Articles | Volume 21, issue 10
https://doi.org/10.5194/acp-21-7749-2021
© Author(s) 2021. 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-21-7749-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Effect of volcanic emissions on clouds during the 2008 and 2018 Kilauea degassing events
Katherine H. Breen
NASA, Goddard Space Flight Center, Greenbelt, MD, USA
Universities Space Research Association, Columbia, MD, USA
Donifan Barahona
CORRESPONDING AUTHOR
NASA, Goddard Space Flight Center, Greenbelt, MD, USA
Tianle Yuan
NASA, Goddard Space Flight Center, Greenbelt, MD, USA
Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, MD, USA
Huisheng Bian
NASA, Goddard Space Flight Center, Greenbelt, MD, USA
Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, MD, USA
Scott C. James
formerly at: Departments of Geosciences and Mechanical Engineering, Baylor University, Waco, TX, USA
deceased
Related authors
Donifan Barahona, Katherine Breen, Karoline Block, and Anton Darmenov
EGUsphere, https://doi.org/10.5194/egusphere-2025-482, https://doi.org/10.5194/egusphere-2025-482, 2025
Short summary
Short summary
Particulate matter impacts Earth's radiation, clouds, and human health, but modeling their size is challenging due to computational and observational limits. We developed a machine learning model to predict aerosol size distributions, which accurately replicates advanced models and field measurements.
Travis Toth, Gregory Schuster, Marian Clayton, Zhujun Li, David Painemal, Sharon Rodier, Jayanta Kar, Tyler Thorsen, Richard Ferrare, Mark Vaughan, Jason Tackett, Huisheng Bian, Mian Chin, Anne Garnier, Ellsworth Welton, Robert Ryan, Charles Trepte, and David Winker
EGUsphere, https://doi.org/10.5194/egusphere-2025-2832, https://doi.org/10.5194/egusphere-2025-2832, 2025
Short summary
Short summary
NASA’s CALIPSO satellite mission observed aerosols (airborne particles) globally from 2006 to 2023. Its final data products update improves its aerosol optical parameters over oceans by adjusting for regional and seasonal differences in a new measurement-model synergistic approach. This results in a more realistic aerosol characterization, specifically near coastlines (where sea salt mixes with pollution), with potential impacts to future studies of science applications (e.g., climate effects).
Ci Song, Daniel T. McCoy, Isabel L. McCoy, Hunter Brown, Andrew Gettelman, Trude Eidhammer, and Donifan Barahona
EGUsphere, https://doi.org/10.5194/egusphere-2025-2009, https://doi.org/10.5194/egusphere-2025-2009, 2025
Short summary
Short summary
This study examines how aerosols from human activities alter cloud microphysical properties. Airborne observations from a field campaign are used to constrain an ensemble of global model configurations and their associated cloud property changes. Results show that airborne in-situ measurements of aerosol and cloud properties do provide insight into global changes in cloud microphysics but are sensitive to uncertainties in both airborne measurements and Earth system model emulators.
Donifan Barahona, Katherine Breen, Karoline Block, and Anton Darmenov
EGUsphere, https://doi.org/10.5194/egusphere-2025-482, https://doi.org/10.5194/egusphere-2025-482, 2025
Short summary
Short summary
Particulate matter impacts Earth's radiation, clouds, and human health, but modeling their size is challenging due to computational and observational limits. We developed a machine learning model to predict aerosol size distributions, which accurately replicates advanced models and field measurements.
Mariya Petrenko, Ralph Kahn, Mian Chin, Susanne E. Bauer, Tommi Bergman, Huisheng Bian, Gabriele Curci, Ben Johnson, Johannes W. Kaiser, Zak Kipling, Harri Kokkola, Xiaohong Liu, Keren Mezuman, Tero Mielonen, Gunnar Myhre, Xiaohua Pan, Anna Protonotariou, Samuel Remy, Ragnhild Bieltvedt Skeie, Philip Stier, Toshihiko Takemura, Kostas Tsigaridis, Hailong Wang, Duncan Watson-Parris, and Kai Zhang
Atmos. Chem. Phys., 25, 1545–1567, https://doi.org/10.5194/acp-25-1545-2025, https://doi.org/10.5194/acp-25-1545-2025, 2025
Short summary
Short summary
We compared smoke plume simulations from 11 global models to each other and to satellite smoke amount observations aimed at constraining smoke source strength. In regions where plumes are thick and background aerosol is low, models and satellites compare well. However, the input emission inventory tends to underestimate in many places, and particle property and loss rate assumptions vary enormously among models, causing uncertainties that require systematic in situ measurements to resolve.
