Earth system model parameterizations improved by language model agents
Can LLM Agents Improve Imperfect Earth System Model Parameterizations?
Machine Learning
Summary
Earth system models simulate weather and climate but rely on simplified formulas called parameterizations to represent small processes that computers can't directly model. The authors tested whether language model agents can help create better formulas by reading scientific papers, coding new versions, and comparing them to detailed simulations. They found that with expert help or iterative feedback, these agents produced working and physically consistent code, sometimes even outperforming traditional versions. However, results varied depending on the language model and task, showing promise but not yet a clear replacement for human experts.
What this means in practice
- •For climate modelers: Create and test new parameterization code automatically from literature summaries and feedback to speed up model development.
- •For computational physicists: Use language model agents to generate differentiable, mass-conserving code for physical process emulation within simulation frameworks.
Authors
Kara D. Lamb
Abstract
Earth system models (ESMs) rely on parameterizations to represent unresolved processes such as convection, cloud microphysics, and turbulence. However, translating advances in physical understanding into stable, validated parameterizations and integrating them into ESMs often takes years. Agentic AI could accelerate this process and enable more systematic exploration of design choices. Using warm-rain collision-coalescence as a test case, we evaluate agents' ability to synthesize scientific literature, reimplement published bulk parameterizations, and propose new functional forms as differentiable JAX code. We compare conventional parameterizations, data-driven emulators, and agent-generated closures against high-fidelity superdroplet simulations, repeating unattended experiments across ten language models. With expert guidance or iterative feedback, agents produced executable, differentiable, mass-conserving code, although unattended performance depended strongly on the scheme and language model. Iterative feedback lowered median rollout error for eight of ten models. Retuned physical schemes matched or outperformed many learned parameterizations at substantially lower computational cost, but two of 100 unattended agent-generated closures achieved slightly lower rain-mass error than the most accurate retuned scheme on held-out simulations.