Benchmarking Regional Thermodynamic Trends in an AI emulator, ACE2, and a hybrid model, NeuralGCM

TL;DR

This study benchmarks AI and hybrid climate models against physics-based models in capturing regional thermodynamic trends, revealing strengths and limitations in their ability to simulate observed climate changes.

Contribution

It provides a comprehensive comparison of AI, hybrid, and physics-based models in regional climate trend simulation, highlighting AI models' potential and current limitations.

Findings

AI models capture Arctic Amplification and midlatitude temperature trends.

ACE2 outperforms others in vertical temperature trend accuracy.

AI models struggle with heat extremes and drying trends in specific regions.

Abstract

AI models have emerged as potential complements to physics-based models, but their skill in capturing observed regional climate trends with important societal impacts has not been explored. Here, we benchmark satellite-era regional thermodynamic trends, including extremes, in an AI emulator (ACE2) and a hybrid model (NeuralGCM). We also compare the AI models' skill to physics-based land-atmosphere models. Both AI models show skill in capturing regional temperature trends such as Arctic Amplification. ACE2 outperforms other models in capturing vertical temperature trends in the midlatitudes. However, the AI models do not capture regional trends in heat extremes over the US Southwest. Furthermore, they do not capture drying trends in arid regions, even though they generally perform better than physics-based models. Our results also show that a data-driven AI emulator can perform comparably to, or better than, hybrid and physics-based models in capturing regional thermodynamic trends.