Climate projections have uncertainties related to components of the climate system and their interactions. A typical approach to quantifying these uncertainties is to use climate models to create ensembles of repeated simulations under different initial conditions. Due to the complexity of these simulations, generating such ensembles of projections is computationally expensive. In this work, we present ArchesClimate, a deep learning-based climate model emulator that aims to reduce this cost. ArchesClimate is trained on decadal hindcasts of the IPSL-CM6A-LR climate model at a spatial resolution of approximately 2.5x1.25 degrees. 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 and can be used to auto-regressively emulate climate model simulations of any length. 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 significantly reduce the cost of climate model simulations.

翻译：气候预测存在与气候系统各组成部分及其相互作用相关的不确定性。量化这些不确定性的典型方法是利用气候模型在不同初始条件下创建重复模拟的集合。由于这些模拟的复杂性，生成此类预测集合的计算成本极高。本研究提出ArchesClimate——一种基于深度学习的气候模型仿真器，旨在降低此项成本。该模型在IPSL-CM6A-LR气候模型约2.5×1.25度空间分辨率的年代际后报数据上进行训练。我们遵循ArchesWeatherGen框架训练流匹配模型，并将其适配于近未来气候预测。训练完成后，该模型可生成提前一个月的气候状态，并能通过自回归方式仿真任意时长的气候模型模拟。实验表明，在长达10年的时间尺度上，这些生成结果具有稳定性与物理一致性。我们还证明，对于多个重要气候变量，ArchesClimate生成的模拟结果与IPSL模型具有可互换性。本研究表明气候模型仿真器可显著降低气候模拟的计算成本。