Articles | Volume 12, issue 3
https://doi.org/10.5194/gmd-12-955-2019
© Author(s) 2019. 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-12-955-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A new method (M3Fusion v1) for combining observations and multiple model output for an improved estimate of the global surface ozone distribution
National Research Council Research Associateship Program, David Skaggs Research Center, Boulder, CO, USA
NOAA Earth System Research Laboratory, Boulder, CO, USA
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
Owen R. Cooper
NOAA Earth System Research Laboratory, Boulder, CO, USA
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
J. Jason West
Department of Environmental Sciences & Engineering, University of North Carolina, Chapel Hill, NC, USA
Marc L. Serre
Department of Environmental Sciences & Engineering, University of North Carolina, Chapel Hill, NC, USA
Martin G. Schultz
Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, Jülich, Germany
Meiyun Lin
NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
Virginie Marécal
Météo-France, Centre National de Recherches Météorologiques, Toulouse, France
Béatrice Josse
Météo-France, Centre National de Recherches Météorologiques, Toulouse, France
Makoto Deushi
Meteorological Research Institute (MRI), Tsukuba, Japan
Kengo Sudo
Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan
Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan
Junhua Liu
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Universities Space Research Association, Columbia, MD, USA
Christoph A. Keller
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Universities Space Research Association, Columbia, MD, USA
John A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA, USA
Related authors
Ju-Mee Ryoo, Laura T. Iraci, Yu Yan Cui, Owen R. Cooper, Matthew S. Johnson, Kai-Lan Chang, Emma L. Yates, Valerie Thouret, Hanna Clark, Philippe Nedelec, and Bastien Sauvage
EGUsphere, https://doi.org/10.5194/egusphere-2026-1157, https://doi.org/10.5194/egusphere-2026-1157, 2026
Short summary
Short summary
Background ozone over Western North America is increasing despite local emission reductions, driven by intensifying transport from Southeast Asia, expanded global shipping, and rising aircraft emissions in the upper troposphere-lower stratosphere (UTLS). By analyzing the ozone 'lower tail' (<33rd percentile), this study identifies growing remote influences and shifting trans-Pacific transport patterns that are fundamentally altering the region's background atmospheric state.
Sebastian H. M. Hickman, Makoto M. Kelp, Paul T. Griffiths, Kelsey Doerksen, Kazuyuki Miyazaki, Elyse A. Pennington, Gerbrand Koren, Fernando Iglesias-Suarez, Martin G. Schultz, Kai-Lan Chang, Owen R. Cooper, Alex Archibald, Roberto Sommariva, David Carlson, Hantao Wang, J. Jason West, and Zhenze Liu
Geosci. Model Dev., 18, 8777–8800, https://doi.org/10.5194/gmd-18-8777-2025, https://doi.org/10.5194/gmd-18-8777-2025, 2025
Short summary
Short summary
Machine learning is being more widely used across environmental and climate science. This work reviews the use of machine learning in tropospheric ozone research, focusing on three main application areas in which significant progress has been made. Common challenges in using machine learning across the three areas are highlighted, and future directions for the field are indicated.
Yu Yan Cui, Ju-Mee Ryoo, Matthew S. Johnson, Kai-Lan Chang, Emma L. Yates, Owen R. Cooper, and Laura T. Iraci
Earth Syst. Sci. Data, 17, 5903–5914, https://doi.org/10.5194/essd-17-5903-2025, https://doi.org/10.5194/essd-17-5903-2025, 2025
Short summary
Short summary
Atmospheric observations show that free tropospheric ozone has increased across the Northern Hemisphere over the past three decades. The sources driving this increase remain unclear. In this study, we developed a source-receptor relationship database combining multiplatform ozone data and advanced atmospheric transport modeling. This database can identify emission regions responsible for ozone increases and can also be used to analyze other co-observed atmospheric constituents.
Roeland Van Malderen, Zhou Zang, Kai-Lan Chang, Robin Björklund, Owen R. Cooper, Jane Liu, Eliane Maillard Barras, Corinne Vigouroux, Irina Petropavlovskikh, Thierry Leblanc, Valérie Thouret, Pawel Wolff, Peter Effertz, Audrey Gaudel, David W. Tarasick, Herman G. J. Smit, Anne M. Thompson, Ryan M. Stauffer, Debra E. Kollonige, Deniz Poyraz, Gérard Ancellet, Marie-Renée De Backer, Matthias M. Frey, James W. Hannigan, José L. Hernandez, Bryan J. Johnson, Nicholas Jones, Rigel Kivi, Emmanuel Mahieu, Isamu Morino, Glen McConville, Katrin Müller, Isao Murata, Justus Notholt, Ankie Piters, Maxime Prignon, Richard Querel, Vincenzo Rizi, Dan Smale, Wolfgang Steinbrecht, Kimberly Strong, and Ralf Sussmann
Atmos. Chem. Phys., 25, 9905–9935, https://doi.org/10.5194/acp-25-9905-2025, https://doi.org/10.5194/acp-25-9905-2025, 2025
Short summary
Short summary
Tropospheric ozone is an important greenhouse gas and an air pollutant whose distribution and time variability are mainly governed by anthropogenic emissions and dynamics. In this paper, we assess regional trends of tropospheric ozone column amounts, based on two different approaches of merging or synthesizing ground-based observations and their trends within specific regions. Our findings clearly demonstrate regional trend differences but also consistently higher pre-COVID than post-COVID trends.
Kai-Lan Chang, Brian C. McDonald, Colin Harkins, and Owen R. Cooper
Atmos. Chem. Phys., 25, 5101–5132, https://doi.org/10.5194/acp-25-5101-2025, https://doi.org/10.5194/acp-25-5101-2025, 2025
Short summary
Short summary
Exposure to high levels of ozone can be harmful to human health. This study shows consistent and robust evidence of decreasing ozone extremes across much of the United States over the period from 1990 to 2023, previously attributed to ozone precursor emission controls. Nevertheless, we also show that the increasing heat wave frequencies are likely to contribute to additional ozone exceedances, slowing the progress of decreasing the frequency of ozone exceedances.
Audrey Gaudel, Ilann Bourgeois, Meng Li, Kai-Lan Chang, Jerald Ziemke, Bastien Sauvage, Ryan M. Stauffer, Anne M. Thompson, Debra E. Kollonige, Nadia Smith, Daan Hubert, Arno Keppens, Juan Cuesta, Klaus-Peter Heue, Pepijn Veefkind, Kenneth Aikin, Jeff Peischl, Chelsea R. Thompson, Thomas B. Ryerson, Gregory J. Frost, Brian C. McDonald, and Owen R. Cooper
Atmos. Chem. Phys., 24, 9975–10000, https://doi.org/10.5194/acp-24-9975-2024, https://doi.org/10.5194/acp-24-9975-2024, 2024
Short summary
Short summary
The study examines tropical tropospheric ozone changes. In situ data from 1994–2019 display increased ozone, notably over India, Southeast Asia, and Malaysia and Indonesia. Sparse in situ data limit trend detection for the 15-year period. In situ and satellite data, with limited sampling, struggle to consistently detect trends. Continuous observations are vital over the tropical Pacific Ocean, Indian Ocean, western Africa, and South Asia for accurate ozone trend estimation in these regions.
Kai-Lan Chang, Owen R. Cooper, Audrey Gaudel, Irina Petropavlovskikh, Peter Effertz, Gary Morris, and Brian C. McDonald
Atmos. Chem. Phys., 24, 6197–6218, https://doi.org/10.5194/acp-24-6197-2024, https://doi.org/10.5194/acp-24-6197-2024, 2024
Short summary
Short summary
A great majority of observational trend studies of free tropospheric ozone use sparsely sampled ozonesonde and aircraft measurements as reference data sets. A ubiquitous assumption is that trends are accurate and reliable so long as long-term records are available. We show that sampling bias due to sparse samples can persistently reduce the trend accuracy, and we highlight the importance of maintaining adequate frequency and continuity of observations.
Davide Putero, Paolo Cristofanelli, Kai-Lan Chang, Gaëlle Dufour, Gregory Beachley, Cédric Couret, Peter Effertz, Daniel A. Jaffe, Dagmar Kubistin, Jason Lynch, Irina Petropavlovskikh, Melissa Puchalski, Timothy Sharac, Barkley C. Sive, Martin Steinbacher, Carlos Torres, and Owen R. Cooper
Atmos. Chem. Phys., 23, 15693–15709, https://doi.org/10.5194/acp-23-15693-2023, https://doi.org/10.5194/acp-23-15693-2023, 2023
Short summary
Short summary
We investigated the impact of societal restriction measures during the COVID-19 pandemic on surface ozone at 41 high-elevation sites worldwide. Negative ozone anomalies were observed for spring and summer 2020 for all of the regions considered. In 2021, negative anomalies continued for Europe and partially for the eastern US, while western US sites showed positive anomalies due to wildfires. IASI satellite data and the Carbon Monitor supported emission reductions as a cause of the anomalies.
Haolin Wang, Xiao Lu, Daniel J. Jacob, Owen R. Cooper, Kai-Lan Chang, Ke Li, Meng Gao, Yiming Liu, Bosi Sheng, Kai Wu, Tongwen Wu, Jie Zhang, Bastien Sauvage, Philippe Nédélec, Romain Blot, and Shaojia Fan
Atmos. Chem. Phys., 22, 13753–13782, https://doi.org/10.5194/acp-22-13753-2022, https://doi.org/10.5194/acp-22-13753-2022, 2022
Short summary
Short summary
We report significant global tropospheric ozone increases in 1995–2017 based on extensive aircraft and ozonesonde observations. Using GEOS-Chem (Goddard Earth Observing System chemistry model) multi-decadal global simulations, we find that changes in global anthropogenic emissions, in particular the rapid increases in aircraft emissions, contribute significantly to the increases in tropospheric ozone and resulting radiative impact.
Joshua Singleton, Hantao Wang, Jerry R. Ziemke, Marc L. Serre, and J. Jason West
EGUsphere, https://doi.org/10.5194/egusphere-2026-4798, https://doi.org/10.5194/egusphere-2026-4798, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Satellite ozone observations have been considered poor predictors of ozone at ground level. Here we evaluate the ability of one satellite product to estimate ground-level ozone globally over 18 years. The satellite product shows a modest correlation with ground-level ozone that does not vary strongly with year, season, world region, or ozone level. Using a linear regression, we translate the satellite product into a global monthly ground-level ozone dataset for use in future data fusion studies.
Andrin Jörimann, Timofei Sukhodolov, Simone Tilmes, David Plummer, Shingo Watanabe, Hideharu Akiyoshi, Gabriel Chiodo, Daniele Visioni, Sandro Vattioni, Eugene Rozanov, Ewa Monika Bednarz, Béatrice Josse, Yousuke Yamashita, and Thomas Peter
Atmos. Chem. Phys., 26, 11207–11234, https://doi.org/10.5194/acp-26-11207-2026, https://doi.org/10.5194/acp-26-11207-2026, 2026
Short summary
Short summary
We study a future scenario where artificial stratospheric aerosol injections counter medium climate change, to understand possible negative side effects like ozone depletion. The injected aerosol layer is implemented uniformly in five climate models, which eliminates some uncertainty from model-specific aerosol evolution. The models agree well on where and how key thermodynamical (heating, circulation) and chemical processes change, however, the strength of the changes varies considerably.
Katharina Perny, Timofei Sukhodolov, Ales Kuchar, Pavle Arsenovic, Bernadette Rosati, Christoph Brühl, Sandip S. Dhomse, Andrin Jörimann, Anton Laakso, Graham Mann, Ulrike Niemeier, Giovanni Pitari, Ilaria Quaglia, Takashi Sekiya, Kengo Sudo, Claudia Timmreck, Simone Tilmes, Daniele Visioni, and Harald E. Rieder
Atmos. Chem. Phys., 26, 10997–11025, https://doi.org/10.5194/acp-26-10997-2026, https://doi.org/10.5194/acp-26-10997-2026, 2026
Short summary
Short summary
Major volcanic eruptions, such as the one of Mt. Pinatubo in 1991, can inject large amounts of sulfur dioxide into the stratosphere. The resulting aerosol cloud affects stratospheric temperature and thereby middle atmospheric dynamics and chemistry. Here we investigate similarities and differences across an ensemble of climate models in reproducing the stratospheric temperature signal following the Mt. Pinatubo eruption.
Ramiyou Karim Mache, Sabine Schröder, Michael Langguth, Ankit Patnala, and Martin G. Schultz
Geosci. Model Dev., 19, 5765–5779, https://doi.org/10.5194/gmd-19-5765-2026, https://doi.org/10.5194/gmd-19-5765-2026, 2026
Short summary
Short summary
The TOAR-classifier model is a data-driven tool that allows for an objective classification of air quality measuring stations as urban, rural, or suburban. Such classification is important in the analysis of air pollutant trends and regional signatures. The model is employed in the second Tropospheric Ozone Assessment Report but can also be used in other research work.
