Estimation of cloud condensation nuclei concentration from aerosol optical quantities: influential factors and uncertainties

Li, Jianjun; Li, Zhanqing

doi:10.5194/acp-14-471-2014

Cited by 90 publications

(98 citation statements)

References 68 publications

(80 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this context, it is also noteworthy to mention that Liu and Li (2014) showed that the product of σ × AE (denoted as Aerosol Index AI, introduced by Nakajima et al, 2001) provides a better correlation with n CCN than n CCN with σ . By using AI instead of σ in the correlation, Liu and Li (2014) consider information on the aerosol type and the related size distribution (high AI for fine-mode-dominated aerosol, low AI for coarse-mode-dominated aerosol conditions).…”

Section: Leipzig and Limassol Long-term Observations Of Mixed Aerosolsmentioning

confidence: 99%

See 1 more Smart Citation

Potential of polarization lidar to provide profiles of CCN- and INP-relevant aerosol parameters

Mamouri¹,

Ansmann²

2016

Atmos. Chem. Phys.

141

184

View full text Add to dashboard Cite

Abstract. We investigate the potential of polarization lidar to provide vertical profiles of aerosol parameters from which cloud condensation nucleus (CCN) and ice nucleating particle (INP) number concentrations can be estimated. We show that height profiles of particle number concentrations n 50,dry considering dry aerosol particles with radius > 50 nm (reservoir of CCN in the case of marine and continental non-desert aerosols), n 100,dry (particles with dry radius > 100 nm, reservoir of desert dust CCN), and of n 250,dry (particles with dry radius > 250 nm, reservoir of favorable INP), as well as profiles of the particle surface area concentration s dry (used in INP parameterizations) can be retrieved from lidar-derived aerosol extinction coefficients σ with relative uncertainties of a factor of 1.5-2 in the case of n 50,dry and n 100,dry and of about 25-50 % in the case of n 250,dry and s dry . Of key importance is the potential of polarization lidar to distinguish and separate the optical properties of desert aerosols from non-desert aerosol such as continental and marine particles. We investigate the relationship between σ , measured at ambient atmospheric conditions, and n 50,dry for marine and continental aerosols, n 100,dry for desert dust particles, and n 250,dry and s dry for three aerosol types (desert, non-desert continental, marine) and for the main lidar wavelengths of 355, 532, and 1064 nm. Our study is based on multiyear Aerosol Robotic Network (AERONET) photometer observations of aerosol optical thickness and column-integrated particle size distribution at Leipzig, Germany, and Limassol, Cyprus, which cover all realistic aerosol mixtures. We further include AERONET data from field campaigns in Morocco, Cabo Verde, and Barbados, which provide pure dust and pure marine aerosol scenarios. By means of a simple CCN parameterization (with n 50,dry or n 100,dry as input) and available INP parameterization schemes (with n 250,dry and s dry as input) we finally compute profiles of the CCN-relevant particle number concentration n CCN and the INP number concentration n INP . We apply the method to a lidar observation of a heavy dust outbreak crossing Cyprus and a case dominated by continental aerosol pollution.

show abstract

Section: Leipzig and Limassol Long-term Observations Of Mixed Aerosolsmentioning

confidence: 99%

“…Former studies indicate also that measured aerosol optical properties (at wavelengths around 500 nm) can be used to estimate CCN number concentrations, CCNC (Ghan and Collins, 2004;Ghan et al, 2006;Andreae, 2009;Jefferson, 2010;Liu and Li, 2014;Shinozuka et al, 2015).…”

Section: R E Mamouri and A Ansmann: Lidar Profiling Of Ccn-and Inpmentioning

confidence: 99%

Potential of polarization lidar to provide profiles of CCN- and INP-relevant aerosol parameters

Mamouri¹,

Ansmann²

2016

Atmos. Chem. Phys.

141

184

View full text Add to dashboard Cite

show abstract

“…Moses Eterigho Emetere 1 atmosphere. This process is called aerosol retention, which is quite interesting and cannot be generalized because of its dynamic properties.…”

Section: Systemic Study Of Aerosol Loading and Retention Over Praia-cmentioning

confidence: 99%

“…Liu et al [1] affi rmed that atmospheric aerosols can increase the heat fl ux over the earth. Verheggen and Weijers [2] clarifi ed that aerosols cause cooling while black carbon aerosol causes warming.…”

Section: Introductionmentioning

confidence: 99%

Systemic Study of Aerosol Loading and Retention over Praia-Cape Verde: Satellite and Ground Observation Analysis

