Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4579-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-4579-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
28 Sep 2020
Model description paper |
| 28 Sep 2020
Simulating the forest fire plume dispersion, chemistry, and aerosol formation using SAM-ASP version 1.0
Chantelle R. Lonsdale
Atmospheric and Environmental Research (AER), Lexington, MA 02421,
USA
Matthew J. Alvarado
CORRESPONDING AUTHOR
Atmospheric and Environmental Research (AER), Lexington, MA 02421,
USA
Anna L. Hodshire
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO 80523, USA
Emily Ramnarine
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO 80523, USA
Jeffrey R. Pierce
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO 80523, USA
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Biomass burning emits particles and vapors that can impact both health and climate. Here, we investigate the role of dilution in the evolution of aerosol size and composition in observed US wildfire smoke plumes. Centers of plumes dilute more slowly than edges. We see differences in concentrations and composition between the centers and edges both in the first measurement and in subsequent measurements. Our findings support the hypothesis that plume dilution influences smoke aging.
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Atmos. Chem. Phys., 19, 6561–6577, https://doi.org/10.5194/acp-19-6561-2019, https://doi.org/10.5194/acp-19-6561-2019, 2019
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Biomass burning aerosols have important global radiative effects that depend on particle size. However, model estimates of these effects do not explicitly account for the coagulation of particles in biomass burning plumes. In this work, we present the first use of a sub-grid coagulation scheme in a global aerosol model to account for in-plume coagulation. We find that this in-plume coagulation leads to important changes in the biomass burning aerosol radiative effects.
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Dimethyl sulfide (DMS) is mostly emitted from ocean surfaces and represents the largest natural source of sulfur to the atmosphere. Once in the atmosphere DMS forms stable oxidation products such as SO2 and H2SO4 which can subsequently contribute to airborne particle formation and growth. In this study we update the DMS oxidation mechanism in the chemical transport model GEOS-Chem and describe resulting changes in particle growth as well as the overall global sulfur budget.
Lixu Jin, Wade Permar, Vanessa Selimovic, Damien Ketcherside, Robert J. Yokelson, Rebecca S. Hornbrook, Eric C. Apel, I-Ting Ku, Jeffrey L. Collett Jr., Amy P. Sullivan, Daniel A. Jaffe, Jeffrey R. Pierce, Alan Fried, Matthew M. Coggon, Georgios I. Gkatzelis, Carsten Warneke, Emily V. Fischer, and Lu Hu
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Haihui Zhu, Randall V. Martin, Betty Croft, Shixian Zhai, Chi Li, Liam Bindle, Jeffrey R. Pierce, Rachel Y.-W. Chang, Bruce E. Anderson, Luke D. Ziemba, Johnathan W. Hair, Richard A. Ferrare, Chris A. Hostetler, Inderjeet Singh, Deepangsu Chatterjee, Jose L. Jimenez, Pedro Campuzano-Jost, Benjamin A. Nault, Jack E. Dibb, Joshua S. Schwarz, and Andrew Weinheimer
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Particle size of atmospheric aerosol is important for estimating its climate and health effects, but simulating atmospheric aerosol size is computationally demanding. This study derives a simple parameterization of the size of organic and secondary inorganic ambient aerosol that can be applied to atmospheric models. Applying this parameterization allows a better representation of the global spatial pattern of aerosol size, as verified by ground and airborne measurements.
Nicole A. June, Anna L. Hodshire, Elizabeth B. Wiggins, Edward L. Winstead, Claire E. Robinson, K. Lee Thornhill, Kevin J. Sanchez, Richard H. Moore, Demetrios Pagonis, Hongyu Guo, Pedro Campuzano-Jost, Jose L. Jimenez, Matthew M. Coggon, Jonathan M. Dean-Day, T. Paul Bui, Jeff Peischl, Robert J. Yokelson, Matthew J. Alvarado, Sonia M. Kreidenweis, Shantanu H. Jathar, and Jeffrey R. Pierce
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Anna L. Hodshire, Ezra J. T. Levin, A. Gannet Hallar, Christopher N. Rapp, Dan R. Gilchrist, Ian McCubbin, and Gavin R. McMeeking
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Mathew Sebastian, Sobhan Kumar Kompalli, Vasudevan Anil Kumar, Sandhya Jose, S. Suresh Babu, Govindan Pandithurai, Sachchidanand Singh, Rakesh K. Hooda, Vijay K. Soni, Jeffrey R. Pierce, Ville Vakkari, Eija Asmi, Daniel M. Westervelt, Antti-Pekka Hyvärinen, and Vijay P. Kanawade
Atmos. Chem. Phys., 22, 4491–4508, https://doi.org/10.5194/acp-22-4491-2022, https://doi.org/10.5194/acp-22-4491-2022, 2022
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Characteristics of particle number size distributions and new particle formation in six locations in India were analyzed. New particle formation occurred frequently during the pre-monsoon (spring) season and it significantly modulates the shape of the particle number size distributions. The contribution of newly formed particles to cloud condensation nuclei concentrations was ~3 times higher in urban locations than in mountain background locations.
Anna L. Hodshire, Ezra J. T. Levin, A. Gannet Hallar, Christopher N. Rapp, Dan R. Gilchrist, Ian McCubbin, and Gavin R. McMeeking
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-29, https://doi.org/10.5194/acp-2022-29, 2022
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The new Continuous Flow Diffusion Chamber-Ice Activation Spectrometer collected 4 months of ice nucleating particle (INP) measurements at a 5-minute resolution at the mountainside Storm Peak Laboratory. Most long-term INP measurements are at a time resolution of a day or longer: our instrument is a promising advance towards high-resolution long-term INP measurements. We observe higher peak INP concentrations than previous mountain studies, possibly due to the higher time resolution of our data.
Michael Cheeseman, Bonne Ford, Zoey Rosen, Eric Wendt, Alex DesRosiers, Aaron J. Hill, Christian L'Orange, Casey Quinn, Marilee Long, Shantanu H. Jathar, John Volckens, and Jeffrey R. Pierce
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-751, https://doi.org/10.5194/acp-2021-751, 2021
Revised manuscript not accepted
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Eric A. Wendt, Casey Quinn, Christian L'Orange, Daniel D. Miller-Lionberg, Bonne Ford, Jeffrey R. Pierce, John Mehaffy, Michael Cheeseman, Shantanu H. Jathar, David H. Hagan, Zoey Rosen, Marilee Long, and John Volckens
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Fine particulate matter air pollution is one of the leading contributors to adverse health outcomes on the planet. Here, we describe the design and validation of a low-cost, compact, and autonomous instrument capable of measuring particulate matter levels directly, via mass sampling, and optically, via mass and sunlight extinction measurements. We demonstrate the instrument's accuracy relative to reference measurements and its potential for community-level sampling.
Anna L. Hodshire, Emily Ramnarine, Ali Akherati, Matthew L. Alvarado, Delphine K. Farmer, Shantanu H. Jathar, Sonia M. Kreidenweis, Chantelle R. Lonsdale, Timothy B. Onasch, Stephen R. Springston, Jian Wang, Yang Wang, Lawrence I. Kleinman, Arthur J. Sedlacek III, and Jeffrey R. Pierce
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Biomass burning emits particles and vapors that can impact both health and climate. Here, we investigate the role of dilution in the evolution of aerosol size and composition in observed US wildfire smoke plumes. Centers of plumes dilute more slowly than edges. We see differences in concentrations and composition between the centers and edges both in the first measurement and in subsequent measurements. Our findings support the hypothesis that plume dilution influences smoke aging.
Betty Croft, Randall V. Martin, Richard H. Moore, Luke D. Ziemba, Ewan C. Crosbie, Hongyu Liu, Lynn M. Russell, Georges Saliba, Armin Wisthaler, Markus Müller, Arne Schiller, Martí Galí, Rachel Y.-W. Chang, Erin E. McDuffie, Kelsey R. Bilsback, and Jeffrey R. Pierce
Atmos. Chem. Phys., 21, 1889–1916, https://doi.org/10.5194/acp-21-1889-2021, https://doi.org/10.5194/acp-21-1889-2021, 2021
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Agnieszka Kupc, Christina J. Williamson, Anna L. Hodshire, Jan Kazil, Eric Ray, T. Paul Bui, Maximilian Dollner, Karl D. Froyd, Kathryn McKain, Andrew Rollins, Gregory P. Schill, Alexander Thames, Bernadett B. Weinzierl, Jeffrey R. Pierce, and Charles A. Brock
Atmos. Chem. Phys., 20, 15037–15060, https://doi.org/10.5194/acp-20-15037-2020, https://doi.org/10.5194/acp-20-15037-2020, 2020
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Tropical upper troposphere over the Atlantic and Pacific oceans is a major source region of new particles. These particles are associated with the outflow from deep convection. We investigate the processes that govern the formation of these particles and their initial growth and show that none of the formation schemes commonly used in global models are consistent with observations. Using newer schemes indicates that organic compounds are likely important as nucleating and initial growth agents.
Lawrence I. Kleinman, Arthur J. Sedlacek III, Kouji Adachi, Peter R. Buseck, Sonya Collier, Manvendra K. Dubey, Anna L. Hodshire, Ernie Lewis, Timothy B. Onasch, Jeffery R. Pierce, John Shilling, Stephen R. Springston, Jian Wang, Qi Zhang, Shan Zhou, and Robert J. Yokelson
Atmos. Chem. Phys., 20, 13319–13341, https://doi.org/10.5194/acp-20-13319-2020, https://doi.org/10.5194/acp-20-13319-2020, 2020
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Aerosols from wildfires affect the Earth's temperature by absorbing light or reflecting it back into space. This study investigates time-dependent chemical, microphysical, and optical properties of aerosols generated by wildfires in the Pacific Northwest, USA. Wildfire smoke plumes were traversed by an instrumented aircraft at locations near the fire and up to 3.5 h travel time downwind. Although there was no net aerosol production, aerosol particles grew and became more efficient scatters.
W. Richard Leaitch, John K. Kodros, Megan D. Willis, Sarah Hanna, Hannes Schulz, Elisabeth Andrews, Heiko Bozem, Julia Burkart, Peter Hoor, Felicia Kolonjari, John A. Ogren, Sangeeta Sharma, Meng Si, Knut von Salzen, Allan K. Bertram, Andreas Herber, Jonathan P. D. Abbatt, and Jeffrey R. Pierce
Atmos. Chem. Phys., 20, 10545–10563, https://doi.org/10.5194/acp-20-10545-2020, https://doi.org/10.5194/acp-20-10545-2020, 2020
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Alma Hodzic, Pedro Campuzano-Jost, Huisheng Bian, Mian Chin, Peter R. Colarco, Douglas A. Day, Karl D. Froyd, Bernd Heinold, Duseong S. Jo, Joseph M. Katich, John K. Kodros, Benjamin A. Nault, Jeffrey R. Pierce, Eric Ray, Jacob Schacht, Gregory P. Schill, Jason C. Schroder, Joshua P. Schwarz, Donna T. Sueper, Ina Tegen, Simone Tilmes, Kostas Tsigaridis, Pengfei Yu, and Jose L. Jimenez
Atmos. Chem. Phys., 20, 4607–4635, https://doi.org/10.5194/acp-20-4607-2020, https://doi.org/10.5194/acp-20-4607-2020, 2020
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Organic aerosol (OA) is a key source of uncertainty in aerosol climate effects. We present the first pole-to-pole OA characterization during the NASA Atmospheric Tomography aircraft mission. OA has a strong seasonal and zonal variability, with the highest levels in summer and over fire-influenced regions and the lowest ones in the southern high latitudes. We show that global models predict the OA distribution well but not the relative contribution of OA emissions vs. chemical production.
