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A novel approach to predict the arctic stratospheric ozone from stratospheric polar vortex dynamics using explainable
Anish Kumar1, Joyjit Mandal2, Sina Mehrdad3
1Institute for Meteorology, Leipzig University, Leipzig, Germany. anish.kumar@uni-leipzig.de.
Scientific Reports
|October 20, 2025
Summary
Arctic ozone levels are decreasing, prompting the need for predictive algorithms. This study uses machine learning on stratospheric polar vortex dynamics to forecast ozone, showing promising accuracy for future climate modeling.
Area of Science:
- Atmospheric Chemistry
- Climate Science
- Machine Learning
Background:
- Arctic stratospheric ozone depletion has trended downwards since 2019.
- An Arctic ozone hole was reported in the Stratospheric Polar Vortex (SPV) in 2020, causing concern.
Purpose of the Study:
- To develop an efficient algorithm for predicting Arctic ozone levels.
- To utilize the morphological and dynamical properties of the SPV for ozone prediction.
Main Methods:
- An explainable machine learning approach using XGBoost.
- Feature engineering based on physics-based properties of the SPV.
Main Results:
- XGBoost achieved an R-squared score of 0.80 and a correlation of 0.91 with observed ozone levels.
- The algorithm accurately predicted daily and seasonal ozone variations, including the 2020 low, with a ~20 Dobson unit overestimation.
- The model demonstrated strong alignment with observations in certain years.
Conclusions:
- Dynamical parameters of the SPV are effective for predicting chemical ozone loss.
- The developed algorithm can serve as a tool for projecting future Arctic ozone variability using climate model data.
- This approach offers a chemistry-independent method for ozone variability prediction.
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