Articles | Volume 24, issue 1
https://doi.org/10.5194/acp-24-427-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-427-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Quantifying the effects of the microphysical properties of black carbon on the determination of brown carbon using measurements at multiple wavelengths
Zhejiang Lab, Hangzhou, Zhejiang 311121, China
Dan Li
Zhejiang Lab, Hangzhou, Zhejiang 311121, China
Yuanyuan Wang
Zhejiang Lab, Hangzhou, Zhejiang 311121, China
Dandan Sun
Zhejiang Lab, Hangzhou, Zhejiang 311121, China
Weizhen Hou
State Environment Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Jinghe Ren
Zhejiang Lab, Hangzhou, Zhejiang 311121, China
Hailing Wu
State Environment Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Peng Zhou
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Jibing Qiu
CORRESPONDING AUTHOR
Zhejiang Lab, Hangzhou, Zhejiang 311121, China
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
Related authors
Jie Luo, Miao Hu, Jibing Qiu, Kaitao Li, Hao He, Yuping Sun, and Xiulin Geng
EGUsphere, https://doi.org/10.5194/egusphere-2024-1155, https://doi.org/10.5194/egusphere-2024-1155, 2024
Preprint archived
Short summary
Short summary
In this work, we first calculate the scattering signal returned from partially-coated black carbon based on the SP2 measurement, and then the mixing states were retrieved using Mie theory, and the difference between the retrieved and "true" mixing states can be the uncertainties of the SP2 -Represent measurement. In addition, the effects on the direct radiative forcing are also evaluated.
Jie Luo, Zhengqiang Li, Chenchong Zhang, Qixing Zhang, Yongming Zhang, Ying Zhang, Gabriele Curci, and Rajan K. Chakrabarty
Atmos. Chem. Phys., 22, 7647–7666, https://doi.org/10.5194/acp-22-7647-2022, https://doi.org/10.5194/acp-22-7647-2022, 2022
Short summary
Short summary
The fractal black carbon was applied to re-evaluate the regional impacts of morphologies on aerosol–radiation interactions (ARIs), and the effects were compared between the US and China. The regional-mean clear-sky ARI is significantly affected by the BC morphology, and relative differences of 17.1 % and 38.7 % between the fractal model with a Df of 1.8 and the spherical model were observed in eastern China and the northwest US, respectively.
Jie Luo, Zhengqiang Li, Cheng Fan, Hua Xu, Ying Zhang, Weizhen Hou, Lili Qie, Haoran Gu, Mengyao Zhu, Yinna Li, and Kaitao Li
Atmos. Meas. Tech., 15, 2767–2789, https://doi.org/10.5194/amt-15-2767-2022, https://doi.org/10.5194/amt-15-2767-2022, 2022
Short summary
Short summary
A single model is difficult to represent various shapes of dust. We proposed a tunable model to represent dust with various shapes. Two tunable parameters were used to represent the effects of the erosion degree and binding forces from the mass center. Thus, the model can represent various dust shapes by adjusting the tunable parameters. Besides, the applicability of the spheroid model in calculating the optical properties and polarimetric characteristics is evaluated.
Jie Luo, Yongming Zhang, and Qixing Zhang
Geosci. Model Dev., 14, 2113–2126, https://doi.org/10.5194/gmd-14-2113-2021, https://doi.org/10.5194/gmd-14-2113-2021, 2021
Short summary
Short summary
In this work, we developed a numerical method to investigate the effects of black carbon (BC) morphology on the estimation of brown carbon (BrC) absorption using the absorption Ångström exponent (AAE) method. We found that BC morphologies have significant impacts on the estimated BrC absorptions. Moreover, we have demonstrated under what conditions the AAE methods can provide good or bad estimations and explored the reasons for why the good or bad estimations were caused.
Jie Luo, Yongming Zhang, Feng Wang, and Qixing Zhang
Atmos. Chem. Phys., 18, 16897–16914, https://doi.org/10.5194/acp-18-16897-2018, https://doi.org/10.5194/acp-18-16897-2018, 2018
Short summary
Short summary
The absorption enhancement of black carbon with brown coatings is investigated. In addition, the ratio of the absorption of BC coated by brown carbon (BrC) to an external mixture of BrC and BC (Eabs_internal) is also investigated. The lensing effect and sunglasses effect are clearly defined. The applicability of the core–shell sphere model was investigated. The effects of the size distribution, fractal dimension, and wavelength dependency are also explored.
Jie Luo, Miao Hu, Jibing Qiu, Kaitao Li, Hao He, Yuping Sun, and Xiulin Geng
EGUsphere, https://doi.org/10.5194/egusphere-2024-1155, https://doi.org/10.5194/egusphere-2024-1155, 2024
Preprint archived
Short summary
Short summary
In this work, we first calculate the scattering signal returned from partially-coated black carbon based on the SP2 measurement, and then the mixing states were retrieved using Mie theory, and the difference between the retrieved and "true" mixing states can be the uncertainties of the SP2 -Represent measurement. In addition, the effects on the direct radiative forcing are also evaluated.
Jie Luo, Zhengqiang Li, Chenchong Zhang, Qixing Zhang, Yongming Zhang, Ying Zhang, Gabriele Curci, and Rajan K. Chakrabarty
Atmos. Chem. Phys., 22, 7647–7666, https://doi.org/10.5194/acp-22-7647-2022, https://doi.org/10.5194/acp-22-7647-2022, 2022
Short summary
Short summary
The fractal black carbon was applied to re-evaluate the regional impacts of morphologies on aerosol–radiation interactions (ARIs), and the effects were compared between the US and China. The regional-mean clear-sky ARI is significantly affected by the BC morphology, and relative differences of 17.1 % and 38.7 % between the fractal model with a Df of 1.8 and the spherical model were observed in eastern China and the northwest US, respectively.
Jie Luo, Zhengqiang Li, Cheng Fan, Hua Xu, Ying Zhang, Weizhen Hou, Lili Qie, Haoran Gu, Mengyao Zhu, Yinna Li, and Kaitao Li
Atmos. Meas. Tech., 15, 2767–2789, https://doi.org/10.5194/amt-15-2767-2022, https://doi.org/10.5194/amt-15-2767-2022, 2022
Short summary
Short summary
A single model is difficult to represent various shapes of dust. We proposed a tunable model to represent dust with various shapes. Two tunable parameters were used to represent the effects of the erosion degree and binding forces from the mass center. Thus, the model can represent various dust shapes by adjusting the tunable parameters. Besides, the applicability of the spheroid model in calculating the optical properties and polarimetric characteristics is evaluated.
Jie Luo, Yongming Zhang, and Qixing Zhang
Geosci. Model Dev., 14, 2113–2126, https://doi.org/10.5194/gmd-14-2113-2021, https://doi.org/10.5194/gmd-14-2113-2021, 2021
Short summary
Short summary
In this work, we developed a numerical method to investigate the effects of black carbon (BC) morphology on the estimation of brown carbon (BrC) absorption using the absorption Ångström exponent (AAE) method. We found that BC morphologies have significant impacts on the estimated BrC absorptions. Moreover, we have demonstrated under what conditions the AAE methods can provide good or bad estimations and explored the reasons for why the good or bad estimations were caused.
B. Y. Ge, Z. Q. Li, W. Z. Hou, Y. Zhang, and K. T. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W9, 51–56, https://doi.org/10.5194/isprs-archives-XLII-3-W9-51-2019, https://doi.org/10.5194/isprs-archives-XLII-3-W9-51-2019, 2019
W. Z. Hou, H. F. Wang, Z. Q. Li, L. L. Qie, B. Y. Ge, C. Fan, and S. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W9, 63–69, https://doi.org/10.5194/isprs-archives-XLII-3-W9-63-2019, https://doi.org/10.5194/isprs-archives-XLII-3-W9-63-2019, 2019
Jie Luo, Yongming Zhang, Feng Wang, and Qixing Zhang
Atmos. Chem. Phys., 18, 16897–16914, https://doi.org/10.5194/acp-18-16897-2018, https://doi.org/10.5194/acp-18-16897-2018, 2018
Short summary
Short summary
The absorption enhancement of black carbon with brown coatings is investigated. In addition, the ratio of the absorption of BC coated by brown carbon (BrC) to an external mixture of BrC and BC (Eabs_internal) is also investigated. The lensing effect and sunglasses effect are clearly defined. The applicability of the core–shell sphere model was investigated. The effects of the size distribution, fractal dimension, and wavelength dependency are also explored.
