Foundation models in Earth system science: Advances, challenges, and future paths

Fenzhen Su; Yinxia Cao; Xin Li; Haoyu Wang; Rong Fan; Shuyuan Huang; Wenxing He; Ziying Chen; Peng Zhang; Liang Zhao; Tinghua Ai; Gang Huang; Haosu Tang; Xiaofeng Li; Zimeng Zhao; Changming Dong; Zhenhong Li; Libo Wang; Junjie Wang; Chengyuan Ju

doi:10.59717/j.tirv.2026.100014

The Innovation Reviews > 2026 Vol. 1 > No. 1 > 100014

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Foundation models in Earth system science: Advances, challenges, and future paths

1.
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2.
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
3.
National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
4.
Key Laboratory of Deep Petroleum Intelligent Exploration and Development, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
5.
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
6.
State Key Laboratory of Earth System Numerical Modeling and Application, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
7.
School of Geography and Planning, University of Sheffield, Sheffield S3 7ND, UK
8.
Key Laboratory of Ocean Observation and Forecasting and Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
9.
School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
10.
State Key Laboratory of Loess Science, Chang’an University, Xi’an 710054, China
11.
College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China
12.
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
13.
These authors contributed equally

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Corresponding author: sufz@lreis.ac.cn
Received Date: April 14, 2026

Accepted Date: September 15, 2026

Published Online: September 20, 2026

The Innovation Reviews 1(1), https://doi.org/10.59717/j.tirv.2026.100014 | Cite this article

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PUBLIC SUMMARY

PUBLIC SUMMARY
1. This review examines foundation models across six fields of Earth system science.
  
  Limited access to usable data and difficulties in respecting physical laws remain major challenges.
  
  Future models could connect information across Earth’s systems and incorporate physical knowledge.
  
  These advances could improve planetary monitoring and simulations of future Earth system changes.

ABSTRACT

ABSTRACT

The convergence of massive Earth observation datasets and emerging foundation models offers an opportunity to address the complex challenges in Earth System Science (ESS). However, the direct application of foundation models to ESS is often hindered by domain gaps, characterized by the need for physical consistency, the complexity of Earth’s spherical geometries, and the non-stationarity of the multisphere coupled Earth systems. Building on prior reviews that prioritize model architecture or view geoscience as a monolithic entity, this study provides a domain-centric synthesis of foundation models across six distinct subfields, including Geology, Geophysics, Atmosphere, Ocean, Geography, and the Integrated Earth System. By analyzing the applications and limitations of Artificial Intelligence (AI) within these sub-fields, this work identifies unique domain barriers and cross-cutting challenges, including the AI-ready Earth data dilemma, representation space fragmentation, physical inconsistency, scientific validation deficiency, and ecosystem barriers. In this context, we provide future paths for the next generation of ESS foundation models, driven by unified Earth representation engine, a physics-embedded neural architecture, an Earth system knowledge graph, multi-scale generative inversion, causal reasoning, and human-AI collaborative discovery interface. In this way, foundation models can potentially complement traditional numerical models to monitor planetary status and simulate its future evolution, thereby equipping the scientific community with new tools for holistic planetary stewardship and complex decision-making.
- Earth system science /
- foundation models /
- artificial intelligence /
- multimodal learning /
- scientific machine learning /
- Earth system modeling

