Articles | Volume 6, issue 4
https://doi.org/10.5194/gmd-6-961-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-6-961-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
18 Jul 2013
Development and technical paper |
| 18 Jul 2013
Improving the representation of secondary organic aerosol (SOA) in the MOZART-4 global chemical transport model
A. Mahmud
Department of Civil & Environmental Engineering, Portland State University, P.O. Box 751-CEE, Portland, OR 97207-0751, USA
K. Barsanti
Department of Civil & Environmental Engineering, Portland State University, P.O. Box 751-CEE, Portland, OR 97207-0751, USA
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William P. L. Carter, Jia Jiang, John J. Orlando, and Kelley C. Barsanti
Atmos. Chem. Phys., 25, 199–242, https://doi.org/10.5194/acp-25-199-2025, https://doi.org/10.5194/acp-25-199-2025, 2025
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This paper describes the scientific basis for gas-phase atmospheric chemical mechanisms derived using the SAPRC mechanism generation system, MechGen. It can derive mechanisms for most organic compounds with C, H, O, or N atoms, including initial reactions of organics with OH, O3, NO3, and O3P or by photolysis, as well as the reactions of the various types of intermediates that are formed. The paper includes a description of areas of uncertainty where additional research and updates are needed.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
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The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Vignesh Vasudevan-Geetha, Lee Tiszenkel, Zhizhao Wang, Robin Russo, Daniel Bryant, Julia Lee-Taylor, Kelley Barsanti, and Shan-Hu Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-2454, https://doi.org/10.5194/egusphere-2024-2454, 2024
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Our laboratory experiments using two high-resolution mass spectrometers show that these OOMs can also form within the particle phase, in addition to gas-to-particle conversion processes. Our results demonstrate that particle-phase formation processes can contribute to the formation and growth of new particles in biogenic environments.
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.
Yutong Liang, Christos Stamatis, Edward C. Fortner, Rebecca A. Wernis, Paul Van Rooy, Francesca Majluf, Tara I. Yacovitch, Conner Daube, Scott C. Herndon, Nathan M. Kreisberg, Kelley C. Barsanti, and Allen H. Goldstein
Atmos. Chem. Phys., 22, 9877–9893, https://doi.org/10.5194/acp-22-9877-2022, https://doi.org/10.5194/acp-22-9877-2022, 2022
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This article reports the measurements of organic compounds emitted from western US wildfires. We identified and quantified 240 particle-phase compounds and 72 gas-phase compounds emitted in wildfire and related the emissions to the modified combustion efficiency. Higher emissions of diterpenoids and monoterpenes were observed, likely due to distillation from unburned heated vegetation. Our results can benefit future source apportionment and modeling studies as well as exposure assessments.
Christos Stamatis and Kelley Claire Barsanti
Atmos. Meas. Tech., 15, 2591–2606, https://doi.org/10.5194/amt-15-2591-2022, https://doi.org/10.5194/amt-15-2591-2022, 2022
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Building on the identification of hundreds of gas-phase chemicals in smoke samples from laboratory and field studies, an algorithm was developed that successfully identified chemical patterns that were consistent among types of trees and unique between types of trees that are common fuels in western coniferous forests. The algorithm is a promising approach for selecting chemical speciation profiles for air quality modeling using a highly reduced suite of measured compounds.
Qi Li, Jia Jiang, Isaac K. Afreh, Kelley C. Barsanti, and David R. Cocker III
Atmos. Chem. Phys., 22, 3131–3147, https://doi.org/10.5194/acp-22-3131-2022, https://doi.org/10.5194/acp-22-3131-2022, 2022
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Chamber-derived secondary organic aerosol (SOA) yields from camphene are reported for the first time. The role of peroxy radicals (RO2) was investigated using chemically detailed box models. We observed higher SOA yields (up to 64 %) in the experiments with added NOx than without due to the formation of highly oxygenated organic molecules (HOMs) when
NOx is present. This work can improve the representation of camphene in air quality models and provide insights into other monoterpene studies.
Zachary C. J. Decker, Michael A. Robinson, Kelley C. Barsanti, Ilann Bourgeois, Matthew M. Coggon, Joshua P. DiGangi, Glenn S. Diskin, Frank M. Flocke, Alessandro Franchin, Carley D. Fredrickson, Georgios I. Gkatzelis, Samuel R. Hall, Hannah Halliday, Christopher D. Holmes, L. Gregory Huey, Young Ro Lee, Jakob Lindaas, Ann M. Middlebrook, Denise D. Montzka, Richard Moore, J. Andrew Neuman, John B. Nowak, Brett B. Palm, Jeff Peischl, Felix Piel, Pamela S. Rickly, Andrew W. Rollins, Thomas B. Ryerson, Rebecca H. Schwantes, Kanako Sekimoto, Lee Thornhill, Joel A. Thornton, Geoffrey S. Tyndall, Kirk Ullmann, Paul Van Rooy, Patrick R. Veres, Carsten Warneke, Rebecca A. Washenfelder, Andrew J. Weinheimer, Elizabeth Wiggins, Edward Winstead, Armin Wisthaler, Caroline Womack, and Steven S. Brown
Atmos. Chem. Phys., 21, 16293–16317, https://doi.org/10.5194/acp-21-16293-2021, https://doi.org/10.5194/acp-21-16293-2021, 2021
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To understand air quality impacts from wildfires, we need an accurate picture of how wildfire smoke changes chemically both day and night as sunlight changes the chemistry of smoke. We present a chemical analysis of wildfire smoke as it changes from midday through the night. We use aircraft observations from the FIREX-AQ field campaign with a chemical box model. We find that even under sunlight typical
nighttimechemistry thrives and controls the fate of key smoke plume chemical processes.
Sabrina Chee, Kelley Barsanti, James N. Smith, and Nanna Myllys
Atmos. Chem. Phys., 21, 11637–11654, https://doi.org/10.5194/acp-21-11637-2021, https://doi.org/10.5194/acp-21-11637-2021, 2021
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We explored molecular properties affecting atmospheric particle formation efficiency and derived a parameterization between particle formation rate and heterodimer concentration, which showed good agreement to previously reported experimental data. Considering the simplicity of calculating heterodimer concentration, this approach has potential to improve estimates of global cloud condensation nuclei in models that are limited by the computational expense of calculating particle formation rate.
Isaac Kwadjo Afreh, Bernard Aumont, Marie Camredon, and Kelley Claire Barsanti
Atmos. Chem. Phys., 21, 11467–11487, https://doi.org/10.5194/acp-21-11467-2021, https://doi.org/10.5194/acp-21-11467-2021, 2021
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This is the first mechanistic modeling study of secondary organic aerosol (SOA) from the understudied monoterpene, camphene. The semi-explicit chemical model GECKO-A predicted camphene SOA yields that were ~2 times α-pinene. Using 50/50 α-pinene + limonene as a surrogate for camphene increased predicted SOA mass from biomass burning fuels by up to ~100 %. The accurate representation of camphene in air quality models can improve predictions of SOA when camphene is a dominant monoterpene.