W. Z. Hou, Z. Q. Li, F. X. Zheng, and L. L. Qie
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 533–537, https://doi.org/10.5194/isprs-archives-XLII-3-533-2018, https://doi.org/10.5194/isprs-archives-XLII-3-533-2018, 2018
D. Li, X. Ding, and J. Wu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-12-2497-2015, https://doi.org/10.5194/hessd-12-2497-2015, 2015
Revised manuscript has not been submitted
Related subject area
Subject: Aerosols | Research Activity: Remote Sensing | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
An emerging aerosol climatology via remote sensing over Metro Manila, the Philippines
Ozone Monitoring Instrument (OMI) UV aerosol index data analysis over the Arctic region for future data assimilation and climate forcing applications
Monitoring multiple satellite aerosol optical depth (AOD) products within the Copernicus Atmosphere Monitoring Service (CAMS) data assimilation system
Comparisons between the distributions of dust and combustion aerosols in MERRA-2, FLEXPART, and CALIPSO and implications for deposition freezing over wintertime Siberia
Atmospheric oxidation mechanism and kinetics of indole initiated by ●OH and ●Cl: a computational study
Identifying the spatiotemporal variations in ozone formation regimes across China from 2005 to 2019 based on polynomial simulation and causality analysis
Aerosol vertical distribution and interactions with land/sea breezes over the eastern coast of the Red Sea from lidar data and high-resolution WRF-Chem simulations
Improved inversion of aerosol components in the atmospheric column from remote sensing data
Retrieval of aerosol components directly from satellite and ground-based measurements
Towards a satellite formaldehyde – in situ hybrid estimate for organic aerosol abundance
Retrieval of desert dust and carbonaceous aerosol emissions over Africa from POLDER/PARASOL products generated by the GRASP algorithm
Estimating the open biomass burning emissions in central and eastern China from 2003 to 2015 based on satellite observation
Intra-annual variations of regional aerosol optical depth, vertical distribution, and particle types from multiple satellite and ground-based observational datasets
Chemical composition of ambient PM2. 5 over China and relationship to precursor emissions during 2005–2012
Synergistic use of Lagrangian dispersion and radiative transfer modelling with satellite and surface remote sensing measurements for the investigation of volcanic plumes: the Mount Etna eruption of 25–27 October 2013
Climatology of the aerosol optical depth by components from the Multi-angle Imaging SpectroRadiometer (MISR) and chemistry transport models
A global aerosol classification algorithm incorporating multiple satellite data sets of aerosol and trace gas abundances
Simulation of GOES-R ABI aerosol radiances using WRF-CMAQ: a case study approach
Absorption properties of Mediterranean aerosols obtained from multi-year ground-based remote sensing observations
The global 3-D distribution of tropospheric aerosols as characterized by CALIOP
A unified approach to infrared aerosol remote sensing and type specification
Interpretation of FRESCO cloud retrievals in case of absorbing aerosol events
Global and regional trends of aerosol optical depth over land and ocean using SeaWiFS measurements from 1997 to 2010
Potential for a biogenic influence on cloud microphysics over the ocean: a correlation study with satellite-derived data
Mixing of dust and NH3 observed globally over anthropogenic dust sources
The composition and variability of atmospheric aerosol over Southeast Asia during 2008
NASA A-Train and Terra observations of the 2010 Russian wildfires
The Eyjafjallajökull eruption in April 2010 – detection of volcanic plume using in-situ measurements, ozone sondes and lidar-ceilometer profiles
Saharan dust infrared optical depth and altitude retrieved from AIRS: a focus over North Atlantic – comparison to MODIS and CALIPSO
Absorption Angstrom Exponent in AERONET and related data as an indicator of aerosol composition
Genevieve Rose Lorenzo, Avelino F. Arellano, Maria Obiminda Cambaliza, Christopher Castro, Melliza Templonuevo Cruz, Larry Di Girolamo, Glenn Franco Gacal, Miguel Ricardo A. Hilario, Nofel Lagrosas, Hans Jarett Ong, James Bernard Simpas, Sherdon Niño Uy, and Armin Sorooshian
Atmos. Chem. Phys., 23, 10579–10608, https://doi.org/10.5194/acp-23-10579-2023, https://doi.org/10.5194/acp-23-10579-2023, 2023
Short summary
Short summary
Aerosol and weather interactions in Southeast Asia are complex and understudied. An emerging aerosol climatology was established in Metro Manila, the Philippines, from aerosol particle physicochemical properties and meteorology, revealing five sources. Even with local traffic, transported smoke from biomass burning, aged dust, and cloud processing, background marine particles dominate and correspond to lower aerosol optical depth in Metro Manila compared to other Southeast Asian megacities.
Blake T. Sorenson, Jianglong Zhang, Jeffrey S. Reid, Peng Xian, and Shawn L. Jaker
Atmos. Chem. Phys., 23, 7161–7175, https://doi.org/10.5194/acp-23-7161-2023, https://doi.org/10.5194/acp-23-7161-2023, 2023
Short summary
Short summary
We quality-control Ozone Monitoring Instrument (OMI) aerosol index data by identifying row anomalies and removing systematic biases, using the data to quantify trends in UV-absorbing aerosols over the Arctic region. We found decreasing trends in UV-absorbing aerosols in spring months and increasing trends in summer months. For the first time, observational evidence of increasing trends in UV-absorbing aerosols over the North Pole is found using the OMI data, especially over the last half decade.
Sebastien Garrigues, Samuel Remy, Julien Chimot, Melanie Ades, Antje Inness, Johannes Flemming, Zak Kipling, Istvan Laszlo, Angela Benedetti, Roberto Ribas, Soheila Jafariserajehlou, Bertrand Fougnie, Shobha Kondragunta, Richard Engelen, Vincent-Henri Peuch, Mark Parrington, Nicolas Bousserez, Margarita Vazquez Navarro, and Anna Agusti-Panareda
Atmos. Chem. Phys., 22, 14657–14692, https://doi.org/10.5194/acp-22-14657-2022, https://doi.org/10.5194/acp-22-14657-2022, 2022
Short summary
Short summary
The Copernicus Atmosphere Monitoring Service (CAMS) provides global monitoring of aerosols using the ECMWF forecast model constrained by the assimilation of satellite aerosol optical depth (AOD). This work aims at evaluating two new satellite AODs to enhance the CAMS aerosol global forecast. It highlights the spatial and temporal differences between the satellite AOD products at the model spatial resolution, which is essential information to design multi-satellite AOD data assimilation schemes.
Lauren M. Zamora, Ralph A. Kahn, Nikolaos Evangeliou, Christine D. Groot Zwaaftink, and Klaus B. Huebert
Atmos. Chem. Phys., 22, 12269–12285, https://doi.org/10.5194/acp-22-12269-2022, https://doi.org/10.5194/acp-22-12269-2022, 2022
Short summary
Short summary
Arctic dust, smoke, and pollution particles can affect clouds and Arctic warming. The distributions of these particles were estimated in three different satellite, reanalysis, and model products. These products showed good agreement overall but indicate that it is important to include local dust in models. We hypothesize that mineral dust effects on ice processes in the Arctic atmosphere might be highest over Siberia, where it is cold, moist, and subject to relatively high dust levels.
Jingwen Xue, Fangfang Ma, Jonas Elm, Jingwen Chen, and Hong-Bin Xie
Atmos. Chem. Phys., 22, 11543–11555, https://doi.org/10.5194/acp-22-11543-2022, https://doi.org/10.5194/acp-22-11543-2022, 2022
Short summary
Short summary
·OH/·Cl initiated indole reactions mainly form organonitrates, alkoxy radicals and hydroperoxide products, showing a varying mechanism from previously reported amines reactions. This study reveals carcinogenic nitrosamines cannot be formed in indole oxidation reactions despite radicals formed from -NH- H abstraction. The results are important to understand the atmospheric impact of indole oxidation and extend current understanding on the atmospheric chemistry of organic nitrogen compounds.
