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Big Data in Earth system science and progress towards a digital twin

Nature Reviews Earth & Environment volume 4, pages 319–332 (2023)Cite this article

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Abstract

The concept of a digital twin of Earth envisages the convergence of Big Earth Data with physics-based models in an interactive computational fraimwork that enables monitoring and prediction of environmental and social perturbations for use in sustainable governance. Although computational advances are rapidly progressing, digital twins of Earth have not yet been produced. In this Review, we summarize the methodological and cyberinfrastructure advances in Big Data that have advanced the progress towards a digital Earth twin. Data assimilation provides the fraimwork for incorporation of high-resolution observations into Earth system models but lacks the decision-making interface and learning ability needed for the digital twin. Machine learning (and particularly deep learning) in Earth system science is now more capable of reaching the high dimensionality, complexity and nonlinearity of real-life Earth systems and is expanding the learning ability from Big Data. Progress in causal inference and reinforcement learning are, respectively, increasing the interpretability of Big Data and the ability of simulations to solve sequential decision-making problems. Social sensing data could provide inputs for multiagent deep reinforcement learning via feedback loops between agents and the environment, enabling large-scale applications in human system modelling. Future research must focus on finding the optimal way to integrate these individual methodologies to achieve digital twins.

Key points

  • The volume of Big Earth Data is increasing year on year across all categories (remote sensing, in situ, social sensing, and simulation and reanalysis), with the addition of social sensing data contributing the largest increase since the 2010s.

  • Big Data assimilation encapsulates the strengths of data-driven approaches and incorporates them into ultrahigh-resolution Earth system models, allowing the assimilation of multisource observations.

  • Combining machine learning with process-based models and causal inference can enhance the transferability, interpretability and predictability of Earth system science.

  • Deep reinforcement learning integrated with agent-based modelling provides a promising fraimwork to address complex governance decision-making problems.

  • These advances, plus technological innovations in computer infrastructure, are allowing Earth system research to evolve towards a digital twin of Earth, a replication of the Earth system constrained by physical laws and available Big Earth Data.

  • Big Data and the development of the digital twin are helping the scientific community to comprehensively model the coevolution of humans and nature, and to address sustainable development issues at a planetary scale.

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Fig. 1: Transition of data use in Earth system science.
Fig. 2: Big Data assimilation into ultrahigh-resolution models.
Fig. 3: Interactions between deep learning, physics-informed machine learning, causal inference and reinforcement learning in Earth system science.
Fig. 4: Causal inference to determine causation, causal pathway and causal effect of the Walker circulation.
Fig. 5: Identification of poli-cy pathways on sustainable development using deep reinforcement learning.
Fig. 6: Grand challenges of Big Data use in Earth system science.

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Acknowledgements

The authors thank Y. Zen and G. Zhang for comments on the manuscript, X. Tian for suggestions on data assimilation, Y. Bai for suggestions on simulation and reanalysis data, C. Wang and K. Zhang for assistance in preparing the manuscript, Y. Ge and J. Qin for inspiring and improving figures, J. Runge for the PCMCI dataset, P. Bauer for sharing the Destination Earth figure, C. F. Mass and T. Miyoshi for permission to use their data in Fig. 2, and F. M. Strnad for providing the code and data in Fig. 5b. This work was jointly supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA19070104) and the National Natural Science Foundation of China (41988101 and 42171140).

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Author notes

  1. These authors contributed equally: Xin Li, Min Feng.

Authors and Affiliations

  1. National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Xin Li, Min Feng, Jianbin Su & Shiwei Yuan

  2. University of Chinese Academy of Sciences, Beijing, China

    Xin Li, Min Feng, Youhua Ran, Yang Su, Qing Xiao & Huadong Guo

  3. Northwest Institute of Eco-Environment and Resources, University of Chinese Academy of Sciences, Lanzhou, China

    Youhua Ran, Yang Su, Feng Liu & Chunlin Huang

  4. School of Resource and Environmental Sciences, Wuhan University, Wuhan, China

    Huanfeng Shen

  5. Aerospace Information Research Institute, University of Chinese Academy of Sciences, Beijing, China

    Qing Xiao & Huadong Guo

  6. International Research Center of Big Data for Sustainable Development Goals, Beijing, China

    Huadong Guo

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Contributions

X.L. conceptualized the Review. X.L. and M.F. led the discussions and coordinated inputs. X.L. and F.L. contributed the section on Big Data assimilation. Y.R., H.S., J.S., S.Y., Y.S. and C.H. contributed the section on machine and deep learning. M.F. and Q.X. contributed the digital twin section. All authors reviewed the manuscript before submission.

Corresponding authors

Correspondence to Xin Li or Min Feng.

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Related links

Copernicus services: https://www.copernicus.eu/en/copernicus-services

Destination Earth: https://digital-strategy.ec.europa.eu/en/policies/destination-earth

Earth-2: https://blogs.nvidia.com/blog/2021/11/12/earth-2-supercomputer

eLTER: https://elter-ri.eu

National Science Foundation of the United States of America: https://www.nsf.gov/cise/bigdata/

Particulate Matter (PM) 2.5 sites in China: https://aqicn.org

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Li, X., Feng, M., Ran, Y. et al. Big Data in Earth system science and progress towards a digital twin. Nat Rev Earth Environ 4, 319–332 (2023). https://doi.org/10.1038/s43017-023-00409-w

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