Machine Learning for Earth System Modeling: Accelerating Pathways to Impact

Climate change is a major concern for human civilization, yet significant uncertainty remains in future warming, change in precipitation patterns, and frequency of climate extremes. Proper adaptation and mitigation demands accurate climate projections capable of simulating the atmosphere, ocean, land, and their interactions. Numerical models exhaustively tuned by domain scientists have been the gold standard for modeling both weather and climate because of their interpretability and ability to simulate “what-if” scenarios not present in the historical record. Although AI forecasts have started to make operational progress in weather prediction, climate projections are a harder problem. For example, High Impact-Low Likelihood events are undersampled in ERA5 reanalysis data, and substantial decadal variability in modes of climate variability (like the El-Niño Southern Oscillation) limit the ability of AI forecasts to reliably extrapolate into the future. This workshop seeks to accelerate progress on using machine learning to improve climate projections, emphasizing areas that domain scientists have deemed amenable to machine learning approaches. Examples include hybrid physics-ML climate models, where machine learning is used to emulate subgrid processes too expensive to resolve explicitly, and dynamical downscaling, where high-resolution climate variables are inferred from coarse-resolution models in a physically consistent manner. In service of this, our workshop will be accompanied by a $50,000 Kaggle competition on the ClimSim dataset (https://leap-stc.github.io/ClimSim/), which won the Outstanding Datasets and Benchmarks Paper award at NeurIPS 2023. We welcome submissions on machine learning topics that can advance earth system model development. Some examples include deep generative models, explainable AI, physics-informed neural networks, and uncertainty quantification. While machine learning is not new to the climate science community, dedicated opportunities for the cross-fertilization of ideas are rare, and machine learning experts motivated to make an impact may not be aware of domain science research directions most in need of their expertise. This workshop directly addresses both of these challenges.