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

Abstract

Traditional trial-and-error tuning of uncertain parameters in global atmospheric general circulation models (GCMs) 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 predefined metric that includes precipitation, temperature, humidity, and longwave/shortwave cloud forcing. The computational cost of the entire optimization procedure is about equivalent to 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 automaticmore » tuning can be a viable supplement to process-oriented model evaluations and improvement.« less

Authors:

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

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• Search DOE PAGES for ORCID "0000-0002-9628-1278"

Xie, Shaocheng ^[8]; Zheng, Weimin ^[9]

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+ Show Author Affiliations

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

Publication Date:

Fri Dec 21 00:00:00 EST 2018

Research Org.:

Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States); Stony Brook Univ., NY (United States); Tsinghua Univ., Beijing (China)

Sponsoring Org.:

USDOE Office of Science (SC), Biological and Environmental Research (BER); National Key Research and Development Program of China; National Natural Science Foundation of China (NSFC)

OSTI Identifier:

1498460

Report Number(s):

LLNL-JRNL-765387
Journal ID: ISSN 1991-9603; 955420

Grant/Contract Number:  

AC52-07NA27344; 2017YFA0604500; 2016YFA0602100; 91530323; 41776010

Resource Type:

Accepted Manuscript

Journal Name:

Geoscientific Model Development (Online)

Additional Journal Information:

Journal Name: Geoscientific Model Development (Online); Journal Volume: 11; Journal Issue: 12; Journal ID: ISSN 1991-9603

Publisher:

European Geosciences Union

Country of Publication:

United States

Language:

English

Subject:

54 ENVIRONMENTAL SCIENCES