This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1017/eds.2022.19. This is version 1 of this Preprint.
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The winter stratospheric polar vortex (SPV) exhibits considerable variability in magnitude and structure, which can result in extreme SPV events. These extremes can subsequently influence weather in the troposphere from weeks to months and thus are important sources of surface predictability. However, the predictability of the SPV extreme events is limited to 1-2 weeks in state-of-the-art prediction systems. Longer predictability timescales of SPV would strongly benefit long-range surface prediction. One potential option for extending predictability timescales is the use of machine learning (ML). However, it is often unclear which predictors and patterns are important for ML models to make a successful prediction. Here we use explainable multiple linear regressions (MLR) and an explainable artificial neural network (ANN) framework to model SPV variations and identify one type of extreme SPV events called sudden stratospheric warmings. We employ a NN attribution method to propagate the ANN's decision-making process backward and uncover feature importance in the predictors. The feature importance of the input is consistent with the known precursors for extreme SPV events. This consistency provides confidence that ANNs can extract reliable and physically meaningful indicators for the prediction of the SPV. In addition, our study shows a simple MLR model can predict the SPV daily variations using sequential feature selection, which provides hints for the connections between the input features and the SPV variations. Our results indicate the potential of explainable ML techniques in predicting stratospheric variability and extreme events, and in searching for potential precursors for these events on extended-range timescales.
Physical Sciences and Mathematics
stratospheric variation, polar vortex extreme events, prediction, feature selection, explainable neural network
Published: 2022-10-01 17:02
Last Updated: 2022-10-02 00:02
Data Availability (Reason not available):
ERA-Interim reanalysis was obtained from the ECMWF server (https://apps.ecmwf.int/datasets/data/interim-full-daily, last access: May 2020).