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REFERENCES

[1]	Steffen W., Richardson K., Rockström J., et al. (2020). The emergence and evolution of Earth system science. Nat. Rev. Earth Environ. 1:54−63. DOI:10.1038/s43017-019-0005-6 View in Article CrossRef Google Scholar
[2]	Kumar A., Nagar S. and Anand S. (2021). Climate change and existential threats. Singh S., Singh P., Selvasembian R., et al (eds.). Global Climate Change (Elsevier), pp: 1–31. DOI: 10.1016/B978-0-12-822928-6.00005-8 View in Article Google Scholar
[3]	Raymond C., Horton R.M., Zscheischler J., et al. (2020). Understanding and managing connected extreme events. Nat. Clim. Change 10:611−621. DOI:10.1038/s41558-020-0790-4 View in Article CrossRef Google Scholar
[4]	Cao Y., Wang H., Su F., et al. (2026). Satellite radar and AIS reveal a 97% decline in shipping traffic through the Strait of Hormuz. The Innovation 7:101367. DOI:10.1016/j.xinn.2026.101367 View in Article CrossRef Google Scholar
[5]	Li X., Feng M., Ran Y., et al. (2023). Big data in Earth system science and progress towards a digital twin. Nat. Rev. Earth Environ. 4:319−332. DOI:10.1038/s43017-023-00409-w View in Article CrossRef Google Scholar
[6]	Guo H., Wang L. and Liang D. (2016). Big earth data from space: a new engine for Earth science. Sci. Bull. 61:505−513. DOI:10.1007/s11434-016-1041-y View in Article CrossRef Google Scholar
[7]	Gray J. (2009). Jim Gray on eScience: A transformed scientific method. Hey T., Tansley S., and Tolle K.M. (eds.). The Fourth Paradigm: Data-Intensive Scientific Discovery (Microsoft Research), pp: xvii–xxxi. View in Article Google Scholar
[8]	Radford A., Narasimhan K., Salimans T., et al. (2018). Improving language understanding by generative pre-training. https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf View in Article Google Scholar
[9]	Guo D., Yang D., Zhang H., et al. (2025). DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning. Nature 645:633−638. DOI:10.1038/s41586-025-09422-z View in Article CrossRef Google Scholar
[10]	Liu Y., Zhang K., Li Y., et al. (2024). Sora: a review on background, technology, limitations, and opportunities of large vision models. arXiv. https://arxiv.org/abs/2402.17177 View in Article Google Scholar
[11]	Nguyen T., Brandstetter J., Kapoor A., et al. (2023). ClimaX: A foundation model for weather and climate. arXiv. https://arxiv.org/abs/2301.10343 View in Article Google Scholar
[12]	Bodnar C., Bruinsma W. P., Lucic A., et al. (2025). A foundation model for the Earth system. Nature 641:1180−1187. DOI:10.1038/s41586-025-09005-y View in Article CrossRef Google Scholar
[13]	Irrgang C., Boers N., Sonnewald M., et al. (2021). Towards neural Earth system modelling by integrating artificial intelligence in Earth system science. Nat. Mach. Intell. 3:667−674. DOI:10.1038/s42256-021-00374-3 View in Article CrossRef Google Scholar
[14]	Reichstein M., Camps-Valls G., Stevens B., et al. (2019). Deep learning and process understanding for data-driven Earth system science. Nature 566:195−204. DOI:10.1038/s41586-019-0912-1 View in Article CrossRef Google Scholar
[15]	Schultz M. G., Betancourt C., Gong B., et al. (2021). Can deep learning beat numerical weather prediction. Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 379:20200097. DOI:10.1098/rsta.2020.0097 View in Article CrossRef Google Scholar
[16]	Dueben P. D. and Bauer P. (2018). Challenges and design choices for global weather and climate models based on machine learning. Geosci. Model Dev. 11:3999−4009. DOI:10.5194/gmd-11-3999-2018 View in Article CrossRef Google Scholar
[17]	Zhang H., Xu J.J., Cui H.W., et al. (2024). When geoscience meets foundation models: Toward a general geoscience artificial intelligence system. IEEE Geosci. Remote Sens. Mag. 2-41. DOI:10.1109/MGRS.2024.3496478 View in Article Google Scholar
[18]	Karpatne A., Ebert-Uphoff I., Ravela S., et al. (2019). Machine learning for the geosciences: challenges and opportunities. IEEE Trans. Knowl. Data Eng. 31:1544−1554. DOI:10.1109/TKDE.2018.2861006 View in Article CrossRef Google Scholar
[19]	Persello C., Wegner J. D., Hänsch R., et al. (2022). Deep learning and Earth observation to support the sustainable development goals: current approaches, open challenges, and future opportunities. IEEE Geosci. Remote Sens. Mag. 10:172−200. DOI:10.1109/MGRS.2021.3136100 View in Article CrossRef Google Scholar
[20]	Zhao T., Wang S., Ouyang C., et al. (2024). Artificial intelligence for geoscience: progress, challenges, and perspectives. The Innovation 5:100691. DOI:10.1016/j.xinn.2024.100691 View in Article CrossRef Google Scholar
