Process‐Based Climate Model Development Harnessing Machine Learning: II. Model Calibration From Single Column to Global
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Abstract
We demonstrate a new approach for climate model tuning in a realistic situation. Our approach, the mathematical foundations and technical details of which are given in Part I, systematically uses a single‐column configuration of a global atmospheric model on test cases for which reference large‐eddy‐simulations are available. The space of free parameters is sampled running the single‐column model from which metrics are estimated in the full parameter space using emulators. The parameter space is then reduced by retaining only the values for which the emulated metrics match large eddy simulations within a given tolerance to error. The approach is applied to the 6A version of the LMDZ model which results from a long investment in the development of physics parameterizations and by‐hand tuning. The boundary layer is revisited by increasing the vertical resolution and varying parameters that were kept fixed so far, which improves the representation of clouds at process scale. The approach allows us to automatically reach a tuning of this modified configuration as good as that of the 6A version. We show how this approach helps accelerate the introduction of new parameterizations. It allows us to maintain the physical foundations of the model and to ensure that the improvement of global metrics is obtained for a reasonable behavior at process level, reducing the risk of error compensations that may arise from over‐fitting some climate metrics. That is, we get things right for the right reasons.
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The targets of the by-hand tuning of IPSL-6A were translated into 14 scalar metrics, 11 for TOA radiative fluxes and 3 for rainfall distribution.
“Following a previous study (45), the targets of the by-hand tuning of the IPSL-6A configuration were translated into 14 scalar metrics. Among them, 11 relate to annual mean top-of-atmosphere (TOA) radiative fluxes, taking averages over the globe or specific regions and decomposing between solar and thermal radiation or clear sky fluxes and radiative effect of clouds, while three metrics relate to”
From Toward machine-assisted tuning avoiding the underestimation of uncertainty in climate change projections (2023) · cites this paper as [45]
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 [45]
Cites
An Overview of CMIP5 and the Experiment Design
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-11-00094.1 · Cited by 14,052 · see citing works
CLIMATE AND THE OCEAN CIRCULATION1
Manabe, S. (1969) · Monthly Weather Review · doi:10.1175/1520-0493(1969)097 · Cited by 1,208 · see citing works
An update on Earth's energy balance in light of the latest global observations
Stephens, G. L., Li, J. F., Wild, M., Clayson, C. A., Loeb, N. G., Kato, S., L’Ecuyer, T., Stackhouse, P. W., Lebsock, M., & Andrews, T. (2012) · Nature Geoscience · doi:10.1038/ngeo1580 · Cited by 1,007 · see citing works
Toward Optimal Closure of the Earth's Top-of-Atmosphere Radiation Budget
Loeb, N. G., Wielicki, B. A., Doelling, D. R., Smith, G. L., Keyes, D. F., Kato, S., Manalo-Smith, N., & Wong, T. (2009) · Journal of Climate · doi:10.1175/2008jcli2637.1 · Cited by 960 · see citing works
Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive
Schmidt, G. A., Kelley, M., Nazarenko, L., Ruedy, R. A., Russell, G. L., Aleinov, I., Bauer, M., Bauer, S. E., Bhat, M. K., Bleck, R., et al. (2014) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2013ms000265 · Cited by 714 · see citing works
The Influence of Gravity Wave Breaking on the General Circulation of the Middle Atmosphere
Holton, J. R. (1983) · Journal of the Atmospheric Sciences · doi:10.1175/1520-0469(1983)040 · Cited by 654 · see citing works
Source-receptor matrix calculation with a Lagrangian particle dispersion model in backward mode
Seibert, P., & Frank, A. (2004) · Atmospheric chemistry and physics · doi:10.5194/acp-4-51-2004 · Cited by 629 · see citing works
The Art and Science of Climate Model Tuning
Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J.-C., Balaji, V., Duan, Q., Folini, D., Ji, D., Klocke, D., Qian, Y., et al. (2017) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-15-00135.1 · Cited by 611 · see citing works
Self-citationTuning the climate of a global model
Mauritsen, T., Stevens, B., Roeckner, E., Crueger, T., Esch, M., Giorgetta, M. A., Helmuth Haak, G., Jungclaus, J. H., Klocke, D., Matei, D., et al. (2012) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2012ms000154 · Cited by 530 · see citing works
Sundqvist, H. (1978) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.49710444110 · Cited by 501 · see citing works
Could Machine Learning Break the Convection Parameterization Deadlock?
