minimum (at 97 nm for the annual mean size distribution; see Table 2) between Aitken and accumulation mode in the bimodal size distribution. Thus, the step to S = 0.47 % represents the onset
of signicant activation in the Aitken-mode size range.
A different representation of these observations is displayed in Fig. 3, which shows the bimodally tted (bimodal logarithmic normal distribution, R2 = 0.99) an
nual mean NCN(D) size distribution. In this annual average representation, the Aitken-mode maximum is located at DAit = 69 [notdef] 1 nm, the accumulation-mode maximum at
DAcc = 149 [notdef] 2 nm, and both are separated by the Hoppel
minimum (compare to Table 2) (Hoppel et al., 1996). Furthermore, Fig. 3 clearly shows that different (S,Da) values are retrieved for the Aitken ( Ait = 0.14 [notdef] 0.03) vs. the
accumulation-mode size range ( Acc = 0.22 [notdef] 0.03). This in
dicates that Aitken- and accumulation-mode particles have different hygroscopicities and thus different chemical compositions. In this case, Aitken-mode particles tend to be more predominantly organic (close to = 0.1) than the
accumulation-mode particles, which tend to contain more inorganic species (i.e., ammonium, sulfates, potassium) (Prenni et al., 2007; Gunthe et al., 2009; Wex et al., 2009; C.Phlker et al., 2012). The enhanced hygroscopicity in the accumulation mode is a well-documented observation for various locations worldwide, which is thought to result from the cloud processing history of this aerosol size fraction (e.g., Paramonov et al., 2013, 2015). For the Amazon Basin, our observed size dependence of (S,Da) agrees well with the values reported by Gunthe et al. (2009) and Whitehead et al. (2016).
The arithmetic mean hygroscopicity parameter at the ATTO site for all sizes (43 nm < Da < 172 nm) and for the entire measurement period is mean = 0.17 [notdef] 0.06. For com
parison, Gunthe et al. (2009) reported mean = 0.16 [notdef] 0.06
(for the early wet season 2008). The observed standard deviation is rather small, which reects the low variability of mean throughout the year (see Fig. 1b).
No perceptible diurnal trend in mean is present in the annually averaged data. This is because the ATTO site is not (strongly) inuenced by aerosol compositional changes that follow pronounced diurnal cycles (i.e., input of anthropogenic emissions). A consequence of this nding is that the overall hygroscopicity of the aerosol at the ATTO site (as a representative measurement station of the central Amazon) is well represented in model studies by using mean = 0.17 [notdef] 0.06 (see also Sect. 3.5.4). Previous long-
term CCN observations from alpine, semi-arid, and boreal background sites have similarly shown that diurnal cycles in (S,Da) (or the related Da(S)) tend to be rather small or even
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Figure 2. CCN activation curves for all measured S levels (S = 0.111.10 %), averaged over the entire measurement
period. Data points represent arithmetic mean values. For NCCN(S,D)/NCN(D), the standard error is plotted, which is very small (due to the large number of scans with comparatively small variability) and therefore not perceptible in this representation. For the diameter, D, the error bars represent the experimental error as specied in Sect. 2.3. The grey vertical band represents the position of the Hoppel minimum (including error range) for the annual mean number size distribution (compare to Fig. 3). Dashed lines provide visual orientation and indicate 0, 50, and 100 % activation. The value at 50 % activation is used for calculation of the hygroscopicity parameter (S,Da). The lines connecting the data points merely serve as visual orientation.
The MAF(S) time series in Fig. 1f represents a valuable additional parameter to determine the abundance of poor CCN (i.e., aerosol particles that are not activated into CCN within the tested S range). For higher S (i.e., S > 0.11 %), MAF(S) is close to unity over the whole year. In contrast, MAF(0.11 %) uctuates around unity during the wet season months; however, it drops below unity during the biomass-burning-impacted dry season and subsequent transition period. For some episodes, MAF(S) shows very pronounced dips, as further discussed in the Part 2 study (M. L. Phlker et al., 2017a).
3.2 Annual means of CCN activation curves and hygroscopicity parameter
Figure 2 displays the annual mean CCN activation curves for all S levels. Thus, it represents an overall characterization of the particle activation behavior, which means that for decreasing S levels the activation diameter, Da(S), increases. In other words, every S corresponds to a certain (and to some extent typical) Da(S) range, where particles start to become activated (see Table 1). As an example, relatively high S conditions (0.471.10 %) yield substantial activation already in the Aitken-mode range, while low S levels (0.110.29 %) correspond to activation of larger particles, mostly in the ac-
15718 M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1
Table 2. Properties (position x0, integral number concentration NCN, width ) of Aitken and accumulation modes from the double log-normal t (compare to R2) of the total particle size distributions. Values are given as annual means and subdivided into seasonal periods of interest as specied in Sect. 3.3 (compare also to Fig. 6). In addition, values for the position of the Hoppel minimum DH as well as estimated average peak supersaturation in cloud Scloud(DH, ) are listed. The errors represent the uncertainty of the t parameters. The error in Scloud(DH, ) is the experimentally derived error in S.
Season Mode NCN x0 R2 DH Scloud(DH, )(cm3) (nm) (nm) (%)
Year Aitken 397 [notdef] 31 0.13 [notdef] 0.03 69 [notdef] 1 0.44 [notdef] 0.02 0.99 97 [notdef] 2 0.29 [notdef] 0.03
accumulation 906 [notdef] 29 0.22 [notdef] 0.05 149 [notdef] 2 0.57 [notdef] 0.01
LRT Aitken 231 [notdef] 8 0.14 [notdef] 0.04 67 [notdef] 1 0.63 [notdef] 0.01 0.99 109 [notdef] 2 0.23 [notdef] 0.02
accumulation 232 [notdef] 10 0.28 [notdef] 0.08 172 [notdef] 1 0.51 [notdef] 0.01
Wet Aitken 246 [notdef] 9 0.13 [notdef] 0.02 70 [notdef] 1 0.53 [notdef] 0.01 0.99 112 [notdef] 2 0.22 [notdef] 0.02
accumulation 145 [notdef] 8 0.21 [notdef] 0.05 170 [notdef] 2 0.42 [notdef] 0.01
Transition Aitken 405 [notdef] 24 0.14 [notdef] 0.02 65 [notdef] 1 0.42 [notdef] 0.01 0.99 92 [notdef] 2 0.34 [notdef] 0.03
accumulation 668 [notdef] 24 0.24 [notdef] 0.04 135 [notdef] 1 0.53 [notdef] 0.01
Dry Aitken 483 [notdef] 49 0.13 [notdef] 0.03 71 [notdef] 2 0.42 [notdef] 0.03 0.99 97 [notdef] 2 0.29 [notdef] 0.03
accumulation 1349 [notdef] 47 0.21 [notdef] 0.04 150 [notdef] 2 0.58 [notdef] 0.01
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Figure 3. Size dependence of the hygroscopicity parameter (S,Da) averaged over the entire measurement period. Values of (S,Da) for every S level are plotted against their corresponding midpoint activation diameter Da(S) (left axis). For (S,Da), the error bars represent 1 standard deviation. For Da(S), the experimentally derived error is shown. In addition, the average number size distribution for the entire measurement period is shown (right axis). Dashed green lines represent the average Aitken and accumulation modes. The standard error of the number size distribution is indicated as grey shading, which is very small and therefore hardly perceptible in this representation due to the large number of scans with comparatively small variability. Distinctly different (S,Da) levels can be observed for the Aitken and accumulation modes with lower variability in the Aitken than in the accumulation mode.
absent (Jurnyi et al., 2011; Levin et al., 2012; Paramonov et al., 2013).
Figure 4 combines the annually averaged size distributions of NCN(D) as well as NCCN(S,D) for all S levels. These curves result from multiplying the NCN(D) size distribution with the CCN activation curves in Fig. 2 and clearly visualize the inverse relationship of Da(S) and S. Following the
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previous discussion of Fig. 2, S ranging between 0.11 and0.29 % mostly activates accumulation-mode particles, while
S ranging between 0.47 and 1.10 % activates the accumulation mode plus a substantial fraction of Aitken-mode particles. For the highest supersaturation (S = 1.10 %) that was
used in this study, almost the entire NCN(D) size distribution is being activated into CCN, which (regarding the very
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dry season periods. The fraction of non-activated particles with D 245 nm at S = 0.11 % is 10 % during the tran
sition period and 20 % during the dry season. Interest
ingly, this effect is only observed for S = 0.11 %, whereas
MAF(> 0.11 %) reaches unity throughout the entire year. An explanation for this observation could be the intrusion of relatively fresh biomass burning aerosol plumes during the transition period and dry season, which contain a fraction of comparatively inefcient CCN. Soot is probably a main candidate here; however, fresh soot should also signicantly reduce the MAF(S) for higher S levels (Rose et al., 2010).Thus, we speculate that probably semi-aged soot particles may be an explanation for the observed activation behavior.
Figure 6 corresponds to Fig. 3 and subdivides the annual mean (S,Da) size distribution ( (S,Da) plotted against all measured Da(S)) as well as the annual mean NCN(D) size distribution into their seasonal counterparts. The particle size distributions were tted with a bimodal logarithmic normal distribution and the corresponding results are listed in detail in Table 2. The differences in the characteristic size distributions for the individual seasons clearly emerge: in addition to the strong variations in total particle number concentration (see Fig. 1), the accumulation mode overwhelms the Aitken mode during the dry season, while accumulation and Aitken modes occur at comparable strength under wet season conditions. In other words, during the dry season, Aitken-mode particles account on average for about 26 % of the total aerosol population (NCN,Ait = 483 [notdef] 49 cm3
vs. NCN,Acc = 1349 [notdef] 47 cm3), whereas during the wet
season, the Aitken mode accounts for about 62 % (NCN,Ait = 246 [notdef] 9 cm3 vs. NCN,Acc = 145 [notdef] 8 cm3) (see
Table 2). The size distribution of the transition period from wet to dry season represents an intermediate state between the wet and dry season extremes. Furthermore, the comparison between wet season conditions with and without LRT inuence reveals comparable distributions. However, a slight increase in the accumulation mode during LRT conditions indicates the presence of dust, smoke, pollution, and aged sea spray on top of the biogenic aerosol population during pristine periods (M. L. Phlker et al., 2017a).
The Hoppel minimum DH (Hoppel et al., 1996) between the Aitken and accumulation modes4 also shows seasonal variations with its largest values around 110 nm in the wet season and its smallest values around 95 nm in the dry season (compare to Fig. 5 and Table 2). Following Krger et al. (2014), the observed DH can be used to determine an effective average cloud peak supersaturation Scloud(DH, ).
4The position of DH was determined as the intersection of the tted and normalized modes (monomodal ts for Aitken and accumulation mode were normalized to equal area). The normalization is necessary for a precise localization of DH because large differences in Aitken- and accumulation-mode strength (e.g., for the dry season conditions) cause biased DH as the intersection of both modes is shifted towards the smaller mode.
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Figure 4. Number size distributions of total aerosol particles, NCN(D), and of cloud condensation nuclei, NCCN(S,D), at all 10 supersaturation levels (S = 0.111.10 %) averaged over the entire
measurement period. The NCCN(S,D) size distributions were calculated by multiplying the average NCN(D) size distributions (in
Fig. 3) with the average CCN activation curves in (Fig. 2).
sparse occurrence of particles < 30 nm) explains the high NCCN(1.10 %)/NCN,10 levels in Fig. 1d.
3.3 Seasonal differences in CCN properties at the ATTO site
Within the seasonal periods in the central Amazon as dened in Sect. 1.2, we have subdivided the annual data set into the following four periods of interest, which represent the contrasting aerosol conditions and/or sources. (a) The rst half of the wet seasons 2014 and 2015 received substantial amounts of long-range transport (LRT) aerosol: mostly African dust, biomass smoke, and fossil fuel emissions (Ansmann et al., 2009; Salvador et al., 2016). Here, the corresponding period of interest will be called LRT season and covers 24 March to 13 April 2014 and 9 January to 10 February 2015. (b) In the late wet season 2014, all pollution indicators approached background conditions. Thus, the period from 13 April to31 May 2014 will be treated as the clean wet season in this study. (c) The months June to July represent the transition period from wet to dry season and will be called transition wet to dry. (d) The period of interest that covers the dry season with frequent intrusion of biomass burning smoke ranges from August to December 2014.
Figure 5 shows the CCN activation curves for all S levels, subdivided into the four seasonal periods of interest. Although the plots for the individual seasons appear to differ only subtly, e.g., in Da(S) position and curve width, there is one major difference: the variable shape of the activation curve for the smallest S = 0.11 %. Particularly, the be
havior of MAF(0.11 %) shows clear seasonal differences. It reaches unity during the wet season, whereas it levels off below unity for the LRT, transition, and particularly for the
15720 M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1
size, concentration, and hygroscopicity as well as cloud supersaturation represent key parameters for a detailed understanding of cloud properties. Figure 6 provides reference values for all these parameters, resolved by seasons and thus provides comprehensive insight into the Amazonian cloud properties.
Comparing the seasonal (S,Da) size distributions in
Fig. 6, it is obvious that the (seasonally averaged) Ait values in the Aitken-mode size range are surprisingly stable between 0.13 and 0.14 throughout the whole year. This indicates that the Aitken-mode aerosol population was persistently dominated by almost pure organic particles throughout the seasons. In contrast, noticeable seasonal differences were observed for (seasonally averaged) Acc values in the accumulation-mode size range, with mean values ranging from around 0.21 to 0.28. This indicates that the accumulation mode also comprises high contents of organic materials, however, with elevated amounts of inorganic ingredi-
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Figure 5. CCN activation curves for all measured S levels (S = 0.111.10 %), subdivided into seasonal periods of interest as specied in
Sect. 3.3. Data points represent arithmetic mean values. For NCCN(S,D)/NCN(D), the standard error is plotted, which is very small (due to the large number of scans with comparatively small variability) and therefore not perceptible in this representation. For the diameter, D, the error bars represent the experimental error as specied in Sect. 2.3. The grey vertical bands represent the (seasonal) position of the Hoppel minima (including error range; compare to Table 2). Dashed horizontal lines provide visual orientation and indicate 0, 50, and 100 % activation. The 50 % activation diameter is used for calculation of the hygroscopicity parameter (S,Da). The lines connecting the data points merely serve as visual orientation.
