2024 Summer Lectures in Climate Data Science

VERONIKA EYRING
DLR / Univ. of Bremen

Understanding and Modelling the Earth System with Machine Learning
Earth System Models (ESMs) are fundamental to understanding and projecting climate change. The models have continued to improve over the years, but systematic errors and large uncertainties in their projections remain. A large contribution to this uncertainty stems from the representation of processes such as clouds and convection that occur at scales smaller than the resolved model grid. This impacts the models’ ability to accurately project global and regional climate change, climate variability, and extremes. New approaches are required with breakthroughs expected in particular from the combination of high-resolution simulations that can resolve small-scale and fast processes, the wealth of Earth observations, and machine learning (ML) techniques. High-resolution, cloud resolving models with horizontal grid resolution of a few kilometers alleviate many biases of coarse-resolution models for deep clouds and convection, wave propagation and precipitation, but they cannot be run at climate timescales for multiple decades or longer due to high computational costs. Yet short simulations from high-resolution models can serve as information to develop ML-based parametrisations that are then incorporated into hybrid ESMs that promise to have significantly reduced systematic errors and enhanced projection capability compared to current ESMs. In contrast to km-scale climate models, ESMs incorporate important Earth system processes and feedbacks while still being fast enough to provide large ensembles important to simulate internal variability and extremes and to improve attribution and understanding. This combination can drive a paradigm shift in current Earth system modelling and analyses towards a new data-driven, yet still physics-aware, science. The key goal is a hybrid modelling approach that maintains physical consistency and realistically extrapolates to unseen climate regimes while reducing climate projection uncertainties and improving Earth system understanding. The talk presents progress in hybrid modelling work with the ICOsahedral Non-hydrostatic (ICON) atmospheric model from the European Research Council (ERC) Synergy Grant on “Understanding and Modelling the Earth System with Machine Learning (USMILE)” as well as key challenges and visions on AI-empowered next-generation multiscale climate modeling for mitigation and adaptation.