Dagny A. Ullmann, Mallory L. Hinks, Adrian M. Maclean, Christopher L. Butenhoff, James W. Grayson, Kelley Barsanti, Jose L. Jimenez, Sergey A. Nizkorodov, Saeid Kamal, and Allan K. Bertram
Atmos. Chem. Phys., 19, 1491–1503, https://doi.org/10.5194/acp-19-1491-2019, https://doi.org/10.5194/acp-19-1491-2019, 2019
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We measured the viscosity and diffusion of organic molecules in secondary organic aerosol (SOA) generated from the ozonolysis of limonene. The results suggest that the mixing times of large organics in the SOA studied are short (< 1 h) for conditions found in the planetary boundary layer. The results also show that the Stokes–Einstein equation gives accurate predictions of diffusion coefficients of large organics within the studied SOA up to a viscosity of 102 to 104 Pa s.
Coty N. Jen, Lindsay E. Hatch, Vanessa Selimovic, Robert J. Yokelson, Robert Weber, Arantza E. Fernandez, Nathan M. Kreisberg, Kelley C. Barsanti, and Allen H. Goldstein
Atmos. Chem. Phys., 19, 1013–1026, https://doi.org/10.5194/acp-19-1013-2019, https://doi.org/10.5194/acp-19-1013-2019, 2019
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Wildfires in the western US are occurring more frequently and burning larger land areas. Smoke from these fires will play a greater role in regional air quality and atmospheric chemistry than in the past. To help fire and climate modelers and atmospheric experimentalists better understand how smoke impacts the environment, we have separated, identified, classified, and quantified the thousands of organic compounds found in smoke and related their amounts emitted to fire conditions.
Lindsay E. Hatch, Albert Rivas-Ubach, Coty N. Jen, Mary Lipton, Allen H. Goldstein, and Kelley C. Barsanti
Atmos. Chem. Phys., 18, 17801–17817, https://doi.org/10.5194/acp-18-17801-2018, https://doi.org/10.5194/acp-18-17801-2018, 2018
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We demonstrate the use of solid-phase extraction (SPE) disks for the untargeted analysis of gas-phase intermediate volatility and semi-volatile organic compounds emitted from biomass burning. SPE and Teflon filter samples collected from laboratory fires were analyzed by two-dimensional gas chromatography, with distinct differences in the observed chromatographic profiles as a function of
fuel type. Fuel-dependent emissions and volatility differences among benzenediol isomers were captured.
Adrian M. Maclean, Christopher L. Butenhoff, James W. Grayson, Kelley Barsanti, Jose L. Jimenez, and Allan K. Bertram
Atmos. Chem. Phys., 17, 13037–13048, https://doi.org/10.5194/acp-17-13037-2017, https://doi.org/10.5194/acp-17-13037-2017, 2017
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Using laboratory data, meteorological fields and a chemical transport model, we investigated how often mixing times are < 1 h within SOA in the planetary boundary layer (PBL). Based on viscosity data for alpha-pinene SOA generated using mass concentrations of ~1000 µg m −3, mixing times in biogenic SOA are < 1h most of the time.
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.
Lindsay E. Hatch, Robert J. Yokelson, Chelsea E. Stockwell, Patrick R. Veres, Isobel J. Simpson, Donald R. Blake, John J. Orlando, and Kelley C. Barsanti
Atmos. Chem. Phys., 17, 1471–1489, https://doi.org/10.5194/acp-17-1471-2017, https://doi.org/10.5194/acp-17-1471-2017, 2017
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The most comprehensive database of gaseous biomass burning emissions to date was compiled. Four complementary instruments were deployed together during laboratory fires. The results generally compared within experimental uncertainty and highlighted that a range of measurement approaches are required for adequate characterization of smoke composition. Observed compounds were binned based on volatility, and priority recommendations were made to improve secondary organic aerosol predictions.
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.
T. Yli-Juuti, K. Barsanti, L. Hildebrandt Ruiz, A.-J. Kieloaho, U. Makkonen, T. Petäjä, T. Ruuskanen, M. Kulmala, and I. Riipinen
Atmos. Chem. Phys., 13, 12507–12524, https://doi.org/10.5194/acp-13-12507-2013, https://doi.org/10.5194/acp-13-12507-2013, 2013
K. C. Barsanti, A. G. Carlton, and S. H. Chung
Atmos. Chem. Phys., 13, 12073–12088, https://doi.org/10.5194/acp-13-12073-2013, https://doi.org/10.5194/acp-13-12073-2013, 2013
M. Xie, K. C. Barsanti, M. P. Hannigan, S. J. Dutton, and S. Vedal
Atmos. Chem. Phys., 13, 7381–7393, https://doi.org/10.5194/acp-13-7381-2013, https://doi.org/10.5194/acp-13-7381-2013, 2013
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The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
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Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
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Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
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This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
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Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
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Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
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This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
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In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
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The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
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The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
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Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
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Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
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Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
Geosci. Model Dev., 17, 7401–7422, https://doi.org/10.5194/gmd-17-7401-2024, https://doi.org/10.5194/gmd-17-7401-2024, 2024
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Atmospheric model users often overlook the impact of the land–atmosphere interaction. This study accessed various setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization allows certain improvement by constraining the soil moisture fields and, through its impact on atmospheric mixing, improves atmospheric transport.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
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We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer
Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024, https://doi.org/10.5194/gmd-17-7263-2024, 2024
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Monitoring greenhouse gas emission reductions requires a combination of models and observations, as well as an initial emission estimate. Each component provides information with a certain level of certainty and is weighted to yield the most reliable estimate of actual emissions. We describe efforts for estimating the uncertainty in the initial emission estimate, which significantly impacts the outcome. Hence, a good uncertainty estimate is key for obtaining reliable information on emissions.
Álvaro González-Cervera and Luis Durán
Geosci. Model Dev., 17, 7245–7261, https://doi.org/10.5194/gmd-17-7245-2024, https://doi.org/10.5194/gmd-17-7245-2024, 2024
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RASCAL is an open-source Python tool designed for reconstructing daily climate observations, especially in regions with complex local phenomena. It merges large-scale weather patterns with local weather using the analog method. Evaluations in central Spain show that RASCAL outperforms ERA20C reanalysis in reconstructing precipitation and temperature. RASCAL offers opportunities for broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024, https://doi.org/10.5194/gmd-17-7199-2024, 2024
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We enhance the WDM6 scheme by incorporating predicted graupel density. The modification affects graupel characteristics, including fall velocity–diameter and mass–diameter relationships. Simulations highlight changes in graupel distribution and precipitation patterns, potentially influencing surface snow amounts. The study underscores the significance of integrating predicted graupel density for a more realistic portrayal of microphysical properties in weather models.
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Geosci. Model Dev., 17, 7001–7027, https://doi.org/10.5194/gmd-17-7001-2024, https://doi.org/10.5194/gmd-17-7001-2024, 2024
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We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.