Ci Song, Daniel McCoy, Andrea Molod, and Donifan Barahona
EGUsphere, https://doi.org/10.5194/egusphere-2024-4108, https://doi.org/10.5194/egusphere-2024-4108, 2025
Short summary
Short summary
The uncertainty in how clouds respond to aerosols limits our ability to predict future warming. This study uses a global reanalysis data, GiOcean, which includes a detailed treatment of cloud microphysics to represent interactions between aerosols and clouds. We evaluate the response of warm clouds to aerosols in GiOcean by comparing variables important for cloud properties from GiOcean with available spaceborne remote sensing observations.
Sampa Das, Peter R. Colarco, Huisheng Bian, and Santiago Gassó
Atmos. Chem. Phys., 24, 4421–4449, https://doi.org/10.5194/acp-24-4421-2024, https://doi.org/10.5194/acp-24-4421-2024, 2024
Short summary
Short summary
The smoke aerosols emitted from vegetation burning can alter the regional energy budget via multiple pathways. We utilized detailed observations from the NASA ORACLES airborne campaign based in Namibia during September 2016 to improve the representation of smoke aerosol properties and lifetimes in our GEOS Earth system model. The improved model simulations are for the first time able to capture the observed changes in the smoke absorption during long-range plume transport.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
Short summary
Short summary
The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Tianle Yuan, Fei Liu, Lok N. Lamsal, and Hua Song
EGUsphere, https://doi.org/10.22541/essoar.168771101.14987378/v1, https://doi.org/10.22541/essoar.168771101.14987378/v1, 2023
Preprint archived
Short summary
Short summary
We train and apply a state-of-the-art deep learning model to detect NO2 plumes emitted by ships using NO2 retrievals from TROPOMI. By applying the model, we can detect individual plumes with excellent fidelity. The aggregated data show major shipping routes, but miss other routes. The missing routes are due to high cloudiness. Our method can be potentially useful for monitoring ship emissions of NOx and verifying compliance of emission standards.
Hamza Ahsan, Hailong Wang, Jingbo Wu, Mingxuan Wu, Steven J. Smith, Susanne Bauer, Harrison Suchyta, Dirk Olivié, Gunnar Myhre, Hitoshi Matsui, Huisheng Bian, Jean-François Lamarque, Ken Carslaw, Larry Horowitz, Leighton Regayre, Mian Chin, Michael Schulz, Ragnhild Bieltvedt Skeie, Toshihiko Takemura, and Vaishali Naik
Atmos. Chem. Phys., 23, 14779–14799, https://doi.org/10.5194/acp-23-14779-2023, https://doi.org/10.5194/acp-23-14779-2023, 2023
Short summary
Short summary
We examine the impact of the assumed effective height of SO2 injection, SO2 and BC emission seasonality, and the assumed fraction of SO2 emissions injected as SO4 on climate and chemistry model results. We find that the SO2 injection height has a large impact on surface SO2 concentrations and, in some models, radiative flux. These assumptions are a
hiddensource of inter-model variability and may be leading to bias in some climate model results.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, https://doi.org/10.5194/amt-16-5387-2023, 2023
Short summary
Short summary
A new database is constructed from over 20 years of satellite records that comprises millions of deep convective clouds and spans the global tropics and subtropics. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Ehud Strobach, Andrea Molod, Donifan Barahona, Atanas Trayanov, Dimitris Menemenlis, and Gael Forget
Geosci. Model Dev., 15, 2309–2324, https://doi.org/10.5194/gmd-15-2309-2022, https://doi.org/10.5194/gmd-15-2309-2022, 2022
Short summary
Short summary
The Green's functions methodology offers a systematic, easy-to-implement, computationally cheap, scalable, and extendable method to tune uncertain parameters in models accounting for the dependent response of the model to a change in various parameters. Herein, we successfully show for the first time that long-term errors in earth system models can be considerably reduced using Green's functions methodology. The method can be easily applied to any model containing uncertain parameters.
