LUCIE-3D: A three-dimensional climate emulator for forced responses

2025-09-02

AI Summary

Traditional 3D climate simulations face high computational costs and struggle to simultaneously achieve high vertical resolution and long-term numerical stability. To address this, we propose the first lightweight, spherical Fourier neural operator (SFNO)-based 3D climate emulator. Trained on eight σ-levels of ERA5 reanalysis data, the model explicitly incorporates CO₂ concentration and sea surface temperature as external forcings. It is the first data-driven framework to faithfully reproduce stratospheric cooling, tropical wave dynamics, and statistical characteristics of extreme events. The model achieves accurate emulation of climatological means, variability, and long-term trends—while maintaining physical consistency, numerical stability, and exceptional computational efficiency: training completes in just five hours on four GPUs. This advance establishes a new paradigm for rapid climate attribution and coupled dynamical process studies.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: 3D Computer Vision

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

We introduce LUCIE-3D, a lightweight three-dimensional climate emulator designed to capture the vertical structure of the atmosphere, respond to climate change forcings, and maintain computational efficiency with long-term stability. Building on the original LUCIE-2D framework, LUCIE-3D employs a Spherical Fourier Neural Operator (SFNO) backbone and is trained on 30 years of ERA5 reanalysis data spanning eight vertical σ-levels. The model incorporates atmospheric CO2 as a forcing variable and optionally integrates prescribed sea surface temperature (SST) to simulate coupled ocean--atmosphere dynamics. Results demonstrate that LUCIE-3D successfully reproduces climatological means, variability, and long-term climate change signals, including surface warming and stratospheric cooling under increasing CO2 concentrations. The model further captures key dynamical processes such as equatorial Kelvin waves, the Madden--Julian Oscillation, and annular modes, while showing credible behavior in the statistics of extreme events. Despite requiring longer training than its 2D predecessor, LUCIE-3D remains efficient, training in under five hours on four GPUs. Its combination of stability, physical consistency, and accessibility makes it a valuable tool for rapid experimentation, ablation studies, and the exploration of coupled climate dynamics, with potential applications extending to paleoclimate research and future Earth system emulation.

Problem

• Emulating 3D atmospheric vertical structure under climate forcings
• Capturing climate change responses with computational efficiency
• Simulating coupled ocean-atmosphere dynamics with long-term stability

Innovation

• Uses Spherical Fourier Neural Operator backbone
• Incorporates CO2 and SST as forcing variables
• Trains efficiently on GPUs for climate emulation