Hantao Wang, Marc L. Serre, Kazuyuki Miyazaki, Juan Cuesta, Jerry R. Ziemke, and J. Jason West
EGUsphere, https://doi.org/10.5194/egusphere-2026-2812, https://doi.org/10.5194/egusphere-2026-2812, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Ground-level ozone poses a significant health risk, yet ground monitors are sparse and satellites lack surface sensitivity. Here we develop a framework to infer surface ozone directly from satellite observations. By leveraging vertical profiles from balloons and aircraft and using chemical reanalysis vertical ratios, we significantly improved the accuracy of ozone estimates. Our 2005–2022 global dataset provides a valuable ground-level ozone background field for regions lacking ground networks.
Paul D. Hamer, Miha Markelj, Oscar Rojas-Munoz, Bertrand Bonan, Jean-Christophe Calvet, Virginie Marécal, Alex Guenther, Heidi Trimmel, Islen Vallejo, Sabine Eckhardt, Gabriela Sousa Santos, Katerina Sindelarova, David Simpson, Norbert Schmidbauer, Heidi Hellén, Pascal Rubli, Stefan Reimann, Anja Claude, Dagmar Kubistin, Julie Cozic, James Dernie, and Leonor Tarrasón
Earth Syst. Sci. Data, 18, 3635–3669, https://doi.org/10.5194/essd-18-3635-2026, https://doi.org/10.5194/essd-18-3635-2026, 2026
Short summary
Short summary
Plants release gases like isoprene that can form ozone and affect air quality. Using models and satellite data, we mapped the emissions of isoprene from plants across Europe and found that droughts can reduce leaf growth, leading to lower emissions. This shows that to understand and predict air quality, we must also understand how drought impacts vegetation. Our findings highlight the value of linking extreme weather, plant health, and pollution in models of the Earth system as a whole.
Takashi Sekiya, Kazuyuki Miyazaki, Henk Eskes, Pieter Rijsdijk, Kengo Sudo, and Yugo Kanaya
EGUsphere, https://doi.org/10.5194/egusphere-2026-1681, https://doi.org/10.5194/egusphere-2026-1681, 2026
Short summary
Short summary
This study evaluates the synergistic effects of satellite aerosol optical depth (AOD) and trace gas data assimilation on aerosol composition analyses. The simultaneous data assimilation (DA) improved the PM2.5 model biases in Northeast Asia compared to conventional DA run that separately assimilate AOD satellite observations. This coupled aerosol and trace gas DA framework offers significant advantages for improving global aerosol composition analyses.
Sindhu Vasireddy, Michael Langguth, and Martin Schultz
EGUsphere, https://doi.org/10.5194/egusphere-2026-1562, https://doi.org/10.5194/egusphere-2026-1562, 2026
Short summary
Short summary
This study evaluates a transformer model for hourly air quality forecasting using past pollution, weather, and anthropogenic metadata (emissions, land use). It outperforms Copernicus Atmosphere Monitoring Service forecasts, especially in urban regions, with lower bias and improved stability. Trained in Germany, it transfers to South Korea with minimal adaptation, preserving geochemical relationships and showing strong cross-regional generalization.
Marta Abalos, Thomas Birner, Andreas Chrysanthou, Sean Davis, Alvaro de la Cámara, Sandip Dhomse, Hella Garny, Michaela I. Hegglin, Daan Hubert, Oksana Ivaniha, James Keeble, Marianna Linz, Daniele Minganti, Jessica Neu, David Plummer, Laura Saunders, Kasturi Shah, Gabriele Stiller, Kleareti Tourpali, Darryn Waugh, Nathan Luke Abraham, Hideharu Akiyoshi, Martyn P. Chipperfield, Patrick Jöckel, Béatrice Josse, Marion Marchand, Patrick Martineau, Olaf Morgenstern, Timofei Sukhodolov, Shingo Watanabe, and Yousuke Yamashita
Atmos. Chem. Phys., 26, 5249–5291, https://doi.org/10.5194/acp-26-5249-2026, https://doi.org/10.5194/acp-26-5249-2026, 2026
Short summary
Short summary
Accurate representation of stratospheric transport in Chemistry-Climate Models is essential for reliable climate projections. This study evaluates three generations of models using observational data and reanalyses, identifying persistent biases and their potential causes. Some biases persist or even worsen in newer models. These findings highlight key limitations and inform efforts to improve models and advance understanding through process-based studies and enhanced observations.
David D. Parrish, Charles A. Mims, Richard G. Derwent, Ian C. Faloona, Henry Bowman, Tongwen Wu, Jie Zhang, Makoto Deushi, and Naga Oshima
EGUsphere, https://doi.org/10.5194/egusphere-2026-1939, https://doi.org/10.5194/egusphere-2026-1939, 2026
Preprint archived
Short summary
Short summary
Nearly a century of ozone concentration measurements (the last half continuous) is now available in the northern mid-latitude troposphere. Atmospheric models cannot yet accurately simulate that record. We compare measured and simulated long-term changes and seasonal cycles of background ozone to quantify some model shortcomings, and suggest that their important causes may be inadequate model treatments of ozone precursor evolution and vertical transport out of the planetary boundary layer.
Ju-Mee Ryoo, Laura T. Iraci, Yu Yan Cui, Owen R. Cooper, Matthew S. Johnson, Kai-Lan Chang, Emma L. Yates, Valerie Thouret, Hanna Clark, Philippe Nedelec, and Bastien Sauvage
EGUsphere, https://doi.org/10.5194/egusphere-2026-1157, https://doi.org/10.5194/egusphere-2026-1157, 2026
Short summary
Short summary
Background ozone over Western North America is increasing despite local emission reductions, driven by intensifying transport from Southeast Asia, expanded global shipping, and rising aircraft emissions in the upper troposphere-lower stratosphere (UTLS). By analyzing the ozone 'lower tail' (<33rd percentile), this study identifies growing remote influences and shifting trans-Pacific transport patterns that are fundamentally altering the region's background atmospheric state.
Sean Davis, William Ball, Yue Jia, Gabriel Chiodo, Justin Alsing, James Keeble, Hideharu Akiyoshi, Carlo Arosio, Ewa Bednarz, Andreas Chrysanthou, Melanie Coldewey-Egbers, Robert Damadeo, Sandip Dhomse, Mohamadou Diallo, Simone Dietmuller, Roland Eichinger, Stacey Frith, Birgit Hassler, Michaela Hegglin, Daan Hubert, Patrick Jöckel, Béatrice Josse, Natalya Kramarova, Diego Loyola, Eliane Maillard Barras, Marion Marchand, Olaf Morgenstern, David Plummer, Robert Portmann, Karen Rosenlof, Alexei Rozanov, Viktoria Sofieva, Johannes Staehelin, Timofei Sukhodolov, Kleareti Tourpali, Ronald Van der A, H. J. Ray Wang, Krzysztof Wargan, Shingo Watanabe, Mark Weber, Jeannette Wild, Yousuke Yamashita, and Jerry Ziemke
EGUsphere, https://doi.org/10.5194/egusphere-2026-532, https://doi.org/10.5194/egusphere-2026-532, 2026
Short summary
Short summary
This study investigates how tropical ozone levels have changed since 2000 in chemistry climate models and satellite observations to determine how well they agree with one another, and to see if current trends can help predict future levels. At some, satellite records disagree significantly on the magnitude of ozone changes. The study shows a connection between recent ozone trends and future ozone levels, suggesting that satellite measurements could help constrain future ozone changes.
Biplob Dey, Toke Due Sjøgren, Peeyush Khare, Georgios I. Gkatzelis, Yizhen Wu, Sindhu Vasireddy, Martin Schultz, Alexander Knohl, Riikka Rinnan, Thorsten Hohaus, and Eva Y. Pfannerstill
Biogeosciences, 23, 1423–1457, https://doi.org/10.5194/bg-23-1423-2026, https://doi.org/10.5194/bg-23-1423-2026, 2026
Short summary
Short summary
Trees release reactive gases that affect air quality and climate. We studied how these emissions from European beech and English oak change under realistic scenarios of combined and single heat and ozone stress. Heat increased emissions, while ozone reduced most of them. When stressors were combined, the effects were complex and varied by species. Machine learning identified key stress-related compounds. Our findings show that future tree stress may alter air quality and climate interactions.
William J. Collins, John S. Daniel, Martyn P. Chipperfield, Martin Cussac, Makoto Deushi, Gregory Faluvegi, Paul Griffiths, Øivind Hodnebrog, Larry W. Horowitz, James Keeble, Douglas Kinnison, Vaishali Naik, Fiona M. O'Connor, Drew Shindell, Simone Tilmes, Kostas Tsigaridis, Zihao Wang, and James Weber
EGUsphere, https://doi.org/10.5194/egusphere-2025-6033, https://doi.org/10.5194/egusphere-2025-6033, 2026
Short summary
Short summary
Ozone depleting substances (ODSs) are also greenhouse gases that cause global warming. However, their destruction of ozone contributes a global cooling. We have used results from climate models that include atmospheric chemistry and found that the cooling effect of the ozone depletion diagnosed in the models was larger than that calculated using a standard method. We find that some ODSs have a net cooling effect whereas for others the warming effect is significantly reduced.
Xiaohua Pan, Mian Chin, Ralph A. Kahn, Hitoshi Matsui, Toshihiko Takemura, Meiyun Lin, Yuanyu Xie, Dongchul Kim, and Maria Val Martin
Atmos. Chem. Phys., 26, 171–196, https://doi.org/10.5194/acp-26-171-2026, https://doi.org/10.5194/acp-26-171-2026, 2026
Short summary
Short summary
Wildfire smoke can travel far from its source, affecting air quality far from the fire itself. This study looks at how two key factors – how much smoke is emitted & how high it rises – affect how smoke spreads. Using data from a major 2008 Siberian wildfire, four models were tested. Results show that models often inject smoke too low & remove it too quickly, missing high-altitude smoke seen by satellites. Better estimates of smoke height and removal are crucial to improve air quality forecasts.
Sebastian H. M. Hickman, Makoto M. Kelp, Paul T. Griffiths, Kelsey Doerksen, Kazuyuki Miyazaki, Elyse A. Pennington, Gerbrand Koren, Fernando Iglesias-Suarez, Martin G. Schultz, Kai-Lan Chang, Owen R. Cooper, Alex Archibald, Roberto Sommariva, David Carlson, Hantao Wang, J. Jason West, and Zhenze Liu
Geosci. Model Dev., 18, 8777–8800, https://doi.org/10.5194/gmd-18-8777-2025, https://doi.org/10.5194/gmd-18-8777-2025, 2025
Short summary
Short summary
Machine learning is being more widely used across environmental and climate science. This work reviews the use of machine learning in tropospheric ozone research, focusing on three main application areas in which significant progress has been made. Common challenges in using machine learning across the three areas are highlighted, and future directions for the field are indicated.
Hantao Wang, Kazuyuki Miyazaki, Haitong Zhe Sun, Zhen Qu, Xiang Liu, Antje Inness, Martin Schultz, Sabine Schröder, Marc Serre, and J. Jason West
Atmos. Chem. Phys., 25, 15969–15990, https://doi.org/10.5194/acp-25-15969-2025, https://doi.org/10.5194/acp-25-15969-2025, 2025
Short summary
Short summary
We compare six datasets of global ground-level ozone, developed using geostatistical, machine learning, or reanalysis methods. The datasets show important differences from one another in ozone magnitude, greater than 5 ppb, and trends, globally and regionally. Compared with measurements, performance varies among datasets, and most overestimate ozone, particularly at lower concentrations. These differences among datasets highlight uncertainties for applications to health and other impacts.
Yasuto Watanabe, Makoto Deushi, and Kohei Yoshida
Clim. Past, 21, 2243–2261, https://doi.org/10.5194/cp-21-2243-2025, https://doi.org/10.5194/cp-21-2243-2025, 2025
Short summary
Short summary
This study uses an Earth System Model, MRI-ESM2.0, to demonstrate that the atmospheric ozone distribution during warm interglacial periods is modified by the changes in the Earth's orbital parameters. We further show that the change in atmospheric ozone works to cool the surface at the high-latitude regions of the northern hemisphere in the past warm interglacial periods (6 and 127 thousand years ago), while its impact is small around Antarctica.