Matthew W. Christensen, Andrew Gettelman, Jan Cermak, Guy Dagan, Michael Diamond, Alyson Douglas, Graham Feingold, Franziska Glassmeier, Tom Goren, Daniel P. Grosvenor, Edward Gryspeerdt, Ralph Kahn, Zhanqing Li, Po-Lun Ma, Florent Malavelle, Isabel L. McCoy, Daniel T. McCoy, Greg McFarquhar, Johannes Mülmenstädt, Sandip Pal, Anna Possner, Adam Povey, Johannes Quaas, Daniel Rosenfeld, Anja Schmidt, Roland Schrödner, Armin Sorooshian, Philip Stier, Velle Toll, Duncan Watson-Parris, Robert Wood, Mingxi Yang, and Tianle Yuan
Atmos. Chem. Phys., 22, 641–674, https://doi.org/10.5194/acp-22-641-2022, https://doi.org/10.5194/acp-22-641-2022, 2022
Short summary
Short summary
Trace gases and aerosols (tiny airborne particles) are released from a variety of point sources around the globe. Examples include volcanoes, industrial chimneys, forest fires, and ship stacks. These sources provide opportunistic experiments with which to quantify the role of aerosols in modifying cloud properties. We review the current state of understanding on the influence of aerosol on climate built from the wide range of natural and anthropogenic laboratories investigated in recent decades.
Maria Sand, Bjørn H. Samset, Gunnar Myhre, Jonas Gliß, Susanne E. Bauer, Huisheng Bian, Mian Chin, Ramiro Checa-Garcia, Paul Ginoux, Zak Kipling, Alf Kirkevåg, Harri Kokkola, Philippe Le Sager, Marianne T. Lund, Hitoshi Matsui, Twan van Noije, Dirk J. L. Olivié, Samuel Remy, Michael Schulz, Philip Stier, Camilla W. Stjern, Toshihiko Takemura, Kostas Tsigaridis, Svetlana G. Tsyro, and Duncan Watson-Parris
Atmos. Chem. Phys., 21, 15929–15947, https://doi.org/10.5194/acp-21-15929-2021, https://doi.org/10.5194/acp-21-15929-2021, 2021
Short summary
Short summary
Absorption of shortwave radiation by aerosols can modify precipitation and clouds but is poorly constrained in models. A total of 15 different aerosol models from AeroCom phase III have reported total aerosol absorption, and for the first time, 11 of these models have reported in a consistent experiment the contributions to absorption from black carbon, dust, and organic aerosol. Here, we document the model diversity in aerosol absorption.
Huisheng Bian, Eunjee Lee, Randal D. Koster, Donifan Barahona, Mian Chin, Peter R. Colarco, Anton Darmenov, Sarith Mahanama, Michael Manyin, Peter Norris, John Shilling, Hongbin Yu, and Fanwei Zeng
Atmos. Chem. Phys., 21, 14177–14197, https://doi.org/10.5194/acp-21-14177-2021, https://doi.org/10.5194/acp-21-14177-2021, 2021
Short summary
Short summary
The study using the NASA Earth system model shows ~2.6 % increase in burning season gross primary production and ~1.5 % increase in annual net primary production across the Amazon Basin during 2010–2016 due to the change in surface downward direct and diffuse photosynthetically active radiation by biomass burning aerosols. Such an aerosol effect is strongly dependent on the presence of clouds. The cloud fraction at which aerosols switch from stimulating to inhibiting plant growth occurs at ~0.8.
Hongbin Yu, Qian Tan, Lillian Zhou, Yaping Zhou, Huisheng Bian, Mian Chin, Claire L. Ryder, Robert C. Levy, Yaswant Pradhan, Yingxi Shi, Qianqian Song, Zhibo Zhang, Peter R. Colarco, Dongchul Kim, Lorraine A. Remer, Tianle Yuan, Olga Mayol-Bracero, and Brent N. Holben
Atmos. Chem. Phys., 21, 12359–12383, https://doi.org/10.5194/acp-21-12359-2021, https://doi.org/10.5194/acp-21-12359-2021, 2021
Short summary
Short summary
This study characterizes a historic African dust intrusion into the Caribbean Basin in June 2020 using satellites and NASA GEOS. Dust emissions in West Africa were large albeit not extreme. However, a unique synoptic system accumulated the dust near the coast for about 4 d before it was ventilated. Although GEOS reproduced satellite-observed plume tracks well, it substantially underestimated dust emissions and did not lift up dust high enough for ensuing long-range transport.