Ruiyuan Li, Miaoqing Xu, Manchun Li, Ziyue Chen, Na Zhao, Bingbo Gao, and Qi Yao
Atmos. Chem. Phys., 21, 15631–15646, https://doi.org/10.5194/acp-21-15631-2021, https://doi.org/10.5194/acp-21-15631-2021, 2021
Short summary
Short summary
We employed ground observations of ozone and satellite products of HCHO and NO2 to investigate spatiotemporal variations of ozone formation regimes across China. Two different models were employed for determining the crucial thresholds that separate three ozone formation regimes, including NOx-limited, VOC-limited, and transitional regimes. The close output from two different models provides a reliable reference for better understanding ozone formation regimes.
Sagar P. Parajuli, Georgiy L. Stenchikov, Alexander Ukhov, Illia Shevchenko, Oleg Dubovik, and Anton Lopatin
Atmos. Chem. Phys., 20, 16089–16116, https://doi.org/10.5194/acp-20-16089-2020, https://doi.org/10.5194/acp-20-16089-2020, 2020
Short summary
Short summary
Both natural (dust, sea salt) and anthropogenic (sulfate, organic and black carbon) aerosols are common over the Red Sea coastal plains. King Abdullah University of Science and Technology (KAUST), located on the eastern coast of the Red Sea, hosts the only operating lidar system in the Arabian Peninsula, which measures atmospheric aerosols day and night. We use these lidar data and high-resolution WRF-Chem model simulations to study the potential effect of dust aerosols on Red Sea environment.
Ying Zhang, Zhengqiang Li, Yu Chen, Gerrit de Leeuw, Chi Zhang, Yisong Xie, and Kaitao Li
Atmos. Chem. Phys., 20, 12795–12811, https://doi.org/10.5194/acp-20-12795-2020, https://doi.org/10.5194/acp-20-12795-2020, 2020
Short summary
Short summary
Observation of atmospheric aerosol components plays an important role in reducing uncertainty in climate assessment. In this study, an improved remote sensing method which can better distinguish scattering components is developed, and the aerosol components in the atmospheric column over China are retrieved based on the Sun–sky radiometer Observation NETwork (SONET). The component distribution shows there could be a sea salt component in northwest China from a paleomarine source in desert land.
Lei Li, Oleg Dubovik, Yevgeny Derimian, Gregory L. Schuster, Tatyana Lapyonok, Pavel Litvinov, Fabrice Ducos, David Fuertes, Cheng Chen, Zhengqiang Li, Anton Lopatin, Benjamin Torres, and Huizheng Che
Atmos. Chem. Phys., 19, 13409–13443, https://doi.org/10.5194/acp-19-13409-2019, https://doi.org/10.5194/acp-19-13409-2019, 2019
Short summary
Short summary
A novel methodology to monitor atmospheric aerosol components using remote sensing is presented. The concept is realized within the GRASP (Generalized Retrieval of Aerosol and Surface Properties) project. Application to POLDER/PARASOL and AERONET observations yielded the spatial and temporal variability of absorbing and non-absorbing insoluble and soluble aerosol species in the fine and coarse size fractions. This presents the global-scale aerosol component derived from satellite measurements.
Jin Liao, Thomas F. Hanisco, Glenn M. Wolfe, Jason St. Clair, Jose L. Jimenez, Pedro Campuzano-Jost, Benjamin A. Nault, Alan Fried, Eloise A. Marais, Gonzalo Gonzalez Abad, Kelly Chance, Hiren T. Jethva, Thomas B. Ryerson, Carsten Warneke, and Armin Wisthaler
Atmos. Chem. Phys., 19, 2765–2785, https://doi.org/10.5194/acp-19-2765-2019, https://doi.org/10.5194/acp-19-2765-2019, 2019
Short summary
Short summary
Organic aerosol (OA) intimately links natural and anthropogenic emissions with air quality and climate. Direct OA measurements from space are currently not possible. This paper describes a new method to estimate OA by combining satellite HCHO and in situ OA and HCHO. The OA estimate is validated with the ground network. This new method has a potential for mapping observation-based global OA estimate.
Cheng Chen, Oleg Dubovik, Daven K. Henze, Tatyana Lapyonak, Mian Chin, Fabrice Ducos, Pavel Litvinov, Xin Huang, and Lei Li
Atmos. Chem. Phys., 18, 12551–12580, https://doi.org/10.5194/acp-18-12551-2018, https://doi.org/10.5194/acp-18-12551-2018, 2018
Short summary
Short summary
This paper introduces a method to use satellite-observed spectral AOD and AAOD to derive three types of aerosol emission sources simultaneously based on inverse modelling at a high spatial and temporal resolution. This study shows it is possible to estimate aerosol emissions and improve the atmospheric aerosol simulation using detailed aerosol optical and microphysical information from satellite observations.
Jian Wu, Shaofei Kong, Fangqi Wu, Yi Cheng, Shurui Zheng, Qin Yan, Huang Zheng, Guowei Yang, Mingming Zheng, Dantong Liu, Delong Zhao, and Shihua Qi
Atmos. Chem. Phys., 18, 11623–11646, https://doi.org/10.5194/acp-18-11623-2018, https://doi.org/10.5194/acp-18-11623-2018, 2018
Short summary
Short summary
In order to support regional modeling impact on air quality and policy making on controlling open biomass burning emissions, accurate open biomass burning emissions were estimated from 2003 to 2015 with high spatial and temporal resolution. Multiple satellite data, updated biomass data and survey results were all used to improve the accuracy. In addition, management policies and all influencing factors in rural areas for open biomass burning emissions were considered.
Bin Zhao, Jonathan H. Jiang, David J. Diner, Hui Su, Yu Gu, Kuo-Nan Liou, Zhe Jiang, Lei Huang, Yoshi Takano, Xuehua Fan, and Ali H. Omar
Atmos. Chem. Phys., 18, 11247–11260, https://doi.org/10.5194/acp-18-11247-2018, https://doi.org/10.5194/acp-18-11247-2018, 2018
Short summary
Short summary
We combine satellite-borne and ground-based observations to investigate the intra-annual variations of regional aerosol column loading, vertical distribution, and particle types. Column aerosol optical depth (AOD), as well as AOD > 800 m, peaks in summer/spring. However, AOD < 800 m and surface PM2.5 concentrations mostly peak in winter. The aerosol intra-annual variations differ significantly according to aerosol types characterized by different sizes, light absorption, and emission sources.
Guannan Geng, Qiang Zhang, Dan Tong, Meng Li, Yixuan Zheng, Siwen Wang, and Kebin He
Atmos. Chem. Phys., 17, 9187–9203, https://doi.org/10.5194/acp-17-9187-2017, https://doi.org/10.5194/acp-17-9187-2017, 2017
Short summary
Short summary
We presented the characteristics of PM2.5 chemical composition over China during 2005–2012 by synthesis of in situ measurement data and satellite-based estimates. We also investigated the driving forces behind the changes by examining the changes in precursor emissions. We found that the decrease in sulfate is partly offset by the increase in nitrate. The results indicate that the synchronized abatement of emissions for multipollutants is necessary for reducing ambient PM2.5 over China.
Pasquale Sellitto, Alcide di Sarra, Stefano Corradini, Marie Boichu, Hervé Herbin, Philippe Dubuisson, Geneviève Sèze, Daniela Meloni, Francesco Monteleone, Luca Merucci, Justin Rusalem, Giuseppe Salerno, Pierre Briole, and Bernard Legras
Atmos. Chem. Phys., 16, 6841–6861, https://doi.org/10.5194/acp-16-6841-2016, https://doi.org/10.5194/acp-16-6841-2016, 2016
Short summary
Short summary
We combine plume dispersion and radiative transfer modelling, and satellite and surface remote sensing observations to study the regional influence of a relatively weak volcanic eruption from Mount Etna (25–27 October 2013) on the optical/micro-physical properties of Mediterranean aerosols. Our results indicate that even relatively weak volcanic eruptions may produce an observable effect on the aerosol properties at the regional scale, with a significant impact on the regional radiative balance.
