对比多种模型结构，发现垂直耦合是模拟平流层变暖的关键
Investigating Inductive Biases for Machine Learning Emulation of Sudden Stratospheric Warmings in Idealised Isca Simulations
- 用三种架构在理想化模拟中测试平流层变暖预测
- 有剧烈平流层变化时，模型表现差异显著扩大
- 即使误差小，也不代表物理过程准确，需警惕虚假拟合
机器学习模拟器在气象预报中的应用日益广泛，有望通过学习动态关键可预测性源，提升次季节至季节尺度的预测能力。一个核心挑战是模型能否利用如平流层变率等可预测锚点，影响对流层环流并超越短期预报。本文通过成对的理想化Isca模拟（仅波-2加热扰动不同），检验了卷积、变压器和图基架构在单步预测中对突然平流层变暖（SSW）动力学的模拟效果。当平流层动态平静时，各模型差异较小；但当出现类似SSW的变率时，模型表现差异显著扩大。结果表明，显式的三维垂直耦合是机器学习模拟平流层动力学的关键归纳偏置。然而，埃利亚森-帕尔姆通量诊断显示，低预测误差并不保证波-平均流相互作用的物理真实性，平流层波驱动结构仍存在系统性误差。
原文摘要 · Abstract (English)
Machine-learning emulators are increasingly used for weather prediction and have the potential to extend skill on subseasonal-to-seasonal timescales by learning dynamically important sources of predictability. A key challenge is whether the models can exploit predictability anchors, such as stratospheric variability, that influence tropospheric circulation beyond short lead times. We test how architectural inductive bias affects emulation of sudden stratospheric warming (SSW) dynamics using paired idealised Isca simulations that differ only in an imposed wave-2 heating perturbation. Across convolutional, transformer, and graph-based architectures trained for one-step prediction, model differences are modest when the stratosphere is dynamically quiet but widen substantially when SSW-like variability is active. Our results identify explicit three-dimensional vertical coupling as a key inductive bias for machine-learning emulation of stratospheric dynamics. However, Eliassen-Palm flux diagnostics show that low forecast error does not guarantee physically faithful wave-mean-flow interaction, with coherent errors remaining in stratospheric wave-driving structure.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。