EyringMLClimateGroup

This respository contains code reproducing the main results of the paper "Data-driven equation discovery of a sea ice albedo parametrisation"

ICON model output evaluation with ESMValTool

This repository contains the Juypter Notebooks and python files to reproduce the main results of the paper: L. Pastori, V. Eyring, M. Schwabe, "Fisher Information, Training and Bias in Fourier Regression Models", arXiv:2510.06945.

Code for quantum neural networks for cloud cover parameterizations in climate models

This repository contains the code and models used in the paper "A multivariate analysis of atmospheric drivers for Western European heatwaves".

This repository presents a code for causal inference to quantify dominant chemical–dynamical pathways that control ozone variability in the tropical middle stratosphere

Accompagnying repository for "Quantum machine learning-based parameterization for atmospheric boundary layer turbulence"

A physics-informed machine learning parameterization for cloud microphysics in ICON