Machine learning for improved understanding and projections of climate change

Abstract

Earth system models are fundamental to understanding and projecting climate change. The models have continued to improve over the years, but considerable biases and uncertainties in their projections remain. A large contribution to this uncertainty stems from differences in the representation of phenomena such as clouds and convection that occur at scales smaller than the resolved model grid. The long-standing deficiencies in cloud parameterizations have motivated developments of global high-resolution cloud-resolving models that can explicitly resolve clouds and convection. Short simulations from the computationally costly high-resolution models together with observations can serve as information to develop machine learning (ML)-based parameterizations that are then incorporated into Earth system models. The ICOsahedral Non-hydrostatic (ICON) model is an open-access modelling framework, which is used on a variety of timescales and resolutions, ranging from numerical weather predictions to climate projections. Here we utilize existing regional and global cloud-resolving ICON simulations with data-driven techniques to train ML-based parametrizations. The newly developed parameterizations are coupled to the ICON Earth system model (ICON-ESM) via the Fortran-Keras Bridge, resulting in the ICON-ESM-ML hybrid model.