Submitted as: model description paper 27 Aug 2021

Submitted as: model description paper | 27 Aug 2021

Review status: this preprint is currently under review for the journal GMD.

Model calibration using ESEm v1.0.0 – an open, scalable Earth System Emulator

Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, and Philip Stier Duncan Watson-Parris et al.
  • Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford, UK

Abstract. Large computer models are ubiquitous in the earth sciences. These models often have tens or hundreds of tuneable parameters and can take thousands of core-hours to run to completion while generating terabytes of output. It is becoming common practice to develop emulators as fast approximations, or surrogates, of these models in order to explore the relationships between these inputs and outputs, understand uncertainties and generate large ensembles datasets. While the purpose of these surrogates may differ, their development is often very similar. Here we introduce ESEm: an open-source tool providing a general workflow for emulating and validating a wide variety of models and outputs. It includes efficient routines for sampling these emulators for the purpose of uncertainty quantification and model calibration. It is built on well-established, high-performance libraries to ensure robustness, extensibility and scalability. We demonstrate the flexibility of ESEm through three case-studies using ESEm to reduce parametric uncertainty in a general circulation model, explore precipitation sensitivity in a cloud resolving model and scenario uncertainty in the CMIP6 multi-model ensemble.

Duncan Watson-Parris et al.

Status: open (until 22 Oct 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Duncan Watson-Parris et al.

Duncan Watson-Parris et al.


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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 which demonstrate the utility of ESEm and, we hope, 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.