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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-152
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-152
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: methods for assessment of models 03 Aug 2020

Submitted as: methods for assessment of models | 03 Aug 2020

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This preprint is currently under review for the journal GMD.

Testing the Reliability of Interpretable Neural Networks in Geoscience Using the Madden-Julian Oscillation

Benjamin A. Toms1, Karthik Kashinath2, Prabhat2, and Da Yang2,3 Benjamin A. Toms et al.
  • 1Department of Atmospheric Science, Colorado State University, Fort Collins, CO
  • 2Lawrence Berkeley National Laboratory, Berkeley, California
  • 3University of California, Davis, Davis, California

Abstract. We test the reliability of two neural network interpretation techniques, backward optimization and layerwise relevance propagation, within geoscientific applications by applying them to a commonly studied geophysical phenomenon, the Madden-Julian Oscillation. The Madden-Julian Oscillation is a multi-scale pattern within the tropical atmosphere that has been extensively studied over the past decades, which makes it an ideal test case to ensure the interpretability methods can recover the current state of knowledge regarding its spatial structure. The neural networks can, indeed, reproduce the current state of knowledge and can also provide new insights into the seasonality of the Madden-Julian Oscillation and its relationships with atmospheric state variables.

The neural network identifies the phase of the Madden-Julian Oscillation twice as accurately as linear regression, which means that nonlinearities used by the neural network are important to the structure of the Madden-Julian Oscillation. Interpretations of the neural network show that it accurately captures the spatial structures of the Madden-Julian Oscillation, suggest that the nonlinearities of the Madden-Julian Oscillation are manifested through the uniqueness of each event, and offer physically meaningful insights into its relationship with atmospheric state variables. We also use the interpretations to identify the seasonality of the MJO, and find that the conventionally defined extended seasons should be shifted later by one month. More generally, this study suggests that neural networks can be reliably interpreted for geoscientific applications and may thereby serve as a dependable method for testing geoscientific hypotheses.

Benjamin A. Toms et al.

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Benjamin A. Toms et al.

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Data for GMD 2020-152 Benjamin Toms https://doi.org/10.5281/zenodo.3968896

Model code and software

Data for GMD 2020-152 Benjamin Toms https://doi.org/10.5281/zenodo.3968896

Benjamin A. Toms et al.

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Latest update: 28 Sep 2020
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Short summary
We test whether a type of machine learning called neural networks can be used trustfully within the geosciences. We do so by challenging the networks to understand the spatial patterns of a commonly studied geoscientific phenomenon. The neural networks can correctly identify the spatial patterns, which lends confidence that similar networks can be used for more uncertain problems. The results of this study may give geoscientists confidence when using neural networks in their research.
We test whether a type of machine learning called neural networks can be used trustfully within...
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