Cloud development and dynamics are highly complex processes in which aerosol particles are activated at different supersaturations. In the context of this study, Scloud(DH, )
is used as a mean cloud supersaturation and serves as an overall reference value; however, it does not reect the complex development of S inside a cloud. Based on our data, Scloud(DH, ) is estimated as a value around 0.29 % during dry season conditions and around 0.22 % during wet season conditions (Table 2). This indicates that Scloud(DH, ) levels tend to be noticeable lower during wet season cloud development compared to the dry season scenario. A plausible cause for the comparatively small DH and high Scloud(DH, )
in the dry season could be invigorated updraft regimes in the convective clouds. This invigoration could be caused by the stronger solar heating during the dry season and/or the increased aerosol load under biomass-burning-impacted conditions, as suggested previously (Andreae et al., 2004; Rosen-feld et al., 2008). As outlined in Sect. 1.1, aerosol particle
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Figure 6. Size dependence of the hygroscopicity parameter (S,Da) subdivided into seasonal periods of interest (color coding) as specied in Sect. 3.3. Values of (S,Da) for every S level are plotted against their corresponding midpoint activation diameter Da(S) (left axis). For (S,Da), the error bars represent 1 standard deviation. For Da(S), the experimentally derived error is shown. In addition, the average number size distributions for the seasonal periods of interest are shown (right axis). The standard error of the number size distributions is indicated as shading, which is very small and therefore hardly perceptible in this representation due to the large number of scans with comparatively small variability. A clear size dependence and seasonal trends in (S,Da) levels can be observed. The averaged number size distributions show very pronounced seasonal differences.
ents (i.e., sulfate, ammonium, and potassium). In the size range around DH, which separates the (apparently) chemically distinct aerosol populations of Aitken and accumulation modes, a step-like increase in (S,Da) is observed. The highest seasonally averaged (S,Da) values (up to 0.28) are observed during intrusion of dust, marine sulfate, and sea-salt-rich LRT plumes. Note that short-term peaks in (S,Da)
can be even higher; see case studies in Part 2 (M. L. Phlker et al., 2017a). In the absence of LRT, the Acc values are also rather stable for most of the year and range between0.21 and 0.24. Overall, a remarkable observation is the high similarity between the wet and dry season (S,Da) size distributions, while many other aerosol parameters undergo substantial seasonal variations (Andreae et al., 2015).
The (S,Da) levels reported here agree well with the corresponding results in the previous Amazonian CCN studies by Gunthe et al. (2009) and Whitehead et al. (2016), which range between 0.1 and 0.4, with a mean around 0.16 [notdef] 0.06.
In a wider context, our results also agree well with previous long-term measurements at other continental background locations (i.e., alpine, semi-arid, and boreal sites) (Jurnyi et al., 2011; Levin et al., 2012; Paramonov et al., 2013;Mikhailov et al., 2015). Comparing these four sites with each other, the following observations can be made. (i) Ait tends to be smaller than Acc at all four background locations. (ii) At the alpine, semi-arid, and boreal sites, (S,Da)
undergoes a rather gradual increase from the Aitken- to the accumulation-mode size range (Paramonov et al., 2013,
and references therein), whereas this increase appears to be steeper (step-like) in the Amazon. This can clearly be seen in the present study (e.g., Fig. 3) as well as in Gunthe et al. (2009) and Whitehead et al. (2016). (iii) Particularly in the vegetated environments (i.e., tropical, boreal, and semi-arid forests), Ait mostly ranges between 0.1 and 0.2, suggesting that the Aitken-mode particles predominantly comprise organic constituents. Furthermore, Ait shows a remarkably small seasonality for these locations. (iv) The Acc levels show a much wider variability throughout the seasons for all locations.
Figure 7 presents the diurnal cycles in mean for the four seasonal periods of interest. No perceptible diurnal trends in mean can be observed for any of the seasons. The only observable difference is an increased variability of mean during the LRT season (see error bars in Fig. 7a). This can be explained by the episodic character of LRT intrusions, which causes an alternating pattern of clean periods with background conditions and periods of elevated concentrations of LRT aerosol (M. L. Phlker et al., 2017a). For comparison, the diurnal cycles in NCN concentration have been added to
Fig. 7, which conrm the absence of strong diurnal variations in the aerosol population.
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15722 M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1
nitrate, sulfate, ammonium, and chloride), the results tend to be dominated by the fraction of larger particles with comparatively high masses (i.e., in the accumulation-mode size range) and are inuenced less by the fraction of small particles with comparatively low masses (i.e., in the Aitken-mode size range). Thus, in order to increase the comparability between ACSM and CCNC, we have chosen the lowest S level (S = 0.11 [notdef] 0.01 %), which represents the largest measured
Da(S) (Da(S) = 172 [notdef] 12 nm).
In Fig. 8, the (0.11 %,Da) values are plotted against the ACSM-derived organic mass fraction (forg). The data were tted with (i) a linear t and (ii) a bivariate regression according to Cantrell (2008). A linear t approach was used by Gunthe et al. (2009) to determine the effective hygroscopicity parameters org = 0.1 of biogenic Ama
zonian SOA (forg = 1) and inorg = 0.6 for the inorganic frac
tion (forg = 0). For the present data set, the same proce
dure results in an acceptable coefcient of determination (R2 = 0.66). We estimated the effective hygroscopicity pa
rameters org = 0.12 [notdef] 0.01 and inorg = 0.61 [notdef] 0.01 based
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Figure 7. Diurnal cycles in hygroscopicity parameter, mean, and total aerosol number concentration, NCN, subdivided into seasonal periods of interest as specied in Sect. 3.3. No diurnal trend is detectable throughout the year. Note that the range of 1 standard deviation of mean around the mean is surprisingly small given that long seasonal time periods and data from all S levels have been averaged. The only perceptible difference is a larger scattering during a period with LRT inuence (a). Grey and yellow shading indicate night and day.
3.4 Aerosol chemical composition and effective hygroscopicity
Continuous ACSM measurements are being conducted at the
ATTO site since March 2014, providing online and non-size-resolved information on the chemical composition of the non-refractory aerosol (Andreae et al., 2015). Here, we compare the ACSM data on the aerosols chemical composition with the CCNC-derived (S,Da) values. This analysis focuses on the dry season months, when ACSM and CCNC were operated in parallel.5 Note that the ACSM covers a size range from 75 to 650 nm (Ng et al., 2010), while the size-resolved CCN measurements provide information only up to particle sizes of about 170 nm. Since the ACSM records the size-integrated masses of dened chemical species (organics,
5Although the ACSM measurements were started in
March 2014, instrumental issues during the initial months caused some uncertainty for the corresponding data. Thus, for this study, we focus only on the data period August to December 2014, when the instrumental issues were resolved.
trations have been suggested (Andreae, 2009; Gunthe et al., 2009; Rose et al., 2010; Deng et al., 2013). Any parametrization strategy seeks, on one hand, an efcient combination of a minimal set of input data and, on the other hand, a good representation of the atmospheric CCN population.
The detailed analysis in this study has shown that the CCN population in the central Amazon is mainly dened by comparatively stable (S,Da) levels, due to the predominance of organic aerosol particles, and rather pronounced seasonal trends in aerosol number size distribution. Particularly, the remarkably stable (S,Da) values suggest that the Amazonian CCN cycling can be parametrized rather precisely for efcient prediction of CCN concentrations. In the following paragraphs, we apply the following CCN parametrization strategies to the present data set and explore their strengths and limitations:
i. CCN prediction based on the correlation between NCCN(0.4 %) and NCN, called the [Delta1]NCCN(0.4 %)/[Delta1]NCN parametrization here;
ii. CCN prediction based on the correlation between NCCN(S) and cCO, called the [Delta1]NCCN(S)/[Delta1]cCO parametrization here;
iii. CCN prediction based on analytical t functions of experimentally obtained CCN spectra, called CCN spectra parametrization;
iv. CCN prediction based on the -Khler model, called -Khler parametrization; and
v. CCN prediction based on a novel and effective parametrization built on CCN efciency spectra, called CCN efciency spectra parametrization.
The prediction accuracy for the individual strategies is summarized in Table 3.
3.5.1 [Delta1]NCCN(0.4 %)/[Delta1]NCN parametrization
Andreae (2009) analyzed CCN data sets from several contrasting eld sites worldwide and found signicant relationships between the satellite-retrieved aerosol optical thickness (AOT) and the corresponding NCCN(0.4 %) levels as well as between the total aerosol number concentration NCN and NCCN(0.4 %). The obtained ratio
NCCN(0.4 %)/NCN = 0.36 [notdef] 0.14 in other words, the glob
ally averaged CCN efciency at S = 0.4 % can be used to
predict CCN concentrations. The corresponding results for the present data set are displayed in Fig. 9a and show a surprisingly tight correlation, given that a globally obtained NCCN(0.4 %)/NCN ratio has been used. However, Fig. 9a also shows a systematic underestimation of the predicted CCN concentration NCCN,p(0.4 %), which can be explained by the comparatively high activated fractions in the Amazon (e.g., NCCN(0.47 %)/NCN,10 ranging from 0.6 to 0.9;
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(S,D a)
M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1 15723
0.8
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0.3
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0.0
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forg
Figure 8. Correlation between (0.11 %, 170 nm) and the organic
mass fraction, forg, determined by the ACSM during the dry season months. The data were tted by a linear and a bivariate regression t. Shading of the t lines shows the standard error of the t. The error bars of the data markers represent the experimental error, which is estimated as 5 % for forg and 10 % for (0.11 %, 170 nm).
on the linear t and extrapolation to forg = 1 and forg = 0,
respectively. This is in good agreement with previous studies (King et al., 2007; Engelhart et al., 2008; Gunthe et al., 2009; Rose et al., 2011). However, a drawback of the linear tting approach is the fact that swapping forg and (0.11 %,Da) on the axes will change the results.
Therefore, we also applied the bivariate regression t, which takes into account that both parameters, forg and (0.11 %,Da), have an experimental error. For the bivariate regression, an error of 5 % in forg and an error of 10 % in (0.11 %,Da) were used. A coefcient of determination of R2 = 0.71 was obtained for the bivariate regression, which
is slightly better than for the linear t. Based on the bivariate regression, we estimated effective hygroscopicity parameters org = 0.10 [notdef] 0.01 and inorg = 0.71 [notdef] 0.01 for the or
ganic and inorganic fractions, respectively.
3.5 CCN parametrizations and prediction of CCN number concentrations
Cloud-resolving models at all scales spanning from large eddy simulations (LESs) to global climate models (GCMs) require simple and efcient parametrizations of the complex microphysical basis to adequately reect the spatiotemporal CCN cycling (Cohard et al., 1998; Andreae, 2009). Previously, several different approaches to predict CCN concen-
15724 M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1
Table3.CharacteristicdeviationbetweenobservedandpredictedCCNnumberconcentrationsNCCN ( S ) and NCCN,p ( S ) based on different parametrization schemes, ac-
0.61[notdef] 0.06 1.02 1.15 1.23 1.55 0.36 0.61 1.47 1.73 0.47 0.67 0.08 0.09 0.08 0.18 0.05 0.09 0.08 0.15 0.05 0.08
0.74[notdef] 0.08 1.50 1.59 1.22 1.51 0.40 0.62 1.37 1.63 0.44 0.64 0.09 0.10 0.09 0.16 0.04 0.06 0.09 0.14 0.04 0.06
0.92[notdef] 0.11 1.11 1.28 1.15 1.42 0.45 0.63 1.18 1.44 0.40 0.60 0.08 0.08 0.05 0.10 0.01 0.03 0.05 0.09 0.01 0.04
1.10[notdef] 0.08 1.12 1.25 1.11 1.35 0.48 0.64 1.05 1.31 0.35 0.57 0.08 0.08 0.04 0.08 0.01 0.04 0.05 0.08 0.01 0.05
All1.501.732.002.270.570.801.892.150.540.750.100.110.190.330.100.200.140.250.060.17
0.11[notdef] 0.01 1.48 1.75 4.68 4.75 1.50 1.57 2.54 2.81 0.61 0.89 0.18 0.22 0.64 0.74 0.24 0.44 0.39 0.53 0.14 0.36
0.15[notdef] 0.02 0.50 1.21 2.78 2.99 0.71 0.92 2.42 2.69 0.62 0.85 0.07 0.11 0.27 0.47 0.10 0.32 0.15 0.36 0.04 0.27
0.20[notdef] 0.02 2.84 2.96 2.46 2.75 0.59 0.85 2.60 2.86 0.70 0.91 0.11 0.13 0.22 0.43 0.13 0.30 0.14 0.33 0.08 0.24
0.24[notdef] 0.03 1.78 1.98 1.93 2.26 0.45 0.74 2.24 2.50 0.64 0.84 0.09 0.10 0.16 0.37 0.12 0.25 0.12 0.28 0.09 0.20
0.29[notdef] 0.03 2.19 2.33 1.74 2.09 0.40 0.71 2.12 2.39 0.62 0.82 0.14 0.14 0.22 0.42 0.14 0.25 0.17 0.32 0.11 0.20
0.40 0.41 0.47
0.47[notdef] 0.04 1.33 1.54 1.36 1.73 0.33 0.63 1.70 1.93 0.50 0.71 0.04 0.06 0.09 0.26 0.07 0.16 0.08 0.20 0.06 0.12
biasdevbiasdevbiasdevbiasdevbiasdevbiasdevbiasdevbiasdevbiasdevbiasdevbiasdev
Twomeypower-lawtErftAnnualaverageResolvedbyseasons
annualseasonalannualseasonal
S(%)[Delta1]NCCN (S )/[Delta1]NCN [Delta1]NCCN (S )/[Delta1]cCO Fits of CCN spectra -Khler Erf t of CCN efciency spectra
cordingtoRoseetal.(2008).ForeveryparametrizationschemeandresolvedbyS,thefollowinginformationisprovided:(i)arithmeticmeanvaluesoftherelativebias
[Delta1]bias NCCN ( S )= ( N CCN,p ( S ) N CCN ( S ))/N CCN ( S ) and (ii) of the total relative deviation [Delta1] dev N CCN ( S )=[notdef] N CCN,p ( S ) N CCN ( S )[notdef] /N CCN ( S ).
see Fig. 1). Activated fractions in other locations worldwide tend to be lower due to the (more persistent) abundance of nucleation-mode particles, as discussed in Sect. 3.1.
In Sect. 3.5.5, we will show that our novel parametrization is an extension of this approach: the NCCN(0.4 %)/NCN parametrization refers to a globally averaged CCN efciency at one specic S, while the CCN efciency spectra parametrization is based on an analytical description of CCN efciencies across the entire (relevant) S range and has been determined specically for the central Amazon.
3.5.2 [Delta1]NCCN(S)/[Delta1]cCO parametrization
Experimentally obtained excess NCCN(S) to excess cCO ratios can be used to calculate NCCN,p(S). Kuhn et al. (2010)
determined [Delta1]NCCN(0.6 %)/[Delta1]cCO = 26 cm3 ppb1 for
biomass burning plumes and [Delta1]NCCN(0.6 %)/[Delta1]cCO = 49 cm3 ppb1 for urban emissions in the area around Manaus, Brazil. Lawson et al. (2015) investigated biomass burning emissions in Australia and found [Delta1]NCCN(0.5 %)/[Delta1]cCO = 9.4 cm3 ppb1. In the context of
the present study, we have calculated [Delta1]NCCN(S)/[Delta1]cCO for a strong biomass burning event in August 2014. This event and its impact on the CCN population is the subject of a detailed discussion in the companion Part 2 paper (M. L. Phlker et al., 2017a). Here, we use the [Delta1]NCCN(S)/[Delta1]cCO ratios from the companion paper to obtain a CCN prediction. The observed [Delta1]NCCN(S)/[Delta1]cCO ratios range between 6.7 [notdef] 0.5 cm3 ppb1 (for S = 0.11 %) and values
around 18.0 [notdef] 1.3 cm3 ppb1 (for higher S) (see summary
in Table 4). Since biomass burning is the dominant source of pollution in the central Amazon, these biomass-burning-related [Delta1]NCCN(S)/[Delta1]cCO ratios in Table 4 were used to calculate NCCN,p(S) for the present data set. The corresponding results in Fig. 9b show a reasonable correlation for highly polluted conditions (NCN > 2000 cm3) and a poor correlation for cleaner states (NCN < 2000 cm3). This behavior can be explained by the fact that the high concentrations in CCN and CO originate from frequent biomass burning plumes during the Amazonian dry season (see Fig. 1). Thus, they can be assigned to the same sources with rather dened [Delta1]NCCN(S)/[Delta1]cCO ratios (Andreae et al., 2012). During the contrasting cleaner periods, CN and CO originate from a variety of different sources, which are often not related and therefore explain the poor correlation for clean to semi-polluted conditions. Overall, Fig. 9b indicates that the quality of CO-based CCN prediction is rather poor, due to the complex interplay of different sources. The overall deviation between NCCN,p(S) and NCCN(S) for this approach is about 170 % (Table 3).
