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Discussion papers | Copyright
https://doi.org/10.5194/gmd-2018-87
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 03 May 2018

Development and technical paper | 03 May 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Automatic tuning of the Community Atmospheric Model CAM5.3 by using short-term hindcasts with an improved downhill simplex optimization method

Tao Zhang1,2, Minghua Zhang4, Yanluan Lin1, Wei Xue1,3, Wuyin Lin2, Haiyang Yu4, Juanxiong He5, Xiaoge Xin6, Hsi-Yen Ma7, Shaochen Xie7, and Weimin Zheng3 Tao Zhang et al.
  • 1Ministry of Education Key Laboratory for Earth System Modeling, and Department for Earth System Science, Tsinghua University, Beijing, China
  • 2Brookhaven National Laboratory, Brookhaven, New York, USA
  • 3Department of Computer Science and Technology, Tsinghua University, Beijing, China
  • 4School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, New York, USA
  • 5Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • 6Beijing Climate Center, China Meteorological Administration, Beijing, China
  • 7Lawrence Livermore National Laboratory, Livermore,California, USA

Abstract. Traditional trial-and-error tuning of uncertain parameters in global atmospheric General Circulation Models (GCM) is time consuming and subjective. This study explores the feasibility of automatic optimization of GCM parameters for fast physics by using short-term hindcasts. An automatic workflow is described and applied to the Community Atmospheric Model (CAM5) to optimize several parameters in its cloud and convective parameterizations. We show that the auto-optimization leads to 10% reduction of the overall bias in CAM5, which is already a well calibrated model, based on a pre-defined metric that includes precipitation, temperature, humidity, and longwave/shortwave cloud forcing. The computational cost of the entire optimization procedure is about equivalent to about a single 12-year atmospheric model simulation. The tuning reduces the large underestimation in the CAM5 longwave cloud forcing by decreasing the threshold relative humidity and the sedimentation velocity of ice crystals in the cloud schemes; it reduces the overestimation of precipitation by increasing the adjustment time in the convection scheme. The physical processes behind the tuned model performance for each targeted field are discussed. Limitations of the automatic tuning are described, including the slight deterioration in some targeted fields that reflect the structural errors of the model. It is pointed out that automatic tuning can be a viable supplement to process-oriented model evaluations and improvement.

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Latest update: 18 Aug 2018
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Short summary
Tuning of uncertain parameters in global atmospheric General Circulation Models has the 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 the CAM5.
Tuning of uncertain parameters in global atmospheric General Circulation Models has the extreme...
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