Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 8
Articles | Volume 16, issue 8
https://doi.org/10.5194/gmd-16-2149-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-16-2149-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 8
Methods for assessment of models
 | Highlight paper
 | 
20 Apr 2023
Methods for assessment of models | Highlight paper |  | 20 Apr 2023

Causal deep learning models for studying the Earth system

Tobias Tesch, Stefan Kollet, and Jochen Garcke

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Cited articles

Adler, B., Kalthoff, N., and Gantner, L.: Initiation of deep convection caused by land-surface inhomogeneities in West Africa: a modelled case study, Meteorol. Atmos. Phys., 112, 15–27, https://doi.org/10.1007/s00703-011-0131-2, 2011. a
Barnes, E. A., Samarasinghe, S. M., Ebert-Uphoff, I., and Furtado, J. C.: Tropospheric and Stratospheric Causal Pathways Between the MJO and NAO, J. Geophys. Res.-Atmos., 124, 9356–9371, https://doi.org/10.1029/2019jd031024, 2019. a
Baur, F., Keil, C., and Craig, G. C.: Soil moisture–precipitation coupling over Central Europe: Interactions between surface anomalies at different scales and the dynamical implication, Q. J. Roy. Meteor. Soc., 144, 2863–2875, https://doi.org/10.1002/qj.3415, 2018. a
Dumoulin, V. and Visin, F.: A guide to convolution arithmetic for deep learning, https://arxiv.org/abs/1603.07285 (last access: 16 April 2023), 2016. a
Ebert-Uphoff, I. and Deng, Y.: Causal discovery in the geosciences – Using synthetic data to learn how to interpret results, Comput. Geosci., 99, 50–60, https://doi.org/10.1016/j.cageo.2016.10.008, 2017. a
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Many papers are currently being published applying deep learning to geoscientific applications. However, most of them only offer proof of concept results on highly idealised scenarios. This paper combines deep learning approaches with the structural causal models popularized by the work of Judea Pearl, and it applies this methodology to a real problem, analyzing soil moisture-precipitation coupling in climate reanalysis data. In contrast to many papers in this field, this promises actual insight in the scientific application of the work.
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A recent statistical approach for studying relations in the Earth system is to train deep learning (DL) models to predict Earth system variables given one or several others and use interpretable DL to analyze the relations learned by the models. Here, we propose to combine the approach with a theorem from causality research to ensure that the deep learning model learns causal rather than spurious relations. As an example, we apply the method to study soil-moisture–precipitation coupling.
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