[21]	Bergen K. J., Johnson P. A., de Hoop M. V., et al. (2019). Machine learning for data-driven discovery in solid Earth geoscience. Science 363:eaau0323. DOI:10.1126/science.aau0323 View in Article CrossRef Google Scholar
[22]	Rhoads B.L. and Thorn C.E. (eds.) (1996). The scientific nature of geomorphology: Proceedings of the 27th Binghamton symposium in geomorphology (Wiley). View in Article Google Scholar
[23]	Burrough P. A. (1981). Fractal dimensions of landscapes and other environmental data. Nature 294:240−242. DOI:10.1038/294240a0 View in Article CrossRef Google Scholar
[24]	Chamberlin T. C. (1890). The method of multiple working hypotheses. Science ns- 15:92−96. DOI:10.1126/science.ns-15.366.92 View in Article CrossRef Google Scholar
[25]	Byers C. W. (1982). Stratigraphy–The fall of continuity. J. Geol. Educ. 30:215−221. DOI:10.5408/0022-1368-30.4.215 View in Article CrossRef Google Scholar
[26]	Harris E. C. (1979). The laws of archaeological stratigraphy. World Archaeol. 11:111−117. DOI:10.1080/00438243.1979.9979753 View in Article CrossRef Google Scholar
[27]	Short N.M. and Blair R.W. Jr. (eds.) (1986). Geomorphology from space: A global overview of regional landforms. Washington DC: Scientific and Technical Information Branch, National Aeronautics and Space Administration, pp: 717. View in Article Google Scholar
[28]	Kondolf G.M., Piégay H., Schmitt L., et al. (2016). Geomorphic classification of rivers and streams. Kondolf G.M. and Piégay H. (eds.). Tools in Fluvial Geomorphology (John Wiley & Sons, Ltd), pp: 133–158. DOI: 10.1002/9781118648551.ch7 View in Article Google Scholar
[29]	Greene M.T. (2017). Geology in the Nineteenth century: Changing views of a changing world. Cornell University Press. DOI: 10.7591/9781501704741 View in Article Google Scholar
[30]	Bokulich A. and Oreskes N. (2017). Models in geosciences. Magnani L. and Bertolotti T. (eds). Springer Handbook of Model-Based Science (Springer International Publishing), pp: 891–911. DOI: 10.1007/978-3-319-30526-4_41 View in Article Google Scholar
[31]	Bauer P., Thorpe A. and Brunet G. (2015). The quiet revolution of numerical weather prediction. Nature 525:47−55. DOI:10.1038/nature14956 View in Article CrossRef Google Scholar
[32]	Turner A.K. and Gable C.W. (2007). A review of geological modeling. Thorleifson L.H., Berg R.C. and Russell H.A.J. (eds). Three-Dimensional Geologic Mapping for Groundwater Applications (Minnesota Geological Survey), pp: 75–79. View in Article Google Scholar
[33]	Johannessen O.M. (1986). Brief overview of the physical oceanography. Hurdle B.G. (ed). The Nordic Seas (Springer), pp: 103–128. DOI: 10.1007/978-1-4615-8035-5_4 View in Article Google Scholar
[34]	Pierce D. W., Barnett T. P., Santer B. D., et al. (2009). Selecting global climate models for regional climate change studies. Proc. Natl. Acad. Sci. 106:8441−8446. DOI:10.1073/pnas.0900094106 View in Article CrossRef Google Scholar
[35]	Schneider T., Lan S., Stuart A., et al. (2017). Earth system modeling 2.0: a blueprint for models that learn from observations and targeted high-resolution simulations. Geophys. Res. Lett. 44. DOI:10.1002/2017GL076101 View in Article Google Scholar
[36]	Li X., Chen Y., Liu X., et al. (2011). Concepts, methodologies, and tools of an integrated geographical simulation and optimization system. Int. J. Geogr. Inf. Sci. 25:633−655. DOI:10.1080/13658816.2010.496370 View in Article CrossRef Google Scholar
[37]	Fox-Kemper B., Adcroft A., Böning C. W., et al. (2019). Challenges and prospects in ocean circulation models. Front. Mar. Sci. 6. DOI:10.3389/fmars.2019.00065 View in Article Google Scholar
[38]	Jakob C. (2010). Accelerating progress in global atmospheric model development through improved parameterizations: Challenges, opportunities, and strategies. Bull. Am. Meteorol. Soc. 91:869−876. DOI:10.1175/2009BAMS2898.1 View in Article CrossRef Google Scholar
[39]	Xu Y., Liu X., Cao X., et al. (2021). Artificial intelligence: a powerful paradigm for scientific research. The Innovation 2:100179. DOI:10.1016/j.xinn.2021.100179 View in Article CrossRef Google Scholar
[40]	Guo X., Lao J., Dang B., et al. (2024). SkySense: A multi-modal remote sensing foundation model towards universal interpretation for Earth observation imagery. arXiv. https://arxiv.org/abs/2312.10115 View in Article Google Scholar
[41]	Jakubik J., Yang F., Blumenstiel B., et al. (2025). TerraMind: Large-scale generative multimodality for Earth observation. arXiv. https://arxiv.org/abs/2504.11171 View in Article Google Scholar
[42]	Brown C. F., Kazmierski M. R., Pasquarella V. J., et al. (2025). AlphaEarth foundations: An embedding field model for accurate and efficient global mapping from sparse label data. arXiv. https://arxiv.org/abs/2507.22291 View in Article Google Scholar
