Causal Climate Emulation with Bayesian Filtering

• URL: http://arxiv.org/abs/2506.09891v1
• Date: Wed, 11 Jun 2025 16:00:55 GMT
• Title: Causal Climate Emulation with Bayesian Filtering
• Authors: Sebastian Hickman, Ilija Trajkovic, Julia Kaltenborn, Francis Pelletier, Alex Archibald, Yaniv Gurwicz, Peer Nowack, David Rolnick, Julien Boussard,
• Abstract summary: 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.
• Score: 17.110430055067756
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional models of climate change use complex systems of coupled equations to simulate physical processes across the Earth system. These simulations are highly computationally expensive, limiting our predictions of climate change and analyses of its causes and effects. Machine learning has the potential to quickly emulate data from climate models, but current approaches are not able to incorporate physics-informed causal relationships. Here, we develop an interpretable climate model emulator based on causal representation learning. We derive a physics-informed approach including a Bayesian filter for stable long-term autoregressive emulation. We demonstrate that our emulator learns accurate climate dynamics, and we show the importance of each one of its components on a realistic synthetic dataset and data from two widely deployed climate models.

Related papers

• Learning to generate physical ocean states: Towards hybrid climate modeling [1.5845117761091052]
  Ocean General Circulation Models require extensive computational resources to reach equilibrium states.<n>Deep learning emulators, despite offering fast predictions, lack the physical interpretability and long-term stability necessary for climate scientists.<n>We propose to take the best from both worlds by leveraging deep generative models to produce physically consistent oceanic states.
  arXiv  Detail & Related papers  (2025-02-04T17:14:41Z)
• Probabilistic Emulation of a Global Climate Model with Spherical DYffusion [15.460280166612119]
  We present the first conditional generative model that produces accurate and physically consistent global climate ensemble simulations. Our model integrates the dynamics-informed diffusion framework (DYffusion) with the Spherical Fourier Neural Operator (SFNO) architecture. The model achieves near gold-standard performance for climate model emulation, outperforming existing approaches and demonstrating promising ensemble skill.
  arXiv  Detail & Related papers  (2024-06-21T00:16:55Z)
• Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests [47.285748922842444]
  We train a deep neural network to predict a phenological index from meteorological time series. We find that this approach outperforms traditional process-based models.
  arXiv  Detail & Related papers  (2024-01-08T15:29:23Z)
• Towards Causal Representations of Climate Model Data [18.82507552857727]
  This work delves into the potential of causal representation learning, specifically the emphCausal Discovery with Single-parent Decoding (CDSD) method. Our findings shed light on the challenges, limitations, and promise of using CDSD as a stepping stone towards more interpretable and robust climate model emulation.
  arXiv  Detail & Related papers  (2023-12-05T16:13:34Z)
• FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation [13.745581787463962]
  We introduce FaIRGP, a data-driven emulator that satisfies the physical temperature response equations of an energy balance model. We show how FaIRGP can be used to obtain estimates of top-of-atmosphere radiative forcing. We hope that this work will contribute to widening the adoption of data-driven methods in climate emulation.
  arXiv  Detail & Related papers  (2023-07-14T08:43:36Z)
• 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)
• Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
  Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
  arXiv  Detail & Related papers  (2022-11-20T06:46:35Z)
• Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processes [61.31361524229248]
  We build on recent scale sparsetemporal GPs to reduce the computational burden. We successfully employ such a doubly sparse GP to construct a probabilistic model of paleoclimate.
  arXiv  Detail & Related papers  (2022-11-15T14:15:04Z)
• 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)

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.