Peter A. Bogenschutz, Jishi Zhang, Qi Tang, and Philip Cameron-Smith
Geosci. Model Dev., 17, 7029–7050, https://doi.org/10.5194/gmd-17-7029-2024, https://doi.org/10.5194/gmd-17-7029-2024, 2024
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Using high-resolution and state-of-the-art modeling techniques we simulate five atmospheric river events for California to test the capability to represent precipitation for these events. We find that our model is able to capture the distribution of precipitation very well but suffers from overestimating the precipitation amounts over high elevation. Increasing the resolution further has no impact on reducing this bias, while increasing the domain size does have modest impacts.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-145, https://doi.org/10.5194/gmd-2024-145, 2024
Revised manuscript accepted for GMD
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The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements in 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Manu Anna Thomas, Klaus Wyser, Shiyu Wang, Marios Chatziparaschos, Paraskevi Georgakaki, Montserrat Costa-Surós, Maria Gonçalves Ageitos, Maria Kanakidou, Carlos Pérez García-Pando, Athanasios Nenes, Twan van Noije, Philippe Le Sager, and Abhay Devasthale
Geosci. Model Dev., 17, 6903–6927, https://doi.org/10.5194/gmd-17-6903-2024, https://doi.org/10.5194/gmd-17-6903-2024, 2024
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Aerosol–cloud interactions occur at a range of spatio-temporal scales. While evaluating recent developments in EC-Earth3-AerChem, this study aims to understand the extent to which the Twomey effect manifests itself at larger scales. We find a reduction in the warm bias over the Southern Ocean due to model improvements. While we see footprints of the Twomey effect at larger scales, the negative relationship between cloud droplet number and liquid water drives the shortwave radiative effect.
Kai Cao, Qizhong Wu, Lingling Wang, Hengliang Guo, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongxing Li, Lina Liu, Dongqing Li, Hao Wu, and Lanning Wang
Geosci. Model Dev., 17, 6887–6901, https://doi.org/10.5194/gmd-17-6887-2024, https://doi.org/10.5194/gmd-17-6887-2024, 2024
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AMD’s heterogeneous-compute interface for portability was implemented to port the piecewise parabolic method solver from NVIDIA GPUs to China's GPU-like accelerators. The results show that the larger the model scale, the more acceleration effect on the GPU-like accelerator, up to 28.9 times. The multi-level parallelism achieves a speedup of 32.7 times on the heterogeneous cluster. By comparing the results, the GPU-like accelerators have more accuracy for the geoscience numerical models.
Ruyi Zhang, Limin Zhou, Shin-ichiro Shima, and Huawei Yang
Geosci. Model Dev., 17, 6761–6774, https://doi.org/10.5194/gmd-17-6761-2024, https://doi.org/10.5194/gmd-17-6761-2024, 2024
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Solar activity weakly ionises Earth's atmosphere, charging cloud droplets. Electro-coalescence is when oppositely charged droplets stick together. We introduce an analytical expression of electro-coalescence probability and use it in a warm-cumulus-cloud simulation. Results show that charge cases increase rain and droplet size, with the new method outperforming older ones. The new method requires longer computation time, but its impact on rain justifies inclusion in meteorology models.
Hilda Sandström and Patrick Rinke
EGUsphere, https://doi.org/10.48550/arXiv.2406.18171, https://doi.org/10.48550/arXiv.2406.18171, 2024
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Machine learning has the potential to aid the identification organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning model in atmospheric sciences.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-99, https://doi.org/10.5194/gmd-2024-99, 2024
Revised manuscript accepted for GMD
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Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rainfall. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and then the model skill is evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with 4 open-source models.
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Geosci. Model Dev., 17, 6571–6587, https://doi.org/10.5194/gmd-17-6571-2024, https://doi.org/10.5194/gmd-17-6571-2024, 2024
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Satellite observations provide crucial information about atmospheric constituents in a global distribution that helps to better predict the weather over sparsely observed regions like the Arctic. However, the use of satellite data is usually conservative and imperfect. In this study, a better spatial representation of satellite observations is discussed and explored by a so-called footprint function or operator, highlighting its added value through a case study and diagnostics.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024, https://doi.org/10.5194/gmd-17-6489-2024, 2024
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The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
Giorgio Veratti, Alessandro Bigi, Sergio Teggi, and Grazia Ghermandi
Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024, https://doi.org/10.5194/gmd-17-6465-2024, 2024
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In this study, we present VERT (Vehicular Emissions from Road Traffic), an R package designed to estimate transport emissions using traffic estimates and vehicle fleet composition data. Compared to other tools available in the literature, VERT stands out for its user-friendly configuration and flexibility of user input. Case studies demonstrate its accuracy in both urban and regional contexts, making it a valuable tool for air quality management and transport scenario planning.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2197, https://doi.org/10.5194/egusphere-2024-2197, 2024
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The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a more efficient implementation of the serial and batch versions of the Ensemble Square Root Filter (EnSRF) algorithm in CIF.
Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and Damu Bala Subrahamanyam
Geosci. Model Dev., 17, 6379–6399, https://doi.org/10.5194/gmd-17-6379-2024, https://doi.org/10.5194/gmd-17-6379-2024, 2024
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A Python successor to the aerosol module of the OPAC model, named AeroMix, has been developed, with enhanced capabilities to better represent real atmospheric aerosol mixing scenarios. AeroMix’s performance in modeling aerosol mixing states has been evaluated against field measurements, substantiating its potential as a versatile aerosol optical model framework for next-generation algorithms to infer aerosol mixing states and chemical composition.
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev., 17, 6365–6378, https://doi.org/10.5194/gmd-17-6365-2024, https://doi.org/10.5194/gmd-17-6365-2024, 2024
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The width of the tropical rain belt affects many aspects of our climate, yet we do not understand what controls it. To better understand it, we present a method to change it in numerical model experiments. We show that the method works well in four different models. The behavior of the width is unexpectedly simple in some ways, such as how strong the winds are as it changes, but in other ways, it is more complicated, especially how temperature increases with carbon dioxide.
Tianning Su and Yunyan Zhang
Geosci. Model Dev., 17, 6319–6336, https://doi.org/10.5194/gmd-17-6319-2024, https://doi.org/10.5194/gmd-17-6319-2024, 2024
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Using 2 decades of field observations over the Southern Great Plains, this study developed a deep-learning model to simulate the complex dynamics of boundary layer clouds. The deep-learning model can serve as the cloud parameterization within reanalysis frameworks, offering insights into improving the simulation of low clouds. By quantifying biases due to various meteorological factors and parameterizations, this deep-learning-driven approach helps bridge the observation–modeling divide.
Siyuan Chen, Yi Zhang, Yiming Wang, Zhuang Liu, Xiaohan Li, and Wei Xue
Geosci. Model Dev., 17, 6301–6318, https://doi.org/10.5194/gmd-17-6301-2024, https://doi.org/10.5194/gmd-17-6301-2024, 2024
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This study explores strategies and techniques for implementing mixed-precision code optimization within an atmosphere model dynamical core. The coded equation terms in the governing equations that are sensitive (or insensitive) to the precision level have been identified. The performance of mixed-precision computing in weather and climate simulations was analyzed.
