Articles | Volume 11, issue 12
https://doi.org/10.5194/gmd-11-5189-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-5189-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Automatic tuning of the Community Atmospheric Model (CAM5) by using short-term hindcasts with an improved downhill simplex optimization method
Tao Zhang
Ministry of Education Key Laboratory for Earth System Modeling, and Department for Earth System Science, Tsinghua University, Beijing, China
Brookhaven National Laboratory, Upton, NY, USA
Minghua Zhang
CORRESPONDING AUTHOR
School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA
Wuyin Lin
Brookhaven National Laboratory, Upton, NY, USA
Yanluan Lin
Ministry of Education Key Laboratory for Earth System Modeling, and Department for Earth System Science, Tsinghua University, Beijing, China
Wei Xue
CORRESPONDING AUTHOR
Ministry of Education Key Laboratory for Earth System Modeling, and Department for Earth System Science, Tsinghua University, Beijing, China
Department of Computer Science and Technology, Tsinghua University, Beijing, China
Haiyang Yu
School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA
Juanxiong He
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Xiaoge Xin
Beijing Climate Center, China Meteorological Administration, Beijing, China
Hsi-Yen Ma
Lawrence Livermore National Laboratory, Livermore, CA, USA
Shaocheng Xie
Lawrence Livermore National Laboratory, Livermore, CA, USA
Weimin Zheng
Department of Computer Science and Technology, Tsinghua University, Beijing, China
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Cited
9 citations as recorded by crossref.
- Development of optimization platform and its application in severe accident management L. Wu et al. 10.1016/j.pnucene.2021.103721
- A Multilevel Surrogate Model-Based Precipitation Parameter Tuning Method for CAM5 Using Remote Sensing Data for Validation X. Wu et al. 10.3390/rs17030408
- Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model R. Pathak et al. 10.1038/s41598-020-74441-x
- An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3) L. Wu et al. 10.5194/gmd-13-41-2020
- Dynamically computed characteristic adjustment time scale for Zhang–McFarlane convective parameterization scheme M. Wang et al. 10.1007/s00382-023-07031-y
- LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM) J. Guo et al. 10.5194/gmd-17-3975-2024
- Impact of the similarity functions of surface layer parametrization in a climate model over the Indian region P. Namdev et al. 10.1002/qj.4400
- The potential for structural errors in emergent constraints B. Sanderson et al. 10.5194/esd-12-899-2021
- Superparameterised cloud effects in the EMAC general circulation model (v2.50) – influences of model configuration H. Rybka & H. Tost 10.5194/gmd-13-2671-2020
9 citations as recorded by crossref.
- Development of optimization platform and its application in severe accident management L. Wu et al. 10.1016/j.pnucene.2021.103721
- A Multilevel Surrogate Model-Based Precipitation Parameter Tuning Method for CAM5 Using Remote Sensing Data for Validation X. Wu et al. 10.3390/rs17030408
- Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model R. Pathak et al. 10.1038/s41598-020-74441-x
- An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3) L. Wu et al. 10.5194/gmd-13-41-2020
- Dynamically computed characteristic adjustment time scale for Zhang–McFarlane convective parameterization scheme M. Wang et al. 10.1007/s00382-023-07031-y
- LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM) J. Guo et al. 10.5194/gmd-17-3975-2024
- Impact of the similarity functions of surface layer parametrization in a climate model over the Indian region P. Namdev et al. 10.1002/qj.4400
- The potential for structural errors in emergent constraints B. Sanderson et al. 10.5194/esd-12-899-2021
- Superparameterised cloud effects in the EMAC general circulation model (v2.50) – influences of model configuration H. Rybka & H. Tost 10.5194/gmd-13-2671-2020
Latest update: 29 Oct 2025
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.
Tuning of uncertain parameters in global atmospheric general circulation models has extreme...