Huikyo Lee, Olga V. Kalashnikova, Kentaroh Suzuki, Amy Braverman, Michael J. Garay, and Ralph A. Kahn
Atmos. Chem. Phys., 16, 6627–6640, https://doi.org/10.5194/acp-16-6627-2016, https://doi.org/10.5194/acp-16-6627-2016, 2016
Short summary
Short summary
The Multi-angle Imaging SpectroRadiometer (MISR) on NASA's TERRA satellite has provided a global distribution of aerosol amount and type information for each month over 16+ years since March 2000. This study analyzes, for the first time, characteristics of observed and simulated distributions of aerosols for three broad classes of aerosols: spherical nonabsorbing, spherical absorbing, and nonspherical – near or downwind of their major source regions.
M. J. M. Penning de Vries, S. Beirle, C. Hörmann, J. W. Kaiser, P. Stammes, L. G. Tilstra, O. N. E. Tuinder, and T. Wagner
Atmos. Chem. Phys., 15, 10597–10618, https://doi.org/10.5194/acp-15-10597-2015, https://doi.org/10.5194/acp-15-10597-2015, 2015
S. A. Christopher
Atmos. Chem. Phys., 14, 3183–3194, https://doi.org/10.5194/acp-14-3183-2014, https://doi.org/10.5194/acp-14-3183-2014, 2014
M. Mallet, O. Dubovik, P. Nabat, F. Dulac, R. Kahn, J. Sciare, D. Paronis, and J. F. Léon
Atmos. Chem. Phys., 13, 9195–9210, https://doi.org/10.5194/acp-13-9195-2013, https://doi.org/10.5194/acp-13-9195-2013, 2013
D. M. Winker, J. L. Tackett, B. J. Getzewich, Z. Liu, M. A. Vaughan, and R. R. Rogers
Atmos. Chem. Phys., 13, 3345–3361, https://doi.org/10.5194/acp-13-3345-2013, https://doi.org/10.5194/acp-13-3345-2013, 2013
L. Clarisse, P.-F. Coheur, F. Prata, J. Hadji-Lazaro, D. Hurtmans, and C. Clerbaux
Atmos. Chem. Phys., 13, 2195–2221, https://doi.org/10.5194/acp-13-2195-2013, https://doi.org/10.5194/acp-13-2195-2013, 2013
P. Wang, O. N. E. Tuinder, L. G. Tilstra, M. de Graaf, and P. Stammes
Atmos. Chem. Phys., 12, 9057–9077, https://doi.org/10.5194/acp-12-9057-2012, https://doi.org/10.5194/acp-12-9057-2012, 2012
N. C. Hsu, R. Gautam, A. M. Sayer, C. Bettenhausen, C. Li, M. J. Jeong, S.-C. Tsay, and B. N. Holben
Atmos. Chem. Phys., 12, 8037–8053, https://doi.org/10.5194/acp-12-8037-2012, https://doi.org/10.5194/acp-12-8037-2012, 2012
A. Lana, R. Simó, S. M. Vallina, and J. Dachs
Atmos. Chem. Phys., 12, 7977–7993, https://doi.org/10.5194/acp-12-7977-2012, https://doi.org/10.5194/acp-12-7977-2012, 2012
P. Ginoux, L. Clarisse, C. Clerbaux, P.-F. Coheur, O. Dubovik, N. C. Hsu, and M. Van Damme
Atmos. Chem. Phys., 12, 7351–7363, https://doi.org/10.5194/acp-12-7351-2012, https://doi.org/10.5194/acp-12-7351-2012, 2012
W. Trivitayanurak, P. I. Palmer, M. P. Barkley, N. H. Robinson, H. Coe, and D. E. Oram
Atmos. Chem. Phys., 12, 1083–1100, https://doi.org/10.5194/acp-12-1083-2012, https://doi.org/10.5194/acp-12-1083-2012, 2012
J. C. Witte, A. R. Douglass, A. da Silva, O. Torres, R. Levy, and B. N. Duncan
Atmos. Chem. Phys., 11, 9287–9301, https://doi.org/10.5194/acp-11-9287-2011, https://doi.org/10.5194/acp-11-9287-2011, 2011
H. Flentje, H. Claude, T. Elste, S. Gilge, U. Köhler, C. Plass-Dülmer, W. Steinbrecht, W. Thomas, A. Werner, and W. Fricke
Atmos. Chem. Phys., 10, 10085–10092, https://doi.org/10.5194/acp-10-10085-2010, https://doi.org/10.5194/acp-10-10085-2010, 2010
S. Peyridieu, A. Chédin, D. Tanré, V. Capelle, C. Pierangelo, N. Lamquin, and R. Armante
Atmos. Chem. Phys., 10, 1953–1967, https://doi.org/10.5194/acp-10-1953-2010, https://doi.org/10.5194/acp-10-1953-2010, 2010
P. B. Russell, R. W. Bergstrom, Y. Shinozuka, A. D. Clarke, P. F. DeCarlo, J. L. Jimenez, J. M. Livingston, J. Redemann, O. Dubovik, and A. Strawa
Atmos. Chem. Phys., 10, 1155–1169, https://doi.org/10.5194/acp-10-1155-2010, https://doi.org/10.5194/acp-10-1155-2010, 2010
Cited articles
Adachi, K., Chung, S. H., Friedrich, H., and Buseck, P. R.: Fractal parameters of individual soot particles determined using electron tomography: Implications for optical properties, J. Geophys. Res.-Atmos., 112, D14202, https://doi.org/10.1029/2006JD008296, 2007. a
Adachi, K., Chung, S. H., and Buseck, P. R.: Shapes of soot aerosol particles and implications for their effects on climate, J. Geophys. Res.-Atmos., 115, D15206, https://doi.org/10.1029/2009JD012868, 2010. a, b
Andreae, M. O. and Gelencsér, A.: Black carbon or brown carbon? The nature of light-absorbing carbonaceous aerosols, Atmos. Chem. Phys., 6, 3131–3148, https://doi.org/10.5194/acp-6-3131-2006, 2006. a
Arola, A., Schuster, G., Myhre, G., Kazadzis, S., Dey, S., and Tripathi, S. N.: Inferring absorbing organic carbon content from AERONET data, Atmos. Chem. Phys., 11, 215–225, https://doi.org/10.5194/acp-11-215-2011, 2011. a
Bahadur, R., Praveen, P. S., Xu, Y., and Ramanathan, V.: Solar absorption by elemental and brown carbon determined from spectral observations, P. Natl. Acad. Sci. USA, 109, 17366–17371, https://doi.org/10.1073/pnas.1205910109, 2012. a
Bao, F., Cheng, T., Li, Y., Gu, X., Guo, H., Wu, Y., Wang, Y., and Gao, J.: Retrieval of black carbon aerosol surface concentration using satellite remote sensing observations, Remote Sens. Environ., 226, 93–108, 2019. a
Baumgardner, D., Kok, G., and Raga, G.: Warming of the Arctic lower stratosphere by light absorbing particles, Geophys. Res. Lett., 31, L06117, https://doi.org/10.1029/2003GL018883, 2004. a
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore, A. M., Li, Q., Liu, H. Y., Mickley, L. J., and Schultz, M. G.: Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation, J. Geophys. Res.-Atmos., 106, 23073–23095, https://doi.org/10.1029/2001JD000807, 2001. a
Bhandari, J., China, S., Chandrakar, K. K., Kinney, G., Cantrell, W., Shaw, R. A., Mazzoleni, L. R., Girotto, G., Sharma, N., Gorkowski, K., et al.: Extensive soot compaction by cloud processing from laboratory and field observations, Sci. Rep., 9, 11824, https://doi.org/10.1038/s41598-019-48143-y, 2019. a
Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K., Hopke, P. K., Jacobson, M. Z., Kaiser, J. W., Klimont, Z., Lohmann, U., Schwarz, J. P., Shindell, D., Storelvmo, T., Warren, S. G., and Zender, C. S.: Bounding the role of black carbon in the climate system: A scientific assessment, J. Geophys. Res.-Atmos., 118, 5380–5552, https://doi.org/10.1002/jgrd.50171, 2013. a, b, c, d, e, f