Sidhant J. Pai, Colette L. Heald, Jeffrey R. Pierce, Salvatore C. Farina, Eloise A. Marais, Jose L. Jimenez, Pedro Campuzano-Jost, Benjamin A. Nault, Ann M. Middlebrook, Hugh Coe, John E. Shilling, Roya Bahreini, Justin H. Dingle, and Kennedy Vu
Atmos. Chem. Phys., 20, 2637–2665, https://doi.org/10.5194/acp-20-2637-2020, https://doi.org/10.5194/acp-20-2637-2020, 2020
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Aerosols in the atmosphere have significant health and climate impacts. Organic aerosol (OA) accounts for a large fraction of the total aerosol burden, but models have historically struggled to accurately simulate it. This study compares two very different OA model schemes and evaluates them against a suite of globally distributed airborne measurements with the goal of providing insight into the strengths and weaknesses of each approach across different environments.
Mark W. Shephard, Enrico Dammers, Karen E. Cady-Pereira, Shailesh K. Kharol, Jesse Thompson, Yonatan Gainariu-Matz, Junhua Zhang, Chris A. McLinden, Andrew Kovachik, Michael Moran, Shabtai Bittman, Christopher E. Sioris, Debora Griffin, Matthew J. Alvarado, Chantelle Lonsdale, Verica Savic-Jovcic, and Qiong Zheng
Atmos. Chem. Phys., 20, 2277–2302, https://doi.org/10.5194/acp-20-2277-2020, https://doi.org/10.5194/acp-20-2277-2020, 2020
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Presented is a description and survey demonstrating the capabilities of the CrIS ammonia product for monitoring, air quality forecast model evaluation, dry deposition estimates, and emission estimates of an agricultural hotspot.
Bonne Ford, Jeffrey R. Pierce, Eric Wendt, Marilee Long, Shantanu Jathar, John Mehaffy, Jessica Tryner, Casey Quinn, Lizette van Zyl, Christian L'Orange, Daniel Miller-Lionberg, and John Volckens
Atmos. Meas. Tech., 12, 6385–6399, https://doi.org/10.5194/amt-12-6385-2019, https://doi.org/10.5194/amt-12-6385-2019, 2019
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This study demonstrates the use of a low-cost sensor in a citizen-science network, Citizen-Enabled Aerosol Measurements for Satellites (CEAMS), to measure air quality in participants’ backyards. The pilot network was conducted in the fall and winter of 2017 in northern Colorado. Measurements of aerosols taken by the citizens are also compared to standard air quality instruments.
Eric A. Wendt, Casey W. Quinn, Daniel D. Miller-Lionberg, Jessica Tryner, Christian L'Orange, Bonne Ford, Azer P. Yalin, Jeffrey R. Pierce, Shantanu Jathar, and John Volckens
Atmos. Meas. Tech., 12, 5431–5441, https://doi.org/10.5194/amt-12-5431-2019, https://doi.org/10.5194/amt-12-5431-2019, 2019
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We introduce a low-cost, compact device (aerosol mass and optical depth (AMOD) sampler) that can be used by citizen scientists to measure air quality. Our paper discusses the development and different components for measuring aerosols. It also shows that measurements made by the AMOD next to reference-grade monitors agreed within 10 %. Coupled with the cost of these instruments, this agreement demonstrates that the AMOD can be widely deployed to monitor air quality by citizen scientists.
George S. Fanourgakis, Maria Kanakidou, Athanasios Nenes, Susanne E. Bauer, Tommi Bergman, Ken S. Carslaw, Alf Grini, Douglas S. Hamilton, Jill S. Johnson, Vlassis A. Karydis, Alf Kirkevåg, John K. Kodros, Ulrike Lohmann, Gan Luo, Risto Makkonen, Hitoshi Matsui, David Neubauer, Jeffrey R. Pierce, Julia Schmale, Philip Stier, Kostas Tsigaridis, Twan van Noije, Hailong Wang, Duncan Watson-Parris, Daniel M. Westervelt, Yang Yang, Masaru Yoshioka, Nikos Daskalakis, Stefano Decesari, Martin Gysel-Beer, Nikos Kalivitis, Xiaohong Liu, Natalie M. Mahowald, Stelios Myriokefalitakis, Roland Schrödner, Maria Sfakianaki, Alexandra P. Tsimpidi, Mingxuan Wu, and Fangqun Yu
Atmos. Chem. Phys., 19, 8591–8617, https://doi.org/10.5194/acp-19-8591-2019, https://doi.org/10.5194/acp-19-8591-2019, 2019
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Effects of aerosols on clouds are important for climate studies but are among the largest uncertainties in climate projections. This study evaluates the skill of global models to simulate aerosol, cloud condensation nuclei (CCN) and cloud droplet number concentrations (CDNCs). Model results show reduced spread in CDNC compared to CCN due to the negative correlation between the sensitivities of CDNC to aerosol number concentration (air pollution) and updraft velocity (atmospheric dynamics).
Emily Ramnarine, John K. Kodros, Anna L. Hodshire, Chantelle R. Lonsdale, Matthew J. Alvarado, and Jeffrey R. Pierce
Atmos. Chem. Phys., 19, 6561–6577, https://doi.org/10.5194/acp-19-6561-2019, https://doi.org/10.5194/acp-19-6561-2019, 2019
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Biomass burning aerosols have important global radiative effects that depend on particle size. However, model estimates of these effects do not explicitly account for the coagulation of particles in biomass burning plumes. In this work, we present the first use of a sub-grid coagulation scheme in a global aerosol model to account for in-plume coagulation. We find that this in-plume coagulation leads to important changes in the biomass burning aerosol radiative effects.
Anna L. Hodshire, Pedro Campuzano-Jost, John K. Kodros, Betty Croft, Benjamin A. Nault, Jason C. Schroder, Jose L. Jimenez, and Jeffrey R. Pierce
Atmos. Chem. Phys., 19, 3137–3160, https://doi.org/10.5194/acp-19-3137-2019, https://doi.org/10.5194/acp-19-3137-2019, 2019
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A global chemical-transport model is used to determine the impact of methanesulfonic acid (MSA) on the aerosol size distribution and associated radiative effects, testing varying assumptions of MSA’s effective volatility and nucleating ability. We find that MSA mass best matches the ATom airborne measurements when volatility varies as a function of temperature, relative humidity, and available gas-phase bases, and the MSA radiative forcing is on the order of -50 mW m-2 over the Southern Ocean.
Betty Croft, Randall V. Martin, W. Richard Leaitch, Julia Burkart, Rachel Y.-W. Chang, Douglas B. Collins, Patrick L. Hayes, Anna L. Hodshire, Lin Huang, John K. Kodros, Alexander Moravek, Emma L. Mungall, Jennifer G. Murphy, Sangeeta Sharma, Samantha Tremblay, Gregory R. Wentworth, Megan D. Willis, Jonathan P. D. Abbatt, and Jeffrey R. Pierce
Atmos. Chem. Phys., 19, 2787–2812, https://doi.org/10.5194/acp-19-2787-2019, https://doi.org/10.5194/acp-19-2787-2019, 2019
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Summertime Arctic atmospheric aerosols are strongly controlled by processes related to natural regional sources. We use a chemical transport model with size-resolved aerosol microphysics to interpret measurements made during summertime 2016 in the Canadian Arctic Archipelago. Our results explore the processes that control summertime aerosol size distributions and support a climate-relevant role for Arctic marine secondary organic aerosol formed from precursor vapors with Arctic marine sources.
Jonathan P. D. Abbatt, W. Richard Leaitch, Amir A. Aliabadi, Allan K. Bertram, Jean-Pierre Blanchet, Aude Boivin-Rioux, Heiko Bozem, Julia Burkart, Rachel Y. W. Chang, Joannie Charette, Jai P. Chaubey, Robert J. Christensen, Ana Cirisan, Douglas B. Collins, Betty Croft, Joelle Dionne, Greg J. Evans, Christopher G. Fletcher, Martí Galí, Roya Ghahreman, Eric Girard, Wanmin Gong, Michel Gosselin, Margaux Gourdal, Sarah J. Hanna, Hakase Hayashida, Andreas B. Herber, Sareh Hesaraki, Peter Hoor, Lin Huang, Rachel Hussherr, Victoria E. Irish, Setigui A. Keita, John K. Kodros, Franziska Köllner, Felicia Kolonjari, Daniel Kunkel, Luis A. Ladino, Kathy Law, Maurice Levasseur, Quentin Libois, John Liggio, Martine Lizotte, Katrina M. Macdonald, Rashed Mahmood, Randall V. Martin, Ryan H. Mason, Lisa A. Miller, Alexander Moravek, Eric Mortenson, Emma L. Mungall, Jennifer G. Murphy, Maryam Namazi, Ann-Lise Norman, Norman T. O'Neill, Jeffrey R. Pierce, Lynn M. Russell, Johannes Schneider, Hannes Schulz, Sangeeta Sharma, Meng Si, Ralf M. Staebler, Nadja S. Steiner, Jennie L. Thomas, Knut von Salzen, Jeremy J. B. Wentzell, Megan D. Willis, Gregory R. Wentworth, Jun-Wei Xu, and Jacqueline D. Yakobi-Hancock
Atmos. Chem. Phys., 19, 2527–2560, https://doi.org/10.5194/acp-19-2527-2019, https://doi.org/10.5194/acp-19-2527-2019, 2019
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The Arctic is experiencing considerable environmental change with climate warming, illustrated by the dramatic decrease in sea-ice extent. It is important to understand both the natural and perturbed Arctic systems to gain a better understanding of how they will change in the future. This paper summarizes new insights into the relationships between Arctic aerosol particles and climate, as learned over the past five or so years by a large Canadian research consortium, NETCARE.
Ningxin Wang, Spiro D. Jorga, Jeffery R. Pierce, Neil M. Donahue, and Spyros N. Pandis
Atmos. Meas. Tech., 11, 6577–6588, https://doi.org/10.5194/amt-11-6577-2018, https://doi.org/10.5194/amt-11-6577-2018, 2018
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The interaction of particles with the chamber walls has been a significant source of uncertainty when analyzing results of secondary organic aerosol formation experiments performed in Teflon chambers. We evaluated the performance of several particle wall-loss correction methods for aging experiments of α-pinene ozonolysis products. Experimental procedures are proposed for the characterization of particle losses during different stages of these experiments.
Anna L. Hodshire, Brett B. Palm, M. Lizabeth Alexander, Qijing Bian, Pedro Campuzano-Jost, Eben S. Cross, Douglas A. Day, Suzane S. de Sá, Alex B. Guenther, Armin Hansel, James F. Hunter, Werner Jud, Thomas Karl, Saewung Kim, Jesse H. Kroll, Jeong-Hoo Park, Zhe Peng, Roger Seco, James N. Smith, Jose L. Jimenez, and Jeffrey R. Pierce
Atmos. Chem. Phys., 18, 12433–12460, https://doi.org/10.5194/acp-18-12433-2018, https://doi.org/10.5194/acp-18-12433-2018, 2018
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We investigate the nucleation and growth processes that shape the aerosol size distribution inside oxidation flow reactors (OFRs) that sampled ambient air from Colorado and the Amazon rainforest. Results indicate that organics are important for both nucleation and growth, vapor uptake was limited to accumulation-mode particles, fragmentation reactions were important to limit particle growth at higher OH exposures, and an H2SO4-organics nucleation mechanism captured new particle formation well.
