# Emulating the Forced Response of Climate Models with Generative Machine Learning

arXiv: https://arxiv.org/abs/2605.16929 · HTML: https://mlsanity.com/paper/2605.16929/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 93 references that are in the map.
- First posted on arXiv 16 May 2026.

## Builds on (most cited first, 21 of 21)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [ClimaX: A foundation model for weather and climate](https://mlsanity.com/paper/2301.10343/) (2023)
- [Neural General Circulation Models for Weather and Climate](https://mlsanity.com/paper/2311.07222/) (2023)
- [GenCast: Diffusion-based ensemble forecasting for medium-range weather](https://mlsanity.com/paper/2312.15796/) (2023)
- [Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast](https://mlsanity.com/paper/2211.02556/) (2022)
- [DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting](https://mlsanity.com/paper/2306.01984/) (2023)
- [AdaSpeech: Adaptive Text to Speech for Custom Voice](https://mlsanity.com/paper/2103.00993/) (2021)
- [Skillful joint probabilistic weather forecasting from marginals](https://mlsanity.com/paper/2506.10772/) (2025)
- [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)
- [Samudra: An AI Global Ocean Emulator for Climate](https://mlsanity.com/paper/2412.03795/) (2024)
- [ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning](https://mlsanity.com/paper/2311.03721/) (2023)
- [Differentiable Programming for Earth System Modeling](https://mlsanity.com/paper/2208.13825/) (2022)
- [Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model](https://mlsanity.com/paper/2406.08632/) (2024)
- [SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators](https://mlsanity.com/paper/2509.12490/) (2025)
- [Swift: An Autoregressive Consistency Model for Efficient Weather Forecasting](https://mlsanity.com/paper/2509.25631/) (2025)
- [The impact of internal variability on benchmarking deep learning climate emulators](https://mlsanity.com/paper/2408.05288/) (2024)
- [Causal Climate Emulation with Bayesian Filtering](https://mlsanity.com/paper/2506.09891/) (2025)
- [ArchesClimate: Probabilistic Decadal Ensemble Generation With Flow Matching](https://mlsanity.com/paper/2509.15942/) (2025)
- [mloz: A Highly Efficient Machine Learning-Based Ozone Parameterization for Climate Sensitivity Simulations](https://mlsanity.com/paper/2509.20422/) (2025)

## Abstract (arXiv)

Global climate models are essential tools to simulate past and potential future pathways of climate change, as well as associated climate impacts. Shared Socioeconomic Pathways (SSPs) describe a range of future scenarios of global economic and demographic development. These SSPs are intrinsically linked to changes in climate forcings -- the external drivers, such as greenhouse gas and aerosol emissions, which change Earth's energy balance over time. These forcings act as boundary conditions in Earth System Models (ESM), providing insight into the potential climatic impacts of each SSP. Running an ESM, however, is extremely computationally expensive, conflicting with the need for large ensemble runs to provide robust estimates in the presence of internal variability and scenario uncertainty. Machine Learning emulators provide a promising avenue towards fast and cheap scenario generation, but until recently lacked uncertainty quantification and the ability to condition on external forcings. Here, we build upon recent work and extend it by training on multiple SSPs. We successfully generate scenarios of IPSL -- CM6A -- LR unseen during training and remain physically consistent with the underlying climate model, even under strong extrapolation scenarios. Our emulator is validated against MESMER -- M, a statistical emulator of land surface temperature. Our research demonstrates that our model, ArchesClimate -- SSP, does not simply imitate scenarios seen during training, but is actually capable of modeling the response of a climate state to diverse forcings. This is an important step towards reliable and rapid climate model scenario generation.