Articles | Volume 11, issue 12
https://doi.org/10.5194/gmd-11-5189-2018
https://doi.org/10.5194/gmd-11-5189-2018
Development and technical paper
 | 
21 Dec 2018
Development and technical paper |  | 21 Dec 2018

Automatic tuning of the Community Atmospheric Model (CAM5) by using short-term hindcasts with an improved downhill simplex optimization method

Tao Zhang, Minghua Zhang, Wuyin Lin, Yanluan Lin, Wei Xue, Haiyang Yu, Juanxiong He, Xiaoge Xin, Hsi-Yen Ma, Shaocheng Xie, and Weimin Zheng

Related authors

ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev., 18, 4103–4117, https://doi.org/10.5194/gmd-18-4103-2025,https://doi.org/10.5194/gmd-18-4103-2025, 2025
Short summary
A Fortran–Python interface for integrating machine learning parameterization into earth system models
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025,https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
Causal Analysis of Aerosol Impacts on Isolated Deep Convection: Findings from TRACER
Dié Wang, Roni Kobrosly, Tao Zhang, Tamanna Subba, Susan van den Heever, Siddhant Gupta, and Michael Jensen
EGUsphere, https://doi.org/10.5194/egusphere-2024-2436,https://doi.org/10.5194/egusphere-2024-2436, 2024
Short summary
An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3)
Li Wu, Tao Zhang, Yi Qin, and Wei Xue
Geosci. Model Dev., 13, 41–53, https://doi.org/10.5194/gmd-13-41-2020,https://doi.org/10.5194/gmd-13-41-2020, 2020
Short summary
Parameter calibration in global soil carbon models using surrogate-based optimization
Haoyu Xu, Tao Zhang, Yiqi Luo, Xin Huang, and Wei Xue
Geosci. Model Dev., 11, 3027–3044, https://doi.org/10.5194/gmd-11-3027-2018,https://doi.org/10.5194/gmd-11-3027-2018, 2018
Short summary

Related subject area

Climate and Earth system modeling
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev., 18, 4009–4021, https://doi.org/10.5194/gmd-18-4009-2025,https://doi.org/10.5194/gmd-18-4009-2025, 2025
Short summary
ICON-HAM-lite 1.0: simulating the Earth system with interactive aerosols at kilometer scales
Philipp Weiss, Ross Herbert, and Philip Stier
Geosci. Model Dev., 18, 3877–3894, https://doi.org/10.5194/gmd-18-3877-2025,https://doi.org/10.5194/gmd-18-3877-2025, 2025
Short summary
Process-based modeling framework for sustainable irrigation management at the regional scale: integrating rice production, water use, and greenhouse gas emissions
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev., 18, 3799–3817, https://doi.org/10.5194/gmd-18-3799-2025,https://doi.org/10.5194/gmd-18-3799-2025, 2025
Short summary
Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration
Carolina A. Bieri, Francina Dominguez, Gonzalo Miguez-Macho, and Ying Fan
Geosci. Model Dev., 18, 3755–3779, https://doi.org/10.5194/gmd-18-3755-2025,https://doi.org/10.5194/gmd-18-3755-2025, 2025
Short summary
Reducing time and computing costs in EC-Earth: an automatic load-balancing approach for coupled Earth system models
Sergi Palomas, Mario C. Acosta, Gladys Utrera, and Etienne Tourigny
Geosci. Model Dev., 18, 3661–3679, https://doi.org/10.5194/gmd-18-3661-2025,https://doi.org/10.5194/gmd-18-3661-2025, 2025
Short summary

Cited articles

Allen, M. R., Stott, P. A., Mitchell, J. F., Schnur, R., and Delworth, T. L.: Quantifying the uncertainty in forecasts of anthropogenic climate change, Nature, 407, 617–620, 2000. a
Bardenet, R., Brendel, M., Kégl, B., and Sebag, M.: Collaborative hyperparameter tuning, in: paper presented at the 30th International Conference on Machine Learning (ICML-13), 199–207, ACM, Atlanta, USA, 2013. a
Boyle, J., Klein, S., Zhang, G., Xie, S., and Wei, X.: Climate model forecast experiments for TOGA COARE, Mon. Weather Rev., 136, 808–832, 2008. a
Bretherton, C. S. and Park, S.: A new moist turbulence parameterization in the Community Atmosphere Model, J. Climate, 22, 3422–3448, 2009. a
Covey, C., Lucas, D. D., Tannahill, J., Garaizar, X., and Klein, R.: Efficient screening of climate model sensitivity to a large number of perturbed input parameters, J. Adv. Model. Earth Sy., 5, 598–610, 2013. a
Download
Short summary
Tuning of uncertain parameters in global atmospheric general circulation models has extreme computational cost. In this study, we provide an automatic tuning method by combining an auto-optimization algorithm with hindcasts to improve climate simulations in CAM5. The tuning improved the overall performance of a well-calibrated model by about 10 %. The computational cost of the entire auto-tuning procedure is just equivalent to a single 20-year simulation of CAM5.
Share