John K. Kodros, Sarah J. Hanna, Allan K. Bertram, W. Richard Leaitch, Hannes Schulz, Andreas B. Herber, Marco Zanatta, Julia Burkart, Megan D. Willis, Jonathan P. D. Abbatt, and Jeffrey R. Pierce
Atmos. Chem. Phys., 18, 11345–11361, https://doi.org/10.5194/acp-18-11345-2018, https://doi.org/10.5194/acp-18-11345-2018, 2018
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The mixing state of black carbon is one of the key uncertainties limiting the ability of models to estimate the direct radiative effect. In this work, we present aircraft measurements from the Canadian Arctic of coating thickness as a function of black carbon core diameter and black-carbon-containing particle number fractions. We use these measurements to inform estimates of the direct radiative effect in Arctic aerosol simulations.
Jingqiu Mao, Annmarie Carlton, Ronald C. Cohen, William H. Brune, Steven S. Brown, Glenn M. Wolfe, Jose L. Jimenez, Havala O. T. Pye, Nga Lee Ng, Lu Xu, V. Faye McNeill, Kostas Tsigaridis, Brian C. McDonald, Carsten Warneke, Alex Guenther, Matthew J. Alvarado, Joost de Gouw, Loretta J. Mickley, Eric M. Leibensperger, Rohit Mathur, Christopher G. Nolte, Robert W. Portmann, Nadine Unger, Mika Tosca, and Larry W. Horowitz
Atmos. Chem. Phys., 18, 2615–2651, https://doi.org/10.5194/acp-18-2615-2018, https://doi.org/10.5194/acp-18-2615-2018, 2018
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This paper is aimed at discussing progress in evaluating, diagnosing, and improving air quality and climate modeling using comparisons to SAS observations as a guide to thinking about improvements to mechanisms and parameterizations in models.
Haihan Chen, Anna L. Hodshire, John Ortega, James Greenberg, Peter H. McMurry, Annmarie G. Carlton, Jeffrey R. Pierce, Dave R. Hanson, and James N. Smith
Atmos. Chem. Phys., 18, 311–326, https://doi.org/10.5194/acp-18-311-2018, https://doi.org/10.5194/acp-18-311-2018, 2018
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Much of what we know about atmospheric new particle formation (NPF) is based on ground-level measurements. We used tethered balloon measurements and remote sensing to study the location in the boundary layer in which NPF events are initiated, the degree to which the boundary layer is well-mixed during NPF, and the potential role that water may play in aerosol particle chemical evolution. This information will improve the representativeness of process level models and laboratory experiments.
Roya Ghahreman, Ann-Lise Norman, Betty Croft, Randall V. Martin, Jeffrey R. Pierce, Julia Burkart, Ofelia Rempillo, Heiko Bozem, Daniel Kunkel, Jennie L. Thomas, Amir A. Aliabadi, Gregory R. Wentworth, Maurice Levasseur, Ralf M. Staebler, Sangeeta Sharma, and W. Richard Leaitch
Atmos. Chem. Phys., 17, 8757–8770, https://doi.org/10.5194/acp-17-8757-2017, https://doi.org/10.5194/acp-17-8757-2017, 2017
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We present spring and summertime vertical profile measurements of Arctic dimethyl sulfide (DMS), together with model simulations to consider what these profiles indicate about DMS sources and lifetimes in the Arctic. Our results highlight the role of local open water as the source of DMS(g) during July 2014 and the influence of long-range transport of DMS(g) from further afield in the Arctic during April 2015.
Bonne Ford, Moira Burke, William Lassman, Gabriele Pfister, and Jeffrey R. Pierce
Atmos. Chem. Phys., 17, 7541–7554, https://doi.org/10.5194/acp-17-7541-2017, https://doi.org/10.5194/acp-17-7541-2017, 2017
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We explore using the percent of Facebook posters mentioning
smokeor
air qualityto assess exposure to wildfire smoke in the western US during summer 2015. We compare this de-identified, aggregated Facebook dataset to satellite observations, surface measurements, and model-simulated concentrations, and we find good agreement in smoke-impacted regions. Our results suggest that aggregate social media data can be used to supplement traditional datasets to estimate smoke exposure.
Petros Vasilakos, Yong-Ηa Kim, Jeffrey R. Pierce, Sotira Yiacoumi, Costas Tsouris, and Athanasios Nenes
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-96, https://doi.org/10.5194/gmd-2017-96, 2017
Revised manuscript not accepted
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Radioactive charging can significantly impact the way radioactive aerosols behave, and as a result their lifetime, but such effects are neglected in predictive model studies of radioactive plumes. We extend a well-established model that simulates the evolution of atmospheric particulate matter to account for radioactive charging effects in an accurate and computationally efficient way. It is shown that radioactivity can strongly impact the deposition patterns of aerosol.
Qijing Bian, Shantanu H. Jathar, John K. Kodros, Kelley C. Barsanti, Lindsay E. Hatch, Andrew A. May, Sonia M. Kreidenweis, and Jeffrey R. Pierce
Atmos. Chem. Phys., 17, 5459–5475, https://doi.org/10.5194/acp-17-5459-2017, https://doi.org/10.5194/acp-17-5459-2017, 2017
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In this paper, we perform simulations of the evolution of biomass-burning organic aerosol in laboratory smog-chamber experiments and ambient plumes. We find that in smog-chamber experiments, vapor wall losses lead to a large reduction in the apparent secondary organic aerosol formation. In ambient plumes, fire size and meteorology regulate the plume dilution rate, primary organic aerosol evaporation rate, and secondary organic aerosol formation rate.
Chantelle R. Lonsdale, Jennifer D. Hegarty, Karen E. Cady-Pereira, Matthew J. Alvarado, Daven K. Henze, Matthew D. Turner, Shannon L. Capps, John B. Nowak, J. Andy Neuman, Ann M. Middlebrook, Roya Bahreini, Jennifer G. Murphy, Milos Z. Markovic, Trevor C. VandenBoer, Lynn M. Russell, and Amy Jo Scarino
Atmos. Chem. Phys., 17, 2721–2739, https://doi.org/10.5194/acp-17-2721-2017, https://doi.org/10.5194/acp-17-2721-2017, 2017
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This study takes advantage of the high-resolution observations of NH3(g) made by the TES satellite instrument over Bakersfield during the CalNex campaign, along with campaign measurements, to compare CMAQ model results in the San Joaquin Valley, California. Additionally we evaluate the CMAQ bi-directional ammonia flux results using the CARB emissions inventory against these satellite and campaign measurements, not previously explored in combination.
Theodora Nah, Renee C. McVay, Jeffrey R. Pierce, John H. Seinfeld, and Nga L. Ng
Atmos. Chem. Phys., 17, 2297–2310, https://doi.org/10.5194/acp-17-2297-2017, https://doi.org/10.5194/acp-17-2297-2017, 2017
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We present a model framework that accounts for coagulation in chamber studies where high seed aerosol surface area concentrations are used. The uncertainties in the calculated SOA mass concentrations and yields between four different particle-wall loss correction methods over the series of α-pinene ozonolysis experiments are also assessed. We show that SOA mass yields calculated by the four methods can deviate significantly in studies where high seed aerosol surface area concentrations are used.
Matthew J. Alvarado, Chantelle R. Lonsdale, Helen L. Macintyre, Huisheng Bian, Mian Chin, David A. Ridley, Colette L. Heald, Kenneth L. Thornhill, Bruce E. Anderson, Michael J. Cubison, Jose L. Jimenez, Yutaka Kondo, Lokesh K. Sahu, Jack E. Dibb, and Chien Wang
Atmos. Chem. Phys., 16, 9435–9455, https://doi.org/10.5194/acp-16-9435-2016, https://doi.org/10.5194/acp-16-9435-2016, 2016
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Understanding the scattering and absorption of light by aerosols is necessary for understanding air quality and climate change. We used data from the 2008 ARCTAS campaign to evaluate aerosol optical property models using a closure methodology that separates errors in these models from other errors in aerosol emissions, chemistry, or transport. We find that the models on average perform reasonably well, and make suggestions for how remaining biases could be reduced.
Anna L. Hodshire, Michael J. Lawler, Jun Zhao, John Ortega, Coty Jen, Taina Yli-Juuti, Jared F. Brewer, Jack K. Kodros, Kelley C. Barsanti, Dave R. Hanson, Peter H. McMurry, James N. Smith, and Jeffery R. Pierce
Atmos. Chem. Phys., 16, 9321–9348, https://doi.org/10.5194/acp-16-9321-2016, https://doi.org/10.5194/acp-16-9321-2016, 2016
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Processes that control the growth of newly formed particles are not well understood and limit predictions of aerosol climate impacts. We combine state-of-the-art measurements at a central-US site with a particle-growth model to investigate the species and processes contributing to growth. Observed growth was dominated by organics, sulfate salts, or a mixture of these two. The model qualitatively captures the variability between different days.
Kimiko M. Sakamoto, James R. Laing, Robin G. Stevens, Daniel A. Jaffe, and Jeffrey R. Pierce
Atmos. Chem. Phys., 16, 7709–7724, https://doi.org/10.5194/acp-16-7709-2016, https://doi.org/10.5194/acp-16-7709-2016, 2016
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We determine how various meteorological and fire factors contribute to shaping the aged biomass-burning particle size distribution through coagulation. The mass emissions flux, fire area, and wind speed are dominant factors controlling the aged size distribution. We parameterize the aged size distribution for global/regional aerosol models. We estimate that the aged biomass-burning particle size distribution may be more sensitive to variability in coagulation than SOA formation.
John K. Kodros, Rachel Cucinotta, David A. Ridley, Christine Wiedinmyer, and Jeffrey R. Pierce
Atmos. Chem. Phys., 16, 6771–6784, https://doi.org/10.5194/acp-16-6771-2016, https://doi.org/10.5194/acp-16-6771-2016, 2016
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We provide a first estimate of the aerosol radiative effects from open, uncontrolled combustion of domestic waste. We find the direct and cloud-albedo indirect radiative effects are predominantly negative (cooling tendency) with regional forcings exceeding −0.4 W m−2; however, the magnitude of these effects depends on the assumed emitted aerosol size, mass, and optical properties.
Betty Croft, Randall V. Martin, W. Richard Leaitch, Peter Tunved, Thomas J. Breider, Stephen D. D'Andrea, and Jeffrey R. Pierce
Atmos. Chem. Phys., 16, 3665–3682, https://doi.org/10.5194/acp-16-3665-2016, https://doi.org/10.5194/acp-16-3665-2016, 2016
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Measurements at high-Arctic sites show a strong annual cycle in atmospheric particle number and size. Previous studies identified poor scientific understanding related to global model representation of Arctic particle number and size, limiting ability to simulate this environment. Here we evaluate state-of-science ability to simulate Arctic particles using GEOS-Chem-TOMAS model, documenting key roles and interconnections of particle formation, cloud-related processes and remaining uncertainties.