Yu Yan Cui, Ju-Mee Ryoo, Matthew S. Johnson, Kai-Lan Chang, Emma L. Yates, Owen R. Cooper, and Laura T. Iraci
Earth Syst. Sci. Data, 17, 5903–5914, https://doi.org/10.5194/essd-17-5903-2025, https://doi.org/10.5194/essd-17-5903-2025, 2025
Short summary
Short summary
Atmospheric observations show that free tropospheric ozone has increased across the Northern Hemisphere over the past three decades. The sources driving this increase remain unclear. In this study, we developed a source-receptor relationship database combining multiplatform ozone data and advanced atmospheric transport modeling. This database can identify emission regions responsible for ozone increases and can also be used to analyze other co-observed atmospheric constituents.
Sergey Khaykin, Slimane Bekki, Sophie Godin-Beekmann, Michael D. Fromm, Philippe Goloub, Qiaoyun Hu, Béatrice Josse, Alexandra Laeng, Mehdi Meziane, David A. Peterson, Sophie Pelletier, and Valérie Thouret
Atmos. Chem. Phys., 25, 14551–14571, https://doi.org/10.5194/acp-25-14551-2025, https://doi.org/10.5194/acp-25-14551-2025, 2025
Short summary
Short summary
In 2023, massive wildfires in Canada injected huge amounts of smoke into the atmosphere. Surprisingly, despite their intensity, the smoke did not rise very high but lingered at flight cruising altitudes, causing widespread pollution. This study shows how two different pathways lifted smoke into the lower stratosphere and reveals new insights into how wildfires affect air quality and climate, challenging what we thought we knew about fire and atmospheric impacts.
Yugo Kanaya, Roberto Sommariva, Alfonso Saiz-Lopez, Andrea Mazzeo, Theodore K. Koenig, Kaori Kawana, James E. Johnson, Aurélie Colomb, Pierre Tulet, Suzie Molloy, Ian E. Galbally, Rainer Volkamer, Anoop Mahajan, John W. Halfacre, Paul B. Shepson, Julia Schmale, Hélène Angot, Byron Blomquist, Matthew D. Shupe, Detlev Helmig, Junsu Gil, Meehye Lee, Sean C. Coburn, Ivan Ortega, Gao Chen, James Lee, Kenneth C. Aikin, David D. Parrish, John S. Holloway, Thomas B. Ryerson, Ilana B. Pollack, Eric J. Williams, Brian M. Lerner, Andrew J. Weinheimer, Teresa Campos, Frank M. Flocke, J. Ryan Spackman, Ilann Bourgeois, Jeff Peischl, Chelsea R. Thompson, Ralf M. Staebler, Amir A. Aliabadi, Wanmin Gong, Roeland Van Malderen, Anne M. Thompson, Ryan M. Stauffer, Debra E. Kollonige, Juan Carlos Gómez Martin, Masatomo Fujiwara, Katie Read, Matthew Rowlinson, Keiichi Sato, Junichi Kurokawa, Yoko Iwamoto, Fumikazu Taketani, Hisahiro Takashima, Mónica Navarro-Comas, Marios Panagi, and Martin G. Schultz
Earth Syst. Sci. Data, 17, 4901–4932, https://doi.org/10.5194/essd-17-4901-2025, https://doi.org/10.5194/essd-17-4901-2025, 2025
Short summary
Short summary
The first comprehensive dataset of tropospheric ozone over oceans/polar regions is presented, including 77 ship/buoy and 48 aircraft campaign observations (1977–2022, 0–5000 m altitude), supplemented by ozonesonde and surface data. Air masses isolated from land for 72+ hours are systematically selected as essentially oceanic. Among the 11 global regions, they show daytime decreases of 11–16 % in the tropics, while near-zero depletions are rare, unlike in the Arctic, implying different mechanisms.
Roeland Van Malderen, Zhou Zang, Kai-Lan Chang, Robin Björklund, Owen R. Cooper, Jane Liu, Eliane Maillard Barras, Corinne Vigouroux, Irina Petropavlovskikh, Thierry Leblanc, Valérie Thouret, Pawel Wolff, Peter Effertz, Audrey Gaudel, David W. Tarasick, Herman G. J. Smit, Anne M. Thompson, Ryan M. Stauffer, Debra E. Kollonige, Deniz Poyraz, Gérard Ancellet, Marie-Renée De Backer, Matthias M. Frey, James W. Hannigan, José L. Hernandez, Bryan J. Johnson, Nicholas Jones, Rigel Kivi, Emmanuel Mahieu, Isamu Morino, Glen McConville, Katrin Müller, Isao Murata, Justus Notholt, Ankie Piters, Maxime Prignon, Richard Querel, Vincenzo Rizi, Dan Smale, Wolfgang Steinbrecht, Kimberly Strong, and Ralf Sussmann
Atmos. Chem. Phys., 25, 9905–9935, https://doi.org/10.5194/acp-25-9905-2025, https://doi.org/10.5194/acp-25-9905-2025, 2025
Short summary
Short summary
Tropospheric ozone is an important greenhouse gas and an air pollutant whose distribution and time variability are mainly governed by anthropogenic emissions and dynamics. In this paper, we assess regional trends of tropospheric ozone column amounts, based on two different approaches of merging or synthesizing ground-based observations and their trends within specific regions. Our findings clearly demonstrate regional trend differences but also consistently higher pre-COVID than post-COVID trends.
Rodrigo J. Seguel, Charlie Opazo, Yann Cohen, Owen R. Cooper, Laura Gallardo, Björn-Martin Sinnhuber, Florian Obersteiner, Andreas Zahn, Peter Hoor, Susanne Rohs, and Andreas Marsing
Atmos. Chem. Phys., 25, 8553–8573, https://doi.org/10.5194/acp-25-8553-2025, https://doi.org/10.5194/acp-25-8553-2025, 2025
Short summary
Short summary
We explored ozone differences between the Northern Hemisphere and Southern Hemispheres in the upper troposphere–lower stratosphere. We found lower ozone (with stratospheric origin) in the Southern Hemisphere, especially during years of severe ozone depletion. Sudden stratospheric warming events increased the ozone in each hemisphere, highlighting the relationship between stratospheric processes and ozone in the upper troposphere, where ozone is an important greenhouse gas.
Paul T. Griffiths, Laura J. Wilcox, Robert J. Allen, Vaishali Naik, Fiona M. O'Connor, Michael Prather, Alex Archibald, Florence Brown, Makoto Deushi, William Collins, Stephanie Fiedler, Naga Oshima, Lee T. Murray, Bjørn H. Samset, Chris Smith, Steven Turnock, Duncan Watson-Parris, and Paul J. Young
Atmos. Chem. Phys., 25, 8289–8328, https://doi.org/10.5194/acp-25-8289-2025, https://doi.org/10.5194/acp-25-8289-2025, 2025
Short summary
Short summary
The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) aimed to quantify the climate and air quality impacts of aerosols and chemically reactive gases. We review its contribution to AR6 (Sixth Assessment Report of the Intergovernmental Panel on Climate Change) and the wider understanding of the role of these species in climate and climate change. We identify challenges and provide recommendations to improve the utility and uptake of climate model data, detailed summary tables of CMIP6 models, experiments, and emergent diagnostics.
Meghana Velagar, Christoph Keller, and J. Nathan Kutz
Geosci. Model Dev., 18, 4667–4684, https://doi.org/10.5194/gmd-18-4667-2025, https://doi.org/10.5194/gmd-18-4667-2025, 2025
Short summary
Short summary
We develop the data-driven method of dynamic mode decomposition for producing a robust and stable surrogate reduced-order model of atmospheric chemistry dynamics. The model is computationally efficient, provides interpretable patterns of activity, and produces uncertainty quantification metrics. It is ideal for the forecasting of atmospheric chemistry in a computationally tractable manner.
Martin Cussac, Martine Michou, Pierre Nabat, Béatrice Josse, and Sophie Pelletier
EGUsphere, https://doi.org/10.5194/egusphere-2025-1933, https://doi.org/10.5194/egusphere-2025-1933, 2025
Preprint archived
Short summary
Short summary
This study evaluates three chemistry schemes of varying complexity, one mainly stratospheric and two tropospheric-stratospheric, in the latest version of the climate model ARPEGE-Climat. Stratospheric ozone and water vapour are better represented. Despite issues with carbon monoxide in one scheme and with winter nitrogen species in the other, tropospheric ozone is overall realistically simulated. These modelling evolutions strengthen future research on chemistry-climate interactions.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
Short summary
Short summary
The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Kai-Lan Chang, Brian C. McDonald, Colin Harkins, and Owen R. Cooper
Atmos. Chem. Phys., 25, 5101–5132, https://doi.org/10.5194/acp-25-5101-2025, https://doi.org/10.5194/acp-25-5101-2025, 2025
Short summary
Short summary
Exposure to high levels of ozone can be harmful to human health. This study shows consistent and robust evidence of decreasing ozone extremes across much of the United States over the period from 1990 to 2023, previously attributed to ozone precursor emission controls. Nevertheless, we also show that the increasing heat wave frequencies are likely to contribute to additional ozone exceedances, slowing the progress of decreasing the frequency of ozone exceedances.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
Short summary
Short summary
Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Hongyu Liu, Bo Zhang, Richard H. Moore, Luke D. Ziemba, Richard A. Ferrare, Hyundeok Choi, Armin Sorooshian, David Painemal, Hailong Wang, Michael A. Shook, Amy Jo Scarino, Johnathan W. Hair, Ewan C. Crosbie, Marta A. Fenn, Taylor J. Shingler, Chris A. Hostetler, Gao Chen, Mary M. Kleb, Gan Luo, Fangqun Yu, Mark A. Vaughan, Yongxiang Hu, Glenn S. Diskin, John B. Nowak, Joshua P. DiGangi, Yonghoon Choi, Christoph A. Keller, and Matthew S. Johnson
Atmos. Chem. Phys., 25, 2087–2121, https://doi.org/10.5194/acp-25-2087-2025, https://doi.org/10.5194/acp-25-2087-2025, 2025
Short summary
Short summary
We use the GEOS-Chem model to simulate aerosol distributions and properties over the western North Atlantic Ocean (WNAO) during the winter and summer deployments in 2020 of the NASA ACTIVATE mission. Model results are evaluated against aircraft, ground-based, and satellite observations. The improved understanding of life cycle, composition, transport pathways, and distribution of aerosols has important implications for characterizing aerosol–cloud–meteorology interactions over WNAO.
Thibaut Lebourgeois, Bastien Sauvage, Pawel Wolff, Béatrice Josse, Virginie Marécal, Yasmine Bennouna, Romain Blot, Damien Boulanger, Hannah Clark, Jean-Marc Cousin, Philippe Nedelec, and Valérie Thouret
Atmos. Chem. Phys., 24, 13975–14004, https://doi.org/10.5194/acp-24-13975-2024, https://doi.org/10.5194/acp-24-13975-2024, 2024
Short summary
Short summary
Our study examines intense-carbon-monoxide (CO) pollution events measured by commercial aircraft from the In-service Aircraft for a Global Observing System (IAGOS) research infrastructure. We combine these measurements with the SOFT-IO model to trace the origin of the observed CO. A comprehensive analysis of the geographical origin, source type, seasonal variation, and ozone levels of these pollution events is provided.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
Short summary
Short summary
This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Audrey Gaudel, Ilann Bourgeois, Meng Li, Kai-Lan Chang, Jerald Ziemke, Bastien Sauvage, Ryan M. Stauffer, Anne M. Thompson, Debra E. Kollonige, Nadia Smith, Daan Hubert, Arno Keppens, Juan Cuesta, Klaus-Peter Heue, Pepijn Veefkind, Kenneth Aikin, Jeff Peischl, Chelsea R. Thompson, Thomas B. Ryerson, Gregory J. Frost, Brian C. McDonald, and Owen R. Cooper
Atmos. Chem. Phys., 24, 9975–10000, https://doi.org/10.5194/acp-24-9975-2024, https://doi.org/10.5194/acp-24-9975-2024, 2024
Short summary
Short summary
The study examines tropical tropospheric ozone changes. In situ data from 1994–2019 display increased ozone, notably over India, Southeast Asia, and Malaysia and Indonesia. Sparse in situ data limit trend detection for the 15-year period. In situ and satellite data, with limited sampling, struggle to consistently detect trends. Continuous observations are vital over the tropical Pacific Ocean, Indian Ocean, western Africa, and South Asia for accurate ozone trend estimation in these regions.
