Capturing missing physics in climate model parameterizations using neural differential equations Article Swipe

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Ali J. Ramadhan , John C. Marshall , André M. C. Souza , Gregory LeClaire Wagner , Manvitha Ponnapati , Christopher Rackauckas ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2010.12559

We explore how neural differential equations (NDEs) may be trained on highly resolved fluid-dynamical models of unresolved scales providing an ideal framework for data-driven parameterizations in climate models. NDEs overcome some of the limitations of traditional neural networks (NNs) in fluid dynamical applications in that they can readily incorporate conservation laws and boundary conditions and are stable when integrated over time. We advocate a method that employs a 'residual' approach, in which the NN is used to improve upon an existing parameterization through the representation of residual fluxes which are not captured by the base parameterization. This reduces the amount of training required and providing a method for capturing up-gradient and nonlocal fluxes. As an illustrative example, we consider the parameterization of free convection of the oceanic boundary layer triggered by buoyancy loss at the surface. We demonstrate that a simple parameterization of the process - convective adjustment - can be improved upon by training a NDE against highly resolved explicit models, to capture entrainment fluxes at the base of the well-mixed layer, fluxes that convective adjustment itself cannot represent. The augmented parameterization outperforms existing commonly used parameterizations such as the K-Profile Parameterization (KPP). We showcase that the NDE performs well independent of the time-stepper and that an online training approach using differentiable simulation via the Julia scientific machine learning software stack improves accuracy by an order-of-magnitude. We conclude that NDEs provide an exciting route forward to the development of representations of sub-grid-scale processes for climate science, opening up myriad new opportunities.

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Artificial neural network Computer science Climate model Representation (politics) Residual Boundary (topology) Parametrization (atmospheric modeling) Process (computing) Applied mathematics Algorithm Mathematics Artificial intelligence Physics Geology Mathematical analysis Operating system Oceanography Quantum mechanics Law Radiative transfer Climate change Politics Political science

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2010.12559
PDF: https://arxiv.org/pdf/2010.12559
OA Status: green
Cited By: 17
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2010.12559

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Title: Capturing missing physics in climate model parameterizations using neural differential equations

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Language: en

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Publication year: 2020

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Publication date: 2020-10-23

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Authors: Ali J. Ramadhan, John C. Marshall, André M. C. Souza, Gregory LeClaire Wagner, Manvitha Ponnapati, Christopher Rackauckas

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Landing page: https://arxiv.org/abs/2010.12559

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Concepts: Artificial neural network, Computer science, Climate model, Representation (politics), Residual, Boundary (topology), Parametrization (atmospheric modeling), Process (computing), Applied mathematics, Algorithm, Mathematics, Artificial intelligence, Physics, Geology, Mathematical analysis, Operating system, Oceanography, Quantum mechanics, Law, Radiative transfer, Climate change, Politics, Political science

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Cited by: 17

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abstract_inverted_index.commonly	185
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abstract_inverted_index.exciting	233
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abstract_inverted_index.showcase	195
abstract_inverted_index.software	220
abstract_inverted_index.surface.	135
abstract_inverted_index.training	101, 154, 209
abstract_inverted_index.K-Profile	191
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abstract_inverted_index.parameterization.	95
abstract_inverted_index.parameterizations	24, 187
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cited_by_percentile_year
countries_distinct_count	0
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sustainable_development_goals[0].id	https://metadata.un.org/sdg/13
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citation_normalized_percentile