N. I. Kristiansen, A. Stohl, D. J. L. Olivié, B. Croft, O. A. Søvde, H. Klein, T. Christoudias, D. Kunkel, S. J. Leadbetter, Y. H. Lee, K. Zhang, K. Tsigaridis, T. Bergman, N. Evangeliou, H. Wang, P.-L. Ma, R. C. Easter, P. J. Rasch, X. Liu, G. Pitari, G. Di Genova, S. Y. Zhao, Y. Balkanski, S. E. Bauer, G. S. Faluvegi, H. Kokkola, R. V. Martin, J. R. Pierce, M. Schulz, D. Shindell, H. Tost, and H. Zhang
Atmos. Chem. Phys., 16, 3525–3561, https://doi.org/10.5194/acp-16-3525-2016, https://doi.org/10.5194/acp-16-3525-2016, 2016
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Processes affecting aerosol removal from the atmosphere are not fully understood. In this study we investigate to what extent atmospheric transport models can reproduce observed loss of aerosols. We compare measurements of radioactive isotopes, that attached to ambient sulfate aerosols during the 2011 Fukushima nuclear accident, to 19 models using identical emissions. Results indicate aerosol removal that is too fast in most models, and apply to aerosols that have undergone long-range transport.
Gregory R. Wentworth, Jennifer G. Murphy, Betty Croft, Randall V. Martin, Jeffrey R. Pierce, Jean-Sébastien Côté, Isabelle Courchesne, Jean-Éric Tremblay, Jonathan Gagnon, Jennie L. Thomas, Sangeeta Sharma, Desiree Toom-Sauntry, Alina Chivulescu, Maurice Levasseur, and Jonathan P. D. Abbatt
Atmos. Chem. Phys., 16, 1937–1953, https://doi.org/10.5194/acp-16-1937-2016, https://doi.org/10.5194/acp-16-1937-2016, 2016
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Air near the surface in the summertime Arctic is extremely clean and typically has very low concentrations of both gases and particles. However, atmospheric measurements taken throughout the Canadian Arctic in the summer of 2014 revealed higher-than-expected amounts of gaseous ammonia. It is likely the majority of this ammonia is coming from migratory seabird colonies throughout the Arctic. Seabird guano (dung) releases ammonia which could impact climate and sensitive Arctic ecosystems.
S. D. D'Andrea, J. Y. Ng, J. K. Kodros, S. A. Atwood, M. J. Wheeler, A. M. Macdonald, W. R. Leaitch, and J. R. Pierce
Atmos. Chem. Phys., 16, 383–396, https://doi.org/10.5194/acp-16-383-2016, https://doi.org/10.5194/acp-16-383-2016, 2016
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We evaluate aerosol size distributions predicted by GEOS-Chem-TOMAS using measurements from the peak of Whistler Mountain. We improve model-measurement comparisons of aerosol number, size, and composition during periods of free-tropospheric and boundary-layer influence by developing simple filtering techniques, and determine the influence of Asian anthropogenic and biomass burning emissions. The low-cost filtering techniques and source apportionment methods can be used for other mountain sites.
R. S. Humphries, R. Schofield, M. D. Keywood, J. Ward, J. R. Pierce, C. M. Gionfriddo, M. T. Tate, D. P. Krabbenhoft, I. E. Galbally, S. B. Molloy, A. R. Klekociuk, P. V. Johnston, K. Kreher, A. J. Thomas, A. D. Robinson, N. R. P. Harris, R. Johnson, and S. R. Wilson
Atmos. Chem. Phys., 15, 13339–13364, https://doi.org/10.5194/acp-15-13339-2015, https://doi.org/10.5194/acp-15-13339-2015, 2015
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An atmospheric new particle formation event that was observed in the pristine East Antarctic pack ice during a springtime voyage in 2012 is characterised in terms of formation and growth rates. Known nucleation mechanisms (e.g. those involving sulfate, iodine and organics) were unable to explain observations; however, correlations with total gaseous mercury were found, leading to the suggestion of a possible mercury-driven nucleation mechanism not previously described.
C. E. Scott, D. V. Spracklen, J. R. Pierce, I. Riipinen, S. D. D'Andrea, A. Rap, K. S. Carslaw, P. M. Forster, P. Artaxo, M. Kulmala, L. V. Rizzo, E. Swietlicki, G. W. Mann, and K. J. Pringle
Atmos. Chem. Phys., 15, 12989–13001, https://doi.org/10.5194/acp-15-12989-2015, https://doi.org/10.5194/acp-15-12989-2015, 2015
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To understand the radiative effects of biogenic secondary organic aerosol (SOA) it is necessary to consider the manner in which it is distributed across the existing aerosol size distribution. We explore the importance of the approach taken by global-scale models to do this, when calculating the direct radiative effect (DRE) & first aerosol indirect effect (AIE) due to biogenic SOA. This choice has little effect on the DRE, but a substantial impact on the magnitude and even sign of the first AIE
Q. Bian, A. A. May, S. M. Kreidenweis, and J. R. Pierce
Atmos. Chem. Phys., 15, 11027–11045, https://doi.org/10.5194/acp-15-11027-2015, https://doi.org/10.5194/acp-15-11027-2015, 2015
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Losses of semi-volatile vapors to Teflon walls may contribute to significant primary particle evaporation during wood-smoke aerosol experiments. These vapor losses may also affect secondary organic aerosol formation during these experiments.
J. K. Kodros, C. E. Scott, S. C. Farina, Y. H. Lee, C. L'Orange, J. Volckens, and J. R. Pierce
Atmos. Chem. Phys., 15, 8577–8596, https://doi.org/10.5194/acp-15-8577-2015, https://doi.org/10.5194/acp-15-8577-2015, 2015
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We examine sensitivities in aerosol concentration and climate effects from biofuel combustion emissions. We find a strong sensitivity in the overall sign and magnitude of the direct radiative effect and cloud-albedo indirect effect due to uncertainties regarding emissions size distribution, composition, mass, and optical mixing state. This uncertainty limits our ability to evaluate black carbon mitigation strategies to counter warming effects from greenhouse gases.
M. J. Alvarado, C. R. Lonsdale, R. J. Yokelson, S. K. Akagi, H. Coe, J. S. Craven, E. V. Fischer, G. R. McMeeking, J. H. Seinfeld, T. Soni, J. W. Taylor, D. R. Weise, and C. E. Wold
Atmos. Chem. Phys., 15, 6667–6688, https://doi.org/10.5194/acp-15-6667-2015, https://doi.org/10.5194/acp-15-6667-2015, 2015
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Being able to understand and simulate the chemical evolution of biomass burning smoke plumes under a wide variety of conditions is a critical part of forecasting the impact of these fires on air quality, atmospheric composition, and climate. Here we use an improved model of this chemistry to simulate the evolution of ozone and secondary organic aerosol within a young biomass burning smoke plume from the Williams prescribed burn in chaparral, which was sampled over California in November 2009.
J. R. Pierce, B. Croft, J. K. Kodros, S. D. D'Andrea, and R. V. Martin
Atmos. Chem. Phys., 15, 6147–6158, https://doi.org/10.5194/acp-15-6147-2015, https://doi.org/10.5194/acp-15-6147-2015, 2015
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In this paper we show that coagulation of cloud droplets with interstitial aerosol particles, a process often neglected in atmospheric aerosol models, has a significant impact on aerosol size distributions and radiative forcings.
S. D. D'Andrea, J. C. Acosta Navarro, S. C. Farina, C. E. Scott, A. Rap, D. K. Farmer, D. V. Spracklen, I. Riipinen, and J. R. Pierce
Atmos. Chem. Phys., 15, 2247–2268, https://doi.org/10.5194/acp-15-2247-2015, https://doi.org/10.5194/acp-15-2247-2015, 2015
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We use modeled estimates of BVOCs from the years 1000 to 2000 to test the effect of anthropogenic BVOC emission changes on SOA formation, aerosol size distributions, and radiative effects using the GEOS-Chem-TOMAS model. Changes of >25% in the number of particles with diameters >80nm are predicted regionally due to extensive land-use changes, leading to increases in combined radiative effect of >0.5 Wm-2. This change in radiative forcing could be an overlooked anthropogenic effect on climate.
M. J. Alvarado, V. H. Payne, K. E. Cady-Pereira, J. D. Hegarty, S. S. Kulawik, K. J. Wecht, J. R. Worden, J. V. Pittman, and S. C. Wofsy
Atmos. Meas. Tech., 8, 965–985, https://doi.org/10.5194/amt-8-965-2015, https://doi.org/10.5194/amt-8-965-2015, 2015
K. M. Sakamoto, J. D. Allan, H. Coe, J. W. Taylor, T. J. Duck, and J. R. Pierce
Atmos. Chem. Phys., 15, 1633–1646, https://doi.org/10.5194/acp-15-1633-2015, https://doi.org/10.5194/acp-15-1633-2015, 2015
M. D. Gibson, J. Haelssig, J. R. Pierce, M. Parrington, J. E. Franklin, J. T. Hopper, Z. Li, and T. J. Ward
Atmos. Chem. Phys., 15, 815–827, https://doi.org/10.5194/acp-15-815-2015, https://doi.org/10.5194/acp-15-815-2015, 2015
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This paper presents a quantitative comparison of the four most commonly used receptor models, namely absolute principal component scores, pragmatic mass closure, chemical mass balance, and positive matrix factorization. The receptor models were used to predict the contributions of boreal wild-fire smoke and other sources to PM2.5 mass in Halifax, Nova Scotia, Canada during the BORTAS-B experiment. This paper also presents a new woodsmoke PM2.5 enrichment factor (levoglucosan x 52).
R. G. Stevens and J. R. Pierce
Atmos. Chem. Phys., 14, 13661–13679, https://doi.org/10.5194/acp-14-13661-2014, https://doi.org/10.5194/acp-14-13661-2014, 2014
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We implement a parameterization of sub-grid new-particle formation in sulfur-rich plumes (P6) for the first time into a global chemical-transport model with online aerosol microphysics. Compared with previous treatments of sub-grid particle formation, use of the P6 parameterization limits sub-grid particle formation in polluted or low-sunlight regions. We also test the sensitivity of sub-grid particle formation to changes in SO2 or NOx emissions due to emissions controls.
V. H. Payne, M. J. Alvarado, K. E. Cady-Pereira, J. R. Worden, S. S. Kulawik, and E. V. Fischer
Atmos. Meas. Tech., 7, 3737–3749, https://doi.org/10.5194/amt-7-3737-2014, https://doi.org/10.5194/amt-7-3737-2014, 2014
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Peroxyacetyl nitrate (PAN) plays an important role in the distribution of lower-atmospheric ozone. PAN can be transported far from the original pollution source, leading to ozone formation and degraded air quality in remote areas. Satellite observations from the Tropospheric Emission Spectrometer (TES) are sensitive to PAN at lower altitude than previous global data sets. We describe characteristics of the data and show elevated PAN associated with boreal fires and outflow of Asian pollution.
