A novel approach to predict the arctic stratospheric ozone from stratospheric polar vortex dynamics using explainable machine learning.

ABSTRACT: A significant decreasing trend of Arctic stratospheric ozone has been observed since 2019, with the first reported ozone hole in the Arctic Stratospheric Polar Vortex (SPV) in 2020, raising concerns for humanity. This underlines that it is essential to develop an algorithm capable of predicting Arctic ozone levels, preferably using minimal computing resources. This study presents a novel approach for ozone prediction based on the morphological and dynamical properties of the SPV utilizing a explainable machine learning approach. XGBoost exhibits good agreement with the observations, achieving an [Formula: see text] score of 0.80 and a correlation of 0.91. The algorithm accurately predicts the daily and seasonal patterns of ozone variations. It successfully captures the pattern of the lowes

SUBMITTER: Kumar A 

PROVIDER: S-EPMC12537872 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature