Inversion of droplet aerosol analyzer data for long-term aerosol–cloud interaction measurements

Open Access Atmospheric Measurement Techniques Atmos. Meas. Tech., 7, 877–886, 2014 www.atmos-meas-tech.net/7/877/2014/ doi:10.5194/amt-7-877-2014 © Author(s) 2014. CC Attribution 3.0 License. Inversion of droplet aerosol analyzer data for long-term aerosol–cloud interaction measurements M. I. A. Berghof1 , G. P. Frank1 , S. Sjogren1,* , and B. G. Martinsson1 1 Department of Physics, Lund University, Lund, Sweden * now at: University of Applied Sciences Northwestern Switzerland, Brugg-Windisch, Switzerland Correspondence to: M. Berghof () Received: 4 October 2013 – Published in Atmos. Meas. Tech. Discuss.: 29 November 2013 Revised: 21 February 2014 – Accepted: 25 February 2014 – Published: 4 April 2014 Abstract. The droplet aerosol analyzer (DAA) was developed to study the influence of aerosol properties on clouds. It measures the ambient particle size of individual droplets and interstitial particles, the size of the dry (residual) particles after the evaporation of water vapor and the number concentration of the dry (residual) particles. A method was developed for the evaluation of DAA data to obtain the three-parameter data set: ambient particle diameter, dry (residual) particle diameter and number concentration. First results from in-cloud measurements performed on the summit of Mt. Brocken in Germany are presented. Various aspects of the cloud–aerosol data set are presented, such as the number concentration of interstitial particles and cloud droplets, the dry residue particle size distribution, droplet size distributions, scavenging ratios due to cloud droplet formation and size-dependent solute concentrations. This data set makes it possible to study clouds and the influence of the aerosol population on clouds. 1 Introduction Clouds affect the Earth’s climate in a number of ways: for example, they regulate the hydrological cycle and redistribute energy via the transport of water vapor and latent heat in the atmosphere. Many factors influence the macrophysics and microphysics of clouds. The macrophysics of clouds are affected by large-scale meteorological conditions such as updraft velocity, turbulent mixing or atmospheric layering. Cloud droplets form by the condensation of water vapor on aerosol particles. As a consequence, the microphysical conditions are influenced by the macrophysics, the particle number concentration and size distribution, the chemical composition, and the mixing state of the atmospheric aerosol. The first (Twomey) indirect aerosol effect describes changes in cloud properties induced by changes in the properties of aerosol particles (Warner, 1968; Twomey, 1974; Albrecht, 1989; Liou and Ou, 1989; Lohmann and Feichter, 2005). It is still being debated as to whether changes in the microphysical properties of clouds also influence the amount of clouds and liquid water content. These changes are also associated with changes in precipitation efficiency and thus cloud lifetime (second indirect aerosol effect; Albrecht, 1989; Stevens and Feingold, 2009). This work deals with an instrument, the droplet aerosol analyzer (DAA; Martinsson, 1996), developed for experimental studies of the interaction between aerosol and clouds. This instrument was especially developed to study the interaction between aerosol particles and cloud/fog droplets. The DAA measures the ambient size of individual droplets and interstitial particles, the size of the dry (residual) particles after the evaporation of water vapor and the number concentration. This gives a unique three-parameter data set (ambient particle diameter, dry (residual) particle diameter, and number concentration) for both cloud droplets and interstitial particles. The DAA can provide a direct relationship between cloud droplet size and the size of its dry residue. Other instrumentation for studying clouds, such as differential mobility particle spectrometers (DMPS), optical particle counters (OPC; Sorensen et al., 2011), the ground-based fog monitor (Spiegel et al., 2012) or the counterflow virtual impactor (CVI) that has a variable cut-off diameter for cloud droplets between 1 and 30 µm (Anderson et al., 1993; Noone et al., 1988; Ogren et al., 1985; Schwarzenboeck and Heintzenberg, Published by Copernicus Publications on behalf of the European Geosciences Union. 878 M. Berghof: Inversion of DAA data for long-term AIC measurements 2000), cannot provide such a relationship, even when used in parallel. Previous studies with the DAA include thorough intercomparisons (Cederfelt et al., 1997; Frank et al., 1998; Martinsson et al., 1999, 2000) with other cloud/aerosol instrumentation. Results from previous studies include relationships between particle and cloud number concentrations and cloud dynamics (Martinsson et al., 1997, 1999) as well as observations of validated cloud droplet number concentrations reaching up to 3000 cm−3 (Martinsson et al., 2000). Findings from previous DAA studies also include that fogs in polluted regions consisted of droplets that were not activated (Frank et al., 1998). In order to improve the statistical description of the aerosol impact on cloud microstructure for different dynamical situations, long-term measurements are needed. The previous version of the DAA required daily service to ensure high-quality data. As a result, the instrument was only used in experiments spanning a month or less. Here we present parts of new developments of the DAA. The overall aims of the developments are twofold: to improve the time resolution in the measurements and to prepare the instrument for unattended long-term operation. The time resolution was improved by a factor of 2 by changing method of voltage change in the differential mobility analyzers (DMA) used in the DAA, increasing the number of DMAs from seven to eight where the new DMA provides more efficient coverage in terms of electrical mobility, and by changing the DMA aerosol to sheath air flow ratio. Methodology for long-term operation includes closedloop sheath air circulation in all DMAs, automated dryers for cloud droplets (Sjogren et al., 2013), automatic fill of liquid consumed by the particle detectors (condensation particle counter (CPC)), regular, remote access to operational parameters, and logging of a large number of parameters. These developments will be presented elsewhere. First results from measurements at Mt. Brocken (Germany) between June and October 2010 will be shown here. These improvements mean that large amounts of data can be produced to study the interaction between aerosols and clouds for different aerosols and for differences in cloud dynamics. The data produced need to be evaluated. The previous method was based on manual fit of the DAA spectra. Here we present an automated methodology to evaluate DAA data. The routine builds on the previous, manual, unpublished method. 2 Outdoor part Ambient conditions Indoor part Laboratory conditions Inlet BiCh DMA 2a CPC 2a DMA 1a BiCh DMA 2b CPC 2b D (...truncated)