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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2018-327
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2018-327
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 17 Jan 2019

Methods for assessment of models | 17 Jan 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Efficient surrogate modeling methods for large-scale Earth system models based on machine learning techniques

Dan Lu1 and Daniel Ricciuto2 Dan Lu and Daniel Ricciuto
  • 1Computational Sciences and Engineering Division, Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
  • 2Environmental Sciences Division, Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA

Abstract. Improving predictive understanding of Earth system variability and change requires data-model integration. Efficient data-model integration for complex models requires surrogate modeling to reduce model evaluation time. However, building a surrogate of a large-scale Earth system model (ESM) with many output variables is computationally intensive because it involves a large number of expensive ESM simulations. In this effort, we propose an efficient surrogate method capable of using a few ESM runs to build an accurate and fast-to-evaluate surrogate system of model outputs over large spatial and temporal domains. We first use singular value decomposition to reduce the output dimensions, and then use Bayesian optimization techniques to generate an accurate neural network surrogate model based on limited ESM simulation samples. Our machine learning based surrogate methods can build and evaluate a large surrogate system of many variables quickly. Thus, whenever the quantities of interest change such as a different objective function, a new site, and a longer simulation time, we can simply extract the information of interest from the surrogate system without rebuilding new surrogates, which significantly saves computational efforts. We apply the proposed method to a regional ecosystem model to approximate the relationship between 8 model parameters and 42 660 carbon flux outputs. Results indicate that using only 20 model simulations, we can build an accurate surrogate system of the 42 660 variables, where the consistency between the surrogate prediction and actual model simulation is 0.93 and the mean squared error is 0.02. This highly-accurate and fast-to-evaluate surrogate system will greatly enhance the computational efficiency in data-model integration to improve predictions and advance our understanding of the Earth system.

Dan Lu and Daniel Ricciuto
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Status: open (until 14 Mar 2019)
Status: open (until 14 Mar 2019)
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Dan Lu and Daniel Ricciuto
Dan Lu and Daniel Ricciuto
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Short summary
This work uses machine learning techniques to advance the predictive understanding of large-scale Earth systems.
This work uses machine learning techniques to advance the predictive understanding of...
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