[43]	Xiang Y., Jin W., Dong H., et al. (2025). ADAF: an artificial intelligence data assimilation framework for weather forecasting. J. Adv. Model. Earth Syst. 17:e2024MS004839. DOI:10.1029/2024MS004839 View in Article CrossRef Google Scholar
[44]	Liu Q., Chen Z. and Ma J. (2025). Foundation models for exploration geophysics. Geophysics 90:H29−H52. DOI:10.1190/geo2024-0916.1 View in Article CrossRef Google Scholar
[45]	Bommasani R., Hudson D. A., Adeli E., et al. (2021). On the opportunities and risks of foundation models. arXiv. https://arxiv.org/abs/2108.07258 View in Article Google Scholar
[46]	Plésiat É., Dunn R. J. H., Donat M. G., et al. (2024). Artificial intelligence reveals past climate extremes by reconstructing historical records. Nat. Commun. 15:9191. DOI:10.1038/s41467-024-53464-2 View in Article CrossRef Google Scholar
[47]	Karniadakis G. E., Kevrekidis I. G., Lu L., et al. (2021). Physics-informed machine learning. Nat. Rev. Phys. 3:422−440. DOI:10.1038/s42254-021-00314-5 View in Article CrossRef Google Scholar
[48]	Zhang R., Zhou L., Gao C., et al. (2024). A transformer-based coupled ocean-atmosphere model for ENSO studies. Sci. Bull. 69. DOI:10.1016/j.scib.2024.04.048 View in Article Google Scholar
[49]	Zhang P., Zhou L., Li Y., et al. (2025). Self-supervised learning of invariant causal representation in heterogeneous information network. Inf. Fusion 123:103246. DOI:10.1016/j.inffus.2025.103246 View in Article CrossRef Google Scholar
[50]	Li X. and Guo Y. (2025). Paradigm shifts from data-intensive science to robot scientists. Sci. Bull. 70:14−18. DOI:10.1016/j.scib.2024.09.029 View in Article CrossRef Google Scholar
[51]	Lam R., Sanchez-Gonzalez A., Willson M., et al. (2023). Learning skillful medium-range global weather forecasting. Science 382:1416−1421. DOI:10.1126/science.adi2336 View in Article CrossRef Google Scholar
[52]	Bi K., Xie L., Zhang H., et al. (2023). Accurate medium-range global weather forecasting with 3D neural networks. Nature 619:533−538. DOI:10.1038/s41586-023-06185-3 View in Article CrossRef Google Scholar
[53]	Pathak J., Subramanian S., Harrington P., et al. (2022). FourCastNet: A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv. https://arxiv.org/abs/2202.11214 View in Article Google Scholar
[54]	Gao Y., Xiong Y., Wang S., et al. (2022). GeoBERT: Pre-training geospatial representation learning on point-of-interest. Appl. Sci. 12:12942. DOI:10.3390/app122412942 View in Article CrossRef Google Scholar
[55]	Lin Z., Deng C., Zhou L., et al. (2024). GeoGalactica: a scientific large language model in geoscience. arXiv. https://arxiv.org/abs/2401.00434 View in Article Google Scholar
[56]	Wu G., Wang H., Zhang K., et al. (2025). GeoProspect: A domain-specific geological large language model with enhanced continual learning. Neurocomputing 650:130801. DOI:10.1016/j.neucom.2025.130801 View in Article CrossRef Google Scholar
[57]	Chen X., Liu Y., Han W., et al. (2025). A vision-language foundation model-based multi-modal retrieval-augmented generation framework for remote sensing lithological recognition. ISPRS J. Photogramm. Remote Sens. 225:328−340. DOI:10.1016/j.isprsjprs.2025.04.015 View in Article CrossRef Google Scholar
[58]	Zhao M., Xiao Z., Zhang M., et al. (2023). DiTingMotion: a deep-learning first-motion-polarity classifier and its application to focal mechanism inversion. Front. Earth Sci. 11:1103914. DOI:10.3389/feart.2023.1103914 View in Article CrossRef Google Scholar
[59]	Si X., Wu X., Sheng H., et al. (2024). SeisCLIP: a seismology foundation model pre-trained by multimodal data for multipurpose seismic feature extraction. IEEE Trans. Geosci. Remote Sens. 62:1−13. DOI:10.1109/TGRS.2024.3354456 View in Article CrossRef Google Scholar
[60]	Li S., Yang X., Cao A., et al. (2024). SeisT: a foundational deep-learning model for earthquake monitoring tasks. IEEE Trans. Geosci. Remote Sens. 62:1−15. DOI:10.1109/TGRS.2024.3371503 View in Article CrossRef Google Scholar
[61]	Sheng H., Wu X., Si X., et al. (2025). Seismic foundation model: a next generation deep-learning model in geophysics. Geophysics 90:IM59−IM79. DOI:10.1190/geo2024-0262.1 View in Article CrossRef Google Scholar
[62]	Price I., Sanchez-Gonzalez A., Alet F., et al. (2025). Probabilistic weather forecasting with machine learning. Nature 637:84−90. DOI:10.1038/s41586-024-08252-9 View in Article CrossRef Google Scholar
[63]	Sun X., Zhong X., Xu X., et al. (2025). A data-to-forecast machine learning system for global weather. Nat. Commun. 16:6658. DOI:10.1038/s41467-025-62024-1 View in Article CrossRef Google Scholar
