SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators
- URL: http://arxiv.org/abs/2509.12490v1
- Date: Mon, 15 Sep 2025 22:27:26 GMT
- Title: SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators
- Abstract summary: SamudrACE produces centuries-long simulations at 1-degree horizontal, 6-hourly atmospheric, and 5-daily oceanic resolution.<n>It is highly stable and has low climate biases comparable to those of its components with prescribed boundary forcing.
- Score: 6.297785124293287
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Traditional numerical global climate models simulate the full Earth system by exchanging boundary conditions between separate simulators of the atmosphere, ocean, sea ice, land surface, and other geophysical processes. This paradigm allows for distributed development of individual components within a common framework, unified by a coupler that handles translation between realms via spatial or temporal alignment and flux exchange. Following a similar approach adapted for machine learning-based emulators, we present SamudrACE: a coupled global climate model emulator which produces centuries-long simulations at 1-degree horizontal, 6-hourly atmospheric, and 5-daily oceanic resolution, with 145 2D fields spanning 8 atmospheric and 19 oceanic vertical levels, plus sea ice, surface, and top-of-atmosphere variables. SamudrACE is highly stable and has low climate biases comparable to those of its components with prescribed boundary forcing, with realistic variability in coupled climate phenomena such as ENSO that is not possible to simulate in uncoupled mode.
Related papers
- Stochastic Emulation of a Fully Coupled Preindustrial E3SMv3 Simulation [1.870475180413669]
We present a coupled emulator of E3SM version 3, built on the SamudrACE framework, which couples an atmosphere emulator with a full-depth ocean emulator (Samudra)<n>We replace the deterministic atmosphere emulator with its counterpart, ACE2S, and fine-tune the coupled system with a probabilistic objective, so that the atmosphere acts as a source of internal variability for the ocean.<n>Trained on 105 years of a pre-industrial control simulation and evaluated on an independent 400 years, the emulator reproduces E3SMv3's mean climate state with biases much smaller than existing model-to-observ
arXiv Detail & Related papers (2026-08-10T22:19:59Z) - Scaling Storm-Resolving Atmospheric AI Simulation to the Entire Planet [4.77834801104581]
STRATA (Storm-resolving Tile-based autoRegressive Atmosphere Transformer Architecture) is the first autoregressive AI emulator for global storm-resolving atmospheric dynamics.<n> STRATA produces stable 24-hour global rollouts with realistic km-scale dynamics across diverse regimes.<n>It achieves 48 simulation days per megawatt-hour -- about 50 times better energy efficiency than the SCREAM physics model.
arXiv Detail & Related papers (2026-06-30T07:24:33Z) - Earth-o1: A Grid-free Observation-native Atmospheric World Model [81.55899748753434]
We present Earth-o1, an observation-native atmospheric world model that overcomes structural limitations.<n>By integrating diverse sensor inputs into a unified, grid-free dynamical field, the model autonomously advances the atmospheric state in space and time.<n>We show that this fundamentally distinct paradigm enables direct, real-time forecasting and cross-sensor inference without the overhead of explicit numerical solvers.
arXiv Detail & Related papers (2026-05-07T14:27:48Z) - Spatiotemporal Pyramid Flow Matching for Climate Emulation [12.682343033641137]
Generative models have the potential to transform the way we emulate Earth's changing climate.<n>Previous generative approaches rely on weather-scale autoregression for climate emulation.<n>SPF is a new class of matching approaches that model data hierarchically across spatial and temporal scales.
arXiv Detail & Related papers (2025-12-01T23:20:03Z) - Benchmarking atmospheric circulation variability in an AI emulator, ACE2, and a hybrid model, NeuralGCM [0.0]
Physics-based atmosphere-land models with prescribed sea surface temperature have notable successes but also biases in their ability to represent atmospheric variability compared to observations.<n>Recently, AI emulators and hybrid models have emerged with the potential to overcome these biases, but still require systematic evaluation against metrics grounded in fundamental atmospheric dynamics.
