mlsanityData to 7 Oct 2026

Will Artificial Intelligence supersede Earth System and Climate Models?

arXiv 2101.09126 ↗First posted 22 Jan 2021stat.ML cs.LG physics.ao-phcited by 37

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Abstract (arXiv)

We outline a perspective of an entirely new research branch in Earth and climate sciences, where deep neural networks and Earth system models are dismantled as individual methodological approaches and reassembled as learning, self-validating, and interpretable Earth system model-network hybrids. Following this path, we coin the term "Neural Earth System Modelling" (NESYM) and highlight the necessity of a transdisciplinary discussion platform, bringing together Earth and climate scientists, big data analysts, and AI experts. We examine the concurrent potential and pitfalls of Neural Earth System Modelling and discuss the open question whether artificial intelligence will not only infuse Earth system modelling, but ultimately render them obsolete.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Builds on 23 of its 144 references that are in the map. First posted on arXiv 22 Jan 2021.

Builds on

23 of 144 references.

References · 23

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  6. 2020WeatherBench: A benchmark dataset for data-driven weather forecastingcited by 272
  7. 2019Tackling Climate Change with Machine Learningcited by 264
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  9. 2018Deep learning to represent sub-grid processes in climate modelscited by 156
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11 more
  1. 2019Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Modelcited by 45
  2. 2019Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variabilitycited by 43
  3. 2018Exascale Deep Learning for Climate Analyticscited by 28
  4. 2019Achieving Conservation of Energy in Neural Network Emulators for Climate Modelingcited by 27
  5. 2019Global AI Ethics: A Review of the Social Impacts and Ethical Implications of Artificial Intelligencecited by 21
  6. 2020Machine Learning Information Fusion in Earth Observation: A Comprehensive Review of Methods, Applications and Data Sourcescited by 19
  7. 2020Climbing down Charney's ladder: Machine Learning and the post-Dennard era of computational climate sciencecited by 18
  8. 2018A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patternscited by 15
  9. 2020Towards Physically-consistent, Data-driven Models of Convectioncited by 13
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 8; 2023: 10; 2024: 8; 2025: 5; 2026: 4.

37 papers.

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  2. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 603
  3. 2023Machine learning with data assimilation and uncertainty quantification for dynamical systems: a reviewcited by 74
  4. 2023W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecastingcited by 28
  5. 2021Climate-Invariant Machine Learningcited by 24
  6. 2022Differentiable Programming for Earth System Modelingcited by 21
  7. 2023Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecastingcited by 13
  8. 2023When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence Systemcited by 13
  9. 2024MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modelingcited by 11
  10. 2023Extrapolating tipping points and simulating non-stationary dynamics of complex systems using efficient machine learningcited by 9
  11. 2021Output-weighted and relative entropy loss functions for deep learning precursors of extreme eventscited by 8
  12. 2024FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmospherecited by 8
25 more
  1. 2022Learning Subgrid-scale Models with Neural Ordinary Differential Equationscited by 4
  2. 2023Reconstructing Historical Climate Fields With Deep Learningcited by 4
  3. 2022Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Reviewcited by 3
  4. 2023Quantile Extreme Gradient Boosting for Uncertainty Quantificationcited by 3
  5. 2024Earth System Data Cubes: Avenues for advancing Earth system researchcited by 3
  6. 2025HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Modelcited by 3
  7. 2023On the choice of training data for machine learning of geostrophic mesoscale turbulencecited by 2
  8. 2023Exploring Geometric Deep Learning For Precipitation Nowcastingcited by 2
  9. 2024A Hybrid Deep-Learning Model for El Ni~no Southern Oscillation in the Low-Data Regimecited by 2
  10. 2025Artificial Intelligence for Atmospheric Sciences: A Research Roadmapcited by 2
  11. 2025Towards fully differentiable neural ocean model with Veroscited by 2
  12. 2022Cooperative control of environmental extremes by artificial intelligent agentscited by 1
  13. 2022Exploring Randomly Wired Neural Networks for Climate Model Emulationcited by 1
  14. 2024Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate Datacited by 1
  15. 2024ICODE: Modeling Dynamical Systems with Extrinsic Input Informationcited by 1
  16. 2025Field-Space Attention for Structure-Preserving Earth System Transformerscited by 1
  17. 2022DL4DS -- Deep Learning for empirical DownScalingcited by 0
  18. 2023Interpretable Geoscience Artificial Intelligence (XGeoS-AI): Application to Demystify Image Recognitioncited by 0
  19. 2024The Importance of Architecture Choice in Deep Learning for Climate Applicationscited by 0
  20. 2024Improving PINNs By Algebraic Inclusion of Boundary and Initial Conditionscited by 0
  21. 2025Conditional updates of neural network weights for increased out of training performancecited by 0
  22. 2026Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equationscited by 0
  23. 2026OceanCBM: A Concept Bottleneck Model for Mechanistic Interpretability in Ocean Forecastingcited by 0
  24. 2026Deep Learning Surrogates for Emulating Stochastic Climate Tipping Dynamicscited by 0
  25. 2026Discovering Multiscale Deep Formulas in Complex Systems via Neural-Guided Lambda Calculuscited by 0