scholarly journals A three-dimensional sectional representation of aerosol mixing state for simulating optical properties and cloud condensation nuclei

2016 ◽  
Vol 121 (10) ◽  
pp. 5912-5929 ◽  
Author(s):  
Joseph Ching ◽  
Rahul A. Zaveri ◽  
Richard C. Easter ◽  
Nicole Riemer ◽  
Jerome D. Fast
Keyword(s):  
Optical Properties ◽  
Cloud Condensation Nuclei ◽  
Three Dimensional ◽  
Condensation Nuclei ◽  
Mixing State ◽  
Aerosol Mixing State

scholarly journals A Review of the Representation of Aerosol Mixing State in Atmospheric Models

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 168 ◽  
Author(s):  
Robin Stevens ◽  
Ashu Dastoor
Keyword(s):  
Optical Properties ◽  
Climate Models ◽  
Research Effort ◽  
Cloud Condensation Nuclei ◽  
Weather Prediction ◽  
Heterogeneous Chemistry ◽  
Computationally Efficient ◽  
Atmospheric Models ◽  
Mixing State ◽  
Aerosol Mixing State

Aerosol mixing state significantly affects concentrations of cloud condensation nuclei (CCN), wet removal rates, thermodynamic properties, heterogeneous chemistry, and aerosol optical properties, with implications for human health and climate. Over the last two decades, significant research effort has gone into finding computationally-efficient methods for representing the most important aspects of aerosol mixing state in air pollution, weather prediction, and climate models. In this review, we summarize the interactions between mixing-state and aerosol hygroscopicity, optical properties, equilibrium thermodynamics and heterogeneous chemistry. We focus on the effects of simplified assumptions of aerosol mixing state on CCN concentrations, wet deposition, and aerosol absorption. We also summarize previous approaches for representing aerosol mixing state in atmospheric models, and we make recommendations regarding the representation of aerosol mixing state in future modelling studies.

scholarly journals Toward Reduced Representation of Mixing State for Simulating Aerosol Effects on Climate

2017 ◽  
Vol 98 (5) ◽  
pp. 971-980 ◽  
Author(s):  
Laura Fierce ◽  
Nicole Riemer ◽  
Tami C. Bond
Keyword(s):  
Optical Properties ◽  
Cloud Condensation Nuclei ◽  
Mixing Time ◽  
Radiative Properties ◽  
Mixing State ◽  
Hygroscopic Properties ◽  
Model Aerosol ◽  
Aerosol Effects ◽  
Aerosol Mixing State ◽  
Individual Particles

Abstract Atmospheric aerosols affect Earth’s energy budget, and hence its climate, by scattering and absorbing solar radiation and by altering the radiative properties and the lifetime of clouds. These two major aerosol effects depend on the optical properties and the cloud-nucleating ability of individual particles, which, in turn, depend on the distribution of components among individual particles, termed the “aerosol mixing state.” Global models have moved toward including aerosol schemes to represent the evolution of particle characteristics, but individual particle properties cannot be resolved in global-scale simulations. Instead, models approximate the aerosol mixing state. The errors in climate-relevant aerosol properties introduced by such approximations may be large but have not yet been well quantified. This paper quantitatively addresses the question of to what extent the aerosol mixing state must be resolved to adequately represent the optical properties and cloud-nucleating properties of particle populations. Using a detailed benchmarking model to simulate gas condensation and particle coagulation, we show that, after the particles evolve in the atmosphere, simple mixing-state representations are sufficient for modeling cloud condensation nuclei concentrations, and we quantify the mixing time scale that characterizes this transformation. In contrast, a detailed representation of the mixing state is required to model aerosol light absorption, even for populations that are fully mixed with respect to their hygroscopic properties.

scholarly journals Estimating cloud condensation nuclei number concentrations using aerosol optical properties: role of particle number size distribution and parameterization

2019 ◽  
Vol 19 (24) ◽  
pp. 15483-15502 ◽  
Author(s):  
Yicheng Shen ◽  
Aki Virkkula ◽  
Aijun Ding ◽  
Krista Luoma ◽  
Helmi Keskinen ◽  
...  
Keyword(s):  
Optical Properties ◽  
Size Distribution ◽  
Cloud Condensation Nuclei ◽  
Size Distributions ◽  
Condensation Nuclei ◽  
Aerosol Optical Properties ◽  
Average Bias ◽  
Angstrom Exponent ◽  
Ångström Exponent ◽  
Ångstrom Exponent