arXiv Detail & Related papers (2025-10-06T03:42:18Z) - Causal Climate Emulation with Bayesian Filtering [17.110430055067756]
We develop an interpretable climate model emulator based on causal representation learning.<n>We derive a physics-informed approach including a Bayesian filter for stable long-term autoregressive emulation.
arXiv Detail & Related papers (2025-06-11T16:00:55Z) - Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control [97.98560001760126]
We introduce Cosmos-Transfer, a conditional world generation model that can generate world simulations based on multiple spatial control inputs.<n>We conduct evaluations to analyze the proposed model and demonstrate its applications for Physical AI, including robotics2Real and autonomous vehicle data enrichment.
arXiv Detail & Related papers (2025-03-18T17:57:54Z) - Simultaneous emulation and downscaling with physically-consistent deep learning-based regional ocean emulators [2.369898950737752]
We propose an AI-based ocean emulation and downscaling framework focusing on the high-resolution regional ocean over Gulf of Mexico.<n>The framework shows both short-term skills as well as accurate long-term statistics in terms of mean and variability.
arXiv Detail & Related papers (2025-01-09T08:28:31Z) - Samudra: An AI Global Ocean Emulator for Climate [27.24070831177446]
We build a global emulator of the ocean component of a state-of-the-art climate model.<n>We use a modified ConvNeXt UNet architecture trained on multi-depth levels of ocean data.<n>We show that the ocean emulator - Samudra - can reproduce the depth structure of ocean variables and their interannual variability.
arXiv Detail & Related papers (2024-12-05T01:25:34Z) - DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulation [54.02069690134526]
We propose DrivingSphere, a realistic and closed-loop simulation framework.
Its core idea is to build 4D world representation and generate real-life and controllable driving scenarios.
By providing a dynamic and realistic simulation environment, DrivingSphere enables comprehensive testing and validation of autonomous driving algorithms.
arXiv Detail & Related papers (2024-11-18T03:00:33Z) - FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems [56.0640340392818]
We introduce a framework, FREE, that enables the use of varying features and available information to train a universal model.<n>The core idea is to map available environmental data into a text space and then convert the traditional predictive modeling task in environmental science to a semantic recognition problem.<n>Our evaluation on two societally important real-world applications, stream water temperature prediction and crop yield prediction, demonstrates the superiority of FREE over multiple baselines.
arXiv Detail & Related papers (2023-11-17T00:53:09Z) - ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation [45.201929285600606]
We present ClimSim-Online, which includes an end-to-end workflow for developing hybrid ML-physics simulators.
The dataset is global and spans ten years at a high sampling frequency.
We provide a cross-platform, containerized pipeline to integrate ML models into operational climate simulators.
arXiv Detail & Related papers (2023-06-14T21:26:31Z) - ClimaX: A foundation model for weather and climate [51.208269971019504]
ClimaX is a deep learning model for weather and climate science.
It can be pre-trained with a self-supervised learning objective on climate datasets.
It can be fine-tuned to address a breadth of climate and weather tasks.
arXiv Detail & Related papers (2023-01-24T23:19:01Z) - DeepClimGAN: A High-Resolution Climate Data Generator [60.59639064716545]
Earth system models (ESMs) are often used to generate future projections of climate change scenarios.
As a compromise, emulators are substantially less expensive but may not have all of the complexity of an ESM.
Here we demonstrate the use of a conditional generative adversarial network (GAN) to act as an ESM emulator.
arXiv Detail & Related papers (2020-11-23T20:13:37Z) - Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
We find a third intermediate stable state in one of the two climate models we consider.
The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
arXiv Detail & Related papers (2020-10-20T15:31:38Z) - HECT: High-Dimensional Ensemble Consistency Testing for Climate Models [1.7587442088965226]
Climate models play a crucial role in understanding the effect of environmental changes on climate to help mitigate climate risks and inform decisions.
Large global climate models such as the Community Earth System Model (CESM), are very complex with millions of lines of code describing interactions of the atmosphere, land, oceans, and ice.
Our work uses probabilistics like tree-based algorithms and deep neural networks to perform a statistically rigorous goodness-of-fit test of high-dimensional and man-made data.
arXiv Detail & Related papers (2020-10-08T15:16:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.