Abstract. When a new, better-formulated physical parameterization is introduced into a global atmospheric model, aspects of the global model solutions are sometimes degraded. Then, in order to use the new global model to address science questions, there is an incentive to restore its accuracy. Oftentimes this restoration is achieved by tuning of model parameter values. Unfortunately, the retuning process is expensive because characterizing the parameter dependence requires numerous time-consuming global simulations.

To reduce the cost of tuning, this manuscript introduces a "poor man's'' model tuner, "QuadTune''. QuadTune carves the globe into regions and approximates the model parameter dependence by use of an uncorrelated quadratic emulator. The simplicity of the emulator reduces the required number of global model simulations and aids explainability.

Tuning removes parametric error but leaves behind model structural error. Structural error manifests itself as regional residual biases, such as stubborn biases and tuning trade-offs. To visualize these residual biases, QuadTune's software includes a set of diagnostic plots. This paper illustrates the use of the plots for characterizing residual biases with an example tuning problem.