Abstract. The concentration of cloud condensation nuclei (CCN) is an essential parameter affecting aerosol–cloud interactions within warm clouds. Long-term CCN number concentration (NCCN) data are scarce; there are a lot more data on aerosol optical properties (AOPs). It is therefore valuable to derive parameterizations for estimating NCCN from AOP measurements. Such parameterizations have already been made, and in the present work a new parameterization is presented. The relationships between NCCN, AOPs, and size distributions were investigated based on in situ measurement data from six stations in very different environments around the world. The relationships were used for deriving a parameterization that depends on the scattering Ångström exponent (SAE), backscatter fraction (BSF), and total scattering coefficient (σsp) of PM10 particles. The analysis first showed that the dependence of NCCN on supersaturation (SS) can be described by a logarithmic fit in the range SS <1.1 %, without any theoretical reasoning. The relationship between NCCN and AOPs was parameterized as NCCN≈((286±46)SAE ln(SS/(0.093±0.006))(BSF − BSFmin) + (5.2±3.3))σsp, where BSFmin is the minimum BSF, in practice the 1st percentile of BSF data at a site to be analyzed. At the lowest supersaturations of each site (SS ≈0.1 %), the average bias, defined as the ratio of the AOP-derived and measured NCCN, varied from ∼0.7 to ∼1.9 at most sites except at a Himalayan site where the bias was >4. At SS >0.4 % the average bias ranged from ∼0.7 to ∼1.3 at most sites. For the marine-aerosol-dominated site Ascension Island the bias was higher, ∼1.4–1.9. In other words, at SS >0.4 % NCCN was estimated with an average uncertainty of approximately 30 % by using nephelometer data. The biases were mainly due to the biases in the parameterization related to the scattering Ångström exponent (SAE). The squared correlation coefficients between the AOP-derived and measured NCCN varied from ∼0.5 to ∼0.8. To study the physical explanation of the relationships between NCCN and AOPs, lognormal unimodal particle size distributions were generated and NCCN and AOPs were calculated. The simulation showed that the relationships of NCCN and AOPs are affected by the geometric mean diameter and width of the size distribution and the activation diameter. The relationships of NCCN and AOPs were similar to those of the observed ones.

scholarly journals Using different assumptions of aerosol mixing state and chemical composition to predict CCN concentrations based on field measurements in urban Beijing

2018 ◽  
Vol 18 (9) ◽  
pp. 6907-6921 ◽  
Author(s):  
Jingye Ren ◽  
Fang Zhang ◽  
Yuying Wang ◽  
Don Collins ◽  
Xinxin Fan ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Black Carbon ◽  
Cloud Condensation Nuclei ◽  
Field Measurements ◽  
Minor Role ◽  
Mixing State ◽  
Mixing Processes ◽  
Size Dependent ◽  
Aerosol Mixing State ◽  
Aerosol Chemical Composition

Abstract. Understanding the impacts of aerosol chemical composition and mixing state on cloud condensation nuclei (CCN) activity in polluted areas is crucial for accurately predicting CCN number concentrations (NCCN). In this study, we predict NCCN under five assumed schemes of aerosol chemical composition and mixing state based on field measurements in Beijing during the winter of 2016. Our results show that the best closure is achieved with the assumption of size dependent chemical composition for which sulfate, nitrate, secondary organic aerosols, and aged black carbon are internally mixed with each other but externally mixed with primary organic aerosol and fresh black carbon (external–internal size-resolved, abbreviated as EI–SR scheme). The resulting ratios of predicted-to-measured NCCN (RCCN_p∕m) were 0.90 – 0.98 under both clean and polluted conditions. Assumption of an internal mixture and bulk chemical composition (INT–BK scheme) shows good closure with RCCN_p∕m of 1.0 –1.16 under clean conditions, implying that it is adequate for CCN prediction in continental clean regions. On polluted days, assuming the aerosol is internally mixed and has a chemical composition that is size dependent (INT–SR scheme) achieves better closure than the INT–BK scheme due to the heterogeneity and variation in particle composition at different sizes. The improved closure achieved using the EI–SR and INT–SR assumptions highlight the importance of measuring size-resolved chemical composition for CCN predictions in polluted regions. NCCN is significantly underestimated (with RCCN_p∕m of 0.66 – 0.75) when using the schemes of external mixtures with bulk (EXT–BK scheme) or size-resolved composition (EXT–SR scheme), implying that primary particles experience rapid aging and physical mixing processes in urban Beijing. However, our results show that the aerosol mixing state plays a minor role in CCN prediction when the κorg exceeds 0.1.

scholarly journals Impact of dry intrusion events on the composition and mixing state of particles during the winter Aerosol and Cloud Experiment in the Eastern North Atlantic (ACE-ENA)

2021 ◽  
Vol 21 (24) ◽  
pp. 18123-18146
Author(s):  
Jay M. Tomlin ◽  
Kevin A. Jankowski ◽  
Daniel P. Veghte ◽  
Swarup China ◽  
Peiwen Wang ◽  
...  
Keyword(s):  
Long Range ◽  
North Atlantic ◽  
Cloud Condensation Nuclei ◽  
Aerosol Size Distribution ◽  
Condensation Nuclei ◽  
Mixing State ◽  
Carbonaceous Particles ◽  
X Ray ◽  
Eastern North Atlantic ◽  
Individual Particles

