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Cited by 121Meteorological Phenomena and SimulationsClimate variability and modelsOceanographic and Atmospheric Processes

The DOI matches its Crossref record; 110 of the 113 references it cites were found in a registry or an index.

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

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Couvreux, Fleur, Hourdin, Frédéric, Williamson, Daniel, Roehrig, Romain, Volodina, Victoria, Villefranque, Najda, Rio, Catherine, Audouin, Olivier, Salter, James, Bazile, Eric, Brient, Florent, Favot, Florence, Honnert, Rachel, Lefebvre, Marie‐Pierre, Madeleine, Jean‐Baptiste, Rodier, Quentin, Xu, Wenzhe·2021·Journal of Advances in Modeling Earth Systems
DOI 10.1029/2020ms002217

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Abstract

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.

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  1. 10.1175/JCLI-D-15-0551 · DOI
  2. Flato G. Marotzke G. Abiodun B. Braconnot P. Chou S. Collins W. et al. (2013).Evaluation of climate models. InT.F.Stocker D.Qin G.-K.Plattner M.Tignor S.K.Allen J.Boschung et al. (Eds.) Climate change 2013: The physical science basis. Contribution of working group I to the fifth Assessment Report of the Intergovernmental panel on climate change.Cambridge United Kingdom & NY:Cambridge University Press.
  3. 10.1175/1520-0469(2002)059⟨3552:APBMFB⟩2.0.CO;2 · DOI
  4. 10.1175/1520-0469(2002)059⟨1105:POTDCB⟩2.0.CO;2 · DOI
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  6. 10.1002/2018MS001409 · DOI
  7. 10.2307/2684253 · DOI · Paper page
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  9. 10.1175/1520-0469(2003)60<1201:ALESIS>2.0.CO;2 · DOI
  10. Volodina V., 2020, Uncertainty quantification for complex computer models with nonstationary output. Bayesian optimal design for iterative refocussing (Unpublished doctoral dissertation)

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Metadata

Authors
Couvreux, F., Hourdin, F., Williamson, D., Roehrig, R., Volodina, V., Villefranque, N., Rio, C., Audouin, O., Salter, J., Bazile, E., Brient, F., Favot, F., Honnert, R., Lefebvre, M., Madeleine, J., Rodier, Q., & Xu, W.
Affiliations
  • Centre National de la Recherche Scientifique (FR)
  • University of Exeter (GB)
  • The Alan Turing Institute (GB)
  • CNRM, University of Toulouse Meteo‐France CNRS Toulouse France
Corresponding author
Couvreux, Fleur
ORCID
Venue
Journal of Advances in Modeling Earth Systems
Publisher
American Geophysical Union (AGU)
Published
2021-02-26
Volume
13
Issue
3
ISSN
1942-2466 (ISSN-L)
DOI
10.1029/2020ms002217
OpenAlex
W3049378437
Cited by
121
References
113
Citations by year
2026: 13 · 2025: 23 · 2024: 15 · 2023: 22 · 2022: 24 · 2021: 22
FWCI
5.65
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Advances in Modeling Earth Systems (publishedVersion, cc-by-nc-nd)
  • HAL (submittedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Funders
Agence Nationale de la Recherche (HIGH‐TUNE,ANR‐16‐CE01‐0010)
Topics
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
  • Physical Sciences › Earth and Planetary Sciences › Oceanography › Oceanographic and Atmospheric Processes
Keywords
climate model parameterization, parameter calibration, Gaussian process regression, uncertainty quantification, model calibration, boundary layer, large eddy simulation, structural uncertainty

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