[64]	Mardani M., Brenowitz N., Cohen Y., et al. (2025). Residual corrective diffusion modeling for km-scale atmospheric downscaling. Commun. Earth Environ. 6:124. DOI:10.1038/s43247-025-02042-5 View in Article CrossRef Google Scholar
[65]	Xiong W., Xiang Y., Wu H., et al. (2023). AI-GOMS: Large AI-driven global ocean modeling system. arXiv. https://arxiv.org/abs/2308.03152 View in Article Google Scholar
[66]	Guo Z., Lyu P., Ling F., et al. (2025). Data-driven global ocean modeling for seasonal to decadal prediction. Sci. Adv. 11:eadu2488. DOI:10.1126/sciadv.adu2488 View in Article CrossRef Google Scholar
[67]	Huang Q., Niu Y., Zhong X., et al. (2026). A deep learning global ocean forecasting model with sub-daily and eddy-resolving resolution. npj Climate and Atmospheric Science. DOI: 10.1038/s41612-026-01444-2 View in Article Google Scholar
[68]	Yang N., Wang C., Zhao Z., et al. (2026). LangYa: a large AI model for global ocean forecasting. Sci. Bull. DOI:10.1016/j.scib.2026.08.001 View in Article Google Scholar
[69]	Li X., Zhang X., Ren Y., et al. (2026). Artificial intelligence for ocean phenomena forecasting. Sci. Bull. 71:2632−2652. DOI:10.1016/j.scib.2026.04.002 View in Article CrossRef Google Scholar
[70]	Wu K., Zhang Y., Ru L., et al. (2025). A semantic-enhanced multi-modal remote sensing foundation model for Earth observation. Nat. Mach. Intell. 7:1235−1249. DOI:10.1038/s42256-025-01078-8 View in Article CrossRef Google Scholar
[71]	Szwarcman D., Roy S., Fraccaro P., et al. (2026). Prithvi-EO-2.0: A versatile multitemporal foundation model for Earth observation applications. IEEE Trans. Geosci. Remote Sens. 64:1-20. DOI:10.1109/TGRS.2025.3642610 View in Article Google Scholar
[72]	Ozdemir F., Cheng Y., Mohebi S., et al. (2026). Earth system foundation model (ESFM): a unified framework for heterogeneous data integration and forecasting. arXiv. https://arxiv.org/abs/2605.00850 View in Article Google Scholar
[73]	Pal A., Sweeney A., Heyblom K., et al. (2026). NIVA: a multimodal foundation model for actionable earth system intelligence. arXiv. https://arxiv.org/abs/2606.28546v1 View in Article Google Scholar
[74]	Wang C., Wei X., Pan H., et al. (2024). Well logging stratigraphic correlation algorithm based on Semantic Segmentation. Appl. Geophys. 21:650−666. DOI:10.1007/s11770-024-1085-8 View in Article CrossRef Google Scholar
[75]	Sun Y., Pang S., Zhao Z., et al. (2024). Interpretable SHAP model combining Meta-learning and vision transformer for lithology classification using limited and unbalanced drilling data in well logging. Nat. Resour. Res. 33:2545−2565. DOI:10.1007/s11053-024-10396-4 View in Article CrossRef Google Scholar
[76]	Jiang C. and Lei Q. (2025). FracLab: A robust 3D DFN generator for coupled stress-flow-transport modeling of complex fractured media. Comput. Geotech. 186:107444. DOI:10.1016/j.compgeo.2025.107444 View in Article CrossRef Google Scholar
[77]	Yang F., Zuo R. and Kreuzer O. P. (2024). Artificial intelligence for mineral exploration: A review and perspectives on future directions from data science. Earth-Sci. Rev. 258:104941. DOI:10.1016/j.earscirev.2024.104941 View in Article CrossRef Google Scholar
[78]	Dagdelen J., Dunn A., Lee S., et al. (2024). Structured information extraction from scientific text with large language models. Nat. Commun. 15:1418. DOI:10.1038/s41467-024-45563-x View in Article CrossRef Google Scholar
[79]	Chen Y., Tian M., Wu Q., et al. (2024). A deep learning-based method for deep information extraction from multimodal data for geological reports to support geological knowledge graph construction. Earth Sci. Inform. 17:1867−1887. DOI:10.1007/s12145-023-01207-0 View in Article CrossRef Google Scholar
[80]	Luo X., Sun J., Zhang R., et al. (2024). A multi-condition denoising diffusion probabilistic model controls the reconstruction of 3D digital rocks. Comput. Geosci. 184:105541. DOI:10.1016/j.cageo.2024.105541 View in Article CrossRef Google Scholar
[81]	McGovern A., Lagerquist R., Gagne D. J., et al. (2019). Making the black box more transparent: Understanding the physical implications of machine learning. Bull. Am. Meteorol. Soc. 100:2175−2199. DOI:10.1175/BAMS-D-18-0195.1 View in Article CrossRef Google Scholar
[82]	Hu Y., Zhang Q., Zhao W., et al. (2021). TransQuake: A transformer-based deep learning approach for seismic P-wave detection. Earthq. Res. Adv. 1:100004. DOI:10.1016/j.eqrea.2021.100004 View in Article CrossRef Google Scholar
[83]	Sun M., Yu M., Lv P., et al. (2021). Man-made threat event recognition based on distributed optical fiber vibration sensing and SE-WaveNet. IEEE Trans. Instrum. Meas. 70:1−11. DOI:10.1109/TIM.2021.3081178 View in Article CrossRef Google Scholar