Sam O. Owens, Dipanjan Majumdar, Chris E. Wilson, Paul Bartholomew, and Maarten van Reeuwijk
Geosci. Model Dev., 17, 6277–6300, https://doi.org/10.5194/gmd-17-6277-2024, https://doi.org/10.5194/gmd-17-6277-2024, 2024
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Designing cities that are resilient, sustainable, and beneficial to health requires an understanding of urban climate and air quality. This article presents an upgrade to the multi-physics numerical model uDALES, which can simulate microscale airflow, heat transfer, and pollutant dispersion in urban environments. This upgrade enables it to resolve realistic urban geometries more accurately and to take advantage of the resources available on current and future high-performance computing systems.
Felipe Cifuentes, Henk Eskes, Folkert Boersma, Enrico Dammers, and Charlotte Bryan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2225, https://doi.org/10.5194/egusphere-2024-2225, 2024
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We tested the capability of the flux divergence approach (FDA) to reproduce known NOX emissions using synthetic NO2 satellite column retrievals derived from high-resolution model simulations. The FDA accurately reproduced NOX emissions when column observations were limited to the boundary layer and when the variability of NO2 lifetime, NOX:NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces a strong model dependency, reducing the simplicity of the original FDA formulation.
Allison A. Wing, Levi G. Silvers, and Kevin A. Reed
Geosci. Model Dev., 17, 6195–6225, https://doi.org/10.5194/gmd-17-6195-2024, https://doi.org/10.5194/gmd-17-6195-2024, 2024
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This paper presents the experimental design for a model intercomparison project to study tropical clouds and climate. It is a follow-up from a prior project that used a simplified framework for tropical climate. The new project adds one new component – a specified pattern of sea surface temperatures as the lower boundary condition. We provide example results from one cloud-resolving model and one global climate model and test the sensitivity to the experimental parameters.
Astrid Kerkweg, Timo Kirfel, Doung H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-117, https://doi.org/10.5194/gmd-2024-117, 2024
Revised manuscript accepted for GMD
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This article introduces the MESSy DWARF. Usually, the Modular Earth Submodel System (MESSy) is linked to full dynamical models to build chemistry climate models. However, due to the modular concept of MESSy, and the newly developed DWARF component, it is now possible to create simplified models containing just one or some process descriptions. This renders very useful for technical optimisation (e.g., GPU porting) and can be used to create less complex models, e.g., a chemical box model.
Philip G. Sansom and Jennifer L. Catto
Geosci. Model Dev., 17, 6137–6151, https://doi.org/10.5194/gmd-17-6137-2024, https://doi.org/10.5194/gmd-17-6137-2024, 2024
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Weather fronts bring a lot of rain and strong winds to many regions of the mid-latitudes. We have developed an updated method of identifying these fronts in gridded data that can be used on new datasets with small grid spacing. The method can be easily applied to different datasets due to the use of open-source software for its development and shows improvements over similar previous methods. We present an updated estimate of the average frequency of fronts over the past 40 years.
Cited articles
Andreae, M. O. and Crutzen, P. J.: Atmospheric aerosols: Biogeochemical sources and role in atmospheric chemistry, Science, 276, 152–158, https://doi.org/10.1126/science.276.5315.1052, 1997.
Barsanti, K. C., Carlton, A. G., and Chung, S. H.: Analyzing experimental data and model parameters: implications for predictions of SOA using chemical transport models, Atmos. Chem. Phys. Discuss., 13, 15907–15947, https://doi.org/10.5194/acpd-13-15907-2013, 2013.
Barth, M. C., Rasch, P. J., Kiehl, J. T., Benkovitz, C. M., and Schwartz, S. E.: Sulfur chemistry in the National Center for Atmospheric Research Community Climate Model: Description, evaluation, features, and sensitivity to aqueous chemistry, J. Geophys. Res., 105, 1387–1415, 2000.
Brasseur, G. P., Hauglustaine, D. A., Walters, S., Rasch, P. J., Muller, J. F., Granier, C., and Tie, X. X.: MOZART, a global chemical transport model for ozone and related chemical tracers 1. Model description, J. Geophys. Res., 103, 28265–28289, https://doi.org/10.1029/98jd02397, 1998.
Chan, C. Y., Xu, X. D., Li, Y. S., Wong, K. H., Ding, G. A., Chan, L. Y., and Cheng, X. H.: Characteristics of vertical profiles and sources of PM2.5, PM10 and carbonaceous species in Beijing, Atmos. Environ., 39, 5113–5124, https://doi.org/10.1016/j.atmosenv.2005.05.009, 2005.
Chen, Q., Farmer, D. K., Schneider, J., Zorn, S. R., Heald, C. L., Karl, T. G., Guenther, A., Allan, J. D., Robinson, N., Coe, H., Kimmel, J. R., Pauliquevis, T., Borrmann, S., Poeschl, U., Andreae, M. O., Artaxo, P., Jimenez, J. L., and Martin, S. T.: Mass spectral characterization of submicron biogenic organic particles in the Amazon Basin, Geophys. Res. Lett., 36, L20806, https://doi.org/10.1029/2009gl039880, 2009.
Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B. N., Duncan, B. N., Martin, R. V., Logan, J. A., Higuarish, A., and Nakajima, T.: Tropospheric aerosol optical thickness from the GOCART model and comparisons with satellite and sun photometer measurements, J. Atmos. Sci., 59, 461–483, 2002.
Chung, S. H. and Seinfeld, J. H.: Global distribution and climate forcing of carbonaceous aerosols, J.Geophys. Res., 107, 4407, https://doi.org/10.1029/2001jd001397, 2002.
Claeys, M., Wang, W., Ion, A. C., Kourtchev, I., Gelencser, A., and Maenhaut, W.: Formation of secondary organic aerosols from isoprene and its gas-phase oxidation products through reaction with hydrogen peroxide, Atmos. Environ., 38, 4093–4098, https://doi.org/10.1016/j.atmosenv.2004.06.001, 2004.
Clarisse, L., Fromm, M., Ngadi, Y., Emmons, L., Clerbaux, C., Hurtmans, D., and Coheur, P.-F.: Intercontinental transport of anthropogenic sulfur dioxide and other pollutants: An infrared remote sensing case study, Geophys. Res. Lett., 38, L19806, https://doi.org/10.1029/2011gl048976, 2011.
Dan, M., Zhuang, G. S., Li, X. X., Tao, H. R., and Zhuang, Y. H.: The characteristics of carbonaceous species and their sources in PM2.5 in Beijing, Atmos. Environ., 38, 3443–3452, https://doi.org/10.1016/j.atmosenv.2004.02.052, 2004.
Delfino, R. J., Sioutas, C., and Malik, S.: Potential role of ultrafine particles in associations between airborne particle mass and cardiovascular health, Environ. Health Persp., 113, 934–946, https://doi.org/10.1289/ehp.7938, 2005.
Donahue, N. M., Robinson, A. L., Stanier, C. O., and Pandis, S. N.: Coupled partitioning, dilution, and chemical aging of semivolatile organics, Environ. Sci. Technol., 40, 2635–2643, https://doi.org/10.1021/es052297c, 2006.