Amir H. Souri, Bryan N. Duncan, Sarah A. Strode, Daniel C. Anderson, Michael E. Manyin, Junhua Liu, Luke D. Oman, Zhen Zhang, and Brad Weir
Atmos. Chem. Phys., 24, 8677–8701, https://doi.org/10.5194/acp-24-8677-2024, https://doi.org/10.5194/acp-24-8677-2024, 2024
Short summary
Short summary
We explore a new method of using the wealth of information obtained from satellite observations of Aura OMI NO2, HCHO, and MERRA-2 reanalysis in NASA’s GEOS model equipped with an efficient tropospheric OH (TOH) estimator to enhance the representation of TOH spatial distribution and its long-term trends. This new framework helps us pinpoint regional inaccuracies in TOH and differentiate between established prior knowledge and newly acquired information from satellites on TOH trends.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
Geosci. Model Dev., 17, 5545–5571, https://doi.org/10.5194/gmd-17-5545-2024, https://doi.org/10.5194/gmd-17-5545-2024, 2024
Short summary
Short summary
Using multi-platform observations, we validated global formaldehyde (HCHO) simulations from a chemistry transport model. HCHO is a crucial intermediate in the chemical catalytic cycle that governs the ozone formation in the troposphere. The model was capable of replicating the observed spatiotemporal variability in HCHO. In a few cases, the model's capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
Kai-Lan Chang, Owen R. Cooper, Audrey Gaudel, Irina Petropavlovskikh, Peter Effertz, Gary Morris, and Brian C. McDonald
Atmos. Chem. Phys., 24, 6197–6218, https://doi.org/10.5194/acp-24-6197-2024, https://doi.org/10.5194/acp-24-6197-2024, 2024
Short summary
Short summary
A great majority of observational trend studies of free tropospheric ozone use sparsely sampled ozonesonde and aircraft measurements as reference data sets. A ubiquitous assumption is that trends are accurate and reliable so long as long-term records are available. We show that sampling bias due to sparse samples can persistently reduce the trend accuracy, and we highlight the importance of maintaining adequate frequency and continuity of observations.
Meng Li, Junichi Kurokawa, Qiang Zhang, Jung-Hun Woo, Tazuko Morikawa, Satoru Chatani, Zifeng Lu, Yu Song, Guannan Geng, Hanwen Hu, Jinseok Kim, Owen R. Cooper, and Brian C. McDonald
Atmos. Chem. Phys., 24, 3925–3952, https://doi.org/10.5194/acp-24-3925-2024, https://doi.org/10.5194/acp-24-3925-2024, 2024
Short summary
Short summary
In this work, we developed MIXv2, a mosaic Asian emission inventory for 2010–2017. With high spatial (0.1°) and monthly temporal resolution, MIXv2 integrates anthropogenic and open biomass burning emissions across seven sectors following a mosaic methodology. It provides CO2 emissions data alongside nine key pollutants and three chemical mechanisms. Our publicly accessible gridded monthly emissions data can facilitate long-term atmospheric and climate model analyses.
Victoria A. Flood, Kimberly Strong, Cynthia H. Whaley, Kaley A. Walker, Thomas Blumenstock, James W. Hannigan, Johan Mellqvist, Justus Notholt, Mathias Palm, Amelie N. Röhling, Stephen Arnold, Stephen Beagley, Rong-You Chien, Jesper Christensen, Makoto Deushi, Srdjan Dobricic, Xinyi Dong, Joshua S. Fu, Michael Gauss, Wanmin Gong, Joakim Langner, Kathy S. Law, Louis Marelle, Tatsuo Onishi, Naga Oshima, David A. Plummer, Luca Pozzoli, Jean-Christophe Raut, Manu A. Thomas, Svetlana Tsyro, and Steven Turnock
Atmos. Chem. Phys., 24, 1079–1118, https://doi.org/10.5194/acp-24-1079-2024, https://doi.org/10.5194/acp-24-1079-2024, 2024
Short summary
Short summary
It is important to understand the composition of the Arctic atmosphere and how it is changing. Atmospheric models provide simulations that can inform policy. This study examines simulations of CH4, CO, and O3 by 11 models. Model performance is assessed by comparing results matched in space and time to measurements from five high-latitude ground-based infrared spectrometers. This work finds that models generally underpredict the concentrations of these gases in the Arctic troposphere.
Davide Putero, Paolo Cristofanelli, Kai-Lan Chang, Gaëlle Dufour, Gregory Beachley, Cédric Couret, Peter Effertz, Daniel A. Jaffe, Dagmar Kubistin, Jason Lynch, Irina Petropavlovskikh, Melissa Puchalski, Timothy Sharac, Barkley C. Sive, Martin Steinbacher, Carlos Torres, and Owen R. Cooper
Atmos. Chem. Phys., 23, 15693–15709, https://doi.org/10.5194/acp-23-15693-2023, https://doi.org/10.5194/acp-23-15693-2023, 2023
Short summary
Short summary
We investigated the impact of societal restriction measures during the COVID-19 pandemic on surface ozone at 41 high-elevation sites worldwide. Negative ozone anomalies were observed for spring and summer 2020 for all of the regions considered. In 2021, negative anomalies continued for Europe and partially for the eastern US, while western US sites showed positive anomalies due to wildfires. IASI satellite data and the Carbon Monitor supported emission reductions as a cause of the anomalies.
Chi-Tsan Wang, Bok H. Baek, William Vizuete, Lawrence S. Engel, Jia Xing, Jaime Green, Marc Serre, Richard Strott, Jared Bowden, and Jung-Hun Woo
Earth Syst. Sci. Data, 15, 5261–5279, https://doi.org/10.5194/essd-15-5261-2023, https://doi.org/10.5194/essd-15-5261-2023, 2023
Short summary
Short summary
Hazardous air pollutant (HAP) human exposure studies usually rely on local measurements or dispersion model methods, but those methods are limited under spatial and temporal conditions. We processed the US EPA emission data to simulate the hourly HAP emission patterns and applied the chemical transport model to simulate the HAP concentrations. The modeled HAP results exhibit good agreement (R is 0.75 and NMB is −5.6 %) with observational data.
Yanfeng He and Kengo Sudo
Atmos. Chem. Phys., 23, 13061–13085, https://doi.org/10.5194/acp-23-13061-2023, https://doi.org/10.5194/acp-23-13061-2023, 2023
Short summary
Short summary
Lightning has big social impacts. Lightning-produced NOx (LNOx) plays a vital role in atmospheric chemistry and climate. Investigating past lightning and LNOx trends can provide essential indicators of all lightning-related phenomena. Simulations show almost flat global lightning and LNOx trends during 1960–2014. Past global warming enhances the trends positively, but increases in aerosol have the opposite effect. Moreover, global lightning decreased markedly after the Pinatubo eruption.
Herizo Narivelo, Paul David Hamer, Virginie Marécal, Luke Surl, Tjarda Roberts, Sophie Pelletier, Béatrice Josse, Jonathan Guth, Mickaël Bacles, Simon Warnach, Thomas Wagner, Stefano Corradini, Giuseppe Salerno, and Lorenzo Guerrieri
Atmos. Chem. Phys., 23, 10533–10561, https://doi.org/10.5194/acp-23-10533-2023, https://doi.org/10.5194/acp-23-10533-2023, 2023
Short summary
Short summary
Volcanic emissions emit large quantities of gases and primary aerosols that can play an important role in atmospheric chemistry. We present a study of the fate of volcanic bromine emissions from the eruption of Mount Etna around Christmas 2018. Using a numerical model and satellite observations, we analyse the impact of the volcanic plume and how it modifies the composition of the air over the whole Mediterranean basin, in particular on tropospheric ozone through the bromine-explosion cycle.
Marina Friedel, Gabriel Chiodo, Timofei Sukhodolov, James Keeble, Thomas Peter, Svenja Seeber, Andrea Stenke, Hideharu Akiyoshi, Eugene Rozanov, David Plummer, Patrick Jöckel, Guang Zeng, Olaf Morgenstern, and Béatrice Josse
Atmos. Chem. Phys., 23, 10235–10254, https://doi.org/10.5194/acp-23-10235-2023, https://doi.org/10.5194/acp-23-10235-2023, 2023
Short summary
Short summary
Previously, it has been suggested that springtime Arctic ozone depletion might worsen in the coming decades due to climate change, which might counteract the effect of reduced ozone-depleting substances. Here, we show with different chemistry–climate models that springtime Arctic ozone depletion will likely decrease in the future. Further, we explain why models show a large spread in the projected development of Arctic ozone depletion and use the model spread to constrain future projections.
Marie Dumont, Simon Gascoin, Marion Réveillet, Didier Voisin, François Tuzet, Laurent Arnaud, Mylène Bonnefoy, Montse Bacardit Peñarroya, Carlo Carmagnola, Alexandre Deguine, Aurélie Diacre, Lukas Dürr, Olivier Evrard, Firmin Fontaine, Amaury Frankl, Mathieu Fructus, Laure Gandois, Isabelle Gouttevin, Abdelfateh Gherab, Pascal Hagenmuller, Sophia Hansson, Hervé Herbin, Béatrice Josse, Bruno Jourdain, Irene Lefevre, Gaël Le Roux, Quentin Libois, Lucie Liger, Samuel Morin, Denis Petitprez, Alvaro Robledano, Martin Schneebeli, Pascal Salze, Delphine Six, Emmanuel Thibert, Jürg Trachsel, Matthieu Vernay, Léo Viallon-Galinier, and Céline Voiron
Earth Syst. Sci. Data, 15, 3075–3094, https://doi.org/10.5194/essd-15-3075-2023, https://doi.org/10.5194/essd-15-3075-2023, 2023
Short summary
Short summary
Saharan dust outbreaks have profound effects on ecosystems, climate, health, and the cryosphere, but the spatial deposition pattern of Saharan dust is poorly known. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This campaign triggered wide interest and over 100 samples. The samples revealed the high variability of the dust properties within a single event.
Daniel C. Anderson, Bryan N. Duncan, Julie M. Nicely, Junhua Liu, Sarah A. Strode, and Melanie B. Follette-Cook
Atmos. Chem. Phys., 23, 6319–6338, https://doi.org/10.5194/acp-23-6319-2023, https://doi.org/10.5194/acp-23-6319-2023, 2023
Short summary
Short summary
We describe a methodology that combines machine learning, satellite observations, and 3D chemical model output to infer the abundance of the hydroxyl radical (OH), a chemical that removes many trace gases from the atmosphere. The methodology successfully captures the variability of observed OH, although further observations are needed to evaluate absolute accuracy. Current satellite observations are of sufficient quality to infer OH, but retrieval validation in the remote tropics is needed.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Cynthia H. Whaley, Kathy S. Law, Jens Liengaard Hjorth, Henrik Skov, Stephen R. Arnold, Joakim Langner, Jakob Boyd Pernov, Garance Bergeron, Ilann Bourgeois, Jesper H. Christensen, Rong-You Chien, Makoto Deushi, Xinyi Dong, Peter Effertz, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Greg Huey, Ulas Im, Rigel Kivi, Louis Marelle, Tatsuo Onishi, Naga Oshima, Irina Petropavlovskikh, Jeff Peischl, David A. Plummer, Luca Pozzoli, Jean-Christophe Raut, Tom Ryerson, Ragnhild Skeie, Sverre Solberg, Manu A. Thomas, Chelsea Thompson, Kostas Tsigaridis, Svetlana Tsyro, Steven T. Turnock, Knut von Salzen, and David W. Tarasick
Atmos. Chem. Phys., 23, 637–661, https://doi.org/10.5194/acp-23-637-2023, https://doi.org/10.5194/acp-23-637-2023, 2023
Short summary
Short summary
This study summarizes recent research on ozone in the Arctic, a sensitive and rapidly warming region. We find that the seasonal cycles of near-surface atmospheric ozone are variable depending on whether they are near the coast, inland, or at high altitude. Several global model simulations were evaluated, and we found that because models lack some of the ozone chemistry that is important for the coastal Arctic locations, they do not accurately simulate ozone there.
Felix Kleinert, Lukas H. Leufen, Aurelia Lupascu, Tim Butler, and Martin G. Schultz
Geosci. Model Dev., 15, 8913–8930, https://doi.org/10.5194/gmd-15-8913-2022, https://doi.org/10.5194/gmd-15-8913-2022, 2022
Short summary
Short summary
We examine the effects of spatially aggregated upstream information as input for a deep learning model forecasting near-surface ozone levels. Using aggregated data from one upstream sector (45°) improves the forecast by ~ 10 % for 4 prediction days. Three upstream sectors improve the forecasts by ~ 14 % on the first 2 d only. Our results serve as an orientation for other researchers or environmental agencies focusing on pointwise time-series predictions, for example, due to regulatory purposes.
Bing Gong, Michael Langguth, Yan Ji, Amirpasha Mozaffari, Scarlet Stadtler, Karim Mache, and Martin G. Schultz
Geosci. Model Dev., 15, 8931–8956, https://doi.org/10.5194/gmd-15-8931-2022, https://doi.org/10.5194/gmd-15-8931-2022, 2022
Short summary
Short summary
Inspired by the success of deep learning in various domains, we test the applicability of video prediction methods by generative adversarial network (GAN)-based deep learning to predict the 2 m temperature over Europe. Our video prediction models have skill in predicting the diurnal cycle of 2 m temperature up to 12 h ahead. Complemented by probing the relevance of several model parameters, this study confirms the potential of deep learning in meteorological forecasting applications.