Johannes Mohrmann, Robert Wood, Tianle Yuan, Hua Song, Ryan Eastman, and Lazaros Oreopoulos
Atmos. Chem. Phys., 21, 9629–9642, https://doi.org/10.5194/acp-21-9629-2021, https://doi.org/10.5194/acp-21-9629-2021, 2021
Short summary
Short summary
Observations of marine-boundary-layer conditions are composited by cloud type, based on a new classification dataset. It is found that two cloud types, representing regions of clustered and suppressed low-level clouds, occur in very similar large-scale conditions but are distinguished from each other by considering low-level circulation and surface wind fields, validating prior results from modeling.
Youhua Tang, Huisheng Bian, Zhining Tao, Luke D. Oman, Daniel Tong, Pius Lee, Patrick C. Campbell, Barry Baker, Cheng-Hsuan Lu, Li Pan, Jun Wang, Jeffery McQueen, and Ivanka Stajner
Atmos. Chem. Phys., 21, 2527–2550, https://doi.org/10.5194/acp-21-2527-2021, https://doi.org/10.5194/acp-21-2527-2021, 2021
Short summary
Short summary
Chemical lateral boundary condition (CLBC) impact is essential for regional air quality prediction during intrusion events. We present a model mapping Goddard Earth Observing System (GEOS) to Community Multi-scale Air Quality (CMAQ) CB05–AERO6 (Carbon Bond 5; version 6 of the aerosol module) species. Influence depends on distance from the inflow boundary and species and their regional characteristics. We use aerosol optical thickness to derive CLBCs, achieving reasonable prediction.
Tianle Yuan, Hua Song, Robert Wood, Johannes Mohrmann, Kerry Meyer, Lazaros Oreopoulos, and Steven Platnick
Atmos. Meas. Tech., 13, 6989–6997, https://doi.org/10.5194/amt-13-6989-2020, https://doi.org/10.5194/amt-13-6989-2020, 2020
Short summary
Short summary
We use deep transfer learning techniques to classify satellite cloud images into different morphology types. It achieves the state-of-the-art results and can automatically process a large amount of satellite data. The algorithm will help low-cloud researchers to better understand their mesoscale organizations.
Jie Gong, Xiping Zeng, Dong L. Wu, S. Joseph Munchak, Xiaowen Li, Stefan Kneifel, Davide Ori, Liang Liao, and Donifan Barahona
Atmos. Chem. Phys., 20, 12633–12653, https://doi.org/10.5194/acp-20-12633-2020, https://doi.org/10.5194/acp-20-12633-2020, 2020
Short summary
Short summary
This work provides a novel way of using polarized passive microwave measurements to study the interlinked cloud–convection–precipitation processes. The magnitude of differences between polarized radiances is found linked to ice microphysics (shape, size, orientation and density), mesoscale dynamic and thermodynamic structures, and surface precipitation. We conclude that passive sensors with multiple polarized channel pairs may serve as cheaper and useful substitutes for spaceborne radar sensors.
María A. Burgos, Elisabeth Andrews, Gloria Titos, Angela Benedetti, Huisheng Bian, Virginie Buchard, Gabriele Curci, Zak Kipling, Alf Kirkevåg, Harri Kokkola, Anton Laakso, Julie Letertre-Danczak, Marianne T. Lund, Hitoshi Matsui, Gunnar Myhre, Cynthia Randles, Michael Schulz, Twan van Noije, Kai Zhang, Lucas Alados-Arboledas, Urs Baltensperger, Anne Jefferson, James Sherman, Junying Sun, Ernest Weingartner, and Paul Zieger
Atmos. Chem. Phys., 20, 10231–10258, https://doi.org/10.5194/acp-20-10231-2020, https://doi.org/10.5194/acp-20-10231-2020, 2020
Short summary
Short summary
We investigate how well models represent the enhancement in scattering coefficients due to particle water uptake, and perform an evaluation of several implementation schemes used in ten Earth system models. Our results show the importance of the parameterization of hygroscopicity and model chemistry as drivers of some of the observed diversity amongst model estimates. The definition of dry conditions and the phenomena taking place in this relative humidity range also impact the model evaluation.
