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

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

doi:10.5194/gmd-2018-87

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

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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 thatmore »« less

Authors:

Zhang, Tao ^[1]; Zhang, Minghua ^[2]; Lin, Yanluan ^[3]; Xue, Wei ^[4]; Lin, Wuyin ^[5]; Yu, Haiyang ^[2]; He, Juanxiong ^[6]; Xin, Xiaoge ^[7]; Ma, Hsi-Yen ^[8]; Xie, Shaochen ^[8]; Zheng, Weimin ^[9]

Tsinghua Univ., Beijing (China). Ministry of Education Key Lab. for Earth System Modeling, and Dept.for Earth System Science; Brookhaven National Lab. (BNL), Upton, NY (United States)
Stony Brook Univ., NY (United States). School of Marine and Atmospheric Sciences
Tsinghua Univ., Beijing (China). Ministry of Education Key Lab. for Earth System Modeling, and Dept.for Earth System Science
Tsinghua Univ., Beijing (China). Ministry of Education Key Lab. for Earth System Modeling, and Dept.for Earth System Science, and Dept. of Computer Science and Technology
Brookhaven National Lab. (BNL), Upton, NY (United States)
Chinese Academy of Sciences (CAS), Beijing (China). Inst. of Atmospheric Physics
China Meteorological Administration, Beijing (China). Beijing Climate Center
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Tsinghua Univ., Beijing (China). Dept. of Computer Science and Technology

Publication Date:: Thu May 03 00:00:00 EDT 2018

Research Org.:: Brookhaven National Lab. (BNL), Upton, NY (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

OSTI Identifier:: 1484867

Report Number(s):: BNL-209642-2018-JAAM
Journal ID: ISSN 1991-962X

Grant/Contract Number:: SC0012704

Resource Type:: Accepted Manuscript

Journal Name:: Geoscientific Model Development Discussions (Online)

Additional Journal Information:: Journal Name: Geoscientific Model Development Discussions (Online); Journal Volume: 11; Journal Issue: 12; Journal ID: ISSN 1991-962X

Publisher:: European Geosciences Union

Country of Publication:: United States

Language:: English

Subject:: 54 ENVIRONMENTAL SCIENCES

Citation Formats


                    Zhang, Tao, Zhang, Minghua, Lin, Yanluan, Xue, Wei, Lin, Wuyin, Yu, Haiyang, He, Juanxiong, Xin, Xiaoge, Ma, Hsi-Yen, Xie, Shaochen, and Zheng, Weimin. Automatic tuning of the Community Atmospheric Model CAM5.3 by using short-term hindcasts with an improved downhill simplex optimization method.  United States: N. p., 2018. 
Web.  doi:10.5194/gmd-2018-87.

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                    Zhang, Tao, Zhang, Minghua, Lin, Yanluan, Xue, Wei, Lin, Wuyin, Yu, Haiyang, He, Juanxiong, Xin, Xiaoge, Ma, Hsi-Yen, Xie, Shaochen, & Zheng, Weimin. Automatic tuning of the Community Atmospheric Model CAM5.3 by using short-term hindcasts with an improved downhill simplex optimization method.  United States.  https://doi.org/10.5194/gmd-2018-87

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                    Zhang, Tao, Zhang, Minghua, Lin, Yanluan, Xue, Wei, Lin, Wuyin, Yu, Haiyang, He, Juanxiong, Xin, Xiaoge, Ma, Hsi-Yen, Xie, Shaochen, and Zheng, Weimin. Thu .  
"Automatic tuning of the Community Atmospheric Model CAM5.3 by using short-term hindcasts with an improved downhill simplex optimization method".  United States.  https://doi.org/10.5194/gmd-2018-87.  https://www.osti.gov/servlets/purl/1484867.

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@article{osti_1484867,

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

  author       = {Zhang, Tao and Zhang, Minghua and Lin, Yanluan and Xue, Wei and Lin, Wuyin and Yu, Haiyang and He, Juanxiong and Xin, Xiaoge and Ma, Hsi-Yen and Xie, Shaochen and Zheng, Weimin},

  abstractNote = {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.},

  doi          = {10.5194/gmd-2018-87},

  journal      = {Geoscientific Model Development Discussions (Online)},

  number       = 12,

  volume       = 11,

  place        = {United States},

  year         = {Thu May 03 00:00:00 EDT 2018},

  month        = {Thu May 03 00:00:00 EDT 2018}

}

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