Abstract. Long-range transport of continental emissions has a far-reaching influence over remote regions, resulting in substantial change in the size, morphology, and composition of the local aerosol population and cloud condensation nuclei (CCN) budget. Here, we investigate the physicochemical properties of atmospheric particles collected on board a research aircraft flown over the Azores during the winter 2018 Aerosol and Cloud Experiment in the Eastern North Atlantic (ACE-ENA) campaign. Particles were collected within the marine boundary layer (MBL) and free troposphere (FT) after long-range atmospheric transport episodes facilitated by dry intrusion (DI) events. Chemical and physical properties of individual particles were investigated using complementary capabilities of computer-controlled scanning electron microscopy and X-ray spectromicroscopy to probe particle external and internal mixing state characteristics. Furthermore, real-time measurements of aerosol size distribution, cloud condensation nuclei (CCN) concentration, and back-trajectory calculations were utilized to help bring into context the findings from offline spectromicroscopy analysis. While carbonaceous particles were found to be the dominant particle type in the region, changes in the percent contribution of organics across the particle population (i.e., external mixing) shifted from 68 % to 43 % in the MBL and from 92 % to 46 % in FT samples during DI events. This change in carbonaceous contribution is counterbalanced by the increase in inorganics from 32 % to 57 % in the MBL and 8 % to 55 % in FT. The quantification of the organic volume fraction (OVF) of individual particles derived from X-ray spectromicroscopy, which relates to the multi-component internal composition of individual particles, showed a factor of 2.06 ± 0.16 and 1.11 ± 0.04 increase in the MBL and FT, respectively, among DI samples. We show that supplying particle OVF into the κ-Köhler equation can be used as a good approximation of field-measured in situ CCN concentrations. We also report changes in the κ values in the MBL from κMBL, non-DI=0.48 to κMBL, DI=0.41, while changes in the FT result in κFT, non-DI=0.36 to κFT, DI=0.33, which is consistent with enhancements in OVF followed by the DI episodes. Our observations suggest that entrainment of particles from long-range continental sources alters the mixing state population and CCN properties of aerosol in the region. The work presented here provides field observation data that can inform atmospheric models that simulate sources and particle composition in the eastern North Atlantic.

scholarly journals Size-resolved cloud condensation nuclei (CCN) activity and closure analysis at the HKUST Supersite in Hong Kong

2014 ◽  
Vol 14 (18) ◽  
pp. 10267-10282 ◽  
Author(s):  
J. W. Meng ◽  
M. C. Yeung ◽  
Y. J. Li ◽  
B. Y. L. Lee ◽  
C. K. Chan
Keyword(s):  
Particle Size ◽  
Hong Kong ◽  
Atmospheric Aerosols ◽  
Cloud Condensation Nuclei ◽  
Particle Diameter ◽  
Volume Ratio ◽  
Condensation Nuclei ◽  
Mixing State ◽  
Scanning Mobility Particle Sizer ◽  
The Mean

Abstract. The cloud condensation nuclei (CCN) properties of atmospheric aerosols were measured on 1–30 May 2011 at the HKUST (Hong Kong University of Science and Technology) Supersite, a coastal site in Hong Kong. Size-resolved CCN activation curves, the ratio of number concentration of CCN (NCCN) to aerosol concentration (NCN) as a function of particle size, were obtained at supersaturation (SS) = 0.15, 0.35, 0.50, and 0.70% using a DMT (Droplet Measurement Technologies) CCN counter (CCNc) and a TSI scanning mobility particle sizer (SMPS). The mean bulk size-integrated NCCN ranged from ~500 cm−3 at SS = 0.15% to ~2100 cm−3 at SS = 0.70%, and the mean bulk NCCN / NCN ratio ranged from 0.16 at SS = 0.15% to 0.65 at SS = 0.70%. The average critical mobility diameters (D50) at SS = 0.15, 0.35, 0.50, and 0.70% were 116, 67, 56, and 46 nm, respectively. The corresponding average hygroscopic parameters (κCCN) were 0.39, 0.36, 0.31, and 0.28. The decrease in κCCN can be attributed to the increase in organic to inorganic volume ratio as particle size decreases, as measured by an Aerodyne high resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS). The κCCN correlates reasonably well with κAMS_SR based on size-resolved AMS measurements: κAMS_SR = κorg × forg + κinorg × finorg, where forg and finorg are the organic and inorganic volume fractions, respectively, κorg = 0.1 and κinorg = 0.6, with a R2 of 0.51. In closure analysis, NCCN was estimated by integrating the measured size-resolved NCN for particles larger than D50 derived from κ assuming internal mixing state. Estimates using κAMS_SR show that the measured and predicted NCCN were generally within 10% of each other at all four SS. The deviation increased to 26% when κAMS was calculated from bulk PM1 AMS measurements of particles because PM1 was dominated by particles of 200 to 500 nm in diameter, which had a larger inorganic fraction than those of D50 (particle diameter < 200 nm). A constant κ = 0.33 (the average value of κAMS_SR over the course of campaign) was found to give an NCCN prediction within 12% of the actual measured values. We also compared NCCN estimates based on the measured average D50 and the average size-resolved CCN activation ratio to examine the relative importance of hygroscopicity and mixing state. NCCN appears to be relatively more sensitive to the mixing state and hygroscopicity at a high SS = 0.70% and a low SS = 0.15%, respectively.

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