J. R. Pierce, D. M. Westervelt, S. A. Atwood, E. A. Barnes, and W. R. Leaitch
Atmos. Chem. Phys., 14, 8647–8663, https://doi.org/10.5194/acp-14-8647-2014, https://doi.org/10.5194/acp-14-8647-2014, 2014
D. M. Westervelt, J. R. Pierce, and P. J. Adams
Atmos. Chem. Phys., 14, 5577–5597, https://doi.org/10.5194/acp-14-5577-2014, https://doi.org/10.5194/acp-14-5577-2014, 2014
C. L. Heald, D. A. Ridley, J. H. Kroll, S. R. H. Barrett, K. E. Cady-Pereira, M. J. Alvarado, and C. D. Holmes
Atmos. Chem. Phys., 14, 5513–5527, https://doi.org/10.5194/acp-14-5513-2014, https://doi.org/10.5194/acp-14-5513-2014, 2014
B. Croft, J. R. Pierce, and R. V. Martin
Atmos. Chem. Phys., 14, 4313–4325, https://doi.org/10.5194/acp-14-4313-2014, https://doi.org/10.5194/acp-14-4313-2014, 2014
R. G. Stevens and J. R. Pierce
Atmos. Chem. Phys., 13, 12117–12133, https://doi.org/10.5194/acp-13-12117-2013, https://doi.org/10.5194/acp-13-12117-2013, 2013
S. D. D'Andrea, S. A. K. Häkkinen, D. M. Westervelt, C. Kuang, E. J. T. Levin, V. P. Kanawade, W. R. Leaitch, D. V. Spracklen, I. Riipinen, and J. R. Pierce
Atmos. Chem. Phys., 13, 11519–11534, https://doi.org/10.5194/acp-13-11519-2013, https://doi.org/10.5194/acp-13-11519-2013, 2013
G. S. Stuart, R. G. Stevens, A.-I. Partanen, A. K. L. Jenkins, H. Korhonen, P. M. Forster, D. V. Spracklen, and J. R. Pierce
Atmos. Chem. Phys., 13, 10385–10396, https://doi.org/10.5194/acp-13-10385-2013, https://doi.org/10.5194/acp-13-10385-2013, 2013
L. A. Lee, K. J. Pringle, C. L. Reddington, G. W. Mann, P. Stier, D. V. Spracklen, J. R. Pierce, and K. S. Carslaw
Atmos. Chem. Phys., 13, 8879–8914, https://doi.org/10.5194/acp-13-8879-2013, https://doi.org/10.5194/acp-13-8879-2013, 2013
Y. H. Lee, J. R. Pierce, and P. J. Adams
Geosci. Model Dev., 6, 1221–1232, https://doi.org/10.5194/gmd-6-1221-2013, https://doi.org/10.5194/gmd-6-1221-2013, 2013
S. A. K. Häkkinen, H. E. Manninen, T. Yli-Juuti, J. Merikanto, M. K. Kajos, T. Nieminen, S. D. D'Andrea, A. Asmi, J. R. Pierce, M. Kulmala, and I. Riipinen
Atmos. Chem. Phys., 13, 7665–7682, https://doi.org/10.5194/acp-13-7665-2013, https://doi.org/10.5194/acp-13-7665-2013, 2013
D. M. Westervelt, J. R. Pierce, I. Riipinen, W. Trivitayanurak, A. Hamed, M. Kulmala, A. Laaksonen, S. Decesari, and P. J. Adams
Atmos. Chem. Phys., 13, 7645–7663, https://doi.org/10.5194/acp-13-7645-2013, https://doi.org/10.5194/acp-13-7645-2013, 2013
M. D. Gibson, J. R. Pierce, D. Waugh, J. S. Kuchta, L. Chisholm, T. J. Duck, J. T. Hopper, S. Beauchamp, G. H. King, J. E. Franklin, W. R. Leaitch, A. J. Wheeler, Z. Li, G. A. Gagnon, and P. I. Palmer
Atmos. Chem. Phys., 13, 7199–7213, https://doi.org/10.5194/acp-13-7199-2013, https://doi.org/10.5194/acp-13-7199-2013, 2013
M. J. Alvarado, V. H. Payne, E. J. Mlawer, G. Uymin, M. W. Shephard, K. E. Cady-Pereira, J. S. Delamere, and J.-L. Moncet
Atmos. Chem. Phys., 13, 6687–6711, https://doi.org/10.5194/acp-13-6687-2013, https://doi.org/10.5194/acp-13-6687-2013, 2013
P. I. Palmer, M. Parrington, J. D. Lee, A. C. Lewis, A. R. Rickard, P. F. Bernath, T. J. Duck, D. L. Waugh, D. W. Tarasick, S. Andrews, E. Aruffo, L. J. Bailey, E. Barrett, S. J.-B. Bauguitte, K. R. Curry, P. Di Carlo, L. Chisholm, L. Dan, G. Forster, J. E. Franklin, M. D. Gibson, D. Griffin, D. Helmig, J. R. Hopkins, J. T. Hopper, M. E. Jenkin, D. Kindred, J. Kliever, M. Le Breton, S. Matthiesen, M. Maurice, S. Moller, D. P. Moore, D. E. Oram, S. J. O'Shea, R. C. Owen, C. M. L. S. Pagniello, S. Pawson, C. J. Percival, J. R. Pierce, S. Punjabi, R. M. Purvis, J. J. Remedios, K. M. Rotermund, K. M. Sakamoto, A. M. da Silva, K. B. Strawbridge, K. Strong, J. Taylor, R. Trigwell, K. A. Tereszchuk, K. A. Walker, D. Weaver, C. Whaley, and J. C. Young
Atmos. Chem. Phys., 13, 6239–6261, https://doi.org/10.5194/acp-13-6239-2013, https://doi.org/10.5194/acp-13-6239-2013, 2013
J. Worden, K. Wecht, C. Frankenberg, M. Alvarado, K. Bowman, E. Kort, S. Kulawik, M. Lee, V. Payne, and H. Worden
Atmos. Chem. Phys., 13, 3679–3692, https://doi.org/10.5194/acp-13-3679-2013, https://doi.org/10.5194/acp-13-3679-2013, 2013
J. R. Pierce, M. J. Evans, C. E. Scott, S. D. D'Andrea, D. K. Farmer, E. Swietlicki, and D. V. Spracklen
Atmos. Chem. Phys., 13, 3163–3176, https://doi.org/10.5194/acp-13-3163-2013, https://doi.org/10.5194/acp-13-3163-2013, 2013
C. R. Lonsdale, R. G. Stevens, C. A. Brock, P. A. Makar, E. M. Knipping, and J. R. Pierce
Atmos. Chem. Phys., 12, 11519–11531, https://doi.org/10.5194/acp-12-11519-2012, https://doi.org/10.5194/acp-12-11519-2012, 2012
Related subject area
Atmospheric sciences
An optimized semi-empirical physical approach for satellite-based PM2.5 retrieval: embedding machine learning to simulate complex physical parameters
Sensitivity of tropospheric ozone to halogen chemistry in the chemistry–climate model LMDZ-INCA vNMHC
Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants
Evaluating precipitation distributions at regional scales: a benchmarking framework and application to CMIP5 and 6 models
The Fire Inventory from NCAR version 2.5: an updated global fire emissions model for climate and chemistry applications
An approach to refining the ground meteorological observation stations for improving PM2.5 forecasts in the Beijing–Tianjin–Hebei region
Assessment of WRF (v 4.2.1) dynamically downscaled precipitation on subdaily and daily timescales over CONUS
Convective-gust nowcasting based on radar reflectivity and a deep learning algorithm
Self-nested large-eddy simulations in PALM model system v21.10 for offshore wind prediction under different atmospheric stability conditions
How does cloud-radiative heating over the North Atlantic change with grid spacing, convective parameterization, and microphysics scheme in ICON version 2.1.00?
Updated isoprene and terpene emission factors for the Interactive BVOC (iBVOC) emission scheme in the United Kingdom Earth System Model (UKESM1.0)
Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM–MPAS variable-resolution model
Intercomparison of the weather and climate physics suites of a unified forecast–climate model system (GRIST-A22.7.28) based on single-column modeling
Halogen chemistry in volcanic plumes: a 1D framework based on MOCAGE 1D (version R1.18.1) preparing 3D global chemistry modelling
PyFLEXTRKR: a flexible feature tracking Python software for convective cloud analysis
CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting
Long-term evaluation of surface air pollution in CAMSRA and MERRA-2 global reanalyses over Europe (2003–2020)
Emulating aerosol optics with randomly generated neural networks
Development of an ecophysiology module in the GEOS-Chem chemical transport model version 12.2.0 to represent biosphere–atmosphere fluxes relevant for ozone air quality
Comparison of ozone formation attribution techniques in the northeastern United States
Improving trajectory calculations by FLEXPART 10.4+ using single-image super-resolution
Data fusion uncertainty-enabled methods to map street-scale hourly NO2 in Barcelona: a case study with CALIOPE-Urban v1.0
Forecasting tropical cyclone tracks in the northwestern Pacific based on a deep-learning model
CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model
Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks
A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME
Improving the representation of shallow cumulus convection with the simplified-higher-order-closure–mass-flux (SHOC+MF v1.0) approach
ISAT v2.0: an integrated tool for nested-domain configurations and model-ready emission inventories for WRF-AQM
GPU-HADVPPM V1.0: high-efficient parallel GPU design of the Piecewise Parabolic Method (PPM) for horizontal advection in air quality model (CAMx V6.10)
Estimation of CH4 emission based on an advanced 4D-LETKF assimilation system
Accelerated estimation of sea-spray-mediated heat flux using Gaussian quadrature: case studies with a coupled CFSv2.0-WW3 system
AMORE-Isoprene v1.0: a new reduced mechanism for gas-phase isoprene oxidation
A method for generating a quasi-linear convective system suitable for observing system simulation experiments
The second Met Office Unified Model–JULES Regional Atmosphere and Land configuration, RAL2
Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar0)
A dynamic ammonia emission model and the online coupling with WRF–Chem (WRF–SoilN–Chem v1.0): development and regional evaluation in China
SCIATRAN software package (V4.6): update and further development of aerosol, clouds, surface reflectance databases and models
Deep learning models for generation of precipitation maps based on numerical weather prediction
An inconsistency in aviation emissions between CMIP5 and CMIP6 and the implications for short-lived species and their radiative forcing
On the use of Infrared Atmospheric Sounding Interferometer (IASI) spectrally resolved radiances to test the EC-Earth climate model (v3.3.3) in clear-sky conditions
Incorporation of aerosol into the COSPv2 satellite lidar simulator for climate model evaluation
The impact of altering emission data precision on compression efficiency and accuracy of simulations of the community multiscale air quality model
AerSett v1.0: a simple and straightforward model for the settling speed of big spherical atmospheric aerosols
The High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.0) Model Enables Fast Dynamic Downscaling to the Hectometer Scale
The development and validation of an Inhomogeneous Wind Scheme for Urban Street
EnKF-based fusion of site-available machine learning air quality predictions from RFSML v1.0 and gridded chemical transport model forecasts from GEOS-Chem v13.1.0
Optimization of weather forecasting for cloud cover over the European domain using the meteorological component of the Ensemble for Stochastic Integration of Atmospheric Simulations version 1.0
Bayesian transdimensional inverse reconstruction of the Fukushima Daiichi caesium 137 release
Simulating heat and CO2 fluxes in Beijing using SUEWS V2020b: Sensitivity to vegetation phenology and maximum conductance
Implementation of HONO into the chemistry–climate model CHASER (V4.0): roles in tropospheric chemistry
Caiyi Jin, Qiangqiang Yuan, Tongwen Li, Yuan Wang, and Liangpei Zhang
Geosci. Model Dev., 16, 4137–4154, https://doi.org/10.5194/gmd-16-4137-2023, https://doi.org/10.5194/gmd-16-4137-2023, 2023
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The semi-empirical physical approach derives PM2.5 with strong physical significance. However, due to the complex optical characteristic, the physical parameters are difficult to express accurately. Thus, combining the atmospheric physical mechanism and machine learning, we propose an optimized model. It creatively embeds the random forest model into the physical PM2.5 remote sensing approach to simulate a physical parameter. Our method shows great optimized performance in the validations.