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M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1 15725
Figure 9. Predicted vs. measured CCN number concentrations calculated from (a) observed ratio NCCN(0.4 %)/NCN = 0.36 in An
dreae (2009) and (b) observed (biomass-burning-related) excess CCN to excess CO ratios in M. L. Phlker et al. (2017a). The color code shows the number of data points falling into the pixel area, following Jurnyi et al. (2011). The black line represents a bivariate regression t of the data.
Table 4. Excess NCCN(S) to excess cCO ratios [Delta1]NCCN(S)/[Delta1]cCO for the individual S levels during peak period of the strong biomass burning event in August 2014. This event is analyzed in detail through a case study in the companion Part 2 paper (M. L. Phlker et al., 2017a). The values [Delta1]NCCN(S)/[Delta1]cCO were obtained from bivariate regression t of scatterplots between NCCN(S) and cCO for individual
S levels (Andreae et al., 2012). The parameter NCCN(S) in this table represents the y axis intercept of the linear regression of NCCN vs. cCO at cCO = 0 ppb and is, therefore, negative (see M. L. Phlker et al., 2017a).
S (%) [Delta1]NCCN(S)/[Delta1]cCO(cm3 ppb1) NCCN(S) (cm3) R2
0.11 [notdef] 0.01 6.7 [notdef] 0.5 603 [notdef] 125 0.86
0.15 [notdef] 0.02 13.6 [notdef] 1.4 1447 [notdef] 354 0.68
0.20 [notdef] 0.02 14.3 [notdef] 0.8 1128 [notdef] 208 0.90
0.24 [notdef] 0.03 16.8 [notdef] 1.0 1460 [notdef] 261 0.86
0.29 [notdef] 0.03 17.4 [notdef] 1.3 1378 [notdef] 296 0.83
0.47 [notdef] 0.04 20.1 [notdef] 1.7 1675 [notdef] 425 0.84
0.61 [notdef] 0.06 17.9 [notdef] 1.3 1206 [notdef] 332 0.88
0.74 [notdef] 0.08 16.5 [notdef] 1.3 933 [notdef] 329 0.88
0.92 [notdef] 0.11 18.1 [notdef] 1.4 1265 [notdef] 355 0.85
1.10 [notdef] 0.08 17.5 [notdef] 1.3 1096 [notdef] 328 0.87
3.5.3 Classical and improved CCN spectra parametrization
The total number of particles that are activated at a given S is regarded as one of the central parameters in cloud formation and evolution (Andreae and Rosenfeld, 2008). Thus, CCN spectra (NCCN(S) plotted against S) are a widely and frequently used representation in various studies to summarize the observed NCCN(S) values over the cloud-relevant S range for a given time period and location (Twomey and Wojciechowski, 1969; Roberts et al., 2002; Rissler et al., 2004;Freud et al., 2008; Gunthe et al., 2009; Martins et al., 2009b).Different analytical t functions of the experimental CCN spectra have been proposed and are used as parametrization schemes for NCCN(S) in modeling studies (e.g., Cohard et
al., 1998; Khain et al., 2000; Pinsky et al., 2012; Deng et al., 2013).
In the context of the present study, the annual mean Amazonian CCN spectrum is shown in Fig. 10. As an analytical representation of the experimental data, we have used Twomeys empirically found (classical) power-law t function (Twomey, 1959):
NCCN (S) = NCCN (1%) [notdef]
[parenleftbigg]
S 1%
k
, (6)
which yields a reasonable coefcient of determination of R2 = 0.88 (Fig. 10a). Besides the annual mean spectrum, we
also conducted a Twomey t for the seasonally resolved CCN spectra (not shown) and summarized the resulting t parameters in Table 5. The obtained t parameters (e.g., for the an-
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15726 M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1
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800 600 400 200
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0
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ln
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1
A
(7)
is related to the physical basis of the tted data and yields a high coefcient of determination (R2 = 0.997). Mathe
matically, this erf represents an integration of a log-normal NCN(D) size distribution. Analogously, the NCCN(D) spectrum represents the cumulative distribution of the relative NCN(D) distribution (compare to Fig. 4). A double-erf t would be even more appropriate for the bimodal Amazon NCN(D) distribution (compare to Fig. 6 and discussion in Sect. 3.5.5). However, the single-erf t proposed above proved to be (already) a very good analytical representation as underlined by the high coefcient of determination (R2 > 0.99). The erf t reects the physically expected saturation behavior of aerosol activation for high S and thus converges against a limit of A = 1067 [notdef] 22 cm3, which
matches well with the mean total number concentration of NCN,10 = 1097 [notdef] 66 cm3. The erf t (if not forced through
the origin) transects the abscissa at S0 = 0.066 %. Therefore,
the erf t cannot describe the CCN activation behavior for low S( 0.07 %), which is also an experimentally unaccessi
ble S range. For this approach, we also summarized the corresponding t parameters for the annual mean CCN spectrum and the seasonally resolved cases in Table 6.
Figure 11a and b show the corresponding NCCN,p(S) vs.
NCCN(S) scatterplots based on the annual mean CCN spectrum using the Twomey and erf ts.6 In general, parametrizations based on CCN spectra yield a mean state based on average concentrations (see t parameters in Fig. 10 as well as Tables 5 and 6) and ignore the temporal variability of the aerosol abundance (Martins et al., 2009a; Rose et al., 2010;Jurnyi et al., 2011). Upon closer inspection, Table 3 shows that the erf t allows somewhat better predictions (e.g., deviation of power-law t about 227 % vs. 215 % for erf t
6The horizontal lines in the scatterplots result from the fact that constant NCCN,p(S) values are obtained for the different S levels.
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0.0
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Figure 10. CCN spectrum (circular markers) averaged over the entire measurement period and tted with the classical Twomey power-law t (a) and an alternative error function t (b). Error bars at the markers represent the measurement error in S and standard error in NCCN(S). The dashed line is a t function with grey shading as uncertainty of the t.
Table 5. Twomey t parameters describing CCN spectra NCCN(S) vs. S as parametrization input data (compare to Figs. 10 and 11a, c).
Fit parameters are provided for annually averaged CCN spectra and resolved by seasons.
Time period NCCN(1 %) (cm3) k R2
Annual 998 [notdef] 60 0.36 [notdef] 0.04 0.88
Wet season 289 [notdef] 7 0.57 [notdef] 0.03 0.98
LRT period 378 [notdef] 9 0.38 [notdef] 0.03 0.94
Transition 970 [notdef] 40 0.49 [notdef] 0.05 0.94
Dry season 1469 [notdef] 78 0.36 [notdef] 0.06 0.86
nual mean CCN spectrum) NCCN(1 %) = 998 cm3 (some
times also called c) and k = 0.36 agree with results from
previous measurements that are summarized by Martins et al. (2009b). The power-law function has become a widely used parametrization due to its simplicity (Cohard et al., 1998). However, because it is based on strong assumptions and not related to the physical basis of the tted data, it has certain drawbacks, such as the poor representation of NCCN(S) at small S (i.e., < 0.2 %), as well as the fact that for larger S (i.e., > 1.2 %) it does not converge against NCN, which is, for physical reasons, the upper limit.
Table 6. Erf t parameters describing CCN spectra NCCN(S) vs. S as parametrization input data (compare to Figs. 10 and 11b, d).
Fit parameters are provided for annually averaged CCN spectra and resolved by seasons.
Time period A (cm3) S0 (%) w0 R2
Annual 1067 [notdef] 22 0.07 [notdef] 0.01 2.1 [notdef] 0.1 0.99
Wet season 340 [notdef] 30 0.08 [notdef] 0.01 2.9 [notdef] 0.2 0.97
LRT period 532 [notdef] 72 0.04 [notdef] 0.01 4.5 [notdef] 1.0 0.98
Transition 1180 [notdef] 37 0.07 [notdef] 0.01 3.0 [notdef] 0.2 0.99
Dry season 1430 [notdef] 24 0.07 [notdef] 0.01 1.8 [notdef] 0.1 0.99
As an alternative, an error function t which is used in this context for the rst time represents the data much better (Fig. 10b). The proposed error function (erf)
NCCN (S) = A [notdef] erf
M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1 15727
Figure 11. Predicted vs. measured CCN number concentrations based on the classical Twomey power-law t (a, c) and an alternative error function t (b, d). The top row (a, b) represents the annually averaged cases, whereas the bottom row (c, d) represents parametrizations based on seasonally resolved CCN spectra. Both predictions are based exclusively on the corresponding average t functions (i.e., the annually averaged CCN spectra in Fig. 10 and seasonally averaged CCN spectra, as specied in Tables 5 and 6) without considering time-resolved aerosol parameters. The color code shows the number of data points falling into the pixel area, following Jurnyi et al. (2011). Predicted and measured CCN concentrations deviate signicantly, showing the inherent limitations of the CCN spectra approach. For the annually averaged data (a, b), no meaningful bivariate regression t could be obtained.
in the case of annual mean and 80 % vs. 75 % for the seasonally resolved case), which can be explained by the fact that the erf t represents the experimental data more appropriately (compare to Fig. 10). Overall, however, the power-law t and the erf t approaches give rather poor correlations due to the missing representation of the aerosols temporal variability. This is particularly obvious for the annual mean case, since the total aerosol abundance varies signicantly between wet and dry season conditions. Accordingly, the CCN spectra parametrization, which operates with constants, predictably underestimates the dry season conditions and overestimates the wet season conditions. In addition to the analytical t approaches for the annual mean spectrum (Fig. 11a and b), we conducted an analogous CCN prediction based on seasonally resolved CCN spectra (Fig. 11c and
d). The prediction accuracy clearly improves (e.g., deviation of erf t for annual mean case equals 215 % vs. 75 % for seasonally resolved case; see Table 3). Figure 11 illustrates that the prediction accuracy of parametrizations that rely on analytical t functions of CCN spectra (i.e., Twomey, erf, and related functions) improves with decreasing variability of the aerosol population (e.g., for shorter periods with less variable aerosol properties). However, the missing representation of the aerosols temporal variability remains an inherent limitation of the CCN spectra parametrization. It can be concluded that this parametrization requires a minimum of aerosol input data (i.e., only the parameters of the corresponding t function), which explains its wide use in various modeling studies. However, Fig. 11 and Table 3 show that this simplicity is clearly at the expense of the prediction accuracy.
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15728 M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1
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Figure 13. CCN efciency spectra averaged over the entire measurement period for the reference concentrations, NCN,10 and
NCN,50. The t functions are error function ts (the dashed line with shading represents the uncertainty of the t). The error bars at the markers represent the measurement error in S and 1 standard deviation (not the standard error as in Fig. 10) in NCCN(S)/NCN,Dcut.
the specic (temporary) aerosol population during the period of the study. The shape of CCN spectra provides some information on the aerosol activation behavior as a function of S. However, the strong variability in the total aerosol abundance makes it difcult to compare the CCN efciency behavior between different locations and/or periods of interest with specic (e.g., seasonal) conditions. For the present data set, Fig. 13 shows annually averaged CCN efciency spectra (NCCN(S)/NCN,Dcut plotted against S) for two different reference aerosol concentrations NCN,10 and NCN,50.8 The corresponding t parameters are summarized in Table 7. The CCN efciency spectra are independent of the total aerosol load and instead reect the fraction of activated particles for the relevant S range. Here, we also use an erf t,
NCCN(S)
NCN,Dcut =
1
2 +
2
R2= 0.99
2 4 6 8
100
2 4 6 8
1000
2 4 6
10
NCCN(S) [cm-3]
Figure 12. Predicted vs. measured CCN number concentrations using the -Khler model approach. This approach requires the following time-resolved aerosol input data: (i) time-resolved aerosol size spectra spanning the CCN-relevant range (e.g., SMPS) and(ii) annual average values for the Aitken and accumulation size range ( Ait = 0.14 and Acc = 0.22). The color code shows the num
ber of data points falling into the pixel area, following Jurnyi et al. (2011). The black line represents a bivariate regression t of the data.
3.5.4 -Khler parametrization
The -Khler model approach has been used in previous studies and gave good CCN predictions (e.g., Gunthe et al., 2009; Rose et al., 2010). For the present data set, the NCCN,p(S) concentrations were calculated according to Rose et al. (2010).7 Here, the annually averaged values Ait = 0.14
and Acc = 0.22 were used for the CCN prediction, since
they accurately represent the stable levels in the central Amazon. Figure 12 shows the corresponding NCCN,p(S) vs.
NCCN(S) scatterplot, in which the areas with the highest density of data points precisely follow the one-to-one line. Table 3 underlines this good agreement, as the observed deviation of around 10 % between NCCN,p(S) and NCCN(S) is the smallest among all tested parametrizations. Accordingly, the -Khler model approach turns out to be a very accurate parametrization. However, it requires a time series of NCN size distributions as input data and is therefore the most data demanding strategy in this regard.
3.5.5 CCN efciency spectra parametrization
It has to be kept in mind that CCN spectra strongly depend on the total aerosol concentration and thus predominantly reect
7Briey, for every SMPS scan, the NCN size distribution has been integrated above the critical diameter Da, in which Da has been obtained based on a given and S.
1.00.80.60.40.20.0
1
2 [notdef] erf0
@
ln
[parenleftBig]
S S1
[parenrightBig]
1
A, (8)
to describe the data for the same reasons as outlined in Sect. 3.5.3. The ts yield high coefcients of determination (R2 = 0.99). Per denition, NCCN(S)/NCN,Dcut spans from
zero to unity. Therefore, the offset y0 of the function as well as the pre-factor A have been set to 0.5. For the atmospherically relevant S range typically S < 0.6 % (see Andreae, 2009) aerosol sizes around 5060 nm are considered the onset of the CCN size range (see also Fig. 4). Accordingly, if Dcut is chosen close to this activation threshold, the corresponding NCCN(S)/NCN,Dcut approaches unity, which can be seen in Fig. 13. The free variable S1 (e.g., S1 = 0.22 [notdef] 0.01 %
for NCN,10 and S1 = 0.19 [notdef] 0.01 % for NCN,50) represents the
8The use of aerosol number concentrations with Dcut = 50 nm
has been suggested by Paramonov et al. (2015) as a reference value to ensure comparability of CCN efciencies from different studies.
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w1
M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1 15729
Table 7. Erf t parameters describing CCN efciency spectra NCCN(S)/NCN,Dcut vs. S as model input data (compare to Figs. 13 and 14). Fit parameters are provided for (i) annually averaged efciency spectra with ve different aerosol number reference concentrations NCN,Dcut and (ii) resolved by seasons for NCN,10 and
NCN,50.
