An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3)

Abstract

Uncertain parameters in physical parameterizations of general circulation models (GCMs) greatly impact model performance. In recent years, automatic parameter optimization has been introduced for tuning model performance of GCMs, but most of the optimization methods are unconstrained optimization methods under a given performance indicator. Therefore, the calibrated model may break through essential constraints that models have to keep, such as the radiation balance at the top of the model. The radiation balance is known for its importance in the conservation of model energy. In this study, an automated and efficient parameter optimization with the radiation balance constraint is presented and applied in the Community Atmospheric Model (CAM5) in terms of a synthesized performance metric using normalized mean square error of radiation, precipitation, relative humidity, and temperature. The tuned parameters are from the parameterization schemes of convection and cloud. The radiation constraint is defined as the absolute difference of the net longwave flux at the top of the model (FLNT) and the net solar flux at the top of the model (FSNT) of less than 1 W m−2. Results show that the synthesized performance under the optimal parameters is 6.3 % better than the control run (CNTL) and the radiation imbalance is as low as 0.1 W m−2. The proposed method provides an insight for physics-guided optimization, and it can be easily applied to optimization problems with other prerequisite constraints in GCMs.