Randall V. Martin, Sebastian D. Eastham, Liam Bindle, Elizabeth W. Lundgren, Thomas L. Clune, Christoph A. Keller, William Downs, Dandan Zhang, Robert A. Lucchesi, Melissa P. Sulprizio, Robert M. Yantosca, Yanshun Li, Lucas Estrada, William M. Putman, Benjamin M. Auer, Atanas L. Trayanov, Steven Pawson, and Daniel J. Jacob
Geosci. Model Dev., 15, 8731–8748, https://doi.org/10.5194/gmd-15-8731-2022, https://doi.org/10.5194/gmd-15-8731-2022, 2022
Short summary
Short summary
Atmospheric chemistry models must be able to operate both online as components of Earth system models and offline as standalone models. The widely used GEOS-Chem model operates both online and offline, but the classic offline version is not suitable for massively parallel simulations. We describe a new generation of the offline high-performance GEOS-Chem (GCHP) that enables high-resolution simulations on thousands of cores, including on the cloud, with improved access, performance, and accuracy.
Amy Christiansen, Loretta J. Mickley, Junhua Liu, Luke D. Oman, and Lu Hu
Atmos. Chem. Phys., 22, 14751–14782, https://doi.org/10.5194/acp-22-14751-2022, https://doi.org/10.5194/acp-22-14751-2022, 2022
Short summary
Short summary
Understanding tropospheric ozone trends is crucial for accurate predictions of future air quality and climate, but drivers of trends are not well understood. We analyze global tropospheric ozone trends since 1980 using ozonesonde and surface measurements, and we evaluate two models for their ability to reproduce trends. We find observational evidence of increasing tropospheric ozone, but models underestimate these increases. This hinders our ability to estimate ozone radiative forcing.
Haolin Wang, Xiao Lu, Daniel J. Jacob, Owen R. Cooper, Kai-Lan Chang, Ke Li, Meng Gao, Yiming Liu, Bosi Sheng, Kai Wu, Tongwen Wu, Jie Zhang, Bastien Sauvage, Philippe Nédélec, Romain Blot, and Shaojia Fan
Atmos. Chem. Phys., 22, 13753–13782, https://doi.org/10.5194/acp-22-13753-2022, https://doi.org/10.5194/acp-22-13753-2022, 2022
Short summary
Short summary
We report significant global tropospheric ozone increases in 1995–2017 based on extensive aircraft and ozonesonde observations. Using GEOS-Chem (Goddard Earth Observing System chemistry model) multi-decadal global simulations, we find that changes in global anthropogenic emissions, in particular the rapid increases in aircraft emissions, contribute significantly to the increases in tropospheric ozone and resulting radiative impact.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Alessandro Damiani, Manish Naja, and Al Mashroor Fatmi
Atmos. Chem. Phys., 22, 12559–12589, https://doi.org/10.5194/acp-22-12559-2022, https://doi.org/10.5194/acp-22-12559-2022, 2022
Short summary
Short summary
Nitrogen dioxide (NO2) and formaldehyde (HCHO) are essential trace graces regulating tropospheric ozone chemistry. These trace constituents are measured using an optical passive remote sensing technique. In addition, NO2 and HCHO are simulated with a computer model and evaluated against the observations. Such evaluations are essential to assess model uncertainties and improve their predictability. The results yielded good agreement between the two datasets with some discrepancies.
Flossie Brown, Gerd A. Folberth, Stephen Sitch, Susanne Bauer, Marijn Bauters, Pascal Boeckx, Alexander W. Cheesman, Makoto Deushi, Inês Dos Santos Vieira, Corinne Galy-Lacaux, James Haywood, James Keeble, Lina M. Mercado, Fiona M. O'Connor, Naga Oshima, Kostas Tsigaridis, and Hans Verbeeck
Atmos. Chem. Phys., 22, 12331–12352, https://doi.org/10.5194/acp-22-12331-2022, https://doi.org/10.5194/acp-22-12331-2022, 2022
Short summary
Short summary
Surface ozone can decrease plant productivity and impair human health. In this study, we evaluate the change in surface ozone due to climate change over South America and Africa using Earth system models. We find that if the climate were to change according to the worst-case scenario used here, models predict that forested areas in biomass burning locations and urban populations will be at increasing risk of ozone exposure, but other areas will experience a climate benefit.
Daniel C. Anderson, Melanie B. Follette-Cook, Sarah A. Strode, Julie M. Nicely, Junhua Liu, Peter D. Ivatt, and Bryan N. Duncan
Geosci. Model Dev., 15, 6341–6358, https://doi.org/10.5194/gmd-15-6341-2022, https://doi.org/10.5194/gmd-15-6341-2022, 2022
Short summary
Short summary
The hydroxyl radical (OH) is the most important chemical in the atmosphere for removing certain pollutants, including methane, the second-most-important greenhouse gas. We present a methodology to create an easily modifiable parameterization that can calculate OH concentrations in a computationally efficient way. The parameterization, which predicts OH within 5 %, can be integrated into larger climate models to allow for calculation of the interactions between OH, methane, and other chemicals.
Yanfeng He, Hossain Mohammed Syedul Hoque, and Kengo Sudo
Geosci. Model Dev., 15, 5627–5650, https://doi.org/10.5194/gmd-15-5627-2022, https://doi.org/10.5194/gmd-15-5627-2022, 2022
Short summary
Short summary
Lightning-produced NOx (LNOx) is a major source of NOx. Hence, it is crucial to improve the prediction accuracy of lightning and LNOx in chemical climate models. By modifying existing lightning schemes and testing them in the chemical climate model CHASER, we improved the prediction accuracy of lightning in CHASER. Different lightning schemes respond very differently under global warming, which indicates further research is needed considering the reproducibility of long-term trends of lightning.
Swantje Preuschmann, Tanja Blome, Knut Görl, Fiona Köhnke, Bettina Steuri, Juliane El Zohbi, Diana Rechid, Martin Schultz, Jianing Sun, and Daniela Jacob
Adv. Sci. Res., 19, 51–71, https://doi.org/10.5194/asr-19-51-2022, https://doi.org/10.5194/asr-19-51-2022, 2022
Short summary
Short summary
The main aspect of the paper is to obtain transferable principles for the development of digital knowledge transfer products. As such products are still unstandardised, the authors explored challenges and approaches for product developments. The authors report what they see as useful principles for developing digital knowledge transfer products, by describing the experience of developing the Net-Zero-2050 Web-Atlas and the "Bodenkohlenstoff-App".
Jason E. Williams, Vincent Huijnen, Idir Bouarar, Mehdi Meziane, Timo Schreurs, Sophie Pelletier, Virginie Marécal, Beatrice Josse, and Johannes Flemming
Geosci. Model Dev., 15, 4657–4687, https://doi.org/10.5194/gmd-15-4657-2022, https://doi.org/10.5194/gmd-15-4657-2022, 2022
Short summary
Short summary
The global CAMS air quality model is used for providing tropospheric ozone information to end users. This paper updates the chemical mechanism employed (CBA) and compares it against two other mechanisms (MOCAGE, MOZART) and a multi-decadal dataset based on a previous version of CBA. We perform extensive validation for the US using multiple surface and aircraft datasets, providing an assessment of biases and the extent of correlation across different seasons during 2014.
Clara Betancourt, Timo T. Stomberg, Ann-Kathrin Edrich, Ankit Patnala, Martin G. Schultz, Ribana Roscher, Julia Kowalski, and Scarlet Stadtler
Geosci. Model Dev., 15, 4331–4354, https://doi.org/10.5194/gmd-15-4331-2022, https://doi.org/10.5194/gmd-15-4331-2022, 2022
Short summary
Short summary
Ozone is a toxic greenhouse gas with high spatial variability. We present a machine-learning-based ozone-mapping workflow generating a transparent and reliable product. Going beyond standard mapping methods, this work combines explainable machine learning with uncertainty assessment to increase the integrity of the produced map.
Takashi Sekiya, Kazuyuki Miyazaki, Henk Eskes, Kengo Sudo, Masayuki Takigawa, and Yugo Kanaya
Atmos. Meas. Tech., 15, 1703–1728, https://doi.org/10.5194/amt-15-1703-2022, https://doi.org/10.5194/amt-15-1703-2022, 2022
Short summary
Short summary
This study gives a systematic comparison of TROPOMI version 1.2 and OMI QA4ECV tropospheric NO2 column through global chemical data assimilation (DA) integration for April–May 2018. DA performance is controlled by measurement sensitivities, retrieval errors, and coverage. Due to reduced errors in TROPOMI, agreements against assimilated and independent observations were improved by TROPOMI DA compared to OMI DA. These results demonstrate that TROPOMI DA improves global analyses of NO2 and ozone.
Henry Bowman, Steven Turnock, Susanne E. Bauer, Kostas Tsigaridis, Makoto Deushi, Naga Oshima, Fiona M. O'Connor, Larry Horowitz, Tongwen Wu, Jie Zhang, Dagmar Kubistin, and David D. Parrish
Atmos. Chem. Phys., 22, 3507–3524, https://doi.org/10.5194/acp-22-3507-2022, https://doi.org/10.5194/acp-22-3507-2022, 2022
Short summary
Short summary
A full understanding of ozone in the troposphere requires investigation of its temporal variability over all timescales. Model simulations show that the northern midlatitude ozone seasonal cycle shifted with industrial development (1850–2014), with an increasing magnitude and a later summer peak. That shift reached a maximum in the mid-1980s, followed by a reversal toward the preindustrial cycle. The few available observations, beginning in the 1970s, are consistent with the model simulations.
Andrew O. Langford, Christoph J. Senff, Raul J. Alvarez II, Ken C. Aikin, Sunil Baidar, Timothy A. Bonin, W. Alan Brewer, Jerome Brioude, Steven S. Brown, Joel D. Burley, Dani J. Caputi, Stephen A. Conley, Patrick D. Cullis, Zachary C. J. Decker, Stéphanie Evan, Guillaume Kirgis, Meiyun Lin, Mariusz Pagowski, Jeff Peischl, Irina Petropavlovskikh, R. Bradley Pierce, Thomas B. Ryerson, Scott P. Sandberg, Chance W. Sterling, Ann M. Weickmann, and Li Zhang
Atmos. Chem. Phys., 22, 1707–1737, https://doi.org/10.5194/acp-22-1707-2022, https://doi.org/10.5194/acp-22-1707-2022, 2022
Short summary
Short summary
The Fires, Asian, and Stratospheric Transport–Las Vegas Ozone Study (FAST-LVOS) combined lidar, aircraft, and in situ measurements with global models to investigate the contributions of stratospheric intrusions, regional and Asian pollution, and wildfires to background ozone in the southwestern US during May and June 2017 and demonstrated that these processes contributed to background ozone levels that exceeded 70 % of the US National Ambient Air Quality Standard during the 6-week campaign.
Abhinna K. Behera, Emmanuel D. Rivière, Sergey M. Khaykin, Virginie Marécal, Mélanie Ghysels, Jérémie Burgalat, and Gerhard Held
Atmos. Chem. Phys., 22, 881–901, https://doi.org/10.5194/acp-22-881-2022, https://doi.org/10.5194/acp-22-881-2022, 2022
Short summary
Short summary
Deep convection overshooting the stratosphere's contribution to the global stratospheric water budget is still being quantified. We ran three different cloud-resolving simulations of an observed case of overshoots in Bauru during the TRO-Pico balloon campaign in the context of upscaling the impact of overshoots at a large scale. These simulations, which have been validated with balloon-borne and S-band radar measurements, shed light on the local-scale variability and composition of overshoots.
Matthieu Plu, Guillaume Bigeard, Bojan Sič, Emanuele Emili, Luca Bugliaro, Laaziz El Amraoui, Jonathan Guth, Beatrice Josse, Lucia Mona, and Dennis Piontek
Nat. Hazards Earth Syst. Sci., 21, 3731–3747, https://doi.org/10.5194/nhess-21-3731-2021, https://doi.org/10.5194/nhess-21-3731-2021, 2021
Short summary
Short summary
Volcanic eruptions that spread out ash over large areas, like Eyjafjallajökull in 2010, may have huge economic consequences due to flight cancellations. In this article, we demonstrate the benefits of source term improvement and of data assimilation for quantifying volcanic ash concentrations. The work, which was supported by the EUNADICS-AV project, is the first one, to our knowledge, that demonstrates the benefit of the assimilation of ground-based lidar data over Europe during an eruption.
