发表机构
Deutsches Zentrum für Luft- und Raumfahrt; University of Bremen; University of Lausanne; University of New South Wales（德国航空航天中心; 不来梅大学; 洛桑大学; 新南威尔士大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过符号蒸馏学习预报变量，为AI对流参数化引入记忆机制，改善气候统计和日循环。
AI 中文摘要
混合AI-物理气候建模旨在通过从高保真数据中学习参数化次网格过程，来改进粗分辨率（约100公里）的地球系统模型。然而，迄今为止，这主要涉及局部时间上的诊断参数化，其中次网格状态仅依赖于当前粗网格状态，而不记忆先前状态，这对于像对流这样具有内在持续性的过程来说是不现实的。为了解决这个问题，我们通过学习预报变量来增强局部时间参数化，这些变量紧凑地携带重要的额外过去信息，而在没有显式次网格信息可用的情况下。首先，我们使用自编码器将过去信息压缩到低维潜在空间中，然后该潜在空间为训练用于参数化目标次网格尺度过程的神经网络提供信息。接着，我们用控制潜在变量时间演化的符号方程替换自编码器，产生额外的预报记忆变量，这些变量可以与解析的大气状态一起积分。我们在两个系统上评估了这种方法：Lorenz-96模型（在线）和高分辨率大气模拟的地表降水（离线）。一个受迫的多变量线性常微分方程在两项实验中恢复了自编码器方法所实现的大部分附加价值。与没有记忆的诊断参数化相比，我们的记忆信息方法改善了气候统计和时间结构，包括热带陆地降水的现实日循环。
英文摘要
Hybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from high-fidelity data. However, this so far mostly involves local-in-time, diagnostic parameterizations, in which the subgrid state depends only on the current coarse state with no memory of previous states, which is unrealistic for processes such as convection that have intrinsic persistence. To address this, we enhance local-in-time parameterizations by learning prognostic variables that compactly carry important, additional past information where no explicit sub-grid information is available. First we compress past information into a low-dimensional latent space using an autoencoder, which then informs a neural network trained to parameterize targeted subgrid-scale processes. We then replace the autoencoder with symbolic equations that govern the time evolution of the latent variables, yielding additional prognostic memory variables that can be integrated alongside the resolved atmospheric state. We evaluate this approach on two systems: the Lorenz-96 model (online) and surface precipitation from high-resolution atmospheric simulations (offline). A forced multivariate linear ordinary differential equation recovers most of the added value achieved by the autoencoder-based approach in both experiments. Benchmarked against diagnostic parameterizations without memory, our memory-informed approach improves climate statistics and temporal structure, including a realistic diurnal cycle of tropical land precipitation.
