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

Abstract

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.

A surrogate model-based ESM parameter tuning scientific workflow management framework for HPC

“…Small variations in these parameters can lead to changes in simulation results. Therefore, parameter tuning and calibration are crucial methods for enhancing the accuracy of ESM simulations (Wu et al, 2020).…”

Surrogate model-based precipitation tuning for CAM5

“…Chinta and Balaji (2020) presented a MO-ASMO method to enhance the prediction accuracy of the Indian summer monsoon (ISM) in the WRF model. Zhang et al (2020) proposed a land surface evapotranspiration (ET) optimization method based on the multivariate adaptive regression splines (MARS) surrogate model and the ASMO method. The mentioned studies have sufficiently shown that surrogate modelbased optimization methods are capable of resolving the ESM parameter tuning problems.…”