Cappa, C. D., Kolesar, K. R., Zhang, X., Atkinson, D. B., Pekour, M. S., Zaveri, R. A., Zelenyuk, A., and Zhang, Q.: Understanding the optical properties of ambient sub- and supermicron particulate matter: results from the CARES 2010 field study in northern California, Atmos. Chem. Phys., 16, 6511–6535, https://doi.org/10.5194/acp-16-6511-2016, 2016. a
Cazorla, A., Bahadur, R., Suski, K. J., Cahill, J. F., Chand, D., Schmid, B., Ramanathan, V., and Prather, K. A.: Relating aerosol absorption due to soot, organic carbon, and dust to emission sources determined from in-situ chemical measurements, Atmos. Chem. Phys., 13, 9337–9350, https://doi.org/10.5194/acp-13-9337-2013, 2013. a
Chakrabarty, R. K., Moosmüller, H., Garro, M. A., Arnott, W. P., Walker, J., Susott, R. A., Babbitt, R. E., Wold, C. E., Lincoln, E. N., and Hao, W. M.: Emissions from the laboratory combustion of wildland fuels: Particle morphology and size, J. Geophys. Res.-Atmos., 111, D07204, https://doi.org/10.1029/2005JD006659, 2006. a
Chakrabarty, R. K., Moosmüller, H., Chen, L.-W. A., Lewis, K., Arnott, W. P., Mazzoleni, C., Dubey, M. K., Wold, C. E., Hao, W. M., and Kreidenweis, S. M.: Brown carbon in tar balls from smoldering biomass combustion, Atmos. Chem. Phys., 10, 6363–6370, https://doi.org/10.5194/acp-10-6363-2010, 2010. a, b
Chang, H.-C. and Charalampopoulos, T.: Determination of the wavelength dependence of refractive indices of flame soot, Proc. R. Soc. Lon. Ser-A, 430, 577–591, 1990. a
Chen, C., Fan, X., Shaltout, T., Qiu, C., Ma, Y., Goldman, A., and Khalizov, A. F.: An unexpected restructuring of combustion soot aggregates by subnanometer coatings of polycyclic aromatic hydrocarbons, Geophys. Res. Lett., 43, 11,080–11,088, https://doi.org/10.1002/2016GL070877, 2016. a
Chen, C., Dubovik, O., Schuster, G. L., Chin, M., Henze, D. K., Lapyonok, T., Li, Z., Derimian, Y., and Zhang, Y.: Multi-angular polarimetric remote sensing to pinpoint global aerosol absorption and direct radiative forcing, Nat. Commun., 13, 7459, 2022. a
Chen, Y. and Bond, T. C.: Light absorption by organic carbon from wood combustion, Atmos. Chem. Phys., 10, 1773–1787, https://doi.org/10.5194/acp-10-1773-2010, 2010. a, b, c
China, S., Mazzoleni, C., Gorkowski, K., Aiken, A. C., and Dubey, M. K.: Morphology and mixing state of individual freshly emitted wildfire carbonaceous particles, Nat. Commun., 4, 1–7, 2013. a
China, S., Salvadori, N., and Mazzoleni, C.: Effect of traffic and driving characteristics on morphology of atmospheric soot particles at freeway on-ramps, Environ. Sci. Technol., 48, 3128–3135, 2014. a
China, S., Scarnato, B., Owen, R. C., Zhang, B., Ampadu, M. T., Kumar, S., Dzepina, K., Dziobak, M. P., Fialho, P., Perlinger, J. A., Hueber, J., Helmig, D., Mazzoleni, L. R., and Mazzoleni, C.: Morphology and mixing state of aged soot particles at a remote marine free troposphere site: Implications for optical properties, Geophys. Res. Lett., 42, 1243–1250, https://doi.org/10.1002/2014GL062404, 2015. a
Chung, C. E., Ramanathan, V., and Decremer, D.: Observationally constrained estimates of carbonaceous aerosol radiative forcing, P. Natl. Acad. Sci. USA, 109, 11624–11629, https://doi.org/10.1073/pnas.1203707109, 2012. a
Cross, E. S., Onasch, T. B., Ahern, A., Wrobel, W., Slowik, J. G., Olfert, J., Lack, D. A., Massoli, P., Cappa, C. D., Schwarz, J. P., Spackman, J. R., Fahey, D. W., Sedlacek, A., Trimborn, A., Jayne, J. T., Freedman, A., Williams, L. R., Ng, N. L., Mazzoleni, C., Dubey, M., Brem, B., Kok, G., Subramanian, R., Freitag, S., Clarke, A., Thornhill, D., Marr, L. C., Kolb, C. E., Worsnop, D. R., and Davidovits, P.: Soot Particle Studies—Instrument Inter-Comparison—Project Overview, Aerosol Sci. Technol., 44, 592–611, https://doi.org/10.1080/02786826.2010.482113, 2010. a
Dasari, S., Andersson, A., Bikkina, S., Holmstrand, H., Budhavant, K., Satheesh, S., Asmi, E., Kesti, J., Backman, J., Salam, A., Bisht, D. S., Tiwari, S., Hameed, Z., and Örjan Gustafsson: Photochemical degradation affects the light absorption of water-soluble brown carbon in the South Asian outflow, Sci. Adv., 5, eaau8066, https://doi.org/10.1126/sciadv.aau8066, 2019. a
Dhaubhadel, R., Pierce, F., Chakrabarti, A., and Sorensen, C.: Hybrid superaggregate morphology as a result of aggregation in a cluster-dense aerosol, Phys. Rev. E, 73, 011404, https://doi.org/10.1103/PhysRevE.73.011404, 2006. a
Dubovik, O., Herman, M., Holdak, A., Lapyonok, T., Tanré, D., Deuzé, J. L., Ducos, F., Sinyuk, A., and Lopatin, A.: Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations, Atmos. Meas. Tech., 4, 975–1018, https://doi.org/10.5194/amt-4-975-2011, 2011. a
Dubovik, O., Lapyonok, T., Litvinov, P., Herman, M., Fuertes, D., Ducos, F., Torres, B., Derimian, Y., Huang, X., Lopatin, A., Chaikovsky, A., Aspetsberger, M., and Federspiel, C.: GRASP: a versatile algorithm for characterizing the atmosphere, SPIE Newsroom, 25, 1–4, https://doi.org/10.1117/2.1201408.005558, 2014. a
Eastham, S. D., Long, M. S., Keller, C. A., Lundgren, E., Yantosca, R. M., Zhuang, J., Li, C., Lee, C. J., Yannetti, M., Auer, B. M., Clune, T. L., Kouatchou, J., Putman, W. M., Thompson, M. A., Trayanov, A. L., Molod, A. M., Martin, R. V., and Jacob, D. J.: GEOS-Chem High Performance (GCHP v11-02c): a next-generation implementation of the GEOS-Chem chemical transport model for massively parallel applications, Geosci. Model Dev., 11, 2941–2953, https://doi.org/10.5194/gmd-11-2941-2018, 2018. a
Feng, Y., Ramanathan, V., and Kotamarthi, V. R.: Brown carbon: a significant atmospheric absorber of solar radiation?, Atmos. Chem. Phys., 13, 8607–8621, https://doi.org/10.5194/acp-13-8607-2013, 2013. a
Filippov, A., Zurita, M., and Rosner, D.: Fractal-like Aggregates: Relation between Morphology and Physical Properties, J. Colloid Interf. Sci., 229, 261–273, https://doi.org/10.1006/jcis.2000.7027, 2000. a
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Giles, D. M., Sinyuk, A., Sorokin, M. G., Schafer, J. S., Smirnov, A., Slutsker, I., Eck, T. F., Holben, B. N., Lewis, J. R., Campbell, J. R., Welton, E. J., Korkin, S. V., and Lyapustin, A. I.: Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements, Atmos. Meas. Tech., 12, 169–209, https://doi.org/10.5194/amt-12-169-2019, 2019. a
Gong, X., Zhang, C., Chen, H., Nizkorodov, S. A., Chen, J., and Yang, X.: Size distribution and mixing state of black carbon particles during a heavy air pollution episode in Shanghai, Atmos. Chem. Phys., 16, 5399–5411, https://doi.org/10.5194/acp-16-5399-2016, 2016. a