Duan, F. K., He, K. B., Ma, Y. L., Jia, Y. T., Yang, F. M., Lei, Y., Tanaka, S., and Okuta, T.: Characteristics of carbonaceous aerosols in Beijing, China, Chemosphere, 60, 355–364, https://doi.org/10.1016/j.chemosphere.2004.12.035, 2005.
Dunlea, E. J., DeCarlo, P. F., Aiken, A. C., Kimmel, J. R., Peltier, R. E., Weber, R. J., Tomlinson, J., Collins, D. R., Shinozuka, Y., McNaughton, C. S., Howell, S. G., Clarke, A. D., Emmons, L. K., Apel, E. C., Pfister, G. G., van Donkelaar, A., Martin, R. V., Millet, D. B., Heald, C. L., and Jimenez, J. L.: Evolution of Asian aerosols during transpacific transport in INTEX-B, Atmos. Chem. Phys., 9, 7257–7287, https://doi.org/10.5194/acp-9-7257-2009, 2009.
Emmons, L. K., Walters, S., Hess, P. G., Lamarque, J.-F., Pfister, G. G., Fillmore, D., Granier, C., Guenther, A., Kinnison, D., Laepple, T., Orlando, J., Tie, X., Tyndall, G., Wiedinmyer, C., Baughcum, S. L., and Kloster, S.: Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4), Geosci. Model Dev., 3, 43–67, https://doi.org/10.5194/gmd-3-43-2010, 2010.
Farina, S. C., Adams, P. J., and Pandis, S. N.: Modeling global secondary organic aerosol formation and processing with the volatility basis set: Implications for anthropogenic secondary organic aerosol, J. Geophys. Res., 115, D09202, https://doi.org/10.1029/2009jd013046, 2010.
Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D. W., Haywood, J., Lean, J., Lowe, D. C., Myhre, G., Nganga, J., Prinn, R., Raga, G., Schulz, M., and Van Dorland, R.: Changes in Atmospheric Constituents and in Radiative Forcing. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2007.
Gilardoni, S., Vignati, E., Marmer, E., Cavalli, F., Belis, C., Gianelle, V., Loureiro, A., and Artaxo, P.: Sources of carbonaceous aerosol in the Amazon basin, Atmos. Chem. Phys., 11, 2747–2764, https://doi.org/10.5194/acp-11-2747-2011, 2011.
Goldstein, A. H. and Galbally, I. E.: Known and unexplored organic constituents in the earth's atmosphere, Environ. Sci. Technol., 41, 1514–1521, https://doi.org/10.1021/es072476p, 2007.
Goto, D., Takemura, T., and Nakajima, T.: Importance of global aerosol modeling including secondary organic aerosol formed from monoterpene, J. Geophys. Res., 113, D07205, https://doi.org/10.1029/2007jd009019, 2008.
Granier, C., Guenther, A., Lamarque, J., Mieville, A., Muller, J., Olivier, J., Orlando, J., Peters, J., Petron, G., Tyndall, G., and Wallens, S.: POET, a database of surface emissions of ozone precursors, available at: http://www.aero.jussieu.fr/projet/ACCENT/POET.php (last access: August 2008), 2005.
Griffin, R. J., Cocker, D. R., Flagan, R. C., and Seinfeld, J. H.: Organic aerosol formation from the oxidation of biogenic hydrocarbons, J. Geophys. Res., 104, 3555–3567, https://doi.org/10.1029/1998jd100049, 1999.
Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron, C.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6, 3181–3210, https://doi.org/10.5194/acp-6-3181-2006, 2006.
Guillaume, B., Liousse, C., Rosset, R., Cachier, H., Van Velthoven, P., Bessagnet, B., and Poisson, N.: ORISAM-TM4: a new global sectional multi-component aerosol model including SOA formation – Focus on carbonaceous BC and OC aerosols, Tellus B, 59, 283–302, https://doi.org/10.1111/j.1600-0889.2006.00246.x, 2007.
Hack, J. J.: Parameterization of moist convection in the National Center for Atmospheric Research community climate model (CCM2), J. Geophys. Res., 99, 5551–5568, https://doi.org/10.1029/93jd03478, 1994.
Hallquist, M., Wenger, J. C., Baltensperger, U., Rudich, Y., Simpson, D., Claeys, M., Dommen, J., Donahue, N. M., George, C., Goldstein, A. H., Hamilton, J. F., Herrmann, H., Hoffmann, T., Iinuma, Y., Jang, M., Jenkin, M. E., Jimenez, J. L., Kiendler-Scharr, A., Maenhaut, W., McFiggans, G., Mentel, Th. F., Monod, A., Prévôt, A. S. H., Seinfeld, J. H., Surratt, J. D., Szmigielski, R., and Wildt, J.: The formation, properties and impact of secondary organic aerosol: current and emerging issues, Atmos. Chem. Phys., 9, 5155–5236, https://doi.org/10.5194/acp-9-5155-2009, 2009.
Heald, C. L., Jacob, D. J., Park, R. J., Russell, L. M., Huebert, B. J., Seinfeld, J. H., Liao, H., and Weber, R. J.: A large organic aerosol source in the free troposphere missing from current models, Geophys. Res. Lett., 32, L18809, https://doi.org/10.1029/2005gl023831, 2005.
Heald, C. L., Jacob, D. J., Turquety, S., Hudman, R. C., Weber, R. J., Sullivan, A. P., Peltier, R. E., Atlas, E. L., de Gouw, J. A., Warneke, C., Holloway, J. S., Neuman, J. A., Flocke, F. M., and Seinfeld, J. H.: Concentrations and sources of organic carbon aerosols in the free troposphere over North America, J. Geophys. Res., 111, D23S47, https://doi.org/10.1029/2006jd007705, 2006.
Heald, C. L., Coe, H., Jimenez, J. L., Weber, R. J., Bahreini, R., Middlebrook, A. M., Russell, L. M., Jolleys, M., Fu, T.-M., Allan, J. D., Bower, K. N., Capes, G., Crosier, J., Morgan, W. T., Robinson, N. H., Williams, P. I., Cubison, M. J., DeCarlo, P. F., and Dunlea, E. J.: Exploring the vertical profile of atmospheric organic aerosol: comparing 17 aircraft field campaigns with a global model, Atmos. Chem. Phys., 11, 12673–12696, https://doi.org/10.5194/acp-11-12673-2011, 2011.
Henze, D. K. and Seinfeld, J. H.: Global secondary organic aerosol from isoprene oxidation, Geophys. Res. Lett., 33, L09812, https://doi.org/10.1029/2006gl025976, 2006.
Henze, D. K., Seinfeld, J. H., Ng, N. L., Kroll, J. H., Fu, T.-M., Jacob, D. J., and Heald, C. L.: Global modeling of secondary organic aerosol formation from aromatic hydrocarbons: high- vs. low-yield pathways, Atmos. Chem. Phys., 8, 2405–2420, https://doi.org/10.5194/acp-8-2405-2008, 2008.