NCN,Dcut Time period S1 (%) w1 R2
NCN,10
Annual
distribution and, in a secondary role, the particles chemical composition, represented by (S,Da) (Dusek et al., 2006).Thus, the seasonally averaged number size distributions and the seasonally averaged (S,Da) size distribution in Fig. 6 have to be considered to explain the different shapes in Fig. 14. Focusing on the contrasting wet and dry season plots, it can be stated that (i) while the (S,Da) size distributions for wet and dry seasons appear to be very similar (same size trend and same values), the number size distributions (i.e., the ratio of Aitken and accumulation modes) differ substantially. (ii) With increasing S, the diameter Da(S) de
creases and is shifted from the accumulation-mode towards the Aitken-mode size range. (iii) Thus, under dry season conditions, comparatively small S levels (S = 0.110.2 %)
can already activate most particles of the pronounced accumulation mode. (iv) In contrast, under wet season conditions, while the same S levels still activate the accumulation-mode particles, the comparatively strong Aitken mode remains unactivated. This means that the ratio of Aitken- and accumulation-mode particles (NCN,Ait/NCN,Acc(wet) = 1.7;
NCN,Ait/NCN,Acc(dry) = 0.4; compare to Table 2) determines
the activated fraction as a function of S and thus also the steepness of the CCN efciency spectra in Fig. 14.
While size appears as the dominant parameter in the CCN activation behavior, in certain cases variability in chemical composition also matters (Dusek et al., 2006). In Fig. 14, this can be seen for the wet season cases with and without LRT inuence: in the presence of LRT aerosol, the 50 % activation occurs already at S1 = 0.22 % for NCN,10, which is
much closer to the dry (S1 = 0.18 % for NCN,10) than to the
wet season (S1 = 0.35 % for NCN,10) behavior. While Fig. 6
shows that the number size distributions for both cases are similar, the observed difference in Fig. 14 can be explained by the deviations in the corresponding (S,Da) size distributions. In other words, the elevated (S,Da) levels during the intrusion of LRT aerosols allows the activation of particle sizes that remain inactivated at the lower (S,Da) levels in the absence of LRT aerosol. Therefore, the differences in chemical composition can explain the decreased S1 in these cases.
In Fig. 14, single-erf ts have been used as analytical descriptions of the CCN efciency spectra. Overall, this approach provides a good representation of the experimental data (see high coefcients of determination in Table 5). However, the single-erf t is merely an approximation, assuming that the aerosol size distribution is monomodal. This is a valid assumption for the dry season (see Fig. 6) and corresponds to a good agreement between t and data points in Fig. 14d. In contrast, the wet season shows pronounced and prevailing bimodal size distributions (see Fig. 6), which corresponds to a clear discrepancy between the t and data points in Fig. 14b (i.e., for S > 0.3 %). For a bimodal size distribution, a double-erf t is the physically more appropriate description (see also discussion in Sect. 3.5.3). Figure 15 illustrates the contrast between a single- and a double-erf t
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0.22 [notdef] 0.01 1.78 [notdef] 0.08 0.99
NCN,20 0.22 [notdef] 0.01 1.78 [notdef] 0.08 0.99
NCN,30 0.22 [notdef] 0.01 1.72 [notdef] 0.07 0.99
NCN,50 0.19 [notdef] 0.01 1.41 [notdef] 0.05 0.99
NCN,10
Wet season 0.35 [notdef] 0.01 1.80 [notdef] 0.06 0.99
LRT period 0.22 [notdef] 0.01 2.39 [notdef] 0.10 0.98
Transition 0.28 [notdef] 0.01 1.70 [notdef] 0.05 0.99
Dry season 0.18 [notdef] 0.01 1.57 [notdef] 0.11 0.98
Wet season 0.26 [notdef] 0.01 1.37 [notdef] 0.12 0.99
LRT period 0.17 [notdef] 0.01 1.58 [notdef] 0.10 0.99
Transition 0.23 [notdef] 0.01 1.38 [notdef] 0.04 0.99
Dry season 0.17 [notdef] 0.01 1.31 [notdef] 0.06 0.92
S value where half of the aerosol particles are activated into cloud droplets. A monodisperse aerosol with a dened composition would yield a steep step-like CCN efciency spectrum, while the complex Amazonian aerosol results in a wide and rather smooth step. In other words, the width of the erf t (here w1 = 1.78 [notdef] 0.08 for NCN,10 and w1 = 1.41 [notdef] 0.05
for NCN,50) is an (indirect) measure for the diversity (i.e., size and composition) of the aerosol population.
Figure 14 shows a direct comparison of the CCN efciency spectra resolved by seasonal periods of interest (compare also to Sect. 3.3), which reveals characteristic differences in the curves shape (i.e., its steepness). The corresponding t parameters are summarized in Table 7. A good numeric indicator for the differences in steepness is the t parameter S1, which species the 50 % activation supersaturation of the total aerosol population. The largest contrast in shape and S1 can be seen between the dry and wet season scenario: during the dry season, the CCN efciency increases steeply with S, and S1 is reached at 0.18 % for NCN,10, whereas during the wet season, the increase of the CCN efciency is rather gradual and S1 is reached only at 0.35 % for NCN,10. The transition period represents (once more) an intermediate state between the dry and wet season extremes (S1 = 0.28 % for NCN,10).
For transition period conditions, Kuhn et al. (2010) reported NCCN(0.6 %)/NCN = 0.66 [notdef] 0.15, which is in good agree
ment with Fig. 14c (NCCN(0.61 %)/NCN,10 = 0.72 [notdef] 0.10).
The observed differences among the CCN efciency spectra in Fig. 14 reect some of the major trends in the aerosol seasonality in Amazonia. A closer look at Fig. 6 helps to understand those. Overall, the key parameters in the CCN activation behavior are (primarily) the aerosol number size
NCN,50
15730 M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1
Figure 14. CCN efciency spectra averaged over the entire measurement period for reference concentrations, NCN,10 and NCN,50, and subdivided into seasonal periods of interest as specied in Sect. 3.3. The t functions are error function ts (the dashed line with shading represents the uncertainty of the t). The error bars at the markers represent the measurement error in S and 1 standard deviation (not the standard error, as in Fig. 10) in NCCN(S)/NCN,Dcut.
1.00.80.60.40.20.0
of the wet season CCN efciency spectrum for NCN,50. As expected, the double-erf t is clearly a better representation of the data across the entire S range. However, in the context of this study, the double-erf t of CCN spectra merely serves as proof of concept. It will be discussed in more detail in a follow-up study (M. L. Phlker et al., 2017b). Thus, in the context of the following CCN parametrization, we will work exclusively with the single-erf t approach for the following reasons: (i) the single-erf t represents the simpler parametrization scheme (two t parameters instead of six) and (ii) the difference in the CCN prediction accuracy of single- vs. double-erf t turns out to be insignicant.
Figure 16 explores the applicability of the CCN efciency spectra parametrization (single-erf ts) to calculate CCN concentrations. The following four modications of the parametrization scheme are compared: annually averaged CCN efciency spectra with (i) Dcut = 10 nm and
(ii) Dcut = 50 nm (compare to Fig. 13) as well as seasonally
resolved CCN efciency spectra with (iii) Dcut = 10 nm, and
(iv) Dcut = 50 nm (compare to Fig. 14). All cases in Fig. 16
show rather tight correlations, which prove the high prediction accuracy of the CCN efciency spectra parametrization. The corresponding deviations between NCCN(S) and
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N CCN(S)/N CN,D50
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Supersaturation [%]
Figure 15. CCN efciency spectrum for the wet season scenario (Fig. 14b) with NCN,50 as reference concentration. The experimental data have been tted with single- and double-erf ts (dashed lines with shading as uncertainty of the ts). The error bars at the markers represent the measurement error in S and 1 standard deviation in NCCN(S)/NCN,50.
M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1 15731
Figure 16. Predicted vs. measured CCN number concentrations, based on our novel parametrization using time-resolved aerosol number concentrations and average error function ts of CCN efciency spectra. The panels show the following four variations of the parametrization:(a) erf t of the annually averaged NCCN(S)/NCN,10 vs. S efciency plot, (b) erf t of the annually averaged NCCN(S)/NCN,50 vs. S efciency plot, (c) erf ts of the NCCN(S)/NCN,10 vs. S efciency plot, resolved by seasons, and (d) erf ts of the NCCN(S)/NCN,50 vs.
S efciency plot, resolved by seasons. This approach requires the following as input data: (i) a time series of total aerosol concentration (e.g., NCN,10 from a CPC measurement or NCN,50 as model output) and (ii) the parameters of the erf t (e.g., as provided in Table 7). The color code shows the number of data points falling into the pixel area, following Jurnyi et al. (2011). The black line represents a bivariate regression t of the data.
NCCN,p(S) are summarized in Table 3. The comparison conrms that the cases with Dcut = 50 nm perform better than
Dcut = 10 nm. Moreover, the seasonally resolved cases show
higher prediction accuracies than the annually averaged scenarios. Thus, the highest deviation of 33 % is observed for the case of Fig. 16a and the lowest deviation (and therefore best performance) with 17 % for the case of Fig. 16d (see Table 3).
In a way, the CCN efciency spectra parametrization represents a compromise between the previously introduced parametrization strategies: it operates with a comparatively small set of input data and still provides good prediction ac-
curacies. The input data require the t parameters S1 and w1 of the single-erf t, which reects the shape of the t functions. This part conveys the specic CCN activation behavior of the given aerosol population (e.g., the wet season scenario). In addition, a time series of NCN,Dcut is required, which accounts for the temporal variability of the aerosol population. The new parametrization approach is currently extended and applied to further data sets worldwide (M. L. Phlker et al., 2017b).
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15732 M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1
4 Conclusions
Size-resolved CCN measurements have been conducted at the remote ATTO site in the central Amazon, spanning a full seasonal cycle from March 2014 to February 2015. These measurements represent the rst long-term study on CCN concentrations and hygroscopicity in this unique and globally important ecosystem. The reported measurements span the aerosol size range of 20245 nm and therefore cover the Aitken and accumulation modes, which dominate the aerosol burden in the Amazon throughout the year (Andreae et al., 2015). The supersaturation in the CCN counter was cycled through 10 levels from S = 0.11 % to S = 1.10 %. Overall,
this study presents an in-depth analysis of the key CCN parameters, based on a continuous sequence of more than 10 000 CCN activation curves with a temporal resolution of4.5 h and therefore allows a detailed analysis of the CCN cycling in the central Amazon Basin.
The Amazonian atmosphere reveals a characteristic bimodal aerosol size distribution, which is dominated by pronounced Aitken and accumulation modes (DAit 70 nm
vs. DAcc 150 nm) as well as the sparse occurrence of
nucleation-mode particles (< 30 nm). This size distribution closely relates to the observed CCN properties, as its entire size range and thus the majority of particles fall into the CCN-active range. Accumulation-mode particles are CCN active at supersaturations between 0.11 and 0.29 %, while supersaturations between 0.47 and 1.10 % activate both the Aitken and accumulation modes. The absence of nucleation-mode particles further explains the high activated fractions NCCN(S)/NCN,10 that were observed throughout all seasons, with NCCN(0.11 %)/NCN,10 reaching up to 0.4 and NCCN(1.10 %)/NCN,10 constantly exceeding 0.9. These values are substantially higher than corresponding activated fractions at other continental background sites worldwide (Jurnyi et al., 2011; Levin et al., 2012; Paramonov et al., 2013). Overall, the CCN concentrations NCCN(S) for all S levels closely follow the pronounced pollution-related seasonal cycle in NCN that is typical for the Amazon region.
The hygroscopicity parameter (S,Da), which reects the chemical composition of the particles, appears to be remarkably stable throughout the entire measurement period with only a weak seasonal cycle and no perceptible diurnal trends.Numerically, the (S,Da) values lie within a rather narrow range from 0.1 to 0.3 for most of the time. The mean hygroscopicity averaged over the entire period and size range and its corresponding standard deviation is mean = 0.17 [notdef] 0.06.
In terms of particle size, (S,Da) reveals a clear size dependence with lower values for the Aitken mode ( Ait =
0.14 [notdef] 0.03) and elevated levels in the accumulation-mode
range ( Acc = 0.22 [notdef] 0.05). Previous studies showed that
the Amazonian aerosol population is dominated by organic aerosols throughout the seasons (Talbot et al., 1988, 1990;Graham et al., 2003; Gunthe et al., 2009; Martin et al., 2010b;Chen et al., 2015). The comparatively low (S,Da) values in
this study underline this observation. However, the observed difference between Ait and Acc shows that the Aitken mode is almost purely organic (close to = 0.1), while the accumu
lation mode is somewhat enriched in inorganic constituents.
Focusing on seasonal differences, substantial changes in the aerosol concentrations and the shape of the size distribution have been observed. During the (clean) wet season, equally strong Aitken and accumulation modes were ob-served, while during the (polluted) dry season the accumulation mode overwhelms the Aitken mode. The transition periods represent intermediate states between these extremes. Interestingly, the strong seasonal variability in aerosol abundance and sources does not correspond to noticeable changes in (S,Da). In other words, Ait and Acc are almost identical for dry and wet season conditions. The only seasonal period where (S,Da) deviates from its typical range is the LRT season when out-of-basin dust, marine sulfate, and sea salt are transported into the Amazon Basin. During this period, a signicant increase in Acc up to 0.28 is observed. In summary, the seasonally averaged CCN populations (represented by the CCN efciency spectra) are mostly dened by particle size (i.e., shape of aerosol size distribution). The only episodes when (besides size) chemical variability also matters are the LRT periods with their enhanced (S,Da) values.
Based on the CCN key parameters that have been obtained in the present study, we show that the CCN population over Amazonia can be modeled very effectively. Different approaches to infer a CCN concentration from basic aerosol parameters have been compared and it turns out that a remarkably good correlation between modeled and measured data can be obtained based on continuous SMPS time series as well as the annually averaged Ait and acc values from this study. Alternatively, CCN concentration can effectively be calculated based on our novel parametrization, which is based on tted CCN efciency spectra and continuous time series of total aerosol number concentrations. These efcient approaches to infer the Amazonian CCN population are expected to help improve future modeling studies.
5 Data availability
The CCN data of this study has been deposited as a Supplement. The data is provided in NASA Ames format (see British Atmospheric Data Centre (BADC), 2008). For specic data requests or detailed information on the deposited data, please refer to the corresponding [email protected].
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M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1 15733
Appendix A
Table A1. List of symbols.
Symbol Quantity and unit
A CN number concentration derived from erf t of CCN spectra, cm3a(S,Di) cumulative Gaussian t of multi-charge CCN activation fraction at a given D and S a(S,D) cumulative Gaussian t of CCN activation fraction at a given S
cCO CO mole fraction, ppb
D mobility equivalent particle diameter, nmDa(S) midpoint activation diameter determined from CCN activation curve, nm
DAit position of Aitken-mode maximum, nmDAcc position of accumulation-mode maximum, nm
Dcut lower cutoff diameter in aerosol number reference concentration NCN,Dcut , nm DH position of Hoppel minimum, nmf (Di) multiple-charged fraction at a given D
forg organic mass fractionfinorg inorganic mass fraction
I number of charges hygroscopicity parameter (S,Da) hygroscopicity parameter determined from CCN activation curve Acc mean hygroscopicity parameter for accumulation-mode particles Ait mean hygroscopicity parameter for Aitken-mode particles mean mean hygroscopicity parameter for all measured S
MAF(S) maximum activated fraction determined by CCN activation curve N number of data pointsNCCN(S) CCN number concentration at a given S, cm3
NCCN,p(S) predicted CCN number concentration at a given S, cm3NCCN(S,Da) CCN number concentration determined from CCN activation curve, cm3
NCCN(S,D)/NCN(D) CCN activation fraction
NCCN(S)/NCN,Dcut CCN efciency for aerosol reference concentration NCN,Dcut
NCN,Dcut aerosol number reference concentration (> Dcut), cm3 NCN,10 aerosol number reference concentration (> 10 nm), cm3
NCN,50 aerosol number reference concentration (> 50 nm), cm3 NCN,Acc CN number concentration for accumulation-mode particles, cm3
NCN,Ait CN number concentration for Aitken-mode particles, cm3 PATTO precipitation rate at ATTO site, mm day1
PTRMM precipitation rate from TRMM mission, mm day1 S water vapor supersaturation, %
Sc critical supersaturation for CCN activation, %
Scloud(DH, ) average cloud peak supersaturation, % s(D) SMPS size distribution, cm3s(Di) multi-charge size distribution of D, cm3
S0 abscissa transect of erf t of CCN spectra, %
S1 midpoint activation supersaturation determined from CCN efciency spectra, %
w0 width of erf t of CCN spectraw1 width of erf t of CCN efciency spectrax0 position of mobility equivalent particle diameter, nm width of log-normal t of Aitken and accumulation modes (S) width of CCN activation curve, nm(S)/Da(S) heterogeneity parameter
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15734 M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1
Table A2. List of abbreviations.
