Elucidating Model Inadequacies in a Cloud Parameterization by Use of an Ensemble-Based Calibration Framework

Abstract

Every cloud parameterization contains structural model errors. The source of these errors is difficult to pinpoint because cloud parameterizations contain nonlinearities and feedbacks. To elucidate these model inadequacies, this paper uses a general-purpose ensemble parameter estimation technique. In principle, the technique is applicable to any parameterization that contains a number of adjustable coefficients. It optimizes or calibrates parameter values by attempting to match predicted fields to reference datasets. Rather than striving to find the single best set of parameter values, the output is instead an ensemble of parameter sets. This ensemble provides a wealth of information. In particular, it can help uncover model deficiencies and structural errors that might not otherwise be easily revealed. The calibration technique is applied to an existing single-column model (SCM) that parameterizes boundary layer clouds. The SCM is a higher-order turbulence closure model. It is closed using a multivariate probability density function (PDF) that represents subgrid-scale variability. Reference datasets are provided by large-eddy simulations (LES) of a variety of cloudy boundary layers. The calibration technique locates some model errors in the SCM. As a result, empirical modifications are suggested. These modifications are evaluated with independent datasets and found to lead to an overall improvement in the SCM’s performance.