Automatic tuning of the Community Atmospheric Model (CAM5) by using short-term hindcasts with an improved downhill simplex optimization method
作者： Weimin. Zhang , Tao , Zhang , Minghua , Lin
关键词:
摘要: 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 automatic optimization of GCM for fast physics by using short-term hindcasts. An automatic workflow described and applied to Community Atmospheric Model (CAM5) to optimize several in its cloud convective parameterizations. We show that auto-optimization leads 10 % reduction overall bias CAM5, which already a well-calibrated model, based on a predefined metric includes precipitation, temperature, humidity, and longwave/shortwave forcing. The computational cost entire optimization procedure about equivalent single 12-year atmospheric model simulation. reduces large underestimation in the CAM5 longwave forcing decreasing threshold relative humidity and sedimentation velocity ice crystals schemes; it reduces overestimation precipitation increasing adjustment time in convection scheme. physical processes behind tuned model performance each targeted field are discussed. Limitations the automatic described, including slight deterioration some targeted fields reflect structural errors model. It is pointed out can be viable supplement to process-oriented evaluations improvement.
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