用深度学习模拟十年尺度气候变率，大幅降低计算成本。
ArchesClimate: Probabilistic Decadal Ensemble Generation With Flow Matching
- 基于流匹配模型，从前期气候状态预测未来一个月气候
- 生成10年气候序列稳定且物理一致，关键变量与真实模型可互换
- 适合需要大规模气候情景的科研与气候风险评估人群
内部变率是年际至十年尺度气候预测不确定性的主要来源。通常通过在不同初始条件下生成大量模拟来分离内部变率与强迫响应。由于地球系统模型复杂，此类大规模模拟计算成本高昂。本文提出 ArchesClimate，一种基于深度学习的气候模型模拟器，旨在降低月度至十年尺度内部变率探索的计算开销。该模型在 IPSL-CM6A-LR 气候模型的十年回溯预报数据上训练。我们采用 ArchesWeatherGen 的流匹配框架，将其适配用于近短期气候预测。训练完成后，模型可从前两个月的状态生成未来一个月的气候状态，并支持自回归式模拟气候模型运行。实验表明，生成序列在长达10年内保持稳定且物理一致；对于多个关键气候变量，生成结果与 IPSL 模型模拟具有可互换性。结果表明，气候模型模拟器有望显著降低大规模气候模拟的计算成本。
原文摘要 · Abstract (English)
Internal variability is a dominant contributor to the uncertainty of predictions at the interannual to decadal timescale. A typical approach to separating the internal variability from forced climate responses is to generate large ensembles of simulations under different initial conditions. Due to the complexity of Earth System Models, generating these large ensembles is computationally expensive. In this work, we present ArchesClimate, a deep learning-based climate model emulator designed to reduce the cost of exploring internal variability at timescales ranging from monthly to decadal. ArchesClimate is trained on decadal hindcasts of the IPSL-CM6A-LR climate model. We train a flow matching model following ArchesWeatherGen, which we adapt to predict near-term climate. Once trained, the model generates states at a one-month lead time from the states of the two preceding months, and can be used to auto-regressively emulate climate model simulations. We show that for up to 10 years, these generations are stable and physically consistent. We also show that for several important climate variables, ArchesClimate generates simulations that are interchangeable with the IPSL model. This work suggests that climate model emulators could reduce the cost of generating large ensembles with climate models.
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