Herron-Thorpe, F. L., Mount, G. H., Emmons, L. K., Lamb, B. K., Chung, S. H., and Vaughan, J. K.: Regional air-quality forecasting for the Pacific Northwest using MOPITT/TERRA assimilated carbon monoxide MOZART-4 forecasts as a near real-time boundary condition, Atmos. Chem. Phys., 12, 5603–5615, https://doi.org/10.5194/acp-12-5603-2012, 2012.
Hess, M., Koepke, P., and Schult, I.: Optical properties of aerosols and clouds: The software package OPAC, Bull. Am. Meteorol. Soc., 79, 831–844, 1998.
Holtslag, A. A. M. and Boville, B. A.: Local versus nonlocal boundary-layer diffusion in a global climate model, J. Climate, 6, 1825–1842, 1993.
Horowitz, L. W., Walters, S., Mauzerall, D. L., Emmons, L. K., Rasch, P. J., Granier, C., Tie, X. X., Lamarque, J. F., Schultz, M. G., Tyndall, G. S., Orlando, J. J., and Brasseur, G. P.: A global simulation of tropospheric ozone and related tracers: Description and evaluation of MOZART, version 2, J. Geophys. Res., 108, 4784, https://doi.org/10.1029/2002jd002853, 2003.
Hoyle, C. R., Berntsen, T., Myhre, G., and Isaksen, I. S. A.: Secondary organic aerosol in the global aerosol – chemical transport model Oslo CTM2, Atmos. Chem. Phys., 7, 5675–5694, https://doi.org/10.5194/acp-7-5675-2007, 2007.
Hoyle, C. R., Myhre, G., Berntsen, T. K., and Isaksen, I. S. A.: Anthropogenic influence on SOA and the resulting radiative forcing, Atmos. Chem. Phys., 9, 2715–2728, https://doi.org/10.5194/acp-9-2715-2009, 2009
Jacob, D. J.: Heterogeneous chemistry and tropospheric ozone, Atmos. Environ., 34, 2131–2159, 2000.
Jiang, F., Liu, Q., Huang, X., Wang, T., Zhuang, B., and Xie, M.: Regional modeling of secondary organic aerosol over China using WRF/Chem, J. Aerosol Sci., 43, 57–73, https://doi.org/10.1016/j.jaerosci.2011.09.003, 2012.
Jimenez, J. L., Canagaratna, M. R., Donahue, N. M., Prevot, A. S. H., Zhang, Q., Kroll, J. H., DeCarlo, P. F., Allan, J. D., Coe, H., Ng, N. L., Aiken, A. C., Docherty, K. S., Ulbrich, I. M., Grieshop, A. P., Robinson, A. L., Duplissy, J., Smith, J. D., Wilson, K. R., Lanz, V. A., Hueglin, C., Sun, Y. L., Tian, J., Laaksonen, A., Raatikainen, T., Rautiainen, J., Vaattovaara, P., Ehn, M., Kulmala, M., Tomlinson, J. M., Collins, D. R., Cubison, M. J., Dunlea, E. J., Huffman, J. A., Onasch, T. B., Alfarra, M. R., Williams, P. I., Bower, K., Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer, S., Demerjian, K., Salcedo, D., Cottrell, L., Griffin, R., Takami, A., Miyoshi, T., Hatakeyama, S., Shimono, A., Sun, J. Y., Zhang, Y. M., Dzepina, K., Kimmel, J. R., Sueper, D., Jayne, J. T., Herndon, S. C., Trimborn, A. M., Williams, L. R., Wood, E. C., Middlebrook, A. M., Kolb, C. E., Baltensperger, U., and Worsnop, D. R.: Evolution of Organic Aerosols in the Atmosphere, Science, 326, 1525–1529, https://doi.org/10.1126/science.1180353, 2009.
Johnson, D., Utembe, S. R., Jenkin, M. E., Derwent, R. G., Hayman, G. D., Alfarra, M. R., Coe, H., and McFiggans, G.: Simulating regional scale secondary organic aerosol formation during the TORCH 2003 campaign in the southern UK, Atmos. Chem. Phys., 6, 403–418, https://doi.org/10.5194/acp-6-403-2006, 2006.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-year reanalysis project, B. Am. Meteorol. Soc., 77, 437–471, https://doi.org/10.1175/1520-0477(1996)077<0437:tnyrp>2.0.co;2, 1996.
Kanakidou, M., Seinfeld, J. H., Pandis, S. N., Barnes, I., Dentener, F. J., Facchini, M. C., Van Dingenen, R., Ervens, B., Nenes, A., Nielsen, C. J., Swietlicki, E., Putaud, J. P., Balkanski, Y., Fuzzi, S., Horth, J., Moortgat, G. K., Winterhalter, R., Myhre, C. E. L., Tsigaridis, K., Vignati, E., Stephanou, E. G., and Wilson, J.: Organic aerosol and global climate modelling: a review, Atmos. Chem. Phys., 5, 1053–1123, https://doi.org/10.5194/acp-5-1053-2005, 2005.
Kavouras, I. G., Mihalopoulos, N., and Stephanou, E. G.: Formation of atmospheric particles from organic acids produced by forests, Nature, 395, 683–686, https://doi.org/10.1038/27179, 1998.
Kistler, R., Kalnay, E., Collins, W., Saha, S., White, G., Woollen, J., Chelliah, M., Ebisuzaki, W., Kanamitsu, M., Kousky, V., van den Dool, H., Jenne, R., and Fiorino, M.: The NCEP-NCAR 50-year reanalysis: Monthly means CD-ROM and documentation, B. Am. Meteorol. Soc., 82, 247–267, https://doi.org/10.1175/1520-0477(2001)082<0247:tnnyrm>2.3.co;2, 2001.
Kleinman, L. I., Springston, S. R., Daum, P. H., Lee, Y.-N., Nunnermacker, L. J., Senum, G. I., Wang, J., Weinstein-Lloyd, J., Alexander, M. L., Hubbe, J., Ortega, J., Canagaratna, M. R., and Jayne, J.: The time evolution of aerosol composition over the Mexico City plateau, Atmos. Chem. Phys., 8, 1559–1575, https://doi.org/10.5194/acp-8-1559-2008, 2008.
Lack, D. A., Tie, X. X., Bofinger, N. D., Wiegand, A. N., and Madronich, S.: Seasonal variability of secondary organic aerosol: A global modeling study, J. Geophys. Res., 109, D03203, https://doi.org/10.1029/2003jd003418, 2004.
Lamarque, J.-F., Kiehl, J. T., Hess, P. G., Collins, W. D., Emmons, L. K., Ginoux, P., Luo, C., and Tie, X. X.: Response of a coupled chemistry-climate model to changes in aerosol emissions: Global impact on the hydrological cycle and the tropospheric burdens of OH, ozone and NOx, Geophys. Res. Lett., 32, L16809, https://doi.org/10.1029/2005GL023419, 2005.
Lane, T. E., Donahue, N. M., and Pandis, S. N.: Simulating secondary organic aerosol formation using the volatility basis-set approach in a chemical transport model, Atmos. Environ., 42, 7439–7451, https://doi.org/10.1016/j.atmosenv.2008.06.026, 2008.
