Influence of Common Assumptions Regarding Aerosol Composition and Mixing State on Predicted CCN Concentration

Mahish, Manasi; Jefferson, Anne; Collins, D. R.

doi:10.3390/atmos9020054

Cited by 10 publications

(9 citation statements)

References 74 publications

(16 reference statements)

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“…For M BC e , c CO , and the particle concentrations in the accumulation mode range (N acc ), in the Aitken mode range (N Ait ), and in the total particle population (N CN,10 ), 4-6 years of ATTO site measurements were available. Additionally, M BC e data measured at the ZF2 site, located 40 km northwest of Manaus, were used to reflect the conditions back to 2008 (e.g., Martin et al, 2010a). In terms of sources and conditions, the ATTO and ZF2 sites are comparable (Saturno et al, 2017a;.…”

Section: Seasonal Cycles Of Remote-sensing and In Situ Datamentioning

confidence: 99%

Long-term observations of cloud condensation nuclei over the Amazon rain forest – Part 2: Variability and characteristics of biomass burning, long-range transport, and pristine rain forest aerosols

et al. 2018

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Size-resolved measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations and hygroscopicity were conducted over a full seasonal cycle at the remote Amazon Tall Tower Observatory (ATTO, March 2014-February 2015. In a preceding companion paper, we presented annually and seasonally averaged data and parametrizations (Part 1;Pöhlker et al., 2016a). In the present study (Part 2), we analyze key features and implications of aerosol and CCN properties for the following characteristic atmospheric conditions:-Empirically pristine rain forest (PR) conditions, where no influence of pollution was detectable, as observed during parts of the wet season from March to May. The PR episodes are characterized by a bimodal aerosol size distribution (strong Aitken mode with D Ait ≈ 70 nm and N Ait ≈ 160 cm −3 , weak accumulation mode with D acc ≈ 160 nm and N acc ≈ 90 cm −3 ), a chemical composition dominated by organic compounds, and relatively low particle hygroscopicity (κ Ait ≈ 0.12, κ acc ≈ 0.18).-Long-range-transport (LRT) events, which frequently bring Saharan dust, African biomass smoke, and sea spray aerosols into the Amazon Basin, mostly during February to April. The LRT episodes are characterized by a dominant accumulation mode (D Ait ≈ 80 nm, N Ait ≈ 120 cm −3 vs. D acc ≈ 180 nm, N acc ≈ 310 cm −3 ), an increased abundance of dust and salt, and relatively high hygroscopicity (κ Ait ≈ 0.18, κ acc ≈ 0.35).The coarse mode is also significantly enhanced during these events.-Biomass burning (BB) conditions characteristic for the Amazonian dry season from August to November. The BB episodes show a very strong accumulation mode (D Ait ≈ 70 nm, N Ait ≈ 140 cm −3 vs. D acc ≈ 170 nm, N acc ≈ 3400 cm −3 ), very high organic mass fractions (∼ 90 %), and correspondingly low hygroscopicity (κ Ait ≈ 0.14, κ acc ≈ 0.17).-Mixed-pollution (MPOL) conditions with a superposition of African and Amazonian aerosol emissions during the dry season. During the MPOL episode presented here as a case study, we observed African aerosols with a broad monomodal distribution (D ≈ 130 nm, N CN,10 ≈ 1300 cm −3 ), with high sulfate mass fractions (∼ 20 %) from volcanic sources and correspondingly high hygroscopicity (κ < 100 nm ≈ 0.14, κ > 100 nm ≈ 0.22), which were periodically mixed with fresh smoke from nearby fires (D ≈ 110 nm, N CN,10 ≈ 2800 cm −3 ) with an organicdominated composition and sharply decreased hygroscopicity (κ < 150 nm ≈ 0.10, κ > 150 nm ≈ 0.20).Insights into the aerosol mixing state are provided by particle hygroscopicity (κ) distribution plots, which indicate largely internal mixing for the PR aerosols (narrow κ distribution) and more external mixing for the BB, LRT, and MPOL aerosols (broad κ distributions).The CCN spectra (CCN concentration plotted against water vapor supersaturation) obtained for the different case studies indicate distinctly different regimes of cloud formation and microphysics depending on aerosol properties and meteorological conditions. The measurement results suggest that CCN activa...

