用随机耦合模型复现气候长期变化，捕捉厄尔尼诺等关键变率。
Stochastic Emulation of a Fully Coupled Preindustrial E3SMv3 Simulation
- 构建大气与海洋的随机耦合模拟器，提升内部变率真实性。
- 在400年独立测试中，气候均值偏差小于观测差异，重现厄尔尼诺谱特征。
- 适合气候模拟、极端事件研究者，尤其关注长期变率建模。
我们基于SamudrACE框架，构建了E3SMv3的随机耦合模拟器，将大气模拟器ACE2替换为随机版本ACE2S，与全深度海洋模拟器Samudra耦合。通过概率目标微调系统，使大气成为海洋内部变率的来源。模型在105年预工业控制模拟数据上训练，独立评估于400年数据，其平均气候态偏差远小于现有模型与观测间的差异。相比确定性基线，随机训练保持了跨时间尺度的内部变率，特别是在厄尔尼诺功率谱、涡旋丰富的海温异常及边缘海冰区海冰变率方面表现优异。日降水模拟准确至99.99百分位，但对最罕见的热带极端事件仍低估。结果表明，随机耦合模拟器可高保真再现长期变率，但对未见极端事件的外推仍是关键挑战。
原文摘要 · Abstract (English)
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.
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