ACE: A fast, skillful learned global atmospheric model for climate prediction
Abstract
Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a 200M-parameter, autoregressive machine learning emulator of an existing comprehensive 100-km resolution global atmospheric model. The formulation of ACE allows evaluation of physical laws such as the conservation of mass and moisture. The emulator is stable for 100 years, nearly conserves column moisture without explicit constraints and faithfully reproduces the reference model's climate, outperforming a challenging baseline on over 90% of tracked variables. ACE requires nearly 100x less wall clock time and is 100x more energy efficient than the reference model using typically available resources. Without fine-tuning, ACE can stably generalize to a previously unseen historical sea surface temperature dataset.
Read the paper
Related studies
- Climate Change Impacts on Marine Ecosystems
- Assisted Gene Flow to Facilitate Local Adaptation to Climate Change
- Climate Change Effects on Plant Disease: Genomes to Ecosystems
- Biodiversity and Climate Change: Integrating Evolutionary and Ecological Responses of Species and Communities
- A systematic review of climate change education: giving children and young people a ‘voice’ and a ‘hand’ in redressing climate change
- Global Monsoon Dynamics and Climate Change
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.