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Process‐Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement

Abstract : • We apply uncertainty quantification to single-column model/large-eddy simulation comparison to calibrate free parameters • We revisit model development strategy with an emphasi The development of parameterizations is a major task in the development of weather and climate models. Model improvement has been slow in the past decades, due to the difficulty of encompassing key physical processes into parameterizations, but also of calibrating or “tuning” the many free parameters involved in their formulation. Machine learning techniques have been recently used for speeding up the development process. While some studies propose to replace parameterizations by data‐driven neural networks, we rather advocate that keeping physical parameterizations is key for the reliability of climate projections. In this paper we propose to harness machine learning to improve physical parameterizations. In particular, we use Gaussian process‐based methods from uncertainty quantification to calibrate the model free parameters at a process level. To achieve this, we focus on the comparison of single‐column simulations and reference large‐eddy simulations over multiple boundary‐layer cases. Our method returns all values of the free parameters consistent with the references and any structural uncertainties, allowing a reduced domain of acceptable values to be considered when tuning the three‐dimensional (3D) global model. This tool allows to disentangle deficiencies due to poor parameter calibration from intrinsic limits rooted in the parameterization formulations. This paper describes the tool and the philosophy of tuning in single‐column mode. Part 2 shows how the results from our process‐based tuning can help in the 3D global model tuning. s on processes for model calibration • The proposed tuning tool allows to formalize the complementary use of multicases with various metrics
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Contributor : Gestionnaire Hal-Su <>
Submitted on : Monday, April 19, 2021 - 2:48:07 PM
Last modification on : Tuesday, May 4, 2021 - 3:24:14 PM


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Fleur Couvreux, Frédéric Hourdin, Daniel Williamson, Romain Roehrig, Victoria Volodina, et al.. Process‐Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement. Journal of Advances in Modeling Earth Systems, American Geophysical Union, 2021, 13 (3), ⟨10.1029/2020ms002217⟩. ⟨hal-03202039⟩



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