#A07 Parametric sensitivity and tuning for ACME-V1 atmosphere model based on short PPE simulations

The ACME V1 (NE30_L72) atmosphere model has included many new features in the physics parameterizations. Complex nonlinear interactions between those new features create a big challenge for understanding the model behaviors and tuning. Using the one-at-a-time method, we often encounter cases where the tuning of one parameter leads to an offset of the accomplishment from the tuning of another parameter, or the improvement in one target variable leads to degradation of model fidelity in another target variable. The PPE simulations provide an opportunity to evaluate and optimize model fidelity in a comprehensive and systematic manner. We have finished 256x12 5-day simulations, in which 18 carefully-selected parameters in various physical processes were perturbed simultaneously using the Latin Hypercube sampling method. In this poster we will briefly introduce the framework of PPE simulations and present the results from the analysis that aimed at quantifying the model response to the most sensitive parameters and estimating the maximum likelihood of model parameter space for a number of important fidelity metrics. We also present the results focusing on a few issues related to model tuning using short PPE simulations, such as model parametric sensitivity with different spatial resolution, simulation lengthy, and skill score function i.e. global mean bias, root mean square error, spatial pattern correlation coefficient, and Taylor score. Finally we discuss a few limitations in using PPE simulations for global model tuning. Results from this analysis provide a more complete picture of the model behavior and improve our understanding of model physics associated with model parameters and their interactions.