Cyril Caram, Sophie Szopa, Anne Cozic, Slimane Bekki, Carlos A. Cuevas, and Alfonso Saiz-Lopez
Geosci. Model Dev., 16, 4041–4062, https://doi.org/10.5194/gmd-16-4041-2023, https://doi.org/10.5194/gmd-16-4041-2023, 2023
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We studied the role of halogenated compounds (containing chlorine, bromine and iodine), emitted by natural processes (mainly above the oceans), in the chemistry of the lower layers of the atmosphere. We introduced this relatively new chemistry in a three-dimensional climate–chemistry model and looked at how this chemistry will disrupt the ozone. We showed that the concentration of ozone decreases by 22 % worldwide and that of the atmospheric detergent, OH, by 8 %.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Grégoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, and Thomas Lauvaux
Geosci. Model Dev., 16, 3997–4016, https://doi.org/10.5194/gmd-16-3997-2023, https://doi.org/10.5194/gmd-16-3997-2023, 2023
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Monitoring of CO2 emissions is key to the development of reduction policies. Local emissions, from cities or power plants, may be estimated from CO2 plumes detected in satellite images. CO2 plumes generally have a weak signal and are partially concealed by highly variable background concentrations and instrument errors, which hampers their detection. To address this problem, we propose and apply deep learning methods to detect the contour of a plume in simulated CO2 satellite images.
Min-Seop Ahn, Paul A. Ullrich, Peter J. Gleckler, Jiwoo Lee, Ana C. Ordonez, and Angeline G. Pendergrass
Geosci. Model Dev., 16, 3927–3951, https://doi.org/10.5194/gmd-16-3927-2023, https://doi.org/10.5194/gmd-16-3927-2023, 2023
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We introduce a framework for regional-scale evaluation of simulated precipitation distributions with 62 climate reference regions and 10 metrics and apply it to evaluate CMIP5 and CMIP6 models against multiple satellite-based precipitation products. The common model biases identified in this study are mainly associated with the overestimated light precipitation and underestimated heavy precipitation. These biases persist from earlier-generation models and have been slightly improved in CMIP6.
Christine Wiedinmyer, Yosuke Kimura, Elena C. McDonald-Buller, Louisa K. Emmons, Rebecca R. Buchholz, Wenfu Tang, Keenan Seto, Maxwell B. Joseph, Kelley C. Barsanti, Annmarie G. Carlton, and Robert Yokelson
Geosci. Model Dev., 16, 3873–3891, https://doi.org/10.5194/gmd-16-3873-2023, https://doi.org/10.5194/gmd-16-3873-2023, 2023
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The Fire INventory from NCAR (FINN) provides daily global estimates of emissions from open fires based on satellite detections of hot spots. This version has been updated to apply MODIS and VIIRS satellite fire detection and better represents both large and small fires. FINNv2.5 generates more emissions than FINNv1 and is in general agreement with other fire emissions inventories. The new estimates are consistent with satellite observations, but uncertainties remain regionally and by pollutant.
Lichao Yang, Wansuo Duan, and Zifa Wang
Geosci. Model Dev., 16, 3827–3848, https://doi.org/10.5194/gmd-16-3827-2023, https://doi.org/10.5194/gmd-16-3827-2023, 2023
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An approach is proposed to refine a ground meteorological observation network to improve the PM2.5 forecasts in the Beijing–Tianjin–Hebei region. A cost-effective observation network is obtained and makes the relevant PM2.5 forecasts assimilate fewer observations but achieve the forecasting skill comparable to or higher than that obtained by assimilating all ground station observations, suggesting that many of the current ground stations can be greatly scattered to avoid much unnecessary work.
Abhishekh Kumar Srivastava, Paul Aaron Ullrich, Deeksha Rastogi, Pouya Vahmani, Andrew Jones, and Richard Grotjahn
Geosci. Model Dev., 16, 3699–3722, https://doi.org/10.5194/gmd-16-3699-2023, https://doi.org/10.5194/gmd-16-3699-2023, 2023
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Stakeholders need high-resolution regional climate data for applications such as assessing water availability and mountain snowpack. This study examines 3 h and 24 h historical precipitation over the contiguous United States in the 12 km WRF version 4.2.1-based dynamical downscaling of the ERA5 reanalysis. WRF improves precipitation characteristics such as the annual cycle and distribution of the precipitation maxima, but it also displays regionally and seasonally varying precipitation biases.
Haixia Xiao, Yaqiang Wang, Yu Zheng, Yuanyuan Zheng, Xiaoran Zhuang, Hongyan Wang, and Mei Gao
Geosci. Model Dev., 16, 3611–3628, https://doi.org/10.5194/gmd-16-3611-2023, https://doi.org/10.5194/gmd-16-3611-2023, 2023
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Due to the small-scale and nonstationary nature of convective wind gusts (CGs), reliable CG nowcasting has remained unattainable. Here, we developed a deep learning model — namely CGsNet — for 0—2 h of quantitative CG nowcasting, first achieving minute—kilometer-level forecasts. Based on the CGsNet model, the average surface wind speed (ASWS) and peak wind gust speed (PWGS) predictions are obtained. Experiments indicate that CGsNet exhibits higher accuracy than the traditional method.
Maria Krutova, Mostafa Bakhoday-Paskyabi, Joachim Reuder, and Finn Gunnar Nielsen
Geosci. Model Dev., 16, 3553–3564, https://doi.org/10.5194/gmd-16-3553-2023, https://doi.org/10.5194/gmd-16-3553-2023, 2023
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Local refinement of the grid is a powerful method allowing us to reduce the computational time while preserving the accuracy in the area of interest. Depending on the implementation, the local refinement may introduce unwanted numerical effects into the results. We study the wind speed common to the wind turbine operational speeds and confirm strong alteration of the result when the heat fluxes are present, except for the specific refinement scheme used.
Sylvia Sullivan, Behrooz Keshtgar, Nicole Albern, Elzina Bala, Christoph Braun, Anubhav Choudhary, Johannes Hörner, Hilke Lentink, Georgios Papavasileiou, and Aiko Voigt
Geosci. Model Dev., 16, 3535–3551, https://doi.org/10.5194/gmd-16-3535-2023, https://doi.org/10.5194/gmd-16-3535-2023, 2023
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Clouds absorb and re-emit infrared radiation from Earth's surface and absorb and reflect incoming solar radiation. As a result, they change atmospheric temperature gradients that drive large-scale circulation. To better simulate this circulation, we study how the radiative heating and cooling from clouds depends on model settings like grid spacing; whether we describe convection approximately or exactly; and the level of detail used to describe small-scale processes, or microphysics, in clouds.
James Weber, James A. King, Katerina Sindelarova, and Maria Val Martin
Geosci. Model Dev., 16, 3083–3101, https://doi.org/10.5194/gmd-16-3083-2023, https://doi.org/10.5194/gmd-16-3083-2023, 2023
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The emissions of volatile organic compounds from vegetation (BVOCs) influence atmospheric composition and contribute to certain gases and aerosols (tiny airborne particles) which play a role in climate change. BVOC emissions are likely to change in the future due to changes in climate and land use. Therefore, accurate simulation of BVOC emission is important, and this study describes an update to the simulation of BVOC emissions in the United Kingdom Earth System Model (UKESM).
Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
Geosci. Model Dev., 16, 3029–3081, https://doi.org/10.5194/gmd-16-3029-2023, https://doi.org/10.5194/gmd-16-3029-2023, 2023
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We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique in that it follows a standard protocol designed for coordinated experiments of regional models. We found negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced due to rapidly changing super computer systems.
Xiaohan Li, Yi Zhang, Xindong Peng, Baiquan Zhou, Jian Li, and Yiming Wang
Geosci. Model Dev., 16, 2975–2993, https://doi.org/10.5194/gmd-16-2975-2023, https://doi.org/10.5194/gmd-16-2975-2023, 2023
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The weather and climate physics suites used in GRIST-A22.7.28 are compared using single-column modeling. The source of their discrepancies in terms of modeling cloud and precipitation is explored. Convective parameterization is found to be a key factor responsible for the differences. The two suites also have intrinsic differences in the interaction between microphysics and other processes, resulting in different cloud features and time step sensitivities.
Virginie Marécal, Ronan Voisin-Plessis, Tjarda Jane Roberts, Alessandro Aiuppa, Herizo Narivelo, Paul David Hamer, Béatrice Josse, Jonathan Guth, Luke Surl, and Lisa Grellier
Geosci. Model Dev., 16, 2873–2898, https://doi.org/10.5194/gmd-16-2873-2023, https://doi.org/10.5194/gmd-16-2873-2023, 2023
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We implemented a halogen volcanic chemistry scheme in a one-dimensional modelling framework preparing for further use in a three-dimensional global chemistry-transport model. The results of the simulations for an eruption of Mt Etna in 2008, including various sensitivity tests, show a good consistency with previous modelling studies.
Zhe Feng, Joseph Hardin, Hannah C. Barnes, Jianfeng Li, L. Ruby Leung, Adam Varble, and Zhixiao Zhang
Geosci. Model Dev., 16, 2753–2776, https://doi.org/10.5194/gmd-16-2753-2023, https://doi.org/10.5194/gmd-16-2753-2023, 2023
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PyFLEXTRKR is a flexible atmospheric feature tracking framework with specific capabilities to track convective clouds from a variety of observations and model simulations. The package has a collection of multi-object identification algorithms and has been optimized for large datasets. This paper describes the algorithms and demonstrates applications for tracking deep convective cells and mesoscale convective systems from observations and model simulations at a wide range of scales.
Yan Ji, Bing Gong, Michael Langguth, Amirpasha Mozaffari, and Xiefei Zhi
Geosci. Model Dev., 16, 2737–2752, https://doi.org/10.5194/gmd-16-2737-2023, https://doi.org/10.5194/gmd-16-2737-2023, 2023
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Formulating short-term precipitation forecasting as a video prediction task, a novel deep learning architecture (convolutional long short-term memory generative adversarial network, CLGAN) is proposed. A benchmark dataset is built on minute-level precipitation measurements. Results show that with the GAN component the model generates predictions sharing statistical properties with observations, resulting in it outperforming the baseline in dichotomous and spatial scores for heavy precipitation.
Aleksander Lacima, Hervé Petetin, Albert Soret, Dene Bowdalo, Oriol Jorba, Zhaoyue Chen, Raúl F. Méndez Turrubiates, Hicham Achebak, Joan Ballester, and Carlos Pérez García-Pando
Geosci. Model Dev., 16, 2689–2718, https://doi.org/10.5194/gmd-16-2689-2023, https://doi.org/10.5194/gmd-16-2689-2023, 2023
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Understanding how air pollution varies across space and time is of key importance for the safeguarding of human health. This work arose in the context of the project EARLY-ADAPT, for which the Barcelona Supercomputing Center developed an air pollution database covering all of Europe. Through different statistical methods, we compared two global pollution models against measurements from ground stations and found significant discrepancies between the observed and the modeled surface pollution.