Cited articles
Abdul-Razzak, H. and Ghan, S.: A parameterization of aerosol activation, 2. Multiple aerosol types, J. Geophys. Res., 105, 6837–6844,
https://doi.org/10.1029/1999JD901161, 2000. a
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness,
Science, 245, 1227–1230, 1989. a
Barahona, D. and Nenes, A.: Parameterizing the competition between homogeneous and heterogeneous freezing in cirrus cloud formation – monodisperse ice nuclei, Atmos. Chem. Phys., 9, 369–381, https://doi.org/10.5194/acp-9-369-2009, 2009. a, b, c
Barahona, D., Rodriguez, J., and Nenes, A.: Sensitivity of the global
distribution of cirrus ice crystal concentration to heterogeneous freezing,
J. Geophys. Res.-Atmos., 115, D23213, https://doi.org/10.1029/2010JD014273, 2010. a
Barahona, D., Molod, A., Bacmeister, J., Nenes, A., Gettelman, A., Morrison,
H., Phillips, V., and Eichmann, A.: Development of two-moment cloud microphysics for liquid and ice within the NASA Goddard Earth Observing System Model (GEOS-5), Geosc. Model Dev., 7, 1733–1766,
https://doi.org/10.5194/gmd-7-1733-2014, 2014. a, b, c, d, e
Barahona, D., Molod, A., and Kalesse, H.: Direct estimation of the global
distribution of vertical velocity within cirrus clouds, Sci. Rep., 7, 6840, https://doi.org/10.1038/s41598-017-07038-6, 2017. a
Beirle, S., Hörmann, C., Penning de Vries, M., Dörner, S., Kern, C., and Wagner, T.: Estimating the volcanic emission rate and atmospheric lifetime of SO2 from space: a case study for Kīlauea volcano, Hawai'i, Atmos. Chem. Phys., 14, 8309–8322, https://doi.org/10.5194/acp-14-8309-2014, 2014. a, b, c, d, e
Bennartz, R.: Global assessment of marine boundary layer cloud droplet number
concentration from satellite, J. Geophys. Res.-Atmos., 112, D02201,
https://doi.org/10.1029/2006JD007547, 2007. a, b
Bennartz, R. and Rausch, J.: Global and regional estimates of warm cloud droplet number concentration based on 13 years of AQUA-MODIS observations, Atmos. Chem. Phys., 17, 9815–9836, https://doi.org/10.5194/acp-17-9815-2017, 2017. a, b
Boo, K.-O., Booth, B. B., Byun, Y.-H., Lee, J., Cho, C., Shim, S., and Kim,
K.-T.: Influence of aerosols in multidecadal SST variability simulations
over the North Pacific, J. Geophys. Res.-Atmos., 120, 517–531, 2015. a
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster,
P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh,
S., Sherwood, S., Stevens, B., and Zhang, X.: in: Climate Change 2013: The
Physical Science Basis, in: Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change: Clouds and Aerosols, edited by: Stocker, T., Qin, D., Plattner, G.-K., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P., Cambridge University Press, Cambridge, UK and New York, NY, USA, 571–657, 2013. a, b, c, d, e
Businger, S., Huff, R., Pattantyus, A., Horton, K., Sutton, A. J., Elias, T.,
and Cherubini, T.: Observing and forecasting Vog dispersion from Kīlauea Volcano, Hawaii, B. Am. Meteorol. Soc., 96, 1667–1686, 2015. a
Carn, S., Clarisse, L., and Prata, A. J.: Multi-decadal satellite measurements of global volcanic degassing, J. Volcanol. Geoth. Res., 311, 99–134, 2016. a
Carn, S., Fioletov, V., McLinden, C., Li, C., and Krotkov, N.: A decade of
global volcanic SO2 emissions measured from space, Sci. Rep., 7, 44095, https://doi.org/10.1038/srep44095, 2017. a, b
Chepfer, H., Bony, S., Winker, D., Cesana, G., Dufresne, J., Minnis, P.,
Stubenrauch, C., and Zeng, S.: The GCM-oriented CALIPSO cloud product (CALIPSO-GOCCP), J. Geophys. Res.-Atmos., 115, D00H16,
