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

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

doi:https://doi.org/10.5194/gmd-11-5189-2018

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.

https://doi.org/10.5194/gmd-11-5189-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 12

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

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Final revised paper (published on 21 Dec 2018)
Supplement to the final revised paper
Preprint (discussion started on 03 May 2018)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

SC1: 'Code Availability', Lutz Gross, 22 Jun 2018
- AC1: 'Move the codes into another site', Tao Zhang, 25 Jun 2018
RC1: 'Review', Anonymous Referee #1, 29 Jun 2018
RC2: 'Zhang, T, (2018): "Automatic tuning of the Community Atmospheric Model CAM5.3 by using short-term hindcasts with an improved downhill simplex optimization method"', Anonymous Referee #2, 20 Sep 2018

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Tao Zhang on behalf of the Authors (16 Nov 2018) Author's response Manuscript

ED: Publish as is (25 Nov 2018) by Patrick Jöckel

AR by Tao Zhang on behalf of the Authors (02 Dec 2018) Manuscript

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.