Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise prediction 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.

翻译：现代气候预测因计算资源限制而缺乏足够的空间与时间分辨率，导致对风暴等关键过程的预测存在不准确和不精确的问题。将物理与机器学习相结合的混合方法开创了新一代高保真气候模拟器，通过将计算密集型的短期高分辨率模拟任务外包给机器学习仿真器，从而绕开摩尔定律的限制。然而，这种机器学习-物理混合模拟方法需要领域特定的处理，且因缺乏训练数据和相关易用工作流而难以被机器学习专家所使用。我们提出ClimSim——迄今为止专为机器学习-物理混合研究设计的最大数据集。该数据集由气候科学家与机器学习研究者组成的联盟共同开发，包含多尺度气候模拟结果，由57亿对多变量输入/输出向量构成，这些向量隔离了局部嵌套的高分辨率高保真物理过程对主气候模拟器宏尺度物理状态的影响。数据集覆盖全球范围，以高频采样跨越多年时间，其设计使得生成的仿真器能够兼容后续耦合至业务化气候模拟器的需求。我们实现了一系列确定性和随机回归基线模型，以突出机器学习面临的挑战及其评估指标。数据集（https://huggingface.co/datasets/LEAP/ClimSim_high-res）与代码（https://leap-stc.github.io/ClimSim）均已开源发布，旨在支持机器学习-物理混合方法与高保真气候模拟的发展，造福科学与社会。