Process‐Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement
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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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Preconditioning by a multi-wave tuning of a single-column version of the model against explicit high-resolution simulations of cloud scenes may significantly accelerate the tuning process.
“We showed in another study how preconditioning by a multi-wave tuning of a single-column version of the model against explicit high-resolution simulations of cloud scenes may significantly accelerate the tuning process (25, 45).”
From Toward machine-assisted tuning avoiding the underestimation of uncertainty in climate change projections (2023) · cites this paper as [25]
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(2021) · Geoscientific model development · doi:10.5194/gmd-2021-39-rc2
Ryan, E. (2021) · Geoscientific model development · doi:10.5194/gmd-2021-39-ac1
(2021) · Geoscientific model development · doi:10.5194/gmd-2021-205-cec1
Watson‐Parris, D., Williams, A. I. L., Deaconu, L. T., et al. (2021) · Geoscientific model development · doi:10.5194/gmd-2021-267-rc2
Reviewer comment on gmd-2021-267
Watson‐Parris, D., Williams, A. I. L., Deaconu, L. T., et al. (2021) · Geoscientific model development · doi:10.5194/gmd-2021-267-rc1
(2021) · Geoscientific model development · doi:10.5194/gmd-2021-205-rc2
Watson‐Parris, D. (2021) · Geoscientific model development · doi:10.5194/gmd-2021-267-ac1
Ma, P. (2022) · Geoscientific model development · doi:10.5194/gmd-2021-298-ac3
Ma, P. (2022) · Geoscientific model development · doi:10.5194/gmd-2021-298-ac2
Ma, P. (2022) · Geoscientific model development · doi:10.5194/gmd-2021-298-ac1
Voldoire, A. (2022) · Geoscientific model development · doi:10.5194/gmd-2021-413-ac4
How to calibrate a climate model with neural network based physics?Preprint
Balogh, B., Saint‐Martin, D., & Ribes, A. (2022) · doi:10.1002/essoar.10510164.1
(2021) · Geoscientific model development · doi:10.5194/gmd-2021-61-rc1
Ryan, E. (2021) · Geoscientific model development · doi:10.5194/gmd-2021-39-ac3
Ryan, E. (2021) · Geoscientific model development · doi:10.5194/gmd-2021-39-ac2
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Räz, T. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.06137 · Cited by 1 · see citing works
(2023) · Geoscientific model development · doi:10.5194/gmd-2022-280-rc1
(2023) · Geoscientific model development · doi:10.5194/gmd-2022-280-rc2
Tarasov, L. (2023) · doi:10.5194/egusphere-2022-1410-ac3
Tarasov, L. (2023) · doi:10.5194/egusphere-2022-1410-ac1
Tarasov, L. (2023) · doi:10.5194/egusphere-2022-1410-ac2
McNeall, D. (2023) · Geoscientific model development · doi:10.5194/gmd-2022-280-ac1
Comment on egusphere-2023-2996
Raoult, N., Beylat, S., Salter, J. M., et al. (2024) · doi:10.5194/egusphere-2023-2996-rc1
Designing a fully-tunable and versatile TKE-l turbulence parameterization for atmospheric modelsPreprint
Vignon, É., Arjdal, K., Cheruy, F., Coulon-Decorzens, M., Dehondt, C., Dubos, T., Fromang, S., Hourdin, F., Lange, L., Raillard, L., et al. (2024) · doi:10.22541/essoar.171415891.19754973/v1
Self-citation
- 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)
- OpenAlex
- W3049378437
- Cited by
- 121
- References
- 113
- Citations by year
- 2026: 13 · 2025: 23 · 2024: 15 · 2023: 22 · 2022: 24 · 2021: 22 · 2020: 1 · 2019: 1
- FWCI
- 5.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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