Paul D. Hamer, Virginie Marécal, Ryan Hossaini, Michel Pirre, Gisèle Krysztofiak, Franziska Ziska, Andreas Engel, Stephan Sala, Timo Keber, Harald Bönisch, Elliot Atlas, Kirstin Krüger, Martyn Chipperfield, Valery Catoire, Azizan A. Samah, Marcel Dorf, Phang Siew Moi, Hans Schlager, and Klaus Pfeilsticker
Atmos. Chem. Phys., 21, 16955–16984, https://doi.org/10.5194/acp-21-16955-2021, https://doi.org/10.5194/acp-21-16955-2021, 2021
Short summary
Short summary
Bromoform is a stratospheric ozone-depleting gas released by seaweed and plankton transported to the stratosphere via convection in the tropics. We study the chemical interactions of bromoform and its derivatives within convective clouds using a cloud-scale model and observations. Our findings are that soluble bromine gases are efficiently washed out and removed within the convective clouds and that most bromine is transported vertically to the upper troposphere in the form of bromoform.
Hossain M. S. Hoque, Kengo Sudo, Hitoshi Irie, Alessandro Damiani, and Al Mashroor Fatmi
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-815, https://doi.org/10.5194/acp-2021-815, 2021
Revised manuscript not accepted
Short summary
Short summary
Nitrogen dioxide (NO2) and formaldehyde (HCHO) profiles, retrieved from remote sensing observations, are used to evaluate the global chemistry transport model CHASER. Overall, CHASER has demonstrated good skills in reproducing the seasonal climatology of NO2 and HCHO on a local scale at sites in South and East Asia. Around mountainous terrains, the model performs better on a regional scale. The improved spatial resolution of CHASER can likely reduce the observed discrepancies in the datasets.
Liam Bindle, Randall V. Martin, Matthew J. Cooper, Elizabeth W. Lundgren, Sebastian D. Eastham, Benjamin M. Auer, Thomas L. Clune, Hongjian Weng, Jintai Lin, Lee T. Murray, Jun Meng, Christoph A. Keller, William M. Putman, Steven Pawson, and Daniel J. Jacob
Geosci. Model Dev., 14, 5977–5997, https://doi.org/10.5194/gmd-14-5977-2021, https://doi.org/10.5194/gmd-14-5977-2021, 2021
Short summary
Short summary
Atmospheric chemistry models like GEOS-Chem are versatile tools widely used in air pollution and climate studies. The simulations used in such studies can be very computationally demanding, and thus it is useful if the model can simulate a specific geographic region at a higher resolution than the rest of the globe. Here, we implement, test, and demonstrate a new variable-resolution capability in GEOS-Chem that is suitable for simulations conducted on supercomputers.
Cited articles
Adachi, Y., Yukimoto, S., Deushi, M., Obata, A., andTaichu. Y. Tanaka, H. N.,
Hosaka, M., Sakami, T., Yoshimura, H., Hirabara, M., Shindo, E., Tsujino, H.,
Mizuta, R., Yabu, S., Koshiro, T., Ose, T., and Kitoh, A.: Basic performance
of a new earth system model of the Meteorological Research Institute
(MRI-ESM1), Pap. Meteorol. Geophys, 64, 1–18, https://doi.org/10.2467/mripapers.64.1,
2013. a
Anenberg, S. C., Horowitz, L. W., Tong, D. Q., and West, J. J.: An estimate
of the global burden of anthropogenic ozone and fine particulate matter on
premature human mortality using atmospheric modeling, Environ. Health Persp.,
118, 1189–1195, https://doi.org/10.1289/ehp.0901220, 2010. a
Banerjee, A., Dunson, D. B., and Tokdar, S. T.: Efficient Gaussian process
regression for large datasets, Biometrika, 100, 75–89,
https://doi.org/10.1093/biomet/ass068, 2012. a, b
Berrocal, V. J., Gelfand, A. E., and Holland, D. M.: Space-time data fusion
under error in computer model output: An application to modeling air quality,
Biometrics, 68, 837–848, https://doi.org/10.1111/j.1541-0420.2011.01725.x, 2012. a
Bolin, D. and Lindgren, F.: Spatial models generated by nested stochastic
partial differential equations, with an application to global ozone mapping,
Ann. Appl. Stat., 5, 523–550, https://doi.org/10.1214/10-AOAS383, 2011. a, b
Brauer, M., Amann, M., Burnett, R. T., Cohen, A., Dentener, F., Ezzati, M.,
Henderson, S. B., Krzyzanowski, M., Martin, R. V., Dingenen, R. V., van
Donkelaar, A., and Thurston, G. D.: Exposure assessment for estimation of the
global burden of disease attributable to outdoor air pollution, Environ. Sci.
Technol., 46, 652–660, https://doi.org/10.1021/es2025752, 2012. a, b, c
Brauer, M., Freedman, G., Frostad, J., van Donkelaar, A., Martin, R. V.,
Dentener, F., van Dingenen, R., Estep, K., Amini, H., Apte, J. S.,
Balakrishnan, K., Barregardh, L., Broday, D., Feigin, V., Ghosh, S., Hopke,
P. K., Knibbs, L. D., Kokubo, Y., Liu, Y., Ma, S., Morawska, L., Sangrador,
J. L. T., Shaddick, G., Anderson, H. R., Vos, T., Forouzanfar, M. H.,
Burnett, R. T., and Cohen, A.: Ambient air pollution exposure estimation for
the global burden of disease 2013, Environ. Sci. Technol., 50, 79–88,
https://doi.org/10.1021/acs.est.5b03709, 2015. a, b, c
Braverman, A., Chatterjee, S., Heyman, M., and Cressie, N.: Probabilistic
evaluation of competing climate models, Adv. Stat. Clim. Meteorol. Oceanogr.,
3, 93–105, https://doi.org/10.5194/ascmo-3-93-2017, 2017. a
Brynjarsdóttir, J. and O'Hagan, A.: Learning about physical parameters:
The importance of model discrepancy, Inverse Probl., 30, 114007,
https://doi.org/10.1088/0266-5611/30/11/114007, 2014. a
Buser, C. M., Künsch, H. R., Lüthi, D., Wild, M., and Schär, C.:
Bayesian multi-model projection of climate: bias assumptions and interannual
variability, Clim. Dynam., 33, 849–868, https://doi.org/10.1007/s00382-009-0588-6,
2009. a
Cameletti, M., Lindgren, F., Simpson, D., and Rue, H.: Spatio-temporal
modeling of particulate matter concentration through the SPDE approach,
AStA Adv. Stat. Anal., 97, 109–131, https://doi.org/10.1007/s10182-012-0196-3, 2013. a
Cariolle, D. and Teyssèdre, H.: A revised linear ozone photochemistry
parameterization for use in transport and general circulation models:
multi-annual simulations, Atmos. Chem. Phys., 7, 2183–2196,
https://doi.org/10.5194/acp-7-2183-2007, 2007. a
CEDA: Centre for Environmental Data Analysis: CCMI archive, available at:
http://data.ceda.ac.uk/badc/wcrp-ccmi/data/CCMI-1/output/, last access:
28 February 2019. a
Chandler, R. E.: Exploiting strength, discounting weakness: combining
information from multiple climate simulators, Philos. T. R. Soc. A, 371,
20120388, https://doi.org/10.1098/rsta.2012.0388, 2013. a
Chang, K.-L. and Guillas, S.: Computer model calibration with large
non-stationary spatial outputs: application to the calibration of a climate
model, J. Roy. Stat. Soc. C-Appl., 68, 51–78, https://doi.org/10.1111/rssc.12309,
2019. a
Chang, K.-L., Guillas, S., and Fioletov, V. E.: Spatial mapping of
ground-based observations of total ozone, Atmos. Meas. Tech., 8, 4487–4505,
https://doi.org/10.5194/amt-8-4487-2015, 2015. a
Chang, K.-L., Petropavlovskikh, I., Cooper, O. R., Schultz, M. G., and Wang,
T.: Regional trend analysis of surface ozone observations from monitoring
networks in eastern North America, Europe and East Asia, Elementa, 5, p. 50,
https://doi.org/10.1525/elementa.243, 2017. a
Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep,
K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V.,
Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y., Martin,
R., Morawska, L., III, C. A. P., Shin, H., Straif, K., Shaddick, G., Thomas,
M., van Dingenen, R., van Donkelaar, A., Vos, T., Murray, C. J. L., and
Forouzanfar, M. H.: Estimates and 25-year trends of the global burden of
disease attributable to ambient air pollution: an analysis of data from the
Global Burden of Diseases Study 2015, The Lancet, 389, 1907–1918,
https://doi.org/10.1016/S0140-6736(17)30505-6, 2017. a
Conti, S. and O'Hagan, A.: Bayesian emulation of complex multi-output and
dynamic computer models, J. Stat. Plan. Infer., 140, 640–651,
https://doi.org/10.1016/j.jspi.2009.08.006, 2010. a
Cooper, O. R., Parrish, D. D., Ziemke, J. R., Balashov, N. V., Cupeiro, M.,
Galbally, I. E., Gilge, S., Horowitz, L., Jensen, N. R., Lamarque, J.-F.,
Naik, V., Oltmans, S. J., Schwab, J., Shindell, D. T., Thompson, A. M.,
Thouret, V., Wang, Y., and Zbinden, R. M.: Global distribution and trends of
tropospheric ozone: An observation-based review, Elementa, 2, p. 000029,
https://doi.org/10.12952/journal.elementa.000029, 2014. a
Cressie, N. and Johannesson, G.: Fixed rank kriging for very large spatial
data sets, J. Roy. Stat. Soc. B, 70, 209–226,
https://doi.org/10.1111/j.1467-9868.2007.00633.x, 2008. a, b
Diggle, P. J., Menezes, R., and Su, T.-l.: Geostatistical inference under
preferential sampling, J. Roy. Stat. Soc. C-App., 59, 191–232, 2010. a
Fleming, Z. L., Doherty, R. M., von Schneidemesser, E., Malley, C. S.,
Cooper, O. R., Pinto, J. P., Colette, A., Xu, X., Simpson, D., Schultz,
M. G., Lefohn, A. S., Hamad, S., Moolla, R., Solberg, S., and Feng, Z.:
Tropospheric Ozone Assessment Report: Present-day ozone distribution and
trends relevant to human health, Elementa, 6, p. 12,
https://doi.org/10.1525/elementa.273, 2018. a
Fuentes, M. and Raftery, A. E.: Model evaluation and spatial interpolation by
Bayesian combination of observations with outputs from numerical models,
Biometrics, 61, 36–45, https://doi.org/10.1111/j.0006-341X.2005.030821.x, 2005. a
Furrer, R. and Sain, S. R.: Spatial model fitting for large datasets with
applications to climate and microarray problems, Stat. Comput., 19, 113–128,
https://doi.org/10.1007/s11222-008-9075-x, 2009. a
Gaudel, A., Cooper, O. R., Ancellet, G., Barret, B., Boynard, A., Burrows,
J. P., Clerbaux, C., Coheur, P. F., Cuesta, J., Cuevas, E., Doniki, S.,
Dufour, G., Ebojie, F., Foret, G., Garcia, O., Muños, M. J. G., Hannigan,
J. W., Hase, F., Huang, G., Hassler, B., Hurtmans, D., Jaffe, D., Jones, N.,
Kalabokas, P., Kerridge, B., Kulawik, S. S., Latter, B., Leblanc, T.,
Flochmoën, E. L., Lin, W., Liu, J., Liu, X., Mahieu, E., McClure-Begley,
A., Neu, J. L., Osman, M., Palm, M., Petetin, H., Petropavlovskikh, I.,
Querel, R., Rahpoe, N., Rozanov, A., Schultz, M. G., Schwab, J., Siddans, R.,
Smale, D., Steinbacher, M., Tanimoto, H., Tarasick, D. W., Thouret, V.,
Thompson, A. M., Trickl, T., Weatherhead, E. C., Wespes, C., Worden, H. M.,
Vigouroux, C., Xu, X., Zeng, G., and Ziemke, J. R.: Tropospheric Ozone
Assessment Report: Present-day distribution and trends of tropospheric ozone
relevant to climate and global atmospheric chemistry model evaluation,
Elementa, 6, p. 39, https://doi.org/10.1525/elementa.291, 2018. a
GBD: Global, regional, and national comparative risk assessment of 79
behavioural, environmental and occupational, and metabolic risks or clusters
of risks in 188 countries, 1990–2013: a systematic analysis for the Global
Burden of Disease Study 2013, The Lancet, 386, 2287–2323,
https://doi.org/10.1016/S0140-6736(15)00128-2, 2015. a
Gelfand, A. E. and Sahu, S. K.: Combining monitoring data and computer model
output in assessing environmental exposure, in: Handbook of Applied Bayesian
Analysis, Oxford University Press, Oxford, UK, 482–510, 2010. a
Gotway, C. A. and Young, L. J.: Combining incompatible spatial data, J. Am.