https://doi.org/10.1029/2009JD012251, 2010. a, b, c, d
Chin, M., Diehl, T., Dubovik, O., Eck, T. F., Holben, B. N., Sinyuk, A., and
Streets, D. G.: Light absorption by pollution, dust, and biomass burning
aerosols: a global model study and evaluation with AERONET measurements,
Ann. Geophys., 27, 3439–3464, https://doi.org/10.5194/angeo-27-3439-2009, 2009. a, b
Colarco, P., da Silva, A., Chin, M., and Diehl, T.: Online simulations of
global aerosol distributions in the NASA GEOS-4 model and comparisons to
satellite and ground-based aerosol optical depth, J. Geophys. Res., 115,
D14207, https://doi.org/10.1029/2009JD012820, 2010. a, b
Durant, A. J., Shaw, R., Rose, W. I., Mi, Y., and Ernst, G.: Ice nucleation and overseeding of ice in volcanic clouds, J. Geophys. Res.-Atmos., 113, D09206, https://doi.org/10.1029/2007JD009064, 2008. a
Elias, T., Kern, C., Horton, K., Sutton, A. J., and Garbeil, H.: Measuring
SO2 emission rates at Kīlauea Volcano, Hawaii USA, 2014–2017, Front. Earth Sci., 6, 214, https://doi.org/10.3389/feart.2018.00214, 2018. a, b
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, 2017. a, b
Gryspeerdt, E., Quaas, J., Ferrachat, S., Gettelman, A., Ghan, S., Lohmann, U., Morrison, H., Neubauer, D., Partridge, D. G., Stier, P., Takemura, T., Wang, H., Wang, M., and Zhang, K.: Constraining the instantaneous aerosol influence on cloud albedo, P. Natl. Acad. Sci. USA, 114, 4899–4904, 2017. a
Hoyle, C. R., Pinti, V., Welti, A., Zobrist, B., Marcolli, C., Luo, B., Höskuldsson, Á., Mattsson, H. B., Stetzer, O., Thorsteinsson, T., Larsen, G., and Peter, T.: Ice nucleation properties of volcanic ash from Eyjafjallajökull, Atmos. Chem. Phys., 11, 9911–9926, https://doi.org/10.5194/acp-11-9911-2011, 2011. a
Hubanks, P., Platnick, S., King, M., and Ridgway, B.: MODIS atmosphere L3 gridded product algorithm theoretical basis document, ATBD Reference Number: ATBD-MOD-30 30 (2008): 96, Goddard Space Flight Center, Greenbelt, MD, 2015. a
Klein, S. A., Zhang, Y., Zelinka, M. D., Pincus, R., Boyle, J., and Gleckler,
P. J.: Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator, J. Geophys. Res.-Atmos., 118, 1329–1342, 2013. a
Koren, I., Kaufman, Y. J., Rosenfeld, D., Remer, L. A., and Rudich, Y.: Aerosol invigoration and restructuring of Atlantic convective clouds, Geophys. Res. Lett., 32, L14828, https://doi.org/10.1029/2005GL023187, 2005. a
Li, C., Krotkov, N. A., and Leonard, P.: OMI/Aura Sulfur Dioxide (SO2) Total Column L3 1 day Best Pixel in 0.25 degree × 0.25 degree V3, GES DISC – Goddard Earth Sciences Data and Information Services Center, Greenbelt, MD, USA, https://doi.org/10.5067/Aura/OMI/DATA3008, 2020. a
Lin, S.-J. and Rood, R. B.: Multidimensional Flux-Form Semi-Lagrangian
Transport Schemes, Mon. Weather Rev., 124, 2046–2070,
https://doi.org/10.1175/1520-0493(1996)124<2046:MFFSLT>2.0.CO;2, 1996. a
Lohmann, U. and Feichter, J.: Global indirect aerosol effects: a review, Atmos. Chem. Phys., 5, 715–737, https://doi.org/10.5194/acp-5-715-2005, 2005. a, b, c
Malavelle, F. F., Haywood, J. M., Jones, A., Gettelman, A., Clarisse, L.,
Bauduin, S., Allan, R. P., Karset, I. H. H., Kristjánsson, J. E.,
Oreopoulos, L., Cho, N., Lee, D., Bellouin, N., Boucher, O., Grosvenor, D. P., Carslaw, K. S., Dhomse, S., Mann, G. W., Schmidt, A., Coe, H., Hartley, M. E., Dalvi, M., Hill, A. A., Johnson, B. T., Johnson, C. E., Knight, J. R., O'Connor, F. M., Partridge, D. G., Stier, P., Myhre, G., Platnick, S., Stephens, G. L., Takahashi, H., and Thordarson, T.: Strong constraints on aerosol–cloud interactions from volcanic eruptions, Nature, 546, 485, https://doi.org/10.1038/nature22974, 2017. a, b, c, d, e, f