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., & Yacalis, G. (2018) · Geophysical Research Letters · doi:10.1029/2018gl078202 · Cited by 497 · see citing works
Prognostic Validation of a Neural Network Unified Physics Parameterization
Brenowitz, N., & Bretherton, C. S. (2018) · Geophysical Research Letters · doi:10.1029/2018gl078510 · Cited by 387 · see citing works
van Zanten, M. C., Stevens, B., Nuijens, L. A., Siebesma, A. P., Ackerman, A. S., Burnet, F., Cheng, A., Couvreux, F., Jiang, H., Khairoutdinov, M. F., et al. (2011) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2011ms000056 · Cited by 367 · see citing works
Self-citationLarge‐eddy simulation of the diurnal cycle of shallow cumulus convection over land
Brown, A. R., Cederwall, R. T., Chlond, A., Duynkerke, P. G., Golaz, J., Khairoutdinov, M. F., Lewellen, D. C., Lock, A. P., Macvean, M. K., Moeng, C., et al. (2002) · Quarterly Journal of the Royal Meteorological Society · doi:10.1256/003590002320373210 · Cited by 348 · see citing works
Improving the Anelastic Approximation
Durran, D. R. (1989) · Journal of the Atmospheric Sciences · doi:10.1175/1520-0469(1989)046 · Cited by 301 · see citing works
Hourdin, F., Grandpeix, J.-Y., Rio, C., Bony, S., Jam, A., Cheruy, F., Rochetin, N., Fairhead, L., Idelkadi, A., Musat, I., et al. (2013) · Climate Dynamics · doi:10.1007/s00382-012-1343-y · Cited by 299 · see citing works
Self-citationLMDZ6A: The Atmospheric Component of the IPSL Climate Model With Improved and Better Tuned Physics
Hourdin, F., Rio, C., Grandpeix, J., Madeleine, J., Cheruy, F., Rochetin, N., Jam, A., Musat, I., Idelkadi, A., Fairhead, L., et al. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2019ms001892 · Cited by 268 · see citing works
Self-citationKrasnopolsky, V. M., Fox‐Rabinovitz, M. S., & Belochitski, A. A. (2013) · Advances in Artificial Neural Systems · doi:10.1155/2013/485913 · Cited by 208 · see citing works
Williamson, D., Goldstein, M., Allison, L. C., Blaker, A. T., Challenor, P., Jackson, L. C., & Yamazaki, K. (2013) · Climate Dynamics · doi:10.1007/s00382-013-1896-4 · Cited by 196 · see citing works
Self-citationOn the Factors Modulating the Stratocumulus to Cumulus Transitions
Sandu, I., & Stevens, B. (2011) · Journal of the Atmospheric Sciences · doi:10.1175/2011jas3614.1 · Cited by 196 · see citing works
Estimation of entrainment rate in simple models of convective clouds
Gregory, D. (2001) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.49712757104 · Cited by 160 · see citing works
A Density Current Parameterization Coupled with Emanuel’s Convection Scheme. Part I: The Models
Grandpeix, J., & Lafore, J. (2009) · Journal of the Atmospheric Sciences · doi:10.1175/2009jas3044.1 · Cited by 159 · see citing works
Unified treatment of dry convective and stratocumulus‐topped boundary layers in the ECMWF model
Köhler, M., Ahlgrimm, M., & Beljaars, A. (2011) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.713 · Cited by 148 · see citing works
Multiscale Statistical Properties of a High-Resolution Precipitation Forecast
Harris, D., Foufoula‐Georgiou, E., Droegemeier, K. K., & Levit, J. J. (2001) · Journal of Hydrometeorology · doi:10.1175/1525-7541(2001)002 · Cited by 137 · see citing works
Ayotte, K. W., Sullivan, P. P., Andr�n, A., Doney, S. C., Holtslag, A. A. M., Large, W. G., McWilliams, J. C., Moeng, C., Otte, M. J., Tribbia, J. J., et al. (1996) · Boundary-Layer Meteorology · doi:10.1007/bf00120078 · Cited by 129 · see citing works
A Thermal Plume Model for the Convective Boundary Layer: Representation of Cumulus Clouds
Rio, C., & Hourdin, F. (2008) · Journal of the Atmospheric Sciences · doi:10.1175/2007jas2256.1 · Cited by 128 · see citing works
