一种物理信息自动学习框架，用于开发ENSO多样性的随机概念模型
A Physics-Informed Auto-Learning Framework for Developing Stochastic Conceptual Models for ENSO Diversity
AI总结：
本文提出一种物理信息自动学习框架，用于推导ENSO多样性随机概念模型，并发现最小四维模型（含三个年际变量和一个季节内变量）能准确表征多样性，弱非线性对重现极端事件至关重要。
摘要：
过去几十年来，对ENSO动力学的理解已得到极大提升。然而，在概念模型中，空间形态、峰值强度和时空演变方面的ENSO多样性仍是一个理解不足或表征不足的方面。本文开发了一种物理信息自动学习框架，用于推导具有不同自由度的ENSO随机概念模型。该框架计算效率高且易于应用。一旦设定目标模型的状态向量，便利用因果推断基于数学函数库构建方程的右侧。与标准非线性回归有根本不同，该自动学习框架通过仅保留能提高与观测动力学一致性的项，提供简洁模型。它还能识别关键潜在变量并提供物理解释。利用一个现实的六维参考充放电振荡器ENSO模型，使用自动学习框架推导出三至六维模型的层级，并通过一套统一的验证标准系统验证，这些标准评估ENSO多样性的动力学和统计特征。结果表明，表征ENSO多样性的最小模型是四维的，包含三个年际变量描述西太平洋温跃层深度、东太平洋和中太平洋海表温度，以及一个用于西风爆发的季节内变量。若没有季节内变量，所得三维模型会低估极端事件且过于规律。模型中有限数量的弱非线性对于重现观测到的极端厄尔尼诺事件以及东太平洋与西太平洋海表温度之间的非线性关系至关重要。
英文摘要：
Understanding ENSO dynamics has tremendously improved over the past decades. However, one aspect still poorly understood or represented in conceptual models is the ENSO diversity in spatial pattern, peak intensity, and temporal evolution. In this paper, a physics-informed auto-learning framework is developed to derive ENSO stochastic conceptual models with varying degrees of freedom. The framework is computationally efficient and easy to apply. Once the state vector of the target model is set, causal inference is exploited to build the right-hand side of the equations based on a mathematical function library. Fundamentally different from standard nonlinear regression, the auto-learning framework provides a parsimonious model by retaining only terms that improve the dynamical consistency with observations. It can also identify crucial latent variables and provide physical explanations. Exploiting a realistic six-dimensional reference recharge oscillator-based ENSO model, a hierarchy of three- to six-dimensional models is derived using the auto-learning framework and is systematically validated by a unified set of validation criteria assessing the dynamical and statistical features of the ENSO diversity. It is shown that the minimum model characterizing ENSO diversity is four-dimensional, with three interannual variables describing the western Pacific thermocline depth, the eastern and central Pacific sea surface temperatures (SSTs), and one intraseasonal variable for westerly wind events. Without the intraseasonal variable, the resulting three-dimensional model underestimates extreme events and is too regular. The limited number of weak nonlinearities in the model are essential in reproducing the observed extreme El Niños and nonlinear relationship between the eastern and western Pacific SSTs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