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012. a
Heald, C. L., Ridley, D. A., Kroll, J. H., Barrett, S. R. H., Cady-Pereira, K. E., Alvarado, M. J., and Holmes, C. D.: Contrasting the direct radiative effect and direct radiative forcing of aerosols, Atmos. Chem. Phys., 14, 5513–5527, https://doi.org/10.5194/acp-14-5513-2014, 2014. a
Hecobian, A., Zhang, X., Zheng, M., Frank, N., Edgerton, E. S., and Weber, R. J.: Water-Soluble Organic Aerosol material and the light-absorption characteristics of aqueous extracts measured over the Southeastern United States, Atmos. Chem. Phys., 10, 5965–5977, https://doi.org/10.5194/acp-10-5965-2010, 2010. a
Heinson, W. R., Sorensen, C. M., and Chakrabarti, A.: Does Shape Anisotropy Control the Fractal Dimension in Diffusion-Limited Cluster-Cluster Aggregation?, Aerosol Sci. Technol., 44, i–iv, https://doi.org/10.1080/02786826.2010.516032, 2010. a
Heinson, W. R., Heinson, Y. W., Liu, P., and Chakrabarty, R. K.: Breakdown of fractal dimension invariance in high monomer-volume-fraction aerosol gels, Aerosol Sci. Technol., 52, 953–956, https://doi.org/10.1080/02786826.2018.1492086, 2018. a
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018. a
Holben, B., Eck, T., Slutsker, I., Tanré, D., Buis, J., Setzer, A., Vermote, E., Reagan, J., Kaufman, Y., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A.: AERONET – A Federated Instrument Network and Data Archive for Aerosol Characterization, Remote Sens. Environ., 66, 1–16, https://doi.org/10.1016/S0034-4257(98)00031-5, 1998. a
Kahnert, M. and Devasthale, A.: Black carbon fractal morphology and short-wave radiative impact: a modelling study, Atmos. Chem. Phys., 11, 11745–11759, https://doi.org/10.5194/acp-11-11745-2011, 2011. a, b
Kinne, S.: Aerosol radiative effects with MACv2, Atmos. Chem. Phys., 19, 10919–10959, https://doi.org/10.5194/acp-19-10919-2019, 2019. a, b
Kirchstetter, T. W., Novakov, T., and Hobbs, P. V.: Evidence that the spectral dependence of light absorption by aerosols is affected by organic carbon, J. Geophys. Res.-Atmos., 109, D21208, https://doi.org/10.1029/2004JD004999, 2004. a, b, c
Kirillova, E. N., Marinoni, A., Bonasoni, P., Vuillermoz, E., Facchini, M. C., Fuzzi, S., and Decesari, S.: Light absorption properties of brown carbon in the high Himalayas, J. Geophys. Res.-Atmos., 121, 9621–9639, https://doi.org/10.1002/2016JD025030, 2016. a
Kondo, Y., Sahu, L., Moteki, N., Khan, F., Takegawa, N., Liu, X., Koike, M., and Miyakawa, T.: Consistency and Traceability of Black Carbon Measurements Made by Laser-Induced Incandescence, Thermal-Optical Transmittance, and Filter-Based Photo-Absorption Techniques, Aerosol Sci. Technol., 45, 295–312, https://doi.org/10.1080/02786826.2010.533215, 2011. a
Lack, D. A. and Cappa, C. D.: Impact of brown and clear carbon on light absorption enhancement, single scatter albedo and absorption wavelength dependence of black carbon, Atmos. Chem. Phys., 10, 4207–4220, https://doi.org/10.5194/acp-10-4207-2010, 2010. a, b
Lack, D. A., Moosmüller, H., McMeeking, G. R., Chakrabarty, R. K., and Baumgardner, D.: Characterizing elemental, equivalent black, and refractory black carbon aerosol particles: a review of techniques, their limitations and uncertainties, Anal. Bioanal. Chem., 406, 99–122, 2014. a
Laskin, A., Laskin, J., and Nizkorodov, S. A.: Chemistry of atmospheric brown carbon, Chem. Rev., 115, 4335–4382, 2015. a
Li, J., Liu, C., Yin, Y., and Kumar, K. R.: Numerical investigation on the Ångström exponent of black carbon aerosol, J. Geophys. Res.-Atmos., 121, 3506–3518, https://doi.org/10.1002/2015JD024718, 2016. a
Liu, C., Li, J., Yin, Y., Zhu, B., and Feng, Q.: Optical properties of black carbon aggregates with non-absorptive coating, J. Quant. Spectrosc. Ra., 187, 443–452, https://doi.org/10.1016/j.jqsrt.2016.10.023, 2017. a, b, c
Liu, C., Chung, C. E., Yin, Y., and Schnaiter, M.: The absorption Ångström exponent of black carbon: from numerical aspects, Atmos. Chem. Phys., 18, 6259–6273, https://doi.org/10.5194/acp-18-6259-2018, 2018. a, b, c, d
Liu, L. and Mishchenko, M. I.: Effects of aggregation on scattering and radiative properties of soot aerosols, J. Geophys. Res.-Atmos., 110, https://doi.org/10.1029/2004JD005649, 2005. a, b
Luo, J., Zhang, Y., Wang, F., and Zhang, Q.: Effects of brown coatings on the absorption enhancement of black carbon: a numerical investigation, Atmos. Chem. Phys., 18, 16897–16914, https://doi.org/10.5194/acp-18-16897-2018, 2018. a, b, c, d
Luo, J., Zhang, Q., Luo, J., Liu, J., Huo, Y., and Zhang, Y.: Optical Modeling of Black Carbon With Different Coating Materials: The Effect of Coating Configurations, J. Geophys. Res.-Atmos., 124, 13230–13253, https://doi.org/10.1029/2019JD031701, 2019. a, b
Luo, J., Zhang, Y., and Zhang, Q.: The Ångström Exponent and Single-Scattering Albedo of Black Carbon: Effects of Different Coating Materials, Atmosphere, 11, 1103, https://doi.org/10.3390/atmos11101103, 2020. a
Luo, J., Zhang, Q., Zhang, C., Zhang, Y., and Chakrabarty, R. K.: The fractal characteristics of atmospheric coated soot: Implication for morphological analysis, J. Aerosol Sci., 157, 105804, https://doi.org/10.1016/j.jaerosci.2021.105804, 2021a. a, b
Luo, J., Zhang, Q., Zhang, Y., and Li, Z.: Radiative Properties of Non-spherical Black Carbon Aerosols, 69–124, Springer International Publishing, Cham, ISBN 978-3-030-87683-8, https://doi.org/10.1007/978-3-030-87683-8_3, 2021b. a
Luo, J., Zhang, Y., and Zhang, Q.: Effects of black carbon morphology on brown carbon absorption estimation: from numerical aspects, Geosci. Model Dev., 14, 2113–2126, https://doi.org/10.5194/gmd-14-2113-2021, 2021c. a, b, c, d
Luo, J., Li, Z., Zhang, C., Zhang, Q., Zhang, Y., Zhang, Y., Curci, G., and Chakrabarty, R. K.: Regional impacts of black carbon morphologies on shortwave aerosol–radiation interactions: a comparative study between the US and China, Atmos. Chem. Phys., 22, 7647–7666, https://doi.org/10.5194/acp-22-7647-2022, 2022. a
Luo, J., Li, Z., Qiu, J., Zhang, Y., Fan, C., Li, L., Wu, H., Zhou, P., Li, K., and Zhang, Q.: The Simulated Source Apportionment of Light Absorbing Aerosols: Effects of Microphysical Properties of Partially-Coated Black Carbon, J. Geophys. Res.-Atmos., 128, e2022JD037291, https://doi.org/10.1029/2022JD037291, 2023. a, b, c, d, e, f, g, h