Lee, A., Goldstein, A. H., Kroll, J. H., Ng, N. L., Varutbangkul, V., Flagan, R. C., and Seinfeld, J. H.: Gas-phase products and secondary aerosol yields from the photooxidation of 16 different terpenes, J. Geophys. Res., 111, D17305, https://doi.org/10.1029/2006jd007050, 2006.
Lee, K. and Chung, C. E.: Observationally-constrained estimates of global fine-mode AOD, Atmos. Chem. Phys., 13, 2907–2921, https://doi.org/10.5194/acp-13-2907-2013, 2013.
Lee-Taylor, J., Madronich, S., Aumont, B., Baker, A., Camredon, M., Hodzic, A., Tyndall, G. S., Apel, E., and Zaveri, R. A.: Explicit modeling of organic chemistry and secondary organic aerosol partitioning for Mexico City and its outflow plume, Atmos. Chem. Phys., 11, 13219–13241, https://doi.org/10.5194/acp-11-13219-2011, 2011.
Liao, H., Henze, D. K., Seinfeld, J. H., Wu, S., and Mickley, L. J.: Biogenic secondary organic aerosol over the United States: Comparison of climatological simulations with observations, J. Geophys. Res., 112, D06201, https://doi.org/10.1029/2006jd007813, 2007.
Lin, G., Penner, J. E., Sillman, S., Taraborrelli, D., and Lelieveld, J.: Global modeling of SOA formation from dicarbonyls, epoxides, organic nitrates and peroxides, Atmos. Chem. Phys., 12, 4743–4774, https://doi.org/10.5194/acp-12-4743-2012, 2012.
Lin, P., Hu, M., Deng, Z., Slanina, J., Han, S., Kondo, Y., Takegawa, N., Miyazaki, Y., Zhao, Y., and Sugimoto, N.: Seasonal and diurnal variations of organic carbon in PM2.5 in Beijing and the estimation of secondary organic carbon, J. Geophys. Res., 114, D00G11, https://doi.org/10.1029/2008jd010902, 2009.
Lin, S. J. and Rood, R. B.: Multidimensional flux-form semi-Lagrangian transport schemes, Mon. Weather Rev., 124, 2046–2070, https://doi.org/10.1175/1520-0493(1996)124<2046:mffslt>2.0.co;2, 1996.
Liousse, C., Penner, J. E., Chuang, C., Walton, J. J., Eddleman, H., and Cachier, H.: A global three-dimensional model study of carbonaceous aerosols, J. Geophys. Res., 101, 19411–19432,https://doi.org/10.1029/95jd03426, 1996.
Murphy, B. N. and Pandis, S. N.: Simulating the Formation of Semivolatile Primary and Secondary Organic Aerosol in a Regional Chemical Transport Model, Environ. Sci. Technol., 43, 4722–4728, https://doi.org/10.1021/es803168a, 2009.
Murphy, B. N., Donahue, N. M., Fountoukis, C., and Pandis, S. N.: Simulating the oxygen content of ambient organic aerosol with the 2D volatility basis set, Atmos. Chem. Phys., 11, 7859–7873, https://doi.org/10.5194/acp-11-7859-2011, 2011.
O'Donnell, D., Tsigaridis, K., and Feichter, J.: Estimating the direct and indirect effects of secondary organic aerosols using ECHAM5-HAM, Atmos. Chem. Phys., 11, 8635–8659, https://doi.org/10.5194/acp-11-8635-2011, 2011.
Odum, J. R., Hoffmann, T., Bowman, F., Collins, D., Flagan, R. C., and Seinfeld, J. H.: Gas/particle partitioning and secondary organic aerosol yields, Environ. Sci. Technol., 30, 2580–2585, https://doi.org/10.1021/es950943+, 1996.
Olivier, J., Peters, J., Granier, C., Petron, G., Müller, J., and Wallens, S.: Present and future surface emissions of atmospheric compounds, POET report #2, EU project EVK2-1999-00011, available at: http://www.aero.jussieu.fr/projet/ACCENT/POET.php (last access: August 2008), 2003.
Pankow, J. F.: An absorption-model of the gas aerosol partitioning involved in the formation of secondary organic aerosol, Atmos. Environ., 28, 185–188, https://doi.org/10.1016/1352-2310(94)90094-9, 1994.
Pankow, J. F. and Barsanti, K. C.: The carbon number-polarity grid: A means to manage the complexity of the mix of organic compounds when modeling atmospheric organic particulate matter, Atmos. Environ., 43, 2829–2835, https://doi.org/10.1016/j.atmosenv.2008.12.050, 2009.
Park, M., Randel, W. J., Emmons, L. K., and Livesey, N. J.: Transport pathways of carbon monoxide in the Asian summer monsoon diagnosed from Model of Ozone and Related Tracers (MOZART), J. Geophys. Res., 114, D08303, https://doi.org/10.1029/2008jd010621, 2009.
Pathak, R. K., Presto, A. A., Lane, T. E., Stanier, C. O., Donahue, N. M., and Pandis, S. N.: Ozonolysis of α-pinene: parameterization of secondary organic aerosol mass fraction, Atmos. Chem. Phys., 7, 3811–3821, https://doi.org/10.5194/acp-7-3811-2007, 2007.
Pfister, G. G., Emmons, L. K., Edwards, D. P., Arellano, A., G Sachse, and Campos, T.: Variability of springtime transpacific pollution transport during 2000–2006: the INTEX-B mission in the context of previous years, Atmos. Chem. Phys., 10, 1345–1359, https://doi.org/10.5194/acp-10-1345-2010, 2010.
Pope, C. A. and Dockery, D. W.: Health effects of fine particulate air pollution: Lines that connect, J. Air Waste Manage., 56, 709–742, 2006.
Presto, A. A. and Donahue, N. M.: Investigation of alpha-pinene plus ozone secondary organic aerosol formation at low total aerosol mass, Environ. Sci. Technol., 40, 3536–3543, https://doi.org/10.1021/es052203z, 2006.
Rasch, P. J., Mahowald, N. M., and Eaton, B. E.: Representations of transport, convection, and the hydrologic cycle in chemical transport models: Implications for the modeling of short-lived and soluble species, J. Geophys. Res., 102, 28127–28138, https://doi.org/10.1029/97jd02087, 1997.
Robinson, N. H., Hamilton, J. F., Allan, J. D., Langford, B., Oram, D. E., Chen, Q., Docherty, K., Farmer, D. K., Jimenez, J. L., Ward, M. W., Hewitt, C. N., Barley, M. H., Jenkin, M. E., Rickard, A. R., Martin, S. T., McFiggans, G., and Coe, H.: Evidence for a significant proportion of Secondary Organic Aerosol from isoprene above a maritime tropical forest, Atmos. Chem. Phys., 11, 1039–1050, https://doi.org/10.5194/acp-11-1039-2011, 2011.