Andrew Geiss, Po-Lun Ma, Balwinder Singh, and Joseph C. Hardin
Geosci. Model Dev., 16, 2355–2370, https://doi.org/10.5194/gmd-16-2355-2023, https://doi.org/10.5194/gmd-16-2355-2023, 2023
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Atmospheric aerosols play a critical role in Earth's climate, but it is too computationally expensive to directly model their interaction with radiation in climate simulations. This work develops a new neural-network-based parameterization of aerosol optical properties for use in the Energy Exascale Earth System Model that is much more accurate than the current one; it also introduces a unique model optimization method that involves randomly generating neural network architectures.
Joey C. Y. Lam, Amos P. K. Tai, Jason A. Ducker, and Christopher D. Holmes
Geosci. Model Dev., 16, 2323–2342, https://doi.org/10.5194/gmd-16-2323-2023, https://doi.org/10.5194/gmd-16-2323-2023, 2023
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We developed a new component within an atmospheric chemistry model to better simulate plant ecophysiological processes relevant for ozone air quality. We showed that it reduces simulated biases in plant uptake of ozone in prior models. The new model enables us to explore how future climatic changes affect air quality via affecting plants, examine ozone–vegetation interactions and feedbacks, and evaluate the impacts of changing atmospheric chemistry and climate on vegetation productivity.
Qian Shu, Sergey L. Napelenok, William T. Hutzell, Kirk R. Baker, Barron H. Henderson, Benjamin N. Murphy, and Christian Hogrefe
Geosci. Model Dev., 16, 2303–2322, https://doi.org/10.5194/gmd-16-2303-2023, https://doi.org/10.5194/gmd-16-2303-2023, 2023
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Source attribution methods are generally used to determine culpability of precursor emission sources to ambient pollutant concentrations. However, source attribution of secondarily formed pollutants such as ozone and its precursors cannot be explicitly measured, making evaluation of source apportionment methods challenging. In this study, multiple apportionment approach comparisons show common features but still reveal wide variations in predicted sector contribution and species dependency.
Rüdiger Brecht, Lucie Bakels, Alex Bihlo, and Andreas Stohl
Geosci. Model Dev., 16, 2181–2192, https://doi.org/10.5194/gmd-16-2181-2023, https://doi.org/10.5194/gmd-16-2181-2023, 2023
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We use neural-network-based single-image super-resolution to improve the upscaling of meteorological wind fields to be used for particle dispersion models. This deep-learning-based methodology improves the standard linear interpolation typically used in particle dispersion models. The improvement of wind fields leads to substantial improvement in the computed trajectories of the particles.
Alvaro Criado, Jan Mateu Armengol, Hervé Petetin, Daniel Rodriguez-Rey, Jaime Benavides, Marc Guevara, Carlos Pérez García-Pando, Albert Soret, and Oriol Jorba
Geosci. Model Dev., 16, 2193–2213, https://doi.org/10.5194/gmd-16-2193-2023, https://doi.org/10.5194/gmd-16-2193-2023, 2023
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This work aims to derive and evaluate a general statistical post-processing tool specifically designed for the street scale that can be applied to any urban air quality system. Our data fusion methodology corrects NO2 fields based on continuous hourly observations and experimental campaigns. This study enables us to obtain exceedance probability maps of air quality standards. In 2019, 13 % of the Barcelona area had a 70 % or higher probability of exceeding the annual legal NO2 limit of 40 µg/m3.
Liang Wang, Bingcheng Wan, Shaohui Zhou, Haofei Sun, and Zhiqiu Gao
Geosci. Model Dev., 16, 2167–2179, https://doi.org/10.5194/gmd-16-2167-2023, https://doi.org/10.5194/gmd-16-2167-2023, 2023
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The past 24 h TC trajectories and meteorological field data were used to forecast TC tracks in the northwestern Pacific from hours 6–72 based on GRU_CNN, which we proposed in this paper and which has better prediction results than traditional single deep-learning methods. The historical steering flow of cyclones has a significant effect on improving the accuracy of short-term forecasting, while, in long-term forecasting, the SST and geopotential height will have a particular impact.
Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
EGUsphere, https://doi.org/10.5194/egusphere-2023-616, https://doi.org/10.5194/egusphere-2023-616, 2023
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We have built a tool called CHEEREIO that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. CHEEREIO uses the difference between the model simulations of the atmosphere and real-world observations to come up with a good guess for the actual composition of our atmosphere, the true emissions of various pollutants, and whatever else they may want to study.
Thomas Berkemeier, Matteo Krüger, Aryeh Feinberg, Marcel Müller, Ulrich Pöschl, and Ulrich K. Krieger
Geosci. Model Dev., 16, 2037–2054, https://doi.org/10.5194/gmd-16-2037-2023, https://doi.org/10.5194/gmd-16-2037-2023, 2023
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Kinetic multi-layer models (KMs) successfully describe heterogeneous and multiphase atmospheric chemistry. In applications requiring repeated execution, however, these models can be too expensive. We trained machine learning surrogate models on output of the model KM-SUB and achieved high correlations. The surrogate models run orders of magnitude faster, which suggests potential applicability in global optimization tasks and as sub-modules in large-scale atmospheric models.
Elena Fillola, Raul Santos-Rodriguez, Alistair Manning, Simon O'Doherty, and Matt Rigby
Geosci. Model Dev., 16, 1997–2009, https://doi.org/10.5194/gmd-16-1997-2023, https://doi.org/10.5194/gmd-16-1997-2023, 2023
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Lagrangian particle dispersion models are used extensively for the estimation of greenhouse gas (GHG) fluxes using atmospheric observations. However, these models do not scale well as data volumes increase. Here, we develop a proof-of-concept machine learning emulator that can produce outputs similar to those of the dispersion model, but 50 000 times faster, using only meteorological inputs. This works demonstrates the potential of machine learning to accelerate GHG estimations across the globe.
Maria J. Chinita, Mikael Witte, Marcin J. Kurowski, Joao Teixeira, Kay Suselj, Georgios Matheou, and Peter Bogenschutz
Geosci. Model Dev., 16, 1909–1924, https://doi.org/10.5194/gmd-16-1909-2023, https://doi.org/10.5194/gmd-16-1909-2023, 2023
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Low clouds are one of the largest sources of uncertainty in climate prediction. In this paper, we introduce the first version of the unified turbulence and shallow convection parameterization named SHOC+MF developed to improve the representation of shallow cumulus clouds in the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM). Here, we also show promising preliminary results in a single-column model framework for two benchmark cases of shallow cumulus convection.
Kun Wang, Chao Gao, Kai Wu, Kaiyun Liu, Haofan Wang, Mo Dan, Xiaohui Ji, and Qingqing Tong
Geosci. Model Dev., 16, 1961–1973, https://doi.org/10.5194/gmd-16-1961-2023, https://doi.org/10.5194/gmd-16-1961-2023, 2023
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This study establishes an easy-to-use and integrated framework for a model-ready emission inventory for the Weather Research and Forecasting (WRF)–Air Quality Numerical Model (AQM). A free tool called the ISAT (Inventory Spatial Allocation Tool) was developed based on this framework. ISAT helps users complete the workflow from the WRF nested-domain configuration to a model-ready emission inventory for AQM with a regional emission inventory and a shapefile for the target region.
Kai Cao, Qizhong Wu, Lingling Wang, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongqing Li, and Lanning Wang
EGUsphere, https://doi.org/10.5194/egusphere-2023-410, https://doi.org/10.5194/egusphere-2023-410, 2023
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Offline performance experiments results show that the GPU-HADVPPM on V100 GPU can achieve up to more than one thousand (1113.6x) speedup to its original version on E5-2682 v4 CPU. A series of optimization measures are taken, the CAMx-CUDA model improves the computing efficiency by 128.4x on a single V100 GPU card. A parallel architecture with an MPI+CUDA hybrid paradigm is presented, and it can achieve up to 4.5x speedup when launch 8 CPU cores and 8 GPU cards.
Jagat S. H. Bisht, Prabir K. Patra, Masayuki Takigawa, Takashi Sekiya, Yugo Kanaya, Naoko Saitoh, and Kazuyuki Miyazaki
Geosci. Model Dev., 16, 1823–1838, https://doi.org/10.5194/gmd-16-1823-2023, https://doi.org/10.5194/gmd-16-1823-2023, 2023
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In this study, we estimated CH4 fluxes using an advanced 4D-LETKF method. The system was tested and optimized using observation system simulation experiments (OSSEs), where a known surface emission distribution is retrieved from synthetic observations. The availability of satellite measurements has increased, and there are still many missions focused on greenhouse gas observations that have not yet launched. The technique being referred to has the potential to improve estimates of CH4 fluxes.
Ruizi Shi and Fanghua Xu
Geosci. Model Dev., 16, 1839–1856, https://doi.org/10.5194/gmd-16-1839-2023, https://doi.org/10.5194/gmd-16-1839-2023, 2023
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Based on the Gaussian quadrature method, a fast algorithm of sea-spray-mediated heat flux is developed. Compared with the widely used single-radius algorithm, the new fast algorithm shows a better agreement with the full spectrum integral of spray flux. The new fast algorithm is evaluated in a coupled modeling system, and the simulations of sea surface temperature, wind speed and wave height are improved. Thereby, the new fast algorithm has great potential to be used in coupled modeling systems.
Forwood Wiser, Bryan K. Place, Siddhartha Sen, Havala O. T. Pye, Benjamin Yang, Daniel M. Westervelt, Daven K. Henze, Arlene M. Fiore, and V. Faye McNeill
Geosci. Model Dev., 16, 1801–1821, https://doi.org/10.5194/gmd-16-1801-2023, https://doi.org/10.5194/gmd-16-1801-2023, 2023
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We developed a reduced model of atmospheric isoprene oxidation, AMORE-Isoprene 1.0. It was created using a new Automated Model Reduction (AMORE) method designed to simplify complex chemical mechanisms with minimal manual adjustments to the output. AMORE-Isoprene 1.0 has improved accuracy and similar size to other reduced isoprene mechanisms. When included in the CRACMM mechanism, it improved the accuracy of EPA’s CMAQ model predictions for the northeastern USA compared to observations.
Jonathan D. Labriola, Jeremy A. Gibbs, and Louis J. Wicker
Geosci. Model Dev., 16, 1779–1799, https://doi.org/10.5194/gmd-16-1779-2023, https://doi.org/10.5194/gmd-16-1779-2023, 2023
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Observing system simulation experiments (OSSEs) are simulated case studies used to understand how different assimilated weather observations impact forecast skill. This study introduces the methods used to create an OSSE for a tornadic quasi-linear convective system event. These steps provide an opportunity to simulate a realistic high-impact weather event and can be used to encourage a more diverse set of OSSEs.
Mike Bush, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Aravindakshan Jayakumar, Huw Lewis, Adrian Lock, Marion Mittermaier, Saji Mohandas, Rachel North, Aurore Porson, Belinda Roux, Stuart Webster, and Mark Weeks
Geosci. Model Dev., 16, 1713–1734, https://doi.org/10.5194/gmd-16-1713-2023, https://doi.org/10.5194/gmd-16-1713-2023, 2023
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Building on the baseline of RAL1, the RAL2 science configuration is used for regional modelling around the UM partnership and in operations at the Met Office. RAL2 has been tested in different parts of the world including Australia, India and the UK. RAL2 increases medium and low cloud amounts in the mid-latitudes compared to RAL1, leading to improved cloud forecasts and a reduced diurnal cycle of screen temperature. There is also a reduction in the frequency of heavier precipitation rates.