Self-citationParameterization of the Vertical Velocity Equation for Shallow Cumulus Clouds
de Roode, S. R., Siebesma, A. P., Jonker, H. J. J., & de Voogd, Y. (2012) · Monthly Weather Review · doi:10.1175/mwr-d-11-00277.1 · Cited by 113 · see citing works
The Distribution of Rainfall over Oceans from Spaceborne Radars
Berg, W. K., L’Ecuyer, T., & Haynes, J. M. (2009) · Journal of Applied Meteorology and Climatology · doi:10.1175/2009jamc2330.1 · Cited by 108 · see citing works
Couvreux, F., Hourdin, F., & Rio, C. (2009) · Boundary-Layer Meteorology · doi:10.1007/s10546-009-9456-5 · Cited by 106 · see citing works
Self-citationObjective calibration of regional climate models
Bellprat, O., Kotlarski, S., Lüthi, D., & Schär, C. (2012) · Journal of Geophysical Research Atmospheres · doi:10.1029/2012jd018262 · Cited by 106 · see citing works
Identifying and removing structural biases in climate models with history matching
Williamson, D., Blaker, A. T., Hampton, C., & Salter, J. M. (2014) · Climate Dynamics · doi:10.1007/s00382-014-2378-z · Cited by 103 · see citing works
Self-citationRio, C., Hourdin, F., Couvreux, F., & Jam, A. (2010) · Boundary-Layer Meteorology · doi:10.1007/s10546-010-9478-z · Cited by 85 · see citing works
Self-citationCouvreux, F., Guichard, F., Redelsperger, J., Kiemle, C., Masson, V., Lafore, J., & Flamant, C. N. (2005) · Quarterly Journal of the Royal Meteorological Society · doi:10.1256/qj.04.167 · Cited by 78 · see citing works
Self-citationTuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model
Williamson, D. B., Blaker, A. T., & Sinha, B. (2017) · Geoscientific Model Development · doi:10.5194/gmd-10-1789-2017 · Cited by 75 · see citing works
Self-citationAutomatic Model Calibration: A New Way to Improve Numerical Weather Forecasting
Gan, Y., Ye, A., Miao, C., Miao, S., Liang, X., & Fan, S. (2016) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-15-00104.1 · Cited by 74 · see citing works
Air moisture control on ocean surface temperature, hidden key to the warm bias enigma
Hourdin, F., Găinuşă-Bogdan, A., Braconnot, P., Dufresne, J., Traore, A., & Rio, C. (2015) · Geophysical Research Letters · doi:10.1002/2015gl066764 · Cited by 69 · see citing works
Self-citationHourdin, F., Jam, A., Rio, C., Couvreux, F., Sandu, I., Lefebvre, M., Brient, F., & Idelkadi, A. (2019) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2019ms001666 · Cited by 46 · see citing works
Self-citationVignon, É., Hourdin, F., Genthon, C., Gallée, H., Bazile, É., Lefebvre, M., Madeleine, J., & van de Wiel, B. J. H. (2017) · Journal of Geophysical Research Atmospheres · doi:10.1002/2017jd026802 · Cited by 43 · see citing works
Self-citationAndrianakis, I., Vernon, I., McCreesh, N., McKinley, T. J., Oakley, J. E., Nsubuga, R. N., Goldstein, M., & White, R. G. (2016) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/rssc.12198 · Cited by 40 · see citing works
Jam, A., Hourdin, F., Rio, C., & Couvreux, F. (2012) · Boundary-Layer Meteorology · doi:10.1007/s10546-012-9789-3 · Cited by 36 · see citing works
Self-citationSalter, J. M., & Williamson, D. (2016) · Environmetrics · doi:10.1002/env.2405 · Cited by 36 · see citing works
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Hourdin, F., Rio, C., Jam, A., Traore, A., & Musat, I. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2019ms001988 · Cited by 24 · see citing works
Self-citationVertical Transports by Plumes within the Moderately Convective Marine Atmospheric Surface Layer
Mason, R. A., Shirer, H. N., Wells, R., & Young, G. S. (2002) · Journal of the Atmospheric Sciences · doi:10.1175/1520-0469(2002)059 · Cited by 5 · see citing works