[84]	Zhao M., Xiao Z., Chen S., et al. (2023). DiTing: a large-scale chinese seismic benchmark dataset for artificial intelligence in seismology. Earthq. Sci. 36:84−94. DOI:10.1016/j.eqs.2022.01.022 View in Article CrossRef Google Scholar
[85]	Ongie G., Jalal A., Metzler C. A., et al. (2020). Deep learning techniques for inverse problems in imaging. IEEE J. Sel. Areas Inf. Theory 1:39−56. DOI:10.1109/JSAIT.2020.2991563 View in Article CrossRef Google Scholar
[86]	Wu X., Ma J., Si X., et al. (2023). Sensing prior constraints in deep neural networks for solving exploration geophysical problems. Proc. Natl. Acad. Sci. 120:e2219573120. DOI:10.1073/pnas.2219573120 View in Article CrossRef Google Scholar
[87]	Hersbach H., Bell B., Berrisford P., et al. (2020). The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146:1999−2049. DOI:10.1002/qj.3803 View in Article CrossRef Google Scholar
[88]	Eyring V., Bony S., Meehl G. A., et al. (2016). Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9:1937−1958. DOI:10.5194/gmd-9-1937-2016 View in Article CrossRef Google Scholar
[89]	Camps-Valls G., Fernández-Torres M.Á., Cohrs K.H., et al. (2025). Artificial intelligence for modeling and understanding extreme weather and climate events. Nat. Commun. 16:1919. DOI:10.1038/s41467-025-56573-8 View in Article CrossRef Google Scholar
[90]	Rasp S., Hoyer S., Merose A., et al. (2024). WeatherBench 2: A Benchmark for the Next Generation of Data‐Driven Global Weather Models. J. Adv. Model. Earth Syst. 16:e2023MS004019. DOI:10.1029/2023MS004019 View in Article CrossRef Google Scholar
[91]	Weyn J. A., Durran D. R. and Caruana R. (2020). Improving data‐driven global weather prediction using deep convolutional neural networks on a cubed sphere. J. Adv. Model. Earth Syst. 12:e2020MS002109. DOI:10.1029/2020MS002109 View in Article CrossRef Google Scholar
[92]	Kochkov D., Yuval J., Langmore I., et al. (2024). Neural general circulation models for weather and climate. Nature 632:1060−1066. DOI:10.1038/s41586-024-07744-y View in Article CrossRef Google Scholar
[93]	Sheng Z., He Y., Wang S., et al. (2025). Dynamics, chemistry, and modeling studies in the aviation and aerospace transition zone. The Innovation 6:101012. DOI:10.1016/j.xinn.2025.101012 View in Article CrossRef Google Scholar
[94]	Chattopadhyay A., Gray M., Wu T., et al. (2024). OceanNet: a principled neural operator-based digital twin for regional oceans. Sci. Rep. 14:21181. DOI:10.1038/s41598-024-72145-0 View in Article CrossRef Google Scholar
[95]	Beauchamp M., Febvre Q., Georgenthum H., et al. (2023). 4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry. Geosci. Model Dev. 16:2119−2147. DOI:10.5194/gmd-16-2119-2023 View in Article CrossRef Google Scholar
[96]	Nowak K., Koldunov N., Jung T., et al. (2025). OceanRep: A foundation model for ocean dynamics. Vienna: EGU General Assembly 2025. DOI: 10.5194/egusphere-egu25-15061 View in Article Google Scholar
[97]	Bi Z., Zhang N., Xue Y., et al. (2024). OceanGPT: A large language model for ocean science tasks. Ku L.W., Martins A. and Srikumar V. (eds). Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics), pp: 3357–3372. DOI: 10.18653/v1/2024.acl-long.184 View in Article Google Scholar
[98]	Brajard J., Carrassi A., Bocquet M., et al. (2021). Combining data assimilation and machine learning to infer unresolved scale parametrization. Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 379:20200086. DOI:10.1098/rsta.2020.0086 View in Article CrossRef Google Scholar
[99]	Zilberman N. V., Thierry V., King B., et al. (2023). Observing the full ocean volume using Deep Argo floats. Front. Mar. Sci. 10:1287867. DOI:10.3389/fmars.2023.1287867 View in Article CrossRef Google Scholar
[100]	Wan L., Garcia Sotillo M., Bell M., et al. (2025). An introduction to operational chains in ocean forecasting. State Planet 5-opsr: 15. DOI: 10.5194/sp-5-opsr-15-2025 View in Article Google Scholar
[101]	Jakubik J., Roy S., Phillips C. E., et al. (2023). Foundation Models for Generalist Geospatial Artificial Intelligence. arXiv. https://arxiv.org/abs/2310.18660 View in Article Google Scholar
[102]	Feng Z., Atzberger C., Jaffer S., et al. (2025). TESSERA: Temporal embeddings of surface spectra for Earth representation and analysis. arXiv. https://arxiv.org/abs/2506.20380 View in Article Google Scholar
[103]	Herzog H., Bastani F., Zhang Y., et al. (2026). OlmoEarth: Stable latent image modeling for multimodal Earth observation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp: 34806–34817. View in Article Google Scholar
