ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
UC Irvine · Lawrence Livermore National Labs · Columbia University · University of California, Berkeley · Massachusetts Institute of Technology · University of California, Irvine · Institute of Atmospheric Physics, German Aerospace Center (DLR) · Columbia University / LDEO · Princeton University · University of Lausanne · Oregon State University · Caltech · NVIDIA · Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany; University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany · NVIDIA Corporation · UC San Diego · New York University · Allen Institute for AI · Lawrence Livermore National Lab · DLR · Brookhaven National Laboratory · SUNY at Stony Brook · Nvidia · University of California-Los Angeles · Colorado State University · Sandia National Labs · University of California-San Diego Scripps Inst of Oceanography
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摘要
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state.The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.