Mackowski, D.: The extension of the Multiple Sphere T Matrix code to include multiple plane boundaries and 2-D periodic systems, J. Quant. Spectrosc. Ra., 290, 108292, https://doi.org/10.1016/j.jqsrt.2022.108292, 2022. a
Mackowski, D. and Mishchenko, M.: A multiple sphere T-matrix Fortran code for use on parallel computer clusters, J. Quant. Spectrosc. Ra., 112, 2182–2192, https://doi.org/10.1016/j.jqsrt.2011.02.019, 2011. a
Mackowski, D. W.: MSTM Version 3.0: April 2013, MSTM [code], https://www.eng.auburn.edu/~dmckwski/scatcodes/ (last access: 30 April 2022), 2013. a
Mackowski, D. W.: MSTM: Multiple Sphere T Matrix code, GitHub [code], https://github.com/dmckwski/MSTM, last access: 30 December 2023. a
Matsui, H., Hamilton, D. S., and Mahowald, N. M.: Black carbon radiative effects highly sensitive to emitted particle size when resolving mixing-state diversity, Nat. Commun., 9, 3446, https://doi.org/10.1038/s41467-018-05635-1, 2018. a, b
Mie, G.: Beiträge zur Optik trüber Medien, speziell kolloidaler Metallösungen, Ann. Phys., 330, 377–445, 1908. a
Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015. a
Moran, J., Fuentes, A., Liu, F., and Yon, J.: FracVAL: An improved tunable algorithm of cluster cluster aggregation for generation of fractal structures formed by polydisperse primary particles, Comput. Phys. Commun., 239, 225–237, https://doi.org/10.1016/j.cpc.2019.01.015, 2019. a
Moteki, N., Kondo, Y., Miyazaki, Y., Takegawa, N., Komazaki, Y., Kurata, G., Shirai, T., Blake, D. R., Miyakawa, T., and Koike, M.: Evolution of mixing state of black carbon particles: Aircraft measurements over the western Pacific in March 2004, Geophys. Res. Lett., 34, L11803, https://doi.org/10.1029/2006GL028943, 2007. a
Myhre, G., Samset, B. H., Schulz, M., Balkanski, Y., Bauer, S., Berntsen, T. K., Bian, H., Bellouin, N., Chin, M., Diehl, T., Easter, R. C., Feichter, J., Ghan, S. J., Hauglustaine, D., Iversen, T., Kinne, S., Kirkevåg, A., Lamarque, J.-F., Lin, G., Liu, X., Lund, M. T., Luo, G., Ma, X., van Noije, T., Penner, J. E., Rasch, P. J., Ruiz, A., Seland, Ø., Skeie, R. B., Stier, P., Takemura, T., Tsigaridis, K., Wang, P., Wang, Z., Xu, L., Yu, H., Yu, F., Yoon, J.-H., Zhang, K., Zhang, H., and Zhou, C.: Radiative forcing of the direct aerosol effect from AeroCom Phase II simulations, Atmos. Chem. Phys., 13, 1853–1877, https://doi.org/10.5194/acp-13-1853-2013, 2013. a
Onofri, F. R. A.: Fractal-like aggregates: Diffusion limited model (random walk algorithm) [code], https://sites.google.com/view/fabriceonofri/aggregates/fractal-like-aggregates-diffusion-model/ (last access: 30 December 2023), 2019. a
Pang, Y., Wang, Y., Wang, Z., Zhang, Y., Liu, L., Kong, S., Liu, F., Shi, Z., and Li, W.: Quantifying the Fractal Dimension and Morphology of Individual Atmospheric Soot Aggregates, J. Geophys. Res.-Atmos., 127, e2021JD036055, https://doi.org/10.1029/2021JD036055, 2022. a, b
Pang, Y., Chen, M., Wang, Y., Chen, X., Teng, X., Kong, S., Zheng, Z., and Li, W.: Morphology and Fractal Dimension of Size-Resolved Soot Particles Emitted From Combustion Sources, J. Geophys. Res.-Atmos., 128, e2022JD037711, https://doi.org/10.1029/2022JD037711, 2023. a, b
Petzold, A. and Schönlinner, M.: Multi-angle absorption photometry—a new method for the measurement of aerosol light absorption and atmospheric black carbon, J. Aerosol Sci., 35, 421–441, https://doi.org/10.1016/j.jaerosci.2003.09.005, 2004. a
Radney, J. G., You, R., Ma, X., Conny, J. M., Zachariah, M. R., Hodges, J. T., and Zangmeister, C. D.: Dependence of soot optical properties on particle morphology: measurements and model comparisons, Environ. Sci. Technol., 48, 3169–3176, 2014. a
Randerson, J., Van Der Werf, G., Giglio, L., Collatz, G., and Kasibhatla, P.: Global Fire Emissions Database, Version 4,(GFEDv4), ORNL DAAC, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1293, 2018. a
Rathod, T. and Sahu, S.: Measurements of optical properties of black and brown carbon using multi-wavelength absorption technique at Mumbai, India, J. Earth Syst. Sci., 131, 32, 2022. a
Russell, P. B., Bergstrom, R. W., Shinozuka, Y., Clarke, A. D., DeCarlo, P. F., Jimenez, J. L., Livingston, J. M., Redemann, J., Dubovik, O., and Strawa, A.: Absorption Angstrom Exponent in AERONET and related data as an indicator of aerosol composition, Atmos. Chem. Phys., 10, 1155–1169, https://doi.org/10.5194/acp-10-1155-2010, 2010. a, b
Sand, M., Samset, B. H., Myhre, G., Gliß, J., Bauer, S. E., Bian, H., Chin, M., Checa-Garcia, R., Ginoux, P., Kipling, Z., Kirkevåg, A., Kokkola, H., Le Sager, P., Lund, M. T., Matsui, H., van Noije, T., Olivié, D. J. L., Remy, S., Schulz, M., Stier, P., Stjern, C. W., Takemura, T., Tsigaridis, K., Tsyro, S. G., and Watson-Parris, D.: Aerosol absorption in global models from AeroCom phase III, Atmos. Chem. Phys., 21, 15929–15947, https://doi.org/10.5194/acp-21-15929-2021, 2021. a
Schnaiter, M., Linke, C., Möhler, O., Naumann, K.-H., Saathoff, H., Wagner, R., Schurath, U., and Wehner, B.: Absorption amplification of black carbon internally mixed with secondary organic aerosol, J. Geophys. Res.-Atmos., 110, D19204, https://doi.org/10.1029/2005JD006046, 2005. a
Schwarz, J. P., Gao, R. S., Fahey, D. W., Thomson, D. S., Watts, L. A., Wilson, J. C., Reeves, J. M., Darbeheshti, M., Baumgardner, D. G., Kok, G. L., Chung, S. H., Schulz, M., Hendricks, J., Lauer, A., Karcher, B., Slowik, J. G., Rosenlof, K. H., Thompson, T. L., Langford, A. O., Loewenstein, M., and Aikin, K. C.: Single-particle measurements of midlatitude black carbon and light-scattering aerosols from the boundary layer to the lower stratosphere, J. Geophys. Res.-Atmos., 111, D16207, https://doi.org/10.1029/2006JD007076, 2006. a
Shaw, G. E.: Sun Photometry, B. Am. Meteorol. Soc., 64, 4–10, https://doi.org/10.1175/1520-0477(1983)064<0004:SP>2.0.CO;2, 1983. a
Shin, S.-K., Tesche, M., Müller, D., and Noh, Y.: Technical note: Absorption aerosol optical depth components from AERONET observations of mixed dust plumes, Atmos. Meas. Tech., 12, 607–618, https://doi.org/10.5194/amt-12-607-2019, 2019. a
Shiraiwa, M., Kondo, Y., Moteki, N., Takegawa, N., Sahu, L. K., Takami, A., Hatakeyama, S., Yonemura, S., and Blake, D. R.: Radiative impact of mixing state of black carbon aerosol in Asian outflow, J. Geophys. Res.-Atmos., 113, D24210, https://doi.org/10.1029/2008JD010546, 2008. a
