Learning Prognostic Variables for AI Convective Parameterizations via Symbolic Distillation

2026-09-21

AI Summary

研究通过学习预估变量和使用符号蒸馏法增强局部时间参数化，以解决对流等过程的内在持久性问题，改进了AI-物理混合气候模型。

Abstract

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.

Problem

• prognostic variables
• subgrid processes
• hybrid AI-physics climate modeling
• diagnostic parameterizations

Innovation

• prognostic variables
• symbolic distillation
• latent space
• memory-informed approach
• subgrid-scale processes