[104]	Chen S., Wang J., Yuan S., et al. (2026). Democratizing planetary-scale analysis: an ultra-lightweight Earth embedding database for accurate and flexible global land monitoring. Earth Syst. Sci. Data 18:5375−5398. DOI:10.5194/essd-18-5375-2026 View in Article CrossRef Google Scholar
[105]	Lessig C., Luise I., Gong B., et al. (2023). AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning. arXiv. https://arxiv.org/abs/2308.13280 View in Article Google Scholar
[106]	Schmude J., Roy S., Trojak W., et al. (2024). Prithvi WxC: Foundation model for weather and climate. arXiv. https://arxiv.org/abs/2409.13598 View in Article Google Scholar
[107]	Zhao X., Zhou Z., Zhang W., et al. (2025). WeatherGFM: learning a weather generalist foundation model via In-context learning. Int. Conf. Learn. Represent. 2025:80665−80685. View in Article Google Scholar
[108]	Klocke D., Frauen C., Engels J. F., et al. (2025). Computing the full earth system at 1 km resolution. arXiv. https://arxiv.org/abs/2511.02021 View in Article Google Scholar
[109]	Wu H., Gao Y., Gou R., et al. (2025). Advanced long-term earth system forecasting by learning the small-scale nature. arXiv. https://arxiv.org/abs/2505.19432 View in Article Google Scholar
[110]	Valcke S., Balaji V., Craig A., et al. (2012). Coupling technologies for Earth System Modelling. Geosci. Model Dev. 5:1589−1596. DOI:10.5194/gmd-5-1589-2012 View in Article CrossRef Google Scholar
[111]	Wilkinson M. D., Dumontier M., Aalbersberg Ij. J., et al. (2016). The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3:160018. DOI:10.1038/sdata.2016.18 View in Article CrossRef Google Scholar
[112]	Liu Z. and Yao T. (2024). Strategizing Earth science data development. Sci. Data 11:693. DOI:10.1038/s41597-024-03531-6 View in Article CrossRef Google Scholar
[113]	Rödder S., Heymann M. and Stevens B. (2020). Historical, philosophical, and sociological perspectives on Earth system modeling. J. Adv. Model. Earth Syst. 12:e2020MS002139. DOI:10.1029/2020MS002139 View in Article CrossRef Google Scholar
[114]	Beucler T. (2021). Enforcing analytic constraints in neural networks emulating physical systems. Phys. Rev. Lett. 126:098302. DOI:10.1103/PhysRevLett.126.098302 View in Article CrossRef Google Scholar
[115]	Pegion K., Kirtman B. P., Becker E., et al. (2019). The subseasonal experiment (SubX): A multimodel subseasonal prediction experiment. B. Am. Meteorol. Soc. 100:2043−2060. DOI:10.1175/BAMS-D-18-0270.1 View in Article CrossRef Google Scholar
[116]	Raissi M., Perdikaris P. and Karniadakis G. E. (2019). Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378:686−707. DOI:10.1016/j.jcp.2018.10.045 View in Article CrossRef Google Scholar
[117]	Hansen D., Maddix D.C., Alizadeh S., et al. (2024). Learning physical models that can respect conservation laws. Physica D 457:133952. DOI:10.1016/j.physd.2023.133952 View in Article CrossRef Google Scholar
[118]	Lee J., Gleckler P.J., Ahn M.S., et al. (2024). Systematic and objective evaluation of Earth system models. Geosci. Model Dev. 17:3919−3948. DOI:10.5194/gmd-17-3919-2024 View in Article CrossRef Google Scholar
[119]	Eyring V., Gleckler P. J., Heinze C., et al. (2016). Towards improved and more routine Earth system model evaluation in CMIP. Earth Syst. Dyn. 7:813−830. DOI:10.5194/esd-7-813-2016 View in Article CrossRef Google Scholar
[120]	Runge J., Gerhardus A., Varando G., et al. (2023). Causal inference for time series. Nat. Rev. Earth Environ. 4:487−505. DOI:10.1038/s43017-023-00431-y View in Article CrossRef Google Scholar
[121]	Runge J., Bathiany S., Bollt E., et al. (2019). Inferring causation from time series in Earth system sciences. Nat. Commun. 10:2553. DOI:10.1038/s41467-019-10105-3 View in Article CrossRef Google Scholar
[122]	Govett M., Bah B., Bauer, P., et al. (2024). Exascale computing and data handling: Challenges and opportunities for weather and climate prediction. Bull. Am. Meteorol. Soc. 105:E2385−E2404. DOI:10.1175/BAMS-D-23-0220.1 View in Article CrossRef Google Scholar
[123]	Luccioni A. S., Viguier S. and Ligozat A.L. (2023). Estimating the carbon footprint of BLOOM, a 176B parameter language model. J. Mach. Learn. Res. 24:1−15. View in Article Google Scholar
[124]	Rozet F. and Louppe G. (2023). Score-based data assimilation. Oh A., Naumann T., Globerson A., et al. (eds). Advances in Neural Information Processing Systems 36 (Curran Associates), pp: 40521–40541. DOI: 10.52202/075280-1763 View in Article Google Scholar
[125]	Tom G., Schmid S. P., Baird S. G., et al. (2024). Self-driving laboratories for chemistry and materials science. Chem. Rev. 124:9633−9732. DOI:10.1021/acs.chemrev.4c00055 View in Article CrossRef Google Scholar