Abbreviation Description
ACSM aerosol chemical speciation monitor
AOT aerosol optical thicknessATTO Amazon Tall Tower ObservatoryACRIDICON aerosol, cloud, precipitation, and radiation interactions and dynamics of convective cloud systems BUNIAACIC BrazilUK network for investigation of Amazonian atmospheric composition and impacts on climate BC black carbonCCN cloud condensation nucleiCCNC cloud condensation nuclei counterCN condensation nucleiCHUVA cloud processes of the main precipitation systems in Brazil: a contribution to cloud resolving modeling and to the GPMs (global precipitation measurements)
CPC condensation particle counterCO carbon monoxideDMA differential mobility analyzerENSO El NioSouthern OscillationErf log-normal error functionGCMs global climate modelsGoAmazon14/5 green ocean Amazon 2014/5HALO high-altitude and long-range research aircraftHTDMA hygroscopicity tandem differential mobility analyzerIN ice nucleiIOP intensive observation periodLES large eddy simulationLRT long-range transportNPF new particle formationONI Oceanic Nio IndexOPC optical particle counterPSL polystyrene latexRH relative humiditySE standard errorSMPS scanning mobility particle sizerSOA secondary organic aerosolSST sea surface temperatureTRMM tropical rainfall measuring missionUTC coordinated universal time
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M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1 15735
The Supplement related to this article is available online at http://dx.doi.org/10.5194/acp-16-15709-2016-supplement
Web End =doi:10.5194/acp-16-15709-2016-supplement .
Acknowledgements. This work has been supported by the Max Planck Society (MPG) and the Max Planck Graduate Center with the Johannes Gutenberg University Mainz (MPGC). 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 Ministrio da Cincia, Tecnologia e Inovao (MCTI/FINEP contract 01.11.01248.00) and the Amazon State University (UEA), FAPEAM, LBA/INPA and SDS/CEUC/RDS-Uatum, and the St. Petersburg state University, Russia (project 11.37.220.2016) as well as the EU FP7 project BACCHUS (project no. 603445). This paper contains results of research conducted under the Technical/Scientic 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 Amaznia (INPA). We would like to especially thank all the people involved in the technical, logistical, and scientic support of the ATTO project, in particular Matthias Srgel, Thomas Disper, Andrew Crozier, Uwe Schulz, Steffen Schmidt, Antonio Ocimar Manzi, Alcides Camargo Ribeiro, Hermes Braga Xavier, Elton Mendes da Silva, Nagib Alberto de Castro Souza, Adi Vasconcelos Brando, Amaury Rodrigues Pereira, Antonio Huxley Melo Nascimento, Thiago de Lima Xavier, Josu Ferreira de Souza, Roberta Pereira de Souza, Bruno Takeshi, and Wallace Rabelo Costa. Further, we thank the GoAmazon2014/5 team for the fruitful collaboration and discussions. We acknowledge technical support by the DMT and Grimm Aerosol Technik teams in the course of the experiments. Moreover, we thank Qiaoqiao Wang, Bettina Weber, Nina Ruckteschler, Bruna Amorim Holanda, Kathrin Reinmuth-Selzle, J. Alex Huffman, Ramon Braga, and Daniel Rosenfeld for support and stimulating discussions.
The article processing charges for this open-access publication were covered by the Max Planck Society.
Edited by: G. FischReviewed by: two anonymous referees
References
Almeida, G. P., Borrmann, S., and Leal Junior, J. B. V.: Cloud condensation nuclei (CCN) concentration in the Brazilian northeast semi-arid region: the inuence of local circulation, Meteorol. Atmos. Phys., 125, 159176, doi:http://dx.doi.org/10.1007/s00703-014-0329-1
Web End =10.1007/s00703-014-0329-1 http://dx.doi.org/10.1007/s00703-014-0329-1
Web End = , 2014.
Andreae, M. O.: Aerosols before pollution, Science, 315, 5051, doi:http://dx.doi.org/10.1126/science.1136529
Web End =10.1126/science.1136529 http://dx.doi.org/10.1126/science.1136529
Web End = , 2007.
Andreae, M. O.: Correlation between cloud condensation nuclei concentration and aerosol optical thickness in remote and polluted regions, Atmos. Chem. Phys., 9, 543556, doi:http://dx.doi.org/10.5194/acp-9-543-2009
Web End =10.5194/acp-9-543-2009 http://dx.doi.org/10.5194/acp-9-543-2009
Web End = , 2009.
Andreae, M. O. and Rosenfeld, D.: Aerosol-cloud-precipitation interactions. Part 1. The nature and sources of cloud-active aerosols, Earth-Sci. Rev., 89, 1341, doi:http://dx.doi.org/10.1016/j.earscirev.2008.03.001
Web End =10.1016/j.earscirev.2008.03.001 http://dx.doi.org/10.1016/j.earscirev.2008.03.001
Web End = , 2008.
Andreae, M. O., Artaxo, P., Brandao, C., Carswell, F. E., Ciccioli, P., da Costa, A. L., Culf, A. D., Esteves, J. L., Gash, J.H. C., Grace, J., Kabat, P., Lelieveld, J., Malhi, Y., Manzi, A.O., Meixner, F. X., Nobre, A. D., Nobre, C., Ruivo, M., Silva-Dias, M. A., Stefani, P., Valentini, R., von Jouanne, J., and Waterloo, M. J.: Biogeochemical cycling of carbon, water, energy, trace gases, and aerosols in Amazonia: The LBA-EUSTACH experiments, J. Geophys. Res.-Atmos., 107, LBA 33-1BA 33-25 doi:http://dx.doi.org/10.1029/2001jd000524
Web End =10.1029/2001jd000524 http://dx.doi.org/10.1029/2001jd000524
Web End = , 2002.
Andreae, M. O., Artaxo, P., Beck, V., Bela, M., Freitas, S., Gerbig,C., Longo, K., Munger, J. W., Wiedemann, K. T., and Wofsy, S.C.: Carbon monoxide and related trace gases and aerosols over the Amazon Basin during the wet and dry seasons, Atmos. Chem.Phys., 12, 60416065, doi:http://dx.doi.org/10.5194/acp-12-6041-2012
Web End =10.5194/acp-12-6041-2012 http://dx.doi.org/10.5194/acp-12-6041-2012
Web End = , 2012.Andreae, M. O., Rosenfeld, D., Artaxo, P., Costa, A. A., Frank,G. P., Longo, K. M., and Silva-Dias, M. A. F.: Smoking rain clouds over the Amazon, Science, 303, 13371342, doi:http://dx.doi.org/10.1126/science.1092779
Web End =10.1126/science.1092779 http://dx.doi.org/10.1126/science.1092779
Web End = , 2004.
Andreae, M. O., Acevedo, O. C., Arajo, A., Artaxo, P., Barbosa, C.G. G., Barbosa, H. M. J., Brito, J., Carbone, S., Chi, X., Cintra,B. B. L., da Silva, N. F., Dias, N. L., Dias-Jnior, C. Q., Ditas, F., Ditz, R., Godoi, A. F. L., Godoi, R. H. M., Heimann, M., Hoff-mann, T., Kesselmeier, J., Knemann, T., Krger, M. L., Lavric,J. V., Manzi, A. O., Lopes, A. P., Martins, D. L., Mikhailov, E.F., Moran-Zuloaga, D., Nelson, B. W., Nlscher, A. C., Santos Nogueira, D., Piedade, M. T. F., Phlker, C., Pschl, U., Quesada, C. A., Rizzo, L. V., Ro, C.-U., Ruckteschler, N., S, L. D.A., de Oliveira S, M., Sales, C. B., dos Santos, R. M. N., Saturno, J., Schngart, J., Srgel, M., de Souza, C. M., de Souza,R. A. F., Su, H., Targhetta, N., Tta, J., Trebs, I., Trumbore,S., van Eijck, A., Walter, D., Wang, Z., Weber, B., Williams,J., Winderlich, J., Wittmann, F., Wolff, S., and Yez-Serrano,A. M.: The Amazon Tall Tower Observatory (ATTO): overview of pilot measurements on ecosystem ecology, meteorology, trace gases, and aerosols, Atmos. Chem. Phys., 15, 1072310776, doi:http://dx.doi.org/10.5194/acp-15-10723-2015
Web End =10.5194/acp-15-10723-2015 http://dx.doi.org/10.5194/acp-15-10723-2015
Web End = , 2015.
Ansmann, A., Baars, H., Tesche, M., Muller, D., Althausen, D., Engelmann, R., Pauliquevis, T., and Artaxo, P.: Dust and smoke transport from Africa to South America: Lidar proling over Cape Verde and the Amazon rainforest, Geophys. Res. Lett., 36, L11802, doi:http://dx.doi.org/10.1029/2009gl037923
Web End =10.1029/2009gl037923 http://dx.doi.org/10.1029/2009gl037923
Web End = , 2009.
Asner, G. P., Townsend, A. R., and Braswell, B. H.: Satellite observation of El Nino effects on Amazon forest phenology and productivity, Geophys. Res. Lett., 27, 981984, doi:http://dx.doi.org/10.1029/1999gl011113
Web End =10.1029/1999gl011113 http://dx.doi.org/10.1029/1999gl011113
Web End = , 2000.
Bony, S., Stevens, B., Frierson, D. M. W., Jakob, C., Kageyama,M., Pincus, R., Shepherd, T. G., Sherwood, S. C., Siebesma,A. P., Sobel, A. H., Watanabe, M., and Webb, M. J.: Clouds, circulation and climate sensitivity, Nat. Geosci., 8, 261268, doi:http://dx.doi.org/10.1038/ngeo2398
Web End =10.1038/ngeo2398 http://dx.doi.org/10.1038/ngeo2398
Web End = , 2015.
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V. M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S. K., Sherwood, S., B., S., and Zhang, X.Y.: Clouds and Aerosols, Cambridge, United Kingdom and New York, NY, US, 571658, 2013.
www.atmos-chem-phys.net/16/15709/2016/ Atmos. Chem. Phys., 16, 1570915740, 2016
15736 M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1
Boulon, J., Sellegri, K., Venzac, H., Picard, D., Weingartner, E.,
Wehrle, G., Collaud Coen, M., Btikofer, R., Fluckiger, E., Baltensperger, U., and Laj, P.: New particle formation and ultrane charged aerosol climatology at a high altitude site in the Alps (Jungfraujoch, 3580 m a.s.l., Switzerland), Atmos. Chem. Phys., 10, 93339349, doi:http://dx.doi.org/10.5194/acp-10-9333-2010
Web End =10.5194/acp-10-9333-2010 http://dx.doi.org/10.5194/acp-10-9333-2010
Web End = , 2010.Brienen, R. J. W., Phillips, O. L., Feldpausch, T. R., Gloor,E., Baker, T. R., Lloyd, J., Lopez-Gonzalez, G., Monteagudo-Mendoza, A., Malhi, Y., Lewis, S. L., Martinez, R. V., Alexiades,M., Davila, E. A., Alvarez-Loayza, P., Andrade, A., Aragao, L., Araujo-Murakami, A., Arets, E., Arroyo, L., Aymard, G. A., Banki, O. S., Baraloto, C., Barroso, J., Bonal, D., Boot, R. G. A., Camargo, J. L. C., Castilho, C. V., Chama, V., Chao, K. J., Chave,J., Comiskey, J. A., Valverde, F. C., da Costa, L., de Oliveira, E.A., Di Fiore, A., Erwin, T. L., Fauset, S., Forsthofer, M., Galbraith, D. R., Grahame, E. S., Groot, N., Herault, B., Higuchi, N., Coronado, E. N. H., Keeling, H., Killeen, T. J., Laurance, W. F., Laurance, S., Licona, J., Magnussen, W. E., Marimon, B. S., Marimon, B. H., Mendoza, C., Neill, D. A., Nogueira, E. M., Nunez,P., Camacho, N. C. P., Parada, A., Pardo-Molina, G., Peacock, J., Pena-Claros, M., Pickavance, G. C., Pitman, N. C. A., Poorter,L., Prieto, A., Quesada, C. A., Ramirez, F., Ramirez-Angulo, H., Restrepo, Z., Roopsind, A., Rudas, A., Salomao, R. P., Schwarz,M., Silva, N., Silva-Espejo, J. E., Silveira, M., Stropp, J., Talbot, J., ter Steege, H., Teran-Aguilar, J., Terborgh, J., Thomas-Caesar, R., Toledo, M., Torello-Raventos, M., Umetsu, R. K., Van der Heijden, G. M. F., Van der Hout, P., Vieira, I. C. G., Vieira, S. A., Vilanova, E., Vos, V. A., and Zagt, R. J.: Long-term decline of the Amazon carbon sink, Nature, 519, p. 344, doi:http://dx.doi.org/10.1038/nature14283
Web End =10.1038/nature14283 http://dx.doi.org/10.1038/nature14283
Web End = , 2015.
British Atmospheric Data Centre (BADC): NASA Ames Format for Data Exchange, available at: http://cedadocs.badc.rl.ac.uk/73/4/index.html
Web End =http://cedadocs.badc.rl.ac.uk/73/4/ http://cedadocs.badc.rl.ac.uk/73/4/index.html
Web End =index.html (last access: 19 December 2016), 2008.
Cantrell, C. A.: Technical Note: Review of methods for linear least-squares tting of data and application to atmospheric chemistry problems, Atmos. Chem. Phys., 8, 54775487, doi:http://dx.doi.org/10.5194/acp-8-5477-2008
Web End =10.5194/acp- http://dx.doi.org/10.5194/acp-8-5477-2008
Web End =8-5477-2008 , 2008.
Chen, Q., Farmer, D. K., Rizzo, L. V., Pauliquevis, T., Kuwata, M., Karl, T. G., Guenther, A., Allan, J. D., Coe, H., Andreae, M. O., Pschl, U., Jimenez, J. L., Artaxo, P., and Martin, S. T.: Submicron particle mass concentrations and sources in the Amazonian wet season (AMAZE-08), Atmos. Chem. Phys., 15, 36873701, doi:http://dx.doi.org/10.5194/acp-15-3687-2015
Web End =10.5194/acp-15-3687-2015 http://dx.doi.org/10.5194/acp-15-3687-2015
Web End = , 2015.
