mlsanityData to 7 Oct 2026

Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations

arXiv 1709.00037 ↗First posted 31 Aug 2017stat.ML physics.ao-phcited by 51

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Abstract (arXiv)

Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems. But rapid progress is now within reach. New computational tools and methods from data assimilation and machine learning make it possible to integrate global observations and local high-resolution simulations in an Earth system model (ESM) that systematically learns from both. Here we propose a blueprint for such an ESM. We outline how parameterization schemes can learn from global observations and targeted high-resolution simulations, for example, of clouds and convection, through matching low-order statistics between ESMs, observations, and high-resolution simulations. We illustrate learning algorithms for ESMs with a simple dynamical system that shares characteristics of the climate system; and we discuss the opportunities the proposed framework presents and the challenges that remain to realize it.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Aug 2017.

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Citing papers per year: 2021: 9; 2022: 7; 2023: 11; 2024: 9; 2025: 10; 2026: 5.

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  1. 2023ClimaX: A foundation model for weather and climatecited by 325
  2. 2023Neural General Circulation Models for Weather and Climatecited by 232
  3. 2021Will Artificial Intelligence supersede Earth System and Climate Models?cited by 37
  4. 2022A posteriori learning for quasi-geostrophic turbulence parametrizationcited by 36
  5. 2021NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledgecited by 33
  6. 2022Differentiable Programming for Earth System Modelingcited by 21
  7. 2021Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observationscited by 20
  8. 2022Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flowcited by 20
  9. 2023Sampling via Gradient Flows in the Space of Probability Measurescited by 20
  10. 2023Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariancecited by 16
  11. 2024When are dynamical systems learned from time series data statistically accurate?cited by 14
  12. 2023Learning About Structural Errors in Models of Complex Dynamical Systemscited by 13
38 more
  1. 2024Fisher-Rao Gradient Flow: Geodesic Convexity and Functional Inequalitiescited by 13
  2. 2021Deep Learning-based Extreme Heatwave Forecastcited by 9
  3. 2023Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challengescited by 9
  4. 2024A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statisticscited by 9
  5. 2024Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flowscited by 9
  6. 2021Using Machine Learning at Scale in HPC Simulations with SmartSim: An Application to Ocean Climate Modelingcited by 7
  7. 2024Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Explorationcited by 7
  8. 2022Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantificationcited by 5
  9. 2023Gradient-free online learning of subgrid-scale dynamics with neural emulatorscited by 5
  10. 2024Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programmingcited by 5
  11. 2024A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training datacited by 5
  12. 2026PEST: Physics-Enhanced Swin Transformer for 3D Turbulence Simulationcited by 5
  13. 2021Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networkscited by 4
  14. 2023Multi-fidelity climate model parameterization for better generalization and extrapolationcited by 4
  15. 2025Efficient Deconvolution in Populational Inverse Problemscited by 4
  16. 2021NLP for Climate Policy: Creating a Knowledge Platform for Holistic and Effective Climate Actioncited by 3
  17. 2021Artificial intelligence for Sustainable Energy: A Contextual Topic Modeling and Content Analysiscited by 3
  18. 2022History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz '96cited by 3
  19. 2023Inductive biases in deep learning models for weather predictioncited by 3
  20. 2024Embed and Emulate: Contrastive representations for simulation-based inferencecited by 3
  21. 2025Stochastic and Non-local Closure Modeling for Nonlinear Dynamical Systems via Latent Score-based Generative Modelscited by 3
  22. 2025Spatiotemporal Forecasting as Planning: A Model-Based Reinforcement Learning Approach with Generative World Modelscited by 3
  23. 2025Bridging Idealized and Operational Models: An Explainable AI Framework for Earth System Emulatorscited by 3
  24. 2025The Ensemble Kalman Inversion Racecited by 3
  25. 2023Flow Annealed Kalman Inversion for Gradient-Free Inference in Bayesian Inverse Problemscited by 2
  26. 2023Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulencecited by 2
  27. 2025Generating realistic global precipitation fields from modelled atmospheric circulationcited by 2
  28. 2022Semi-automatic tuning of coupled climate models with multiple intrinsic timescales: lessons learned from the Lorenz96 modelcited by 1
  29. 2024Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systemscited by 1
  30. 2025Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulencecited by 1
  31. 2025Reduced-Order Modeling of Cyclo-Stationary Time Series Using Score-Based Generative Methodscited by 1
  32. 2025Hierarchical Graph Networks for Accurate Weather Forecasting via Lightweight Trainingcited by 1
  33. 2026Synergizing Transport-Based Generative Models and Latent Geometry for Stochastic Closure Modelingcited by 1
  34. 2026Optimal scenario design for climate emulationcited by 1
  35. 2021Learning landmark geodesics using Kalman ensemblescited by 0
  36. 2022Data-driven and machine-learning based prediction of wave propagation behavior in dam-break floodcited by 0
  37. 2025Performance and Efficiency of Climate In-Situ Data Reconstruction: Why Optimized IDW Outperforms kriging and Implicit Neural Representationcited by 0
  38. 2026No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisationcited by 0

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