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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-2019-319
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-319
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 07 Jan 2020

Submitted as: development and technical paper | 07 Jan 2020

Review status
A revised version of this preprint is currently under review for the journal GMD.

Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz96 case study (v1.0)

Stephan Rasp Stephan Rasp
  • Technical University of Munich, Germany

Abstract. Over the last couple of years, machine learning parameterizations have emerged as a potential way to improve the representation of sub-grid processes in Earth System Models (ESMs). So far, all studies were based on the same three-step approach: first a training dataset was created from a high-resolution simulation, then a machine learning algorithms was fitted to this dataset, before the trained algorithms was implemented in the ESM. The resulting online simulations were frequently plagued by instabilities and biases. Here, coupled online learning is proposed as a way to combat these issues. Coupled learning can be seen as a second training stage in which the pretrained machine learning parameterization, specifically a neural network, is run in parallel with a high-resolution simulation. The high-resolution simulation is kept in sync with the neural network-driven ESM through constant nudging. This enables the neural network to learn from the tendencies that the high-resolution simulation would produce if it experienced the states the neural network creates. The concept is illustrated using the Lorenz 96 model, where coupled learning is able to recover the "true" parameterizations. Further, detailed algorithms for the implementation of coupled learning in 3D cloud-resolving models and the super parameterization framework are presented. Finally, outstanding challenges and issues not resolved by this approach are discussed.

Stephan Rasp

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Status: final response (author comments only)
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Stephan Rasp

Stephan Rasp

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Latest update: 03 Apr 2020
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Short summary
Sub-grid parameterizations are largely responsible for uncertainties in climate models. Recently, several studies tried to improve the representation of sub-grid processes by learning parameterization directly from high-resolution modeling data. In this paper, the current state-of-the-art of this research direction is summarized and an algorithm is proposed to combat major problems with existing approaches, namely instabilities and biases.
Sub-grid parameterizations are largely responsible for uncertainties in climate models....
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