Coe, M. T., Marthews, T. R., Costa, M. H., Galbraith, D. R., Greenglass, N. L., Imbuzeiro, H. M. A., Levine, N. M., Malhi,Y., Moorcroft, P. R., Muza, M. N., Powell, T. L., Saleska,S. R., Solorzano, L. A., and Wang, J.: Deforestation and climate feedbacks threaten the ecological integrity of south-southeastern Amazonia, Philos. T. Roy. Soc. B, 368, 19, doi:http://dx.doi.org/10.1098/rstb.2012.0155
Web End =10.1098/rstb.2012.0155 http://dx.doi.org/10.1098/rstb.2012.0155
Web End = , 2013.
Cohard, J. M., Pinty, J. P., and Bedos, C.: Extending Twomeys analytical estimate of nucleated cloud droplet concentrations from CCN spectra, J. Atmos. Sci., 55, 33483357, doi:http://dx.doi.org/10.1175/1520-0469(1998)055<3348:etsaeo>2.0.co;2
Web End =10.1175/1520- http://dx.doi.org/10.1175/1520-0469(1998)055<3348:etsaeo>2.0.co;2
Web End =0469(1998)055<3348:etsaeo>2.0.co;2 , 1998.
Cotton, W. R. and Anthes, R. A.: Storm and cloud dynamics, SanDiego, Academic Press, 833 pp., 1989.
Davidson, E. A., de Araujo, A. C., Artaxo, P., Balch, J. K., Brown,I. F., Bustamante, M. M. C., Coe, M. T., DeFries, R. S., Keller,M., Longo, M., Munger, J. W., Schroeder, W., Soares-Filho, B.
S., Souza Jr., C. M., and Wofsy, S. C.: The Amazon basin in transition, Nature, 481, 321328, doi:http://dx.doi.org/10.1038/nature10717
Web End =10.1038/nature10717 http://dx.doi.org/10.1038/nature10717
Web End = , 2012. Deng, Z. Z., Zhao, C. S., Ma, N., Ran, L., Zhou, G. Q., Lu, D.R., and Zhou, X. J.: An examination of parameterizations for the CCN number concentration based on in situ measurements of aerosol activation properties in the North China Plain, Atmos. Chem. Phys., 13, 62276237, doi:http://dx.doi.org/10.5194/acp-13-6227-2013
Web End =10.5194/acp-13-6227- http://dx.doi.org/10.5194/acp-13-6227-2013
Web End =2013 , 2013.
Dusek, U., Frank, G. P., Hildebrandt, L., Curtius, J., Schneider, J.,
Walter, S., Chand, D., Drewnick, F., Hings, S., Jung, D., Borrmann, S., and Andreae, M. O.: Size matters more than chemistry for cloud-nucleating ability of aerosol particles, Science, 312, 13751378, doi:http://dx.doi.org/10.1126/science.1125261
Web End =10.1126/science.1125261 http://dx.doi.org/10.1126/science.1125261
Web End = , 2006. Engelhart, G. J., Asa-Awuku, A., Nenes, A., and Pandis, S. N.: CCN activity and droplet growth kinetics of fresh and aged monoterpene secondary organic aerosol, Atmos. Chem. Phys., 8, 3937 3949, doi:http://dx.doi.org/10.5194/acp-8-3937-2008
Web End =10.5194/acp-8-3937-2008 http://dx.doi.org/10.5194/acp-8-3937-2008
Web End = , 2008.
Fernandes, K., Giannini, A., Verchot, L., Baethgen, W., and Pinedo-Vasquez, M.: Decadal covariability of Atlantic SSTs and western Amazon dry-season hydroclimate in observations and CMIP5 simulations, Geophys. Res. Lett., 42, 67936801, doi:http://dx.doi.org/10.1002/2015gl063911
Web End =10.1002/2015gl063911 http://dx.doi.org/10.1002/2015gl063911
Web End = , 2015.
Frank, G. P., Dusek, U., and Andreae, M. O.: Technical note: A method for measuring size-resolved CCN in the atmosphere, Atmos. Chem. Phys. Discuss., 6, 48794895, doi:http://dx.doi.org/10.5194/acpd-6-4879-2006
Web End =10.5194/acpd-6- http://dx.doi.org/10.5194/acpd-6-4879-2006
Web End =4879-2006 , 2006.
Freud, E., Rosenfeld, D., Andreae, M. O., Costa, A. A., and Artaxo, P.: Robust relations between CCN and the vertical evolution of cloud drop size distribution in deep convective clouds, Atmos. Chem. Phys., 8, 16611675, doi:http://dx.doi.org/10.5194/acp-8-1661-2008
Web End =10.5194/acp-8-1661-2008 http://dx.doi.org/10.5194/acp-8-1661-2008
Web End = , 2008.
Fu, R., Dickinson, R. E., Chen, M. X., and Wang, H.: How do tropical sea surface temperatures inuence the seasonal distribution of precipitation in the equatorial Amazon?, J. Climate, 14, 4003 4026, doi:http://dx.doi.org/10.1175/1520-0442(2001)014<4003:hdtsst>2.0.co;2
Web End =10.1175/1520-0442(2001)014<4003:hdtsst>2.0.co;2 http://dx.doi.org/10.1175/1520-0442(2001)014<4003:hdtsst>2.0.co;2
Web End = , 2001.
Gloor, M., Barichivich, J., Ziv, G., Brienen, R., Schongart, J., Peylin, P., Cintra, B. B. L., Feldpausch, T., Phillips, O., and Baker, J.: Recent Amazon climate as background for possible ongoing and future changes of Amazon humid forests, Global Biogeochem. Cy., 29, 13841399, doi:http://dx.doi.org/10.1002/2014gb005080
Web End =10.1002/2014gb005080 http://dx.doi.org/10.1002/2014gb005080
Web End = , 2015.
Graham, B., Guyon, P., Maenhaut, W., Taylor, P. E., Ebert, M., Matthias-Maser, S., Mayol-Bracero, O. L., Godoi, R. H. M., Artaxo, P., Meixner, F. X., Moura, M. A. L., Rocha, C., Van Grieken, R., Glovsky, M. M., Flagan, R. C., and Andreae, M. O.: Composition and diurnal variability of the natural Amazonian aerosol, J. Geophys. Res.-Atmos., 108, 4765, doi:http://dx.doi.org/10.1029/2003jd004049
Web End =10.1029/2003jd004049 http://dx.doi.org/10.1029/2003jd004049
Web End = , 2003.
Gunthe, S. S., King, S. M., Rose, D., Chen, Q., Roldin, P., Farmer,D. K., Jimenez, J. L., Artaxo, P., Andreae, M. O., Martin, S.T., and Pschl, U.: Cloud condensation nuclei in pristine tropical rainforest air of Amazonia: size-resolved measurements and modeling of atmospheric aerosol composition and CCN activity, Atmos. Chem. Phys., 9, 75517575, doi:http://dx.doi.org/10.5194/acp-9-7551-2009
Web End =10.5194/acp-9-7551- http://dx.doi.org/10.5194/acp-9-7551-2009
Web End =2009 , 2009.
Hamilton, D. S., Lee, L. A., Pringle, K. J., Reddington, C. L., Spracklen, D. V., and Carslaw, K. S.: Occurrence of pristine
Atmos. Chem. Phys., 16, 1570915740, 2016 www.atmos-chem-phys.net/16/15709/2016/
M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1 15737
aerosol environments on a polluted planet, P. Natl. Acad. Sci.
USA, 111, 1846618471, doi:http://dx.doi.org/10.1073/pnas.1415440111
Web End =10.1073/pnas.1415440111 http://dx.doi.org/10.1073/pnas.1415440111
Web End = , 2014.Hoppel, W. A., Frick, G. M., and Fitzgerald, J. W.: Deducing droplet concentration and supersaturation in marine boundary layer clouds from surface aerosol measurements, J. Geophys.Res.-Atmos., 101, 2655326565, doi:http://dx.doi.org/10.1029/96jd02243
Web End =10.1029/96jd02243 http://dx.doi.org/10.1029/96jd02243
Web End = , 1996.Junk, W. J.: Current state of knowledge regarding South America wetlands and their future under global climate change, Aquat.Sci., 75, 113131, doi:http://dx.doi.org/10.1007/s00027-012-0253-8
Web End =10.1007/s00027-012-0253-8 http://dx.doi.org/10.1007/s00027-012-0253-8
Web End = , 2013.Jurnyi, Z., Gysel, M., Weingartner, E., Bukowiecki, N., Kammer-mann, L., and Baltensperger, U.: A 17 month climatology of the cloud condensation nuclei number concentration at the high alpine site Jungfraujoch, J. Geophys. Res.-Atmos., 116, D10204, doi:http://dx.doi.org/10.1029/2010JD015199
Web End =10.1029/2010JD015199 http://dx.doi.org/10.1029/2010JD015199
Web End = , 2011.
Khain, A., Ovtchinnikov, M., Pinsky, M., Pokrovsky, A., and Krugliak, H.: Notes on the state-of-the-art numerical modeling of cloud microphysics, Atmos. Res., 55, 159224, doi:http://dx.doi.org/10.1016/s0169-8095(00)00064-8
Web End =10.1016/s0169-8095(00)00064-8 http://dx.doi.org/10.1016/s0169-8095(00)00064-8
Web End = , 2000.
King, S. M., Rosenoern, T., Shilling, J. E., Chen, Q., and Martin,S. T.: Cloud condensation nucleus activity of secondary organic aerosol particles mixed with sulfate, Geophys. Res. Lett., 34, 2007GL030390, doi:http://dx.doi.org/10.1029/2007GL030390
Web End =10.1029/2007GL030390 http://dx.doi.org/10.1029/2007GL030390
Web End = , 2007.
Khler, H.: The nucleus in and the growth of hygroscopic droplets,T. Faraday Soc., 32, 11521161, doi:http://dx.doi.org/10.1039/tf9363201152
Web End =10.1039/tf9363201152 http://dx.doi.org/10.1039/tf9363201152
Web End = , 1936.
Koren, I., Kaufman, Y. J., Remer, L. A., and Martins, J. V.: Measurement of the effect of Amazon smoke on inhibition of cloud formation, Science, 303, 13421345, doi:http://dx.doi.org/10.1126/science.1089424
Web End =10.1126/science.1089424 http://dx.doi.org/10.1126/science.1089424
Web End = , 2004.
Koren, I., Altaratz, O., Remer, L. A., Feingold, G., Martins, J.V., and Heiblum, R. H.: Aerosol-induced intensication of rain from the tropics to the mid-latitudes, Nat. Geosci., 5, 118122, doi:http://dx.doi.org/10.1038/ngeo1364
Web End =10.1038/ngeo1364 http://dx.doi.org/10.1038/ngeo1364
Web End = , 2012.
Krger, M. L., Mertes, S., Klimach, T., Cheng, Y. F., Su, H., Schneider, J., Andreae, M. O., Pschl, U., and Rose, D.: Assessment of cloud supersaturation by size-resolved aerosol particle and cloud condensation nuclei (CCN) measurements, Atmos. Meas. Tech., 7, 26152629, doi:http://dx.doi.org/10.5194/amt-7-2615-2014
Web End =10.5194/amt-7-2615-2014 http://dx.doi.org/10.5194/amt-7-2615-2014
Web End = , 2014.
Kuhn, U., Ganzeveld, L., Thielmann, A., Dindorf, T., Schebeske,G., Welling, M., Sciare, J., Roberts, G., Meixner, F. X., Kesselmeier, J., Lelieveld, J., Kolle, O., Ciccioli, P., Lloyd, J., Trentmann, J., Artaxo, P., and Andreae, M. O.: Impact of Manaus City on the Amazon Green Ocean atmosphere: ozone production, precursor sensitivity and aerosol load, Atmos. Chem. Phys., 10, 92519282, doi:http://dx.doi.org/10.5194/acp-10-9251-2010
Web End =10.5194/acp-10-9251-2010 http://dx.doi.org/10.5194/acp-10-9251-2010
Web End = , 2010.
Kulmala, M., Vehkamaki, H., Petaja, T., Dal Maso, M., Lauri,A., Kerminen, V. M., Birmili, W., and McMurry, P. H.: Formation and growth rates of ultrane atmospheric particles: a review of observations, J. Aerosol Sci., 35, 143176, doi:http://dx.doi.org/10.1016/j.jaerosci.2003.10.003
Web End =10.1016/j.jaerosci.2003.10.003 http://dx.doi.org/10.1016/j.jaerosci.2003.10.003
Web End = , 2004.
Lawrence, D. and Vandecar, K.: Effects of tropical deforestation on climate and agriculture, Nature Climate Change, 5, 2736, doi:http://dx.doi.org/10.1038/nclimate2430
Web End =10.1038/nclimate2430 http://dx.doi.org/10.1038/nclimate2430
Web End = , 2015.
Lawson, S. J., Keywood, M. D., Galbally, I. E., Gras, J. L., Cainey,J. M., Cope, M. E., Krummel, P. B., Fraser, P. J., Steele, L. P., Bentley, S. T., Meyer, C. P., Ristovski, Z., and Goldstein, A. H.: Biomass burning emissions of trace gases and particles in marine air at Cape Grim, Tasmania, Atmos. Chem. Phys., 15, 13393 13411, doi:http://dx.doi.org/10.5194/acp-15-13393-2015
Web End =10.5194/acp-15-13393-2015 http://dx.doi.org/10.5194/acp-15-13393-2015
Web End = , 2015.
Levin, E. J. T., Prenni, A. J., Petters, M. D., Kreidenweis, S. M., Sullivan, R. C., Atwood, S. A., Ortega, J., DeMott, P. J., and Smith, J. N.: An annual cycle of size-resolved aerosol hygroscopicity at a forested site in Colorado, J. Geophys. Res.-Atmos., 117, D06201, doi:http://dx.doi.org/10.1029/2011JD016854
Web End =10.1029/2011JD016854 http://dx.doi.org/10.1029/2011JD016854
Web End = , 2012.
Lohmann, U. and Feichter, J.: Global indirect aerosol effects: a review, Atmos. Chem. Phys., 5, 715737, doi:http://dx.doi.org/10.5194/acp-5-715-2005
Web End =10.5194/acp-5-715- http://dx.doi.org/10.5194/acp-5-715-2005
Web End =2005 , 2005.
Machado, L. A. T., Silva Dias, M. A. F., Morales, C., Fisch, G., Vila, D., Albrecht, R., Goodman, S. J., Calheiros, A. J. P., Biscaro, T., Kummerow, C., Cohen, J., Fitzjarrald, D., Nascimento,E. L., Sakamoto, M. S., Cunningham, C., Chaboureau, J.-P., Petersen, W. A., Adams, D. K., Baldini, L., Angelis, C. F., Sapucci, L. F., Salio, P., Barbosa, H. M. J., Landulfo, E., Souza,R. A. F., Blakeslee, R. J., Bailey, J., Freitas, S., Lima, W. F.A., and Tokay, A.: THE CHUVA PROJECT How Does Convection Vary across Brazil?, B. Am. Meteorol. Soc., 95, 13651380, doi:http://dx.doi.org/10.1175/bams-d-13-00084.1
Web End =10.1175/bams-d-13-00084.1 http://dx.doi.org/10.1175/bams-d-13-00084.1
Web End = , 2014.