Skorupski, K., Mroczka, J., Wriedt, T., and Riefler, N.: A fast and accurate implementation of tunable algorithms used for generation of fractal-like aggregate models, Physica A, 404, 106–117, https://doi.org/10.1016/j.physa.2014.02.072, 2014. a
Sorensen, C. M.: Light Scattering by Fractal Aggregates: A Review, Aerosol Sci. Technol., 35, 648–687, https://doi.org/10.1080/02786820117868, 2001. a, b, c
Sorensen, C. M.: The Mobility of Fractal Aggregates: A Review, Aerosol Sci. Technol., 45, 765–779, https://doi.org/10.1080/02786826.2011.560909, 2011. a
Sorensen, C. M. and Roberts, G. C.: The Prefactor of Fractal Aggregates, J. Colloid Interf. Sci., 186, 447–452, https://doi.org/10.1006/jcis.1996.4664, 1997. a
Tesche, M., Müller, D., Gross, S., Ansmann, A., Althausen, D., Freudenthaler, V., Weinzierl, B., Veira, A., and Petzold, A.: Optical and microphysical properties of smoke over Cape Verde inferred from multiwavelength lidar measurements, Tellus B, 169, 162–174, https://doi.org/10.1111/j.1600-0889.2011.00549.x, 2011. a
Tuccella, P., Curci, G., Pitari, G., Lee, S., and Jo, D. S.: Direct Radiative Effect of Absorbing Aerosols: Sensitivity to Mixing State, Brown Carbon, and Soil Dust Refractive Index and Shape, J. Geophys. Res.-Atmos., 125, e2019JD030967, https://doi.org/10.1029/2019JD030967, 2020. a
Wang, J., Nie, W., Cheng, Y., Shen, Y., Chi, X., Wang, J., Huang, X., Xie, Y., Sun, P., Xu, Z., Qi, X., Su, H., and Ding, A.: Light absorption of brown carbon in eastern China based on 3-year multi-wavelength aerosol optical property observations and an improved absorption Ångström exponent segregation method, Atmos. Chem. Phys., 18, 9061–9074, https://doi.org/10.5194/acp-18-9061-2018, 2018. a
Wang, X., Heald, C. L., Sedlacek, A. J., de Sá, S. S., Martin, S. T., Alexander, M. L., Watson, T. B., Aiken, A. C., Springston, S. R., and Artaxo, P.: Deriving brown carbon from multiwavelength absorption measurements: method and application to AERONET and Aethalometer observations, Atmos. Chem. Phys., 16, 12733–12752, https://doi.org/10.5194/acp-16-12733-2016, 2016. a, b, c, d, e, f, g, h
Wang, Y., Liu, F., He, C., Bi, L., Cheng, T., Wang, Z., Zhang, H., Zhang, X., Shi, Z., and Li, W.: Fractal Dimensions and Mixing Structures of Soot Particles during Atmospheric Processing, Environ. Sci. Technol. Lett., 4, 487–493, https://doi.org/10.1021/acs.estlett.7b00418, 2017. a, b, c
Wang, Y., Pang, Y., Huang, J., Bi, L., Che, H., Zhang, X., and Li, W.: Constructing Shapes and Mixing Structures of Black Carbon Particles With Applications to Optical Calculations, J. Geophys. Res.-Atmos., 126, e2021JD034620, https://doi.org/10.1029/2021JD034620, 2021. a
Wentzel, M., Gorzawski, H., Naumann, K.-H., Saathoff, H., and Weinbruch, S.: Transmission electron microscopical and aerosol dynamical characterization of soot aerosols, J. Aerosol Sci., 34, 1347–1370, 2003. a
Woźniak, M.: CHARACTERIZATION OF NANOPARTICLE AGGREGATES WITH LIGHT SCATTERING TECHNIQUES, Theses, Aix-Marseille Université, https://tel.archives-ouvertes.fr/tel-00747711 (last access: 30 June 2023), 2012. a
Xie, M., Chen, X., Holder, A. L., Hays, M. D., Lewandowski, M., Offenberg, J. H., Kleindienst, T. E., Jaoui, M., and Hannigan, M. P.: Light absorption of organic carbon and its sources at a southeastern U.S. location in summer, Environ. Pollut., 244, 38–46, https://doi.org/10.1016/j.envpol.2018.09.125, 2019. a
Zeng, L., Zhang, A., Wang, Y., Wagner, N. L., Katich, J. M., Schwarz, J. P., Schill, G. P., Brock, C., Froyd, K. D., Murphy, D. M., Williamson, C. J., Kupc, A., Scheuer, E., Dibb, J., and Weber, R. J.: Global Measurements of Brown Carbon and Estimated Direct Radiative Effects, Geophys. Res. Lett., 47, e2020GL088747, https://doi.org/10.1029/2020GL088747, 2020a. a
Zeng, L., Zhang, A., Wang, Y., Wagner, N. L., Katich, J. M., Schwarz, J. P., Schill, G. P., Brock, C., Froyd, K. D., Murphy, D. M., Williamson, C. J., Kupc, A., Scheuer, E., Dibb, J., and Weber, R. J.: Global Measurements of Brown Carbon and Estimated Direct Radiative Effects, Geophys. Res. Lett., 47, e2020GL088747, https://doi.org/10.1029/2020GL088747, 2020b. a
Zhang, H. and Wang, Z.: Advances in the Study of Black Carbon Effects on Climate, Adv. Clim. Change Res., 2, 23–30, https://doi.org/10.3724/SP.J.1248.2011.00023, 2011. a
Zhang, A., Wang, Y., Zhang, Y., Weber, R. J., Song, Y., Ke, Z., and Zou, Y.: Modeling the global radiative effect of brown carbon: a potentially larger heating source in the tropical free troposphere than black carbon, Atmos. Chem. Phys., 20, 1901–1920, https://doi.org/10.5194/acp-20-1901-2020, 2020. a
Zhang, G., Peng, L., Lian, X., Lin, Q., Bi, X., Chen, D., Li, M., Li, L., Wang, X., and Sheng, G.: An Improved Absorption Ångström Exponent (AAE)-Based Method for Evaluating the Contribution of Light Absorption from Brown Carbon with a High-Time Resolution, Aerosol Air Qual. Res., 19, 15–24, https://doi.org/10.4209/aaqr.2017.12.0566, 2019. a
Zhang, R., Khalizov, A. F., Pagels, J., Zhang, D., Xue, H., and McMurry, P. H.: Variability in morphology, hygroscopicity, and optical properties of soot aerosols during atmospheric processing, P. Natl. Acad. Sci. USA, 105, 10291–10296, 2008. a
Zhang, X., Mao, M., Yin, Y., and Wang, B.: Numerical Investigation on Absorption Enhancement of Black Carbon Aerosols Partially Coated With Nonabsorbing Organics, J. Geophys. Res.-Atmos., 123, 1297–1308, https://doi.org/10.1002/2017JD027833, 2018. a, b, c, d
Zhang, X., Mao, M., Chen, H., and Tang, S.: The Angstrom exponents of black carbon aerosols with non-absorptive coating: A numerical investigation, J. Quant. Spectrosc. Ra., 257, 107362, https://doi.org/10.1016/j.jqsrt.2020.107362, 2020. a, b, c
Short summary
Remote sensing of brown carbon is very important for climate research, and current optical methods rely mainly on spectral properties for inversion. However, the influence of the microscopic properties of black carbon has rarely been considered by previous studies. This paper shows how the remote sensing of brown carbon is affected by the microphysical properties of black carbon and highlights the adaptability of remote sensing methods.
Remote sensing of brown carbon is very important for climate research, and current optical...
Altmetrics
Final-revised paper
Preprint