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

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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
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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.
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