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Section: Seasonal Cycles Of Remote-sensing and In Situ Datamentioning

confidence: 99%

Long-term observations of cloud condensation nuclei over the Amazon rain forest – Part 2: Variability and characteristics of biomass burning, long-range transport, and pristine rain forest aerosols

et al. 2018

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“…Number concentrations fluctuate in response to the nitrate and organic aerosol cycle on short time scales and synoptic weather variability on longer time scales. During winter months, the inorganic aerosol composition at the site is dominated by nitrate aerosols (Jefferson et al, 2017;Mahish et al, 2018) and hygroscopicity derived from scattering measurements is largest during those months (Jefferson et al, 2017).…”

Section: Growth Factormentioning

confidence: 99%

A Software Package to Simplify Tikhonov Regularization with Examples for Matrix-Based Inversion of SMPS and HTDMA Data

Petters

2021

Preprint

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Abstract. Tikhonov regularization is a tool for reducing noise amplification during data inversion. This work introduces RegularizationTools.jl, a general-purpose software package to apply Tikhonov regularization to data. The package implements well-established numerical algorithms and is suitable for systems of up to ~1000 equations. Included is an abstraction to systematically categorize specific inversion configurations and their associated hyperparameters. A generic interface translates arbitrary linear forward models defined by a computer function into the corresponding design matrix. This obviates the need to explicitly write out and discretize the Fredholm integral equation, thus facilitating fast prototyping of new regularization schemes associated with measurement techniques. Example applications include the inversion involving data from scanning mobility particle sizers (SMPS) and humidified tandem differential mobility analyzers (HTDMA). Inversion of SMPS size distributions reported in this work builds upon the freely-available software DifferentialMobilityAnalyzers.jl. The speed of inversion is improved by a factor of ~200, now requiring between 2 and 5 ms per SMPS scan when using 120 size bins. Previously reported occasional failure to converge to a valid solution is reduced by switching from the L-curve method to generalized cross-validation as the metric to search for the optimal regularization parameter. Higher-order inversions resulting in smooth, denoised reconstructions of size distributions are now included in DifferentialMobilityAnalyzers.jl. This work also demonstrates that an SMPS-style matrix-based inversion can be applied to find the growth factor frequency distribution from raw HTDMA data, while also accounting for multiply-charged particles. The outcome of the aerosol-related inversion methods is showcased by inverting multi-week SMPS and HTDMA datasets from ground-based observations, including SMPS data obtained at Bodega Bay Marine Laboratory during the Calwater 2/ACAPEX campaign, and co-located SMPS and HTDMA data collected at the U.S. Department of Energy observatory located at the Southern Great Plains site in Oklahoma, U.S.A. Results show that the proposed approaches are suitable for unsupervised, nonparametric inversion of large-scale datasets as well as inversion in real-time during data acquisition on low-cost reduced-instruction-set architectures used in single-board computers. The included software implementation of Tikhonov regularization is freely-available, general, and domain-independent, and thus can be applied to many other inverse problems arising in atmospheric measurement techniques and beyond.

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“…Depending on location and local emissions, heterogeneity in hygroscopicity frequently decreases in the early afternoon, when photochemistry increases the available concentrations of condensible species (e.g., [48,53]). Mahish et al (2018) [54] compared predicted CCN concentrations based on different simplifications of the mixing-state using long-term observations from the Southern Great Plains site in Oklahoma, USA. They found that a size-resolved internally-mixed representation of the aerosol mixing state performed modestly better (normalized mean bias (NMB) 1 to 2%, coefficients of determination (R 2 ) about 0.85) compared to either a fully internally-mixed, size-resolved externally-mixed, or a fully externally-mixed representation of the mixing state (NMB 10 to 21%, −9%, and 0 to 7%, respectively; R 2 about 0.8).…”

Section: Impacts On Ccn Concentrationsmentioning

confidence: 99%

“…These errors depend on model spatial resolution, and on the representations of the aerosol size distribution, aerosol processes, and aerosol optical properties, in addition to the true mixing state of the aerosol population. With these caveats in mind, a size-resolved internally-mixed representation has been shown to frequently overestimate CCN concentrations by 10% to 20% [54,[56][57][58]61], and absorption coefficients of BC by 20% to 40% [49,[56][57][58]. In the case of CCN concentrations and BC absorption, these overestimates will be greater near source regions, while overestimates in wet deposition will lead to greater errors in hydrophobic aerosol concentrations far from source regions (e.g., [64]).…”

Section: Summary and Recommendationsmentioning

confidence: 99%

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

Stevens

Dastoor

2019

Atmosphere

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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.

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Influence of Common Assumptions Regarding Aerosol Composition and Mixing State on Predicted CCN Concentration

Cited by 10 publications

References 74 publications

Long-term observations of cloud condensation nuclei over the Amazon rain forest – Part 2: Variability and characteristics of biomass burning, long-range transport, and pristine rain forest aerosols

Long-term observations of cloud condensation nuclei over the Amazon rain forest – Part 2: Variability and characteristics of biomass burning, long-range transport, and pristine rain forest aerosols

A Software Package to Simplify Tikhonov Regularization with Examples for Matrix-Based Inversion of SMPS and HTDMA Data

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

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