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Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)
Hickmon, N. L., Varadharajan, C., Hoffman, F. M., Wainwright, H. M., & Collis, S. (2022) · doi:10.2172/1888810 · Cited by 15 · see citing works
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Tarasov, L. (2023) · doi:10.5194/egusphere-2022-1410-ac2
Cole, J. N. S. (2023) · doi:10.5194/egusphere-2023-120-ac1
Cole, J. N. S. (2023) · doi:10.5194/egusphere-2023-120-ac2
Schneider, T., Leung, L. R., & Wills, R. C. J. (2024) · doi:10.5194/egusphere-2024-20-rc1
Schneider, T., Leung, L.-Y. R., & Wills, R. C. J. (2024) · doi:10.5194/egusphere-2024-20-rc2
Comment on egusphere-2023-2996
Raoult, N., Beylat, S., Salter, J. M., et al. (2024) · doi:10.5194/egusphere-2023-2996-rc1
Jones, C. G., Adloff, F., Booth, B., et al. (2024) · doi:10.5194/egusphere-2024-453-rc1
Schneider, T. (2024) · doi:10.5194/egusphere-2024-20-ac1
Schneider, T. (2024) · doi:10.5194/egusphere-2024-20-ac2
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-citationComment on egusphere-2023-2996
Raoult, N. M., Beylat, S., Salter, J. M., et al. (2024) · doi:10.5194/egusphere-2023-2996-rc2
Raoult, N. (2024) · doi:10.5194/egusphere-2023-2996-ac2
Raoult, N. (2024) · doi:10.5194/egusphere-2023-2996-ac1
Wu, X., Hu, L. Z., Zheng, J., Wang, L., Lu, H., & Fu, H. (2025) · Remote Sensing · doi:10.3390/rs17030408
Kumar, S., & Reddy, B. R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-14030-3.00006-1
QuadTune version 1: a regional tuner for global atmospheric models
Larson, V. E., Guo, Z., Stephens, B. A., Zarzycki, C. M., Dikta, G., Qian, Y., & Xie, S. (2025) · Geoscientific model development · doi:10.5194/gmd-18-9767-2025
Zhang, Y., Su, T., Tang, H., Bogenschutz, P., Klein, S. A., Jackson, C., & Caldwell, P. (2026) · Geophysical Research Letters · doi:10.1029/2025gl120241
Investigating Cloud–Radiation Error Compensations Stemming From Climate Model Calibration
Coulon-Decorzens, M., Hourdin, F., & Villefranque, N. (2026) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2025ms005579
Self-citation
- Authors
- Hourdin, F., Williamson, D., Rio, C., Couvreux, F., Roehrig, R., Villefranque, N., Musat, I., Fairhead, L., Diallo, F. B., & Volodina, V.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- University of Exeter (GB)
- The Alan Turing Institute (GB)
- Corresponding author
- Hourdin, Frédéric
- ORCID
- Venue
- Journal of Advances in Modeling Earth Systems
- Publisher
- American Geophysical Union (AGU)
- Published
- 2021-06
- Volume
- 13
- Issue
- 6
- Pages
- e2020MS002225
- ISSN
- 1942-2466 (ISSN-L)
- OpenAlex
- W3047510468
- Cited by
- 83
- References
- 43
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 15 · 2023: 18 · 2022: 21 · 2021: 12 · 2020: 1
- FWCI
- 4.25
- 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, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Alan Turing Institute (N510129)
- Agence Nationale de la Recherche (ANR-16-CE01-0010)
- Centre National de la Recherche Scientifique
- Grand Équipement National De Calcul Intensif
- Engineering and Physical Sciences Research Council (N510129, EP/N510129/1)
- 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 › Environmental Science › Global and Planetary Change › Atmospheric aerosols and clouds
- Keywords
- climate model tuning, model calibration, single-column model, surrogate models, emulator, parameter space reduction, large eddy simulation, boundary layer parameterization, cloud representation, physics parameterization, error compensation
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.
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