Preprints
https://doi.org/10.5194/egusphere-2023-2739
https://doi.org/10.5194/egusphere-2023-2739
11 Jan 2024
 | 11 Jan 2024
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Technical note: Challenges of 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

Abstract. High quality long-term observational records are essential to ensure appropriate and reliable trend detection of tropospheric ozone. However, the necessity for maintaining high sampling frequency, in addition to continuity, is often under-appreciated. A common assumption is that so long as long-term records (e.g., span of a few decades) are available, (1) the estimated trends are accurate and precise, and (2) the impact of small-scale variability (e.g., weather) can be eliminated. In this study we show that the undercoverage bias (e.g., a type of sampling error resulting from statistical inference based on sparse or insufficient samples, such as once-per-week sampling frequency) can persistently reduce the trend accuracy of free tropospheric ozone, even if multi-decadal time series are considered. We use over 40 years of nighttime ozone observations measured at Mauna Loa, Hawaii (representative of the lower free troposphere) to make this demonstration, and quantify the bias in monthly means and trends under different sampling strategies. We also show that short-term meteorological variability remains a cause for an inflated long-term trend uncertainty. To improve the trend precision and accuracy due to sampling bias, two remedies are proposed: (1) a data variability attribution of colocated meteorological influence can efficiently reduce estimation uncertainty and moderately reduce the impact of sparse sampling; and (2) an adaptive sampling strategy based on anomaly detection enables us to greatly reduce the sampling bias, and produce more accurate trends using fewer samples compared to an intense regular sampling strategy.

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

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Interesting statistical study on biases due to sparse sampling.', Anonymous Referee #1, 15 Feb 2024 reply
  • CC1: 'Comment on egusphere-2023-2739', Raeesa Moolla, 28 Feb 2024 reply
Kai-Lan Chang, Owen R. Cooper, Audrey Gaudel, Irina Petropavlovskikh, Peter Effertz, Gary Morris, and Brian C. McDonald
Kai-Lan Chang, Owen R. Cooper, Audrey Gaudel, Irina Petropavlovskikh, Peter Effertz, Gary Morris, and Brian C. McDonald

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Short summary
The great majority of observational trend studies of free tropospheric ozone use sparsely sampled ozonesonde and aircraft measurements as reference datasets. 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 highlight the importance of maintaining adequate frequency and continuity of observations.