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
                                        
                                    Viewed
                        
                            Total article views: 4,112 (including HTML, PDF, and XML)
                        
                            
                                
                                
                            
                                
                                
                            
                        
                        
                            Cumulative views and downloads 
                                         (calculated since 03 May 2018)
                        
                        
                            
                                
                            
                    
        
                    
                    | HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,890 | 1,137 | 85 | 4,112 | 461 | 139 | 173 | 
- HTML: 2,890
- PDF: 1,137
- XML: 85
- Total: 4,112
- Supplement: 461
- BibTeX: 139
- EndNote: 173
                        
                            Total article views: 3,167 (including HTML, PDF, and XML)
                        
                            
                                
                                
                            
                                
                                
                            
                        
                        
                            Cumulative views and downloads 
                                         (calculated since 21 Dec 2018)
                        
                        
                            
                                
                            
                    
                    
                    | HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,422 | 672 | 73 | 3,167 | 247 | 123 | 156 | 
- HTML: 2,422
- PDF: 672
- XML: 73
- Total: 3,167
- Supplement: 247
- BibTeX: 123
- EndNote: 156
                        
                            Total article views: 945 (including HTML, PDF, and XML)
                        
                            
                                
                                
                            
                                
                                
                            
                        
                        
                            Cumulative views and downloads 
                                         (calculated since 03 May 2018)
                        
                        
                            
                                
                            
                    
        
                
            | HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 468 | 465 | 12 | 945 | 214 | 16 | 17 | 
- HTML: 468
- PDF: 465
- XML: 12
- Total: 945
- Supplement: 214
- BibTeX: 16
- EndNote: 17
Viewed (geographical distribution)
                                Total article views: 4,112 (including HTML, PDF, and XML)
                                
                                Thereof 3,629 with geography defined
                                    and 483 with unknown origin. 
                            
        
                            
                                Total article views: 3,167 (including HTML, PDF, and XML)
                                
                                Thereof 2,739 with geography defined
                                    and 428 with unknown origin. 
                            
        
                            
                                Total article views: 945 (including HTML, PDF, and XML)
                                
                                Thereof 890 with geography defined
                                    and 55 with unknown origin. 
                            
                    | Country | # | Views | % | 
|---|
| Country | # | Views | % | 
|---|
| Country | # | Views | % | 
|---|
| Total: | 0 | 
| HTML: | 0 | 
| PDF: | 0 | 
| XML: | 0 | 
- 1
1
                            | Total: | 0 | 
| HTML: | 0 | 
| PDF: | 0 | 
| XML: | 0 | 
- 1
1
                            | Total: | 0 | 
| HTML: | 0 | 
| PDF: | 0 | 
| XML: | 0 | 
- 1
1
                            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: 31 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...
            
         
 
                             
                             
             
             
            