Deep learning and process understanding for data-driven Earth system science
Open accessAbstract
Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.
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Methods based on various forms of artificial intelligence are already today being applied in a number of research fields related to climate change and environmental monitoring.
“Methods based on various forms of artificial intelligence are already today being applied in a number of research fields related to climate change and environmental monitoring.”
From Artificial intelligence, systemic risks, and sustainability (2021) · cites this paper as [26]
Hybrid models leveraging both deep learning and bio-physical models tend to be less data-intensive, avoid incoherent predictions, and are generally more interpretable than purely data-driven models.
“such hybrid models tend to be less data-intensive, avoid incoherent predictions, and are generally more interpretable than purely data-driven models.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [48]
The hybrid models paradigm is one of the most exciting avenues in modern ML and promises real collaboration between domains of application and ML, especially when coupled with algorithmic designs that allow interpretation and understanding of visual cues.
“This “hybrid models” paradigm48,50 is one of the most exciting avenues in modern ML and promises real collaboration between domains of application and ML, especially when coupled with algorithmic designs that allow interpretation and understanding of the visual cues that are being used136.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [48]
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Machine learning and artificial intelligence to aid climate change research and preparedness
Huntingford, C., Jeffers, E. S., Bonsall, M. B., Christensen, H. M., Lees, T., & Yang, H. (2019) · Environmental Research Letters · doi:10.1088/1748-9326/ab4e55 · Cited by 469 · see citing works
Neural general circulation models for weather and climate
Kochkov, D., Yuval, J., Langmore, I., Norgaard, P., Smith, J., Mooers, G., Klöwer, M., Lottes, J., Rasp, S., Düben, P., et al. (2024) · Nature · doi:10.1038/s41586-024-07744-y · Cited by 454 · see citing works
“…longer timescales 17,18 , these models have not demonstrated the ability to outperform existing GCMs. Hybrid models that combine GCMs with machine learning are appealing because they build on the interpretability, extensibility and successful track record of traditional atmospheric models 19,20 .”
BackgroundHeat Waves: Physical Understanding and Scientific Challenges
Barriopedro, D., García‐Herrera, R., Ordóñez, C., Miralles, D. G., & Salcedo‐Sanz, S. (2023) · Reviews of Geophysics · doi:10.1029/2022rg000780 · Cited by 404 · see citing works
Towards neural Earth system modelling by integrating artificial intelligence in Earth system science
Irrgang, C., Boers, N., Sonnewald, M., Barnes, E. A., Kadow, C., Staneva, J., & Saynisch-Wagner, J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00374-3 · Cited by 308 · see citing works
A review of Earth Artificial Intelligence
Sun, Z., Sandoval, L., Crystal-Ornelas, R., Mousavi, S. M., Wang, J., Lin, C., Cristea, N., Tong, D., Carande, W. H., Ma, X., et al. (2022) · Computers & Geosciences · doi:10.1016/j.cageo.2022.105034 · Cited by 271 · see citing works
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID
Long, Y., Xia, G.-S., Li, S., Yang, W., Yang, M. Y., Zhu, X. X., Zhang, L., & Li, D. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3070368 · Cited by 250 · see citing works
Analog forecasting of extreme-causing weather patterns using deep learning
Chattopadhyay, A., Nabizadeh, E., & Hassanzadeh, P. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2019ms001958 · Cited by 224 · see citing works
Battery Safety: Machine Learning-Based Prognostics
Zhao, J., Feng, X., Pang, Q., Fowler, M., Lian, Y., Ouyang, M., & Burke, A. F. (2024) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2023.101142 · Cited by 216 · see citing works
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?
Schlund, M., Lauer, A., Gentine, P., Sherwood, S. C., & Eyring, V. (2020) · Earth System Dynamics · doi:10.5194/esd-11-1233-2020 · Cited by 178 · see citing works
Replication across space and time must be weak in the social and environmental sciences
Goodchild, M. F., & Li, W. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2015759118 · Cited by 157 · see citing works
Li, W., & Hsu, C.-Y. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11070385 · Cited by 147 · see citing works
Towards a predictor for CO2 plume migration using deep neural networks
Wen, G., Tang, M., & Benson, S. M. (2021) · International Journal of Greenhouse Gas Control · doi:10.1016/j.ijggc.2020.103223 · Cited by 124 · see citing works
Materia, S., García, L. P., van Straaten, C., O, S., Mamalakis, A., Cavicchia, L., Coumou, D., de Luca, P., Kretschmer, M., & Donat, M. (2024) · WIREs Climate Change · doi:10.1002/wcc.914 · Cited by 130 · see citing works
“This era of “ big data ” has, in turn, fueled the application of artificial intelligence (AI) in many domains of Earth science (Boukabara et al., 2021; Huntingford et al., 2019; Irrgang et al., 2021; Reichstein et al., 2019; Sun et al., 2022).”