Martin, S. T., Andreae, M. O., Althausen, D., Artaxo, P., Baars, H., Borrmann, S., Chen, Q., Farmer, D. K., Guenther, A., Gunthe,S. S., Jimenez, J. L., Karl, T., Longo, K., Manzi, A., Mller, T., Pauliquevis, T., Petters, M. D., Prenni, A. J., Pschl, U., Rizzo,L. V., Schneider, J., Smith, J. N., Swietlicki, E., Tota, J., Wang,J., Wiedensohler, A., and Zorn, S. R.: An overview of the Amazonian Aerosol Characterization Experiment 2008 (AMAZE-08), Atmos. Chem. Phys., 10, 1141511438, doi:http://dx.doi.org/10.5194/acp-10-11415-2010
Web End =10.5194/acp- http://dx.doi.org/10.5194/acp-10-11415-2010
Web End =10-11415-2010 , 2010a.
Martin, S. T., Andreae, M. O., Artaxo, P., Baumgardner, D., Chen,Q., Goldstein, A. H., Guenther, A., Heald, C. L., Mayol-Bracero,O. L., McMurry, P. H., Pauliquevis, T., Pschl, U., Prather, K.A., Roberts, G. C., Saleska, S. R., Dias, M. A. S., Spracklen,D. V., Swietlicki, E., and Trebs, I.: Sources and properties of Amazonian aerosol particles, Rev. Geophys., 48, RG2002, doi:http://dx.doi.org/10.1029/2008rg000280
Web End =10.1029/2008rg000280 http://dx.doi.org/10.1029/2008rg000280
Web End = , 2010b.
Martin, S. T., Artaxo, P., Machado, L. A. T., Manzi, A. O., Souza, R.A. F., Schumacher, C., Wang, J., Andreae, M. O., Barbosa, H. M.J., Fan, J., Fisch, G., Goldstein, A. H., Guenther, A., Jimenez, J.L., Pschl, U., Silva Dias, M. A., Smith, J. N., and Wendisch, M.: Introduction: Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5), Atmos. Chem. Phys., 16, 4785 4797, doi:http://dx.doi.org/10.5194/acp-16-4785-2016
Web End =10.5194/acp-16-4785-2016 http://dx.doi.org/10.5194/acp-16-4785-2016
Web End = , 2016.
Martins, J. A., Dias, M., and Goncalves, F. L. T.: Impact of biomass burning aerosols on precipitation in the Amazon: A modeling case study, J. Geophys. Res.-Atmos., 114, 8393, doi:http://dx.doi.org/10.1029/2007jd009587
Web End =10.1029/2007jd009587 http://dx.doi.org/10.1029/2007jd009587
Web End = , 2009a.
Martins, J. A., Goncalves, F. L. T., Morales, C. A., Fisch, G. F., Pinheiro, F. G. M., Leal, J. B. V., Jr., Oliveira, C. J., Silva, E. M., Oliveira, J. C. P., Costa, A. A., and Silva Dias, M. A. F.: Cloud condensation nuclei from biomass burning during the Amazonian dry-to-wet transition season, Meteorol. Atmos. Phys., 104, 8393, doi:http://dx.doi.org/10.1007/s00703-009-0019-6
Web End =10.1007/s00703-009-0019-6 http://dx.doi.org/10.1007/s00703-009-0019-6
Web End = , 2009b.
McFiggans, G., Artaxo, P., Baltensperger, U., Coe, H., Facchini, M.C., Feingold, G., Fuzzi, S., Gysel, M., Laaksonen, A., Lohmann,U., Mentel, T. F., Murphy, D. M., ODowd, C. D., Snider, J. R., and Weingartner, E.: The effect of physical and chemical aerosol properties on warm cloud droplet activation, Atmos. Chem.Phys., 6, 25932649, doi:http://dx.doi.org/10.5194/acp-6-2593-2006
Web End =10.5194/acp-6-2593-2006 http://dx.doi.org/10.5194/acp-6-2593-2006
Web End = , 2006.Mikhailov, E., Vlasenko, S., Martin, S. T., Koop, T., and Pschl,U.: Amorphous and crystalline aerosol particles interacting with
www.atmos-chem-phys.net/16/15709/2016/ Atmos. Chem. Phys., 16, 1570915740, 2016
15738 M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1
water vapor: conceptual framework and experimental evidence for restructuring, phase transitions and kinetic limitations, Atmos. Chem. Phys., 9, 94919522, doi:http://dx.doi.org/10.5194/acp-9-9491-2009
Web End =10.5194/acp-9-9491-2009 http://dx.doi.org/10.5194/acp-9-9491-2009
Web End = , 2009.
Mikhailov, E., Vlasenko, S., Rose, D., and Pschl, U.: Mass-based hygroscopicity parameter interaction model and measurement of atmospheric aerosol water uptake, Atmos. Chem. Phys., 13, 717 740, doi:http://dx.doi.org/10.5194/acp-13-717-2013
Web End =10.5194/acp-13-717-2013 http://dx.doi.org/10.5194/acp-13-717-2013
Web End = , 2013.
Mikhailov, E. F., Mironov, G. N., Phlker, C., Chi, X., Krger,M. L., Shiraiwa, M., Frster, J.-D., Pschl, U., Vlasenko, S. S., Ryshkevich, T. I., Weigand, M., Kilcoyne, A. L. D., and Andreae, M. O.: Chemical composition, microstructure, and hygroscopic properties of aerosol particles at the Zotino Tall Tower Observatory (ZOTTO), Siberia, during a summer campaign, Atmos. Chem. Phys., 15, 88478869, doi:http://dx.doi.org/10.5194/acp-15-8847-2015
Web End =10.5194/acp-15-8847- http://dx.doi.org/10.5194/acp-15-8847-2015
Web End =2015 , 2015.
Mishra, A. K., Lehahn, Y., Rudich, Y., and Koren, I.: Co-variability of smoke and re in the Amazon basin, Atmos. Environ., 109, 97104, doi:http://dx.doi.org/10.1016/j.atmosenv.2015.03.007
Web End =10.1016/j.atmosenv.2015.03.007 http://dx.doi.org/10.1016/j.atmosenv.2015.03.007
Web End = , 2015.Moran-Zuloaga, D., Ditas, F., Walter, D., Arajo, A., Brito, J., Carbone, S., Chi, X., Hrabe de Angelis, I., Lavric, J. V., Ming, J., Phlker, M. L., Ruckteschler, N., Saturno, J., Wang, Y., Wang,Q., Weber, B., Wolff, S., Artaxo, P., Andreae, M. O., and Phlker, C.: Coarse mode aerosols in the Amazon rainforest, Atmos.Chem. Phys. Discuss., in preparation, 2017.
Nenes, A. and Seinfeld, J. H.: Parameterization of cloud droplet formation in global climate models, J. Geophys. Res.-Atmos., 108, AAC 7-1ACC 7-14, doi:http://dx.doi.org/10.1029/2002jd002911
Web End =10.1029/2002jd002911 http://dx.doi.org/10.1029/2002jd002911
Web End = , 2003.Ng, N. L., Canagaratna, M. R., Zhang, Q., Jimenez, J. L., Tian,J., Ulbrich, I. M., Kroll, J. H., Docherty, K. S., Chhabra, P.S., Bahreini, R., Murphy, S. M., Seinfeld, J. H., Hildebrandt,L., Donahue, N. M., DeCarlo, P. F., Lanz, V. A., Prvt, A. S.H., Dinar, E., Rudich, Y., and Worsnop, D. R.: Organic aerosol components observed in Northern Hemispheric datasets from Aerosol Mass Spectrometry, Atmos. Chem. Phys., 10, 4625 4641, doi:http://dx.doi.org/10.5194/acp-10-4625-2010
Web End =10.5194/acp-10-4625-2010 http://dx.doi.org/10.5194/acp-10-4625-2010
Web End = , 2010.
Ng, N. L., Herndon, S. C., Trimborn, A., Canagaratna, M. R., Croteau, P. L., Onasch, T. B., Sueper, D., Worsnop, D. R., Zhang,Q., Sun, Y. L., and Jayne, J. T.: An Aerosol Chemical Speciation Monitor (ACSM) for Routine Monitoring of the Composition and Mass Concentrations of Ambient Aerosol, Aerosol Sci.Tech., 45, 780794, doi:http://dx.doi.org/10.1080/02786826.2011.560211
Web End =10.1080/02786826.2011.560211 http://dx.doi.org/10.1080/02786826.2011.560211
Web End = , 2011.Olivares, I., Svenning, J. C., van Bodegom, P. M., and Balslev,H.: Effects of Warming and Drought on the Vegetation and Plant Diversity in the Amazon Basin, Bot. Rev., 81, 4269, doi:http://dx.doi.org/10.1007/s12229-014-9149-8
Web End =10.1007/s12229-014-9149-8 http://dx.doi.org/10.1007/s12229-014-9149-8
Web End = , 2015.
Ortega, J., Turnipseed, A., Guenther, A. B., Karl, T. G., Day, D. A., Gochis, D., Huffman, J. A., Prenni, A. J., Levin, E. J. T., Kreidenweis, S. M., DeMott, P. J., Tobo, Y., Patton, E. G., Hodzic, A., Cui, Y. Y., Harley, P. C., Hornbrook, R. S., Apel, E. C., Monson,R. K., Eller, A. S. D., Greenberg, J. P., Barth, M. C., Campuzano-Jost, P., Palm, B. B., Jimenez, J. L., Aiken, A. C., Dubey, M. K., Geron, C., Offenberg, J., Ryan, M. G., Fornwalt, P. J., Pryor, S.C., Keutsch, F. N., DiGangi, J. P., Chan, A. W. H., Goldstein, A.H., Wolfe, G. M., Kim, S., Kaser, L., Schnitzhofer, R., Hansel,A., Cantrell, C. A., Mauldin, R. L., and Smith, J. N.: Overview of the Manitou Experimental Forest Observatory: site description and selected science results from 2008 to 2013, Atmos. Chem.Phys., 14, 63456367, doi:http://dx.doi.org/10.5194/acp-14-6345-2014
Web End =10.5194/acp-14-6345-2014 http://dx.doi.org/10.5194/acp-14-6345-2014
Web End = , 2014.
Paramonov, M., Aalto, P. P., Asmi, A., Prisle, N., Kerminen, V.-M., Kulmala, M., and Petj, T.: The analysis of size-segregated cloud condensation nuclei counter (CCNC) data and its implications for cloud droplet activation, Atmos. Chem. Phys., 13, 1028510301, doi:http://dx.doi.org/10.5194/acp-13-10285-2013
Web End =10.5194/acp-13-10285-2013 http://dx.doi.org/10.5194/acp-13-10285-2013
Web End = , 2013. Paramonov, M., Kerminen, V.-M., Gysel, M., Aalto, P. P., Andreae,M. O., Asmi, E., Baltensperger, U., Bougiatioti, A., Brus, D., Frank, G. P., Good, N., Gunthe, S. S., Hao, L., Irwin, M., Jaatinen, A., Jurnyi, Z., King, S. M., Kortelainen, A., Kristensson,A., Lihavainen, H., Kulmala, M., Lohmann, U., Martin, S. T., McFiggans, G., Mihalopoulos, N., Nenes, A., ODowd, C. D., Ovadnevaite, J., Petj, T., Pschl, U., Roberts, G. C., Rose, D., Svenningsson, B., Swietlicki, E., Weingartner, E., Whitehead, J., Wiedensohler, A., Wittbom, C., and Sierau, B.: A synthesis of cloud condensation nuclei counter (CCNC) measurements within the EUCAARI network, Atmos. Chem. Phys., 15, 1221112229, doi:http://dx.doi.org/10.5194/acp-15-12211-2015
Web End =10.5194/acp-15-12211-2015 http://dx.doi.org/10.5194/acp-15-12211-2015
Web End = , 2015.
Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud condensation nucleus activity, Atmos. Chem. Phys., 7, 19611971, doi:http://dx.doi.org/10.5194/acp-7-1961-2007
Web End =10.5194/acp-7- http://dx.doi.org/10.5194/acp-7-1961-2007
Web End =1961-2007 , 2007.
Pinsky, M., Khain, A., Mazin, I., and Korolev, A.: Analytical estimation of droplet concentration at cloud base, J. Geophys. Res.-Atmos., 117, D18211, doi:http://dx.doi.org/10.1029/2012jd017753
Web End =10.1029/2012jd017753 http://dx.doi.org/10.1029/2012jd017753
Web End = , 2012. Phlker, C., Wiedemann, K. T., Sinha, B., Shiraiwa, M., Gunthe, S. S., Smith, M., Su, H., Artaxo, P., Chen, Q., Cheng,Y. F., Elbert, W., Gilles, M. K., Kilcoyne, A. L. D., Moffet,R. C., Weigand, M., Martin, S. T., Pschl, U., and Andreae,M. O.: Biogenic Potassium Salt Particles as Seeds for Secondary Organic Aerosol in the Amazon, Science, 337, 1075 1078, doi:http://dx.doi.org/10.1126/science.1223264
Web End =10.1126/science.1223264 http://dx.doi.org/10.1126/science.1223264
Web End = , 2012.
Phlker, M. L., Phlker, C., Ditas, F., Klimach, T., Hrabe de Angelis, I., Araujo, A., Brito, J., Carbone, S., Chi, X., Cheng, Y., Ditz,R., Gunthe, S. S., Kesselmeier, J., Knemann, T., Lavric, J. V., Martin, S. T., Moran, D., Rose, D., Saturno, J., Su, H., Thalman,R., Walter, D., Wang, J., Wolff, S., Barbosa, H. M. J., Artaxo,P., Andreae, M. O., and Pschl, U.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 2: Near-pristine episodes, ultrane particle bursts, biomass burning and long range transport events, in preparation, 2017a. Phlker, M. L., Rose, D., Phlker, C., Klimach, T., Hrabe de Angelis, I., Ditas, F., Cheng, Y., Gunthe, S. S., Su, H., Andreae, M.O., and Pschl, U.: Parameterizations for model predictions of cloud condensation nuclei (CCN) concentration and hygroscopicity from tropical to polar environments, in preparation, 2017b. Pschl, U.: Atmospheric aerosols: Composition, transformation, climate and health effects, Angew. Chem.-Int. Edit., 44, 7520 7540, doi:http://dx.doi.org/10.1002/anie.200501122
Web End =10.1002/anie.200501122 http://dx.doi.org/10.1002/anie.200501122
Web End = , 2005.
Prenni, A. J., Petters, M. D., Kreidenweis, S. M., DeMott,P. J., and Ziemann, P. J.: Cloud droplet activation of secondary organic aerosol, J. Geophys. Res.-Atmos., 112, 402405, doi:http://dx.doi.org/10.1029/2006jd007963
Web End =10.1029/2006jd007963 http://dx.doi.org/10.1029/2006jd007963
Web End = , 2007.
Reutter, P., Su, H., Trentmann, J., Simmel, M., Rose, D., Gunthe,S. S., Wernli, H., Andreae, M. O., and Pschl, U.: Aerosol- and updraft-limited regimes of cloud droplet formation: inuence of particle number, size and hygroscopicity on the activation of cloud condensation nuclei (CCN), Atmos. Chem. Phys., 9, 7067 7080, doi:http://dx.doi.org/10.5194/acp-9-7067-2009
Web End =10.5194/acp-9-7067-2009 http://dx.doi.org/10.5194/acp-9-7067-2009
Web End = , 2009.