Marchant, B., Platnick, S., Meyer, K., Arnold, G. T., and Riedi, J.: MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP, Atmos. Meas. Tech., 9, 1587–1599, https://doi.org/10.5194/amt-9-1587-2016, 2016. a, b
Mastin, L. G., Guffanti, M., Servranckx, R., Webley, P., Barsotti, S., Dean,
K., Durant, A., Ewert, J. W., Neri, A., Rose, W. I., Schneider, D., Siebert, L., Stunder, B., Swanson, G., Tupper, A., Volentik, A., and Waythomas, C. F.: A multidisciplinary effort to assign realistic source parameters to models of volcanic ash-cloud transport and dispersion during eruptions, J. Volcanol. Geoth. Res., 186, 10–21, 2009. a, b
Maters, E. C., Dingwell, D. B., Cimarelli, C., Müller, D., Whale, T. F., and Murray, B. J.: The importance of crystalline phases in ice nucleation by volcanic ash, Atmos. Chem. Phys., 19, 5451–5465, https://doi.org/10.5194/acp-19-5451-2019, 2019. a, b
Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the
GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosc. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015. a
Morrison, H. and Gettelman, A.: A New Two-Moment Bulk Stratiform Cloud
Microphysics Scheme in the Community Atmosphere Model, Version 3 (CAM3). Part I: Description and Numerical Tests, J. Climate, 21, 3642–3659, https://doi.org/10.1175/2008JCLI2105.1, 2008. a, b, c
NASA: GEOS-5 Modeling Software, available at: http://opensource.gsfc.nasa.gov/projects/GEOS-5/ (last access: 12 May 2021), 2021a. a
NASA: NTRS – NASA Technical Reports Server, available at: https://ntrs.nasa.gov (last access: 12 May 2021), 2021b. a
NASA: NASA-Funded Research Results, available at: https://www.nasa.gov/open/researchaccess/pubspace (last access: 12 May 2021), 2021c. a
Neal, C., Brantley, S., Antolik, L., Babb, J., Burgess, M., Calles, K., Cappos, M., Chang, J., Conway, S., Desmither, L., Dotray, P., Elias, T., Fukunaga, P., Fuke, S., Johanson, I. A., Kamibayashi, K., Kauahikaua, J., Lee, R. L., Pekalib, S., Miklius, A., Million, W., Moniz, C. J., Nadeau, P. A., Okubo, P., Parcheta, C., Patrick, M. R., Shiro, B., Swanson, D. A., Tollett, W., Trusdell, F., Younger, E. F., Zoeller, M. H., Montgomery-Brown, E. K., Anderson, K. R., Poland, M. P., Ball, J. L., Bard, J., Coombs, M., Dietterich, H. R., Kern, C., Thelen, W. A., Carvelli, P. F., Orr, T., Houghton, B. F., Gansecki, C., Hazlett, R., Lundgren, P., Diefenbach, A. K., Lerner, A. H., Waite, G., Kelly, P., Clor, L., Werner, C., Mulliken, K., Fisher, G., and Damby, D.: The 2018 rift eruption and summit collapse of Kīlauea Volcano, Science, 363, 367–374, 2019. a, b, c, d, e
NOAA: Historical El Niño/La Niña episodes (1950–present);
Cold & Warm Episodes by Season, available at:
https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php, last access: 15 June 2020. a
Patrick, M., Dietterich, H., Lyons, J., Diefenbach, A., Parcheta, C., Anderson, K., Namiki, A., Sumita, I., Shiro, B., and Kauahikaua, J.: Cyclic lava effusion during the 2018 eruption of Kīlauea Volcano: data release, US Geological Survey data release, https://doi.org/10.5066/P9PJZ17R, 2019. a, b
Pattantyus, A. K., Businger, S., and Howell, S. G.: Review of sulfur dioxide to sulfate aerosol chemistry at Kīlauea Volcano, Hawai'i, Atmos. Environ., 185, 262–271, 2018. a
Pincus, R., Platnick, S., Ackerman, S. A., Hemler, R. S., and Hofmann, R.