Machine Learning Methods for Weather Forecasting: A Survey
Zhang, H., Liu, Y., Zhang, C., & Li, N. (2025) · Atmosphere · doi:10.3390/atmos16010082 · Cited by 113 · see citing works
Digitalization and the Anthropocene
Creutzig, F., Acemoglu, D., Bai, X., Edwards, P. N., Hintz, M. J., Kaack, L. H., Kilkis, S., Kunkel, S., Luers, A., Milojevic-Dupont, N., et al. (2022) · Annual Review of Environment and Resources · doi:10.1146/annurev-environ-120920-100056 · Cited by 112 · see citing works
Berardi, D., Brzostek, E., Blanc‐Betes, E., Davison, B., DeLucia, E. H., Hartman, M. D., Kent, J., Parton, W. J., Saha, D., & Hudiburg, T. W. (2020) · GCB Bioenergy · doi:10.1111/gcbb.12730 · Cited by 102 · see citing works
Machine learning for the physics of climate
Bracco, A., Brajard, J., Dijkstra, H. A., Hassanzadeh, P., Lessig, C., & Monteleoni, C. (2024) · Nature Reviews Physics · doi:10.1038/s42254-024-00776-3 · Cited by 79 · see citing works
Scientific novelty beyond the experiment
Hallsworth, J. E., Udaondo, Z., Pedrós‐Alió, C., Höfer, J., Benison, K. C., Lloyd, K. G., Cordero, R. J. B., de Campos, C. B. L., Yakimov, M. M., & Amils, R. (2023) · Microbial Biotechnology · doi:10.1111/1751-7915.14222 · Cited by 62 · see citing works
Dueben, P. D., Schultz, M. G., Chantry, M., Gagne, D. J., Hall, D. M., & McGovern, A. (2022) · Artificial Intelligence for the Earth Systems · doi:10.1175/aies-d-21-0002.1 · Cited by 64 · see citing works
Algal biomass mapping of eutrophic lakes using a machine learning approach with MODIS images
Lai, L., Zhang, Y., Cao, Z., Liu, Z., & Yang, Q. (2023) · Science of The Total Environment · doi:10.1016/j.scitotenv.2023.163357 · Cited by 43 · see citing works
Random sketch learning for deep neural networks in edge computing
Li, B., Chen, P., Liu, H., Guo, W., Cao, X., Du, J., Zhao, C., & Zhang, J. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00039-6 · Cited by 40 · see citing works
Shaw, T. A., & Stevens, B. (2025) · Nature · doi:10.1038/s41586-025-08680-1 · Cited by 37 · see citing works
Self-citationTraceable machine learning real-time quality control based on patient data
Zhou, R., Wang, W., Padoan, A., Wang, Z., Feng, X., Han, Z., Chen, C., Liang, Y., Wang, T., Cui, W., et al. (2022) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2022-0548 · Cited by 33 · see citing works
Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation
Xiong, Z., Wang, Y., Zhang, F., Stewart, A. J., Hanna, J., Borth, D., Papoutsis, I., Saux, B. L., Camps-Valls, G., & Zhu, X. X. (2024) · arXiv · doi:10.48550/arxiv.2403.15356 · Cited by 26 · see citing works
Self-citationKang, Y., Bassiouni, M., Gaber, M., Lu, X., & Keenan, T. F. (2025) · Earth System Science Data · doi:10.5194/essd-17-3009-2025 · Cited by 22 · see citing works
A Bayesian Deep Learning Approach to Near‐Term Climate Prediction
Luo, X., Nadiga, B. T., Park, J. H., Ren, Y., Xu, W., & Yoo, S. (2022) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2022ms003058 · Cited by 18 · see citing works
Chishtie, F. A., Brunet, D., White, R. H., Michelson, D., Jiang, J., Lucas, V., Ruboonga, E., Imaash, S., Westland, M., Chui, T., et al. (2024) · arXiv · doi:10.48550/arxiv.2411.13496
“Future research will also aim to explore the integration of DI-GNN with other machine learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers, to enhance predictive performance for diverse spatiotemporal datasets [15, 13].”
- Authors
- Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat
- Affiliations
- Max Planck Institute for Biogeochemistry (DE)
- Friedrich Schiller University Jena (DE)
- Lawrence Berkeley National Laboratory (US)
- Corresponding author
- Reichstein, Markus
- Venue
- Nature
- Published
- 2019-02
- Volume
- 566
- Issue
- 7743
- Pages
- 195-204
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2913323966
- Cited by
- 5,673
- References
- 87
- Citations by year
- 2026: 1399 · 2025: 960 · 2024: 824 · 2023: 726 · 2022: 690 · 2021: 640 · 2020: 331 · 2019: 102 · 2018: 1
- FWCI
- 30.68
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Keywords
- deep learning, Earth system science, spatio-temporal context, process understanding, seasonal forecasting, multiscale modeling, hybrid physical-machine learning models, geospatial data, data-driven modeling
- MeSH terms
- Facial Recognition, Deep Learning, Big Data, Computer Simulation, Female, Forecasting, Humans, Pattern Recognition, Automated, Regression, Psychology, Seasons, Time Factors, Translating, Weather, Reproducibility of Results, Knowledge, Uncertainty, Earth Sciences, Spatio-Temporal Analysis, Geographic Mapping
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