Articles | Volume 14, issue 12
https://doi.org/10.5194/gmd-14-7659-2021
https://doi.org/10.5194/gmd-14-7659-2021
Model description paper
 | 
20 Dec 2021
Model description paper |  | 20 Dec 2021

Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator

Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, and Philip Stier

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Cited articles

Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Contr., 19, 716–723, https://doi.org/10.1109/tac.1974.1100705, 1974. 
Beucler, T., Pritchard, M., Rasp, S., Ott, J., Baldi, P., and Gentine, P.: Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems, Phys. Rev. Lett., 126, 098302, https://doi.org/10.1103/physrevlett.126.098302, 2021. 
Brajard, J., Carrassi, A., Bocquet, M., and Bertino, L.: Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model, J. Comput. Sci.-Neth., 44, 101171, https://doi.org/10.1016/j.jocs.2020.101171, 2020. 
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Short summary
The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide variety of Earth science datasets and tools for constraining (or tuning) models of any complexity. Three distinct use cases are presented that demonstrate the utility of ESEm and provide some insight into the use of machine learning for emulation in these different settings. The open-source Python package is freely available so that it might become a valuable tool for the community.