J. P.: Reconciling Simulated and Observed Views of Clouds: MODIS, ISCCP, and
the Limits of Instrument Simulators, J. Climate, 25, 4699–4720,
https://doi.org/10.1175/JCLI-D-11-00267.1, 2012. a
Potter, L., Kreidenweis, S., Huebert, B., Howell, S., Zhuang, J., and Morman,
M.: Variability of sulfate aerosol concentrations at Mauna Loa observatory, Hawaii, in: AIP Conference Proceedings, vol. 1527, American Institute of Physics, 23–28 June 2013, Fort Collins, Colorado, USA, 519–522, 2013. a
Randles, C. A., da Silva, A. M., Buchard, V., Colarco, P. R., Darmenov, A.,
Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., Hair, J., Shinozuka,
Y., and Flynn, C. J.: The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation, J. Climate, 30, 6823–6850, https://doi.org/10.1175/JCLI-D-16-0609.1, 2017. a, b
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., and Wang, W.: An
improved in situ and satellite SST analysis for climate, J. Climate, 15,
1609–1625, 2002. a
Rienecker, M., Suarez, M., Todling, R., Bacmeister, J., Takacs, L., Liu, H.-C., Gu, W., Sienkiewicz, M., Koster, R., Gelaro, R., Stajner, I., and Nielsen, J.: The GEOS-5 Data Assimilation System – Documentation of
Versions 5.0.1, 5.1.0, and 5.2.0., in: vol. 27 of Technical Report Series
on Global Modeling and Data Assimilation, NASA Goddard Space Flight Center, Greenbelt, MD, USA, 2008. a
Seinfeld, J. H., Bretherton, C., Carslaw, K. S., Coe, H., DeMott, P. J.,
Dunlea, E. J., Feingold, G., Ghan, S., Guenther, A. B., Kahn, R., Kraucunas, I., Kreidenweis, S. M., Molina, M. J., Nenes, A., Penner, J. E., Prather, K. A., Ramanathan, V., Ramaswamy, V., Rasch, P. J., Ravishankara, A. R., Rosenfeld, D., Stephens, G., and Wood, R.: Improving our fundamental understanding of the role of aerosol–cloud interactions in the climate system, P. Natl. Acad. Sci. USA, 113, 5781–5790, 2016. a
Takacs, L. L., Suárez, M. J., and Todling, R.: The stability of incremental analysis update, Mon. Weather Rev., 146, 3259–3275, 2018. a
Takahashi, C. and Watanabe, M.: Pacific trade winds accelerated by aerosol
forcing over the past two decades, Nat. Clim. Change, 6, 768–772, 2016. a
Tang, Y., Tong, D. Q., Yang, K., Lee, P., Baker, B., Crawford, A., Luke, W.,
Stein, A., Campbell, P. C., Ring, A., Flynn, J., Wang, Y., McQueen, J., Pan, L., Huang, J., and Stajner, I.: Air quality impacts of the 2018 Mt. Kilauea Volcano eruption in Hawaii: A regional chemical transport model study with satellite-constrained emissions, Atmos, Environ., 237, 117648, https://doi.org/10.1016/j.atmosenv.2020.117648, 2020. a
Wilson, D., Elias, T., Orr, T., Patrick, M., Sutton, J., and Swanson, D.: Small explosion from new vent at Kilauea's summit, Eos Trans. Am. Geophys. Union, 89, 203–203, 2008. a
Yang, K.: OMPS-NPP L2 NM Sulfur Dioxide (SO2) Total and Tropospheric Column swath orbital V2, GES DISC – Goddard Earth Sciences Data and Information Services Center, Greenbelt, MD, USA, https://doi.org/10.5067/A9O02ZH0J94R, 2017. a
Short summary
Increases in atmospheric aerosols affect the scattering and absorption of solar radiation by altering the macrophysical and microphysical processes of clouds. We analyzed aerosol–cloud interactions in response to degassing events from the Kilauea volcano in 2008 and 2018 by comparing satellite and simulated cloud properties. Results showed a threshold response to overcome meteorological effects that is largely controlled by aerosol concentration, composition, plume height, and ENSO state.
Increases in atmospheric aerosols affect the scattering and absorption of solar radiation by...
Altmetrics
Final-revised paper
Preprint