Calibrating the GAMIL3-1° climate model using a derivative-free optimization method

Abstract. Parameterization in climate models often involves parameters that are poorly constrained by observations or theoretical understanding alone. Manual tuning by experts can be time-consuming, subjective, and prone to underestimating uncertainties. Automated tuning methods offer a promising alternative, enabling faster, objective improvements in model performance and better uncertainty quantification. This study presents an automated parameter-tuning framework that employs a derivative-free optimization solver (DFO-LS) to simultaneously perturb and tune multiple convection-related and microphysics parameters. The framework explicitly accounts for observational and initial condition uncertainties (internal variability) to calibrate a 1° resolution atmospheric model (GAMIL3). To evaluate its performance, two main tuning experiments were conducted, targeting 10 and 20 parameters, respectively. In addition, three sensitivity experiments tested the effect of varying initial parameter values in the 10-parameter case. Both tuning experiments achieved a rapid reduction in the cost function. The 10-parameter optimization improved model accuracy for 24 of 34 key variables, while expanding to 20 parameters yielded improvement for 25 variables, though some structural model biases appeared. Ten-year AMIP simulations validated the robustness and stability of the tuning results, showing that the improvements persisted over extended simulations. Additionally, evaluations of the coupled model with optimized parameters showed, compared to the default parameters settings, reduced climate drift, a more stable climate system, and more realistic sea surface temperatures, despite a residual global energy imbalance of 2.0 W m−2 (about 1.4 W m−2 arising from the intrinsic imbalance of the atmospheric component) and some remaining regional biases. The sensitivity experiments further underscored the efficiency of the tuning algorithm and highlight the importance of expert judgment in selecting initial parameter values. This tuning framework is broadly applicable to other general circulation models (GCMs), supporting comprehensive parameter tuning and advancing model development.