Stat. Assoc., 97, 632–648, https://doi.org/10.1198/016214502760047140, 2002. a
Gramacy, R. B. and Apley, D. W.: Local Gaussian process approximation for
large computer experiments, J. Comput. Graph. Stat., 24, 561–578,
https://doi.org/10.1080/10618600.2014.914442, 2015. a
Guillas, S., Tiao, G. C., Wuebbles, D. J., and Zubrow, A.: Statistical
diagnostic and correction of a chemistry-transport model for the prediction
of total column ozone, Atmos. Chem. Phys., 6, 525–537,
https://doi.org/10.5194/acp-6-525-2006, 2006. a
He, Y. and Xiu, D.: Numerical strategy for model correction using physical
constraints, J. Comput. Phys., 313, 617–634,
https://doi.org/10.1016/j.jcp.2016.02.054, 2016. a
Heath, A., Manolopoulou, I., and Baio, G.: Estimating the expected value of
partial perfect information in health economic evaluations using integrated
nested Laplace approximation, Stat. Med., 35, 4264–4280,
https://doi.org/10.1002/sim.6983, 2016. a
Heaton, M. J., Datta, A., Finley, A. O., Furrer, R., Guinness, J.,
Guhaniyogi, R., Gerber, F., Gramacy, R. B., Hammerling, D., Katzfuss, M.,
Lindgren, F., Nychka, D. W., Sun, F., and Zammit-Mangion, A.: A case study
competition among methods for analyzing large spatial data, J. Agric. Biol.
Envir. S., 1–28, https://doi.org/10.1007/s13253-018-00348-w, online first, 2018. a
Hoeting, J. A., Davis, R. A., Merton, A. A., and Thompson, S. E.: Model
selection for geostatistical models, Ecol. Appl., 16, 87–98,
https://doi.org/10.1890/04-0576, 2006. a, b
Hu, L., Keller, C. A., Long, M. S., Sherwen, T., Auer, B., Da Silva, A.,
Nielsen, J. E., Pawson, S., Thompson, M. A., Trayanov, A. L., Travis, K. R.,
Grange, S. K., Evans, M. J., and Jacob, D. J.: Global simulation of
tropospheric chemistry at 12.5 km resolution: performance and evaluation of
the GEOS-Chem chemical module (v10-1) within the NASA GEOS Earth system model
(GEOS-5 ESM), Geosci. Model Dev., 11, 4603–4620,
https://doi.org/10.5194/gmd-11-4603-2018, 2018. a, b, c
Hyde, R., Hossaini, R., and Leeson, A. A.: Cluster-based analysis of
multi-model climate ensembles, Geosci. Model Dev., 11, 2033–2048,
https://doi.org/10.5194/gmd-11-2033-2018, 2018. a, b
Jerrett, M., Burnett, R. T., Pope III, C. A., Ito, K., Thurston, G., Krewski,
D., Shi, Y., Calle, E., and Thun, M.: Long-term ozone exposure and mortality,
N. Engl. J. Med., 360, 1085–1095, https://doi.org/10.1056/NEJMoa0803894, 2009. a
Josse, B., Simon, P., and Peuch, V.-H.: Radon global simulations with the
multiscale chemistry and transport model MOCAGE, Tellus B, 56, 339–356,
https://doi.org/10.1111/j.1600-0889.2004.00112.x, 2004. a
Jun, M. and Stein, M. L.: Statistical comparison of observed and CMAQ
modeled daily sulfate levels, Atmos. Environ., 38, 4427–4436,
https://doi.org/10.1016/j.atmosenv.2004.05.019, 2004. a
Jun, M. and Stein, M. L.: An approach to producing space–time covariance
functions on spheres, Technometrics, 49, 468–479,
https://doi.org/10.1198/004017007000000155, 2007. a
Jun, M. and Stein, M. L.: Nonstationary covariance models for global data,
Ann. Appl. Stat., 2, 1271–1289, https://doi.org/10.1214/08-AOAS183, 2008. a
Jun, M., Knutti, R., and Nychka, D. W.: Spatial analysis to quantify
numerical model bias and dependence: how many climate models are there?, J.
Am. Stat. Assoc., 103, 934–947, https://doi.org/10.1198/016214507000001265, 2008. a
Kammann, E. and Wand, M. P.: Geoadditive models, J. Roy. Stat. Soc. C-App.,
52, 1–18, https://doi.org/10.1111/1467-9876.00385, 2003. a
Kennedy, M. C. and O'Hagan, A.: Bayesian calibration of computer models,
J. Roy. Stat. Soc. B, 63, 425–464, https://doi.org/10.1111/1467-9868.00294, 2001. a
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., and Meehl, G. A.: Challenges
in combining projections from multiple climate models, J. Climate, 23,
2739–2758, https://doi.org/10.1175/2009JCLI3361.1, 2010. a
Lefohn, A. S., Malley, C. S., Smith, L., Wells, B., Hazucha, M., Simon, H.,
Naik, V., Mills, G., Schultz, M. G., Paoletti, E., De Marco, A., Xu, X.,
Zhang, L., Wang, T., Neufeld, H. S., Musselman, R. C., Tarasick, D., Brauer,
M., Feng, Z., Tang, H., Kobayashi, K., Sicard, P., Solberg, S., and Gerosa,
G.: Tropospheric ozone assessment report: Global ozone metrics for climate
change, human health, and crop/ecosystem research, Elementa, 6, p. 28,
https://doi.org/10.1525/elementa.279, 2018. a
Liang, F., Cheng, Y., Song, Q., Park, J., and Yang, P.: A resampling-based
stochastic approximation method for analysis of large geostatistical data,
J. Am. Stat. Assoc., 108, 325–339, 2013. a
Lin, M., Fiore, A. M., Horowitz, L. W., Cooper, O. R., Naik, V., Holloway,
J., Johnson, B. J., Middlebrook, A. M., Oltmans, S. J., Pollack, I. B.,
Ryerson, T. B., Warner, J. X., Wiedinmyer, C., Wilson, J., and Wyman, B.:
Transport of Asian ozone pollution into surface air over the western
United States in spring, J. Geophys. Res., 117, D00V07,
https://doi.org/10.1029/2011JD016961, 2012. a
Lin, M., Horowitz, L. W., Oltmans, S. J., Fiore, A. M., and Fan, S.:
Tropospheric ozone trends at Mauna Loa Observatory tied to decadal climate
variability, Nat. Geosci., 7, 136–143, https://doi.org/10.1038/NGEO2066, 2014. a
Lin, M., Horowitz, L. W., Payton, R., Fiore, A. M., and Tonnesen, G.: US
surface ozone trends and extremes from 1980 to 2014: quantifying the roles of
rising Asian emissions, domestic controls, wildfires, and climate, Atmos.
Chem. Phys., 17, 2943–2970, https://doi.org/10.5194/acp-17-2943-2017, 2017. a
Lindgren, F. and Rue, H.: Bayesian spatial and spatiotemporal modelling with
R-INLA, J. Stat. Softw., 63, 1–25, https://doi.org/10.18637/jss.v063.i19, 2015. a, b
Liu, X. and Guillas, S.: Dimension reduction for Gaussian process emulation:
an application to the influence of bathymetry on tsunami heights, SIAM/ASA J.
Uncertain. Quantif., 5, 787–812, https://doi.org/10.1137/16M1090648, 2017. a
Malley, C. S., Henze, D. K., Kuylenstierna, J. C., Vallack, H. W., Davila,
Y., Anenberg, S. C., Turner, M. C., and Ashmore, M. R.: Updated global
estimates of respiratory mortality in adults ≥30 years of age
attributable to long-term ozone exposure, Environ. Health Persp., 125, 9 pp.,
https://doi.org/10.1289/EHP1390, 2017. a, b
Mills, G., Pleijel, H., Malley, C. S., Sinha, B., Cooper, O. R., Schultz,
M. G., Neufeld, H. S., Simpson, D., Sharps, K., Feng, Z., Gerosa, G.,
Harmens, H., Kobayashi, K., Saxena, P., Paoletti, E., Sinha, V., and Xu, X.:
Tropospheric Ozone Assessment Report: Present-day tropospheric ozone
distribution and trends relevant to vegetation, Elementa, 6, p. 47,
https://doi.org/10.1525/elementa.302, 2018. a
Morgenstern, O., Giorgetta, M. A., Shibata, K., Eyring, V., Waugh, D. W.,
Shepherd, T. G., Akiyoshi, H., Austin, J., Baumgaertner, A. J. G., Bekki, S.,
Braesicke, P., Brühl, C., Chipperfield, M., Cugnet, D., Dameris, M.,
Dhomse, S., Frith, S. M., Garny, H., Gettelman, A., Hardiman, S. C., Hegglin,
M. I., Jöckel, P., Kinnison, D. E., Lamarque, J.-F., Mancini, E.,
Manzini, E., Marchand, M., Michou, M., Nakamura, T., Nielsen, J. E.,
Olivié, D., Pitari, G., Plummer, D. A., Rozanov, E., Scinocca, J. F.,
Smale, D., Teyssèdre, H., Toohey, M., Tian, W., and Yamashita, Y.: Review
of the formulation of present-generation stratospheric chemistry-climate
models and associated external forcings, J. Geophys. Res., 115, D00M02,
https://doi.org/10.1029/2009JD013728, 2010. a
Morgenstern, O., Hegglin, M. I., Rozanov, E., O'Connor, F. M., Abraham, N.
L., Akiyoshi, H., Archibald, A. T., Bekki, S., Butchart, N., Chipperfield, M.
P., Deushi, M., Dhomse, S. S., Garcia, R. R., Hardiman, S. C., Horowitz, L.
W., Jöckel, P., Josse, B., Kinnison, D., Lin, M., Mancini, E., Manyin, M.
E., Marchand, M., Marécal, V., Michou, M., Oman, L. D., Pitari, G.,
Plummer, D. A., Revell, L. E., Saint-Martin, D., Schofield, R., Stenke, A.,
Stone, K., Sudo, K., Tanaka, T. Y., Tilmes, S., Yamashita, Y., Yoshida, K.,
and Zeng, G.: Review of the global models used within phase 1 of the
Chemistry–Climate Model Initiative (CCMI), Geosci. Model Dev., 10, 639–671,
https://doi.org/10.5194/gmd-10-639-2017, 2017. a, b
NASA Center for Climate Simulation (NCCS) Dataportal:
https://portal.nccs.nasa.gov/datashare/G5NR-Chem/Heracles/12.5km/DATA/,
Curator: Bill McHale, last access: 28 February 2019. a
Nguyen, H., Cressie, N., and Braverman, A.: Spatial statistical data fusion
for remote sensing applications, J. Am. Stat. Assoc., 107, 1004–1018,
https://doi.org/10.1080/01621459.2012.694717, 2012. a
Oman, L. D., Ziemke, J. R., Douglass, A. R., Waugh, D. W., Lang, C.,
Rodriguez, J. M., and Nielsen, J. E.: The response of tropical tropospheric
ozone to ENSO, Geophys. Res. Lett., 38, L13706, https://doi.org/10.1029/2011GL047865,
2011. a
R Core Team: R: A language and environment for statistical computing, R
Foundation for Statistical Computing, Vienna, Austria, 2013. a
Rasmussen, C. E. and Williams, C. K. I.: Gaussian processes for machine
learning, The MIT Press, Cambridge, MA, USA, 2006. a
Rue, H. and Held, L.: Gaussian Markov random fields: theory and
applications, CRC Press, New York, USA, 2005. a
Rue, H., Martino, S., and Chopin, N.: Approximate Bayesian inference for
latent Gaussian models by using integrated nested Laplace approximations, J.
Roy. Stat. Soc. B, 71, 319–392, https://doi.org/10.1111/j.1467-9868.2008.00700.x,
2009. a, b, c
Rue, H., Riebler, A., Sørbye, S. H., Illian, J. B., Simpson, D. P., and
Lindgren, F. K.: Bayesian computing with INLA: a review, Annu. Rev. Stat.