[126]	Rußwurm M., Wang S., Kellenberger B., et al. (2024). Meta-learning to address diverse earth observation problems across resolutions. Commun. Earth Environ. 5:37. DOI:10.1038/s43247-023-01146-0 View in Article CrossRef Google Scholar
[127]	Batzner S., Musaelian A., Sun L., et al. (2022). E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13:2453. DOI:10.1038/s41467-022-29939-5 View in Article CrossRef Google Scholar
[128]	Shan S., Wang P., Chen S., et al. (2024). PiRD: Physics-informed residual diffusion for flow field reconstruction. arXiv. https://arxiv.org/abs/2404.08412v2 View in Article Google Scholar
[129]	Raskin R. G. and Pan M. J. (2005). Knowledge representation in the semantic web for Earth and environmental terminology (SWEET). Comput. Geosci. 31:1119−1125. DOI:10.1016/j.cageo.2004.12.004 View in Article CrossRef Google Scholar
[130]	Richard S.M. and CGI Interoperability Working Group (2007). GeoSciML – A GML application for geoscience information interchange. Soller D.R. (ed). Digital Mapping Techniques’06—Workshop Proceedings (U.S. Geological Survey), pp: 47–59. https://pubs.usgs.gov/of/2007/1285/pdf/Richard.pdf View in Article Google Scholar
[131]	Wang C., Ma X., Chen J., et al. (2018). Information extraction and knowledge graph construction from geoscience literature. Comput. Geosci. 112:112−120. DOI:10.1016/j.cageo.2017.12.007 View in Article CrossRef Google Scholar
[132]	Zhang X., Huang Y., Zhang C., et al. (2022). Geoscience knowledge graph (GeoKG): development, construction and challenges. Trans. GIS 26:2480−2494. DOI:10.1111/tgis.12985 View in Article CrossRef Google Scholar
[133]	Zhou C., Wang H., Wang C., et al. (2021). Geoscience knowledge graph in the big data era. Sci. China Earth Sci. 64:1105−1114. DOI:10.1007/s11430-020-9750-4 View in Article CrossRef Google Scholar
[134]	Zhu Y., Sun K., Wang S., et al. (2023). An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships. Sci. China Earth Sci. 66:2563−2578. DOI:10.1007/s11430-022-1169-9 View in Article CrossRef Google Scholar
[135]	Ho J., Jain A. and Abbeel P. (2020). Denoising diffusion probabilistic models. arXiv. https://arxiv.org/abs/2006.11239v2 View in Article Google Scholar
[136]	Schölkopf B., Locatello F., Bauer S., et al. (2021). Towards causal representation learning. arXiv. https://arxiv.org/abs/2102.11107 View in Article Google Scholar
[137]	Runge J., Nowack P., Kretschmer M., et al. (2019). Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5:eaau4996. DOI:10.1126/sciadv.aau4996 View in Article CrossRef Google Scholar
[138]	Bareinboim E. and Pearl J. (2016). Causal inference and the data-fusion problem. Proc. Natl. Acad. Sci. 113:7345−7352. DOI:10.1073/pnas.1510507113 View in Article CrossRef Google Scholar
[139]	Wei J., Yang Y., Zhang X., et al. (2025). From AI for science to agentic science: a survey on autonomous scientific discovery. arXiv. https://arxiv.org/abs/2508.14111 View in Article Google Scholar
[140]	Bell A., Aides A., Helmy A., et al. (2026). Earth AI: Unlocking geospatial insights with foundation models and cross-modal reasoning. arXiv. https://arxiv.org/abs/2510.18318 View in Article Google Scholar
[141]	Feng P., Lv Z., Ye J., et al. (2026). Earth-agent: unlocking the full landscape of Earth observation with agents. Int. Conf. Learn. Represent. 2026:55701−55764. View in Article Google Scholar
[142]	Zhao S., Liu F., Zhang X., et al. (2026). OpenEarth-agent: from tool calling to tool creation for open-environment earth observation. arXiv. https://arxiv.org/abs/2603.22148 View in Article Google Scholar
[143]	Coley C. W., Thomas D. A., Lummiss J. A. M., et al. (2019). A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365:eaax1566. DOI:10.1126/science.aax1566 View in Article CrossRef Google Scholar
[144]	Shields B. J., Stevens J., Li J., et al. (2021). Bayesian reaction optimization as a tool for chemical synthesis. Nature 590:89−96. DOI:10.1038/s41586-021-03213-y View in Article CrossRef Google Scholar
[145]	King R. D., Rowland J., Oliver S. G., et al. (2009). The automation of science. Science 324:85−89. DOI:10.1126/science.1165620 View in Article CrossRef Google Scholar

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Su F., Cao Y., Li X., et al. (2026). Foundation models in Earth system science: Advances, challenges, and future paths. The Innovation Reviews 1:100014. https://doi.org/10.59717/j.tirv.2026.100014

Su F., Cao Y., Li X., et al. (2026). Foundation models in Earth system science: Advances, challenges, and future paths. The Innovation Reviews 1:100014. https://doi.org/10.59717/j.tirv.2026.100014

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