Christoph Neuhauser, Maicon Hieronymus, Michael Kern, Marc Rautenhaus, Annika Oertel, and Rüdiger Westermann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-27, https://doi.org/10.5194/gmd-2023-27, 2023
Revised manuscript accepted for GMD
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Numerical weather prediction models rely on parameterizations for sub-grid scale processes, which represent a source of uncertainty. We present novel visual analytics solutions to analyze interactively the sensitivities of a selected prognostic variable to multiple model parameters along trajectories wrt. similarities in temporal development and spatio-temporal relationships. The proposed workflow is applied to cloud microphysical sensitivities along coherent strongly ascending trajectories.
Chuanhua Ren, Xin Huang, Tengyu Liu, Yu Song, Zhang Wen, Xuejun Liu, Aijun Ding, and Tong Zhu
Geosci. Model Dev., 16, 1641–1659, https://doi.org/10.5194/gmd-16-1641-2023, https://doi.org/10.5194/gmd-16-1641-2023, 2023
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Ammonia in the atmosphere has wide impacts on the ecological environment and air quality, and its emission from soil volatilization is highly sensitive to meteorology, making it challenging to be well captured in models. We developed a dynamic emission model capable of calculating ammonia emission interactively with meteorological and soil conditions. Such a coupling of soil emission with meteorology provides a better understanding of ammonia emission and its contribution to atmospheric aerosol.
Linlu Mei, Vladimir Rozanov, Alexei Rozanov, and John P. Burrows
Geosci. Model Dev., 16, 1511–1536, https://doi.org/10.5194/gmd-16-1511-2023, https://doi.org/10.5194/gmd-16-1511-2023, 2023
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This paper summarizes recent developments of aerosol, cloud and surface reflectance databases and models in the framework of the software package SCIATRAN. These updates and developments extend the capabilities of the radiative transfer modeling, especially by accounting for different kinds of vertical inhomogeneties. Vertically inhomogeneous clouds and different aerosol types can be easily accounted for within SCIATRAN (V4.6). The widely used surface models and databases are now available.
Adrian Rojas-Campos, Michael Langguth, Martin Wittenbrink, and Gordon Pipa
Geosci. Model Dev., 16, 1467–1480, https://doi.org/10.5194/gmd-16-1467-2023, https://doi.org/10.5194/gmd-16-1467-2023, 2023
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Our paper presents an alternative approach for generating high-resolution precipitation maps based on the nonlinear combination of the complete set of variables of the numerical weather predictions. This process combines the super-resolution task with the bias correction in a single step, generating high-resolution corrected precipitation maps with a lead time of 3 h. We used using deep learning algorithms to combine the input information and increase the accuracy of the precipitation maps.
Robin N. Thor, Mariano Mertens, Sigrun Matthes, Mattia Righi, Johannes Hendricks, Sabine Brinkop, Phoebe Graf, Volker Grewe, Patrick Jöckel, and Steven Smith
Geosci. Model Dev., 16, 1459–1466, https://doi.org/10.5194/gmd-16-1459-2023, https://doi.org/10.5194/gmd-16-1459-2023, 2023
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We report on an inconsistency in the latitudinal distribution of aviation emissions between two versions of a data product which is widely used by researchers. From the available documentation, we do not expect such an inconsistency. We run a chemistry–climate model to compute the effect of the inconsistency in emissions on atmospheric chemistry and radiation and find that the radiative forcing associated with aviation ozone is 7.6 % higher when using the less recent version of the data.
Stefano Della Fera, Federico Fabiano, Piera Raspollini, Marco Ridolfi, Ugo Cortesi, Flavio Barbara, and Jost von Hardenberg
Geosci. Model Dev., 16, 1379–1394, https://doi.org/10.5194/gmd-16-1379-2023, https://doi.org/10.5194/gmd-16-1379-2023, 2023
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The long-term comparison between observed and simulated outgoing longwave radiances represents a strict test to evaluate climate model performance. In this work, 9 years of synthetic spectrally resolved radiances, simulated online on the basis of the atmospheric fields predicted by the EC-Earth global climate model (v3.3.3) in clear-sky conditions, are compared to IASI spectral radiance climatology in order to detect model biases in temperature and humidity at different atmospheric levels.
Marine Bonazzola, Hélène Chepfer, Po-Lun Ma, Johannes Quaas, David M. Winker, Artem Feofilov, and Nick Schutgens
Geosci. Model Dev., 16, 1359–1377, https://doi.org/10.5194/gmd-16-1359-2023, https://doi.org/10.5194/gmd-16-1359-2023, 2023
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Aerosol has a large impact on climate. Using a lidar aerosol simulator ensures consistent comparisons between modeled and observed aerosol. We present a lidar aerosol simulator that applies a cloud masking and an aerosol detection threshold. We estimate the lidar signals that would be observed at 532 nm by the Cloud-Aerosol Lidar with Orthogonal Polarization overflying the atmosphere predicted by a climate model. Our comparison at the seasonal timescale shows a discrepancy in the Southern Ocean.
Michael S. Walters and David C. Wong
Geosci. Model Dev., 16, 1179–1190, https://doi.org/10.5194/gmd-16-1179-2023, https://doi.org/10.5194/gmd-16-1179-2023, 2023
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A typical numerical simulation that associates with a large amount of input and output data, applying popular compression software, gzip or bzip2, on data is one good way to mitigate data storage burden. This article proposes a simple technique to alter input, output, or input and output by keeping a specific number of significant digits in data and demonstrates an enhancement in compression efficiency on the altered data but maintains similar statistical performance of the numerical simulation.
Sylvain Mailler, Laurent Menut, Arineh Cholakian, and Romain Pennel
Geosci. Model Dev., 16, 1119–1127, https://doi.org/10.5194/gmd-16-1119-2023, https://doi.org/10.5194/gmd-16-1119-2023, 2023
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Large or even
giantparticles of mineral dust exist in the atmosphere but, so far, solving an non-linear equation was needed to calculate the speed at which they fall in the atmosphere. The model we present, AerSett v1.0 (AERosol SETTling version 1.0), provides a new and simple way of calculating their free-fall velocity in the atmosphere, which will be useful to anyone trying to understand and represent adequately the transport of giant dust particles by the wind.
Dylan Stewart Reynolds, Ethan Gutmann, Bert Kruyt, Michael Haugeneder, Tobias Jonas, Franziska Gerber, Michael Lehning, and Rebecca Mott
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-16, https://doi.org/10.5194/gmd-2023-16, 2023
Preprint under review for GMD
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The challenge of running geophysical models is often compounded by a question of where to obtain appropriate data to give as input to a model. Here we present the HICAR model, which is a simplified atmospheric model capable of running at spatial resolutions of hectometers for long time series or over large domains. This makes physically consistent atmospheric data available at spatial and temporal scales needed for some terrestrial modeling applications, for example seasonal snow forecasting.
Yuanhao Chen, Zhenxin Liu, Yuhang Wang, Cheng Liu, Shuhua Liu, and Hong Liao
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-267, https://doi.org/10.5194/gmd-2022-267, 2023
Revised manuscript accepted for GMD
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The heterogenous layout of urban buildings leads to the complex wind field in and over the urban. Large discrepancies exist between the observation and the current simulations without considering the character. The Inhomogeneous Wind Scheme in Urban Street (IWSUS) is developed to simulation the complex wind speed in a typical street. It can better simulate the wind field in and over the inhomogenous street canyon then improve the simulated energy budget in lower atmosphere layer over the urban.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Bufan Xu, Wei Han, Mijie Pang, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-301, https://doi.org/10.5194/gmd-2022-301, 2023
Revised manuscript accepted for GMD
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Machine learning model have gained great popularity in air quality prediction. However, they are only available at the air quality monitoring stations. In contrast, chemical transport models (CTM) provide forecasts that are continuous in 3D field. Owing to complex error sources, they are typically biased. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our regional-feature-selection machine learning prediction (RFSML v1.0) and a CTM forecast.
Yen-Sen Lu, Garrett H. Good, and Hendrik Elbern
Geosci. Model Dev., 16, 1083–1104, https://doi.org/10.5194/gmd-16-1083-2023, https://doi.org/10.5194/gmd-16-1083-2023, 2023
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The Weather Forecasting and Research (WRF) model consists of many parameters and options that can be adapted to different conditions. This expansive sensitivity study uses a large-scale simulation system to determine the most suitable options for predicting cloud cover in Europe for deterministic and probabilistic weather predictions for day-ahead forecasting simulations.
Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan
Geosci. Model Dev., 16, 1039–1052, https://doi.org/10.5194/gmd-16-1039-2023, https://doi.org/10.5194/gmd-16-1039-2023, 2023
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When radionuclides are released into the atmosphere, the assessment of the consequences depends on the evaluation of the magnitude and temporal evolution of the release, which can be highly variable as in the case of Fukushima Daiichi.
Here, we propose Bayesian inverse modelling methods and the reversible-jump Markov chain Monte Carlo technique, which allows one to evaluate the temporal variability of the release and to integrate different types of information in the source reconstruction.
Yingqi Zheng, Minttu Havu, Huizhi Liu, Xueling Cheng, Yifan Wen, Hei Shing Lee, Joyson Ahongshangbam, and Leena Järvi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-305, https://doi.org/10.5194/gmd-2022-305, 2023
Revised manuscript accepted for GMD
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The performance of Surface Urban Energy and Water Balance Scheme (SUEWS) is evaluated against the observed surface exchanges (fluxes) of heat and carbon dioxide in Beijing. The heat flux modelling is noticeably improved by using observed maximum conductance and by optimizing vegetation phenology modelling. SUEWS also performs well in simulating carbon dioxide flux. We believe that this study provides new insights into improving urban surface flux modelling.
Phuc Thi Minh Ha, Yugo Kanaya, Fumikazu Taketani, Maria Dolores Andrés Hernández, Benjamin Schreiner, Klaus Pfeilsticker, and Kengo Sudo
Geosci. Model Dev., 16, 927–960, https://doi.org/10.5194/gmd-16-927-2023, https://doi.org/10.5194/gmd-16-927-2023, 2023
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HONO affects tropospheric oxidizing capacity; thus, it is implemented into the chemistry–climate model CHASER. The model substantially underpredicts daytime HONO, while nitrate photolysis on surfaces can supplement the daytime HONO budget. Current HONO chemistry predicts reductions of 20.4 % for global tropospheric NOx, 40–67 % for OH, and 30–45 % for O3 in the summer North Pacific. In contrast, OH and O3 winter levels in China are greatly enhanced.
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Short summary
The System for Atmospheric Modelling (SAM) has been coupled with the detailed gas/aerosol chemistry model, the Aerosol Simulation Program (ASP), to capture cross-plume concentration gradients as fire plumes evolve downwind. SAM-ASP v1.0 will lead to the development of parameterizations of near-source biomass burning chemistry that can be used to more accurately simulate biomass burning chemical and physical transformations of trace gases and aerosols within coarser chemical transport models.
The System for Atmospheric Modelling (SAM) has been coupled with the detailed gas/aerosol...