Russell, L. M.: Aerosol organic-mass-to-organic-carbon ratio measurements, Environ. Sci. Technol., 37, 2982–2987, https://doi.org/10.1021/es026123w, 2003.
Schwartz, J.: The effects of particulate air pollution on daily deaths: a multi-city case crossover analysis, Occup. Environ. Med., 61, 956–961, https://doi.org/10.1136/oem.2003.008250, 2004.
Simpson, D., Yttri, K. E., Klimont, Z., Kupiainen, K., Caseiro, A., Gelencser, A., Pio, C., Puxbaum, H., and Legrand, M.: Modeling carbonaceous aerosol over Europe: Analysis of the CARBOSOL and EMEP EC/OC campaigns, J. Geophys. Res., 112, D23S14, https://doi.org/10.1029/2006jd008158, 2007.
Slowik, J. G., Stroud, C., Bottenheim, J. W., Brickell, P. C., Chang, R. Y.-W., Liggio, J., Makar, P. A., Martin, R. V., Moran, M. D., Shantz, N. C., Sjostedt, S. J., van Donkelaar, A., Vlasenko, A., Wiebe, H. A., Xia, A. G., Zhang, J., Leaitch, W. R., and Abbatt, J. P. D.: Characterization of a large biogenic secondary organic aerosol event from eastern Canadian forests, Atmos. Chem. Phys., 10, 2825–2845, https://doi.org/10.5194/acp-10-2825-2010, 2010.
Spracklen, D. V., Jimenez, J. L., Carslaw, K. S., Worsnop, D. R., Evans, M. J., Mann, G. W., Zhang, Q., Canagaratna, M. R., Allan, J., Coe, H., McFiggans, G., Rap, A., and Forster, P.: Aerosol mass spectrometer constraint on the global secondary organic aerosol budget, Atmos. Chem. Phys., 11, 12109–12136, https://doi.org/10.5194/acp-11-12109-2011, 2011.
Tang, Y., Lee, P., Tsidulko, M., Huang, H.-C., McQueen, J. T., DiMego, G. J., Emmons, L. K., Pierce, R. B., Thompson, A. M., Lin, H.-M., Kang, D., Tong, D., Yu, S., Mathur, R., Pleim, J. E., Otte, T. L., Pouliot, G., Young, J. O., Schere, K. L., Davidson, P. M., and Stajner, I.: The impact of chemical lateral boundary conditions on CMAQ predictions of tropospheric ozone over the continental United States, Environ. Fluid Mech., 9, 43–58, https://doi.org/10.1007/s10652-008-9092-5, 2009.
Tie, X., Brasseur, G., Emmons, L., Horowitz, L., and Kinnision, D.: Effects of aerosols on tropospheric oxidants: A global model study, J. Geophys. Res., 106, 2931–2964, 2001.
Tie, X., Madronich, S., Walters, S., Edwards, D., Ginoux, P., Mahowald, N., Zhang, R., Luo, C., and Brasseur, G.: Assessment of the global impact of aerosols on tropospheric oxidants, J. Geophys. Res., 110, D03204, https://doi.org/10.1029/2004JD005359, 2005.
Tsigaridis, K. and Kanakidou, M.: Secondary organic aerosol importance in the future atmosphere, Atmos. Environ., 41, 4682–4692, https://doi.org/10.1016/j.atmosenv.2007.03.045, 2007.
Tsimpidi, A. P., Karydis, V. A., Zavala, M., Lei, W., Molina, L., Ulbrich, I. M., Jimenez, J. L., and Pandis, S. N.: Evaluation of the volatility basis-set approach for the simulation of organic aerosol formation in the Mexico City metropolitan area, Atmos. Chem. Phys., 10, 525–546, https://doi.org/10.5194/acp-10-525-2010, 2010.
Valorso, R., Aumont, B., Camredon, M., Raventos-Duran, T., Mouchel-Vallon, C., Ng, N. L., Seinfeld, J. H., Lee-Taylor, J., and Madronich, S.: Explicit modelling of SOA formation from α-pinene photooxidation: sensitivity to vapour pressure estimation, Atmos. Chem. Phys., 11, 6895–6910, https://doi.org/10.5194/acp-11-6895-2011, 2011.
van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Kasibhatla, P. S., and Arellano Jr., A. F.: Interannual variability in global biomass burning emissions from 1997 to 2004, Atmos. Chem. Phys., 6, 3423–3441, https://doi.org/10.5194/acp-6-3423-2006, 2006.
Volkamer, R., Jimenez, J. L., San Martini, F., Dzepina, K., Zhang, Q., Salcedo, D., Molina, L. T., Worsnop, D. R., and Molina, M. J.: Secondary organic aerosol formation from anthropogenic air pollution: Rapid and higher than expected, Geophys. Res. Lett., 33, L17811, https://doi.org/10.1029/2006gl026899, 2006.
Wespes, C., Emmons, L., Edwards, D. P., Hannigan, J., Hurtmans, D., Saunois, M., Coheur, P.-F., Clerbaux, C., Coffey, M. T., Batchelor, R. L., Lindenmaier, R., Strong, K., Weinheimer, A. J., Nowak, J. B., Ryerson, T. B., Crounse, J. D., and Wennberg, P. O.: Analysis of ozone and nitric acid in spring and summer Arctic pollution using aircraft, ground-based, satellite observations and MOZART-4 model: source attribution and partitioning, Atmos. Chem. Phys., 12, 237–259, https://doi.org/10.5194/acp-12-237-2012, 2012.
Zhang, Q., Canagaratna, M. R., Jayne, J. T., Worsnop, D. R., and Jimenez, J. L.: Time- and size-resolved chemical composition of submicron particles in Pittsburgh: Implications for aerosol sources and processes, J. Geophys. Res., 110, D07S12, https://doi.org/10.1029/2004jd004649, 2005.
Zhang, Q., Jimenez, J. L., Canagaratna, M. R., Allan, J. D., Coe, H., Ulbrich, I., Alfarra, M. R., Takami, A., Middlebrook, A. M., Sun, Y. L., Dzepina, K., Dunlea, E., Docherty, K., DeCarlo, P. F., Salcedo, D., Onasch, T., Jayne, J. T., Miyoshi, T., Shimono, A., Hatakeyama, S., Takegawa, N., Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer, S., Demerjian, K., Williams, P., Bower, K., Bahreini, R., Cottrell, L., Griffin, R. J., Rautiainen, J., Sun, J., Y., Zhang, Y. M., and Worsnop, D. R.: Ubiquity and dominance of oxygenated species in organic aerosols in anthropogenically-influenced Northern Hemisphere midlatitudes, Geophys. Res. Lett., 34, L13801, https://doi.org/10.1029/2007GL029979, 2007.
Zhang, X. Y., Wang, Y. Q., Niu, T., Zhang, X. C., Gong, S. L., Zhang, Y. M., and Sun, J. Y.: Atmospheric aerosol compositions in China: spatial/temporal variability, chemical signature, regional haze distribution and comparisons with global aerosols, Atmos. Chem. Phys., 12, 779–799, https://doi.org/10.5194/acp-12-779-2012, 2012.