Appl., 4, 395–421, https://doi.org/10.1146/annurev-statistics-060116-054045, 2017. a
Sang, H. and Huang, J. Z.: A full scale approximation of covariance functions
for large spatial data sets, J. Roy. Stat. Soc. B, 74, 111–132,
https://doi.org/10.1111/j.1467-9868.2011.01007.x, 2012. a
Sang, H., Jun, M., and Huang, J. Z.: Covariance approximation for large
multivariate spatial data sets with an application to multiple climate model
errors, Ann. Appl. Stat., 5, 2519–2548, https://doi.org/10.1214/11-AOAS478, 2011. a
Schneider, T.: Analysis of incomplete climate data: Estimation of mean values
and covariance matrices and imputation of missing values, J. Climate, 14,
853–871, 2001. a
Schultz, M. G., Schröder, S., Lyapina, O., Cooper, O. R., Galbally, I.,
Petropavlovskikh, I., von Schneidemesser, E., Tanimoto, H., Elshorbany, Y.,
Naja, M., Seguel, R., Dauert, U., Eckhardt, P., Feigenspahn, S., Fiebig, M.,
Hjellbrekke, A.-G., Hong, Y.-D., Kjeld, P. C., Koide, H., Lear, G., Tarasick,
D., Ueno, M., Wallasch, M., Baumgardner, D., Chuang, M.-T., Gillett, R., Lee,
M., Molloy, S., Moolla, R., Wang, T., Sharps, K., Adame, J. A., Ancellet, G.,
Apadula, F., Artaxo, P., Barlasina, M., Bogucka, M., Bonasoni, P., Chang, L.,
Colomb, A., Cuevas, E., Cupeiro, M., Degorska, A., Ding, A., Fröhlich,
M., Frolova, M., Gadhavi, H., Gheusi, F., Gilge, S., Gonzalez, M. Y., Gros,
V., Hamad, S. H., Helmig, D., Henriques, D., Hermansen, O., Holla, R., Huber,
J., Im, U., Jaffe, D. A., Komala, N., Kubistin, D., Lam, K.-S., Laurila, T.,
Lee, H., Levy, I., Mazzoleni, C., Mazzoleni, L., McClure-Begley, A., Mohamad,
M., Murovic, M., Navarro-Comas, M., Nicodim, F., Parrish, D., Read, K. A.,
Reid, N., Ries, L., Saxena, P., Schwab, J. J., Scorgie, Y., Senik, I.,
Simmonds, P., Sinha, V., Skorokhod, A., Spain, G., Spangl, W., Spoor, R.,
Springston, S. R., Steer, K., Steinbacher, M., Suharguniyawan, E., Torre, P.,
Trickl, T., Weili, L., Weller, R., Xu, X., Xue, L., and Zhiqiang, M.:
Tropospheric Ozone Assessment Report: Database and metrics data of global
surface ozone observations, Elementa, 5, p. 58, https://doi.org/10.1525/elementa.244,
2017. a, b
Seltzer, K. M., Shindell, D. T., and Malley, C. S.: Measurement-based
assessment of health burdens from long-term ozone exposure in the United
States, Europe, and China, Environ. Res. Lett., 13, 104018,
https://doi.org/10.1088/1748-9326/aae29d, 2018. a
Shaddick, G. and Zidek, J. V.: A case study in preferential sampling: Long
term monitoring of air pollution in the UK, Spatial Statistics, 9, 51–65,
2014. a
Shaddick, G., Thomas, M. L., Green, A., Brauer, M., Donkelaar, A., Burnett,
R., Chang, H. H., Cohen, A., Dingenen, R. V., Dora, C., Gumy, S., Liu, Y.,
Martin, R., Waller, L. A., West, J. J., Zidek, J. V., and
Prüss-Ustün, A.: Data integration model for air quality: a
hierarchical approach to the global estimation of exposures to ambient air
pollution, J. R. Stat. Soc. C-Appl., 67, 231–253, https://doi.org/10.1111/rssc.12227,
2018. a, b
Sofen, E. D., Bowdalo, D., and Evans, M. J.: How to most effectively expand
the global surface ozone observing network, Atmos. Chem. Phys., 16,
1445–1457, https://doi.org/10.5194/acp-16-1445-2016, 2016. a
Stainforth, D. A., Allen, M. R., Tredger, E. R., and Smith, L. A.:
Confidence, uncertainty and decision-support relevance in climate
predictions, Philos. T. Roy. Soc. A, 365, 2145–2161,
https://doi.org/10.1098/rsta.2007.2074, 2007. a
Stein, M. L.: Interpolation of spatial data: in: Some theory for kriging,
Springer Science and Business Media, New York, USA, 2012. a
Stevenson, D. S., Dentener, F. J., Schultz, M. G., Ellingsen, K., Noije, T.
P. C. V., Wild, O., Zeng, G., Amann, M., Atherton, C. S., Bell, N., Bergmann,
D. J., Bey, I., Butler, T., Cofala, J., Collins, W. J., Derwent, R. G.,
Doherty, R. M., Drevet, J., Eskes, H. J., Fiore, A. M., Gauss, M.,
Hauglustaine, D. A., Horowitz, L. W., Isaksen, I. S. A., Krol, M. C.,
Lamarque, J.-F., Lawrence, M. G., Montanaro, V., Müller, J.-F., Pitari,
G., Prather, M. J., Pyle, J. A., Rast, S., Rodriguez, J. M., Sanderson,
M. G., Savage, N. H., Shindell, D. T., Strahan, S. E., Sudo, K., and Szopa,
S.: Multimodel ensemble simulations of present-day and near-future
tropospheric ozone, J. Geophys. Res., 111, D08301,
https://doi.org/10.1029/2005JD006338, 2006. a, b
Strode, S. A., Rodriguez, J. M., Logan, J. A., Cooper, O. R., Witte, J. C.,
Lamsal, L. N., Damon, M., Van Aartsen, B., Steenrod, S. D., and Strahan,
S. E.: Trends and variability in surface ozone over the United States, J.
Geophys. Res.-Atmos., 120, 9020–9042, https://doi.org/10.1002/2014JD022784, 2015. a
Sudo, K., Takahashi, M., and Akimoto, H.: CHASER: A global chemical model
of the troposphere, 2. Model results and evaluation, J. Geophys. Res., 107,
4586, https://doi.org/10.1029/2001JD001114, 2002a. a
Sudo, K., Takahashi, M., Kurokawa, J., and Akimoto, H.: CHASER: A global
chemical model of the troposphere, 1. Model description, J. Geophys. Res.,
107, 4339, https://doi.org/10.1029/2001JD001113, 2002b. a
Teyssèdre, H., Michou, M., Clark, H. L., Josse, B., Karcher, F.,
Olivié, D., Peuch, V.-H., Saint-Martin, D., Cariolle, D., Attié,
J.-L., Nédélec, P., Ricaud, P., Thouret, V., van der A, R. J.,
Volz-Thomas, A., and Chéroux, F.: A new tropospheric and stratospheric
Chemistry and Transport Model MOCAGE-Climat for multi-year studies:
evaluation of the present-day climatology and sensitivity to surface
processes, Atmos. Chem. Phys., 7, 5815–5860, https://doi.org/10.5194/acp-7-5815-2007,
2007. a
Turner, M. C., Jerrett, M., Pope III, C. A., Krewski, D., Gapstur, S. M.,
Diver, W. R., Beckerman, B. S., Marshall, J. D., Su, J., Crouse, D. L., and
Burnett, R. T.: Long-term ozone exposure and mortality in a large prospective
study, Am. J. Resp. Crit. Care, 193, 1134–1142,
https://doi.org/10.1164/rccm.201508-1633OC, 2016. a, b, c
US Environmental Protection Agency: Integrated Science Assessment (ISA) for
Ozone and Related Photochemical Oxidants, Office of Research and Development,
Research Triangle Park, NC, EPA/600/R-10/076F, 2013. a
Voulgarakis, A., Naik, V., Lamarque, J.-F., Shindell, D. T., Young, P. J.,
Prather, M. J., Wild, O., Field, R. D., Bergmann, D., Cameron-Smith, P.,
Cionni, I., Collins, W. J., Dalsøren, S. B., Doherty, R. M., Eyring, V.,
Faluvegi, G., Folberth, G. A., Horowitz, L. W., Josse, B., MacKenzie, I. A.,
Nagashima, T., Plummer, D. A., Righi, M., Rumbold, S. T., Stevenson, D. S.,
Strode, S. A., Sudo, K., Szopa, S., and Zeng, G.: Analysis of present day and
future OH and methane lifetime in the ACCMIP simulations, Atmos. Chem. Phys.,
13, 2563–2587, https://doi.org/10.5194/acp-13-2563-2013, 2013. a
Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H.,
Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E.,
Takata, K., Emori, S., and Kawamiya, M.: MIROC-ESM 2010: model description
and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4,
845–872, https://doi.org/10.5194/gmd-4-845-2011, 2011. a
Weatherhead, E. C., Bodeker, G. E., Fassò, A., Chang, K.-L., Lazo, J. K.,
Clack, C. T. M., Hurst, D. F., Hassler, B., English, J. M., and Yorgun, S.:
Spatial coverage of monitoring networks: A climate observing system
simulation experiment, J. Appl. Meteorol. Clim., 56, 3211–3228,
https://doi.org/10.1175/JAMC-D-17-0040.1, 2017. a
Weigel, A. P., Knutti, R., Liniger, M. A., and Appenzeller, C.: Risks of
model weighting in multimodel climate projections, J. Climate, 23,
4175–4191, https://doi.org/10.1175/2010JCLI3594.1, 2010. a
Williamson, D., Blaker, A. T., Hampton, C., and Salter, J.: Identifying and
removing structural biases in climate models with history matching, Clim.
Dynam., 45, 1299–1324, https://doi.org/10.1007/s00382-014-2378-z, 2015. a, b
Wood, S. N. and Augustin, N. H.: GAMs with integrated model selection using
penalized regression splines and applications to environmental modelling,
Ecol. Model., 157, 157–177, 2002. a
Wood, S. N., Bravington, M. V., and Hedley, S. L.: Soap film smoothing, J. R.
Stat. Soc. B-Met., 70, 931–955, https://doi.org/10.1111/j.1467-9868.2008.00665.x,
2008. a
World Health Organization: Air quality guidelines global update 2005:
Particulate matter, ozone, nitrogen dioxide, and sulfur dioxide, WHO,
Regional Office for Europe, Copenhagen,
http://www.euro.who.int/__data/assets/pdf_file/0005/78638/E90038.pdf
(last access: 28 February 2019), 2005. a
World Meteorological Organization: Scientific Assessment of Ozone Depletion:
2010: Pursuant to Article 6 of the Montreal Protocol on Substances that
Deplete the Ozone Layer, World Meterological Organization, Geneva,
Switzerland, 2011. a
Wu, S., Mickley, L. J., Jacob, D. J., Rind, D., and Streets, D. G.: Effects
of 2000–2050 changes in climate and emissions on global tropospheric ozone
and the policy-relevant background surface ozone in the United States, J.
Geophys. Res., 113, D18312, https://doi.org/10.1029/2007JD009639, 2008. a
Young, P. J., Archibald, A. T., Bowman, K. W., Lamarque, J.-F., Naik, V.,
Stevenson, D. S., Tilmes, S., Voulgarakis, A., Wild, O., Bergmann, D.,
Cameron-Smith, P., Cionni, I., Collins, W. J., Dalsøren, S. B., Doherty,
R. M., Eyring, V., Faluvegi, G., Horowitz, L. W., Josse, B., Lee, Y. H.,
MacKenzie, I. A., Nagashima, T., Plummer, D. A., Righi, M., Rumbold, S. T.,
Skeie, R. B., Shindell, D. T., Strode, S. A., Sudo, K., Szopa, S., and Zeng,
G.: Pre-industrial to end 21st century projections of tropospheric ozone from
the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP),
Atmos. Chem. Phys., 13, 2063–2090, https://doi.org/10.5194/acp-13-2063-2013, 2013. a, b
Young, P. J., Naik, V., Fiore, A. M., Gaudel, A., Guo, J., Lin, M., Neu,
J. L., Parrish, D. D., Rieder, H. E., Schnell, J. L., Tilmes, S., Wild, O.,
Zhang, L., Ziemke, J. R., Brandt, J., Delcloo, A., Doherty, R. M., Geels, C.,
Hegglin, M. I., Hu, L., Im, U., Kumar, R., Luhar, A., Murray, L., Plummer,
D., Rodriguez, J., Saiz-Lopez, A., Schultz, M. G., Woodhouse, M. T., and
Zeng, G.: Tropospheric Ozone Assessment Report: Assessment of global-scale
model performance for global and regional ozone distributions, variability,
and trends, Elementa, 6, p. 10, https://doi.org/10.1525/elementa.265, 2018. a, b, c
Short summary
We developed a new method for combining surface ozone observations from thousands of monitoring sites worldwide with the output from multiple atmospheric chemistry models. The result is a global surface ozone distribution with greater accuracy than any single model can achieve. We focused on an ozone metric relevant to human mortality caused by long-term ozone exposure. Our method can be applied to studies that quantify the impacts of ozone on human health and mortality.
We developed a new method for combining surface ozone observations from thousands of monitoring...