Quantifying stratospheric ozone trends over 1984–2020: a comparison of ordinary and regularized multivariate regression models

Li, Yajuan; Dhomse, Sandip S.; Chipperfield, Martyn P.; Feng, Wuhu; Bian, Jianchun; Xia, Yuan; Guo, Dong

doi:https://doi.org/10.5194/acp-23-13029-2023

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.

https://doi.org/10.5194/acp-23-13029-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 20

Research article

|

16 Oct 2023

Research article |

| 16 Oct 2023

Quantifying stratospheric ozone trends over 1984–2020: a comparison of ordinary and regularized multivariate regression models

Yajuan Li, Sandip S. Dhomse, Martyn P. Chipperfield, Wuhu Feng, Jianchun Bian, Yuan Xia, and Dong Guo

Supplement

https://doi.org/10.5194/acp-23-13029-2023-supplement

Data sets

The Stratospheric Water and Ozone Satellite Homogenized (SWOOSH) database: a long-term database for climate studies (https://csl.noaa.gov/groups/csl8/swoosh/) S. M. Davis, K. H. Rosenlof, B. Hassler, D. F. Hurst, W. G. Read, H. Vömel, H. Selkirk, M. Fujiwara, and R. Damadeo https://doi.org/10.5194/essd-8-461-2016

ML-TOMCAT V1.0: Machine-Learning-Based Satellite-Corrected Global Stratospheric Ozone Profile Dataset S. S. Dhomse, M. P. Chipperfield, W. Feng, C. Arosio, M. Weber, and A. Rozanov https://doi.org/10.5281/zenodo.5651194

Effects of Reanalysis Forcing Fields on Ozone Trends and Age of Air from a Chemical Transport Model Y. Li, S. Dhomse, M. Chipperfield, W. Feng, A. Chrysanthou, Y. Xia, and D. Guo https://doi.org/10.5281/zenodo.6988615

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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.