Evaluating foehn occurrence in a changing climate based on reanalysis and climate model data using machine learning

Abstract

This study explores the possibilities of employing machine learning algorithms to predict foehn occurrence in Switzerland at a north Alpine (Altdorf) and south Alpine (Lugano) station from its synoptic fingerprint in reanalysis data and climate simulations. This allows for an investigation on a potential future shift in monthly foehn frequencies. First, inputs from various atmospheric fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERAI) were used to train an XGBoost model. Here, similar predictive performance to previous work was achieved, showing that foehn can accurately be diagnosed from the coarse synoptic situation. In the next step, the algorithm was generalized to predict foehn based on the Community Earth System Model (CESM) ensemble simulations of a present-day and warming future climate. The best generalization between ERAI and CESM was obtained by including the present-day data in the training procedure and simultaneously optimizing two objective functions, namely, the negative log loss and squared mean loss, on both datasets, respectively. It is demonstrated that the same synoptic fingerprint can be identified in CESM climate simulation data. Finally, predictions for present-day and future simulations were verified and compared for statistical significance. Our model is shown to produce valid output for most months, revealing that south foehn in Altdorf is expected to become more common during spring, while north foehn in Lugano is expected to become more common during summer.