Simulating the QBO remains a formidable challenge partly due to uncertainties in representing convectively generated gravity waves. We develop an end-to-end uncertainty quantification workflow that calibrates these gravity wave processes in E3SM to yield a more realistic QBO. Central to our approach is a domain knowledge-informed, compressed representation of high-dimensional spatio-temporal wind fields. By employing a parsimonious statistical model that learns the fundamental frequency of the underlying stochastic process from complex observations, we extract a concise set of interpretable and physically meaningful quantities of interest capturing key attributes, such as oscillation amplitude and period. Building on this, we train a probabilistic surrogate model. Leveraging the Karhunen-Loeve decomposition, our surrogate efficiently represents these characteristics as a set of orthogonal features, thereby capturing the cross-correlations among multiple physics quantities evaluated at different stratospheric pressure levels, and enabling rapid surrogate-based inference at a fraction of the computational cost of inference reliant only on full-scale simulations. Finally, we analyze the inverse problem using a multi-objective approach. Our study reveals a tension between amplitude and period that constrains the QBO representation, precluding a single optimal solution that simultaneously satisfies both objectives. To navigate this challenge, we quantify the bi-criteria trade-off and generate a representative set of Pareto optimal physics parameter values that balance the conflicting objectives. This integrated workflow not only improves the fidelity of QBO simulations but also advances toward a practical framework for tuning modes of variability and quasi-periodic phenomena, offering a versatile template for uncertainty quantification in complex geophysical models.

翻译：模拟准两年振荡（QBO）仍然是一项艰巨挑战，部分原因在于对流生成重力波的表征存在不确定性。我们开发了一个端到端的不确定性量化工作流，用于校准E3SM中的这些重力波过程，从而产生更真实的QBO。我们方法的核心在于采用领域知识启发的、高维时空风场的压缩表示。通过使用一个从复杂观测数据中学习底层随机过程基频的简约统计模型，我们提取了一组简洁且可解释的、具有物理意义的关注量，这些量捕捉了关键属性，如振荡幅度和周期。在此基础上，我们训练了一个概率代理模型。利用Karhunen-Loeve分解，我们的代理模型将这些特征高效地表示为一组正交特征，从而捕捉了在不同平流层压力水平上评估的多个物理量之间的互相关性，并实现了基于代理的快速推断，其计算成本仅为依赖全尺度模拟的推断的一小部分。最后，我们采用多目标方法分析了这一反问题。我们的研究揭示了幅度与周期之间存在一种张力，这种张力制约了QBO的表征，使得无法获得一个同时满足两个目标的单一最优解。为了应对这一挑战，我们量化了双准则权衡，并生成了一组具有代表性的帕累托最优物理参数值，以平衡这些相互冲突的目标。这个集成工作流不仅提高了QBO模拟的保真度，而且朝着一个用于调整变率模态和准周期现象的实用框架迈进，为复杂地球物理模型中的不确定性量化提供了一个通用模板。