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Harmonizing models and observations: Data assimilation in Earth system science

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Abstract

Models and observations are two fundamental methodological approaches in Earth system science (ESS). They evolve collaboratively and enhance one another. However, neither of these two approaches is perfect, and they have incompatibilities due to their methodological differences. The emergence of data assimilation (DA) has enabled these two approaches to develop in conjunction and form a harmonic ESS methodology. As a result, DA has shown a fresh vitality and applicability in ESS. This paper reviews the application of DA in the main branches of ESS, traces the coordinated evolution of DA with the methodologies of rationalism and empiricism, analyzes the relationships of DA with estimation theory and cybernetics, summarizes the advances of DA in China, and presents an outlook on the challenges facing the development of a uniform DA for ESS. DA theories and methods will continue to evolve and provide an increasingly mature methodology for enhancing the understanding and prediction of Earth as a system.

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Acknowledgements

This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19070104), the National Natural Science Foundation of China (Grant Nos. 41801270 and 41701046), and the 13th Five-year Informatization Plan of the Chinese Academy of Sciences (Grant No. XXH13505-06).

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  1. National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China

    Xin Li

  2. CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, 100101, China

    Xin Li

  3. Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China

    Feng Liu & Miao Fang

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Li, X., Liu, F. & Fang, M. Harmonizing models and observations: Data assimilation in Earth system science. Sci. China Earth Sci. 63, 1059–1068 (2020). https://doi.org/10.1007/s11430-019-9620-x

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