# ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses

arXiv: https://arxiv.org/abs/2411.11268 · HTML: https://mlsanity.com/paper/2411.11268/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 71 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Builds on (most cited first, 14 of 14)

- [Machine learning accelerated computational fluid dynamics](https://mlsanity.com/paper/2102.01010/) (2021)
- [FuXi: A cascade machine learning forecasting system for 15-day global weather forecast](https://mlsanity.com/paper/2306.12873/) (2023)
- [Neural General Circulation Models for Weather and Climate](https://mlsanity.com/paper/2311.07222/) (2023)
- [WeatherBench 2: A benchmark for the next generation of data-driven global weather models](https://mlsanity.com/paper/2308.15560/) (2023)
- [Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere](https://mlsanity.com/paper/2306.03838/) (2023)
- [GenCast: Diffusion-based ensemble forecasting for medium-range weather](https://mlsanity.com/paper/2312.15796/) (2023)
- [Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere](https://mlsanity.com/paper/2003.11927/) (2020)
- [Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model](https://mlsanity.com/paper/2001.01520/) (2020)
- [ACE: A fast, skillful learned global atmospheric model for climate prediction](https://mlsanity.com/paper/2310.02074/) (2023)
- [LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles](https://mlsanity.com/paper/2405.16297/) (2024)
- [Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh](https://mlsanity.com/paper/2311.06253/) (2023)
- [Climate-Invariant Machine Learning](https://mlsanity.com/paper/2112.08440/) (2021)
- [Probabilistic Emulation of a Global Climate Model with Spherical DYffusion](https://mlsanity.com/paper/2406.14798/) (2024)
- [Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators](https://mlsanity.com/paper/2408.03100/) (2024)

## Built on by (most cited first, 30 of 54)

- [SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators](https://mlsanity.com/paper/2509.12490/) (2025)
- [Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting](https://mlsanity.com/paper/2601.18111/) (2026)
- [Numerical models outperform AI weather forecasts of record-breaking extremes](https://mlsanity.com/paper/2508.15724/) (2025)
- [Simultaneous emulation and downscaling with physically-consistent deep learning-based regional ocean emulators](https://mlsanity.com/paper/2501.05058/) (2025)
- [Generative assimilation and prediction for weather and climate](https://mlsanity.com/paper/2503.03038/) (2025)
- [CondensNet: Enabling stable long-term climate simulations via hybrid deep learning models with adaptive physical constraints](https://mlsanity.com/paper/2502.13185/) (2025)
- [Dynamical errors in machine learning forecasts](https://mlsanity.com/paper/2504.11074/) (2025)
- [Probing forced responses and causality in data-driven climate emulators: conceptual limitations and the role of reduced-order models](https://mlsanity.com/paper/2506.22552/) (2025)
- [What You See is Not What You Get: Neural Partial Differential Equations and The Illusion of Learning](https://mlsanity.com/paper/2411.15101/) (2024)
- [PEAR: Equal Area Weather Forecasting on the Sphere](https://mlsanity.com/paper/2505.17720/) (2025)
- [Causal Climate Emulation with Bayesian Filtering](https://mlsanity.com/paper/2506.09891/) (2025)
- [CERA: A Framework for Improved Generalization of Machine Learning Models to Changed Climates](https://mlsanity.com/paper/2509.00010/) (2025)
- [Spatiotemporal Pyramid Flow Matching for Climate Emulation](https://mlsanity.com/paper/2512.02268/) (2025)
- [FloeNet: A mass-conserving global sea ice emulator that generalizes across climates](https://mlsanity.com/paper/2603.12449/) (2026)
- [Can AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rollouts](https://mlsanity.com/paper/2605.30184/) (2026)
- [Finetuning a Weather Foundation Model with Lightweight Decoders for Unseen Physical Processes](https://mlsanity.com/paper/2506.19088/) (2025)
- [Deep Learning Atmospheric Models Reliably Simulate Out-of-Sample Land Heat and Cold Wave Frequencies](https://mlsanity.com/paper/2507.03176/) (2025)
- [LUCIE-3D: A three-dimensional climate emulator for forced responses](https://mlsanity.com/paper/2509.02061/) (2025)
- [ArchesClimate: Probabilistic Decadal Ensemble Generation With Flow Matching](https://mlsanity.com/paper/2509.15942/) (2025)
- [Bridging Idealized and Operational Models: An Explainable AI Framework for Earth System Emulators](https://mlsanity.com/paper/2510.13030/) (2025)
- [AI-boosted rare event sampling to characterize extreme weather](https://mlsanity.com/paper/2510.27066/) (2025)
- [WIND: Weather Inverse Diffusion for Zero-Shot Atmospheric Modeling](https://mlsanity.com/paper/2602.03924/) (2026)
- [Forecasting the Future with Yesterday's Climate: Temperature Bias in AI Weather and Climate Models](https://mlsanity.com/paper/2509.22359/) (2025)
- [Reframing Generative Models for Physical Systems using Stochastic Interpolants](https://mlsanity.com/paper/2509.26282/) (2025)
- [The Equilibrium Response of Atmospheric Machine-Learning Models to Uniform Sea Surface Temperature Warming](https://mlsanity.com/paper/2510.02415/) (2025)
- [Long-Range Distillation: Distilling 10,000 Years of Simulated Climate into Long Timestep AI Weather Models](https://mlsanity.com/paper/2512.22814/) (2025)
- [Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators](https://mlsanity.com/paper/2602.16090/) (2026)
- [Towards accurate extreme event likelihoods from diffusion model climate emulators](https://mlsanity.com/paper/2605.03802/) (2026)
- [Data-driven Seasonal Climate Predictions via Variational Inference and Transformers](https://mlsanity.com/paper/2503.20466/) (2025)
- [Advancing Seasonal Prediction of Tropical Cyclone Activity with a Hybrid AI-Physics Climate Model](https://mlsanity.com/paper/2505.01455/) (2025)

## Abstract (arXiv)

Existing machine learning models of weather variability are not formulated to enable assessment of their response to varying external boundary conditions such as sea surface temperature and greenhouse gases. Here we present ACE2 (Ai2 Climate Emulator version 2) and its application to reproducing atmospheric variability over the past 80 years on timescales from days to decades. ACE2 is a 450M-parameter autoregressive machine learning emulator, operating with 6-hour temporal resolution, 1deg horizontal resolution and eight vertical layers. It exactly conserves global dry air mass and moisture and can be stepped forward stably for arbitrarily many steps with a throughput of about 1500 simulated years per wall clock day. ACE2 generates emergent phenomena such as tropical cyclones, the Madden Julian Oscillation, and sudden stratospheric warmings. Furthermore, it accurately reproduces the atmospheric response to El Ni~no variability and global trends of temperature over the past 80 years. However, its sensitivities to separately changing sea surface temperature and carbon dioxide are not entirely realistic.