Emetere

Akinyemi

Ojewumi

2018

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

The situation in Praia may be critical at the moment because of its current aerosol loading and retention. In this study we obtained 14-year (2000-13) data set from satellite and ground observation from a multi-angle imaging spectro-radiometer (MISR) and an aerosol robotic network (AERONET). The analytic and statistical techniques were used to investigate the aerosol loading and retention over Praia-Cape Verde. The average annual aerosol retention over the study area increased by 18%. The highest aerosol retention rate of 39% was recorded in 2010. The aerosol loading was found to be higher between December to February. Since the ventilation over Praia is low, aerosol loading impact on the health and welfare of life forms may be threatened. The study also suggested other sectors of the economy that can be affected by the increased aerosol loading over Praia.

show abstract

“…It has been found that the correlation of AOD with CCN number concentrations, a key assumption in this approach, is strongly dependent on ambient relative humidity and aerosol types. Furthermore, these correlations become less reliable when sea salt and mineral dust constitute an important fraction of the particle number, a situation which can be relevant over the ocean or deserts (Liu and Li, 2014). This makes in-situ measurements indispensable 30 and therefore numerous studies of CCN activity have been carried out in a variety of environments, ranging from remote marine over continental background to urban locations, and in the laboratory (e.g., Andreae, 2009a;Asmi et al, 2012; Atmos.…”

Section: Introductionmentioning

confidence: 99%

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

Schmale

Henning

Decesari

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract.Aerosol-cloud interactions (ACI) constitute the single largest uncertainty in anthropogenic radiative forcing. To reduce the uncertainties and gain more confidence in the simulation of ACI, models need to be evaluated against observations, in particular against measurements of cloud condensation nuclei (CCN). Numerous observations of CCN number concentration exist, and many closure studies have been performed to predict CCN number concentrations based on particle number size 5 distributions, chemical composition, and the κ-Köhler theory. Most of these studies provide details for short time periods or focus on special environmental conditions. These observations, however, cannot address questions of large-scale temporal and spatial CCN variability. Here we analyze long-term observations of CCN number concentrations, particle number size distributions and chemical composition from twelve sites on three continents. Eight of these stations are part of the European Aerosols, Clouds, and Trace gases Research InfraStructure (ACTRIS). 10We group the observatories into categories according to their official classification: coastal background (Barrow, Alaska; Mace Head, Ireland; Finokalia, Crete; Noto Peninsula, Japan), rural background (Melpitz, Germany; Cabauw, the Netherlands; Vavihill, Sweden), alpine sites (Puy de Dôme, France; Jungfraujoch, Switzerland), remote forest sites (ATTO, Brazil; SMEAR, Finland) and the urban environment (Seoul, South Korea). Expectedly, CCN characteristics are highly variable across regions. However, they also vary within categories, most strongly in the coastal background group, where 15 CCN number concentrations can vary by up to a factor of 30 within one season. In terms of particle activation behavior, most continental stations exhibit very similar relative activation ratios across the range of 0.1 to 1.0 % supersaturation. At the coastal sites the activation ratios spread more widely across the SS spectrum.Several stations show strong seasonal cycles of CCN number concentrations and particle number size distributions, e.g., atBarrow (Arctic Haze in spring), at the alpine stations (stronger influence of polluted boundary layer air masses in summer), 20 the rain forest (wet and dry season), or Finokalia (forest fire influence in fall). The rural background and urban sites exhibit relatively little variability throughout the year while short-term variability can be high especially at the urban site.The average hygroscopicity parameter, κ, calculated from the chemical composition of submicron particles, was highest at the coastal site of Mace Head (0.6) and the lowest at the rain forest station ATTO (0.2 -0.3). We performed closure studies to predict CCN number concentrations from the particle number size distribution and chemical composition measurements. 25The prediction accuracy for the average concentrations is high. The ratio between predicted and measured CCN concentrations is between 0.87 and 1.4. The temporal variability is also well represented, as reflected by Pearson c...

show abstract

Estimation of cloud condensation nuclei concentration from aerosol optical quantities: influential factors and uncertainties

Cited by 90 publications

References 68 publications

Potential of polarization lidar to provide profiles of CCN- and INP-relevant aerosol parameters

Potential of polarization lidar to provide profiles of CCN- and INP-relevant aerosol parameters

Systemic Study of Aerosol Loading and Retention over Praia-Cape Verde: Satellite and Ground Observation Analysis

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

Contact Info

Product

Resources

About