Articles | Volume 24, issue 10
https://doi.org/10.5194/acp-24-6197-2024
https://doi.org/10.5194/acp-24-6197-2024
Technical note
 | 
28 May 2024
Technical note |  | 28 May 2024

Technical note: Challenges in detecting free tropospheric ozone trends in a sparsely sampled environment

Kai-Lan Chang, Owen R. Cooper, Audrey Gaudel, Irina Petropavlovskikh, Peter Effertz, Gary Morris, and Brian C. McDonald

Related authors

Ground-based Tropospheric Ozone Measurements: Regional tropospheric ozone column trends from the TOAR-II/ HEGIFTOM homogenized datasets
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-3745,https://doi.org/10.5194/egusphere-2024-3745, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research
Sebastian H. M. Hickman, Makoto 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, Alexander T. Archibald, Roberto Sommariva, David Carlson, Hantao Wang, J. Jason West, and Zhenze Liu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3739,https://doi.org/10.5194/egusphere-2024-3739, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Surface ozone trend variability across the United States and the impact of heatwaves (1990–2023)
Kai-Lan Chang, Brian C. McDonald, and Owen R. Cooper
EGUsphere, https://doi.org/10.5194/egusphere-2024-3674,https://doi.org/10.5194/egusphere-2024-3674, 2024
Short summary
Tropical tropospheric ozone distribution and trends from in situ and satellite data
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
Fingerprints of the COVID-19 economic downturn and recovery on ozone anomalies at high-elevation sites in North America and western Europe
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

Related subject area

Subject: Gases | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
Estimating the variability in NOx emissions from Wuhan with TROPOMI NO2 data during 2018 to 2023
Qianqian Zhang, K. Folkert Boersma, Chiel van der Laan, Alba Mols, Bin Zhao, Shengyue Li, and Yuepeng Pan
Atmos. Chem. Phys., 25, 3313–3326, https://doi.org/10.5194/acp-25-3313-2025,https://doi.org/10.5194/acp-25-3313-2025, 2025
Short summary
Enhanced understanding of atmospheric blocking modulation on ozone dynamics within a high-resolution Earth system model
Wenbin Kou, Yang Gao, Dan Tong, Xiaojie Guo, Xiadong An, Wenyu Liu, Mengshi Cui, Xiuwen Guo, Shaoqing Zhang, Huiwang Gao, and Lixin Wu
Atmos. Chem. Phys., 25, 3029–3048, https://doi.org/10.5194/acp-25-3029-2025,https://doi.org/10.5194/acp-25-3029-2025, 2025
Short summary
Natural emissions of VOC and NOx over Africa constrained by TROPOMI HCHO and NO2 data using the MAGRITTEv1.1 model
Beata Opacka, Trissevgeni Stavrakou, Jean-François Müller, Isabelle De Smedt, Jos van Geffen, Eloise A. Marais, Rebekah P. Horner, Dylan B. Millet, Kelly C. Wells, and Alex B. Guenther
Atmos. Chem. Phys., 25, 2863–2894, https://doi.org/10.5194/acp-25-2863-2025,https://doi.org/10.5194/acp-25-2863-2025, 2025
Short summary
Anthropogenic emission controls reduce summertime ozone–temperature sensitivity in the United States
Shuai Li, Haolin Wang, and Xiao Lu
Atmos. Chem. Phys., 25, 2725–2743, https://doi.org/10.5194/acp-25-2725-2025,https://doi.org/10.5194/acp-25-2725-2025, 2025
Short summary
Investigating the response of China's surface ozone concentration to the future changes of multiple factors
Jinya Yang, Yutong Wang, Lei Zhang, and Yu Zhao
Atmos. Chem. Phys., 25, 2649–2666, https://doi.org/10.5194/acp-25-2649-2025,https://doi.org/10.5194/acp-25-2649-2025, 2025
Short summary

Cited articles

Barnes, E. A., Fiore, A. M., and Horowitz, L. W.: Detection of trends in surface ozone in the presence of climate variability, J. Geophys. Res.-Atmos., 121, 6112–6129, https://doi.org/10.1002/2015JD024397, 2016. a
Bateson, T. F. and Schwartz, J.: Selection bias and confounding in case-crossover analyses of environmental time-series data, Epidemiology, 12, 654–661, 2001. a
Chang, K.-L., Cooper, O. R., Gaudel, A., Petropavlovskikh, I., and Thouret, V.: Statistical regularization for trend detection: an integrated approach for detecting long-term trends from sparse tropospheric ozone profiles, Atmos. Chem. Phys., 20, 9915–9938, https://doi.org/10.5194/acp-20-9915-2020, 2020. a, b, c
Chang, K.-L., Schultz, M. G., Lan, X., McClure-Begley, A., Petropavlovskikh, I., Xu, X., and Ziemke, J. R.: Trend detection of atmospheric time series: Incorporating appropriate uncertainty estimates and handling extreme events, Elementa: Science of the Anthropocene, 9, 00035, https://doi.org/10.1525/elementa.2021.00035, 2021. a, b, c, d, e, f
Chang, K.-L., Cooper, O. R., Gaudel, A., Allaart, M., Ancellet, G., Clark, H., Godin-Beekmann, S., Leblanc, T., Van Malderen, R., Nédélec, P., Petropavlovskikh, I., Steinbrecht, W., Stübi, R., Tarasick, D. W., and Torres, C.: Impact of the COVID-19 economic downturn on tropospheric ozone trends: an uncertainty weighted data synthesis for quantifying regional anomalies above western North America and Europe, AGU Advances, 3, e2021AV000542, https://doi.org/10.1029/2021AV000542, 2022. a, b, c
Download
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.
Share
Altmetrics
Final-revised paper
Preprint