Articles | Volume 23, issue 20
https://doi.org/10.5194/acp-23-13029-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-13029-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Oct 2023
Research article |
| 16 Oct 2023
| 16 Oct 2023
Quantifying stratospheric ozone trends over 1984–2020: a comparison of ordinary and regularized multivariate regression models
Yajuan Li
CORRESPONDING AUTHOR
School of Electronic Engineering, Nanjing Xiaozhuang University, Nanjing, China
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
School of Earth and Environment, University of Leeds, Leeds, UK
National Centre for Earth Observation (NCEO), University of Leeds, Leeds, UK
Martyn P. Chipperfield
School of Earth and Environment, University of Leeds, Leeds, UK
National Centre for Earth Observation (NCEO), University of Leeds, Leeds, UK
Wuhu Feng
School of Earth and Environment, University of Leeds, Leeds, UK
National Centre for Atmospheric Science (NCAS), University of Leeds, Leeds, UK
Jianchun Bian
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China
College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
School of Electronic Engineering, Nanjing Xiaozhuang University, Nanjing, China
Dong Guo
Key Laboratory of Meteorological Disaster, Ministry of Education–Joint International Research Laboratory of Climate and Environment Change–Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, China
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Cited
11 citations as recorded by crossref.
- The improved Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST): update, validation and applications Z. Zang et al. https://doi.org/10.5194/acp-24-13889-2024
- Reply to Godin-Beekmann et al.: Quantitative agreement between the CRE theory and spatiotemporal ozone observations is well founded Q. Lu https://doi.org/10.1073/pnas.2537183123
- Lagged ENSO teleconnection mechanisms driving Antarctic stratospheric ozone depletion variability H. He et al. https://doi.org/10.1016/j.atmosres.2025.108539
- Validation and Correction of In-Orbit Tangent Height for the Ozone Monitoring Suite-Limb onboard FY-3F S. Liu et al. https://doi.org/10.1007/s13351-026-5189-3
- Detection of ozone recovery in the Arctic from ground-based measurements C. Jonas et al. https://doi.org/10.5194/acp-26-8089-2026
- An enhanced statistical-AI numerical method for modeling trace gas dispersion in atmospheric flows A. AJDOUR et al. https://doi.org/10.59277/RomRepPhys.2026.78.704
- Updated global and regional trends of stratospheric ozone profiles V. Sofieva et al. https://doi.org/10.5194/acp-26-7387-2026
- Influence of Natural Tropical Oscillations on Ozone Content and Meridional Circulation in the Boreal Winter Stratosphere T. Ermakova et al. https://doi.org/10.3390/atmos15060717
- Cosmic ray–driven electron-induced reaction theory quantifies spatiotemporal variations in lower-stratospheric ozone and temperature Q. Lu https://doi.org/10.1073/pnas.2506469122
- Tracing the signatures of ozone recovery in the Arctic ozone S. Anjali & J. Kuttippurath https://doi.org/10.1038/s41598-025-19373-0
- Causal inference for quantifying chemical–dynamical pathways controlling tropical middle stratospheric ozone variability E. Galytska et al. https://doi.org/10.5194/acp-26-8185-2026
11 citations as recorded by crossref.
- The improved Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST): update, validation and applications Z. Zang et al. https://doi.org/10.5194/acp-24-13889-2024
- Reply to Godin-Beekmann et al.: Quantitative agreement between the CRE theory and spatiotemporal ozone observations is well founded Q. Lu https://doi.org/10.1073/pnas.2537183123
- Lagged ENSO teleconnection mechanisms driving Antarctic stratospheric ozone depletion variability H. He et al. https://doi.org/10.1016/j.atmosres.2025.108539
- Validation and Correction of In-Orbit Tangent Height for the Ozone Monitoring Suite-Limb onboard FY-3F S. Liu et al. https://doi.org/10.1007/s13351-026-5189-3
- Detection of ozone recovery in the Arctic from ground-based measurements C. Jonas et al. https://doi.org/10.5194/acp-26-8089-2026
- An enhanced statistical-AI numerical method for modeling trace gas dispersion in atmospheric flows A. AJDOUR et al. https://doi.org/10.59277/RomRepPhys.2026.78.704
- Updated global and regional trends of stratospheric ozone profiles V. Sofieva et al. https://doi.org/10.5194/acp-26-7387-2026
- Influence of Natural Tropical Oscillations on Ozone Content and Meridional Circulation in the Boreal Winter Stratosphere T. Ermakova et al. https://doi.org/10.3390/atmos15060717
- Cosmic ray–driven electron-induced reaction theory quantifies spatiotemporal variations in lower-stratospheric ozone and temperature Q. Lu https://doi.org/10.1073/pnas.2506469122
- Tracing the signatures of ozone recovery in the Arctic ozone S. Anjali & J. Kuttippurath https://doi.org/10.1038/s41598-025-19373-0
- Causal inference for quantifying chemical–dynamical pathways controlling tropical middle stratospheric ozone variability E. Galytska et al. https://doi.org/10.5194/acp-26-8185-2026
Saved (final revised paper)
Latest update: 23 Jun 2026
Short summary
For the first time a regularized multivariate regression model is used to estimate stratospheric ozone trends. Regularized regression avoids the over-fitting issue due to correlation among explanatory variables. We demonstrate that there are considerable differences in satellite-based and chemical-model-based ozone trends, highlighting large uncertainties in our understanding about ozone variability. We argue that caution is needed when interpreting results with different methods and datasets.
For the first time a regularized multivariate regression model is used to estimate stratospheric...
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