完全耦合的前工业E3SMv3模拟的随机仿真器

中文摘要 AI 辅助

我们提出了一种基于SamudrACE框架构建的E3SM第3版随机耦合仿真器，该框架将大气仿真器（ACE2）与全深度海洋仿真器（Samudra）耦合。我们用随机版本ACE2S取代确定性大气仿真器，并使用概率目标对耦合系统进行微调，使大气成为海洋内部变率的来源。该仿真器基于105年的前工业控制模拟进行训练，并在独立的400年数据上进行评估，其再现的E3SMv3平均气候态偏差远小于现有模型与观测值的差异。与确定性基线相比，随机训练在各时间尺度上保持了内部变率，最显著的是ENSO功率谱、多涡旋海表温度异常以及边缘冰区的海冰变率。该仿真器能准确捕捉日降水量直至99.99百分位的情况，但低估了最罕见的热带极端事件。这些结果表明，随机耦合仿真器可高保真地再现长时间尺度的变率，而对未知极端事件的外推仍是关键挑战。

英文摘要

We present a stochastic coupled emulator of E3SM version 3, built on the SamudrACE framework, which couples an atmosphere emulator (ACE2) with a full-depth ocean emulator (Samudra). We replace the deterministic atmosphere emulator with its stochastic counterpart, ACE2S, and fine-tune the coupled system with a probabilistic objective, so that the atmosphere acts as a source of internal variability for the ocean. Trained on 105 years of a pre-industrial control simulation and evaluated on an independent 400 years, the emulator reproduces E3SMv3's mean climate state with biases much smaller than existing model-to-observation differences. Relative to a deterministic baseline, stochastic training maintains internal variability across timescales, most notably in the ENSO power spectrum, eddy-rich SST anomalies, and sea ice variability in the marginal ice zone. The emulator captures daily precipitation accurately up to the 99.99th percentile, but underestimates the rarest tropical extremes. These results show that stochastic coupled emulators can reproduce long-timescale variability with high fidelity, while extrapolation to unseen extremes remains a key challenge.

发表机构

• Allen Institute for Artificial Intelligence (Ai2)（艾伦人工智能研究所）
• Pacific Northwest National Laboratory（太平洋西北国家实验室）
• Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
• Brookhaven National Laboratory（布鲁克海文国家实验室）
• Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）

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