Articles | Volume 23, issue 20
https://doi.org/10.5194/acp-23-13029-2023
https://doi.org/10.5194/acp-23-13029-2023
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

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Cited articles

Abalos, M., Randel, W. J., Kinnison, D. E., and Garcia, R. R.: Using the Artificial Tracer e90 to Examine Present and Future UTLS Tracer Transport in WACCM, J. Atmos. Sci., 74, 3383–3403, https://doi.org/10.1175/JAS-D-17-0135.1, 2017. 
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Ball, W., Haigh, J., Rozanov, E., Kuchar, A., Sukhodolov, T., Tummon, F., Shapiro, A., and Schmutz, W.: High solar cycle spectral variations inconsistent with stratospheric ozone observations, Nat. Geosci., 9, 206–209, https://doi.org/10.1038/ngeo2640, 2016. 
Ball, W. T., Alsing, J., Mortlock, D. J., Rozanov, E. V., Tummon, F., and Haigh, J. D.: Reconciling differences in stratospheric ozone composites, Atmos. Chem. Phys., 17, 12269–12302, https://doi.org/10.5194/acp-17-12269-2017, 2017. 
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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.
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