Atmos. Chem. Phys., 16, 1570915740, 2016 www.atmos-chem-phys.net/16/15709/2016/
M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1 15739
Rissler, J., Swietlicki, E., Zhou, J., Roberts, G., Andreae, M. O., Gatti, L. V., and Artaxo, P.: Physical properties of the submicrometer aerosol over the Amazon rain forest during the wetto-dry season transition comparison of modeled and measured CCN concentrations, Atmos. Chem. Phys., 4, 21192143, doi:http://dx.doi.org/10.5194/acp-4-2119-2004
Web End =10.5194/acp-4-2119-2004 http://dx.doi.org/10.5194/acp-4-2119-2004
Web End = , 2004.
Rissler, J., Vestin, A., Swietlicki, E., Fisch, G., Zhou, J., Artaxo,P., and Andreae, M. O.: Size distribution and hygroscopic properties of aerosol particles from dry-season biomass burning in Amazonia, Atmos. Chem. Phys., 6, 471491, doi:http://dx.doi.org/10.5194/acp-6-471-2006
Web End =10.5194/acp- http://dx.doi.org/10.5194/acp-6-471-2006
Web End =6-471-2006 , 2006.
Roberts, G. C. and Nenes, A.: A continuous-ow stream-wise thermal-gradient CCN chamber for atmospheric measurements, Aerosol Sci. Technol., 39, 206221, doi:http://dx.doi.org/10.1080/027868290913988
Web End =10.1080/027868290913988 http://dx.doi.org/10.1080/027868290913988
Web End = , 2005.
Roberts, G. C., Artaxo, P., Zhou, J. C., Swietlicki, E., and Andreae, M. O.: Sensitivity of CCN spectra on chemical and physical properties of aerosol: A case study from the Amazon Basin, J. Geophys. Res.-Atmos., 107, LBA 37-1LBA 37-18 doi:http://dx.doi.org/10.1029/2001jd000583
Web End =10.1029/2001jd000583 http://dx.doi.org/10.1029/2001jd000583
Web End = , 2002.
Roberts, G. C., Nenes, A., Seinfeld, J. H., and Andreae, M. O.: Impact of biomass burning on cloud properties in the Amazon Basin, J. Geophys. Res.-Atmos., 108, ACC 9-1ACC 9-19., doi:http://dx.doi.org/10.1029/2001jd000985
Web End =10.1029/2001jd000985 http://dx.doi.org/10.1029/2001jd000985
Web End = , 2003.
Roberts, M. C., Andreae, M. O., Zhou, J. C., and Artaxo, P.: Cloud condensation nuclei in the Amazon Basin: Marine conditions over a continent?, Geophys. Res. Lett., 28, 28072810, 2001.Ronchail, J., Cochonneau, G., Molinier, M., Guyot, J. L., Chaves,A. G. D., Guimaraes, V., and de Oliveira, E.: Interannual rainfall variability in the Amazon basin and sea-surface temperatures in the equatorial Pacic and the tropical Atlantic Oceans, Int. J. Climatol., 22, 16631686, doi:http://dx.doi.org/10.1002/joc.815
Web End =10.1002/joc.815 http://dx.doi.org/10.1002/joc.815
Web End = , 2002.
Rose, D., Gunthe, S. S., Mikhailov, E., Frank, G. P., Dusek, U., Andreae, M. O., and Pschl, U.: Calibration and measurement uncertainties of a continuous-ow cloud condensation nuclei counter (DMT-CCNC): CCN activation of ammonium sulfate and sodium chloride aerosol particles in theory and experiment, Atmos. Chem. Phys., 8, 1153-1179, doi:http://dx.doi.org/10.5194/acp-8-1153-2008
Web End =10.5194/acp-8-1153- http://dx.doi.org/10.5194/acp-8-1153-2008
Web End =2008 , 2008a.
Rose, D., Gunthe, S. S., Mikhailov, E., Frank, G. P., Dusek, U., Andreae, M. O., and Pschl, U.: Calibration and measurement uncertainties of a continuous-ow cloud condensation nuclei counter (DMT-CCNC): CCN activation of ammonium sulfate and sodium chloride aerosol particles in theory and experiment, Atmos. Chem. Phys., 8, 11531179, doi:http://dx.doi.org/10.5194/acp-8-1153-2008
Web End =10.5194/acp-8-1153- http://dx.doi.org/10.5194/acp-8-1153-2008
Web End =2008 , 2008b.
Rose, D., Nowak, A., Achtert, P., Wiedensohler, A., Hu, M., Shao,M., Zhang, Y., Andreae, M. O., and Pschl, U.: Cloud condensation nuclei in polluted air and biomass burning smoke near the mega-city Guangzhou, China Part 1: Size-resolved measurements and implications for the modeling of aerosol particle hygroscopicity and CCN activity, Atmos. Chem. Phys., 10, 3365 3383, doi:http://dx.doi.org/10.5194/acp-10-3365-2010
Web End =10.5194/acp-10-3365-2010 http://dx.doi.org/10.5194/acp-10-3365-2010
Web End = , 2010.
Rose, D., Gunthe, S. S., Su, H., Garland, R. M., Yang, H., Berghof,M., Cheng, Y. F., Wehner, B., Achtert, P., Nowak, A., Wiedensohler, A., Takegawa, N., Kondo, Y., Hu, M., Zhang, Y., Andreae,M. O., and Pschl, U.: Cloud condensation nuclei in polluted air and biomass burning smoke near the mega-city Guangzhou, China Part 2: Size-resolved aerosol chemical composition, di-
urnal cycles, and externally mixed weakly CCN-active soot particles, Atmos. Chem. Phys., 11, 28172836, doi:http://dx.doi.org/10.5194/acp-11-2817-2011
Web End =10.5194/acp-11- http://dx.doi.org/10.5194/acp-11-2817-2011
Web End =2817-2011 , 2011.
Rosenfeld, D., Lohmann, U., Raga, G. B., ODowd, C. D., Kulmala,M., Fuzzi, S., Reissell, A., and Andreae, M. O.: Flood or drought: How do aerosols affect precipitation?, Science, 321, 13091313, doi:http://dx.doi.org/10.1126/science.1160606
Web End =10.1126/science.1160606 http://dx.doi.org/10.1126/science.1160606
Web End = , 2008.
Salvador, P., Almeida, S. M., Cardoso, J., Almeida-Silva, M.,
Nunes, T., Cerqueira, M., Alves, C., Reis, M. A., Chaves,P. C., Artano, B., and Pio, C.: Composition and origin of PM10 in Cape Verde: Characterization of long-range transport episodes, Atmos. Environ., 127, 326339, doi:http://dx.doi.org/10.1016/j.atmosenv.2015.12.057
Web End =10.1016/j.atmosenv.2015.12.057 http://dx.doi.org/10.1016/j.atmosenv.2015.12.057
Web End = , 2016.
Stevens, B., Farrell, D., Hirsch, L., Jansen, F., Nuijens, L., Serikov,I., Brgmann, B., Forde, M., Linne, H., Lonitz, K., and Prospero,J. M.: The Barbados Cloud Observatory Anchoring Investigations of Clouds and Circulation on the Edge of the ITCZ, B. Am.Meteorol. Soc., 0, 787801, doi:http://dx.doi.org/10.1175/BAMS-D-14-00247.1
Web End =10.1175/BAMS-D-14-00247.1 http://dx.doi.org/10.1175/BAMS-D-14-00247.1
Web End = , 2016.
Su, H., Rose, D., Cheng, Y. F., Gunthe, S. S., Massling, A., Stock,M., Wiedensohler, A., Andreae, M. O., and Pschl, U.: Hygroscopicity distribution concept for measurement data analysis and modeling of aerosol particle mixing state with regard to hygroscopic growth and CCN activation, Atmos. Chem. Phys., 10, 74897503, doi:http://dx.doi.org/10.5194/acp-10-7489-2010
Web End =10.5194/acp-10-7489-2010 http://dx.doi.org/10.5194/acp-10-7489-2010
Web End = , 2010.
Talbot, R. W., Andreae, M. O., Andreae, T. W., and Harriss,R. C.: Regional aerosol chemistry of the amazon basin during the dry season, J. Geophys. Res.-Atmos., 93, 14991508, doi:http://dx.doi.org/10.1029/JD093iD02p01499
Web End =10.1029/JD093iD02p01499 http://dx.doi.org/10.1029/JD093iD02p01499
Web End = , 1988.
Talbot, R. W., Andreae, M. O., Berresheim, H., Artaxo, P., Garstang,M., Harriss, R. C., Beecher, K. M., and Li, S. M.: Aerosol chemistry during the wet season in central amazonia the inuence of long-range transport, J. Geophys. Res.-Atmos., 95, 16955 16969, doi:http://dx.doi.org/10.1029/JD095iD10p16955
Web End =10.1029/JD095iD10p16955 http://dx.doi.org/10.1029/JD095iD10p16955
Web End = , 1990.
Thalman, R., de S, S. S., Palm, B. B., Barbosa, H. M. J., Plker, M.L., Alexander, M. L., Carbone, S., Day, D. A., Kuang, C., Manzi,A. O., Ng, N. L., Sedlacek, A., Souza, R. A. F., Springston, S., Watson, T., Phlker, C., Pschl, U., Andreae, M. O., Artaxo, P., Jimenez, J. L., Martin, S. T., and Wang, J.: CCN activity and organic hygroscopicity of Amazonian aerosols seasonal and diel variations and impact of anthropogenic emissions, in preparation, 2017.
Twomey, S.: The nuclei of natural cloud formationpart II: The supersaturation in natural clouds and the variation of cloud droplet concentration, Geos Pura e Appl, 43, 243249, 1959.Twomey, S. and Wojciechowski, T. A.: Observations of the geographical variation of cloud nuclei, J. Atmos. Sci., 26, 684688, 1969.
Vestin, A., Rissler, J., Swietlicki, E., Frank, G. P., and Andreae, M. O.: Cloud-nucleating properties of the Amazonian biomass burning aerosol: Cloud condensation nuclei measurements and modeling, J. Geophys. Res.-Atmos., 112, D14201, doi:http://dx.doi.org/10.1029/2006jd8104
Web End =10.1029/2006jd8104 http://dx.doi.org/10.1029/2006jd8104
Web End = , 2007.
Wendisch, M., Pschl, U., Andreae, M. O., Machado, L. A. T., Albrecht, R., Schlager, H., Rosenfeld, D., Martin, S. T., Abdelmonem, A., Afchine, A., Arajo, A., Artaxo, P., Aufmhoff, H., Barbosa, H. M. J., Borrmann, S., Braga, R., Buchholz, B., Cecchini, M. A., Costa, A., Curtius, J., Dollner, M., Dorf, M., Dreiling, V., Ebert, V., Ehrlich, A., Ewald, F., Fisch, G., Fix, A., Frank,
www.atmos-chem-phys.net/16/15709/2016/ Atmos. Chem. Phys., 16, 1570915740, 2016
15740 M. L. Phlker et al.: Long-term observations of cloud condensation nuclei in the Amazon rain forest Part 1
F., Ftterer, D., Heckl, C., Heidelberg, F., Hneke, T., Jkel, E., Jrvinen, E., Jurkat, T., Kanter, S., Kstner, U., Kenntner, M., Kesselmeier, J., Klimach, T., Knecht, M., Kohl, R., Klling, T., Krmer, M., Krger, M., Krisna, T. C., Lavric, J. V., Longo, K., Mahnke, C., Manzi, A. O., Mayer, B., Mertes, S., Minikin, A., Molleker, S., Mnch, S., Nillius, B., Pfeilsticker, K., Phlker,C., Roiger, A., Rose, D., Rosenow, D., Sauer, D., Schnaiter, M., Schneider, J., Schulz, C., de Souza, R. A. F., Spanu, A., Stock, P., Vila, D., Voigt, C., Walser, A., Walter, D., Weigel, R., Weinzierl,B., Werner, F., Yamasoe, M. A., Ziereis, H., Zinner, T., and Zger, M.: ACRIDICON-CHUVA Campaign: Studying Tropical Deep Convective Clouds and Precipitation over Amazonia Using the New German Research Aircraft HALO, B. Am. Meteorol.Soc., 97, 18851908, doi:http://dx.doi.org/10.1175/bams-d-14-00255.1
Web End =10.1175/bams-d-14-00255.1 http://dx.doi.org/10.1175/bams-d-14-00255.1
Web End = , 2016.Wex, H., Petters, M. D., Carrico, C. M., Hallbauer, E., Massling,A., McMeeking, G. R., Poulain, L., Wu, Z., Kreidenweis, S. M., and Stratmann, F.: Towards closing the gap between hygroscopic growth and activation for secondary organic aerosol: Part 1 Evidence from measurements, Atmos. Chem. Phys., 9, 39873997, doi:http://dx.doi.org/10.5194/acp-9-3987-2009
Web End =10.5194/acp-9-3987-2009 http://dx.doi.org/10.5194/acp-9-3987-2009
Web End = , 2009.
Whitehead, J. D., Darbyshire, E., Brito, J., Barbosa, H. M. J., Crawford, I., Stern, R., Gallagher, M. W., Kaye, P. H., Allan, J. D., Coe, H., Artaxo, P., and McFiggans, G.: Biogenic cloud nuclei in the central Amazon during the transition from wet to dry season, Atmos. Chem. Phys., 16, 97279743, doi:http://dx.doi.org/10.5194/acp-16-9727-2016
Web End =10.5194/acp-16-9727- http://dx.doi.org/10.5194/acp-16-9727-2016
Web End =2016 , 2016.
Winderlich, J., Chen, H., Gerbig, C., Seifert, T., Kolle, O., Lavric,J. V., Kaiser, C., Hfer, A., and Heimann, M.: Continuous low-maintenance CO2/CH4/H2O measurements at the Zotino Tall
Tower Observatory (ZOTTO) in Central Siberia, Atmos. Meas. Tech., 3, 11131128, doi:http://dx.doi.org/10.5194/amt-3-1113-2010
Web End =10.5194/amt-3-1113-2010 http://dx.doi.org/10.5194/amt-3-1113-2010
Web End = , 2010. Yez-Serrano, A. M., Nlscher, A. C., Williams, J., Wolff, S.,
Alves, E., Martins, G. A., Bourtsoukidis, E., Brito, J., Jardine,K., Artaxo, P., and Kesselmeier, J.: Diel and seasonal changes of biogenic volatile organic compounds within and above an Amazonian rainforest, Atmos. Chem. Phys., 15, 33593378, doi:http://dx.doi.org/10.5194/acp-15-3359-2015
Web End =10.5194/acp-15-3359-2015 http://dx.doi.org/10.5194/acp-15-3359-2015
Web End = , 2015.
Zhou, J., Swietlicki, E., Hansson, H. C., and Artaxo, P.: Submicrometer aerosol particle size distribution and hygroscopic growth measured in the Amazon rain forest during the wet season, J. Geophys. Res., 107, 8055, doi:http://dx.doi.org/10.1029/2000jd000203
Web End =10.1029/2000jd000203 http://dx.doi.org/10.1029/2000jd000203
Web End = , 2002.
Atmos. Chem. Phys., 16, 1570915740, 2016 www.atmos-chem-phys.net/16/15709/2016/
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Copyright Copernicus GmbH 2016
Abstract
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 245nm. The mean critical diameters of CCN activation range from 43nm at S = 1.10% to 172nm at S = 0.11%. The particle hygroscopicity exhibits a pronounced size dependence with lower values for the Aitken mode (κ<sub>Ait</sub> = 0.14±0.03), higher values for the accumulation mode (κ<sub>Acc</sub> = 0.22±0.05), and an overall mean value of κ<sub>mean</sub> = 0.17±0.06, consistent with high fractions of organic aerosol.The hygroscopicity parameter, κ, 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.
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